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Institut de Ciències del Mar (CSIC)
Universitat Politècnica de Catalunya
Effects of different allochthonous carbon sources on marine bacterioplankton diversity and function
Memòria presentada per optar al grau de Doctor en Ciències del Mar per la
Universitat Politècnica de Catalunya per
Itziar Lekunberri García
Vist-i-plau del director de tesi
Dr. Josep M. GasolInvestigador–CSIC
“Lo esencial es invisible a los ojos”
“El Principito” (Antoine de Saint-Exupéry)
A mis padres y a mi hermanica, Maitane
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Contents
SUMMARY/ RESUM/ RESUMEN/LABURPENA
GENERAL INTRODUCTION
Chapter I Increased marine bacterioplankton diversity detected by combining culturing and culture-independent techniques
Chapter II Changes in bacterial activity and community composition caused by realistic exposure to oil in mesocosms experiment
Chapter III Effects of different types of phytoplankton blooms caused by nutrient additions on bacterial heterotrophic activity and assemblage composition in sweater mesocososms
Chapter IV Effects of Sahara Dust deposition on marine microbial abundance, bacterial community structure and ecosystem function
Chapter V Transplant experiments show dominance of bottom-up control of prokaryote abundance, heterotrophic production and bacterial community structure in the NE Atlantic Ocean
SYNTHESIS AND DISCUSSION
ANNEXES
I: Traditional methods for bacterial culturing
II: Protocols for Scanning Electronic Microscopy (SEM) of bacteria
REFERENCES (Introduction, discussion and annexes sections)
AGRADECIMIENTOS
7
SUMMARY
Bacteria are the most abundant organisms on Earth and mediate many critical ecosystem processes. This indicates the great importance of the knowledge of the identity and population dynamics of the indigenous microorganisms. Initial studies in aquatic microbiology were carried out by traditional cultivation techniques. Later on, cell counts by epi uorescence techniques demonstrated how abundant bacteria were, and molecular techniques were developed and used to detect the most representatives bacteria in the environment. This is mainly because the culture media have nutrient contents (both organic and inorganic) which are several times more concentrated than those in the natural environment. This makes retrieving dominant organisms from an oligotrophic environment not an easy task, and generates selectivity for particular bacterial types, which are not very representative. A similar selection of bacteria also occurs in the natural environment, at another scale, when different allochthonous inputs take place. These can be due to natural processes, such as river run-offs, aerosol deposition, etc., or may have an anthropogenic origin, such as oil spills. In this thesis we evaluate the effect of several of these additions on bacterial community structure and function.
There is an increasing interest in the isolation of naturally dominating organisms since it provides a better information about the metabolic capabilities of the organisms that are growing in the environment. Thus, we decided to evaluate how representative were the isolates obtained in the Mediterrranean Sea as compared to the sequences retrieved by molecular techniques such as cloning and DGGE ngerprinting. Our analysis highlighted the selectivity created by the growth media, but simultaneously allowed to increase our knowledge about the diversity in our study area, and provided a means for looking into the “rare microbial biosphere”, which cannot be analyzed with other techniques.
To address the question of how nutrient addition modi es bacterial community structure and affects microbial function, we performed different mesocosm experiments, where we added different types of nutrients to water collected from Blanes Bay (NW Mediterranean Sea). Another mesocosm experiment was carried out to evaluate the effect of the oil spill that took place after the Prestige tanker accident in front of the Galician coast. Mesocosms experiments have been widely used to evaluate addition effects as a means to reproduce of the natural conditions. During these experiments different environmental parameters were measured and also bacterial production and abundance were monitored. Bacterial community structure was determined by the DGGE ngerprinting technique, which allows the comparison of different samples and further analysis of the dominant richness by the Shannon-Weaver index, based on the presence and absence of bands. These analyses showed the in uence of grazers on the bacterial selection after the different additions and, for example, explained the effects of Saharan Dust deposition on microbial function and community metabolism, as an effect of the inorganic phosphorus and organic carbon contributed by the dust. In the oil spill mesocosm experiments, effects on bacterial structure, production and abundance were detected only with additions four times higher than the concentration found after the Prestige tanker accident.
We also include an experimental study carried out in the North Atlantic during two different cruises, in which using dyalisis bags and transplant experiments we tested whether bottom-up conditions were stronger than top-down factors in determining microbial function and community structure. The results showed differences between experiments, but a general higher effect of bottom-up conditions than the effect of predators.
The different experimental work included in this thesis allows for some generalizations, but also show some discrepancies, which we discuss in the last chapter.
8
RESUM
Els bacteris són els organismes més abundants a la Terra i els encarregats de pariticipar multitud de processos que són crítics per a l´entorn. Això fa que sigui de gran importància el coneixement de la identitat i dinàmica de les poblacions de microorganismes indígenes. Els primers estudis en microbiologia es van realitzar mitjançant tècniques de cultiu tradicional. Més endavant, tècniques de comptatge per epi uorescència van mostrar com n’eren d’abundants els bacteris, i es van emprar també tècniques moleculars per a detectar els bacteris més representatius del medi. Però les concentracions de bacteris en els medis de cultiu (tant orgànics com inorgànics) són diverses vegades superiors a les que es troben en el medi natural. En conseqüència, l’obtenció dels cultius dels organismes dominants en un medi oligotrò c no és una tasca fàcil i acaba seleccionant bacteris que no són gaire representatius. Una selecció similar de bacteris també es dóna en el medi natural, a diferent escala, quan hi ha aports de diferents compostos al·lòctons. Aquests es poden deure a processos naturals com ara la descàrregues de rius, deposicions mitjançant aerosols, etc., o bé poden ser d’origen antropogènic, com els vessaments de petroli. En aquesta tesi avaluem l’efecte de diverses d’aquestes addicions en la diversitat, l´estructura de les comunitats i la funció bacterianes.
Hi ha un interès creixent en l’aïllament d’organismes dominants en el medi, ja que això permet tenir més informació sobre les capacitats metabòliques dels organismes que hi estan creixent. Per tant, vam decidir mesurar ns a quin punt són representatius els aïllats obtinguts en el mar Mediterrani comparant-los amb les seqüències obtingudes a través de tècniques moleculars com ara les biblioteques de clons i tècniques coma ara la DGGE ngerprinting. La nostra anàlisi destaca la selectivitat creada pel medi marí, però alhora permet tenir un coneixement més ampli sobre la diversitat en la nostra àrea d’estudi, i proporciona mitjans per a explorar la “biosfera microbiana rara”, que no pot ésser analitzada mitjançant altres tècniques.
Per a abordar la pregunta de com l’addició de nutrients selecciona la diversitat bacteriana i afecta la funció microbiana, vam realitzar una sèrie d’experiments de mesocosmos, on es van afegir diferents tipus de nutrients a aigua recollida a la Badia de Blanes (Nord-oest del mar Mediterrani). Un altre experiment de mesocosmos va ser realitzat amb l’objectiu d’avaluar l’efecte del vessament de petroli que va donar-se després de l’enfonsament del petrolier Prestige davant les costes gallegues. Els experiments de mesocosmos han estat utilitzats àmpliament per a determinar els efectes de les addicions amb l’intenció de simular les condicions naturals. Durant aquests experiments es van avaluar diferents paràmetres ambientals i també es van monitoritzar la producció i l’abundància bacterianes. L’estructura de la comunitat bacteriana va ser determinada amb la tècnica de DGGE
ngerprinting, que permet la comparació de diferents mostres i una anàlisi posterior de la diversitat dominant mitjançant l’índex de Shannon-Weaver, basat en la presència i absència de bandes. Aquestes anàlisis mostren la in uència dels depredadors en la selecció bacteriana després de les diferents addicions i, per exemple, explica els efectes de la deposició de pols del Sàhara en la funció microbiana i en el metabolisme de la comunitat, especialment per causa del fòsfor inorgànic i el carboni aportats amb la pols. En els experiments de vessament de petroli, es van poder detectar canvis en l’estructura, producció i abundància bacterianes només en el cas d’addicions quatre vegades més grans que la concentració trobada després de l’accident del Prestige.
També incloem un experiment realitzat a l’Atlàntic Nord durant dues campanyes diferents, en les quals es van utilitzar bosses de diàlisi i experiments de trasplantaments per a demostrar com les condicions de bottom-up van ser més grans que la regulació top-down en la determinació de la funció i estructura de la comunitat microbiana. Els resultats mostren diferències entre experiments, però en general es van detectar efectes més grans de bottom-up que no pas deguts a depredadors.
Finalment, els diferents treballs experimentals inclosos en aquesta tesi permeten fer algunes generalitzacions però també mostren algunes discrepàncies, fet que es discuteix en l’últim apartat.
9
RESUMEN
Las bacterias son los organismos más abundantes de la Tierra y los encargados de mediar en multitud de procesos críticos para el medio ambiente. Esto hace que sea de gran importancia el conocimiento de la identidad y dinámica de poblaciones de los microorganismos indígenas. Los primeros estudios en microbiología se realizaron mediante técnicas de cultivo tradicionales. Más tarde, técnicas de contaje por epi uorescencia mostraron lo abundantes que eran las bacterias, y técnicas moleculares se usaron también para detectar las bacterias más representativas del medio. Pero la concentración de nutrientes en los medios de cultivo (tanto orgánicos como inorgánicos), son varias veces superiores a los que se encuentran en el medio natural. En consecuencia, la obtención de los organismos dominantes en un medio oligotró co no es una tarea fácil y acaba seleccionando organismos bacterianos, que no son muy representativos. Una selección similar de bacterias ocurre también en el medio natural, a diferente escala, cuando hay aportes de diferentes compuestos alóctonos. Esto puede ser debido a procesos naturales tales como descargas de ríos, deposiciones de aerosoles, etc., o bien pueden ser de origen antropogénico, como los derrames de petróleo. En esta tesis evaluamos el efecto de varias de estas adiciones en la diversidad y función bacterianas.
Hay un creciente interés en el aislamiento de organismos dominantes en el medio, ya que esto permite tener mayor información sobre las capacidades metabólicas de los organismos que están creciendo. Por tanto, decidimos evaluar hasta qué punto son representativos los aislados obtenidos en el mar Mediterráneo comparándolos con las secuencias obtenidas mediante técnicas moleculares como bibliotecas de clones y DGGE ngerprinting. Nuestro análisis destaca la selectividad creada por el medio de cultivo, pero a la vez permite ampliar el conocimiento sobre la diversidad en nuestra área de estudio, y proporciona medios para explorar la “biosfera microbiana rara”, que no puede ser analizada mediante otras técnicas.
Para abordar la cuestión de cómo la adición de nutrientes selecciona la diversidad bacteriana y afecta a la función microbiana, realizamos diferentes experimentos de mesocosmos, donde añadimos diferentes tipos de nutrientes a agua recogida en la Bahía de Blanes (Noroeste del mar Mediterráneo). Otro experimento de mesocosmos fue llevado a cabo para evaluar el efecto del derrame de petróleo que tuvo lugar después del hundimiento del petrolero Prestige frente a las costas gallegas. Los experimentos de mesocosmos han sido ampliamente utilizados para determinar los efectos de adiciones con la intención de simular las condiciones naturales. Durante estos experimentos se evaluaron diferentes parámetros ambientales y también se monitorizó la producción y la abundancia bacterianas. La estructura de la comunidad bacteriana fue determinada por la técnica de DGGE ngerprinting, que permite la comparación de diferentes muestras y un análisis posterior de la diversidad dominante mediante el índice de Shannon-Weaver, basado en la presencia y ausencia de bandas. Estos análisis muestran la in uencia de los depredadores en la selección bacteriana después de las diferentes adiciones y, por ejemplo, explica los efectos de la deposición de polvo del Sáhara en la función microbiana y en el metabolismo de la comunidad, especialmente por el fósforo inorgánico y el carbono aportado por el polvo. En los experimentos de derrame de petróleo, sólo se detectaron efectos en la estructura, producción y abundancia bacterianas en el caso de adiciones cuatro veces mayores que la concentración encontrada después del accidente del petrolero Prestige.
También incluimos un experimento llevado acabo en el Atlántico Norte durante dos campañas diferentes, en las que se usaron bolsas de diálisis y experimentos de trasplantes para demostrar cómo las condiciones de bottom-up fueron mayores a los factores de top-down en la determinación de la función y estructura de la comunidad microbiana. Los resultados mostraron diferencias entre experimentos, pero en general se detectaron mayores efectos de bottom-up que los debidos a predadores.
Los diferentes trabajos experimentales incluidos en esta tesis permiten hacer algunas generalizaciones, pero también muestran algunas discrepancias, lo que se discute en el último apartado.
10
LABURPENA
Bakterioak Lurreko izakiriki oparoenak dirak eta ingurumeneko prozesu kritiko askotan bitartekari. Guzti honek izaugarrizko garrantzia ematen dio lekuko mikroorganismoen nortasuna eta taldeko dinamikaren berri izateari. Lehen mikrobiologiako ikerketak ohiko kultibo tekniken bidez egin ziren. Ondoren, epi uoreszentzia bidez egindako zenbatze teknikek bakterioen ugaritasuna azaldu zuten, eta teknika molekularrak bakterio adierazgarrienak aurkitzeko erabili izan ziren. Baino elikagai bilketa kultibo baliabideetan (nahiz organiko nahiz inorganikoak) hainbat aldiz haundiagoa da ingurune naturalean aurkitu daitekeena baino. Ondorioz, organismo gainartzaileen lorpena ingurune oligotro koetan ez da lan erreza eta adierazgarriak ez diren bakterioak aukeratzen bukatzen da. Antzeko aukeraketa bat gertazten da ingurune naturalean, maila ezberdin batean, bertakoak ez diren konposatuen ekarpenak daudenean. Hau prozesu naturalen ondorio izan daiteke hala nola erreken isuria, aerosolen uzteen bidez, e.a. edo gizakiaren jardueraren ondorioz, petrolio isuriak bezela. Tesi honetan hauetako gehitze batzuk bakterioen dibertsitate eta funtzioetan duten eragina aztertzen dugu.
Geroz eta jakin min handiagoa dago bere ingurunean organismo gainartzaileen isolamenduan, honek hazten ari diren organismoen ahalmen metabolikoari buruzko informazioa edukitzen lagunduko baitu. Beraz, aztertzea erabaki duguna zera da, Mediterraneo itsasoan isolatutakoen adierazgarritasuna, klonen liburutegi eta DGGE ngerprinting teknika molekularren bidez lortutako sekuentziekin alderatuz. Gure analisiak kultibo baliabideen
bidez sortutako hautaketa nabarmentzen du, baina era berean gure azterketa eremuko dibertsitateari buruzko ezaguera haunditzea laguntzen digu eta beste tekniken bidez aztertu ezin daitekeen “mikrobiar biosfera arraroa” aztertzeko baliabideak ahalbideratzen dizkigu.
Elikagaien gehitzeak nola bakterioen dibertsitatea aukeratzen eta mikrobiar funtzioan eragiten duen aztertzeko, mesokosmosean saiakuntza ezberdinak egin ditugu, non Blaneseko badian (Mediterraneo itsasoko ipar-mendebaldea) hartutako ur laginei elikagai ezberdinak gehitu dizkiogun. Mesokosmoseko beste saiakuntza bat galizar kostaren parean egin zen, Prestige petroliontzia ondoratu ondoren, petrolio isuriak duen eragina aztertzeko. Mesokosmoseko saiakuntzak ugariak izan dira gehitzeen ondorioak zehazteko, egoera naturalak simulatzeko intentzioarekin. Saiakuntza hauetan ingurune parametro ezberdinak aztertu ziren, era berean bakterio produkzioaren eta ugaritasunaren datuak bildu ziren. Bakteriar taldearen estruktura DGGE ngerprintingteknikaren bidez nkatu zen, teknika honek lagin ezberdinak alderatzea eta dibertsitate gainartzailearen geroagoko analisi bat ahalbideratzen ditu Shannon-Weaver indizearen bidez, banden presentzia eta ausentzian oinarrituz. Analisi hauek, gehiketa ezberdinen ondoren, harrapariek bakterioen aukeraketan duten eragina erakusten dute eta, adibidez, Saharako hautsen pilatzeak mikrobiar funtzioetan eta taldeko metabolismoan duen eragina argitzen du, bereziki hautsak ekartzen duen fosforo inorganiko eta karbonoaren ondorioz. Petrolio isurien saiakuntzetan, bakterioen estruktura, produkzio eta ugaritasunean ondorioak antzeman ziren, isuriak Prestige petroliontziaren ezbeharraren ondoren bilatutako kontzentrazioak laukoizten ziren gehitzeetan soilik.
Bi kanpaina ezberdinetan, Atlantiko Iparraldean egindako saiakuntza bat ere gehitzen dugu, non dialisi boltsak eta trasplante saiakuntzak erabili ziren, mikrobiar komunitatearen funtzioa eta estruktura zehazteko garaian, bottom-up egoerak top-down faktoreak baino gehiago izan zirela egiaztatzeko. Emaitzek saiakuntzen arteko ezberdintasunak erakutsi zituzten, baina orokorrean bottom-up faktoreen eragin handiagoak antzeman ziren harrapariei dagokiena baino.
Tesi hontan azaltzen diren saiakuntza ezberdinak jeneralizazio batzuk aurkeztea zilegitzen digu, nahiz eta desadostasun batuk agertu, hauek azken atalean eztabaidatzen dira.
13
Introduction
GENERAL INTRODUCTION
BACTERIA IN THE ENVIRONMENT
The ocean is probably the oldest and largest ecosystem on the planet. Microorganisms living there
are the closest living descendants of the original forms of life on Earth (Hunter-Cervera et al. 2005).
By increasing the oxygen concentration in the atmosphere they shaped a new aerobic environment
where plants, animals and all other life forms evolved.
Bacteria are the most abundant and species-rich groups of microorganisms. They mediate many
critical ecosystem processes that sustain life on Earth, and other organisms are unable to achieve
(Horne-Devine et al. 2003). Some examples of processes carried out exclusively by microorganisms
are the key processes that set the pace of the nitrogen cycle. Nitri cation, denitri cation and half of
the microbially-mediated nitrogen xation occur in the oceans. Some bacteria play a role in cloud
formation by cycling compounds such as dimethylsul de into the atmosphere. Other prokaryotes
adapted to extreme environments are known to participate in the constant ef ux of methane from
the surface of the oceans, although the process is not yet entirely understood. These are only some
of the physiological characteristics that have been discovered so far, but the huge microbial diversity
indicate that there may be many more metabolic capabilities yet to be discovered.
APPROACHES TO PROKARYOTE DIVERSITY: CULTURING vs MOLECULAR
Early marine microbiologists were convinced that a high percentage of abundant pelagic could
be recovered on substrate-amended agar plates (Zobell 1943). Lately, epi uorescense microscopy
revealed that the bacteria readily cultivable on standard rich growth media were only about 0.1 percent
of the observable bacteria. (Ferguson et al 1984). Jannash & Jones (1959) reported such observations
in seawater, although this problem was universal, and was later named “the great plate anomaly”
(Staley & Konopka 1985).
14
Introduction
In the mid 1980s molecular methods were introduced in Microbial Ecology. Somehow surprisingly,
the results generated by these new techniques did not match the results obtained by culturing. They
revealed that some dominant bacteria are not cultivable using standard methodologies, and others
that are easily cultivable occur at such low abundance in the natural environment that molecular
techniques cannot detect them. Molecular tools made possible to obtain the genetic sequence data of
the dominant organisms in spite of not being able to culture them.
While by ngerprinting techniques not more than 100 sequences are commonly detected, it has
been shown that with a higher effort in cloning, ca1600 sequences can be obtained (Acinas et al. 2004).
Recently appeared different molecular approaches allow massive sequencing of the bacterioplankton
community. Some examples are the study by Venter et al. (2004) in the Sargasso Sea that used a
brute-force shotgun sequencing approach that permit them to estimate that the sample analyzed
contained ca.1800 different bacterial taxa. Sogin et al. (2006), using a more powerful and cheaper
sequencing approach to analyze deep-water samples from the North Atlantic, estimating richness to
be of thousand taxa. Recent molecular studies thus show that the taxonomic composition of natural
bacterial communities comprises a tremendous diversity, although relatively few species appear to
form the bulk of the biomass (Pedrós-Alió 2006).
Fig.1 Plots of number of individuals versus taxon rank. The total curve represents biodiversity and is postulate by Pedros-Alió (2006) to be composed of two sections: dominant diversity (red) and to rare taxa (blue), which survive in the ecosystem at low abundances. Molecular techniques can presumably access most taxa in the diversity part of the curve and the biodiversity tail is usually not accessible, however isolation can retrieve some taxa from this part of the curve.
15
Introduction
Margalef (1994) distinguished between ecosystem diversity (a property of the ecosystem) and
biodiversity, de ned as the total genetic information on Earth (or any part of it) while diversity, in
turn, would be the components that are active and abundant at one particular time and place. Based
on this statement, Pedrós-Alió (2006) suggested to consider the taxa retrieved by PCR as a proxy for
the diversity of an ecosystem. However, rare taxa (the seed bank) that form the long tail of a rank-
abundance curve are necessary to complete the biodiversity. Also, it is important to note that the
number of representatives of a given group is not necessarily linked to the importance of that group
in the functioning to the community (Fig.1).
In relation with this idea, Chapter 1 of this thesis focuses in trying to increase the diversity
previously estimated by molecular approaches (DGGE and clone libraries) at the Blanes Bay Microbial
Observatory (BBMO) with the diversity that would be retrieved by traditional culturing techniques.
Questions at play are not only a (bacterial) group by group comparison of the differences between
what is retrieved by culturing and what is retrieved by molecular techniques, but also whether there
are new bacterial groups appearing, whether some cultured bacteria are good representatives of groups
of sequences appearing in situ, etc. A special attention is given to the microdiversity patterns present
in the culture collection vs. in the molecular techniques. Microdiversity can be de ned as the diversity
measured above the division of species: sequences that are more than 97% similar in their 16S rRNA,
but that they are not exactly identical
RELATING BACTERIAL DIVERSITY AND FUNCTION
There is an increasing interest in relating bacterial diversity to their function in the environment.
However, bacterial activity has mostly been studied in bulk. There are numerous approaches useful
for establishing a functional role for bacterioplankton species and some are used for determining the
activity of microorganisms that are uncultured. Some examples are the use of bromodeoxyuridiene
uptake to identify growing cells (Urbach et al. 1999), and 13C incorporation into lipids to identify
organisms that assimilate acetate. The MARFISH technique combines autoradiography and in situ
hybridation to identify the cells that are able to assimilate speci c organic compounds (Cottrell &
Kirchman 2000). Another approach that would allow us to investigate the organisms’ physiology is to
isolate it and sequence the genome. Indeed, sequencing of complete microbial genomes, now routinely
reveals organisms capacities to perform unexpected metabolic functions, that were impossible to
identify before.
16
Introduction
Traditional methods (e.g. culturing on ZobBell’s marine growth media) have limitations, because
this media has ca. 170-fold more DOC than natural seawater and thus, selects for “eutrophic” bacteria,
those that can develop in such a large amount of carbon. Consequently, the isolation of microbes
found in oligotrophic environments was a great challenge. Many culture techniques have been used
in order to obtain isolates of the most representative members of the bacterial communities. One
approach is the “dilution to extinction” culturing method for the isolation of oligotrophic bacteria
in seawater (Button et al. 1993) and later modi ed by Rappé et al. (2002). Other approaches are
microencapsulation techniques, and the use of low-nutrient agar plates (Eilers et al. 2001). Relatively
new strains that represent many of the most abundant marine clades have been isolated. This is the
case of members of the SAR11 clade, which is the most abundant group of bacteria detected in
seawater DNA by gene cloning and by FISH (Morris et al. 2002, Rappé et al. 2002).
Cultures are important because they provide complete genomes and the means to test the hypotheses
that emerge from genomic data. Cultures supported by predictions from genomic data, together with
metagenomics are a powerful combination. The major success of metagenomics was the discovery
of the light-driven, proton pump proteorhodopsin in the fragmentary genome data of SAR86 (Bejá
et al. 2000). This was the rst report about the presence of such retinaldehyde protein in the domain
bacteria, and this report sparked considerable interest in this potentially important mechanism for
capturing light energy in marine ecosystems.
The Moore Foundation had the initiative to sequence the whole genome of isolates from different
environments, and 9 of our isolates were selected, giving the possibility to learn more about their
function in the environment. For example the manual annotation of M152 (Polaribacter sp.) revealed
the presence of proteorhodopsine (Gonzalez et al. 2008). Furthermore inM134 (Dokdonia sp.) the
bacterial light-dependent proton pump was shown to provide M134 with phototrophic potential
(Gómez-Consarnau et al. 2007).
MARINE MICROBIAL ECOSYSTEMS
Marine microorganisms are known to live in every corner of the ocean, as they are metabolically
versatile to adapt to any environment. The microbial ecologists’ trade is to examine whether bacterial
communities differ according to the type of environment. Although there are likely millions of
species of bacteria, we are only starting to investigate patterns in their diversity and which are the
forces that governs these patterns (Ward et al. 1998). Decades ago it was unclear whether marine
17
Introduction
microbial species are either cosmopolitan, or are more provincial and limited to certain geographical
areas. Fenchel & Finlay (2004), after Baas-Becking (1934), proposed that the majority of marine
planktonic microorganism were cosmopolitan. The arguments for the “everything is everywhere” idea
is that spatial barriers do not occur due to the great capability for dispersal into new locations (Finlay
2002, Fenchel & Finlay 2004, Collins et al. 2001). The Baas-Becking (1934) saying continued as
“everything is everywhere, but the environment selects”. This theory argued that spatial differentiation
would depend on the capability of each microorganism to succeed in a new environment where it
is transported. An example of clear differences in actual community composition are presented by
Pommier et al. (2007), showing 16S rRNA gene libraries collected at nine distant localities spread
around the world were quite different, and some organisms could not be considered cosmopolitan. Of
course, cosmpolitanism could be present in the “rare species” pool (sensu Pedrós-Alió 2006) and the
Pommier study would have failed to detect that part of the bacterial biodiversity, because it had used
a technique that sampled only the “diversity” (Fig. 2).
A priori, each physical, chemical and biological parameter in the oceans could have an impact on
the diversity of microbial communities which would express in a relationship between the observed
diversity pattern as a function of the contextual environmental parameter (Ramette et al. 2007).
Fig.2 Global sampling of marine bacterioplankton communities. (Pommier et al. 2007)
18
Introduction
Bacterioplankton composition has been shown to change along temporal (seasonal cycles, Pinhassi
& Hagstrom 2000, Morris et al. 2005, Schauer et al. 2003, Eilers et al. 2001) and spatial scales
(Pinhassi & Berman et al. 2003, Selje et al. 2004). Since bacteria respond quickly to biotic and abiotic
changes of their environment, the vertical distribution of physical and chemical parameters in the
marine water column should be the basis for a pronounced strati cation of the prominent microbial
populations (Rheinheimer & Gocke 1994, Ramsig et al.1996. Morris et al. 2004, Giovannoni et
al. 1996, Wright et al. 1997). Diversity (or bacterial community structure) has also been found to
vary following latitudinal gradients (Selje et al. 2004, Pommier et al. 2005). Thus, bacterial species
composition in the ocean appears to be determined by factors known to affect the species in culture,
such as temperature, light, nutrients, and the quantity and quality of DOM. Those are all parameters
that we can consider “bottom-up” forces.
To evaluate the magnitude of that “bottom-up” forcing compared to the effect caused by trophic
interactions (predators, “top-down”), we designed a transplant experiment, that appears in Chapter 5,
using dialysis bags to incubate water from different depths in the original conditions and at different
depths with their associated ecological conditions. We aimed to address the possibility of bacterial
community structure to signi cantly vary depending on the environmental conditions and the presence/
absence of bacterial predators.
EFFECTS OF NUTRIENT ADDITIONS ON BACTERIOPLANKTON COMMUNITY STRUCTURE
Changes in inorganic nutrient availability are of decisive importance for de ning ocean primary
productivity and for regulating phytoplankton community composition, succession and distribution.
In response to the increase or decay of different phytoplankton species, bacterioplankton community
structure changes relatively fast (Pinhassi et al. 2004, Sapp et al. 2007). Since heterotrophic bacteria
are the major consumers of dissolved organic matter in the aquatic environment, limitation of bacteria
by organic or inorganic nutrients can have important consequences in biogeochemical Carbon cycling.
The speci c limiting nutrients for bacterial growth may differ depending on the site. Blanes Bay,
and the whole of the Mediterranean show P-limitation (Thingstad et al. 1998, Pinhassi et al. 2006)
although not always (Sala et al. 2002).
19
Introduction
The function and diversity of the marine microbial communities is especially affected in coastal
areas, which exhibit variability in factors such as salinity, and organic and inorganic nutrients. This
variability is consequence of external forcing factors due to natural causes (river-inputs, rainfalls) or
anthropogenic effects. Human activities are responsible for most of the allochthonous additions, and
industrialization is a component of global change and most of the allochthonous sources of carbon
and inorganic nutrients to the ocean are predicted to vary in response to global change. One of the
consequences of climate change is a decrease in rainfalls that might lead to deserti cation of many
areas and to an increase in the deposition of dust originated in these areas.
Besides these processes, organic contamination may also cause bacterioplankton community
successions (Revilla et al. 2000, Schendel et al. 2004). Coastal eutrophication caused by human
activities provides allochthonous sources of nutrients for bacterioplankton communities. Some of
the most widespread contaminants in the environments are the petroleum hydrocarbons (Santas et
al. 1999) and oil spills are a constant problem in many coastal waters. Bacteria can use the PAHs
(Policyclic Aromatic Hidrocarbons, a component of oil) as an allochthonous source of carbon and
energy (Castle et al. 2006), but these compounds can also negatively affect bacterial composition and
function, as quite a number of studies have shown (Siron et al. 1993, Macnaughton et al. 1999, Kasai
et al. 2001, Ogino et al. 2001, Castle et al. 2006, Ohwada et al. 2003, Maruyama et al. 2003, Capello
et al. 2006 and 2007, Teira et al. 2007)
The aim of Chapters 2, 3 and 4 of this Thesis is to assess the effects caused by different additions on
bacterial function and bacterioplankton community structure. In Chapter 2 we simulated the Prestige
oil spill in waters of the Ría de Vigo kept in mesocosm tanks. Chapters 2 and 3, in contrast, were
performed in the NW Mediterranean. In Chapter 3 we wanted to determine the effect of different
inorganic nutrients on phytoplankton species development and the subsequent effects that they could
cause on bacterioplankton. Chapter 4 evaluated the effect of a Sahara dust deposition event on the
microbial communities of the coastal NW Mediterranean.
20
Introduction
BACTERIAL DIVERSITY DETERMINED BY ENVIRONMENTAL CONDITIONS
Different environmental conditions (temperature, salinity, nutrient concentrations, light…) are
responsible for structuring microbial communities in the ocean. Many past studies have focused on
de ning the selective role of organic and inorganic nutrient availability on the growth of speci c
bacteria (Pinhassi et al. 2004, Allers et al. 2007). In some areas the dominant phylogenetic groups
differ depending on the season. For example, Eilers et al. (2001) found that Bacteroidetes dominate
during spring time and Alphaproteobacteria dominate during summer in the Baltic Sea. However,
in the North Sea, the dominant group during spring was Bacteroidetes while Gammaproteobacteria
took over during summer time. Therefore, bacterial successions present diverse patterns in different
environments.
The Alphaproteobacteria may have special adaptation which allow them to use compounds in
low concentrations and may explain why the SAR11 clade is that abundant in open oceans (Morris
et al. 2002, Malmstrom et al. 2007). Concretely in the NW Mediterranean sea Alphaproteobacteria
was found to account for 40% of total cell counts during summer and spring (Alonso-Sáez et al.
2007). Cytophaga-like bacteria are known to be abundant on particles in estuarines and other marine
environments (DeLong et al. 1993, Crump et al. 1999). Indeed, Pinhassi et al. (2004) found that
Flavobacteriaceae could be particularly important in the processing of organic matter during algal
blooms. Gammaproteobacteria have been shown to have the capacity for fast growth in response to
elevated nutrient concentrations (Pinhassi & Berman 2003).
Changes in bacterial composition have also been reported from the Mediterranean Sea (Schauer et
al. 2003, Alonso-Sáez et al. 2007) where different studies have shown shifts in bacterial community
structure due to inorganic nutrient additions (Schäfer et al. 2001, Lebaron et al. 1999, Pinhassi et al.
2006, Allers et al. 2007). Microbial communities from ecosystems receiving nutrient additions tend
to be dominated by those organisms capable of competitively dominate the uptake of the nutrients
added. Microbial communities from contaminated ecosystems also tend to be dominated by those
organisms capable of utilizing the toxic compounds. As a result, these communities are less diverse
in comparison to pristine environments (Torsvik et al. 2002). Examples of this specialization of the
marine communities due to the addition of oil spills are reported by Macnaughton et al. (1999), Ogino
et al. (2001), Kasai et al. (2002), Teira et al. (2007). After an exposition to petroleum contamination
there is a strong selection for hydrocarbon degrading bacteria, such as genus Alcanivorax and
Cycloclasticus.
21
Introduction
PREDATION PRESSURE ON BACTERIAL ACTIVITY AND COMMUNITY STRUCTURE
Depending on the type of marine system, the main factors responsible for the regulation of bacterial
activity and growth can be temperature (Shiah & Ducklow 1994), grazing (Thingstad et al. 1997) or
competition for limiting nutrients (Rivkin & Anderson 1997, Thingstad et al. 1997). Indeed, grazing,
especially due to heterotrophic nano agellates (HNF), has been identi ed as the main factor leading
to bacterial loss (Fenchel et al. 1982, Sherr & Sher 1984, Wright & Cof n 1984, Pace 1988), and
can often balance bacterial production. In oligotrophic environments the substrate supply is more
relevant than predators (Sanders et al. 1992). Several studies indicate that agellates tend to graze
preferentially upon the active component of the bacterioplankton assemblage (del Giorgio et al. 1996),
thus selectively exerting grazing pressure upon some speci c groups of bacteria, a phenomenon that
could modulate assemblage composition (Pernthaler 2005, Pernthaler & Amann 2005).
Although top-down and bottom-up controls are widely known concepts in aquatic microbial ecology
(Pace & Cole 1994), there is still disagreement to whether the abundance and productivity of bacteria
are determined mainly by predators or nutrients (Pernthaler et al. 1997). Furthermore, from a structural
point of view, both predators and nutrients may be major forces shaping the genotypic composition
of bacterial communities (Jürgens & Güde 1994). In addition, succession patterns might appear as
shaped by selective mortality, since heterotrophic nano agelates or viral lysis can be phylotype-
selective and affect the relative proportions of different microbial populations (Thingstad et al. 2000,
Pernthaler 2005). Allers et al. (2007) reported selective agellate grazing on the bacterial population
in response to glucose and phosphorus manipulations in a mesocosm. Grazing resulted in a change in
bacterial community composition, varing from Alteromonadaceae dominance to Rhodobacteriaceae
dominance. Viruses are also important in the control of bacterial abundance (Bratbak et al. 1990)
In all the experiments performed in this thesis we wanted to address the question of which were the
bottom-up and top-down factors controlling the bacterial communities. In order to do so we took into
consideration the grazing effect, either by measuring the HNF abundance or by controlling predator
presence (by ltration).
22
Introduction
EXPERIMENTAL APPROACHES
Biological oceanographers try to understand how marine organisms and biological processes
affect and are affected by physical, chemical and geochemical ocean processes. The relative marine
microbial activities and their in uences in global oceanic processes are dif cult to assess. There
are two approaches to address these questions. The rst, and most realistic one, is to perform in
situ sampling of different environments. Negative aspects of this approach include that it is time
consuming, that it requires economical effort, and that depends on favourable natural conditions.
Because of these problems with carrying out in situ studies, laboratory experiments are designed to
determine the factors affecting metabolism and diversity of bacteria in the environment. However,
this second approach is handicapped by the dif culty of simulating natural conditions. As reported by
Pinhassi & Berman (2003), growth of bacteria from particular phylogenetic lineages are commonly
stimulated by the input of certain labile substrates which might be released during experimental
manipulations.
Hence, it is important to restrict unnecessary manipulations. To avoid these laboratory limitations
mesocosms experiments are commonly used in aquatic ecology to simulate natural behaviour of
communities. These are easy to manipulate and minimize disturbances in the experimental setup by
increasing the incubation volume (Odum et al. 1984). Many studies have performed these approaches
in order to evaluate the response of marine microorganisms to nutrient additions concretely in the
Mediterranean sea: Schäfer et al. (2001), Pinhassi et al. (2006) and Allers et al. (2007). In Chapter
3 and 4 we also used mesocosms experiments to determine the effect of different resources in Blanes
Bay (NW Mediterranean) communities. However, this kind of experiments can also mimic other
situations in the environment such as the release of contaminants to the environment e.g. oil-spills
(Siron et al. 1993, Ohwada et al. 2003, Kasai et al. 2002, Teira et al. 2007). In Chapter 2 we used a
mesocosm experiment with a similar set-up as reported by Olsen et al. 2006). It allows generalization
of some conclusions to situations other than the one assayed, but also allow to see differences between
experiments. A more realistic approach to study the in uences of the environmental conditions to a
speci c bacterial community is the used of the dyalisis bags, a kind of “cage “ that allows the ux
of nutrients. In Chapter 5 we combine mesocosm with transplants using dyalisis bags to assess the
bottom-up or top-down factors controlling the microbial communities at different depths.
23
Introduction
RESOURCES
Fig. 3 Conceptual outline of the thesis. Bacterial community composition and functioning are controlled by two different groups of factors (top-down and bottom-up). The aim of the thesis is to assess the effects of substrates from different origins. In Chapter 1, by determining the bacteria thast grow on traditional culturing, media. In Chapter 2, by evaluating inputs of anthropogenic origin (oil-spills). In Chapter 3, by atmospheric depositions (Saharan dust). In Chapter 4, by stimulation of phytoplankton blooms and the subsequent effect on bacteria, and in Chapter 5, by the effect of mixing of waters of different origin along the vertical strati cationin the ocean.
Top-down
Bottom-up
24
Introduction
IN SUMMARY, AIMS OF THIS THESIS:
The main goal of this thesis was to determine how the different allochthonous resources
affected bacterial community structure, abundance and activity. In order to assess this question
we used different approaches, such as traditional culture techniques, mesocosm experiments
and transplants: which include a combination of mesocosms and dialysis bags.
Schauer et al. (2003) and Alonso-Sáez et al. (2007) assessed the seasonality of bacterioplankton
diversity by clone libraries and DGGE (a ngerprinting technique) in Blanes Bay. As a
consequence of the increasing interest for isolation of microorganisms, we put some effort at
isolating in plates bacteria from this environment. Chapter 1 was focused on the comparison
of the sequences retrieved by culturing with the sequences obtained by culture independent
techniques previously reported in other studies.
Mesocosms experiments was an approach used in this thesis to determine the effect of different
allochthonous additions, of natural or anthropogenic origin. In Chapter 2 we described the
simulation of the Prestige oil spill in front of the Galician coast, in Chapter 3 we analyze bacterial
function and community structure after different phytoplankton bloom stimulation experiments
using different nutrient sources in the NW Mediterranean sea. In Chapter 4 we mimicked a
Saharan dust deposition event in the NW Mediterranean and describe their consequences on the
bacterial community structure and on microbial community function, including the metabolic
balance.
In Chapter 5 we considered “allochthonous” the water from depths different from those where
the resident bacteria developed. We used a transplant experiment to determine the controlling
factors of bacterioplankton communities of the epipelagic, the DCM and the mesopelagic in the
North Atlantic Ocean.
Increased marine bacterioplankton diversity detected by combining culturing and culture-independent techniques
Itziar Lekunberri, Josep M Gasol, Silvia G. Acinas, Laura Gómez-Consarnau, Emilio O. Casamayor, Ramon Massana, Carlos Pedrós-Alió and Jarone Pinhassi
FEMS Microbial Ecology, submitted
CHAPTER 1
27
Chapter I
ABSTRACT
Despite the recognition that microbial biodiversity is an important characteristic of marine
ecosystems, knowledge about the diversity of marine bacteria that can be cultured on traditional
media is very limited. We studied the phylogenetic diversity of 136 bacterial isolates obtained from
coastal Mediterranean seawater at the Blanes Bay Microbial Observatory (www.icm.csic/bio/projects/
icmicrobis/bbmo). Nearly all isolates belonged to the Gammaproteobacteria, Alphaproteobacteria, or
the Bacteroidetes (70, 40 and 20 isolates respectively). The 16S rRNA gene sequences of the isolates
were compared to environmental sequences previously obtained by molecular methods, including
DGGE (60 sequences) and clone libraries (303 sequences), from samples collected at different seasons.
Nine cases of complete or nearly complete identity between isolates and in situ sequences were found:
ve in the Gammaproteobacteria, three in the Alphaproteobacteria and one in the Bacteroidetes. This
suggests that culturing to some extent provides representative organisms for physiological response
experiments for various taxa detected by molecular methods. The limited overlap between sequences
obtained by culturing and molecular techniques resulted in a substantial increase in the bacterial
diversity (richness) detected at this sampling site by combining the two approaches. Rarefaction curve
analysis showed similar patterns for sequences from isolates and molecular approaches, except for
Alphaproteobacteria where culturing retrieved a higher diversity per unit effort. Around 30% of the
environmental clones and isolates formed microdiversity clusters constrained at 99% 16S rRNA gene
sequence similarity. The nding of microdiversity clusters among isolate and clone sequences alike,
suggests that this type of microdiversity is not an artifact and that similar evolutionary processes act on
cultured and uncultured bacteria. Thus, marine bacterial isolates could serve as models to investigate
how microdiversity clusters originate and provide indications of the ecological signi cance of such
genetic variability.
28
Isolate bacterioplankton diversity in the NW Mediterranean
INTRODUCTION
The application of molecular methods to retrieve small subunit rRNA sequences from natural
environments revolutionized microbial ecology (Pace et al. 1985). About 20 years later, environmental
sequences are increasingly reported to data bases and have signi cantly expanded the known diversity
of bacteria (Giovannoni et al. 1989), archaea (DeLong 1994, Fuhrman et al. 1994) and eukarya (Díez
et al. 2001, López-García et al. 2001, Moon van der Stay et al. 2001). For most of this period, culturing
has been looked upon almost with suspicion, as a technique that provided a biased picture of the
“true” diversity of natural communities. In fact, the number of studies of cultured diversity has been
extremely low compared to those reporting clone libraries or analyses using different ngerprinting
techniques (Donachie et al. 2007).
Ribosomal RNA sequences, however, are a poor substitute for the organisms themselves. The
cultured organisms can be used as model organisms for physiological studies (Giovannoni &Stingl
2007, Gómez-Consarnau et al. 2007) and their genomes can be sequenced (Moran et al. 2004, Bauer
et al. 2006, González et al. 2008) and used as scaffolds to reassemble genomes from metagenomic
analyses (Venter et al. 2004, Rusch et al. 2007). Recently, novel protocols for isolating bacteria have
been developed (Zengler et al. 2002, Simu et al. 2005, Giovanonni et al. 2007, Stingl et al. 2007,
Nichols et al. 2008) generating new interest in culturing. However, even with the traditional culturing
methods, new microorganisms can be easily isolated from any environment (Donachie et al. 2007).
Obviously, these microorganisms are present in concentrations that are too low to be retrieved by
molecular methods that essentially detect only the most abundant sequences (Pedrós-Alió 2007).
These easily cultured organisms may form part of the rare biosphere (Pedrós-Alió 2007): the very
large collection of microorganisms present in ecosystems at very low abundance (Sogin et al. 2006).
This low abundance protects them from viral lysis and, partially, from predation, and they can persist
for very long periods of time with little or no activity. It has been suggested that if conditions change,
members of the rare biosphere can quickly multiply and become part of the abundant taxa and
participate actively in carbon and energy ow (Pedrós-Alió 2006). Thus, they constitute a “seed-bank”
that may become extremely useful for the community to respond to changing conditions, including
the appearance of pollutants or other human-made perturbations. The biodiversity of any ecosystem
is not completely known until this rare biosphere is characterized (Pedrós-Alió 2006, Donachie et al.
2007). Since molecular methods are generally incapable of retrieving these rare organisms, isolation
in pure culture remains one of the best windows into the rare biosphere.
29
Chapter I
Comparisons between molecular and culturing approaches have been attempted in a very limited
extent, comparing only one or a few samples and a low number of clones and/or cultures (Suzuki et al.
1997, Eilers et al. 2000, 2001, Hagström et al. 2002, Kisand & Wikner 2003, Stevens et al. 2005). Here,
we analyze a collection of bacteria isolated in pure culture from the Blanes Bay Microbial Observatory
(BBMO). This is an oligotrophic coastal site in the Northwestern Mediterranean Sea, where extensive
molecular data exist (Schauer et al. 2003, Alonso-Sáez et al. 2007) allowing for comparisons between
both approaches. Thus, the aim of the present study was to compare the diversity of cultured isolates
to the diversity of environmental sequences obtained by cultivation independent techniques. Further,
we aimed at comparing the patterns of microdiversity among cultures and clones. In addition, nine of
the isolates were selected by the Gordon and Betty Moore Foundation Marine Microbiology initiative
(www.moore.org/marine-micro.aspx) for whole genome sequencing. The present study intends to
provide the molecular environmental context for these bacteria.
MATERIALS & METHODS
Study area and sampling. The site studied was Blanes Bay, in the NW Mediterranean (The Blanes
Bay Microbial Observatory), at 70 km North of Barcelona. Water was sampled at about 1 km offshore
(41º40’N, 2º 48’E) from a boat, and immediately ltered through a 200 µm mesh net. Sampling was
done in parallel to studies of carbon and sulfur cycling (see details in Alonso-Sáez et al. 2008 and
Vila-Costa et al. 2008) through the period 1998-2004 (see details in Table 4). Seawater was kept in
25-l polycarbonate carboys and transported under dim light to the laboratory (less than 2 h).
Origin of bacterial DNA and isolates.
1) Natural seawater. Samples were collected as explained above and inoculated directly on agar
plates as detailed below.
2) Whole water enrichments. The effect of nutrient additions on the growth of heterotrophic
bacteria was examined in un ltered, whole-water samples (Pinhassi et al. 2006a). Brie y, nutrients
were added to 250 ml samples in nal concentrations of 40 µM C (as glucose), 2 µM N (NH4Cl),
and 0.6 µM P (NaH2PO
4), singly and in all different combinations. After incubation for 24 h at in situ
temperature in the dark, samples for bacterial production, total bacterial numbers and colony forming
units were collected.
30
Isolate bacterioplankton diversity in the NW Mediterranean
3) Dilution cultures. Several times during the period 2001-2004, 1.9 l of seawater were used as
growth media after ltration through 0.2 µm pore-size sterivex units (Durapore-Millipore) using a
peristaltic pump at pressures <200 mm Hg (for details see Pinhassi et al. 2006a). Inocula consisted in
100 ml seawater prepared by gravity ltration through 0.8 µm pore-size lters (Nuclepore). Bacteria
developed in 2 L Nalgene bottles maintained at in situ temperatures (15-22ºC) and in the dark.
Cultures were unamended controls or enriched with C and P, alone or in combination. Substrates
added were e.g. glucose, dimethylsulfoniopropionate (DMSP), inorganic P, ATP, or DNA, all at a nal
concentrations of 20 µM C (Glucose, DMSP) or 0.6 µM P (phosphate, ATP, or DNA).
4) Mesocosm experiments. Two mesocosm (20-100 L) experiments reported in detail in Pinhassi
et al. (2004) and Allers et al. (2007) provided additional sequences. For these experiments surface
seawater was collected from Blanes Bay and was transferred to the laboratory. Bacterial growth and
diversity were monitored in mesocosms enriched with N and P and/or C for approximately one week
(see details in the references mentioned above).
Collection of community DNA. To collect in situ microbial biomass, between 5-15 L of seawater
were ltered using a peristaltic pump through a 5 µm pore size Durapore lter (Milipore) and a 0.2 µm
Sterivex lter (Durapore, Milipore) in succession. The 0.2 µm Sterivex unit was lled with 1.8 ml of
lysis buffer (40 mM EDTA, 50 mM Tris-HCl, 0.75 M sucrose) and stored at –70ºC. Microbial biomass
from the experiments was collected by ltering 0.5-1 L water onto 0.2 µm-pore-size polycarbonate
lters (Milipore) at <200 mm Hg, and lters were stored in lysis buffer at –70ºC. Nucleic acids were
extracted by a standard protocol using phenol/chloroform (details in Schauer et al. 2003)
Origin of the sequences used in the phylogenetic comparison.
1) Isolates. A total of 625 colonies were collected from the above-mentioned sources, from which we
selected 136 isolates for partial 16S rDNA sequencing based on differences in colony morphology and
abundance on agar plates at different sampling occasions or experiments. The isolates were obtained
by plating 100 µl of undiluted, or 10x, 100x and 1000x diluted seawater of untreated or experimental
samples, in triplicates, onto Zobell agar plates (Zobell 1943). Agar plates were incubated at in situ
temperature in the dark for 10-15 days. The 16S rDNA sequences of the 136 bacterial isolates were
ampli ed by means of PCR using Taq polymerase (Boehringer-Mannheim) from DNA preparations
using bacterial 16S rDNA primers 27f and 1492r. Isolates were partially sequenced with internal
primer 358f, which yielded sequences of approximately 700 - 850 bp length. Accession numbers of
the isolates are given in the Table 4.
31
Chapter I
2) DGGE bands. DGGE bands from both environmental samples (27) and enrichment cultures (33)
(whole seawater enrichments and dilution cultures) were retrieved. DGGE ngerprints were performed
as previously described (Schauer et al. 2003, Sánchez et al. 2007). The 16S fragments (around 550
bp in length) were ampli ed with bacterial speci c primer 358f complementary to position 341 to
358, with GC-clamp and universal primer 907rm complimentary to position 927 to 907 (Muyzer et al.
1997). The PCR products were loaded in a 6% polyacrylamide gel with a DNA-denaturant gradient
ranging from 40 to 80%. The gel was run at 100 V for 16 h at 60°C in 1x TAE running buffer. The
DGGE bands were excised, reampli ed, and veri ed by a second DGGE. Bands were sequenced
using primer 358f without the GC-clamp; with the Big Dye terminator cycle-sequencing kit (Perkin
Elmer Corporation) and an ABI PRISM model 377 (v3.3) automated sequencer. These sequences
have been reported in Schauer et al. (2003), Alonso-Sáez et al. (2007), Pinhassi et al. (2004, 2006a),
and Allers et al. (2007).
3) Clone libraries. Clone libraries were constructed from in situ samples collected on 3 March,
14 May, 14 July, 4 August and 21 October 2003 (Alonso-Sáez et al. 2007). Brie y, 16S rDNA
sequences were ampli ed from total community DNA by PCR with universal primers 27f and 1492r,
and resulting amplicons were cloned using the TOPO-TA cloning kit. PCR amplicons were digested
with the restriction enzyme HaeIII (Invitrogen), and the RFLP patterns were compared to identify
unique clones. Clones were partially sequenced with internal primer 358f, which yielded sequences
of approximately 700 - 850 bp.
4) Additional sequences included in the analysis. For comparison, eight DGGE sequences from
the neighboring Banyuls Bay (ca. 150 km away) were included (Schäfer et al. 2001).
Phylogenetic analysis. The 16S rRNA gene partial sequences of our isolates were compared to
existing prokaryotic sequences in GenBank (NCBI) using BLAST (basic local alignment search
tool). Comparison of BLAST results from database searches with different regions of the sequences
showed that none of our sequences were chimeras. Phylogenetic analyses were performed using the
parsimony methods implemented in the ARB sequence analysis package by which sequences were
grouped into major phylogenetic groups. The trees were calculated from sequences approximately
corresponding to nucleotide positions 420 to 770 (E. coli numbering). Sequences of close relatives
included in GenBank after the BLAST process were included in the trees as references in order to
delimitate and name different clusters of sequences.
32
Isolate bacterioplankton diversity in the NW Mediterranean
Comparison of sequence diversity. Rarefaction analysis and diversity estimates by the non-
parametric Chao-1 richness estimator were carried out using the online rarefaction calculator
software (http:// www2.biology.ualberta.ca/jbrzusto/rarefact.php). For clustering sequence analysis,
the software Clusterer was used (Klepac-Ceraj et al. 2006). This allows grouping of sequences into
percent similarity clusters (100, 99% etc.) by the nearest neighbor approach (i.e., sequences are added
to clusters if there is at least one sequence which is within a set similarity threshold). These clusters
based on 99% similarity cut-off served as the basic units for statistical extrapolation of total sequence
diversity using the Chao-1 non-parametric richness estimator and for comparison of sequence
composition among the major phylogenetic groups and between molecular and isolation approaches.
RESULTS
General patterns of richness. The 136 bacterial cultures (84 unique) isolated from Blanes Bay
and analyzed here are detailed in the Table 4, where their origin, closest sequence, and closest cultured
relative are presented. The 16S rRNA sequences of these isolates were compared with 363 sequences
(194 unique) obtained from 16S rDNA clone libraries and denaturing gradient gel electrophoresis
(DGGE) from the same site, retrieved in previous studies (Schauer et al. 2003, Alonso-Sáez et al.
2007). Fig. 1 shows the main groups of bacteria retrieved from the BBMO indicating whether they
were found by culturing, molecular methods or both.
We explored how the percent similarity level chosen would affect the number of OTUs for isolates
and clones (Fig. 2A). When the % similarity was increased from 99 to 100% the number of clone
OTUs increased substantially while the number of isolate OTUs remained nearly the same. This
increase would appear to indicate a larger microdiversity among the clones compared to the cultures.
However, when the same exercise was carried out within each of the three main groups of bacteria,
signi cant differences appeared. In the Alphaproteobacteria there was a large increase in OTUs
among the clones, while only minor increases in OTUs among isolates was observed (Fig. 2B). The
Gammaproteobacteria showed precisely the opposite trend, with a marked increase in the microdiversity
of the cultures and a very small increase in that of clones (Fig. 2C). The Bacteroidetes showed almost
no changes either in isolates or in clones (Fig. 2D). Other than this, there was a clear discontinuity
above 97%, suggesting that this percentage is a reasonable cutoff point for this data set. Both the
Alpha- and Gammaproteobacteria showed the in ection point around 97% similarity. Interestingly,
33
Chapter I
the Bacteroidetes showed a much more uniform increase of OTUs with increases in percent similarity.
We also combined all sequences (isolates, clones, and DGGE bands) for each of the three major
groups (Fig. 2E), showing the clear difference in microdiversity between the Proteobacteria, on the
one hand, and the Bacteroidetes on the other. The next analysis consisted of calculating rarefaction
curves for all the sequences together (Fig. 3A) or for the three groups separating isolates and clones
Fig. 1.- Schematic phylogenetic tree displaying the major lineages in the marine prokaryotic plankton. The color of each branch indicates whether only clones (green), isolates (red) or both, clones and isolates (yellow), were retrieved from Blanes Bay. Lack of color indicates that we could not detect this group with any of these techniques. Cyanobacteria and algae are drawn in dark green since they appeared in the clone libraries in this study but no effort was made to isolate them.
Bacteroidetes
Archaea
Planctomyces
Alteromonas
Marinomonas
Halomonas
Epsilonproteobacteria
Pelagibacter
Roseobacter
Sphingomonas
Rhodobacter
Betaproteobacteria
Chloroflexi
Fibrobacter
Sphingobacteria
BacteroidetesFlavobacteria
Cyanobacteria Algae
Marine Actinobacteria
SAR86
Deltaproteobacteria
Gammaproteobacteria
Alphaproteobacteria
Verrumicrobia
Firmicutes
34
Isolate bacterioplankton diversity in the NW Mediterranean
Fig. 2.- Number of OTUs as a function of the percentage of similarity cutoff chosen to group sequences. A) The three major phylogenetic groups of bacteria combined as retrieved by culturing, cloning or both together B) Number of Alphaproteobacteria OTUs obtained with each technique. C) Same for Gammaproteobacteria; D) Same for Bacteroidetes. E) Total number of OTUs obtained using clone libraries and isolation techniques combined for each of the three main groups.
0
10
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30
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50
60
80 85 90 100
Gammaproteobacteria
0
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35
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Bacteroidetes
Nu
mb
er o
f OTU
sN
um
ber
of O
TUs
Percentage of similarity (%)
(A) (B)
(C) (D)
Percentage of similarity (%)
Nu
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er o
f OTU
s
Percentage of similarity (%)
All Alphaproteobacteria
0
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Group comparison AlphaGammaCFB
95
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totClonesIsolates
(E)
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35
Chapter I
Table 1.- Number of characterized isolates, clones, and DGGE bands belonging
to different taxonomic groups retrieved from Blanes Bay.
__________________________________________________________________________
Isolates Clones DGGE bands
Total Unique Total Unique In situ Experiments__________________________________________________________________________
Alphaproteobacteria 40 20 219* 80 16 17
Betaproteobacteria 2 2 1 1 0 0
Gammaproteobacteria 70 39 50 26 4 2
Deltaproteobacteria 0 0 0 0 1 0
Epsilonproteobacteria 0 0 1 1 0 0
Bacteroidetes 20 19 18 14 7 13
Firmicutes 3 3 0 0 0 0
Actinobacteria 1 1 4 3 0 0
Verrucomicrobia 0 0 10 9 0 0
All 136 84 303 134 28 32__________________________________________________________________________
* 95 belonged to the SAR11 group.
(Figs. 3B-D). The pooled data set was used to estimate the total number of OTUs present in Blanes
Bay by using the Chao 1 index. When the percent similarity was increased from 97 to 100%, the total
richness increased from 125 to 420 OTUs.
Next, we chose a 99% similarity level to compare the estimates of richness provided by isolates,
clones or the two combined, for the three main groups (Table 2, Figs. 3B-D). Unexpectedly, the
Chao estimates from isolates were similar to (Alphaproteobacteria and Bacteroidetes), or larger than
(Gammaproteobacteria), the estimates from clones alone (Table 2). Thus, there was at least as much
richness in the isolate collection as in the clone libraries. It must be recalled that all cultures were
isolated in Zobell medium under similar conditions, with no particular efforts to provide a variety of
culture conditions. Combination of isolates and clones provided Chao estimates similar to, or larger
than, those obtained with either isolates or clones separately (Table 2). Thus, the total richness estimate
obtained by combining the molecular and the isolation approaches was similar to that obtained for the
isolates alone. The rarefaction curves con rmed the differences among the three groups: more OTUs
from cultures for the Gammaproteobacteria, and similar estimates for the Alphaproteobacteria and
the Bacteroidetes (Fig. 3B-D).
36
Isolate bacterioplankton diversity in the NW Mediterranean
Fig. 3.- Rarefaction analysis for sequences obtained by isolation and by molecular approaches. A) All sequences together. B) Alphaproteobacteria. C) Gammaproteobacteria. D) Bacteroidetes.
0
50
100
150
200
0 50 100 150 200 250 300 350 400
100% 166 420.3 49.1 99% 116 231.8 28.3 98% 94 159 19.2 97% 74 125.6 17.6
Number of sequences screened
Num
ber o
f OTU
s obs
erve
d
OTUs Chao1 SD
0
5
10
15
20
25
0 10 20 30 40 50 60 70 80
Gammaproteobacteria
isolatesclones
0
5
10
15
20
0 5 10 15 20 25
Bacteroidetesisolatesclones
Number of sequences screened Number of sequences screened
Number of sequences screened
All sequences
Num
ber o
f OTU
s obs
erve
d Nu
mbe
r of O
TUs o
bser
ved
Num
ber o
f OTU
s obs
erve
d
0
5
10
15
20
25
30
35
0 50 100 150 200
Alphaproteobacteria
isolatesclones
(A)
(B)
(C)
(D)
37
Chapter I
Tabl
e 2.
-Cha
o1 r
ichn
ess
estim
ates
for
the
maj
or b
acte
rial
gro
ups,
bas
ed o
n 99
% s
imila
rity
cut
-off
usin
g th
e se
quen
ces
from
eith
er
isol
ates
and
clo
ne li
brar
ies
sepa
rate
ly, o
r al
l com
bine
d.
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
Is
olat
es
Clo
nes
Isol
ates
and
clo
nes
Taxo
n A
ll O
TU
s C
hao
SD
All
OT
Us
Cha
o SD
A
ll O
TU
s C
hao
SD
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
Alp
hapr
oteo
bact
eria
40
19
47
16
18
3 33
58
12
22
3 52
14
0 31
Gam
map
rote
obac
teri
a 70
27
10
8 39
46
16
36
14
11
6 39
92
23
Bac
tero
idet
es
20
19
34
11
19
16
44
16
39
32
77
19
Tota
l 13
0 67
24
6 52
24
8 62
12
5 21
37
8 11
6 23
2 28
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
38
Isolate bacterioplankton diversity in the NW Mediterranean
Identity of the cultures.
Alphaproteobacteria. Phylogenetic analysis of the Alphaproteobacteria revealed that a substantial
fraction of the isolates belonged to different clusters within the Roseobacter group (clusters Alpha
1 to 4, Fig. 4). In addition, we found isolates belonging to Erythrobacteraceae (cluster Alpha 5),
Hyphomonadaceae and Aurantimonadaceae.
Clusters Alpha 1 and 4 included only sequences obtained by culture independent techniques (Fig.
4, Table 3). Sequences within cluster Alpha 1 (related to clone NAC11-7) and within cluster Alpha 4
(related to clone CHAB-1-5) showed within group similarities >95% and >96% respectively (Table
3). On the other hand, cluster Alpha 2 included only sequences from isolates and P-enrichment
experiments (Mes9 and B-260) but none retrieved directly from the in situ samples. Isolate M193
within this cluster has been whole-genome sequenced. In addition, six isolates represented bacterial
groups that were not detected by molecular techniques (M588, M464, M441, M624, M623 and
M463).
Two clusters containing both isolates and environmental sequences were found. In cluster Alpha 3
the sequences showed within group similarities larger than 97% and belonged to the genus Nereida.
The isolate M128 was 99.8% similar to the clone sequence SPR23 and 99.6% to DGGE bands
B90 and B94, obtained in P, ATP and DNA addition experiments. Cluster Alpha 5 consisted of the
genus Erythrobacter, with four isolates, two clone library sequences, and one in situ DGGE band.
Erythrobacter isolate M442 was 99.8% similar to the clone sequence AUT4 and was identical to
DGGE band 27. Apart from these clusters, isolate M483 was 99.8% similar to clone SPR41. Thus,
isolates M128, M442, and M483 seem good candidates for investigating pure cultures representative
of marine bacterioplankton.
All together, 64% of the 16S rRNA sequences grouped into >97% similarity clusters. The
Alphaproteobacteria had substantial microdiversity with only 33% of the OTUs grouped at 99%
similarity level (Fig. 2B). The rarefaction curves for the isolates were steeper than for the environmental
clones (Fig 3B), implying that, with the same effort, there is a potential to recover larger species
diversity through isolation than through clone analysis.
39
Chapter I
Fig. 4.- Maximum Parsimony tree illustrating the relationships among the partial 16S rRNA sequences from Blanes Bay for the Alphaproteobacteria. Colors indicate the origin of the sequences: bacteria isolated on solid media (red), DGGE bands from experiments (orange), DGGE bands from in situ samples (green), and clones retrieved from in situ samples (blue). Clusters are indicated by vertical red lines. Identities between isolates and sequences are marked by white arrows. The sequences marked with asterisks have been genome sequenced. The SAR11 sequences are not displayed in this tree.
SUM32, Clone
M128, Isolate M149, Isolate
Alpha 1
Alpha 2
Alpha 3
Alpha 4
Alpha 5
0.10
NAC11-7, AF245635 SPR9, Clone WIN4, Clone BL98-28, DGGE band in situ BL03-35, DGGE band in situ
Roseobacter sp. DQ660384.1 B115, DGGE band experiments B140, DGGE band experiments
Roseovarius crassostreae, AF114484Mes23, DGGE band Mesocosmos
Roseoisalinus antarticus, AJ605747.2 SPR10, Clone
BL98-31, DGGE band in situ Mes38, DGGE band Mesocosmos
M612, Isolate Mes11, DGGE band Mesocosmos
Aquamarinicola italica, AM904563M605, Isolate
Mes26, DGGE band Mesocosmos Mes47, DGGE band Mesocosmos Ruegeria gelatinovorans, AF247972M606, Isolate
Mes28, DGGE band Mesocosmos Silicibacter pomeroyi, CF000031
B-230, DGGE band experiments BL98-33, DGGE band in situ
M193, Isolate * Mes9, DGGE band Mesocosmos B-260, DGGE band experiments
M165, Isolate M618, Isolate
M483, Isolate SPR41, Clone Roseobacter litoralis, X78312
BL03-37, DGGE band in situ B147, DGGE band experiments
SPR2, Clone Octadecabacter orientus, DQ167247
Jannashia cystaugens, AB121783M588, Isolate Nereida ignava, AJ748748
SPR23, Clone B90, DGGE band experiments B94, DGGE band experiments
M613, Isolate Mes8, DGGE band Mesocosmos
Rhodobacteraceae bacterium, AY962292.1 Mes16, DGGE band Mesocosmos
AUT20, Clone CHAB-1-5,AJ240910
AY-57, DGGE band Banyuls B79, DGGE band experiments BL98-34, DGGE band in situ BL03-43, DGGE band in situ
BL98-36, DGGE band in situ AY-51, DGGE band Banyuls
Mes15, DGGE band Mesocosmos Loktanella hongkongensis,AY600300.1
M464, Isolate Paracoccus sp.DQ108402.1
M441, Isolate AY-58, DGGE band Banyuls
BL03-55, DGGE band in situ Uncultured organism, DQ396058.1
M623, Isolate Hyphomonas oceanitis, F261046Maricaulis maris, AJ227803.1
M624, Isolate Aurantimonas coralicida, AY065627
M463, Isolate Unculture alphaproteobacteria, AF245619.1
AUT80, Clone Citromicrobium sp., DQ985045.1
M458, Isolate M443, Isolate
Erythrobacter citreus, AF118020 AUT4, Clone
M442, Isolate AUT12, Clone
BL03-48, DGGE in situ Erythrobacter aquimaris, AY461443
M155, Isolate Nisaea denitrificans, DQ665839.1
BL98-50, DGGE band in situ BL03-51, DGGE band in situ
Candidatus Pelagibacte ubique, CP000084.1
40
Isolate bacterioplankton diversity in the NW Mediterranean
Gammaproteobacteria. The Gammaproteobacteria was the most diverse group in terms of richness
(Table 1, Fig. 5) and could be divided into 6 major clusters. In cluster Gamma 1 (Alteromonas) there
were several coincidences of isolates with sequences from in situ clone libraries. This cluster consisted
of 9 isolates from different experiments with combined enrichments of inorganic P and C sources like
glycerol, glucose and DMSP. In this cluster we found that three isolates (M111, M185 and M292) were
identical or nearly identical (>99%) to DGGE bands (labeled as AY and BY) obtained in a mesocosm
experiment carried out in 1996 in Banyuls, ca. 150 km northeast of Blanes Bay (Schäffer et al. 2001).
Furthermore, isolate M169 was identical to DGGE band B145 from an experiment (isolate M604 was
99.8% similar to this sequence). All the sequences from this Alteromonas group were distantly related
to sequence JUL4, which represented 30% of the clones from the July clone library).
Three clusters were formed only by isolates: Gamma 2 (Pseudoalteromonas), Gamma 3 (Vibrio),
and Gamma 4 (Marinomonas). Among the Vibrio sequences there was a substantial phylogenetic
diversity (within-cluster similarity 94%, Table 3). The Pseudoalteromonas cluster included isolates
obtained from unenriched seawater samples from different seasons (March, June and November). In
contrast, most Vibrio sequences were isolated from C+P enrichment experiments. Sequences related
to the Marinomonas genus were primarily obtained from enrichments with ATP
Isolate MED266, from unenriched seawater cultures, was 99.3% similar to clone JUL18 that
was found in three copies in the July clone library. Isolates af liated with the genus Marinobacter
were retrieved from enrichments with three different carbon sources (glucose, pyruvate or DMSP) in
combination with phosphate. Cluster Gamma 5 consisted of distantly related sequences containing
only clone sequences.
The Gammaproteobacteria, like the Alphaproteobacteria, showed high levels of microdiversity:
only 32% of the OTUs were grouped at 99% similarity level. In contrast to the Alphaproteobacteria,
this was due to the large microdiversity of isolates but not of clones (Fig. 2C). Moreover, based on
the Chao-1 estimator, Gammaprotebacteria had the largest number of OTUs (108). This number of
taxa was slightly higher when only isolates were taken into account than with the combination of both
molecular and isolation approaches (Table 2).
Six genera were detected by isolation that had not been detected by molecular techniques: Pseudoalteromanoas, Vibrio, Halomonas, Marinomonas, Oleispira and Marinobacter. Isolates M222,
M121 and M297 were whole-genome sequenced. MED297 has been formally described as the new
species Reinekea blandensis (Pinhassi et al. 2007).
41
Chapter I
Fig. 5.- As previous gure, for the Gammaproteobacteria.
BY-92, DGGE Banyuls Gamma 1
Gamma 2
Gamma 3
Gamma 4
Gamma 5
0.10
M113, Isolate SUM81, Clone
M609, Isolate Alteromonas sp., EF061412.1
M275, Isolate Alteromonas alvinellae, AF288360
B145, DGGE experiments M169, Isolate M604, Isolate
M111, Isolate SUM78 , Clone
AY-86, DGGE Banyuls M185, Isolate
AY-84, DGGE Banyuls M292, Isolate
Alteromonas macleodii, Y18231 AY-85, DGGE Banyuls
Alteromonas addita, AY682202 M517, Isolate
B-227, DGGE experiments JUL4_14, Clone
Glaciecola mesoplila, AY771709 M620, Isolate
M107, Isolate M290, Isolate
Pseudoalteromonas atlantica, X82134 Alteromonas sp., AB016267.1
M188, Isolate Pseudoalteromonas citrea, X82137
M246, Isolate Idiomarina sp., DQ985070.1
M206, Isolate M181, Isolate
M428, Isolate M140, Isolate
M222, Isolate* Vibrio lentus, AJ278880.1
M511, Isolate M227, Isolate
M241, Isolate Vibrio splendidus, AY227706
Vibrio parahaemolyticus, DQ026024.1M418, Isolate
Vibrio pectenicida, Y13830.1 M535, Isolate
M608, Isolate Enterovibrio norvegicus, AJ437193
M126, Isolate Aeromonas sobria, X74683
JUL3, Clone M297, Isolate*
Reinekea marinisedimentorum, AJ561121M121, Isolate*
M238, Isolate M254, Isolate
M123, Isolate M119, Isolate
M269, Isolate M302, Isolate
Marinomonas pontica, AY539835 JUL18_3, Clone
M266, Isolate Oleispira antarticam, AJ426421
M498, Isolate Marinobacter aquaeolei, AF173969
M503, Isolate M203, Isolate
Halomonas marisflava, AF251143 M543, Isolate
AUT70, Clone JUL12, Clone
Noriaki katanozaka, AB022713.1 AUT94, Clone
WIN38, Clone AUT48_2, Clone
Marine gammaproteobacterium, AY3863391 SPR78, Clone
WIN52, Clone WIN63, Clone
42
Isolate bacterioplankton diversity in the NW Mediterranean
Bacteroidetes. Most of the Bacteroidetes sequences belonged to the Flavobacteriaceae family
(Fig. 6, Table 3). Most isolates belonged to clusters Bacteroidetes 1 and 3. Cluster Bacteroidetes
1 contained ve different genera: Gramella, Suf avibacter, Dokdonia (including Krokinobacter),
Salegentibacter and Leeuwenhoekiella (Fig. 6). The last two genera had not been found in Blanes by
molecular techniques. There were three subclusters in cluster Bacteroidetes 3: sequences related to
Polaribacter including isolates and DGGE sequences from P-enrichment experiments and mesocosms
algal blooms; sequences related to Tenacibaculum with a similar origin; and isolate M341 related to
two sequences (BL98-5 and BL03-7) obtained by DGGE analysis of winter samples.
Cluster Bacteroidetes 2 included a large number of sequences af liated to the Delaware Bay
sequences ZD0403 and CF6 (Kirchman et al. 2003). These sequences were obtained exclusively by
molecular techniques. Cluster Bacteroidetes 4 was also composed of uncultivated phylotypes and
largely corresponded to the AGG58 clade (O’Sullivan et al. 2004). Isolates M466 and the M571,
belonging to the genus Microscilla, had not been found before in Blanes Bay by cultivation-independent
techniques.
Only one coincidence between isolates and environmental sequences was found: isolate M134
was very similar to a DGGE band from Banyuls and to the reference strain Dokdonia donghaensis.
This isolate has been whole-genome sequenced and is a promising candidate for environmentally
relevant physiological studies. In fact, MED134 is the only marine bacterium that has been shown to
grow better in the light than in the dark thanks to the presence of proteorhodopsin (Gómez-Consarnau
et al. 2007). Isolates M217 (Leeuwenhoekiella blandensis) and M152 (Polaribacter sp.) have also
been whole-genome sequenced. The genome of MED152 has been manually annotated and published
(González et al. 2008) and MED217 has been formally described as a new species (Pinhassi et al.
2006b).
43
Chapter I
Fig. 6.- As previous gures, for the Bacteroidetes.
Bacteroidetes 1
Bacteroidetes 2
Bacteroidetes 3
Bacteroidetes 4
0.10
Gramella echinicolla, AY608409 M454, Isolate M367, Isolate
WIN75, Clone Sufflavibacter litoralis, DQ868538.2
M220, Isolate Krokinobacter genikus, AB198086 BY-64, DGGE band Banyuls
Dokdonia donghaensis, DQ003277.1 M134, Isolate*
Dokdonia sp., EU195942.1M329, Isolate
Salegentibacter salinus, EF486353 M532, Isolate M445, Isolate Leeuwenhoekiella aequorea, AJ278780
M217, Isolate* M495, Isolate
Croceibacter atlanticus, AY163576.1 B-203.2, DGGE band experiments
Maribacter forsetii, AM712900 M381, Isolate
Auerimarina marisflavi, EF108215.1 B122, DGGE band experiments
SUM25, Clone WIN97, Clone
Unculture marine bacterium ZD0403, AJ400347 BL98-14, DGGE band in situ
B76-2, DGGE band experiments BL03-28, DGGE band in situ
SUM18_2, Clone WIN28, Clone
AUT25, Clone Unculture Bacteroidetes CF6, AY274860
AUT33, Clone Polaribacter dokdonensis, DQ004686
M152, Isolate* B-240, DGGE band experiments
Polaribacter irgensii, AY771712 B98, DGGE band experiments
M568, Isolate Tenacibaculum maritimum, M64629
M595, Isolate M621, Isolate
M622, Isolate B170, DGGE band experiments
M341, Isolate BL98-5, DGGE band in situ B100, DGGE band experiments BL03-7, DGGE band in situ
Flavobacterium succinicans, AM230494.1 AUT87, Clone
Fluviicola taffensis, AF493694.2B108, DGGE band experiments
AUT100, Clone WIN14, Clone
B180, DGGE band experiments B131, DGGE band experiments
B88, DGGE band experiments B-270, DGGE band experiments
Marine Eubacterial AGG58, L10946 B175, DGGE band experiments
SUM2, Clone AUT19_2, Clone
BL03-11, DGGE band in situ SPR33, Clone
Flexibacter tractuosus, M58789.1 M599, Isolate
AUT46, Clone Cyclobacterium marium, M62788
Microscilla arenaria, AB078078 M466, Isolate
M571, Isolate AUT77, Clone
Chitinophaga pinensis, AF078775 BL98-40, DGGE band in situ
44
Isolate bacterioplankton diversity in the NW Mediterranean
DISCUSSION
Since the introduction of molecular biology techniques in marine microbial ecology, a great effort
to describe the diversity of marine bacterioplankton communities has resulted in a comprehensive
inventory of primarily uncultured bacteria (e.g. Giovannoni & Stingl, 2007). As a consequence,
a great majority (86%) of clones of our different clone libraries were more than 97% similar to
sequences already deposited in GenBank. At the same time, a number of microbial ecology studies
have investigated the diversity of cultured isolates (Pinhassi et al. 1997; Eilers et al. 2000, 2001;
Hagström et al. 2000; Long & Azam 2001; Pinhassi & Berman, 2003; Stevens et al. 2005; Suzuki et
al. 1997; Selje et al. 2005). The increase in information about culturable marine bacteria is evident
when one considers that at the time of the pioneering study on the Roseobacter group by González
et al. (1997), the group consisted of only 6 described species while in 2005 more than 35 species
had been described forming up to 13 different major clusters (Buchan et al. 2005). The potential of
culturing to increase the known richness of any environment was also manifest in the present study.
The three main groups of bacteria increased the number of OTUs signi cantly. Moreover, at least for
the Gammaproteobacteria, the shape of the rarefaction curve suggested that potentially more diversity
could be uncovered by culturing than by cloning.
The most abundant groups with different techniques. Both culture dependent and independent
methods indicated that Alpha- and Gammaproteobacteria and Bacteroidetes were the predominant
groups. Together, these three groups accounted for 96% of the isolates, 95% of the clones, and 96% of
the DGGE bands (Table 1). A seasonal study carried out at the same sampling site by FISH indicated
that these three groups accounted for 75% of the EUB+ count (Alonso-Sáez et al. 2007). On average,
Alpha-, Gammaproteobacteria and Bacteroidetes accounted for 30, 4-8, and 11% of the DAPI count
respectively. If we take FISH to be the most quantitative technique to identify bacteria in marine
waters, Gammaproteobacteria were overrepresented in the three sets of sequences considered here,
particularly in the culture collection, where they represented 51% of the isolates, but also in clones
(17%) and DGGE bands (14%, Table 1). Alphaproteobacteria, on the other hand, showed the same
importance in the culture collection (30%) as in FISH, and were overrepresented by the two molecular
approaches (72% of clones and 57% of DGGE bands). Finally, the Bacteroidetes were represented
in percentages not too different form FISH by the three approaches (15% in cultures, 6% in clone
libraries, and 25% in DGGE bands). These comparisons should be kept in mind to interpret our
results.
45
Chapter I
Among the least represented groups, Betaproteobacteria and Actinobacteria were recovered by
both culturing and molecular approaches, while Deltaproteobacteria, Epsilonproteobacteria, and
Verrucomicrobia were only found by molecular techniques (Fig. 1). Obviously, our sequencing and
isolating efforts were not suf cient to retrieve these other groups in signi cant numbers, and we can
say very little about their diversity. The culturing and sequencing effort would have to be increased
at least by one order of magnitude to retrieve signi cant numbers of these groups. Recently, a sample
from the BBMO collected on September 2007 was analyzed by 454 pyrosequencing within the
International Census of Marine Microbes program (http://icomm.mbl.edu). Of the 8605 tag sequences
that could be assigned a taxonomic identity and were not cyanobacteria or chloroplasts, sequence
tags were distributes as follows: Proteobacteria 84%, Bacteroidetes 9%, Verrucomicrobia 2.5%,
Actinobacteria 1.7%, Firmicutes 1.9%, and ten other groups making less than 0.5% each (unpublished
data). Therefore, it is clear that Proteobacteria and Bacteroidetes were the major components of the
bacterioplankton, while the remaining groups were quantitatively minor components.
Estimates of richness in Blanes Bay. It is well known that bacteria isolated in pure culture frequently
do not coincide with sequences retrieved by molecular methods from the same ecosystem. Thus, it is
not surprising that the culturing effort contributed signi cantly to increase the richness of bacterial taxa
known from the BBMO. At least two new genera of Bacteroidetes and six of Gammaproteobacteria
were added to the taxa list of Blanes Bay. In terms of OTUs, known richness increased from 62 (clones
alone) to 116 (clones plus isolates). And considering the estimates from the Chao 1 indicator, richness
increased from 125 to 232 based on clones plus isolates. The degree of novelty provided by cultures
was highest for the Gammaproteobacteria and lowest for the Alphaproteobacteria. However, this is
still a minor fraction of the total richness in Blanes Bay, as a comparison to the 454 pyrosequencing
data mentioned above makes obvious: from a total of 14775 tags, there were 2719 unique ones
(representing different OTUs). This is one order of magnitude higher than the highest estimates from
the culturing and clone library approaches combined, and increases our knowledge about microbial
diversity on the one hand, and exemplify the large efforts needed to completely characterize it. With
the used methods we have barely scratched the surface of microbial biodiversity in this site.
46
Isolate bacterioplankton diversity in the NW Mediterranean
Isolates of potential environmental relevance. Both molecular and culturing methods showed
the major groups to be Alphaproteobacteria, Gammaproteobacteria and Bacteroidetes, as in most other
marine waters (Hagström et al. 2002). However, at the level of speci c 16S rRNA gene sequences
we found only minimal overlap between cultivated and uncultivated bacteria. This suggested that
most of the isolates were of low signi cance in the studied environment at the time of sampling.
This is a fairly general situation (e.g. Donachie et al. 2007). Thus, Suzuki et al. (1997) compared
127 isolates (37 different) and 58 clones (25 different) from one coastal Paci c seawater sample.
They found little overlap between isolate and clone sequences, although a few coincidences occurred
in the Gammaproteobacteria (1 Pseudomonas) and in the Alphaproteobacteria (2 Roseobacter, 1
Sphingomonas). In a similar comparison in North Sea tidal ats, Stevens et al. (2005) found four
identical clone and isolate sequences among a total of 188 isolates and 77 clones. In Blanes we
found 9 cases of identical or nearly identical 16S rRNA gene sequences shared by isolates and in situ
sequences (from clones or DGGE bands): 5 in the gammaproteobacteria, 3 in the alphaproteobacteria
and 1 in the Bacteroidetes (marked with arrows in Figs. 2-4). Obviously these cultures are good
candidates for laboratory studies of environmentally relevant microorganisms.
Expecting the results mentioned above, we reasoned that enrichment cultures would be a good
source of isolates of potential importance in the environment. By incubating sea water samples with
different carbon sources and/or nutrients we hoped to increase the abundance of bacteria that could
respond to such enrichments in nature. It is a frequent observation that novel bands appear in DGGE
gels in response to experimental manipulations (e.g. Massana et al. 2001, Allers et al. 2007). We had
speculated that these bands could correspond to bacteria that could be relatively easy to isolate in
pure culture. Indeed, we found some coincidences between isolates and DGGE bands that appeared
in response to enrichment experiments (Figs. 4-6). We envision these enrichment experiments as an
opportunity for bacteria in the seed-bank to grow and become members of the diversity of the system,
hopefully mimicking episodic enrichments in nature. One particular example is shown in Fig. 7.
The DGGE gel shown corresponds to an enrichment experiment with different P-rich compounds.
A speci c band (band B90) was seen to increase in intensity in the three treatments with P-rich
compounds, but it disappeared in the unenriched control. This band was identical to isolate M128, a
member of the genus Nereida. The band can be appreciated in the lane corresponding to the natural
samples before the beginning of the experiment. This band was too faint for successful sequencing
and, thus, would have remained unidenti ed by conventional molecular techniques. However, under
the appropriate conditions, it became one of the dominant members of the assemblage. This suggests
that M128 is normally a member of the rare biosphere, but may become important when episodes of
47
Chapter I
P enrichment occur. This also indicates that the composition of the bacterial assemblage likely has a
strong dependence on nutrient supply (or bottom-up control), something that gives an explanation for
the recurrence of the same bacterial populations year after year at the same season (Fuhrman et al.
2006).
Fig. 7.- DGGE gel showing the band patterns from different treatments in a nutrient addition experiment.
The in situ sample (labeled T0) showed a larger number of bands than the enrichments. A band increased in
intensity in P additions (B90 and B94), and was identical to isolate M128 (see Supplementary Table). This
band was also present at time 0, but in a much lower relative abundance.
To PO4 ATP DNA K
specific for P =M128 in situ as “seed bank”
48
Isolate bacterioplankton diversity in the NW Mediterranean
Microdiversity. We observed that a high fraction of the total diversity for both environmental
clones and isolates formed microdiversity clusters, constrained at 99% 16S rRNA gene sequence
similarity according to Acinas et al. (2004). While microdiversity clusters are often observed in
environmental clone libraries (Field et al. 1997, Acinas et al. 2004, Morris et al. 2005, Pommier et
al. 2006), we are not aware of any report on microdiversity for larger collections of marine bacterial
isolates. Thirty percent of the total observed OTUs (by both approaches) and 45% of the total species
estimated by Chao-1 were grouped as microdiversity clusters (Figs. 2A and 3A). However, this
microdiversity fraction was not equally distributed among all taxonomic groups, it was mostly due
to the Gammaproteobacteria in the case of isolates and to the Alphaproteobacteria in the case of
clones, while Bacteroidetes exhibited the lowest microdiversity. This low microdiversity could be
the effect a lower number of Bacteroidetes sequences (45) compared to 223 and 116 sequences from
Alpha- and Gammaproteobacteria, respectively. However, the low microdiversity of Bacteroidetes
found in our study is a characteristic also observed in other studies (Acinas et al. 2004; Pommier et
al. 2006). The ecological signi cance of the relatively high abundance of very divergent lineages for
Bacteroidetes remains unresolved. Previous studies show that microdiversity clusters could represent
important units of evolutionary differentiation as ecotypes in natural populations of bacteria (Cohan
2006, Thompson et al. 2005, Polz et al. 2007, Cohan & Perry 2007, Zo et al. 2008). The nding of
microdiversity clusters among isolate and clone sequences alike, suggests that similar evolutionary
processes act on cultured and uncultured bacteria. Thus, marine bacterial isolates could serve as
models to investigate how microdiversity clusters arise and provide indications of the ecological
signi cance of such genetic variability.
ACKNOWLEDGEMENTS
We thank Vanessa Balagué for her work with the clone libraries, and Laura Alonso-Sáez for sharing
data. Thanks also to Isabel Ferrera who helped with Figure 1, and to Thomas Pommier for the
preliminary 454 data. This work was supported by EU-project BASICS (CTM2005-04795/MAR),
Spanish MEC projects GenµMAR (CTM2004-02586/MAR), MODIVUS (CTM2005-04795/MAR)
and GEMMA (CTM2007-63753-C02-01/MAR), and the Swedish Research Council (grant 621-203-
2692 to JP).
49
Chapter I
Tabl
e 4.
- Id
entit
y of
the
bac
teri
a is
olat
ed f
rom
Bla
nes
Bay
. The
seq
uenc
es h
ave
diffe
rent
ori
gins
and
com
e fr
om d
iffer
ent
year
s. d
iffer
ent
seas
ons.
and
al
so f
rom
diff
eren
t ty
pes
of e
nric
hmen
t ex
peri
men
ts. I
ndic
ated
is
the
% s
eque
nce
sim
ilari
ty t
o ot
her
isol
ates
or
sequ
ence
s in
Gen
Ban
k. S
imila
rity
val
ues
are
base
d on
app
roxi
mat
ely
500
base
pai
rs. A
cces
sion
num
bers
are
sho
wn
for
the
sequ
ence
s in
clud
ed in
Gen
Ban
k. D
ate
of is
olat
ion
in d
d/m
m/y
y. O
rigi
ns:
in s
itu. f
rom
unt
reat
ed s
eaw
ater
sam
ples
; A
TP. f
rom
ATP
-am
ende
d se
awat
er c
ultu
res;
P. f
rom
inor
gani
c P
-am
ende
d se
awat
er c
ultu
res;
Con
trol
. sea
wat
er
cultu
res
with
out
enri
chm
ents
; et
c.
Whe
n th
ere
is m
ore
than
one
Gen
Ban
k ac
cess
ion
num
ber
for
a gi
ven
isol
ate
type
, onl
y th
e on
e w
ithou
t pa
rent
hesi
s is
us
ed in
the
text
.
Acc
#
Nam
e D
ate
isol
. O
rigi
n M
ost s
imila
r or
gani
sm(s
) si
mila
rity
lin
eage
fa
mily
• D
Q68
1131
M
107
16/0
3/01
C
ontr
ol
Pseu
doal
tero
mon
as a
gari
vora
ns A
J417
594
99.8
G
amm
a Ps
eudo
alte
rom
onad
acea
e (
DQ
6811
65)
M27
1 13
/11/
01
Con
trol
M
107
DQ
6811
31
100
(E
U25
3571
) M
306
13/1
1/01
P
M10
7 D
Q68
1131
10
0
• D
Q68
1132
M
111
16/0
3/01
C
ontr
ol
Alte
rom
onas
sp.
AY
5766
89
100
Gam
ma
Alte
rom
onad
acea
e
• D
Q68
1133
M
113
16
/03/
01
Con
trol
A
ltero
mon
as a
lvin
ella
e A
F288
360
98.7
G
amm
a A
ltero
mon
adac
eae
Alte
rom
onas
sp.
AJ3
9119
1 99
.8
• D
Q68
1134
M
114
16/0
3/01
C
ontr
ol
Tha
lass
obac
illus
dev
oran
s A
J717
299
99.8
Fi
rmic
utes
B
acill
acea
ea
• D
Q68
1136
M
119
20/0
3/01
A
TP
Mar
inom
onas
dok
done
nsis
DQ
0115
27.1
95.7
G
amm
a M
arin
omon
as (
DQ
6811
35)
M11
7 16
/03/
01
Con
trol
M
119
DQ
6811
36
99
• D
Q40
3809
M
121
20/0
3/01
A
TP
Mar
inom
onas
dok
done
nsis
DQ
0115
26
95
Gam
ma
Mar
inom
onas
Mar
ine
bact
eriu
m A
Y02
8196
98
.9
M23
4 25
/06/
01
DM
SP/P
M
252
25/0
6/01
A
TP
• D
Q68
1137
M
123
20/0
3/01
A
TP
Mar
inom
onas
pon
tica
AY
5398
35
95.5
G
amm
a
Hal
omon
adac
eae
M
221
23/0
5/01
A
TP
M23
6 25
/06/
01
DM
SP/P
• D
Q68
1138
M
126
20/0
3/01
A
TP
Ent
erov
ibri
o no
rveg
icus
AJ4
3719
3 99
.8
Gam
ma
Vib
rion
acea
e
V
ibri
o sp
. AJ3
1620
7 10
0
50
Isolate bacterioplankton diversity in the NW Mediterranean•
DQ
6811
39
M12
8-M
130
20/0
3/01
C
ontr
ol
Ner
eida
igna
va A
J748
748
99.4
A
lpha
R
hodo
bact
erac
eae
Alp
ha p
rote
obac
teri
um A
Y61
2764
99
.8
M15
6-M
157
20/0
3/01
In
situ
M
479
22/0
3/04
C
ontr
ol
M58
4-M
585
29/0
6/04
In
situ
M
592
29/0
6/04
In
situ
M
625
11/1
0/04
C
ontr
ol
(
DQ
6811
40)
M13
2 20
/03/
01
Con
trol
M
128
DQ
6811
39
100
• D
Q48
1462
M
134
20/0
3/01
C
ontr
ol
Dok
doni
a do
ngha
ensi
s D
Q00
3277
99
.4
CFB
Fl
avob
acte
riac
eae
Kro
kino
bact
er g
enik
us A
B19
8086
10
0
• D
Q68
1142
M
140
20/0
3/01
G
lu/P
V
ibri
o sp
. AF2
4227
4 99
.6
Gam
ma
Vib
rion
acea
e
V
ibri
o sp
. DQ
2193
66
100
• D
Q68
1143
M
146
20/0
3/01
P
Tha
lass
obac
illus
dev
oran
s A
J114
484
99.8
Fi
rmic
utes
B
acill
acea
e
H
alob
acill
us s
p. A
Y55
3123
10
0
• D
Q68
1144
M
149
20/0
3/01
P
Ros
eova
rius
cra
ssos
trea
e A
F114
484
98.1
A
lpha
R
hodo
bact
erac
eae
Unc
ultu
red
alph
apro
teob
acte
rium
DQ
3761
56
100
• D
Q48
1463
M
152
20/0
3/01
P
Pola
riba
cter
dok
done
nsis
DQ
0046
86
99.5
C
FB
Flav
obac
teri
acea
e
M
arin
e ba
cter
ium
AY
7458
62
99.9
• D
Q68
1145
M
155
20/0
3/01
In
situ
E
ryth
roba
cter
lito
ralis
AF4
6583
6 99
.0
Alp
ha
Ery
thro
bact
erac
eae
• D
Q68
1146
M
165
15/0
5/01
P
Phae
obac
ter
inhi
bens
AY
1777
12
98.4
A
lpha
R
hodo
bact
erac
eae
Ros
eoba
cter
sp.
AY
5766
90
99.8
• D
Q68
1147
M
169
15/0
5/01
P
Alte
rom
onas
alv
inel
lae
AF2
8836
0 99
.1
Gam
ma
Alte
rom
onad
aeca
e
U
ncul
ture
d m
arin
e ba
cter
ium
DQ
0711
62
100
M20
9 23
/05/
01
Gly
cero
l
M
480
22/0
3/04
In
situ
M
496
22/0
3/04
D
MSP
• D
Q68
1148
M
181
15/0
5/01
P
Vib
rio
tasm
anie
nsis
AJ5
1491
2 99
.1
Gam
ma
Vib
rion
acea
e
V
ibri
o sp
. AF0
2240
9
51
Chapter I
• D
Q68
1149
M
185
15/0
5/01
P
Alte
rom
onas
add
ita A
Y68
2202
99
.6
Gam
ma
Alte
rom
onad
acea
e
A
ltero
mon
as s
p. A
SP29
4361
10
0
M
188
Pseu
doal
tero
mon
as s
p. E
U44
0056
• D
Q68
1150
M
193
15/0
5/01
P
Phae
obac
ter
inhi
bens
AY
1777
12
97
Alp
ha
Rho
doba
cter
acea
eM
177-
M16
3-M
192-
M19
4 15
/05/
01
P
• D
Q68
1151
M
203
23/0
5/01
Py
ruva
te
Mar
inob
acte
r aq
uaeo
lei A
J000
726
100
Gam
ma
Alte
rom
onad
acea
e
M
. hyd
roca
rbon
ocla
stic
us A
Y66
9169
10
0
• D
Q68
1152
M
206
23/0
5/01
In
situ
Id
iom
arin
a se
osin
ensi
s A
Y63
5468
99
.2
Gam
ma
Idio
mar
inac
eae
Mar
inob
acte
r sp
. DQ
2707
61
99.9
• D
Q68
1154
M
216
23/0
5/01
P
Plan
ococ
cus
citr
eus
AF5
0000
8 99
.6
Firm
icut
es
Plan
ococ
cace
ae
Pl
anoc
occu
s sp
. DQ
1773
34
99.9
• D
Q29
4291
M
217
23/0
5/01
P
Lee
uwen
hoek
iella
acc
omm
odim
aris
AJ7
8098
0 97
C
FB
Flav
obac
teri
acea
e
C
ytop
haga
sp.
AY
7458
17
99.6
• D
Q68
1155
M
219
23/0
5/01
P
Koc
uria
pol
aris
AJ2
7886
8 99
.7
Aci
noba
ct.
Mic
roco
ccac
eae
Koc
uria
ros
ea D
Q17
6452
10
0
• D
Q68
1156
M
220
23/0
5/01
P
Sale
gent
ibac
ter
mis
hust
inae
AY
5766
53
94.1
C
FB
Flav
obac
teri
acea
e
U
ncul
ture
d or
gani
sm D
Q39
6362
99
.9
• D
Q68
1157
M
222
25/0
6/01
G
lu/P
V
ibri
o sp
. AF2
4227
4 10
0 G
amm
a V
ibri
onac
eae
Vib
rio
sp. D
Q49
2722
10
0
M
226
25/0
6/01
G
lu/P
• D
Q68
1158
M
227
25/0
6/01
G
lu/P
V
ibri
o gi
gant
is A
J582
807
97.8
G
amm
a V
ibri
onac
eae
Mar
ine
bact
eriu
m A
Y02
8201
98
.5
M23
0 25
/06/
01
Glu
/P
• D
Q68
1159
M
238
25/0
6/01
D
MSP
M
arin
omon
as b
land
ensi
s D
Q40
3809
99
.8
Gam
ma
O
cean
ospi
rilla
les
52
Isolate bacterioplankton diversity in the NW Mediterranean•
DQ
6811
60
M24
1 25
/06/
01
Glu
/P
Vib
rio
giga
ntis
AJ5
8280
7 10
0 G
amm
a V
ibri
onac
eae
M
285
13/1
1/01
C
ontr
ol
M30
0 13
/11/
01
P
(D
Q68
1141
) M
137
20/0
3/01
(E
U25
3572
) M
310
28/0
1/03
P
M24
1 D
Q68
1160
99
(E
U25
3575
) M
344
04/0
3/03
P
M
241
DQ
6811
60
99 (
EU
2535
91)
M50
9 22
/03/
04
Am
ino
acid
s M
241
DQ
6811
60
98
• D
Q68
1161
M
246
25/0
6/01
D
NA
Ps
eudo
alte
rom
onas
av
ipul
chra
AF2
9795
8 99
G
amm
a Ps
eudo
alte
rom
onad
acea
e
• D
Q68
1162
M
254
25/0
6/01
A
TP
Mar
ine
bact
eriu
m A
Y02
8196
99.2
G
amm
a M
arin
omon
as
M
arin
omon
as p
ontic
a A
Y53
9835
95.2
• D
Q68
1163
M
266
13/1
1/01
C
ontr
ol
Sacc
haro
phag
us d
egra
dans
AF0
5526
9 93
.6
Gam
ma
Oce
anos
piri
llale
s
O
leis
pira
ant
arct
icam
AJ4
2642
1 92
M
299-
M29
4-M
279
13/1
1/01
C
ontr
ol
M27
8-M
268
1
3/11
/01
C
ontr
ol
M31
9-M
324-
M32
6-M
327
28/0
1/03
C
ontr
ol
• D
Q68
1164
M
269
13/1
1/01
C
ontr
ol
Mar
inom
onas
dok
done
nsis
DQ
0115
27
97.6
G
amm
a M
arin
omon
as
M
arin
e ba
cter
ium
AY
0282
04
• D
Q68
1166
M
275
13/1
1/01
C
ontr
ol
Alte
rom
onas
alv
inel
lae
AF2
8836
0 99
.6
Gam
ma
Alte
rom
onad
acea
e
G
amm
a pr
oteo
bact
eriu
m A
B01
0855
(
DQ
6811
53)
M21
2 23
/05/
01
Gly
cero
l M
275
DQ
6811
66
99
M61
4 11
/10/
04
P
• D
Q68
1167
M
290
13/1
1/01
In
situ
Ps
eudo
alte
rom
onas
aga
rivo
rans
AJ4
1759
4 99
.6
Gam
ma
Pseu
doal
tero
mon
acea
e
Ps
eudo
alte
rom
onas
sp.
AY
7811
55
99.8
• D
Q68
1168
M
292
13/1
1/01
In
situ
A
ltero
mon
as a
ddita
AY
6822
02
99.6
G
amm
a A
ltero
mon
adac
eae
Unc
ultu
red
Alte
rom
onas
sp.
AY
6642
13
99.6
(E
U25
3590
) M
506
22/0
3/04
D
MSP
M
292
DQ
6811
68
99
• D
Q40
3810
M
297
13/1
1/01
In
situ
R
eine
kea
mar
inis
edim
ento
rum
AJ5
6112
1 94
.8
Gam
ma
Rei
neke
a
U
ncul
ture
d ba
cter
ium
AY
1723
02
96.1
53
Chapter I
• D
Q68
1169
M
302
13/1
1/01
P
Mar
inom
onas
dok
done
nsis
DQ
0115
27
98.3
G
amm
a M
arin
omon
as
U
ncul
ture
d M
arin
omon
as s
p. D
Q42
1663
98
.4
• E
U25
3573
M
329
04/0
3/03
In
situ
K
roki
noba
cter
dia
feri
tkos
AB
1980
89
96
CFB
Fl
avob
acte
race
ae
• E
U25
3574
.1
M34
1
Te
naci
bacu
lum
sp.
EU
0212
93
95
CFB
Fl
avob
acte
race
ae
• E
U25
3577
M
367
13/0
5/03
In
situ
G
ram
ella
ech
inic
ola
AY
6084
09
97
CFB
Fl
avob
acte
race
ae
• E
U25
3579
M
381
13/0
5/03
C
ontr
ol
Mar
ine
bact
eriu
m A
F359
541
99
CFB
Fl
avob
acte
race
ae
M
arib
acte
r fo
rset
ii su
bsp.
For
setii
AM
7190
0 97
• E
U25
3580
M
418
16/0
9/03
P
Gam
ma
prot
eoba
cter
ium
AF3
8414
2 99
G
amm
a V
ibri
onac
eae
• E
U25
3581
M
428
16/0
9/03
P
Gam
ma
prot
eoba
cter
ium
AF3
8414
2 99
G
amm
a V
ibri
onac
eae
• E
U25
3582
M
441
16/0
9/03
C
ontr
ol
Para
cocc
us s
p. A
B26
4129
99
A
lpha
R
hodo
bact
erac
eae
• D
Q68
1170
M
442
16/0
9/03
C
ontr
ol
Ery
thro
bact
er c
itreu
s A
F118
020
100
Alp
ha
Ery
thro
bact
erac
eae
M
537
25/0
5/04
In
situ
(E
U25
3576
) M
365
13/0
5/03
In
situ
M
442
DQ
6811
70
100
(E
U25
3578
) M
380
13/0
5/03
In
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Chapter I
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57
Chapter I
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ZoBell CE (1943) The effect of solid surfaces upon bacterial activity. J Bacteriol 46: 39-56
Changes in bacterial activity and community composition caused by realistic exposure to oil in mesocosm experiments
Itziar Lekunberri, Alejandra Calvo-Díaz, Eva Teira, Xelu A.G. Morán, Francesc Peters, Mar Nieto-Cid, Oscar Espinoza-González, Isabel G. Teixeira and Josep M Gasol
CHAPTER 2
63
Chapter II
ABSTRACT
We studied the effects of the Prestige oil spill on the Ría de Vigo bacterial abundance, production
and community structure, by using mesocosms experiments (ca. 3500 L) lled with water from the
center of the Ría, to which we added a realistic concentration of polyciclic aromatic hydrocarbons
(approximately 20-30 μg L-1 chrysene eq). We followed the changes in bacterial activity by leucine
and thymidine incorporation, and changes in bacterial assemblage structure by 16S-rDNA DGGE as
well as CARD-FISH, during the four relevant periods of the seasonal cycle: spring bloom, summer
strati cation, autumn downwelling and winter. In addition, simultaneous to one of the mesocosms
experiments, we run microcosms with fuel additions equivalent to 0.5x, 1x, 2x and 4x the treatment
imposed to the mesocosms. We detected effects on bacterial diversity (increased richness) and
function (more bacteria and more production) only in the summer experiment. In the microcosm
experiments we found similar effects at PAH concentrations of ca. 40 µg L-1 (2x 20 µg L-1) and
clear detrimental effects on phytoplankton at concentrations of ca. 80 µg L-1, with large development
of gammaproteobacteria. We also show that the development of heterotrophic nano agellate in the
mesocosms is what causes the most important changes in bacterial assemblage structure.
64
Effects of oil on bacterial activity and diversity
INTRODUCTION
Bacterioplankton communities inhabiting the plankton of coastal regions use multiple sources of
carbon and nutrients (e.g. Moran et al. 1999, Mou et al. 2008). These sources have different origins:
autochthonous phytoplankton production, carbon and nutrients coming from the watersheds, or
allochthonous nutrient and C sources that have originated because of human activities. Petroleum
hydrocarbons are some of these organic carbon sources, and are some of the most widespread
contaminants in the environment (Santas et al. 1999). Polyciclic aromatic hydrocarbons (PAHs) are
particularly in uential because of their continuous release into the water, their persistence, and their
well-known detrimental effects on marine invertebrates (Preston, 2002), which make them subject of
public concern.
But bacteria are able to decompose these carbon molecules (e.g. Harayama et al. 1999, Kasai
et al. 2002) and may consider chronic PAHs inputs to seawater just one allochthonous source of
carbon and energy (Castle et al. 2006). However, immediately after an oil spill, the soluble fraction
of polycyclic aromatic hydrocarbons (PAHs) is released into the water column and may represent
point concentrations of PAHs much higher than the values measured in chronic exposures and, thus,
affect bacterioplankton communities in ways different than just as a regular C source. The effect
of oil spills on the natural bacterioplankton communities of coastal ecosystems has been studied
using in situ measurements (e.g. Bode et al. 2006), laboratory experiments (Maruyama et al. 2003,
Castle et al. 2006, Cappello et al. 2006), and experimental systems such as mesocosms (Siron et al.
1993, Ohwada et al. 2003, Cappello et al. 2007). This last type of approach avoids the effects caused
by advection, diffusion and mixing under natural conditions, and it also offers the possibility of
comparison with natural populations to which PAHs were not added, thus allowing statistical testing
of hypotheses. These types of analyses have reported contradictory results about the effects of the oil
spills on plankton communities. Often, no effect on primary producers has been found (e.g. Varela
et al. 2006) while simultaneous signi cant effects on bacterial variables have been reported (Bode
et al. 2006). In several experiments, bacteria have been shown to be stimulated (e.g. Delille & Siron
1993, Ohwada et al. 2003, Bode et al. 2006, Castle et al. 2006, Cappello et al. 2007, Dalby et al.
2008), neutrally affected (Maruyama et al. 2003) or repressed (Garcia et al. 1998, Sargian et al. 2005)
by high concentrations of PAHs. In most cases, a shift in bacterial community structure has been
observed (Gerdts et al. 2004, Castle et al. 2006, McKew et al. 2007) resulting in decreasing richness
65
Chapter II
(Röling et al. 2002) and elevated contributions of gammaproteobacteria (Yakimov et al. 2004, Gerdts
et al. 2004, Cappello et al. 2007), particularly groups such as Alcalinovorax (Cappelloet al. 2007) or
Cycloclasticus (Kasai et al. 2002, Teira et al. 2007).
The concentration of PAHs used to experimentally simulate oil spills have ranged from 900 mg crude
oil L-1 (Cappello et al. 2007) to 4.5 µg L-1 chrysene equivalents (Ohwada et al. 2003), with all possible
intermediate values. It might be possible that some of the reported effects of oil are concentration-
dependent, so that they are signi cant at concentrations which are never experienced by the natural
bacteria assemblages. In fact, the lack of effect of oil spills on phytoplankton communities has been
attributed to the reduction in exposure concentration related to the dispersion of the oil caused of the
hydrographic water movements. Accordingly, it can be hypothesized that if PAHs do not cause effects
on microbial diversity and function, it is because the communities are habituated to a certain level of
these substances in the water, as parts of their normal carbon pools used for growth.
To explore this hypothesis, we added realistic oil concentrations to mesocosms in order to test the
effect of PAHs on plankton communities of the Ría de Vigo. To evaluate the response of bacterioplankton
we measured bacterial activity, diversity and community structure in a series of experiments. Bacterial
activity and diversity measurements can represent useful tools for characterizing the way in which
natural and human stressors drive changes in natural ecosystems. We used relatively low PAHs
concentrations (20-30 μg L-1chrysene eq.) that were, however, sixfold higher than the 90% percentile
of the concentrations found along the Galician coast after the accident (González et al. 2006). To assess
the changes in bacterial assemblage structure we used the DGGE ngerprinting technique, which
serves well to compare microbial assemblages and assess temporal and spatial changes in structure
(Casamayor et al. 2002), also providing estimates of diversity. In general, we found little effects with
the chosen concentrations and, thus, we also performed a concentration-dependent experiment using
a gradient from 0 to 80 µg L-1 chrysene eq. to explore the threshold concentration at which bacterial
assemblage composition and metabolism were affected.
The experiments were part of the project IMPRESION, designed to test the effect of the Prestige
oil spill on plankton communities of the Ría de Vigo. This coastal embayment is an area affected by
marked seasonal cycles of coastal winds (Nogueira et. al. 1997). The annual cycle is divided in an
upwelling season (March to September), with short relaxation intervals that enhance productivity
(Álvarez-Salgado et al. 2002), and a downwelling season (from October to March), characterized
by low phytoplankton biomass and primary production. Microbial plankton composition is different
66
Effects of oil on bacterial activity and diversity
in the different situations: upwelling, downwelling or transition (Figueiras et al. 2002, Nogueira et
al. 1997, Alonso-Gutiérrez et al. submited, Teira et al. 2008). Thus, we explored the effects of oil
on bacterial community structure and activity at the four relevant parts of the seasonal cycle: spring
bloom, summer strati cation, autumn upwelling and winter.
MATERIALS & METHODS
Experimental setup and sampling. The experimental setup was established in a small harbor of
a protected bay, four times during a year. As detailed in Teira et al. (2007), six mesocosms of 1.5 m in
diameter and 2 m deep were lled with seawater at the center of the Ría and transported to the harbor
to which they were tight. The bags were lled from their bottom through a 200 µm mesh in order to
exclude mesozooplankton and facilitate good replication within bags. Two of the mesocosms were
used as controls, received no additions and were not treated in any way except for the 200 µm pre-
ltration. Two were added a low concentration of soluble PAHs (5-10 µg L-1 chrysene equivalents)
and two a high concentration of soluble PAHs (approx. 20-30 µg L-1 chrysene eq.). The experiments
lasted 9 days after the addition. Samples were taken every day during the rst 5 days, and thereafter
every 2 days. Since during the rst two experiments there was no response in the low-concentration
mesocosms, in the subsequent experiments this treatment was eliminated and instead we triplicated
the control and the high-PAH concentration treatments.
The PAHs were added as a soluble fraction which was prepared from 15 kg of Prestige-like heavy
oil provided by the “O cina Técnica de Coordinación del Programa de Intervención Cientí ca en la
Catástrofe del Prestige” mixed with 300 L of 0.2 µm- ltered seawater. The mixture was vigorously
stirred during 4 hours and the resulting extract, of approximately 700 µg L-1 soluble PAHs, was separated
from the insoluble fuel oil by decantation, collected on 25 litres polyethylene barrels, from which it
was added to the mesocosms. PAH concentrations were measured following the MARPOLMON
protocol (UNESCO, 1984), with modi ed volumes, and referred to a chrysene standard (see details
in González et al. 2006).
The Prestige oil spill was found to consist of a complex mixture of hydrocarbons, where the aromatic
fraction (mainly naphthalene, phenanthrene, and alkyl derivatives) comprised ca. 53% (Alzaga et al.
2004). PAHs represented 99.7% of the water soluble fraction of the Prestige oil and alkanes were
almost undetectable in that fraction (J. Albaigés, pers. comm.). Although it was not possible to use
exactly the Prestige oil, we used oil with a very similar composition.
67
Chapter II
Addition of oil was done after having taken the time 0 sample, and sampling of the mesocosms
was then done daily at sunset with an integrated tube that collected water down to 1 meter above
the bottom of the tanks. The experiments were carried out at the 4 periods of the seasonal cycle in
the coastal NE Iberian Atlantic waters (Nogueira et al. 1997): spring bloom (March 2005), summer
strati cation (July 2005), autumn upwelling (September 2005), and winter (January 2006).
Additional microcosm experiments. As described in Teira et al. (2007), a microcosm experiment
was run in parallel to the winter mesocosms using the same initial seawater, in order to determine the
response of the community to a larger gradient of PAH concentration. For the microcosm experiment
ten 5 L-PET bottles were kept opened and refrigerated by circulating surface seawater. A gradient of
four concentrations (10, 20, 40 and 80 µg L-1 equivalent chrysene) plus the control were prepared in
duplicates. This is equivalent to 0.5x, 1x, 2x, and 4x what was added in the simultaneous mesocosms.
The microcosms were kept for 8 days, sampling every 24 h for bacterial abundances, bulk heterotrophic
production and bacterial community structure analyses.
Organism abundances. Chlorophyll a concentration was used as an estimator of phytoplankton
abundance. Two hundred and fty millilitres of seawater were sequentially ltered through 20, 2
and 0.2 µm polycarbonate lters. The lters were frozen and chlorophyll-a extracted in 90% acetone
for 24 h at 4°C. The uorescence of the extracted chlorophyll-a was measured with a Turner-TD-
700 uorometer previously calibrated with pure chlorophyll-a. Total chlorophyll-a concentration was
obtained by addition of the concentration in each of the three size classes.
Bacterial abundances were measured by ow cytometry as described in Gasol & del Giorgio (2000).
The samples were run in a Becton-Dickinson FACSCalibur cytometer after staining with SybrGreen
2.5 µM nal concentration, Molecular Probes) and bacteria were detected by their signature in a plot
of side scatter (SCC) vs FL1 (green uorescence). Calibration of the cytometer, and of the SSC-size
relationship was done as described in Calvo-Díaz & Morán (2006).
HNF abundances- Samples for nano agellate abundance were collected four or ve times in every
experiment. Subsamples of 60-80 mL were xed with glutaraldehyde (1%, nal concentration).
The samples were ltered through 0.6 µm black polycarbonate lters (Milipore), and stained with
DAPI (4, 6 - diamidino-2- phenylindole, Porter and Feig, 1980) at a nal concentration of 5 µg mL-1
(Sieracki et al. 1995). Abundance of these microorganisms was determined at 1000X magni cation
with an Olympus-BX40-102/E epi uorescence microscope. The heterotrophic and phototrophic
nano agellates were counted under both UV excitation (blue uorescence) and blue excitation (B2
68
Effects of oil on bacterial activity and diversity
lter). Under blue light we discriminated phototrophic nano agellates (PNF, showing red-orange
auto uorescence, and/or plastidic structures) from colorless nano agellates classi ed as heterotrophic
(HNF). With this method we could not distinguish mixotrophic nano agellates. Random 10 mm
transects (100 µm width) were examined and between 20 and 100 HNF, and between 10 and 50 PNF
per lter were counted.
Bacterial activity and production. Bacterial heterotrophic production was estimated using both
the 3H-leucine (Kirchman et al. 1985) and 3H-thymidine (Fuhrman & Azam 1982) incorporation
methods. Triplicate or cuatriplicate aliquots of 1.2 ml were taken for each sample and one or two TCA
killed controls. The Leu tracer was used at 40 nM and the TdR tracer at 20 nM nal concentrations
in incubations lasting approximately 2 h. The incorporation was stopped with the addition of 120 µl
of cold 50% TCA to the samples and, after mixing, they were kept frozen at –20ºC until processing,
which was carry out by the centrifugation method of Smith & Azam (1992). The samples were counted
on a Beckman scintillation counter, 24 h after addition of 1 ml of scintillation cocktail. We used the
standard conversion factors of 3.1 kg C mol Leu-1 and 40 kg C mol TdR-1, since we had previously
determined CFs in the Ría to be on average close to these theoretical ones (Morán et al. 2002). The
nal value of heterotrophic bacterial production was computed as the average of the Leucine and
Thymidine-based determinations.
Collection of community DNA. Microbial biomass was collected by sequentially ltering around
5 L of seawater through a 3 μm pore size polycarbonate lter (Milipore, 46 mm) and 0.2 μm sterivex
lter (Milipore). The Sterivex units were lled with 1.8 ml of lysis buffer (50 mM Tris-HCl pH 8.3, 40
mM EDTA pH 8.0, 0.75 M sucrose) and stored at -80ºC. We used the sterivex units for the analyses.
Microbial biomass was treated with lysozime, proteinase K and sodium dodecyl sulphate, and the
nucleic acids were extracted with phenol and concentrated in a Centricon-100 (Milipore). Nucleic
acids were extracted by a standard protocol using phenol/chloroform (see details in Schauer et al.
2003).
Fingerprinting analysis. DGGE and gel analysis were performed essentially as described before
(Schauer et al. 2000; Sánchez et al. 2007). Brie y, 16S rRNA gene fragments (around 550 bp in length)
were ampli ed by PCR using the universal primer 907rm and the bacterial speci c primer 358f, with
a GC-clamp. PCR products were loaded on a 6% polyacrylamide gel with a DNA-denaturant gradient
ranging from 40-80%. The gel was run at 100V for 16h at 60ºC in 1x TAE running buffer. DGGE gel
images were analyzed using the Diversity Database software (BIO-RAD). A matrix was constructed
69
Chapter II
for all lanes taking into account the relative contribution of each band (in percentage) to the total
intensity of the lane. Based on this matrix, we obtained a dendogram by UPGMA clustering method
(Euclidean distances, software Statistica 6.0).
Bacterial community composition. We used CARDFISH to study the composition of the bacterial
communities in the microcosm experiment. Five mililitres were xed with paraformaldehyde (2% nal
concentration) and after 12–18 h storage at 4°C in the dark were ltered through a 0.2 µm polycarbonate
lter (Millipore, GTTP, 25 mm lter diameter) supported by a cellulose nitrate lter (Millipore, HAWP,
0.45 mm), washed twice with Milli-Q water, dried and stored in a microfuge vial at -20°C until further
processing. We used oligonucleotide probes speci c for the domain Eubacteria (EUB338) (Amann et
al. 1990), the Alpha- (ALF968) (Glöckner et al. 1999) and Gammaproteobacteria (GAM42a) (Manz
et al. 1992) subclasses and the class Flavobacteria of phylum Bacteroidetes (CF319a) (Manz et al.
1996). We also used probe CYU829 for Cycloclasticus (Maruyama et al. 2003, Teira et al. 2007).
The Eub antisense probe Non 338 probe was used as negative control. Further details in Teira et al.
(2008).
Statistical analysis. We used ANOVA to test for the differences between treatments. A repeated
measures ANOVA (RMANOVA) with one within-subjects factor (treatment) was conducted to
discriminate the treatment from the time effects and all possible interactions. Time is a within-subject
factor because the same mesocosms is sampled at sequential time periods (every-24-48 h). One way
ANOVA was done to determine differences in the microcosm experiment. We used software Statistica
6.0 and JMP 7 for all analyses.
RESULTS
The initial environmental conditions were very different for each season (Table 1). Inorganic
nutrients were very abundant in winter and spring, while chlorophyll was very high particularly in
autumn, when dissolved inorganic nitrogen (DIN) -but not silicate- was also high, as our sampling
coincided with the decline of an algal bloom (Fig. 1C). Bacterial abundance and heterotrophic
production were high in summer and fall.
70
Effects of oil on bacterial activity and diversity
Tabl
e 1.
- Ini
tial c
ondi
tions
of t
he m
esoc
osm
s ex
peri
men
ts. T
he m
icro
cosm
exp
erim
ent w
as s
imul
tane
ous
to th
e Ja
nuar
y’06
mes
ocos
ms.
Mea
n ±
stan
dard
err
ors.
Tem
pera
ture
(T)
in ºC
, sal
inity
(Sa
l), d
isso
lved
inor
gani
c ni
trog
en (
DIN
) in
μM
, dis
solv
ed in
orga
nic
phos
phor
us (
DIP
) in
μM
, si
licat
e (S
iO4)
in μ
M, p
artic
ulat
e or
gani
c ca
rbon
(P
OC
) in
μM
, par
ticul
ate
orga
nic
nitr
ogen
(P
ON
) in
μM
, chl
orop
hyll-
a co
ncen
trat
ion
(Chl
a) in
m
g m
-3, t
otal
pro
kary
otic
abu
ndan
ce (P
A) i
n x1
05 cel
l ml-1
and
BH
P in
μg
C L
-1 d
-1 (m
ean
of L
eu a
nd T
dR w
ith s
tand
ard
conv
ersi
on fa
ctor
(CF
) 3.1
kg
C m
ol L
eu-1 a
nd 4
0 kg
C m
ol T
dR).
N=
6.
Exp
erim
ent
Dat
e T
Sa
l D
IN
DIP
Si
O4
Chl
a PA
B
HP
Spri
ng
02/0
3/05
10
.5 ±
0.0
35
.48 ±
0.02
4.
40 ±
0.0
8 0.
52 ±
0.0
0 3.
17 ±
0.0
4 3.
01 ±
0.0
1 6.
13 ±
0.1
8 3.
05 ±
0.4
Sum
mer
02
/07/
05
20.8
± 0
.0
35.0
2 ±
0.01
0.
58 ±
0.1
1 0.
15 ±
0.0
1 0.
59 ±
0.0
5 1.
50 ±
0.0
1 9.
92 ±
0.7
2 69
.94 ±
3.4
Aut
um
22/0
9/05
15
.4 ±
0.0
35
.73 ±
0.01
5.
66 ±
0.7
3 0.
51 ±
0.0
8 0.
41 ±
0.0
2 9.
22 ±
0.6
3 13
.82 ±
0.40
26
.25 ±
8.11
Win
ter
24/0
1/06
12
.4 ±
0.0
35
.60 ±
0.00
7.
70 ±
0.4
4 0.
48 ±
0.0
2 3.
72 ±
0.1
5 0.
54 ±
0.0
1 5.
29 ±
0.0
6 16
.88 ±
0.31
71
Chapter II
The added PAHs disappeared from the water in a similar exponential way in the 4 experiments
(Fig. 1). On day 2 of the experiments, only ca. 35% of the added PAHs were found in solution except
in summer, where the high ambient temperature caused faster volatilization of the added compounds
and only 10% remained in the water (Fig. 1). No PAHs were detected in summer after day 4, while
in the other months between 5 and 15% of the added substrates remained in the mesocosms at day 4.
The PAHs exponential decay rate was pretty similar in all the experiments (around -0.35 d-1) except
for the summer experiment in which it was higher -0.7 d-1 (Table 2).
Fig 1.- Changes in dissolved PAH concentration and in chlorophyll a concentration in the control mesocosms (white circles) and the mesocosms to which PAHs had been added (black circles). Spring experiment (A), Summer experiment (B), Autumn experiment (C) and Winter experiment (D). Average plus standard deviation of 2 (spring) or 3 (the other months) replicated mesocosms.
Chla
(mg
m-3)
Spring Summer
% o
f PAH
s rem
ainin
g
Autumn
0
5
10
15
0
20
40
60
80
100
120
0 2 4 6 8
Winter
Chla
(mg m
-3)
% of
PAHs
rem
ainin
g
0
5
10
15
0
20
40
60
80
100
120
0 2 4 6 8
0
5
10
15
0
20
40
60
80
100
120
0 2 4 6 80
5
10
15
0
20
40
60
80
100
120
0 2 4 6 8
Control
PAHs
PAHs concentration
A
C
B
D
Time (days)Time (days)
72
Effects of oil on bacterial activity and diversity
Chlorophyll a. The evolution of chlorophyll in the mesocosms was different in the different
experiments. In spring (Fig 1A, C) water con nement caused a bloom (up to 10 mg m-3), probably
because we had sampled growing healthy algae. In contrast, in autumn, the starting values of Chla
were 9 mg m-3 and decreased drastically during the four subsequent days. In summer and winter there
were few changes in chlorophyll during the experiments, just small slow increases. In general, no
differences were found because of the oil additions. They were only signi cant (<0.01) in the summer
experiment (Fig 1B, Table 3) when the increase after day four to the end of the experiment, was higher
(ca. 2x) in the oil-added mesocosms (Fig. 1B).
Bacterial abundance. The changes in bacterial abundances in the experiments also showed different
trends in the different seasons (Fig. 2). Except in the autumn experiment, there was a general initial
increase in abundance (day 1) that soon was followed by a decrease in bacterial abundance (days 3-4)
and by a second peak, higher in magnitude and at about days 6-8. The autumn experiment showed just
one peak. The effect of oil was obvious in the summer experiment (Fig 2b) when the two peaks were
higher in magnitude when PAHs had been added. When separating the rst (days 0-4) from the second
(days 5-8) part of the experiments, the PAH treatments affected the second part of the experiment in
all experiments (Table 3). This suggests that the signi cance observed using all the data together is
due to the effect in the second part of the experiment.
Table 2.- Conditions of the treatments with added PAHs in the different experiments. Average of2 or 3 replicates ±Stdev.
Experiment Date Initial PAHs Conc Exponential decay rate
(µg l-1) (d-1)
Spring 02/03/05 14.95 ± 5.59 -0.370 ± 0.024
Summer 02/07/05 29.03 ± 6.08 -0.714 ± 0.015
Autum 22/09/05 23.63 ± 6.11 -0.415 ± 0.045
Winter 24/01/06 18.51 ± 4.68 -0.326 ± 0.014
Microcosms (Winter):
0.5x 9.60 ± 0.99 -0.231 ± 0.009
1x 19.81 ± 0.42 -0.241 ± 0.001
2x 38.85 ± 0.64 -0.234 ± 0.003
4x 73.21 ± 2.97 -0.212 ± 0.009
73
Chapter II
Fig 2.- Changes in dissolved PAHs concentration and in bacterial abundances in the control mesocosms (white circles) and the mesocosms to which PAHs had been added (black circles). Spring experiment (A), Summer experiment (B), Autumn experiment (C) and Winter experiment (D). Average plus standard deviation of 2 (spring) or 3 (the other months) replicated mesocosms.
0
1 10 6
2 10 6
3 10 6
4 10 6
5 10 6
0
20
40
60
80
100
120
0 2 4 6 8
ControlPAHsPAHs concentration
A
0
1 10 6
2 10 6
3 10 6
4 10 6
5 10 6
0
20
40
60
80
100
120
0 2 4 6 8
C
Time (days)
0
1 10 6
2 10 6
3 10 6
4 10 6
5 10 6
0
20
40
60
80
100
120
0 2 4 6 8
BSpring Summer
0
1 10 6
2 10 6
3 10 6
4 10 6
5 10 6
0
20
40
60
80
100
120
0 2 4 6 8
D
Time (days)
Autum Winter
% of
PAHs
rem
ainin
g%
of PA
Hs re
main
ing
Bacte
rial a
bund
ance
(cell
ml-1
)Ba
cteria
l abu
ndan
ce (c
ell m
l-1)
74
Effects of oil on bacterial activity and diversity
Table 3.- Results of the repeated measures ANOVA with one within-subject factor (time) and treatment as a between-subjects factor. N = 2-3 for the mesocosms experiments, and N = 4 for the microcosm experiment. Signi cant (P< 0.05) effects are in bold.
Treatment Time (treatment) F-value Prob. F-value Prob.
Chlorophyll a
Spring 0.43 0.525 68.04 <0.01Summer 9.81 <0.01 4.09 <0.01Autumn 0.11 0.733 45.55 <0.01Winter 3.21 0.084 15.63 <0.01Microcosms 130.85 <0.01 79.49 <0.01
Total prokaryotic abundance
Spring 16.10 <0.01 85.07 <0.01 rst half 1.18 0.31 second half 32.91 <0.01Summer 35.08 <0.01 7.59 <0.01 rst half 1.54 0.24 second half 37.29 <0.01Autumn 5.95 0.021 4.67 <0.01 rst half 1.34 0.29 second half 60.89 <0.01Winter 20.31 <0.01 32.60 <0.01 rst half 2.25 0.15 second half 8.88 0.02Microcosms 727.79 <0.01 94.81 <0.01
Prokaryotic heterotrophic production
Spring 4.71 0.47 55.65 <0.01Summer 43.54 <0.01 5.44 <0.01Autumn 2.35 0.136 9.43 <0.01Winter 0.215 0.646 2.73 0.014Microcosms 325.44 <0.01 87.15 <0.01
Bacterial growth rate
Spring 0.56 0.47 103.09 <0.01Summer 0.92 0.36 6.55 <0.01Autumn 3.98 0.06 5.21 <0.01Winter 5.46 0.03 12.82 <0.01Microcosms 4.39 <0.01 18.53 <0.01
75
Chapter II
Fig. 3.- Changes in dissolved PAHs concentration and in bacterial production (leucine incorporation) in the control mesocosms (white circles) and the mesocosms to which PAHs had been added (black circles). Spring experiment (A), Summer experiment (B), Autumn experiment (C) and Winter experiment (D). Average plus standard deviation of 2 (spring) or 3 (the other months) replicated mesocosms.
0
20
40
60
80
100
120
140
160
0
20
40
60
80
100
120
4 6 80
20
40
60
80
100
120
140
160
0
20
40
60
80
100
120
0 2 4 6 8
AControl
PAHs
PAHs concentration
0
20
40
60
80
100
120
140
160
0
20
40
60
80
100
120
0 2 4 6 8
Bacte
rial p
rodu
ction
(mg
C L-1
d-1)
Bacte
rial p
rodu
ction
(mg
C L-
1 d-
1)
0
20
40
60
80
100
120
140
160
0
20
40
60
80
100
120
0 2 4 6 8
0 2
% o
f PAH
s rem
ainin
g%
of P
AHs r
emain
ing
B
C D
Spring Summer
Autum Winter
Times (days)Times (days)
76
Effects of oil on bacterial activity and diversity
Fig. 4.- Evolution of heterotrophic nano agellate abundance in the control (empty symbols) and the oil-added treatment (solid symbols) in the Spring experiment. Averages of 2 replicated mesocosms ± Stdev. Bacterial abundances in the same control (empty squares) and treatment (solid squares) mesocosms are shown for reference (same data of Fig. 2A).
Table 4.- Heterotrophic nano agellate evolution in the rst three mesocosms with experiments and microcosm. Changes in abundance (growth rates) before and after the abundance peak reached in each experiment. Data are not available for the Winter experiment and for 8x treatment of microcosm.
INITIAL FINAL
Time interval (d) GR (d-1) Time interval (d) GR (d-1)
Spring 0-4 0.48 >4 -0.23Summer 0-2 0.71 >2 -0.59Autumn 0-2 0.40 >2 -0.76Winter - - - -
Microcosms 0x 0-3 0.3 >3 0.02 2x 0-3 -0.04 >3 0.06 4x 0-3 -0.07 >3 0.18
0
5 105
1 106
1.5 106
2 106
2.5 106
0 2 4 6 8
Time (days)
0
1000
2000
3000
4000
5000
Hete
rotro
phic
nano
flage
llate
s (c
ells
ml-1
)Bacteria (cells m
l -1)
77
Chapter II
Bacterial production. Thymidine and leucine incorporations were well correlated throughout the
experiments (TdR = 51.9*Leu^0.69, r2= 0.56, N=165), with an average ratio Leu:TdR of 30.4 ± 2.8.
The ratio was lower (average 12.8 ± 1.2) in summer, and larger in spring. It was relatively stable
throughout the experiment in summer and winter, but increased throughout the spring experiment
(46.0 ± 8.9), and decreased in the autumn experiment. Using repeated measures ANOVA the only
signi cant differences between the ratio Leu:TdR in PAHs treated vs. control mesocosms was observed
in summer (details not shown), when it was signi cantly lower in the mesocosms that had received
PAHs (9.7 ± 0.7) than in those that had not (13.7 ± 0.9).
Bacterial production was much higher in the autumn experiment than in the rest of the experiments
(Fig. 3), in concordance with the fact that this experiment coincided with the end of a phytoplankton
bloom that slowly decomposed during our mesocosm experiment (Fig. 1C). In general, bacterial
production tended to reproduce the same pattern as bacterial abundances (Fig. 2), with an initial peak
and a secondary peak after day 4. This was evident in the spring and winter experiments (Fig. 3A
and 3D), less obvious in the summer experiment, and different in the autumn experiment (Fig. 3C).
Differences between treatments were only signi cant in the summer experiment (Fig. 3B and Table 3),
when the mesocosms that had received PAHs had productions about 1.5 fold higher than the controls.
The autumn experiment showed a small tendency for production being higher in the mesocosms that
had received PAHs (Fig 3).
Bacterial growth rates showed a pattern similar to that of production, with initial values ranging
from 0.3 d-1 (spring) to 3 d-1 (summer) (details not shown). They were not signi cantly affected by
the oil addition, except in the microcosm experiment (see below), where bacterial growth rates at the
end of the experiment in the control treatment were twice those of the treatment that had received the
maximum amount of oil. There was also a slight effect of oil on bacterial growth rates in the winter
experiment, where bacterial growth rates were slightly faster in the oil-treatments than in the controls
(ca. 25% decrease in growth rate) between day 3 and day 8.
Heterotrophic nano agellates. Flagellates were enumerated in the rst three experiments. The
evolution was rather similar: they rst increased up to day 3-4, and then decreased until the end of
the experiment (see example in Fig. 4, growth rates for the two different phases in Table 4). The
peak of HNF abundance was reached at different time in the different seasons, at day 4 in the spring
experiment (Fig. 4), at day 2 in the summer and autumn experiments (Table 4). At the peak of agellate
development, the abundance was higher in the mesocosms that had received PAHs than in the controls
(see example for the spring experiment in Fig. 4).
78
Effects of oil on bacterial activity and diversity
Fig. 5.- Time course evolution in the microcosm experiments: PAH concentration (A), Chlorophyl a(B), Bacterial abundance (C) and Prokaryotic production (D). A gradient of four concentrations represented: control (white circle), 20 μg L-1 (black circle), 40 μgL-1 (white square) and 80 μg L-1 (black square). For simplicity, we omit the treatment with ca. 10 µg L-1.
Bacte
rail
prod
uctio
n (m
g C L
-1 h-1
)
0
5 10 5
1 10 6
1,5 10 6
2 10 6
0 1 2 3 4 5 6 7
Bacte
rial a
bund
ance
(cell
s ml-1
)
0
20
40
60
80
100
120
0 1 2 3 4 5 6 7
Control20 µg L-1
40 µg L-1
80 µg L-1
Time (days) Time (days)
Chla
(mg m
-3)
% PA
Hs re
main
ing
A B
C D
0
50
100
150
200
250
0 1 2 3 4 5 6 7
0
0.5
1
1.5
2
0 1 2 3 4 5 6 7
79
Tabl
e 5.
-C
ompo
sitio
n of
the
bact
eria
l ass
embl
age
in th
e m
icro
cosm
exp
erim
ent d
eter
min
ed b
y C
AR
DF
ISH
: C
ells
pos
itive
with
pro
be A
LF96
8 fo
r A
lpha
prot
eoba
cter
ia (
Alp
ha),
pro
be G
AM
42a
for
Gam
map
rote
obac
teri
a (G
amm
a) a
nd p
robe
CF
319a
for
Bac
tero
idet
es (
CF
B).
Mea
sure
s at
th
e be
ginn
ing
(t0)
and
at t
he e
nd (
tf) o
f the
mic
roco
sm e
xper
imen
t in
the
four
diff
eren
t con
ditio
ns:
cont
rol (
0x),
20
mg
L-1 e
q. c
hrys
ene
(1x)
, 40
mg
L-1 (
2x)
and
80 m
gL-1(4
x). T
he “
Cha
nge
in a
bund
ance
” is
the
num
ber
of c
ells
pro
duce
d (o
r lo
st)
duri
ng th
e ex
peri
men
t.
A
lpha
prot
eoba
cter
ia
Gam
map
rote
obac
teri
a B
acte
roid
etes
t0
tf
C
hang
e in
abu
nd.
t0
tf
Cha
nge
in a
bund
. t0
tf
C
hang
e in
abu
nd.
0x
1.48
105
3.31
104
-115
,000
± 5
,300
9.
25 1
04 0.
76 1
05 -1
6,00
0 ±
800
2.50
105
0.18
105
-232
,000
± 1
4,00
01x
1.
32 1
05 0.
68 1
04 -1
26,0
00 ±
6,0
00
5.09
104
1.97
105
146,
000 ±
13,0
00
1.83
105
0.51
105
-132
,000
± 5
,700
2x
0.6
105
1.70
104
-59,
000 ±
1,70
0 4.
07 1
04 1.
29 1
05 88
,000
± 6
,000
1.
68 1
05 0.
64 1
05 -1
04,0
00 ±
2,8
004x
1.
63 1
05 6.
36 1
04 -9
9,00
0 ±
5,10
0 8.
14 1
04 6.
87 1
05 60
6,00
0 ±
44,0
00
1.83
105
1.
11 1
05 -7
19,0
00 ±
2,7
00
80
Effects of oil on bacterial activity and diversity
Fig 6.- Examples of DGGE gels of bacterial 16S rRNA gene fragments from the Spring (A), Summer (B) and microcosm (C) experiments and dendogram classi cation (Ward’s Method, Euclidean distances according to the band pattern). DGGE lanes represent the different treatments: control mesocosms (C1 and C2) and PAHs-amended mesocosms (A1 and A2) and the samplings times in days (T0, T2, T4, T6 and T8). In the microcosm experiment the different PAHs concentrations (control, 20 μg L-1, 40 μg L-1 and 80 μg L-1) and the samplings days (T0, T3 and T7). Different grey bands reunite together samples that cluster together.
0A1C1C1C2C1A1C1A1A1C1 T2
T6ToToT8T8T6T6T4T2
C1 A1 C1 A1 C1 A1 C1 C2 A1 A2 C1 C2 A1 A2
To T2 T4 T6 T8
0 5 10 15 20 25 30T
AC1A1
2
C2 T8T8T8T6T6T6T6T6T4T4T2T2
A1 A2 C1 A1 C2 A1 A2 C1 C2 A2 C1 C2
A T2T2
C1A1 T4
T4
2 T6T6T6T6T6
C2 T8T8T8
A
B
0 5 10 15 20 25 30
20 �g/L
80 �g/L
40 �g/L
40 �g/L20 �g/L
control
Linkage Distance
ToToToT3T3T3ToT3T7T7T7T7
control
20 �g/L
80 �g/L
control
40 �g/L80 �g/L
20 �g/L T7T7T7
40 �g/L80 �g/Llortnoc 02
�Lg
1-
04�
Lg1-
08�
Lg1- lo rtno c 0 2
�Lg
1-
04�
Lg1-
08�
Lg1- lor tnoc 0 2
�Lg
1-
04�
Lg1-
08�
Lg1-
To T3 T7
A
2C
CCA
A
2
1
1
1A2C
21
C
Summer
Minicosmos
Spring
Linkage Distance
Time (days)
Time (days)
0 50 100 150 200 250
C1 T0A1 T0A1 T2C1 T2C2 T6C1 T8C1 T4C2 T8C1 T6A1 T4A2 T6A1 T8A1 T6A2 T8
T2 T4 T6 T8
A1 T4A2 T6A1 T8A1 T6A2 T8
C1 T0A1 T0
Linkage Distance
81
Chapter II
Microcosm experiments. As we have shown before (Teira et al. 2008), the evolution of bacterial
communities was similar in the mesocosms and the microcosms that had received the same oil additions.
PAHs, however, disappeared more slowly from the microcosms (Fig. 5A) than from the mesocosms
(Fig 1D), probably because the water surface in contact with the atmosphere was smaller in the case of
the microcosms. PAHs had an effect on chlorophyll a (Fig. 5B, Table 3), which was much stronger at
80 µg L-1. Bacterial abundance was also affected by the added PAHs, so that the decrease in the peak
occurred later the more PAHs had been added, and did not occur when the maximal concentration of
80 µg L-1 was added (Fig. 5C). Bacterial production did not show any signi cant differences between
treatments until time 4, when the 80 µg L-1 sample increase tremendously (Fig. 5D). HNF developed
much more in the control of the microcosms (up to 3500 cells mL-1) than in the PAH treatments,
which were all rather similar and had few changes in HNF concentration (ca. 1500 mL-1 with a slight
increase towards the end of the experiment, details no shown).
Bacterial community structure and estimates of diversity. Figure 6 shows two examples
of the ngerprints of bacterial community structure. In three cases (spring, autumn and winter) the
band patterns showed no differences between controls and PAHs-added mesoscosms, as exempli ed
by the March DGGE gel and corresponding dendrogram (Fig. 6A). Only in the case of the summer
experiment there was a detectable oil effect (Fig. 6B), the dendrogram showing a cluster of samples
labeled “A” (PAH addition) after day 4 onwards. The presence and absence of bands in the DGGE
analysis was used to estimate bacterial richness of each sample. Band number varied between 7 and
17 with a global average of 13. Differences in bacterial richness were not signi cant for any of the
experiments, except for the summer one, where we detected higher diversity in the PAHs-ammended
mesocosms, particularly in the rst part of the experiment.
The DGGE analysis and the associated dendrogram of the microcosm experiment showed a clear
clustering for the highest concentration treatments, particularly at the start of the experiment (times 0
and 3, Fig. 6C). The samples from time 7 all clustered together, indicating that the time evolution was
probably more important than the PAHs additions in determining community structure. The lowest
richness was detected with the highest PAHs concentration.
In the microcosm experiments we also followed the changes in bacterial subgroup abundance by
CARDFISH. Alphaproteobacteria abundances had similar decreases (1x105 cells mL-1) in all treatments
except in the 2x, when it was lower (5x104 cells mL-1). Bacteroidetes showed a clearly higher decrease
in the tank that was amended with higher concentration of PAHs (Table 5). Somehow different results
were observed when comparing the relative contribution of each subgroup to total DAPI-stained
82
Effects of oil on bacterial activity and diversity
cells abundance instead of the absolute abundance (Teira et al. 2007). The most relevant changes
occurred, however, also in the Gammaproteobacteria, which slightly decreased in the treatment that
received no PAHs, but increased a lot in the treatment that had received most PAHs. Community
structure in the microcosms that had received PAHs shifted from ca. 50% Bacteroidetes, 35%
Alphaproteobacteria and 15% Gammaproteobacteria to 20% Bacteroidetes, 10% Alphaproteobacteria
and 73% Gammaproteobacteria. Community structure also shifted in the microcosms that had not
received PAHs, but the dominance by Gammaproteobacteria was smaller (55%) and driven by the
large decrease in Bacteroidetes (from 50 to 14%). Gammaproteobacteria in the control microcosm
changed little, and Alphaproteobacteria lost a large number of cells, but continued to be a relatively
large part of the assemblage, ca. 26%.
DISCUSSION
We used a mesocosms approach to test for the effects of realistic additions of oil on bacterial
function and assemblage structure. We repeated the experiment four times at different phases of the
seasonal cycle of Ría de Vigo to discern whether the assayed levels of PAHs affected microbial
communities at all times or only depending on the initial microbial communities or environmental
settings. Furthermore we also run a variable-concentration experiment to nd the threshold generating
signi cant effects. The four experiments started in four different plankton conditions. Spring with a
starting phytoplankton bloom, summer with little development of algae, autumn corresponded to a
declining algal bloom, and winter had low chlorophyll values. The fact that the algal communities
maintained constant populations in the mesocosms in summer and winter indicates that our sampling
and bag- lling protocols were accurate and did not disturb too much the communities.
Following the accident of the Prestige tanker in front of the Galician coast in November 2002,
several research projects were carried out to determine the effects of the spill on the ecosystem. Our
objective was to test to what extend the bacterial assemblages of Ría de Vigo were pre-adapted to the
oil concentrations likely to be found in an oil spill accident. We hypothesized that if the assemblages
did not change in diversity nor in function after point additions of PAHs, it would indicate that the
assemblages were accustomed to the presence of similar PAHs concentrations in the waters, and that
they could use and metabolize this type of allochthonous carbon, which would then not dramatically
modify the structure and the function of the bacterial community.
83
Chapter II
The concentrations of PAHs measured after similar oil spill accidents are very variable (as are in fact
the methodologies used to measure the concentration). After the accident in Paraíso Bay, Antarctica,
values of 50-100 µg L-1 were measured, while the values measured after the Exxon Valdez spill were
much lower, of ca. 10-30 ng L-1 (González et al. 2006). The values measured in the area of the Prestige
oil spill a few weeks after the accident were on average 0.3 µg L-1 (González et al. 2006).
It is somehow dif cult to compare different units of PAHs concentrations reported before because
not all the units are interchangeable. In any case, the values used in our experiment tend to be much
lower than those used in the past in experimental studies. Siron et al. (1993), for example, used a
range of oil concentrations of 0.7-4.4 mg L-1 in mesocosms.Some authors used very large crude oil
concentrations 900 mg L-1, Cappello et al. 2006; 100 mg L-1, Cappello et al. 2007) because they
targeted the isolation of hydrocarbon-degrading bacteria, or wanted to detect the speci c use of one
PAH molecule (naphtalene, 6.4 g L-1, Castle et al. 2006). It is certain that when crude oil is added,
the real concentration of PAHs that the microorganisms experience will be much smaller. In our
experiments we chose values (Table 2) intermediate between what was detected in the sea, and what
had been used in previous similar studies, concentrations that we believed are more realistically close
to what in situ bacteria could experience. The fast disappearance of PAHs from the water (Table 1)
further assured that PAH concentrations in the water in our experiments were realistic.
Our results indicate that the concentrations of PAHs tested in the mesocosms appeared to affect
bacterial abundance in all experiments while, however, chlorophyll, bacterial production and growth
rates were only affected signi cantly during the summer experiment (Table 2). This contrasting result
(effects on abundance and not on the rest of the variables) can be explained by the fact that the
effects on bacterial abundance can be seen in the second part of the experiment, after agellates
have grazed on the community and have, presumably, affected its composition (see below). That the
effects are signi cative in summer can be assigned to the fact that in the period with less available
inorganic nutrients (summer, Table 1), the increased mineralization activity by bacteria stimulated
by the introduced PAHs probably generated more inorganic nutrients, which in turn stimulated algal
growth, while in winter the excess of nutrients in the water would mask the increase caused by
increased bacterioplankton mineralization. It is interesting to note that we found the highest effect in
the season in which PAHs disappeared faster from the water (0.70 d-1 in summer and 0.35 d-1 in the
other seasons) probably due to the higher temperatures (Table 1). Temperature also plays a signi cant
role in controlling the nature and extent of microbial hydrocarbon metabolism (Nedwell et al. 1999),
and directly affects both the rate of biodegradation (Brakstad et al. 2006), and the physico-chemical
behaviour of oil hydrocarbons, such as viscosity, diffusion and volatilization, which changes oil
84
Effects of oil on bacterial activity and diversity
composition, and bioavailability of the water-soluble components (Northcott & Jones, 2000, Rowland
et al. 2000). Coulon et al. (2007) showed that the change in temperature had a much more pronounced
effect on the oil-degrading microbial communities than nutrient additions, even though biodegradation
was possible at all temperatures.
The summer season in the NW Iberian zone is characterized by the upwelling due to the dominance
of North winds over the adjacent shelf. Surface water leaves the Ría moving offshore and this water
is replaced by subsurface oceanic Eastern North Atlantic Central Water, which enters the Ría and has
a profound effect on its hydrography (e.g. Álvarez-Salgado et al. 1993). The bacterial assemblages
at this time of the year, previously surviving in deep oceanic waters, are probably less adapted to the
amounts of allochthonous organic matter present in the Ría. In fact, we detected using CARD-FISH the
presence of some Betaproteobacteria (typically considered “freshwater” groups, Methé et al. 1998) at
all seasons except Summer, and the gammaproteobacteria PAH-degrading genus Cycloclasticus was
detected in September and January, but not in summer (Alonso-Gutiérrez et al. submitted). SAR11
was also not detected in summer, something which is consistent with a deep-water origin of the
bacterial assemblage (SAR11 decreases at mesopelagic depths, e.g. Baltar et al. 2007).
The microcosm experiments run in parallell to the January mesocosms demonstrate that the lack
of effect of the added PAH concentrations in the spring, autumn and winter experiments was most
probably due to a too low PAH concentration being assayed. In that experiment, ca. 20 µg L-1 of PAHs
had no effect, while twice this amount started to be detectable, and 4x this amount had a very relevant
effect on the microbial communities, decreasing algal development, increasing bacterial abundance
and production, and decreasing bacterial richness. The PAHs addition affected positively bacterial
abundance and production both in the summer experiment and in the microcosm experiments (Fig. 2
and Fig. 5), but had contrasting effects on chlorophyll development, which was stimulated in summer,
and depressed in the microcosms. Previous studies reported a stimulatory effect of oil addition to
autotrophs but a toxic effect at higher concentrations, while bacterial response was however positive
at all concentrations (Wang & Koshikawa 2007).
Inspection of the DGGE gels let us conclude that there were signi cant changes in bacterial
community structure caused by the PAH additions only in the summer experiment where an increase
in the number of bands was observed (an estimate of higher richness). This result differs from the
previous idea that hydrocarbon polluted areas should have low diversity values and a high dominance
of a few bacterial groups (Harayama et al. 1999, MacNaughton et al. 1999, Kasai et al. 2001, Röling
et al. 2002), and also differs from Schäfer et al. (2000) that reported that experimental manipulations
85
Chapter II
should reduce the diversity of microbial communities. Since the experimentally added PAHs can be
considered as a C source additional to other C sources present in the water, it could be speculated that
increased richness would indicate the appearance of more niches for bacterial development when the
PAHs were present. Interestingly, in the microcosm experiments we observed an increase in richness
with the additions of 20 and 40 µg L-1 as compared to the control, but then a decrease with the highest
concentration (80 µg L-1). The global structure of the community changed little, however, as detected
by DGGE (Fig. 6C), and it seemed to do so more at the end of the experiment, when the 80 µg L-1
treatment was more distinct from the control treatment. This is in contrast with the CARD FISH results
(Table 5) that indicate a clear shift towards gamma-proteobacteria in the 4x (80 µg L-1) treatment,
also apparent, but much lower in magnitude, in the 1x and 2x treatments. This discrepancy is maybe
due to the fact that the high addition treatment presents much higher abundances respect to the other
treatments. Bacteroidetes and alphaproteobacteria decreased in all treatments, but with contrasting
patterns: alphaproteobacteria decreased more at low than at high oil additions, while Bacteroidetes
were replaced by gammaproteobacteria particularly at the highest PAH addition. These results at
times seem to contradict a previous analysis (Teira et al. 2008) which was based on %contribution to
each group and not in strict abundance. The differences can be assigned to a different variable being
observed.
In previous research, gammaproteobacteria have generally been seen to dominate after oil
additions (e.g. Grossman et al. 1990, Kasai et al. 2001, Syutsubo et al. 2001, Röling et al. 2002,
McKew et al. 2007, Cappello et al. 2007, etc.). For example, Alcanivorax were shown to dominate
in oil-contaminated seawater when nutrients were adequately supplied (Kasai et al. 2002) and to
have broad substrate speci city for alkane degradation (Hara et al. 2003). One of the predominant
oil degraders after the Nakhodka oil spill was the aromatic hydrocarbon decomposer Cycloclasticus
pugetii (23-25%), followed by the aliphatic hydrocarbon decomposer Alcanivorax borkumensis (4-
7%) (Maruyama et al. 2003). Since the type of oil we used was rich in PAHs and poor in aliphatics,
we expected to see the development of Cycloclasticus. Indeed, an increase of Cycloclasticus bacteria
in the four mesocosms experiments, reaching different levels depending on the season, was reported
by Teira et al. (2007) and in the microcosm experiments this organism showed a proportional increase
depending on the amount of added PAHs. The Cycloclasticus genus is well known to be implicated in
PAH degradation (Kasai et al. 2002, Yakimov et al. 2004). In the microcosm experiments this group
was a large fraction of the total gammaproteobacteria specially in the middle of the experiment. As
an example, 3, 12, 28, 43 and 89% of the cells positive with the gamma- probe (GAM42a) were also
postive with probe CYPU829 for Cycloclasticus in the 0x, 0.5x, 1x, 2x and 4x treatments respectively
at day 5. Later on in the experiment, Cycloclasticus disappeared from the microcosms (Teira et al.
2007) and were replaced by other gammaprotebacteria. It is important to note, however, that probe
86
Effects of oil on bacterial activity and diversity
GAM42a has a broad gammaproteobacteria group coverage of only 76% (Amann & Fuchs 2008),
and does not target Alcanivorax, nor many other typically hydrocarbon-degrading bacteria, such
Cycloclasticus, Oleispira, Thalassolituus, etc. It targets, however, other gammaproteobacteria with
capability to degrade hydrocarbons, such as Pseudomonas or Marinobacter hydrocarbonoclasticus
(unpublished results).
For the analyses of bacterial community structure in this experiments we used the DGGE approach,
as other studies have done in oil polluted marine environments. (Macnaughton et al. 1999, Ogino et al.
2001, Kasai et al. 2001, Castle et al. 2006) Bacterial richness (as estimated by DGGE) or community
structure was not affected in the experiments other than in the summer one. Observation of the DGGE
band patterns (Fig. 6) and the statistical analyses reported in Table 3 indicates that “Time” is one of the
most important factors determining the changes in bacterial community structure in the experiments,
and also is one of the main factors determining the changes in chlorophyll, prokaryote abundance and
prokaryotic heterotrophic production (Table 3). Since we showed that the oil addition had little effect
on bacterial diversity and function, we could speculate that besides the environmental factors (nutrient
levels, DOC content, water, temperature, etc.) protist development could be one of the reasons why
bacterial function and community structure varied in the different experiments. We expected that
changes in bacterial community structure (as detected by DGGE) should be correlated to the changes
in bacterial activity.
Indeed, the similarity between bacterial community structure in two consecutive time points was
correlated to the total bacterial activity between these two points (Fig. 7A) so that the lower bacterial
production, the closer are the two communities, and vice-versa. The relationship seemed to be different
in the summer experiment than in the others, again supporting the idea that the summer community
developed differently from the bacterial communities of the other experiments. The correlations were
signi cant but not great. To improve them, we analyzed separately the rst part of the experiments
(Fig. 7B), when HNF developed (see example in Fig. 5) with positive growth rates (Table 4) from
the second part of the experiment, when HNF declined (Fig. 4) and presented negative growth rates
(Table 4). The changes in bacterial community structure were well linked to bacterial production in
the rst part of the experiment, when HNF probably induced relatively small changes in the bacterial
assemblage. Later on, once HNF had developed and depleted the bacterial population (Fig. 4), bacterial
community structure changes were not linked any more to bacterial production.
As in many other published mesocosms experiments (e.g. Lebaron et al. 1999, Pinhassi et al.
2006, Allers et al. 2007) bacterial abundance in our study had a small initial positive response to the
87
Chapter II
mesocosms startup (days 1-2), then they decreased in abundances (days 3-4), and then they increased
again towards the end of the experiments. It was this second bacterial peak the one that was most
affected by oil.
There is still disagreement as to whether the abundance, productivity and diversity of pelagic
bacteria are determined mainly by predators or nutrients (Pernthaler 2005). Grazing has been shown
to in uence bacterial community composition in laboratory and eld experiments (van Hannen et
al. 1999, Gasol et al. 2002, Simek et al. 2002). Allers et al. (2007), in a mesocosm experiment with
Mediterranean waters found a succession of bacterial communities. The initial abundance peak consisted
of predominantly Alteromonadaceae, very similar phylotypes in the different treatments. These
organisms were depleted by an increase in HNF numbers and afterwards different Rhodobacteraceae
phylotypes developed in the different treatments, particularly those that had received P additions. If
Fig. 7.- A) Relationships between integrated bacterial production between samples (X axis) and the similarity in bacterial community structure (Y, Pearson correlation coef cients between two consecutive lanes in a DGGE analysis, considering presence/absence of bands as well as intensity), with separation of experiments run in Spring, Autumn and Winter (solid circles) from the Summer experiment (empty circles). A similitude of 1 indicates that the community did not change. For the Summer experiment, N=8, r=-0.71, P<0.05, for the other experiments, N=22, r=-0.58, P<0.005. B) Same plot for the three experiments of Spring, Autumn and Winter, separating the points before the time= 4 (solid squares) and after that time point (empty squares). This division corresponding to changes before the peak of agellate development, or after that peak (see Fig. 5). See text for further explanations. The relationship is only signi cant for the period before agellate development, N=10, r=-0.94, P<0.005. Regression lines are drawn for orientation only
Bacterial production between two time points (µgC l-1 d-1)
Spring, Autumn and Winter experimentsSummer experiment
Sim
ilarit
y of
bac
teria
l com
mun
ity s
truct
ure
A-0.2
0
0.2
0.4
0.6
0.8
1
0 50 100 150 200 250-0.2
0
0.2
0.4
0.6
0.8
1
0 20 40 60 80 100 120 140
B
Bacterial production between two time points (µgC l-1 d-1)
From time=0 to time=4From time=4 to time=8
88
Effects of oil on bacterial activity and diversity
the bacterial assemblages of coastal waters are composed of rather stable populations with peaks of
fast-growing organisms showing rapid temporal uctuations, manipulation of the ecosystem might
destabilize this system and promote the growth of these fast-growing populations, that are the ones
that will be responsible for the changes in bacterial production and community structure (Pernthaler
2005).
Conclusion
Policyclic aromatic hydrocarbons (PAHs), which are well-known to have toxic effects on
organisms also are, however, a signi cant allochthonous C source for bacteria. Our microcosm study
showed that the addition of PAHSs resulted in an immediate change in bacterial community structure
and rapid rate of oil degradation, suggesting the presence of a pre-adapted oil-degrading microbial
community assemblage, in agreement with the data presented by Coulon et al. (2007). Our mesocosms
experiments showed that, at least in three of the seasons, the natural assemblage seldom reacted to the
PAHs additions, did not show any toxic effect, and did not produce any bacterial stimulation, maybe
because the concentration was negligible compared to that of the total biovailable DOC. In the Ría de
Vigo, we had observed that PAH addition triggered short-lived peaks of Cycloclasticus (Teira et al.
2007) that used the PAHs, and then were virally-lysed or predated by protists. The organisms with the
potential to use the allochthonous added substances were present, albeit at very low concentrations,
and had the potential to develop immediately after PAHs additions. Our results also indicate that when
realistic (i.e. not exagerately large) oil or PAHs concentrations are added in experimental containers,
the response of the communities will be more likely to shed light into the realistic behaviour of natural
microbial communities.
ACKNOWLEDGEMENTS
This work was supported by project IMPRESION (VEM2003-20021). We thank all the people
involved, particularly those who helped with the preparation and sampling of the mesocosms during
the four experiments, and F.G. Figueiras and X.A. Álvarez-Salgado who were the driving forces
behind the whole project. We also thank J. González (Universidad de Vigo) for access to data and
D. Vaqué for help with the HNF counts. We would also like to thank the captain and crew on board
RV Mytilus. Financial support to IL and ACD was provided by PhD fellowships from the Spanish
government (MEC).
89
Chapter II
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Different types of phytoplankton blooms caused by different additions in seawater mesocosms, and their effect on bacterial heterotrophic activity and assemblage composition
Itziar Lekunberri, Thomas Lefort,Clara Ruíz-González, Montse Coll, Clara Cardelús, Cèlia Marrasé and Josep M. Gasol
CHAPTER 3
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ABSTRACT
Bacterial community structure is thought to be in part related to the type of phytoplankton species
dominating during algal blooms. The proliferation of speci c organisms in such blooms is due to solar
radiation and the presence of speci c nutrients. We performed a mesocosms experiment to generate
different phytoplankton blooms through the additions of: silica (SiO4), urea and/or phosphorous to
oligotrophic water from the Blanes Bay Microbial Observatory (NW Mediterranean). Over 10 days
incubations, bacterial activity, abundance, nutrient composition and bacterial community structure
were analyzed. The aims of this experiment were: i) to relate different additions to the phytoplankton
and bacterioplankton community composition. ii) to investigate whether the changes in bacterial
community structure occur by a bottom-up or top-down control. Our results reveal that the change in
the bacterial composition is due to a combination of changes in the phytoplankton communities and
grazing pressure. And the differences in abundances and activity of bacteria are due to a shift in the
bacterial composition.
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Bacterial response to different types of phytoplankton
INTRODUCTION
Phytoplankton blooms consist in a marked increase in algal populations, reaching concentrations of
up to millions of cells per milliliter, involving typically only one or a few species. These phenomena
occur when light and nutrients are available in excess. Cullen et al. (2002) de ned 3 types of blooms:
upwelling blooms which are induced by deep (nutrient-rich) waters reaching the lit layers of the
ocean, entrainment blooms those that appear when the mixed layer deepens due to wind erosion
or solar heating reduction, and detraiment blooms, which occur when near-surface strati cation is
induced in a deep mixed layer of nutrient-rich water. In coastal waters algal blooms are frequently the
result of nutrients inputs (Rosenberg et al. 1990, Smayda 1990) or of the start of strati cation. And
these inputs, even if seasonally recurrent, can be equated with sporadic inputs. Variation in nutrient
regimes affects phytoplankton production, diversity and succession, and also in uences the overall
structure of the microbial food web (Smayda & Reynolds 2001, 2003, Cullen et al. 2002).
Previous studies have correlated increases in biomass of individual phytoplankton species with
nutrient inputs to coastal regions (Smayda 1990, Hallegraeff 1993). Diatom growth has been reported
to be dependent on silica availability (e.g. Allen et al. 2005), and urea was found to be preferentially
be used as a nitrogen source by some cyanobacteria and dino agellates (Glibert et al. 2008), thus
potentially selecting for these species. Other algae are adapted to low nutrient concentrations, and
these species bloom after opportunistic species have done it and exhausted nutrients. For example,
blooms of the eukaryotic picoplankter Aureococcus anophagefferens occur mainly after nitrate and
ammonium have decreased below detection limits (Keller & Rice 1989, Smayda & Villareal 1989).
Primary producers release DOC that is consumed by bacteria (Hagström et al. 1979, Fuhrman &
Azam 1980) and comparative studies show a dependence of bacterial abundances on Chl a suggesting
a control on bacterial abundance derived from the availability of resources derived from primary
production (Bird & Kalff 1984, Cole et al. 1988, Gasol & Duarte 2000). Thingstad & Lignell (1997)
enumerated the 5 factors which in uence growth of heterotrophic bacteria: i) DOC, ii) inorganic
phosphate, iii) organic/inorganic nitrogen, iv) protozoan predation and v) lysis by viruses. Control
by resources of bacterial abundance and activity is often named “bottom-up control” and control by
predation or lysis is called “top-down”.
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Chapter III
While many studies have shown that the speci city of a phytoplankton blooms is a consequence of
the speci c nutrient added, few have related this selection to shifts in bacterial community structure
(but see Schäfer et al. 2001, Pinhassi et al. 2006). Laboratory enrichment experiments show a
phytoplankton response followed by changes in bacterioplankton community composition (Pinhassi
et al. 2006, Allers et al. 2007) for example diatom blooms tended to be followed by Flavobacteria
developments. Besides Sapp et al. (2007a) found strong shifts in the associated bacterial communities
(members of the Gammaproteobacteria) to diatom species. However, Sapp et al (2007b) reported that
it could not be generalized whether there were bacterial communities strictly microalgal species-
speci c. Similarly, Garcés et al. (2007) found little correspondance between the type of dino agellate
species developing in blooms and their associated bacterial communities.
Different mesocosms experiments have been used to study the effect of nutrient additions: globally
on the phytoplankton communities (Duarte et al. 2000), on the activity and diversity of bacterial
assemblages (Schäfer et al. 2001), and on microbial succession after nutrient additions (Allers
et al. 2007). In this study we conducted a mesocosms experiment in which we tried to generate
three different bloom situations, in order to determine the effect of the different additions on the
phytoplankton, and of different phytoplankton on bacterioplankton community structure and function
during the summer season in Blanes Bay (NW Mediterranean). Experimental studies have always
shown a general phosphorous limitations in the NW Mediterranean ( Thinsgstad et al. 1998, Pinhassi
et al. 2006), particularly during the summer (e.g. Pinhassi et al. 2006). To avoid any nutrient limitation
phosphorous was added to all the tanks except for the controls. The three enriched treatments included
addition of silica, urea, and P alone. We monitored phytoplankton diversity daily and at the same
time sampled for bacterial abundance, physiological status, activity and diversity were collected. In
addition we also monitored the development of the HNF community to explore whether bottom-up or
top-down control led to bacterial population changes.
MATERIALS & METHODS
Experimental setting. The mesocoms experiment was carried out with water collected from Blanes
Bay (The Blanes Bay Microbial Observatory, NW Mediterranean). Eight 100 L tanks (2 replicates, 4
treatments) were lled with seawater ltered by 150 µm, in duplicates with different additions, and
incubated at in situ temperature (19ºC) with a 12 h photoperiod to simulate natural conditions. The
rst sample was taken 1 h after nutrient addition. The experiment was conducted during 10 days from
the 5 th to 15 th June 2007.
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Bacterial response to different types of phytoplankton
Additions. Within the 8 tanks, 2 were left as controls with no nutrient additions (Ka, Kb) and 6
received phosphate in the same proportion (1 µM nal concentration). Of these 6, 4 were enriched
with 16 µM N nal concentration, 2 with inorganic N (NO3) and 2 with organic N as urea (CO(NH
2)
2).
The NO3 amended tanks also received SiO
4. To keep the silicate in excess we added silicate twice
(in day 1 and day 2), and obtained a concentration (23-22 µM) similar to a target of 25 µM (thus
generating an addition with the ratio P:N:Si of 1:16:25, higher than the standard Red eld’s (1:16:16
to assure that the Si was in excess).To all tanks we added a metal solution in the same proportion to
phosphate as in the f/2 medium (Guillard et al. 1975).
Microplankton enumeration. For the identi cation of microphytoplankton (mainly diatoms,
dino agelates and coccolithophorids), samples were xed with formalin-hexamine solution (0.4%
nal concentration). Counts were made with the Utermöhl et al. (1958) methodology using 50 cm3
settling chambers. One transect of the chamber was observed at 400x magni cation to count the
smaller (<20 µm) and more frequent organisms. Additionally, 1 transect of half of the chamber was
inspected at 200x to enumerate cells of intermediate size (roughly 20 and 50 µm), and the whole
chamber was scanned at 200x to count the larger forms.
Pico- and nanoplankton abundances. Determination of algal and bacterial abundance was
performed with ow cytometry (Marie et al. 1999, Gasol & del Giorgio 2000). Samples were run in
a Becton-Dickinson FACSCalibur ow cytometer with a laser emiting at 488 nm. For phytoplankton,
the samples were not xed and were run at high speed (ca. 100 µl min-1), Four populations
(Prochlorococcus, Synechococcus, picoeukaryotes and nanoeukaryotes) were differentiated according
to size and red and orange uorescence. For non-phototrophic bacteria, 1.2 ml samples were xed with
a 1% paraformaldehyde + 0.05% glutaraldehyde solution and deep-frozen in liquid N2. Afterwards,
the samples were unfrozen, stained with SybrGreen at 1:10,000 dilution and run at low speed (ca 10
µl min-1). Cells were identi ed in a plot of side scatter vs green uorescence (FL1).
Heterotrophic nano agellate abundances were measured following the Rose et al. (2004) protocol.
From a stock solution of 1 mM Lysotracker Green (Molecular Probes) 1 µl was added to 99 µl of <
0.2 µm MiliQ, and 3.8 µl of the diluted Lysotracker were added to 0.5 ml of the sample, generating
a 75 nM Lysotracker nal concentration. We analyzed the samples as in Rose et al. (2004), using
a combination of side scatter and green and red uorescence. The samples were run alive, at high
(ca. 100 µl min-1) speed. Concentrations were obtained from weighting measurement of the volume
analyzed.
101
Chapter III
Bacterial single-cell activity. Measurements of the physiological status of bacteria were done in
two ways: i) Highly respiring prokaryotes, as those able to reduce 5-cyano-2,3-ditiolyl tetrazolium
chloride (CTC; Polysciences), CTC turns into a red uorescent formazan that is detectable by
epi uorescence and ow cytometry (Sherr et al. 1999, Sieracki et al. 1999). Sample aliquots (0.4
ml) were amended with 5 mM CTC (from a fresh stock solution at 50 mM) immediately following
collection and were incubated for 90 min in the dark at room temperature. CTC-positive (CTC+) cells
were enumerated by ow cytometry using the FL2-versus-FL3 dot plot (see Gasol & Arístegui 2007).
For these analyses, we used a high speed (ca. 100 µl ml-1) and a threshold set in red uorescence.
ii) Cells with intact membranes were enumerated using the NADS viability protocol, based on the
combination of the cell-permanent nucleic acid strain SybrGreen I (Molecular Probes, Eugene, OR)
and the cell-impermeant propidium iodine (PI, Sigma Chemical Co.) uorescent probe. We used a
1:10,000 SG1 and 10 µg ml-1 PI concentrations. After simultaneous addition of each stain, the samples
were incubated for 20 min in the dark at room temperature and then analyzed by ow cytometry.
SG1 and PI uorescence were detected in the green (FL1) and orange-red (FL3) cytometric channels,
respectively. A dot plot of red versus green uorescence allowed distinction of the “live” cell cluster
(i.e., cells with intact membranes and DNA present) from the “dead” cell one (i.e., with compromised
membranes) (Gregori et al. 2003, Falcioni et al. 2008).
Bacterial production. Bacterial heterotrophic production was estimated using the 3H-leucine
incorporation method (Kirchman et al. 1985). Quadruplicate aliquots of 1.2 ml and 2 TCA-killed
controls were taken daily. The samples were incubated with 40 nM nal concentration for about 2 h in
the dark and at in situ temperature. The incorporation was stopped with the addition of 120 μl of cold
TCA 50% to each replicate and the samples were kept frozen at –20ºC until processing, which was
carried out by the centrifugation method described by of Smith & Azam (1992).
Collection of community DNA. Microbial biomass was collected by sequential ltration of about
5 l of seawater through a 3 µm pore size polycarbonate lter (Millipore, 46 mm) and a 0.2 µm
sterivex lter unit (Millipore). Microbial biomass was treated with lysozyme, proteinase K and sodium
dodecyl sulphate, and the nucleic acids were extracted with phenol and concentrated in a Centricon-
100 (Milipore). The nucleic acids were extracted by a standard protocol using phenol/chloroform
(Schauer et al. 2003).
102
Bacterial response to different types of phytoplankton
Fingerprinting analysis. DGGE and gel analysis were performed essentially as previously described
(Schauer et al. 2000). Brie y, 16S rRNA gene fragments (around 550 bp in length) were ampli ed by
PCR, using the universal primer 907rm and the bacterial speci c primer 358f, with a GC-clamp. The
PCR products were loaded on a 6% polyacrylamide gel with a DNA-denaturant gradient ranging from
40-80%. The gel was run at 100V for 16 h at 60ºC in 1x TAE running buffer. DGGE gel images were
analyzed using the Diversity Database software (BIO-RAD).
Statistical analysis. A matrix was constructed for all DGGE lanes taking into account the relative
contribution of each band (in percentage) to the total intensity of the lane. Based on this matrix, we
obtained a dendrogram by the Ward’s clustering method (Euclidean distances, Statistica 6.0), and
ordinations of nonmetric multidimensional scaling based on the Bray-Curtis similarity index (MDS,
Kruskal & Wish 1979, Clarke & Green 1988). A one-way ANOSIM was computed to test the observe
differences between treatments and the control samples, and another to contrast differences among
time within treatment samples (software Primer v5). We also used one-way ANOVAs to test for the
differences in Chla, bacterial abundance and bacterial activity between treatments at the different
times when samples were taken for bacterial diversity.
RESULTS
Evolution of inorganic nutrient concentrations. Variations of the inorganic nutrient
concentrations for the different treatments during the experiment are shown in Fig.1. Except from
the control, all tanks had received 1 µM phosphate, which was rapidly consumed in the N amended
treatments (Si and U) but remained quite high in the P tank. The N source in the Si tank was NO3,
which started to decrease after day 6 (Fig 1C). In the representation of the evolution of silica (Fig
1B) the maximal value in treatment Si was only reached on day 2 (25 µM, after the second nutrient
addition), and this concentration was only reduced to ca. 10 µM at the end of the experiment, thus
indicating that there was much leftover unused Si in this treatment. Si remained at the initial value
of ca 4 µM in the control treatment, but it was used in the Urea treatment after day 3, and in the P
treatment after day 5, down to values < 0.1 µM.
103
Table 1.- Concentrations of nutrients (µM) in the 4 different experimental conditions 1 h after the addition. aand b are the replicates.
Experimental Nomenclature PO4 NO
3 SiO
4 DON
conditions
Initial water 0.02 0.13 0.21 -
Control Ka, Kb 0.02 ± 0.00 0.34 ± 0.03 0.38 ± 0.01 4.48 ± 0.41Phosphorous Pa, Pb 0.67 ± 0.03 0.40 ± 0.05 0.34 ± 0.03 6.92 ± 1.05Urea+P Ua, Ub 0.79 ± 0.01 0.031 ± 0.01 0.31 ± 0.00 16.70 ± 1.09N+P+SiO
3 Sia, Sib 0.75 ± 0.01 12.5 ± 1.03 22.7 ± 0.79* 17.08 ± 0.70
*The Silicate values correspond to day 2 (after the second addtion of the SiO3, see text)
Fig.1.- Nutrient evolution throughout the mesocosms experiment. Phosphate (A), Silicate (B) and nitrate+nitrite (C). Treatments: K tank ( ), P tank ( ), Si tank ( ) and U tank ( ). The arrow indicates the second addition of silicate.
0
0.2
0.4
0.6
0.8
1
0 2 4 6 8 10Time (days)
KaKbPaPbSiaSibUaUb
PO43+
(μM
)
SiO 4-2
(μM
)
NO3- +
NO2(μ
M)
Time (days)
0.01
0.1
1
10
100
0 2 4 6 8 10
C
Time (days)
0.001
0.01
0.1
1
10
100
0 2 4 6 8 10
BA
104
Bacterial response to different types of phytoplankton
Time (days)
Leuc
ine i
ncor
pora
tion
(pm
ol L
eu L
-1h-1
)
HNF
(cell
s ml-1
)BA
(cell
s ml-1
)
Chlo
roph
yll (
mg
m-3)
Time (days)
100
1000
104
0 2 4 6 8 100
1000
2000
3000
4000
0 2 4 6 8 10
0.1
1
10
100
0 2 4 6 8 10
AKaKbPaPbSiaSibUaUb
6 105
8 105
106
3 106
5 106
7 106
0 2 4 6 8 10
C D
B
Dynamics of chlorophyll a and phytoplankton composition. The initial value of chlorophyll a
(Chl a) was around 0.2 µg L-1 in all tanks (Fig. 2A). Signi cant differences (<0.05) between treatments
were found after time 3. The Si tank reached concentrations about 16 fold higher than the control and
the P tank while the U tanks reached values ca 5-fold those of the K and P tanks. Chl a in all treatments
remained high until the end of the experiment. Maximum value of Chl a in the P and K tanks was
reached at day 2, in the U tank at day 4, and in the Si tank at day 6.
Fig. 2.- Chlorophyll a concentration (A), bacterial abundance (B), leucine incorporation rates (C) and heterotrophic nano agelate abundance (D) throughout the mesocosms experiment. Treatments: K tank ( ), P tank ( ), Si tank ( ) and U tank ( ).Points used for the statistical analyses are indicated with arrows.
105
Chapter III
0
20
40
60
80
100
2 4 6 8 10
A%Dinoflagellates%Diatoms%Cocolitoforales%NanoflagellatesOthers
0
20
40
60
80
100
2 4 6 8 10
B
0
20
40
60
80
100
2 4 6 8 10
C
0
20
40
60
80
100
2 4 6 8 10
D
0 0
Time (days) Time (days)00
% V
olum
e (�m
3 )%
Vol
ume (
�m3 )
CONTROL P
Si U
The evolution of phytoplankton composition along the experiment in terms of contribution to
biovolume is shown in Fig. 3. The K and P tanks present similar patterns, with alternation between
diatoms and dino agellates. The Si tank showed a large increase in the diatoms comprising ca. 90%
of the community. In contrast the U tank had a decrease in the diatom composition compensated by
an increased in the dino agellates from time 4 to the end.
Fig. 3.- Phytoplankton diversity as % of volume. K tank (A), P tank (B), Si tank (C) and U tank (D). Dino agellates ( ), diatoms ( ), cocolitoforids ( ), nano agellates (X) and others ( ).
106
Bacterial response to different types of phytoplankton
On the basis of size and pigment content, we identi ed two different populations of picophytoplankton
(Synechococcus and picoeukaryotes). Prochlorococcus, are not present in Blanes Bay at this time of
the year (details not shown). Synechococcus in the control and P treatments had a peak at day 3 and
then decreased to remain with populations similar to the initial ones. The U and Si treatments had
highest values with a peak at day 5 and then a decrease to the disappearance at the end (Fig. 4A).
Picoeukaryotes had a clear peak at day 4 with maximal values in the Si and U tanks (Fig. 4B).
0
5 104
1 105
1.5 105
2 105
0 2 4 6 8 10
KaKbPaPbSiaSibUaUb
Syne
choc
occu
s (ce
lls m
l-1)
Pico
euka
ryot
es (c
ells m
l-1)
Time (days)
0
5000
1 104
1.5 104
2 104
2.5 104
3 104
3.5 104
0 2 4 6 8 10
B
A
Fig. 4.- Evolution of Synechococcus (A) and Picoeukaryotes (B). Treatments: K tank ( ), P tank ( ), Si tank ( ) and U tank ( ).
107
Chapter III
Bacterial abundance. Initial bacterial cell numbers were 1.2 x 106 cells ml-1 (Fig. 2B). There was
an initial peak at time 2-3 days in all treatments, which was much larger in treatments P, U and Si than
in the control. A second peak at times 6-8 did not occur in the control. This peak was higher in the U
treatment than in the Si and P treatments. There were very signi cant differences (one-way ANOVA
P<0.01, Table 2) between controls and amended tanks at the beginning (time 3) of the experiment
Bacterial production. All the treatments presented an increased at days 2-3 but no signi cant
differences within the amended tanks were found (Fig 2C), at the beginning they were all very different
from the control tank. While the Si treatment maintained a high value al through the experiment, the
P treatment decreased at time 3, and the U treatment at time 7.
Heterotrophic nano agellates. They were determined by Lysotracker staining. The numbers
generally followed the changes in bacterial abundances with a time lag, and HNF reached highest
densities always at least 1 day after the peak of bacterial abundances (Fig. 2D). The rst maxima
occurred at day 3-4, and a second peak at day 9. In the rst peak there were not strong differences
between treatments, with a slightly higher value for treatment Si. The second peak was higher for
treatments Si and U.
Bacterial single-cell activity. All the amended treatments presented a signi cantly (<0.05) higher
number of respiring cells compared to the controls (Fig. 5A, Table 2). The U tanks show the highest
number of respiring cells, followed by the Si and then the P tanks. The %NADS, which is another
indicator of the physiological stage of the bacteria, did not show differences between treatments except
for the controls which decreased from time 4 towards the end of the experiments (Fig. 5B). Differences
between treatments were also observed by measuring the % of high nucleic acid containing bacteria
(Fig. 5C), which showed almost exactly the same pattern as the amount of CTC+ cells.
Bacterial community composition and estimates of diversity. DNA samples were collected
at day 0, 3, 6, 8 and 10. The analysis of the bacterial DGGE ngerprints (Fig. 6A) by the Ward’s
clustering method, (Fig. 6B) and that of the MDS analyses (Fig. 6C). show similar results: the samples
were organized by time rather than by treatment, thus indicating that the internal evolution in the tanks
superseded the effects of the additions. The one-way ANOSIM analysis of similarity showed that the
108
Bacterial response to different types of phytoplankton
105
106
0 2 4 6 8 10
CTC
(cell
s ml-1
)
% L
ive c
ells (
NAD
S)
% H
DNA
Time (days)
Time (days)
Time (days)
KaKbPaPbSiaSibUaUb
20
30
40
50
60
70
80
90
0 2 4 6 8 10
C
A
70
75
80
85
90
95
0 2 4 6 8 10
B
Fig. 5.- Measures of single-cell bacterial activity: CTC-positive cell abundance (A), % of NADS-determined live cells (B) and %HNA (C). Treatments: K tank ( ), P tank ( ), Si tank ( ) and U tank ( ). Points used for the statistical analyses are indicated with arrows.
109
Chapter III
samples from treatments clustered together by timing (R= 0.517, P: 0.003) and were different from
the control samples (R= 0.549, P: 0.002). The Shannon-Weaver diversity index for phytoplankton did
not change along the experiment in any of the treatments except for the Si tank where it decreased
drastically from time 2 towards the end (Fig. 7A). Bacterial diversity (estimated from the DGGE) had
a similar magnitude than phytoplankton (ca. 2 bits), and showed non-signi cant differences between
treatments (Fig. 7B).
Table 2.- Results of one-way ANOVAs done at every time point when DNA samples for the DGGE analysis were taken. N = 2 for all the mesocosm experiments. Signi cant (P< 0.05) effects are in bold. Treatments with the same letters are not signi cantly different from each other.
T3 T6 T8 T10
Chlorophyll a P 0.011 0.002 <0.0001 0.057
U AB B B ASi A A A AP B A C AK B A C A
Bacterial abundance P <0.001 0.032 0.003 NS
U A A A ASi A AB AB AP A AB BC AK B B C A
Bacterial production P 0.004 0.005 0.008 0.012
U A AB B ABSi A A A AP A BC B BK B C B B
CTC + cell abundance P 0.009 0.011 0.001 NS
U A A A ASi A A A AP A AB B AK B B B A
110
Bacterial response to different types of phytoplankton
Tabl
e.3.
-Phy
topl
ankt
onpo
pula
tions
at T
7 (a
t the
pea
k of
the
bloo
m)
In
pare
ntes
is a
re th
e ab
unda
nce
perc
enta
ge o
f eac
h gr
oup.
The
“O
ther
s” g
roup
incl
ude
the
rest
of p
hyto
plan
kton
pop
ulat
ions
whi
ch a
re n
ot r
epre
sent
ed in
the
prev
ious
gro
ups.
In
situ
K
(ce
lls L
-1)
P (c
ells
L-1)
Si (
cells
L-1)
U (
cells
L-1)
Din
oag
lela
tes
2290
(5.
7%)
4350
(2.
8%)
5854
(3.
76%
) 81
41 (
0.04
%)
9861
(3.
64%
)
Dia
tom
s 16
340
(41.
3%)
6670
8 (4
3.8%
) 11
4783
(74
%)
20,0
00,0
00 (
99.5
%)
2017
22(7
4.5%
)
Coc
olith
ofor
ids
3060
(7.
7%)
1031
8 (6
.8%
) 48
52 (
3.1%
) 59
70 (
0.03
%)
5990
(2.
2%)
Nan
oag
ella
tes
1782
0 (4
5%)
7082
1 (4
6.5%
) 29
646
(19%
) 82
380
(0.4
1%)
5215
5(19
.3%
)
Oth
ers
40(0
.1%
) 18
0 (0
.1%
) 36
0 (0
.2%
) 60
(0.
0%)
829
(0.3
%)
111
Chapter III
K P Si U K P Si U U2º K P Si U K P Si U T3 T6 T8 T10T0
Transform: Square rootResemblance: S17 Bray Curtis similarity
Treatment
K
P
Si
U
0
3
3
3
3
6
666
6
88
8
8
10
10
10
10
2D Stress: 0.16
0 50 100 150 200 250 300
Linkage Distance
U T10
Si T10
P T10
U2º T6
U T6
Si T6
P T6
Si T8
U T8
P T8
K T10
U T3
Si T3
K T8
P T3
K T6
K T3
T0
T3
A
B C
Fig. 6.- A) Denaturing Gradient Gel Electrophoresis (DGGE) of bacterial 16S rRNA gene fragments from samples of different treatments and at different times of incubation. Two different gels are presented, and one lane (U t6) is presented in both gels. B) Dendogram classi cation (Ward’s Method, Euclidean distances according to the band pattern) of DNA samples. (C), Nonmetric multidimensional scaling (NMDS) of the bacterial assemblage composition data.
112
Bacterial response to different types of phytoplankton
DISCUSSION
By adding different nutrients to a NW Mediterranean plankton community, we expected to
generate differences in phytoplankton biomass levels and in phytoplankton community structure and
diversity. And, as a consequence, we expected to generate changes in bacterial biomass, in bacterial
function, and in bacterial community structure and diversity. While we did observe the changes in
phytoplankton biomass (Fig. 2) and community structure (Fig. 3), we only observed strong changes
in bacterial function (Fig 2C), while we observed little changes in bacterial abundances (Fig. 2B) and
bacterial diversity (Fig 7). And bacterial community structure was seen to vary mainly following time,
and much less following the different nutrient additions (Fig. 6).
Our study area (NW Mediterranean) is known to be phosphorous-limited mainly during the
summer (Thingstad et al. 1998, Pinhassi et al. 2006). As the experiment was carried out in June, and
to avoid P limitation, all treatments except for the control (K tank) were amended with phosphate
(PO4) at 1 µM nal concentration. In one tank we added only this phosphorous (the P tank) where we
expected the development of picoeukaryotes and small algae. In another treatment urea was added as
an organic nitrogen source (U tank). Our interest in urea addition was because it has been suggested
that urea enrichment should preferentially lead to dominance by Cyanobacteria, picoeukaryotes and
dino agellates rather than diatoms (Gilbert et al. 2008 and references therein). The last treatment
(Si tank) had NO3 additions besides the phosphate addition to ensure nutrient suf ciency, and also
received SiO4 to promote diatoms dominance, as is well known that they need silica for growth (Allen
et al. 2005).
Indeed, diatoms developed in the Si tank, but they also did so in the other treatments specially in
the rst part of the experiments. This is clearly seen from the decreases in Si concentration in the tanks
(Fig 1B). Phosphate was rapidly consumed in the tanks, which had some N sources (urea or NO3) but
remained mostly unused in the P tank probably because of the lack of a N source. This indicates that
a simple nutrient addition was not enough to promote the selection of a speci c algal group as there
was co-limitation and the added nutrient was in most cases not fully used (Fig. 1).
The Chla plot shows the dramatic differences between treatments caused mainly by the Si
treatment, which reached around 14 fold higher concentrations than the rest of treatments. In this case
the sample phytoplankton diversity decreased strongly (Fig. 8). In the U treatment dino agellates
increased towards the end (Fig. 5), and Synechococcus reached the higher values from time 5 to
time 9 compared to the other treatments. In contrast, phytoplankton diversity in the K, U and P tanks
113
Chapter III
did not show much differences throughout the experiment. In summary, we did induce changes on
phytoplankton species by the different additions, but those were particularly clear in the Si treatment,
while the P and U treatments evolved rather similarly, with little clear-cut differences.
In terms of bacterial abundance and bacterial activity measured as bacterial production and CTC+
cells, at time 3 there were signi cant differences (One way ANOVA P<0.005) between the control
tanks respect to the others (Table 2), a further demonstration of bacterioplankton P limitation in the
NW Mediterranean. We believe this strong limitation, well known from other studies (Sala et al. 2002,
Pinhassi et al. 2006, Thingstad et al. 1998 and references therein) is what drove mainly the initial
evolution of the bacterial community, which reached a rather similar peak at times 2-3 (Fig. 2B),
and presented changes in community structure that were very similar between treatments from time
0 to time 3 (Fig. 6). Thus, this suggests that the bacterioplankton rst of all responded to the nutrient
addition they were mostly dependent, i.e. P. Afterwards, HNF developed (Fig 2C) and depleted
bacterial communities which, only after that situation became differentiated, with differences between
treatments in abundance and activity. For example, the differences in CTC + cells were caused by the
U and Si tanks, but in bacterial production only the Si tank differs from the rest.
The DGGE technique has been widely used in comparative microbial ecology to assess the diversity
of microbial communities and their response to changing environments (Labbé et al. 2007, Castle et
al. 2006, Macnaughton et al. 1999). The ordination of the samples by non-metric multidimensional
scaling (NMDS) based on the DGGE gel band pattern agreed with the clustering results by Ward’s
Methods using Euclidean distances. These analyses show that the samples from amended tanks cluster
together due to different incubation times. The ANOSIM analyses determined a quite good clustering
of samples by the factor of time (R= 0.517, P:0.003). However the control samples had an independent
evolution (R=0.549, P:0.002). Thus, in spite of the observed changes in phytoplankton biomass and
diversity, bacteria showed much less changes. We believe, mostly driven by the initial P limitation and
posterior selection by agellate predation.
In fact, the small changes detected in the community structure analysis at time 3 (Fig. 6) corresponded
with some changes in the Shannon diversity index, which were lower in the K and P treatments at
time = 3 (the rst bacterial abundance peak), and then also slightly lower in the U treatment during
the second bacterial peak (times 6-8). Unfortunately, we had no replicates of these measurements, and
no proper statistical tests can be run. But the lower diversity values in the U plot during the second
peak suggest some specialization of bacterial composition, in agreement with Schäfer et al. (2001)
that found that the DGGE patterns indicated that the addition of nutrients not only had a quantitative
114
Bacterial response to different types of phytoplankton
effect on bacterial community dynamics (higher bacterial cell counts and production, Lebaron et al
1999), but also had a qualitative effect on the development of the bacterial composition.
It is interesting to mention here that the maximum Shannon diversity values were seen in the Si
treatment, simultaneous to the minimum phytoplankton diversity values (Fig. 7). Although the DGGE-
based estimates of richness are subject to polemics (Bent et al. 2007, Danovaro et al. 2007) some
authors think they are as good (or as bad) as any other estimate, since accuate richness estimates are
impossible (Pedrós-Alió 2006). There are two implications to this observation: one is that in ecology
it is commonly believed that diversity is a property that applies to all parts of the ecosystem, and that
by measuring diversity of a group of organisms we are estimating diversity of all other groups. Our
data boot work againsts that hypothesis. Secondly, it is perfectly possible that an evolving diatom
bloom, in which the blooming species age, has different associated bacterial communities, depending
on whether the bloom is young and growing, or old and decomposing, each with different bacterial
communities of similar richness.
0
0,5
1
1,5
2
2,5
0 2 4 6 8 10
Bacterial richness
0
0,5
1
1,5
2
2,5
2 4 6 8 10
Phytoplankton richness
0
Shan
non-
Wea
ver I
ndex
Shan
non-
Wea
ver I
ndex
SiKUP
Time (days) Time (days)
A B
Fig.7.- Shanon-Weaver diversity index of the (micro)phytoplankton (A) and bacteria (B) in the different treatments: K tank ( ), P tank ( ), Si tank ( ) and U tank ( )
115
Chapter III
HNF are assumed to be the primary grazers of bacteria. Thus, an important effect of HNF on
bacterial community structure is expected. Lebaron et al. (1999) suggested that nutrients and grazing
play a key role in shaping the morphologic, genotypic and phenotypic composition of bacterial
communities. The pattern of the HNF abundance in our experiment was the same as that of bacterial
abundance pattern but with one day lag (Fig. 2D), and also the bacterial biomass increase during
the second peak was follow by increases of HNF biomass. Since bacterial abundance and activity
parameters showed strong differences between treatments in the second bacterial peak, and even the
Shannon index showed differentiation of the samples in the second peak, grazing pressure seemed to
be important in the shift of the bacterial assemblage.
Many mesocosms experiments have been performed to investigate the effect of nutrient addition
on marine planktonic communities in the Mediterranean Sea (Lebaron et al. 1999, Schäfer et al.
2001, Olsen et al. 2006, Allers et al. 2007) The input of either phosphorus or nitrogen has often been
reported to be signi cant in the Mediterranean, particularly during strati cation periods (Eker-Develi
et al. 2006). Recent studies in the NW Mediterranean have found differentiation in the bacterial
composition due to different nutrient addition (Schäfer et al. 2001 and Pinhassi et al. 2004) and the
effect of grazing on bacterial biomass (Lebaron et al. 1999). Our results concur with Allers et al. (2007)
that a combination of bottom-up and top-down factors controls bacterial succession and growth in
these type of experiments. We observed that the initial P de cit of the bacterial communities strongly
determined the evolution of the communities after nutrient additions. After an initial very similar
bacterial bloom, depleted by HNF grazing activity, however, we observed that the different nutrients
added did modify slightly bacterial community structure and function. The changes observed were
more clear in the Silica treatment, as the addition of all potentially limiting nutrients (Si, P, N) allowed
the development of a clear phytoplankton bloom, with high chlorophyll and low diversity values.
But even during this bloom we observed an evolution of bacterial diversity probably shaped by the
evolution of HNF predators. The bacterial community then, seems to be controlled by the top-down
factors in contrast to the bottom-up factors that control the initial bacterial bloom as the differences
between treatments in bacterial abundances and activity were not detected until after theHNF grazing
pressure forced changes.
ACKNOWLEDGMENTS
This work was supported by the NoE Marine Genomics Europe and MARBEF, and by Spanish
project MODIVUS (CTM2005-04795/MAR). We thank Vanessa Balagué, Irene Forn, and all the
people participating from the Blanes Bay sampling program, particularly Montse Sala, Evaristo
Vázquez-Domínguez and Cristina Rodríguez for various help.
116
Bacterial response to different types of phytoplankton
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Effects of Saharan Dust deposition on marine microbial abundance, activity and bacterial community structure and ecosystem function
Itziar Lekunberri, Thomas Lefort, Estela Romero, Evaristo Vázquez-Domínguez, Cèlia Marrasé, Francesc Peters, Markus Weinbauer and Josep M Gasol
CHAPTER 4
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ABSTRACT
The Mediterranean coast receives large inputs of Saharan dust. Due to its nutrient content, dust has a
potential fertilization effect on this oligotrophic environment. The aim of this paper was to evaluate
the effect of dust depositions on bacterial abundance, activity and community structure, and on the
metabolic balance on the microbial community. In order to assess this goal we performed a 10 day
mesocosms experiment with additions of realistic dust concentration (0.05 g L-1). Due to the known
P limitation of our study area, we included a P amended tank to distinguish the effect of dust alone
from that of the phosphorus contained in the dust. To determine the saturating concentration of dust
we included a higher dust concentration tank (0.5 g L-1). We found that dust increased the initial water
phosphorus concentration in 0.3 μM and the DOC concentration in 14 μM. Dust addition increased
bacterial abundance (1.8 fold) and bacterial production (5 fold) thus creating higher growth rates at
the initial times of the incubation. Primary production and community respiration were stimulated by
dust and by P, but the net result of the addition of low amounts of dust was a heterotrophic system,
while the net result of the high dust and P additions were net autotrophic communities. Bacterial
community structure changed little between the P-addition and the 0.5 g L-1 dust addition, but the
resulting communities were different from the control and from the high-dust addition communities.
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INTRODUCTION
Coastal microbial communities are subject to external forcing events, such as the inputs of
allochthonous nutrient from rivers, coastal runoff, submarine groundwater discharge, resuspension
of benthic particles, etc. These allochthonous inputs contain organic and inorganic nutrients, which
affect microbial diversity and function (e.g. Wassmann & Olli 2004, Markaki et al. 2003, Bonnet et
al. 2005, Pulido-Villena et al. 2008). Among these sources of allochthonous nutrients to the coastal
ocean, atmospheric transport is a particularly important type (Herut et al. 1999). Wind-transported
particles can settle by wet deposition associated with rain, and this has been shown to be an effective
mechanism of dust deposition in the western Mediterranean Sea (Prodi & Fea 1979, Guerzoni et al.
1997, Loyë-Pilot & Martin 1996). The Sahara and the Sahel are, by far, the largest source of dust
particles (Prospero et al. 2003): they are responsible for more than half of the world’s mineral dust
emission (Perez et al. 2007) and the Mediterranean Sea, because of its closeness to this area, receives
large inputs of Saharan Dust (Löye-Pilot & Marin 1996, Guerzoni et al. 1999). Most of the SD
(Saharan dust) deposition events in the coastal NW Mediterranean occur during late spring and early
summer (Guerzoni et al. 1999, Ridame & Guieu 2002), when “dust rains” are common.
The Mediterranean Sea, and particularly the NW Mediterranean, are known to have phytoplankton
and bacterial communities which are P-limited, particularly during the summer season (Thingstad
et al. 1998, Pinhassi et al. 2006), and since SD has been seen to contain bioavailable phosphorus
(Herut et al. 2002), SD deposition events can stimulate the activity and the production of P-limited
microorganisms. In particular, primary production has been seen to respond to SD inputs (Baroli et al.
2005, Bonnet et al. 2005, Lenes et al. 2001, Herut et al. 1999, 2002, 2005, Ridame & Guieu 2002).
However also bacterial abundance and activity have been seen to respond to SD enrichments (Herut et
al. 2005, Pulido-Villena et al. 2008). A priori, one could speculate that bacteria should be stimulated
after algae would be stimulated rst by the nutrients contributed in the deposition.
But SD contains nutrients other than P, particularly N and Fe (Jickells et al.1995), and is also
known to be also a source of organic carbon to the aquatic ecosystems (Pulido-Villena et al. 2008).
Heterotrophic bacteria are often the best competitors for phosphorus uptake (Currie 1984), and the
simultaneous addition of DOC and P might shift the competition for P to be won by bacteria (Thingstad
et al. 1999). Thus, it could be hypothesized that the processes driven by heterotrophic bacteria would
be more stimulated by a SD event than the autotrophic processes. SD events would, thus, have the
capacity to shift a system towards net heterotrophy by stimulating bacterial respiration more than
primary production. Bacterial respiration has been seen to be stimulated by P additions (Obernosterer
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et al. 2003) and the addition of DOC, presumably of low quality (one can pressume the DOC travelling
in the SD is not very labile), in the presence of enough inorganic P but no other sources of labile DOC,
could force bacteria to respire most of the low-quality C, thus greatly increasing community respiration.
In fact, Pulido-Villena et al. (2008) observed that a natural dust deposition event of 2.6 g m-2 induced a
1.5-fold increase in bacterial abundance and a 2-fold increase in respiration, while experimental dust
additions (between 2 and 20 g m-2) also stimulated abundance and respiration (1.5-3 fold increases).
These authors, however, did not speci cally compare the heterotrophy stimulation caused by the dust
with the simultaneous expected stimulation of primary production (as found by others, e.g. Herut et
al. 2005). Thus, while SD inputs are expected to stimulate both bacterial and phytoplanton function,
it is uncertain whether the input will shift the metabolic balance of the community.
Addition of P and DOC from the SD, but also changes in phytoplankton community structure caused
by the fertilization (e.g. Pinhassi et al. 2004), can affect the structure of the bacterial assemblages.
It is particularly interesting to understand whether it is the P or the DOC component of the SD what
in uences in a larger degree community structure, and whether there are phylotypes particularly
stimulated by the SD pulses. In addition to the inputs of nutrients associated with dust, viable bacteria
can be transported long-distances (Kellogg & Grif n 2006), with unknown effects on the resident
bacterial community structure.
We designed an experiment to i) con rm the stimulation of primary production (PP) and bacterial
function caused by additions of SD to a P-limited oligotrophic plankton community, ii) tell apart the
effects of the fertilization by the P contained in the SD, from the effects of the other components of
the SD, particularly the bioavailable DOC, iii) test whether SD additions would shift the microbial
community to autotrophic or heterotrophic net metabolism, and iv) see whether SD fertilization would
alter bacterial community structure in a way fundamentally different from the changes caused by P
fertilization alone. We added dust to a series of microcosms at an “experimental” concentration of 50
mg l-1 and used two “controls” on top of a no-addition treatment: one with inorganic P alone at the same
concentration that in the SD addition, and one with a much higher SD addition to observe the effects
of SD in a saturation pulse of the same magnitude as found sporadically in the Mediterranean (Loyë-
Pilot & Martin 1996). Furthermore, as the dust depositions in this area are due to storms (Ridame
& Guieu 2002), we added a turbulence treatment to discriminate the effects of the associated water
movement from the effects of SD alone.
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Effects of Saharan dust on bacterial activity and community structure
MATERIALS & METHODS
Experiment setting. The experiment was carried out with water collected in Blanes Bay, NW
Mediterranean (The Blanes Bay Microbial Observatory, 41°40’0“ N, 2°48’0“ E) the 16 May 2006.
Eight tanks of 7.5 L were used for the experiment lled with seawater ltered by 150 μm. Of the
8 tanks, four were established as controls, four received a low dust concentration (0.05 g L-1), two
received a high dust concentration (0.5 g L-1) and two received an inorganic phosphorus addition. Half
of the tanks were incubated with turbulence, and the other half without. While tank replicability was
ensured by using two replicates per condition in half of the treatments, space constraints limited the
possibility of establishing 2 replicates of all treatments. The incubation was done at in situ temperature
(17ºC), and the experiment lasted for 8 days. At the end of the experiment 66% of the volume still
remained in the tanks.
The Saharan dust used in the experiment was collected during an intense dust storm over
Villefranche-sur-Mer, France (43°42’18“ N, 7°18’45“ E) on February 21, 2004. A hard plastic tray
was exposed 1 m above the ground to the rain (during ca. 24 h). The tray had 4 cm high side walls
and the dust accumulated at the bottom of the tray. Several hours after the rain had stopped, the
supernatant was poured off and the dust was transferred with a plastic spatula into an acid-washed
Nalgene high-density polyethylene (HDPE) bottle. The dust was then dried at 60ºC. The dry dust was
grinned before a single initial addition to the experimental tanks. Saharan dust deposition events carry
between 5 and 8000 mg of dust per litre (Ridame & Guieu 2002). A 0.05 g L-1 dust addition released
0.38 ± 0.08 µmol PO4
3- L-1 (Romero et al. in prep.), and this value was used as our low addition level
(DL) and a 10x higher value (0.5 g L-1) as our high addition level (DH). P treatments were enriched
with inorganic PO4
3- at a concentration of 0.5 µM, again as a single initial addition. This was estimated
to be the amount of P that we would add with the Saharan Dust, and would act as a control of the effect
of a P addition without the rest of components of the Saharan Dust.
Small-scale turbulence was generated with vertically-oscillating grids with the mechanical device
described before (Peters et al. 2002). We used a turbulent kinetic energy dissipation rate of 10-2 cm2
s-3. This value is within the range of turbulence intensities in coastal areas (Kiørboe & Saiz, 1995). We
applied turbulence only during the rst three days of the experiment, a typical duration of turbulence
events of the applied mean intensity in the Blanes area (Guadayol & Peters, 2006). Thus, containers
corresponding to turbulence (T) treatments underwent turbulent conditions for three days and remained
still until the end of the experiment, whereas still (S) treatments were kept still all along. Temperature
was adjusted to the in situ water temperature (17ºC) and light conditions were set to 225 µmol photons
m-2 s-1 inside the containers. The light:dark cycle was also adjusted to that of the time of the year. The
experiment started within 3 to 4 hours of water collection.
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Chlorophyll a and nutrients. Samples (100 ml) for estimating chlorophyll a concentration (Chla)
were ltered through Whatman GF/F lters of 25 mm diameter, which were immediately frozen
until extraction. Filters were then placed in 90% acetone at 4ºC for 24 h and the uorescence of the
extract measured using a Turner Designs uorometer (Yentsch & Menzel 1963). Total phosphorus
was determined by wet oxidation and colorimetry following standard protocols (further details in
Romero et al. in prep). For analysis of DOC concentration, ltered samples (20 ml) were acidi ed
with 16 mM HCl ( nal conc.) in acid-clean polypropylene tubes and stored at 4°C until analysis. DOC
was measured with a high temperature carbon analyzer (Shimadzu TOC 5000).
Organism abundance. Bacterial abundance and the percentage of high nucleic acid cells were
measured by ow cytometry (Gasol & del Giorgio 2000). Samples were run in a Becton-Dickinson
FACSCalibur cytometer after staining with SybrGreen (1:10,000 nal concentration, Molecular
Probes), and bacteria were detected by their signature in a plot of side scatter (SCC) vs FL1 (green
uorescence). Regions were established on the SSC versus green uorescence plot in order to
discriminate cells with HNA (high nucleic acid) content from cells with LNA (low nucleic acid)
content. In the same SybrGreen stained samples, we were able to enumerate the concentration of
dust particles (see below). Cell abundance was determined for each subgroup. Bacterial biomass was
calculated from abundance assuming a carbon content of 12 fg C cell-1 (Fukuda et al. 1998).
Live samples were analyzed in a FACSCalibur ow cytometer (Becton Dickinson) in unstained
aliquots for the enumeration of picophytoplankton populations. Milli-Q water was used as sheath
uid and 1-µm yellow-green latex Polysciences beads were used as an internal uorescence standard.
The samples were run at the highest possible speed (around 60 µl min-1) for 5 min, and the data were
acquired in log mode. Abundances were calculated by the ratiometric method from the known amount
of added beads, calibrated daily against TrueCount (Becton & Dickinson) beads. Synechococcus
were detected by their signature in a plot of orange uorescence (FL2) vs. red uorescence (FL3),
picoeukaryotes (Peuk) had higher FL3 signals and no FL2 signals. No Prochlorococcus were present
at the time of sampling.
Single-cell bacterial physiology. We used the activity probe CTC (5 cyano-2,3 ditolyl tetrazolium
chloride, Polysciences) to enumerate highly actively-respiring prokaryotes, as an indication of the
number of very active cells. Two 0.5 ml sample aliquots were spiked with 5 μM nal concentration
of CTC from a freshly prepared stock solution (50 mM in Milli-Q water) and incubated for 90 min
in the dark, at in situ temperature. The samples were immediately run through the FACSCalibur ow
cytometer. An additional 0.5 ml aliquot was xed with paraformaldehyde at time 0, and spiked with 5
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Effects of Saharan dust on bacterial activity and community structure
mM nal concentration of CTC for a background control of CTC uorescence on dead samples. CTC-
formazan is excited by wavelengths between 460 and 530 nm and has bright red uorescence. CTC+
particles were those that showed red uorescence (above 630 nm, FL3 in our instrument), higher than
the background uorescence level (Gasol & Arístegui 2007). A dual plot of 90° light scatter and red
uorescence was used to separate CTC+ cells from background noise. The FL2 (orange uorescence)
vs FL3 (red uorescence) plot was used to differentiate the populations of photosynthetic microbes
from the CTC+ particles.
Bacterial production. Bacterial heterotrophic production was estimated using the 3H-leucine
incorporation method (Kirchman et al. 1985). Triplicate or cuatriplicate aliquots of 1.2 ml were
taken for each sample and one or two TCA killed controls. The Leu tracer was used at 40 nM nal
concentration in incubations of approximately 2h. The incorporation was stopped with the addition
of 120 µl of cold TCA 50% to the samples and, after mixing, they were kept frozen at –20ºC until
processing, which was carry out by the centrifugation method of Smith & Azam (1992).
Community metabolism.For primary production, two light and one dark acid-cleaned polycarbonate
bottles were lled with 160 ml water samples from each sample, inoculated with ~3.7 105 Bq (10 µCi)
of 14C-bicarbonate (VKI, Denmark). Incubations were done in the same chamber where the main
samples were incubating, and with the same light levels, and lasted for ~2 h at in situ temperature.
Samples were then ltered onto Whatman GF/F lters (25 mm diameter). Filters were placed in 6 ml
vials and fumed overnight with HCl 35%. Finally, 4.5 ml of ReadySafe liquid scintillation cocktail
were added before determination of disintegrations per minute (dpm) in the laboratory by means of a
Beckman LS6500 liquid scintillation counter. Dark bottle dpm were subtracted for correction of non-
photosynthetic 14C xation. No isotope discrimination factor was used. Conversion to carbon units
was performed assuming an ambient inorganic C concentration of 25000 mg C m-3.
For community respiration, we followed the changes in dissolved oxygen during dark incubations
of un ltered water. Eight BOD bottles were carefully lled, and three replicate bottles were
immediately xed with Winkler reagents to determine the initial oxygen concentration. Four replicate
bottles were incubated in the darkness at in situ temperature and xed with Winkler reagents after
approximately 24 hours. Dissolved oxygen measurements were made with an automatic titrator
(DL50 Graphix, Mettler Toledo) based on potentiometric endpoint detection (Outdot et al. 1988).
The rate of respiration was determined by regressing the oxygen concentration against the time in
which the samples were withdrawn. This estimation assumes that the disappearance of oxygen was
linear (Model I regression) and the slope of the regression is equal to the respiration rate. We assumed
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Chapter IV
a respiratory quotient of 0.88 (see further details in Alonso-Sáez et al, 2008). Since, on an annual
basis, bacterial respiration at the site is 71±12% of community respiration (Alonso-Sáez et al. 2008),
we estimated bacterial respiration from the value of community respiration in order to obtain some
estimates of bacterial growth ef ciency, which is the fraction of bacterial production over bacterial
production+respiration.
Collection of community DNA. Microbial biomass was collected by sequentially ltering around
5 L of seawater through a 3 µm pore size polycarbonate lter (Millipore, 46 mm) and a 0.2 µm
polycarbonate lter (Millipore). Microbial biomass was treated with lysozime, proteinase K and sodium
dodecyl sulphate, and the nucleic acids were extracted with phenol and concentrated in a Centricon-
100 (Millipore). Nucleic acids were extracted by a standard protocol using phenol/chloroform (see
details in Schauer et al. 2003).
Fingerprinting analysis. DGGE and gel analysis were performed essentially as previously described
(Schauer et al. 2003, Sánchez et al. 2007). Brie y, 16S rRNA gene fragments (around 550 bp in length)
were ampli ed by PCR, using the universal primer 907rm and the bacterial speci c primer 358f, with
a GC-clamp. the PCR products were loaded on a 6% polyacrylamide gel with a DNA-denaturant
gradient ranging from 40-80%. The gel was run at 100V for 16 h at 60ºC in 1x TAE running buffer.
DGGE gel images were analyzed using the Diversity Database software (BIO-RAD). A matrix was
constructed for all lanes taking into account the relative contribution of each band (in percentage) to
the total intensity of the lane. Based on this matrix, we obtained a dendogram by UPGMA clustering
method (Euclidean distances, Statistica 6.0). DGGE bands were excised, reampli ed, and veri ed by
a second DGGE. Bands were sequenced using primer 358f without the GC clamp, with the BigDye
terminator cycle-sequencing kit (Perkin Elmer Corporation) and an ABI PRISM model 377 (v3.3)
automated sequencer.
Non-metric multidimensional scaling plots (NMDS) were also constructed from the binary matrix.
NMDS plots present data in an Euclidean plane (with dimensions of no special signi cance) in
which similar measurements appear close together. Each band pattern is a point in the plane and, by
connecting consecutive points, changes in community structure can be visualized and interpreted
(see van Hannen et al. 1999). Dendrograms and NMDS plots are a robust and widely used way of
comparison of microbial assemblage structure.
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Effects of Saharan dust on bacterial activity and community structure
RESULTS
General experimental considerations. Our low SD additions resulted in an increase in P
concentration of ca. 0.3 µM above the 0.1 µM present in the water at the time of sampling (Table
1). In the DH (High dust) treatment the increase was ten times higher, up to 3.3 µM. Similarly, the
addition of low dust (DL) concentrations enriched the background DOC concentration in ca. 14 µM
(from 59 to 73 µM), while the DH treatment supposed an enrichment of slightly more than 10-fold, up
to ca. 260 µM (Table 1). Maximum DOC recorded at Blanes Bay is ca. 170 µM (Pinhassi et al. 2006,
Alonso-Sáez et al. 2008) and, thus, the DH treatment was well above the common values at the Bay.
Total P at the Bay oscillates between 0.01 and 0.2 µM (Guadayol et al. unpublished)
It was possible to detect the Saharan dust particles by ow cytometry. The particles appeared in the
cytograms that were used to enumerate bacteria by SybrGreen I (Fig. 1). They appeared as particles
with high SSC (light scatter) and FL3 (red uorescence), but low FL1 (green uorescence) as is shown
in the plots of FL3 vs FL1. The measured particles were present at ca. 3 x 106 particles ml-1 in the DH
treatments, and at about 6 x 105 ml-1 in the DL treatments. Dust particles slowly settled or disappeared
during the rst three days of the experiment and were absent at day 4. Perhaps counterintuitively, dust
disappeared faster in the T than in the S treatments (Fig. 1).
Fig.1.- Representation of Dust particles measurements by ow cytometry: concentration (left side) and green uorescence (FL1) vs red uorescence (FL3) plot of a dust sample (upper panel) and control (lower).
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Chapter IV
Microorganism abundance. The experiment started with an initial chlorophyll a (Chla) value of
3 mg m-3, which is one of the largest values that can be detected in the Bay (see Alonso-Sáez et al.
2008), and that indicates that we had sampled a bloom. The bloom decayed throughout the experiment
(Fig. 2A), but did less so in the DL treatment. The P treatment reached values intermediate between
those of the C and DL treatments, with a small bloom developing towards the end of the experiment.
In the DH treatment, a bloom developed reaching values of up to 10 mg m-3 (Fig. 2B). Treatment
was a signi cant variable explaining the data (Table 2), particularly between treatments DH and
C. Treatment DL maintained on average 1.5x the Chla in the control. Synechococcus, which were
initially at 3 x 104 cells ml-1 disappeared from all treatments at similar rates (details not shown), while
picoeukaryotes responded different to the different treatments: they maintained abundances and even
increased slightly at the end of the experiment in the DL treatment (Fig 2C), while they decreased in
the control. They reached a peak of 4 x 104 cells ml-1 in the DH treatment in days 3-4, and a peak of
2 x 105 ml-1 in the P treatments towards the end of the experiment (Fig. 2D). Average Picoeukaryote
(Peuk) abundance was 2.7x in the DL treatment as compared to the C treatment (Table 2). Comparison
of the evolution of Chla and Peuk abundance suggests that while the Chla peak at the end of the
experiment can, in part, be assigned to the Peuk development, the one occurring in the DH treatment
needs be assigned to cells larger than picoeukaryotes. Indeed, the responsible for this Chla peak in the
DH treatment were nano agellates and diatoms, mostly of genus Nitzschia (Romero et al. in prep).
Table 1.- Concentrations of total phosphorus (µM) and of DOC (µM) at the start of the experiment (t0), but after addition of dust (SD) and inorganic phosphorus (IP), and after three days of incubation (t3). C stands for Control, DL for low dust concentration, DH for High dust concentration, P for Phosphorus addition, T for turbulence treatment, and S for Still treatment. When possible, average ± st.dev. of two replicated tanks.
Target concentrations Measured concentrations SD IP Total phosphorus (µM) DOC(µM) (g L-1) (µM) t0 t3 t(18h)
source seawater 0.095 ± 0.007 58.98 ± 0.65CT 0 0 0.115 ± 0.021 CS 0 0 0.120 ± 0.014 DHT 0.5 0 3.34 264.84 ± 2.55DHS 0.5 0 3.37 DLT 0.05 0 0.425 ± 0.021 73.17 ± 1.96DLS 0.05 0 0.445 ± 0.007 PT 0 0.5 0.42 PS 0 0.5 0.47
130
Effects of Saharan dust on bacterial activity and community structure
1
10
0 1 2 3 4 5 6 7
1
10
0 1 2 3 4 5 6 7
104
105
0 1 2 3 4 5 6 7
104
105
0 1 2 3 4 5 6 7
Control- StillControl - TurbulenceDust (low) - StillDust (low) - Turbulence
Dust (high) - StillDust (high) - TurbuencePhosphorus - StillPhosphorus - TurbulenceA B
C D
Time (days) Time (days)
Fig.2.- Upper panels: Changes in chlorophyll a concentrations (mg m-3): In the control tank (white circles) and dust low concentration tank (black circles) in still and turbulence conditions (A). In phosphorus addition tank (white squares) and dust high concentration tank (black squares)in still and turbulence conditions (B). Lower panels: Picoeukaryotes abundances (cell ml-1) in the same treatments.
131
Chapter IV
While heterotrophic bacteria decreased in the control tanks until day 3, and then increased towards
the end of the experiment (Fig. 3A), they presented an initial peak in treatment DL at day 1, which was
suppressed in day 2. The response in the DH treatment was much higher (> 10x in just one day, Fig.
3B), and the high bacterial concentrations lasted for three days after coming back to concentrations
similar than those in the control treatment at day 4. In both, P and DH treatments, bacteria increased
steadely from day 4 to the end of the experiment. The nal bacterial yield in the P and DH treatments
was slightly higher (4-8 106 cells ml-1) than in the C or DL treatments (3-5 106 cells ml-1). Addition of
dust (DL) increased bacterial abundance by a factor of 1.8 on average (Table 2).
The differences between treatments were higher in the physiological structure of the bacterial
assemblage. The %HNA cells increased immediately in all treatments, particularly in the DL, except
in the C (Fig 3C-D). Some effects of turbulence were obvious in this parameter, with %HNA being
higher in the T replicates of the C, P and DL treatments. The number of actively-respiring bacteria
Table 2.- Results of a One-way ANOVA with the time-integrated (for production) or time-averaged (for stocks) values for each treatment. Treatments with the same letters are not signi cantly different from each other. The Post-hoc tests give the probability of DL treatments being different from the C treatments, and of the P treatmens from the DL treatments. The “factor” is the ratio between the time-integrated value of treatment DL to that of treatment C.
Chla total BA BP Peuk %HNA P/B
Global ANOVA P <0.001 <0.001 0.005 0.002 0.002 NS
TreatmentCS D D D C D C ACT C D D C D D B C ADLS B C C A B A ADLT B C C A B B C A ADHS A B A B B A ADHT A A A B C B A APS C D C D B C D A A B APT B C C D A B C A A A
Post-Hoc tests
P Difference DL / C 0.001 0.004 0.002 0.007 <0.001 NSFactor (x) 1.5 1.8 2.3 2.7 1.4 -
P Difference DL / P NS NS NS 0.01 NS NS
132
Effects of Saharan dust on bacterial activity and community structure
� �� ��
�� � ����
�
Fig.3.- Upper panels: Bacterial abundance (cell ml-1): In the control tank (white circles) and dust low concentration tank (black circles) in still and turbulence conditions (A). In the phosphorus addition tank (white squares) and dust high concentration tank (black squares) in still and turbulence conditions (B). Lower panels: Physiological state measured by %HNA in the same treatments.
133
Chapter IV
(CTC+) followed a pattern quite similar to that of the %HNA cells, with a clear difference between the
DL and the C treatments (Table 3), a much larger response in the DH, and a relatively higher response
in the turbulent replicates of the C, P and DL treatments. While the differences in %HNA between
the C and DL treatments (Fig. 3) lasted for the whole incubation, those in the number or proportion
of CTC+ cells were mostly apparent during the rst 4 days of incubation. Table 3 was split into two
parts (times 1-3 and 4-7 days) to re ect better these differences. In any case, the difference in the
number of CTC+ cells was signi cant between the C and DL treatments for both periods, and it was
also signi cant for the %CTC+ cells in the rst period (Table 3).
Bacterial activity. Dust stimulated bacterial production by a factor of ca. 5x, from the control
treatment to that of the DL treatment (Fig. 4A, Table 2). The effect of the addition of P was more
pronounced when turbulence was applied in the incubation (Fig. 4B), but a positive enhancement
in the turbulent treatments was evident in almost all additions. Bacterial growth rates were higher
Table 3.- Average concentrations of CTC+ cells, %CTC (over total bacteria), for days 1 to 3, and for days 4 to 7. Average values ± SE of all replicates and times. In the lower part, results of the Student’s t comparisons between treatments C (control) and LD (Low Dust), or between treatments P (Phosphorus) and LD (Low Dust). In this last case, separating Still from Turbulence treatments.
CTC+ abund %CTC Days 1-3 Days 4-7 Days 1-3 Days 4-7
CS 0.82 105 ± 0.20 1.91 105 ± 0.28 10.4 ± 2.9 7.7 ± 3.0
CT 0.94 105 ± 0.05 0.74 105 ± 0.12 10.9 ± 2.3 5.2 ± 0.7
DHS 49.30 105 ± 14.7 32.46 105 ± 0.05 45.7 ± 22.4 6.3 ± 0.9
DHT 51.22 105 ± 14.60 3.55 105 ± 2.29 29.3 ± 8.2 26.1 ± 17.1
DLS 3.85 105 ± 0.81 3.52 105 ± 0.85 26.8 ± 8.5 6.9 ± 1.1
DLT 6.80 105 ± 1.82 2.17 105 ± 0.25 30.6 ± 6.2 8.5 ± 2.0
PS 0.91 105 ± 0.19 1.49 105 ± 0.48 9.7 ± 2.1 4.0 ± 0.4PT 2.24 105 ± 0.21 2.76 105 ± 1.07 16.2 ± 1.4 8.4 ± 2.4
Prob. diff DL / C P < 0.001 P < 0.001 P < 0.001 NSFactor (X) 6.02 2.15 2.69 1.20Prob. diff DL / P Still NS NS NS NS Turb P < 0.001 NS NS NS
134
Effects of Saharan dust on bacterial activity and community structure
� �� ��
�
�� � ����
in the DL treatment than in the C at days 2 and 3, but were similar later on (Fig. 4C). Since days 2-
3 are when bacterial abundances decreased, this could indicate the effects of grazing by agellates
developing and feeding on bacteria at those times. The addition of a high amount of Dust (DH) did
not promote bacterial production above the values created by the addition of lower amounts of dust
(DL). It is possible that incorporation at the added 40 nM leucine was actually saturated in these
samples that had, at the beginning ca. 107 cells ml-1. The relatively low growth rates measured for the
DH treatments at the start of the experiment (Fig. 4D), are not compatible with the large increase in
abundances measured (Fig. 3B), supports this possibility, and suggests that the measured values of
production and growth rates in the DH treatments might be unreliable.
Fig.4.- Upper panels: Bacterial production (μg C L-1): In the control tank (white circles) and dust low concentration tank (black circles) in still and turbulence conditions (A). In the phosphorus addition tank (white squares) and dust high concentration tank (black squares) in still and turbulence conditions (B). Lower panels: Bacterial growth rate (d-1) in the same treatments.
135
Chapter IV
Tree Diagram for 13 CasesUnweighted pair-group average
Euclidean distances
0 10 20 30 40 50 60Linkage Distance
ToCS T3CT T3
DHS T3DHT T3DHS T7DHT T7DLS T3DLT T3PS T3PT T3PS T7PT T7
Resemblance: S17 Bray Curtis similarity
PT T7
PS T7
DHT T7 DHS T7
PT T3
PS T3
DHT T3DHS T3
DLT T3DLS T3
CT T3CS T3
To
2D Stress: 0.07
T7T3T0
CS CT DLS DLT DHS DHT PS PT DHS DHT PS PT
SD1SD2SD3SD4 SD6
SD7SD8SD10
SD11SD12SD13SD14SD15
Community metabolism. We measured primary production twice during the experiment, and
community respiration once. Dust addition increased both, PP and CR (Table 4), the low dust addition
by factors ranging from 1.6 to 3.6. The DH addition produced much larger increases, with factors of
6-15. In all cases the increases in metabolism caused by the dust were stronger for PP than for CR,
thus turning the communities towards net autotrophy from a near-balanced initial community. There
was a clear difference between the DL and the P treatments: P stimulated PP much more than DL,
while CR was similarly stimulated by both treatments. The net result of the addition of DL was a
heterotrophic system, while the net result of the DH and P additions were autotrophic communities.
It is somehow dif cult to compare to the controls since CR in the control-Turbulence treatment was
highly stimulated.
Fig.5.- Denaturing Gradient Gel Electrophoresis (DGGE) of bacterial 16S rRNA gene fragments from samples of different treatments and at different times of incubation (A). Dendogram classi cation (Ward’s Method, Euclidean distances according to the band pattern) of DNA samples. (B). Nonmetric multidimensional scaling (MDS) of the bacterial assemblage composition data (C).
136
Effects of Saharan dust on bacterial activity and community structure
Bacterial community structure. We ngerprinted bacterial assemblage composition by
denaturing gradient gel electrophoresis (DGGE). The Nonmetric multidimensional scaling (MDS)
and Ward’s clustering method were used in order to explore the similarities between the samples
based on bacterial assemblage structure, and to compare the effect of the dust addition to those of
phosphorus and control treatments. The P and DL communities after the addition clustered together
and were distant from the C or DH communites. (Fig. 4). The control tanks were similar to the initial
sample. There were some effects of Turbulence, more obvious at the end of the experiment (Time 7)
than at time 3. We retrieved 13 bands that were dominant in different treatments (Table 5). All the
sequences that could be retrieved from the DGGE gel were found to have similarities in GenBank to
previously described organisms, with values > 97% (Table 5). Just one phylotype was dominant in all
the treatments (SD2), four appeared only in phosphorus treatments, 3 only where Saharan dust had
been added, and 3 in both P and Dust treatments.
DISCUSSION
The fertilizing nature of Saharan dust. The amount of SD that we added in our main experimental
treatment, 50 mg L-1 was slightly higher than the doses used experimentally in a couple of recent
studies (Herut et al. 2005, Pulido-Villena et al. 2008), but were within the limits of what can be
expected to occur in the ocean. According to Ridame & Guieu (2002), 60% of ‘Saharan rains’ carry
between 5 and 8000 mg of dust per liter. A 0.05 g L-1 dust concentration released, abiotically, 0.38 ±
0.08 µmol PO43- L-1 (Romero et al. in prep) a value which is somewhat lower than the range reported
in Ridame & Guieu (2002), where 0.05 g L-1 of dust released anywhere between 0.97 and 3.2 µmol
PO4
3- L-1. In the experimental tanks, however, we obtained ca. 3 µmol PO43- per gram of dust (close
to the yields reported by Pan et al. 2002, and Herut et al. 2005), which led to nal concentrations of
1.5 µmol PO4
3- L-1 and 0.15 µmol PO4
3- L-1 in the DH and DL containers respectively (Romero et al.
in prep). This is an amount of P that increased the initial P concentration by a modest factor of 1.3
(Table 1) and, thus, addition of a smaller dust amount would most probably not have affected the
microbial communities of the coastal waters we used for the experiment. This might contrast with
the experiments of Herut et al. (2005) and Pulido-Villena et al. (2008), which used oceanic waters,
initially more oligotrophic than the waters we used.
In spite of the fact that the P addition was relatively modest, it triggered clear effects in Chla
development (an average factor of 1.5x, Fig 2), in picoeukaryotes development (an average factor of
2.7x), and in primary production (an average factor of 1.5x at day 3 and 2.7x at day 7, Table 4). That
SD enhances primary production had been previously demonstrated in several experimental studies
(Blain et al. 2004, Mills et al. 2004, Herut et al. 2005), and the yield we observed (on average 0.5 µg
137
Chapter IV
Chla per 0.05 g. dust, i.e. 50 µg g-1) is exactly the same yield observed by Herut et al. (2005) in the
Eastern Mediterranean, but smaller than the observations of Blain et al. (2004) and Mills et al.(2004)
in the North Atlantic. Enhancement of chlorophyll in the DH treatment was on the order of 10x the
enhancement generated in the DL treatment (Fig. 2), which is consistent with the 10-fold difference in
the addition. Since SD contains nutrients other than P (Jickells et al. 2005), the hypothesis that other
limiting nutrients were the responsible for the primary producers enhancement cannot be disregarded
and, in fact, the addition of P alone resulted in a smaller enhancement than the DL addition (Fig 2),
although on average the difference was not statistically signi cant (Table 2).
Bacteria were also stimulated by the dust addition: abundance (an average factor of 1.8x),
production (factor of 2.3x), number of actively respiring cells (factors of 2-6x), and also the single-
cell indications of cellular activity, such as the %HNA (factor of 1.4x) and the %CTC+ cells (a
factor of 2x). However, bacterial growth rates were not statistically enhanced by the DL addition. The
stimulation of bacterial heterotrophic activity resulted also in a stimulation of community respiration
(at this site, BR is aprox. 70% of CR, Alonso-Sáez et al. 2008), by a factor of 1.6-2.4 (Table 4).
Bacterial abundance and production were, thus, enhanced by a factor higher than the enhancement of
chlorophyll a. P enrichment in the DL treatment was of a factor of 1.3x and DOC enrichment was also
of a similar factor (1.24x). However, the background DOC concentration at the start of the experiment
(59 µM, Table 1) is typically formed by mostly nonreactive C (Hansell & Carlson 1998). The 14 µM
added with the DL treatment could indicate a large enrichment factor, although since we do not know
the nature of this DOC, we can not con rm this observation except through its effects on bacterial
abundance, single-cell activity and heterotrophic production.
A response of the heterotrophic community to the SD addition had already been observed by
Bonnet et al. (2005) and Herut et al. (2005), even though they did not measure the DOC added. In
these studies, as in our DL treatment, the stimulation of bacterial production was not followed by a
proportional stimulation of bacterial abundance, something that they assigned to HNF development
cropping the stimulated production. HNF reached a peak at day 3 in our experiment (Romero et al. in
prep) and likely cropped most of the new production in the rst days of the experiment, but not after
day 4 (Fig. 3), maybe because bacteria then developed grazing-defence strategies. Bacteria seemed to
escape HNF checking in the DH treatments (Fig. 3B): the burst of growth caused by the large nutrient
dose escaped grazer control.
The bacterial activity “yield” that we obtained was similar to that measured by Herut et al. (2005):
they obtained ca. 75 pmol leucine h-1 per mg dust, and we obtained 70 pmol h-1 per mg dust. The
bacterial abundance yield we measured (2 x 109 cells per 50 mg dust, in the DL treatment and 2 x
138
Effects of Saharan dust on bacterial activity and community structure
1010 cells per 500 mg in the DH treatment) was consistent between treatments, but lower than the 1
x 109 cells per 2 mg measured by Pulido-Villena et al. (2008). In the DH treatment, however, leucine
incorporation rates should had been 10x those we measured in the DL treatment and were similar,
something that we think was caused because we added too low amount of leucine tracer for the
enrichment we had generated.
Biogeochemical effects of SD: autotrophy vs. heterotrophy. Saharan Dust enhanced
community respiration in our experiment, as it did in the experiments and natural observations of
Pulido-Villena et al. (2008). These authors measured a 2x enhancement of bacterial respiration, while
we measured a factor of 1.6-2.4x (Table 4) with a higher SD dose. Our results suggest that this
enhancement had to do with the inorganic nutrient fertilization caused by the SD, but also with the
DOC that was introduced with the dust. SD contains carbon in amounts that might easily reach 1% dw
(Eglinton et al. 2002). This carbon originated in the past in vegetation res accumulated and stored
in soil (Eglinton et al. 2002), and is thus, likely not to be very labile. Our results suggest some use of
this carbon by the resident (see below) bacterial community.
We only measured respiration at the end of the experiment (Table 4), when respiration in the Still
treatment was not signi cantly different between the P and DL treatments, but was much higher in the
DL treatment under turbulence. A similar result could be observed in the number of actively respiring
cells (CTC+, Table 2), which were, under turbulence, much higher in the DL treatment than in the P
treatment. In some studies the number of CTC+ bacterial cells have been seen to vary coherently with
bacterial respiration (Smith, 1998) and increases in the number of CTC+ cells are usually associated
to bursts in growth of bacteria (del Giorgio & Gasol, 2008).
The metabolic balance of the microbial community was altered by the dust addition in a way
different from the alteration caused by P addition. P shifted the community to net autotrophic ( nal
CR/PP values of 0.4-0.5). In the DL treatment the values were net heterotrophic (1.47 ± 0.73 and
1.63 ± 0.52). The control treatment stayed close to 1 (0.95 ± 0.35) in the still treatment but became
very heterotrophic under turbulence (see discussion in next section). Interestingly, the high addition
of dust (DH, 0.5 g L-1) shifted the community towards net autotrophy while the DL treatment shifted
it towards heterotrophy. In the DH treatment a large diatom bloom developed with Chla values of ca.
10 mg m-3 (see details in Romero et al. in prep). In contrast, the 1.5x increase in Chla measured in
the DL treatment consisted in pico- and nanoeukaryotic cells. Apparently, at least from what can be
extrapolated from our few data, a low addition of SD can shift the system to heterotrophy, but a larger
input might trigger the development of diatom blooms that would turn the system net autotrophic.
139
Chapter IV
Table 4.- Values of community metabolism in the different treatments. When errors are present, they correspond to the standard error of two replicated microcosms.
Primary Production Community Respiration CR/PP
(mgC m-3 h-1)
Initial 5.02 4.66 0.93Control – Still day 3 2.32 ± 0.23 day 7 1.24 ± 0.38 1.13 ± 0.10 0.95 ± 0.35
Control – Turbulence day 3 2.40 ± 0.28 day 7 1.04 ± 0.06 3.40 ± 0.11 3.27 ± 0.08
Dust (low) – Still day 3 3.64 ± 0.16 day 7 2.26 ± 0.78 2.74 ± 0.50 1.21 ± 0.73
Dust (low) – Turbulence day 3 3.25 ± 0.12 day 7 3.71 ± 1.16 5.42 ± 0.03 1.46 ± 0.52
FACTOR DL / C day 7-Still 1.82 2.42 day 7-Turbulence 3.56 1.59
Dust (high) – Still day 3 15.76 day 7 39.29 18.27 0.46
Dust (high) – Turbulence day 3 25.20 day 7 21.70 19.70 0.91
Phosphorus – Still day 3 1.55 day 7 6.21 3.08 0.50
Phosphorus – Turbulence day 3 2.13 day 7 8.98 3.62 0.40
FACTOR P/DL day 7-Still 2.75 1.12 day 7-Turbulence 2.42 0.67
140
Effects of Saharan dust on bacterial activity and community structure
The type of algae using the added P might explain this difference, as well as the possibility that
some nutrient thresholds exist that shift the “winners” from picoeukaryotes to diatoms. Silica, which
was known to be present in SD but with low availability (Bonnet et al. 2005) might be used by the
autotrophic plankton when added at some concentrations. Indeed, we observed that the SD that we
used contained silica, that this was released into the water, and that it was presumably used up by the
microbial community in the DH treatment (Romero et al. in prep).
In this sense it is interesting to refer to the models of the microbial food web developed by Thingstad
(in e.g. Thingstad et al. 2007). The models idealize competition for P between bacteria, agellates
and diatoms. Addition of labile-DOC shifts the uptake towards the bacteria (and, thus, the system
would turn net heterotrophic), while addition of silicate shifts it towards the diatoms (and the system
would turn net autotrophic). We hypothesize that our DL treatment would be a situation of the rst
type, while the DH treatment would be of the second type. The metabolic response of the plankton
community to Saharan Dust additions would be concentration-dependent.
Pulido-Villena et al. (2008) presented evidence that, on top of the fertilizing nature of SD events
that could increase primary production and C drawback to the ocean, the stimulation of heterotrophic
bacteria could actually reduce the amount of C sequestration caused by these events. These authors
calculated that the dust-induced bacterial growth could mineralize up to 70% of the annual bioavailable
DOC annually exported to the deep Mediterranean. Our results concur with those of these authors
in assigning a very relevant role to bacteria in the use of the nutrients accompanying the dust pulse,
but suggest that the real metabolic effects are concentration-dependent, and might depend on the
bioavailability of the P, Si and organic C attached to the dust, something that is currently very poorly
constrained.
Interactions with turbulence. Our experiment was not designed to be a proper test of the effect of
turbulence as an external factor modifying the response of he microbial community. We just wanted to
create a situation likely to be close to that encountered by the plankton community at the time when a
dust pulse is more probable. Perhaps counterintuitively, the applied turbulence levels facilitated dust
sedimentation or dissolution (Fig. 1), instead of retarding it. This result is consistent with experimental
work that shows that algae sediment faster under turbulence than under still conditions (Ruiz et
al. 2004) and with work showing that at low levels of the shear caused by turbulence, the balance
between aggregation and coagulation of particles of sizes 2-10 µm was dominated by coagulation
(Colomer et al. 2005). Cytometric detection of dust was likely not complete since very small particles
probably escaped detection by the ow cytometer (Fig. 1). In the SSC (a surrogate of particle size)
141
Chapter IV
Tabl
e 5.
-Phy
loge
netic
af
liatio
n of
16S
rR
NA
gen
e se
quen
ces
from
exc
ised
DG
GE
ban
ds o
btai
ned
in th
e di
ffere
nt tr
eatm
ents
.
For
each
phy
lotyp
e, th
e clo
sest
rela
tive
in G
enBa
nk a
re s
hown
, tog
ether
with
their
acc
essio
n nu
mber
s an
d se
quen
ce s
imila
rity.
We a
lso
indi
cate
wheth
er th
e phy
lotyp
e was
dete
cted
in th
e P tr
eatm
ents,
in th
e D tr
eatm
ents,
or w
heth
er th
e ban
d ten
ded
to b
e mor
e com
mon
in th
e Tu
rbul
ence
trea
tmen
ts.
Ban
d ID
# P
D
T
C
lose
st r
elat
ive
Acc
ess.
#
% s
imila
rity
Ph
ylog
enet
ic
grou
p
SD1
X
- -
U
ncul
ture
d Fl
avob
acte
ria
bact
eriu
m c
lone
MS0
24-1
F E
F202
335
99%
B
acte
roid
etes
Te
naci
bacu
lum
sp.
E
F416
572
98%
SD2
X
X
-
Unc
ultu
red
Tena
ciba
culu
m s
p.
AY
5735
21
100%
B
acte
roid
etes
Po
lari
bact
er d
okdo
nens
is s
trai
n M
ED
152
AY
5735
21
98%
SD3
- X
X
Unc
ultu
red
Tena
ciba
culu
m s
p.
AY
5735
21
99%
B
acte
roid
etes
Te
naci
bacu
lum
sp.
A
B27
4770
98
%
SD4
- X
-
U
ncul
ture
d Te
naci
bacu
lum
sp.
A
Y57
3521
99
%
Bac
tero
idet
es
Pola
riba
cter
dok
done
nsis
str
ain
ME
D15
2 D
Q48
1463
98
%
SD6
- X
X
Unc
ultu
red
cyto
phag
a sp
. A
J487
533
98%
B
acte
roid
etes
SD7
X
- X
Unc
ultu
red
Bac
tero
idet
es
AB
2660
10
96%
B
acte
roid
etes
L
ewin
ella
nig
rica
ns
AB
3016
15
92%
SD8
X
- -
U
ncul
ture
d B
acte
roid
etes
A
B26
6010
99
%
Bac
tero
idet
es
Lew
inel
la n
igri
cans
A
B30
1615
90
%
SD10
X
-
-
Unc
ultu
red
alph
apro
teob
acte
rium
E
F506
911
99%
A
lpha
prot
eoba
cter
ia
Mar
inos
ulfo
nom
onas
met
hylo
trop
ha
AY
7717
69
97%
SD11
X
X
-
U
ncul
ture
d al
phap
rote
obac
teri
um
AY
9222
43
97%
A
lpha
prot
eoba
cter
ia
Ros
eoba
cter
gal
laec
iens
is
AJ8
6725
5 96
%
SD12
X
-
-
Unc
ultu
red
alph
apro
teob
acte
rium
D
Q44
6171
98
%
Alp
hapr
oteo
bact
eria
T
hala
ssob
ius
sp.
EF5
8795
1 97
%
SD13
-
X
-
Ros
eoba
cter
sp.
A
Y25
8088
97
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142
Effects of Saharan dust on bacterial activity and community structure
vs. uorescence plots (cytogram not shown), dust particles appeared to range from sizes smaller than
bacteria to >20 µm and, thus, we can expect the dust particles detected by the cytometer to be subject,
most likely, to the same aggregation/coagulation regulation shown by Colomer et al. (2005).
We also found a systematic, but weak, positive effect of turbulence on Chla development (Fig. 2A-
B). Experimental turbulence has been seen to enhance chlorophyll development in several laboratory
studies (e.g. Arin et al. 2002, Pinhassi et al. 2004), and in non-enriched and slightly enriched waters
of Blanes Bay particularly in spring (Guadayol et al. unpublish). Turbulence had more effect on
the sample receiving P (Fig. 2B) and less effect in the control sample, something that was also seen
in Arin et al. (2002). While Chla showed a positive enhancement by turbulence, picoeukaryote
development (Fig 2C-D and Table 2) and bacteria (Table 2) were less affected. Shifts in food web
structure and agellate feeding rates caused by turbulence could explain these differences (Peters et
al. 1998, 2002). The most relevant effects on bacterial activity were again seen in the treatment that
had received inorganic nutrients alone (P), which showed a large difference in leucine incorporation
(Fig. 4) and in the number of CTC+ cells (Table 3). These effects resulted in relatively similar levels
of autotrophic and heterotrophic metabolism at day 7 in the P treatment (Table 4), but in very strong
differences in the control treatment, which respired more under turbulence than under still conditions
(t-test, P<0.001). The DL treatment also respired more under turbulence than under still conditions
(t-test, P<0.001), while the balance CR/PP was signi cantly higher for the control under turbulence
than for the still control (t-test, P<0.001). The metabolic balance difference was not signi cant for
treatment DL.
It has to be taken into account that turbulence was applied only from day 1 to day 3 and, thus,
the difference reported in Table 4 occurred 4 days after turbulence had stopped. In the only study to
date in which microbial community respiration has been studied under turbulence or still conditions,
turbulence always generated a higher level of respiration, and a higher heterotrophic metabolic
balance (Alcaraz et al. 2002). Other previous studies had not found effects of turbulence on respiration
(Peters et al. 1998). Turbulence affected little bacterial community structure (Fg. 5), something that
is compatible with the explanation in Pinhassi et al. (2004) that claimed that changes in community
structure were linked to the changes in phytoplankton community structure caused by the turbulence
and not to the physical stress by itself.
The potential for affecting bacterial community structure. We tested whether bacterial
community structure was affected by the different treatments. In particular, we were interested to
see whether the DL and P treatments were signi cantly different. It has been speculated that airborne
143
Chapter IV
bacteria can travel associated to Saharan dust particles (Grif n et al. 2001, Kellogg & Grif n 2006,
Grif n 2007). If that were to be a relevant source of allochthonous bacteria able to develop in the
ocean, we should see it in our analyses. Of course, if that were only a tiny contribution of inactive
bacteria, we would not see it with DGGE, as this technique has a low resolution (picks up only <30
dominant phylotypes, Muyzer et al. 1997).
The main differences observed in community structure were those between the DH treatments and
the DL and P treatments (Fig. 5), indicating that the evolution with time of the bacterial community
caused by the low addition of SD was very similar to that caused by P alone. Even the high addition
of dust didn’t seem to affect the number of bands that were detected (about 12-15 per sample).
Mainly alphaproteobacteria and bacteroidetes could be retrieved from the DGGE and identi ed. We
did not retrieve any gammaproteobacteria, but this might be related to the fact that gammaproteobacteria,
particularly Alteromonas, tend to develop at the beginning of these mesocosm experiments (Schäfer
et al. 2001, Allers et al. 2007) and tend to be replaced by alphaproteobacteria at the end of these same
experiments, particularly by Rhodobacteraceae of types similar to the SD11 and SD13 bands seen in
our experiment.
Several of the Bacteroidetes that we retrieved were similar to Tenacibaculum and Polaribacter
(Table 5). This group contains the genome-sequenced Polaribacter dokdonensis strain M152 (very
similar to band SD2) which has been seen to contain a substantial number of genes for attachment
to surfaces or particles, gliding motility, and polymer degradation (in agreement with the currently
assumed life strategy of marine Bacteroidetes), but also the proteorhodopsin gene, which together
with a remarkable suite of genes to sense and respond to light, may provide a survival advantage
in the nutrient-poor sun-lit ocean surface when in search of fresh particles to colonize (González et
al. 2008). Organisms of this group have been retrieved in culture, in DGGE bands from enrichment
experiments, but are also appearing in situ in Blanes Bay (Lekunberri et al. submitted Chapter 1),
something that make them part of the most interesting marine Bacteroidetes, as they grow in culture
but at times dominate in situ. Some Tenacibaculum-like organisms (SD3 and SD4) appeared in SD
additions, but the sequence closer to M152 (SD2) appeared in almost all experimental treatments.
Bacteroidetes of the Lewinella type (bands SD7 and SD8), relatives of Chitinophaga, are not
common in the area (Lekunberri et al. submitted Chapter 1). They appeared only in the P treatment.
Within the alphaproteobacteria identi ed, bacteria of the Ruegeria/Silicibacter cluster appeared in
the D and P treatments at the end of the experiment (SD14 and SD15). These are common organisms
144
Effects of Saharan dust on bacterial activity and community structure
in Blanes Bay, easy to isolate in plates, and common in mesocosmss experiments (Lekunberri et al.
submitted Chapter 1). The other alphaproteobacteria identi ed (SD10 to SD13) were also commonly
developing in mesocosmss experiments (Allers et al. 2007). In that study different types of sequences
related to Rhodobacteraceae dominated the different treatments in nutrient-enriched mesocosmsss.
Most of the treatments elicited small phytoplankton growth (the mesocosmsss were light-limited),
and the development of picoeukaryotes. This might be the reason for the similitude of these sequences
to those of Allers et al. (2007) and for the difference between the DL and DH treatments (Fig 5). As
pointed out by Pinhassi et al. (2004), the type of algae developing in the system determined bacterial
community structure.
Bands 3, 4, 6, 13, 14 and 15 were associated in some way with the dust addition, but most of them
appeared also as faint bands in some P treatments (e.g. band SD15). Band SD2, which was very
similar to band SD4, appeared in all treatments. It is, thus, unlikely that any of the bands identi ed
can be considered as speci c of the SD addition treatments, and the af liation of the bands also do
not support this speci city. Bacterial community structure in the DL treatment was likely to be mainly
generated by the P pulse, and the associated development of algae. The situation was probably slightly
different in the DH treatment.
Conclusion
A small Saharan dust addition affected bacterial abundance, activity and diversity in our mesocosm
experiments. It also shifted the metabolic balance of the community to heterotrophy, something that we
assign to the DOC addition simultaneous to the P addition with the dust. However, a higher addition of
dust caused a shift towards autotrophy. Bacterial community structure was affected simialrly by dust
than by P alone. The results reinforce the potential for Sahara Dust inputs to affect bacterial diversity
and function, but clearly indicate the need of knowing better the composition of the inputs, as the
results encountered were, in part, dependent on the concentration of added nutrients.
ACKNOWLEDGEMENTS
We thank Raffaela Cattaneo and Òscar Guadayol for help during the experiment. We also thank V.
Balagué, C. Cardelús and I. Forn for careful organization of sampling at the Microbial Observatory.
This work was supported by Spanish projects MODIVUS (CTM2005-04795/MAR) and VARITEC
(CTM2004-0442-C02), and is a contribution to the european Networks of Excellence Eur-Oceans and
MARBEF.
145
Chapter IV
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Transplant experiments demonstrate strong bottom-up control of prokaryote abundance, heterotrophic production and bacterial community structure in the NE Atlantic Ocean
Itziar Lekunberri, Federico Baltar, Alejandra Calvo-Díaz, Eva Teira, Javier Arístegui and Josep M. Gasol
CHAPTER 5
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ABSTRACT
Two transplant experiments between different depths were conducted in the eastern North Atlantic
Ocean to test whether predators or nutrient supply were more relevant in determining bacterial biomass,
production and community structure. To attain this goal, un ltered and 0.8 µm- ltered water from
surface or from deeper layers (either mesopelagic or DCM) were placed in dialysis bags. These bags
were incubated in surface and depth conditions tanks (in tanks containing surface or deep water and
the corresponding temperature). The rst experiment, took place during the RODA cruise in August
2006 south of the Canary Islands (26º50´N, 16º18´W), and the second during the CARPOS cruise in
October 2006 near Madeira (31º50´N, 16º40´W). Dialysis bags of 12000-14000 Da pore size were used
to maintain the microbial communities and let them be in uenced by the surrounding environmental
conditions (allowing the free exchange of nutrient salts and dissolved organic matter). We did not
observe any strong effect of predators in any of the different conditions. Bacterial activity increased
faster when incubated at surface than at depth conditions. Signi cant differences were found between
incubation conditions in the bacterial community structure analyzed in the rst experiment. Together,
our results indicate that bottom-up forces are much more important than top-down regulation in order
to determine bacterial community structure and function, at least in the NE Atlantic Ocean.
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INTRODUCTION
The microbial loop hypothesis postulates a major role for bacteria in the oceanic carbon cycle
(Pomeroy 1974, Williams 1981, Azam et al. 1983), actively growing through the consumption
of DOC released by primary producers, C of allochthonous origin, and C originated from trophic
interactions between plankton organisms (e.g. Nagata 2000). Bacterioplankton can consume 30-60%
of the primary production via dissolved organic matter (Williams 1981, Azam et al. 1983, Cole et al.
1988), reaching close to 100% in very oligotrophic sites (Gasol et al. submitted). Bacterial biomass
often comprises a major fraction of particulate organic carbon, in oligotrophic systems at times even
surpassing phytoplankton biomass (Cho & Azam 1988, Fuhrman et al. 1989, Simon et al. 1992, Gasol
et al. 1997).
Depending on the speci c characteristics of each environment, factors that may control prokaryote
communities include temperature (Shiah & Ducklow 1994), grazing pressure (Thingstad et al. 1997),
and availability of limiting nutrients (Rivkin & Anderson 1997, Thingstad et al. 1997). Bacterial
production is considered to be controlled by the rate of nutrient supply (bottom-up), whereas nal
standing stocks and the rates of speci c growth are considered to be determined by predation pressure
(top-down), by substrate supply (bottom-up), or by both mechanisms (Wright & Cof n 1984, Pace &
Cole 1994, Thingstad & Lignell 1997). Thus, more nutrients would mean more biomass and production,
and more predators could mean less biomass or activity. Grazing is also known to be a selective factor
determining bacterial community structure. Predators prefer to predate the largest bacteria (Jürgens &
Güde 1994), the most active cells (del Giorgio et al 1996), and prefer some speci c bacterial groups
over others (Simek et al 1997, Jürgens et al 1999, Suzuki 1999).
It is generally accepted that bacterial communities seem to be rather similar at relatively large
spatial scales on the horizontal axis (Acinas et al. 1997, Baldwin et al. 2005), except where different
water masses meet (Suzuki et al. 2001, Pinhassi et al. 2003). Since the largest distinction in microbial
communities occurs at the boundary of the euphotic and aphotic zones, light and the vertical distribution
of autotrophs seem to be an important factor determining bacterial distributions, through their effects on
the DOC pool, although vertical clines in the composition of DOC are as yet poorly resolved (Carlson
et al. 2002). Furthermore, temperature, nitrogen and phosphorus availability, vary dramatically on the
vertical axis, so these factors must be considered together to understand the vertical strati cation of
microbial communities.
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Chapter V
The assessment and comprehension of the factors that control bacterial abundance and production
will facilitate the understanding of how carbon and nutrients circulate in planktonic food webs.
Because of the dif culty of naturally replicating nutrient supply in mesocosm, transplant experiments
are a research approach of choice for the study of these factors (e.g. Gasol et al. 2002).
Here we describe two transplant experiments designed to assess the factors that control prokaryote
abundance, activity, and bacterial community composition at two different depths in the eastern North
Atlantic Ocean. To assess these questions, un ltered and 0.8 µm- ltered water from surface or from
depth (deeper layers, either mesopelagic or DCM) were placed in dialysis bags.. We used dialysis
bags that are sort of “microbial cages” that allow exposure of the microbes to ambient nutrient
concentrations (Herndl et al. 1993). Samples from the two different depths were incubated in dialysis
bags placed in times of in situ conditions and transplanted to another tank with the conditions of the
other depth. The experiments lasted 6-8 days and the bags were sampled at 24 h intervals to estimate
bacterial abundance and whole community activity.
MATERIALS & METHODS
Study site and water collection. We present data from two experiments conducted in the North
Atlantic Ocean in August and October 2006. In the August experiment the water was collected during
the RODA cruise near the Canary Islands (26º50´N, 16º18´W), and in the October experiment the
water was collected during the CARPOS cruise near the Island of Madeira (31º50´ N, 16º40´ W). In
the August experiment, the water used in the experiments came from the surface (ca. 5 m) and the
mesopelagic zone (500 m). In October the water for the experiments was collected from the surface
(ca. 5 m) and from the depth of the DCM (deep chlorophyll maximum), which was located at 100 m.
In both cruises the water was collected with Niskin bottles and the parameters of temperature, salinity
and uorescence in each station were recorded by a SeaBird 911 CTD system.
Transplant experiments. The two experiments started by collecting surface and deep water (from
the mesopelagic in the August experiment, and from the DCM in the October experiment). Two acid
clean 50 L tanks were lled up with water one from each different depth and placed to incubate in the
dark at in situ temperatures. The temperature conditions wer simulated with running super cial water
for the super cial water conditions tank and with an incubator for the depth water-conditions tanks
(DCM or mesopelagic). In both tanks 4 dialysis bags (of 1 L each) were placed with un ltered water
from the two different depths and with water that was gently pre- ltered through 0.8 µm polycarbonate
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Regulation of bacterial activity and community structure
lters, to simultaneously address the effects of grazers and of changes in the natural conditions on the
bacterial communities. The dialysis bags (Spectrum laboratories), with a cut-off size of 12000-14000
Da and a maximal width of ~ 8 cm, were cut in lengths of 50 cm to hold ca, 1000 ml. The bags were
thoroughly washed in HCl 4% overnight, and then soaked for >3h in Milli-Q water before use. After
they were lled, they were closed and placed in the two tanks, the one holding surface water and the
other one having deep water. Samples for bacterial abundance and bacterial heterotrophic production
were taken daily from the dialysis bags, making sure not to contaminate the inside of the bags with
surrounding water, and DNA to analyze bacterial community structure was sampled at the beginning
and at the end of the experiments. Unfortunately, the DNA samples of the CARPOS cruise experiment
were lost during transport.
Environmental variables. Measurements of chlorophyll a (Chla) and nutrients were done in both
cruises (Table 1). Particulate organic carbon (POC) and particulate organic nitrogen (PON) were
measured only in the RODA cruise. Organic carbon was determined with a CHN elemental analyzer
after ltration through Whatman GF/F lters (POC) or catalytic combustion at high temperature of
un ltered and acidi ed samples (TOC). Dissolved organic carbon (DOC) was computed as DOC =
TOC-POC. The procedures employed are detailed in previous studies describing the variability of
these parameters in the study area (Alonso-Sáez et al. 2007, Arístegui et al. 1997).
Prokaryote abundance and biomass. Heterotrophic prokaryotes were measured by ow
cytometry as described in Gasol & del Giorgio (2000). The samples were run in a Becton-Dickinson
FACSCalibur cytometer after staining with SybrGreen (1:10,000 nal concentration, Molecular
Probes), and bacteria were detected by their signature in a plot of side scatter (SCC) vs FL1 (green
uorescence). Calibration of the cytometer, and of the uorescence-size relationship was done as
described in Calvo-Díaz & Morán (2006).
Prokaryotic heterotrophic production. Prokaryotic heterotrophic production was estimated
using the 3H-leucine incorporation method (Kirchman et al., 1985). Quadruplicate aliquots of 1.2 ml
were taken for each sample plus two TCA killed controls. The Leu tracer was used at 40 nM nal
concentrations in incubations lasting 2-3 h. The incorporation was stopped with the addition of 120 µl
of cold 50% TCA to the samples and, after mixing, were kept frozen at -20ºC until processing, which
was carried out by the centrifugation method of Smith & Azam (1992). The samples were counted on
a Beckman scintillation counter, 24 h after addition of 1 ml of scintillation cocktail.
155
Chapter V
Bacterial community structure. Microbial biomass was collected by ltering around 250-500 ml
of seawater through 0.2 µm polycarbonate lter (Millipore, 25 mm). The lters were stored at -80ºC.
Microbial biomass was treated with lysis buffer (50 mM Tris-HCl pH 8.3, 40 mM EDTA pH 8.0, 0.75
M sucrose), lysozime, proteinase K and sodium dodecyl sulphate. Nucleic acids were extracted by a
standard protocol using phenol/chloroform (see details in Schauer et al. 2003).
Fingerprinting analysis. DGGE and gel analysis were performed essentially as described (Schauer
et al. 2000; Sánchez et al., 2007). Brie y, 16S rRNA gene fragments (around 550 bp in length) were
ampli ed by PCR using the universal primer 907rm and the bacterial speci c primer 358f, with a
GC-clamp. PCR products were loaded on a 6% polyacrylamide gel with a DNA-denaturant gradient
ranging from 40-80%. The gel was run at 100V for 16 h at 60ºC in 1x TAE running buffer. DGGE gel
images were analyzed using the Diversity Database software (BIO-RAD).
Statistical analysis. A matrix was constructed for all DGGE lanes taking into account the relative
contribution of each band (in percentage) to the total intensity of the lane. Based on this matrix,
we obtained a dendrogram by the Ward’s clustering method (Euclidean distances, Statistica 6.0),
and ordinations of nonmetric multidimensional scaling based on Bray-Curtis similarity index (MDS,
Kruskal & Wish 1979, Clarke & Green 1988). An statistic test (one way ANOSIM) was used to test
for differences between treatments and the control samples, and another to contrast differences among
time within treatments (Primer v5).
156
Regulation of bacterial activity and community structure
0
50
100
150
200
250
30014 16 18 20 22 24 26
193.7 200 206.2 212.5 218.7 225 231.2
Oxigen (�mol Kg-1)
Salinity (PSU)
Temperature (ºC)
0 0.1 0.2 0.3 0.4 0.5
Fluorescence (vol)
36 36.2 36.4 36.6 36.8 37
0
200
400
600
800
10005 10 15 20 25
Temperature (ºC)
120 140 160 180 200 220 240 260
Oxigen (�mol Kg-1)34 34.5 35 35.5 36 36.5 37
Salinity (PSU)
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7Fluorescence(vol)
A B
RESULTS
As expected, we used for our experiments very
different source waters (Fig. 2 and Table 1). In cruise
RODA, we compared surface with mesopelagic water.
This last one was colder (10 degrees lower), nutrient-
replete, and with half the concentration of bacteria than
to the surface. It was also 20x less active than the surface
community (Table 1). Stn E01B of cruise CARPOS was
relatively richer (surface Chla was 0.18 as compared
to < 0.05 mg m-3 in Stn 64 of cruise RODA), and we
chose to perform the transplant experiment with water
of the deep chlorophyll maximum (DCM), which was
very well formed and which reached 0.46 mg m-3 of chlorophyll (Fig. 2). For the interpretation of the
results it has to be taken into account that the incubations of the experiments were carried out at dark
conditions and at in situ temperature. Since the photosynthetic processes were suppressed, this could
affect the original DOC quality during the incubation.
Fig.1.- Map to the sampling area showing the two station locations.
Fig.2.- CTD pro les of water temperature (red), oxygen (blue), uorescence (green) and salinity (black) of the stations in RODA (A) and CARPOS (B) from where the experiments were stablished. Arrows indicate the selected depths.
S
O
T
F
S
TO
F
157
Chapter V
Bacterial production was 2 orders of magnitude higher at the DCM than at the surface, while
bacterial abundance was higher at the surface and, thus, bacterial growth rates were even higher at
depth. That the surface community of RODA was more active than that of the mesopelagic, and that
the DCM community of CARPOS was more active than that of the surface was also evidenced by the
%HNA cells (Table 1).
Bacte
rial a
bund
ance
(cell
ml-1
)Ba
cteria
l abu
ndan
ce (c
ell m
l-1)
Bacterial abundance
Time (days)
Leuc
ine i
ncor
pora
tion (
pmol
Leu
L-1 h-1
)
Leucine incorporation
Leuc
ine i
ncor
pora
tion (
pmol
Leu
L-1 h
-1)
Time (days)
MESOPELAGIC MESOPELAGIC
SURFACE SURFACE
A B
DC104
105
106
107
0 1 2 3 4 5 6
Super CTLS s 0.8 S s SFS m 0.8S m SF
104
105
106
107
0 1 2 3 4 5 6
Meso CTLM 0.8 mM SF mM 0.8 sM SF s
1
10
100
1000
104
0 1 2 3 4 5 6
0,1
1
10
100
1000
104
0 1 2 3 4 5 6
Fig.3.- Estimates of bacterial abundance (left panels, BA) and leucine incorporation rates (right panels, LIR) in the RODA mesopelagic-epipelagic transplant experiment. The upper panels (A, B) correspond to the surface water. The lower panels to the mesopelagic water. In both panels, the control of the tank (outside the bags) is referred with the black round symbol and a continuous line. Water incubated in bags placed in the same type of water are labelled with white symbols (this is Surface water incubated in surface water, or S s, and Mesopelagic water incubated in mesopelagic water, or M m). Transplanted water (S m and M s), stands with dark symbols. Water ltered through 0.8 µm (w/o predators) is plotted with round symbols (e.g. S s 0.8), while untouched water is referred as UF and is plotted with round symbols.
158
Regulation of bacterial activity and community structure
Both, average (and maximal) bacterial abundances were strongly determined by the characteristics
of the water where the samples were incubated in the transplant experiment done with meso- and
epipelagic waters (cruise RODA experiment, Table 2). For the surface water (Fig. 3A) there was
slightly more growth in the incubations at the surface (S s) than in the mesopelagic water (S m).
105
106
107
0 1 2 3 4 5 6
Bacterial abundance S controlS s 0.8S s UFS dcm 0.8S dcm UF
0.1
1
10
100
1000
104
1 2 3 4 5 6
Leucine incorporation
105
106
107
0 1 2 3 4 5 6
DCM controlDCM dcm 0.8DCM dcm UFDCM s 0.8DCM s UF
1
10
100
1000
104
0 1 2 3 4 5 6
0
Time (days) Time (days)
DCM DCM
SURFACEBacte
rial a
bund
ance
(cell
ml-1
)Ba
cteria
l abu
ndan
ce (c
ell m
l-1)
Leuc
ine i
ncor
pora
tion
(pm
ol L
eu L
-1 h-1
)Le
ucin
e inc
orpo
ratio
n (pm
ol L
eu L
-1 h-1
)
SURFACE
A
C D
B
Fig.4.- Estimates of bacterial abundance (left panels, BA) and leucine incorporation rates (right panels, LIR) in the CARPOS DCM-epipelagic transplant experiment. The upper panels (A, B) correspond to the surface water. The lower panels to the DCM water. In both panels, the control of the tank (outside the bags) is referred with the black round symbol and a continuous line. Water incubated in bags placed in the same type of water are labelled with white symbols (this is Surface water incubated in surface water, or S s, and DCM water incubated in mesopelagic water, or DCM dcm). Transplanted water (S dcm and DCM s), stands with dark symbols. Water ltered through 0.8 µm (w/o predators) is plotted with round symbols (e.g. S s 0.8), while untouched water is
referred as UF and is plotted with round symbols.
159
Chapter V
Treatment S-s-UF (un ltered) was rather similar to what happened outside the bags, and there
was little difference between the un ltered and the ltered samples in bacterial abundance, but the
statistical tests show signi cant effects of ltration on bacterial activity (Table 2, Fig. 5B). In terms
of leucine incorporation (Fig 3B), all samples started incorporation at about the same day (day 2), but
samples incubated in the mesopelagic had an initial lowering, an then they recovered and increased
leucine incorporation at a rate very similar to that of the water incubated at the surface, in spite of the
difference in incubation temperature. Bacterial activity was slightly higher in the un ltered than in
ltered samples. Some more differences were visible when we incubated water originated from the
mesopelagic. Bacteria developed strongly when incubated in surface water (Fig. 3C), reaching 4 106
cells ml-1 from a start of 1.5 105 cells ml-1 in 6 days (growth rate of 4.3 d-1). The un ltered sample
developed a higher yield (Fig. 5) than the ltered sample, suggesting that ltration through 0.8 µm
retained some growing bacteria, probably those attached to particles. The rate of growth of the ltered
and un ltered bacteria was roughly the same. When incubated in the mesopelagic, however, there was
a signi cant difference between the samples that had been ltered through 0.8 µm, which did not
develop until day 5, and those which were un ltered, that developed similarly as the control external
water (Fig. 3C). Leucine incorporation rates started to be higher earlier in the samples incubated at
a higher temperature (i.e. samples M s, Fig 3D). Paired Student’s t-tests indicate that the maximum
abundance was reached when incubated at the surface, while the maximum production was achieved
in the presence of predators (Table 2).
In the second experiment, with transplanted water from the surface and the depth of the deep
chlorophyll maximum (DCM), the starting situation was different: the DCM sample was richer and
more active than the surface sample. Surface water incubated at the DCM reached higher abundances
than incubated at the surface (Fig. 4A, Fig 5C). The control water was similar to the S-s-UF sample.
In terms of activity, however, values were rather similar between treatments with a systematic trend of
less production in the UF sample than in the F sample (Table 2, Fig. 5). DCM waters presented a very
similar pattern: they developed more when incubated in DCM water, while activity was rather similar
between treatments but systematically lower in the UF sample than in the F treatment.
160
Regulation of bacterial activity and community structure
Fig. 5 presents two integrative variables of what happened in the experiment: the average abundance
of bacteria in each treatment, and the total integrated bacterial production. The analysis in Table 2
includes two extra variables, the maximal abundance of bacteria and the maximal leucine incorporation
rates: these variables show similar trends than the average abundance and the integrated production.
Fig. 5 clearly shows the effect on the microbial community due to the incubation water conditions
(more abundance and more production in the samples incubated in the surface in RODA ), and a
slight, but signi cant effect of predators, mainly on activity.
Fig.5.- Left-side panels, maximum bacterial yield in each treatment of experiment RODA (A) and CARPOS (C), and integrated leucine incorporation (right-side panels) through the whole of the experiment RODA (B) and CARPOS (D). Letters indicate the source water, the incubation water, and the treatment (e.g. M s 0.8 indicates mesopelagic water incubated at surface and after ltration through 0.8 µm).
0
5 105
1 106
1,5 106
2 106
2,5 106
3 106
SUPE
R
S s
0.8
S s
UF
S m
0.8
S m
UF
MES
O
M m
0.8
M m
UF
M s
0.8
M s
UF
0
5 105
1 106
1,5 106
2 106
SUPE
R
S s
0.8
S s
UF
S m
0.8
S m
UF
MES
O
M m
0.8
M m
UF
M s
0.8
M s
UF
RODA
CARPOS CARPOS
0
1000
2000
3000
4000
SUPE
R
S s
0.8
S s
UF
S m
0.8
S m
UF
MES
O
M m
0.8
M m
UF
M s
0.8
M s
UF
0
2000
4000
6000
8000
1 104
SUPE
R
S s
0.8
S s
UF
S m
0.8
S m
UF
MES
O
M m
0.8
M m
UF
M s
0.8
M s
UF
RODA
Aver
age b
acter
ial ab
unda
nce (
cells
ml-1
)Av
erag
e bac
terial
abun
danc
e (ce
lls m
l-1)
Integ
rated
leuc
ine i
ncor
pora
tion (
pM)
Integ
rated
leuc
ine i
ncor
pora
tion (
pM)
A
C
B
D
161
Chapter V
In the RODA experiment, we analyzed community structure by PCR-16S rRNA DGGE
ngerprinting. The Ward’s clustering method showed a separation between the tanks, all samples
incubated at surface conditions clustered together and all samples incubated in Mesopelagic conditions
grouped together (Fig. 6a). The ordination of the samples by NMDS agrees well with the clustering
results, and shows separation of the samples incubated at the different depths (Fig. 6b). The ANOSIM
analysis determined signi cant differences between initial and nal time of incubations (R=0.915,
P:0.015) and incubation conditions (R=1, P: 0.008).
Ward`s methodCity-block (Manhattan) distances
0 50 100 150 200 250 300 350 400
Linkage Distance
MESO Tf
MmUF
SmUF
Mm0.8
Sm0.8
SUPER Tf
MsUF
Ms0.8
SsUF
Ss0.8
MESO To
SUPER To
DGGE gelDendogram
Resemblance: S17 Bray Curtis similarity
SUPER To
Ss0.8
SsUF
Ms0.8MsUF
SUPER Tf
MESOTo
Sm0.8
SmUF
Mm0.8
MmUFMESO Tf
2D Stress: 0.08
A B
C
SUPER Ss0.8 SsUF Ms0.8 MsUF SUPER MESO Sm0.8 SmUF Mm0.8 MmUF MESO To Tf To Tf
Fig.6.- DGGE gel of the RODA experiment (A). Dendogram classi cation (B)(Ward’s Method, Euclidean distances according to the band pattern). S (water from surface origin), M (water form mesopelagic origin), s (incubation conditions: surface tank), m (incubation conditions: mesopelagic tank). 0.8 (sample ltrated by 0.8 µm) and UF (sample without ltration). Nonmetric multidimensional scaling (NMDS) (C) of the bacterial assemblage composition data (DGGE band pattern). The samples have been clustered in agreement with the Ward’s clustering methods results.
162
Regulation of bacterial activity and community structure
DISCUSSION
Nutrient, light and temperature conditions vary dramatically on the vertical axis in the ocean.
Bacterial function, particularly bacterial production, varies also dramatically with depth (with
exponential slopes of -0.90 km-1, Arístegui et al., submitted). Bacterial community structure is also
known to vary dramatically on the vertical axis, more so than in the horizontal plane (Hewson et al.
2006). It is thus interesting to evaluate the effect that an input of surface waters could have in the DCM
or mesopelagic zone, as it would occur in an anticyclonic eddy. In this situation DOC accumulated
in the surface water masses could be available for the upper mesopelagic microorganisms. Carlson
et al. (2004) showed that DOC that could not be degraded by the surface bacterial community could
be easily remineralized at depth. Similarly, we were interested in predicting the effects caused by a
cyclonic eddy, when deep water masses would move upwards to the surface layer carrying inorganic
nutrients.
The objective of this study was to know to which extent bacterioplankton communities were
determined by its original conditions (e.g. nutrient concentration, abiotic factors), depending on
the depth at which the community had been formed. Hence, we hypothesized that a change in the
conditions should reveal the factor that determines a speci c level of bacterial metabolism and a
speci c community structure. As laboratory experiments have the dif culty of mimicking natural
nutrient conditions, to address this question we performed a transplant experiment using dialysis bags.
We used the concept of a “transplant” experiment, because in this approach organisms are allowed to
grow at different environmental conditions from their origin, sort of in a “microbial cage” (Herndl et
al. 1993).
In addition, we wanted to test whether the growth was nutrient controlled (“bottom-up”) and/
or biomass predator-controlled (“top-down”). A complex interaction exists between the top-down
and bottom-up controls of bacteria in plankton environments. Bacterial production is thought to be
regulated by the supply of nutrients and nal abundances are either determined both by predation-
pressure and substrate supply (Wright & Cof n 1984) or just by predators (Thingstad & Lignell 1997).
However in compiling the results of several experimental and empirical studies, Pace & Cole (1994)
found little evidence of systems where predators were limiting bacterial abundances. Similarly, we
found little evidence of abundances being determined by the presence or absence of predators (Table
2).
163
Chapter V
Table 2.- Results of paired Student’s t-tests (tests whether the average difference is signi cantly different from 0) between each paired treatment in the experiments: source water (e.g. surface vs. mesopelagic water), Incubation water, or presence/absence of predators. The variables analyzed are Maximum and experiment-average bacterial abundance, and integrated and maximum leucine incorporation rates. The trend explains whether the Surface (S) value is higher than the mesopelagic (M) or DCM value, or whether the 0.8 µm ltered (F) is higher or lower than the ltered (F) value. The ratio is the average increase in the variable within the experiments (Surface/Mesopelagic, UnFiltered/Filtered or Surface/DCM).
Exp. Variable Effect Prob. Trend Ratio
RODA Max BA Source 0.010 S > M 1.8 ± 0.5 Incubation 0.009 S > M 1.7 ± 0.3 Predators 0.009 UF > F 1.6 ± 0.3 Average BA Source 0.014 S > M 2.7 ± 1.1 Incubation 0.032 S > M 2.2 ± 1.1 Predators 0.041 UF > F 1.6 ± 0.4 Integrated LIR Source 0.055 S > M 1.7 ± 0.5 Incubation NS 1.0 ± 0.3 Predators 0.051 UF > F 0.7 ± 0.2 Max LIR Source NS 1.5 ± 0.7 Incubation 0.058 M > S 0.7 ± 0.3 Predators 0.086 UF > F 0.6 ± 0.3
CARPOS Max BA Source NS 1.2 ± 0.6 Incubation NS 1.3 ± 0.4 Predators NS 1.2 ± 0.4 Average BA Source NS 0.9 ± 0.4 Incubation NS 1.1 ± 0.4 Predators NS 1.0 ± 0.3 Integrated LIR Source NS 1.1 ± 0.1 Incubation NS 1.0 ± 0.1 Predators 0.019 UF > F 1.2 ± 0.1 Max LIR Source NS 1.0 ± 0.2 Incubation 0.002 DCM > S 0.7 ± 0.1 Predators 0.048 UF > F 1.1 ± 0.7
164
Regulation of bacterial activity and community structure
In both of our experiments, the bags which should be similar to the control –the incubation
tank itself, (e.g. the S-s-UF sample)- showed a similar pattern in bacterial production and bacterial
abundance, thus suggesting that the evolution of bacteria in the bags was not affected further than the
global effect caused by the con nement in the tank. As a general pattern, there was a clear tendency
for the bags with the same origin (e.g. surface water) to diverge depending on whether they were
transplanted or incubated in the same water. Furthermore, when e.g. surface water was transplanted
into more oligotrophic deep (mesopelagic), growth was lower than in the control, while when the
surface water was transplanted into more active (DCM) water, growth was enhanced. In general, the
effect of predators was small, and mainly affected leucine incorporation (Fig. 3-5) The abundance was
higher in the treatments without ltration that is contrary to what we should expect, an explanation
would be that the hot-spots of bacterial activity were excluded in the ltration process.
Grazing, particularly by heterotrophic nano agellates (HNF), has been identi ed as the main
bacterial loss factor (Fenchel 1982, Sherr & Sherr 1984, Wright & Cof n 1984, Pace et al. 1988) and
has been considered to balance more or less bacterial production. In a nutrient addition experiment
with waters of Blanes Bay, Allers et al. (2007) detected a shift in bacterial diversity after an increase
in agellate abundance in nutrient-enriched mescosms. Thus, this suggests that nutrient supply may
favour a bacterial increase which would be followed by a agellate increase, which will act as a
selective force on bacterial community structure (Pernthaler 2005). However, as we did not nd any
major differences between bags with or without predators, the bottom-up factors seemed to explain
(P< 0.01) differences in community structure (Fig. 6). Previous studies have reported that succession
within the bacterioplankton in coastal marine systems may be lead by autochthonous processes, or by
the input of certain labile organic substrates (nutrient-rich runoff from rivers or organic contamination,
Revilla et al. 2000, Schendel et al 2004). This may stimulate the growth of bacteria from particular
phylogenetic lineages (e.g. fast-growing-gammaproteobacteria, Pinhassi & Berman 2003). Our data
seem to suggest the existence of a shift in bacterioplankton community structure due to the waters in
which the samples are incubated.
Gasol et al. (2002) run a transplant experiment in a canyon-shaped reservoir, submitting bacterial
communities from the river side to the conditions in the reservoir side. Even with very different growth
rates for different bacterial groups, the nal community structure was quite similar to the initial one. In
contrast, in our experiments we found signi cant differences in the community composition between
incubation conditions revealing bottom-up effect on the community structure. This agrees with the
results of Carlson et al. (2004), that suggests controls on the bacterial community in space and time
during transplant experiments.Fuhrman et al. (2007) showed that the presence of annually reoccurring
165
Chapter V
bacterial communities in the Southern California coast, and that the occurrence of each population
could be predicted from the ocean environmental conditions. This would imply a large degree of
bottom-up control of bacterial community structure, as predators tend to vary more stochastically
than the nutrient sources (Tanaka & Taniguchi 1999) that are most likely driven by temperature,
light availability, rain and its direct effects on bacteria, or through effects mediated by phytoplankton
species succession, all parameters that we consider “bottom-up”. Our results show experimentally
this to be the case for surface, mesopelagic and DCM bacterial communities of the North Atlantic
Ocean.
ACKNOWLEDGEMENTS
We thank the chief scientist of cruise CARPOS, Dr. Ramiro Varela as well as the rest of coworkers
on both cruises for various help. We particularly thank A. Bode for ancillary data and X.A.G. Morán
for the opportunity to join cruise CARPOS. The eld work of this study was supported by projects
RODA (CTM 2004-06842-C03/MAR) and CARPOS (REN 2003-09532-C03-01), and the lab work
was supported by project MODIVUS (CTM2005-04795/MAR). This is a contribution to the EUR-
OCEANS and MARBEF European Networks of Excellence.
166
Regulation of bacterial activity and community structure
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SYNTHESIS AND DISCUSSION
The aim of this thesis was to describe the effects of different allochthonous carbon sources on the
diversity and function of marine bacterioplankton. In some of the chapters (#1 particularly), the focus
was on diversity, and in some others it was placed on the link between the changes in diversity and
those in bacterial function, in some cases extending beyond the purely bacterial function to that of
the whole microbial ecosystem (e.g. Chapter 4). In Chapter 1 we compared the diversity obtained
by traditional culture techniques and by molecular approaches in the NW Mediterranean. The culture
media can be considered a special type of allochthonous carbon source, and we encountered low
similitude between the bacteria present in the environment, and those selected by the media, suggesting
a strong effect of the resource types and levels on diversity The diversity detected by cultivation was
largely invisible by the molecular PCR-based techniques, and using both methodologies we increased
substantially the diversity reported from the area in previous papers. Coastal planktonic communities
are subject to the introduction of C and nutrients of anthropogenic origin. We analyzed the effect
of one of these sources of C and nutrients of basically human origin in Chapter 2, in which, to
determine the effects of the Prestige tanker oil spill tanker in front of the Galician coast, we used
realistic additions of oil to mesocosms. We detected little effects of oil on the diversity and function
of the authochtonous bacterial microbiota. To evaluate the effect of different nutrient inputs from
different origins (terrestrial or athmospheric), two enrichment experiments were carried out in the NW
Mediterranean coast. We added different sources to stimulate phytoplankton blooms and determined
changes in bacterial community structure, but little differences were found even when a diatom bloom
developed (Chapter 3), In the experiment that tried to mimick an event of Saharan dust deposition
(Chapter 4) we saw clear effects on bacterial function. In Chapter 5, however, and we investigated
the effects of different types of “nutrient environments” on diversity and function of epipelagic, and
mesopelagic bacterioplankton. We did so with transplant experiments, and we found that, in contrast
to what we had seen in the NW Mediterranean and partly in the Atlantic coast, the bottom-up factors
(nutrient environment) strongly determined the nal results of the experiments.
Several elements were common between the different experiments/chapters. For example, i) we
used a mesocosms approach in 3 of the chapters (2, 3 and 4). In these experiments we detected a
common pattern of development of bacterial abundance that was also seen in previously published
experiments. Below, we analyze the meaning and implications of these patterns, also as they have
to do with agellate development. ii) Two of the experiments/chapters were done in Blanes Bay
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(NW Mediterranean) waters, but started at different times of the year (chapters 3 and 4). Similarly,
in Chapter 2 we considered the variation in effects depending on the time of the year in which the
experiment was done. Also, iii) we used a genetic ngerprinting technique (DGGE) in all the chapters.
In Chapter 1 we compared the bands appearing in DGGEs done in Blanes Bay at different years with
the diversity seen by other techniques (clone libraries and isolation). In other chapters we analyzed
changes in bacterial community structure in the different experiments. This allowed us to compare the
“amount of change” in structure between different manipulations and in different seasonal studies.
In the following sections we explore some of these general issues that appear different times
throughout this thesis.
TRANSPLANTS OIL-SPILLSIMULATION SAHARAN DUST
DEPOSITIONPHYTOPLANKTON BLOOMS INDUCTION
+ REALISM -
- CONTROL +
Fig.1.- Summary of different approaches used in this thesis and the associated levels of control, inversely related to the realism levels. From the more realistic and less controlled, to the less realistic and more controlled: transplant experiments (Chapter 5), oil addition (Chapter 2), Saharan dust addition (Chapter 3) and inorganic nutrient addition and phytoplankton bloom induction (Chapter 4). Chapter 1 could somehow be included in the right side of the graph, as the culture media are well de ned, and we know well that provides unrealistic views of bacterial community structure.
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Synthesis and dicussion
i) EXPERIMENTAL APPROCHES
To address the different questions posed in this thesis, we mainly used mesocosms and transplant
experiments using dialysis bags. These two kinds of approaches are very useful to experimentally
mimic natural situations. Mesocosms are a common tool used in aquatic ecology to simulate natural
behaviour of communities, at a scale that can be handled and providing a degree of realism not possible
in the laboratory (Odum 1984). They have been widely used to study the effect of nutrient additions
on phytoplankton communities (e.g. Duarte et al. 2000), on the activity and diversity of bacterial
assemblages (e.g. Schäfer et al. 2001), and on microbial succession (e.g. Allers et al. 2007). This type
of experimental approach avoids the effects caused by advection, diffusion and mixing under natural
conditions and also offers the possibility of comparison with natural populations where there were no
additions, thus allowing statistical testing of hypotheses. We have performed mesocosms experiments
to evaluate the effects of oil spills in Chapter 2, to stimulate different phytoplankton blooms Chapter
3 and to mimic a Saharan dust deposition in Chapter 4. And we even used in Chapter 5 mesocoms
approaches as a way to maintain the transplant experiment with the dialysis bags. These bags are
sort of microbial cages that allow nutrients to ow, and are thus, very useful to exposure a determine
bacterial community to a particular ambient nutrient concentration (Herndl et al. 1993).
To the enclosures we added different types of substrates, ranging from simple inorganic nutrients,
somehow uncharacterized dust originated from the Sahara, or the soluble fraction of Prestige oil.
Furthermore, in one case we applied a “black-box” approach to the substrates by incubating samples
from some depths into other depths that we expected should have different nutrient elds (DOC and
inorganic nutrients, but also temperature). Our target variables (bacterial diversity and function) and
our questions (do the target variables change with these additions?) were analyzed in all these different
types of approaches. They can be ordered in two types of opposite gradients (Fig. 1) depending on
the degree of control we can have over the nutrients added, or on the degree of realism that we can
assume each approach has. The dialysis bag experiments are probably more realistic than the other
approaches, but we control little the nutrient eld. In contrast, in the nutrient-addition experiments of
Chapter 3 we controlled well the nutrients added, but the situation was probably the less realistic one
and the one that shed less light into what really occurs in nature. Plate growth media as those used in
Chapter 1 are an extreme example of unrealistic, but well controlled, approach.
It is well known that enclosures can either enhance the growth of certain microzooplankton by
excluding their predators, or induce their mortality, and this generates the question of whether the
mesocosms can also affect the microbial communities. The Mediterranean sea is known to be an
oligotrophic ecosystem and, thus, it is likely that the so-called “bottle effect” (Marshall et al. 1971) is
more intense than in a relatively more eutrophic system such as Ría de Vigo (Chapter 2). These bottle-
effects, maybe related to the dominant P-limitation of Mediterranean Sea bacterioplankton (Thingstad
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et al. 1998, Pinhassi et al. 2006) seemed larger in the initial times of the experiments performed in
the Mediterranean. Figures 2-4 show a comparison between the changes recorded in the experiments
of Chapters 3 and 4 with two other experiments done with waters from the NW Mediterranean, and
published in Pinhassi et al. (2004) and Allers et al. (2007). These plots show that the initial start of
the these experiments presented shift-ups of 2-3 orders of magnitude in bacterial production, which
are much higher than the shift-ups seen in the Ría de Vigo mesocosms experiments (Fig. 3 in Chapter
2).
�
� ���
�
�
�
� �
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Fig.2.- Chlorophyll a values (mg m-3) along the incubation of four different mesocosms experiments with NW Mediterranean waters: (A) nutrient addition and turbulence experient (Pinhassi et al. 2004); (B) inorganic and organic addition experiment (Allers et al. 2007); (C) phytoplankton bloom stimulation experiment (Chapter 3) and (D) Saharan dust deposition experiment (Chapter 4). Treatments are K: Control, C: Carbon addition, P, Phosphorus addition, CP: both additions. Si: Silica and P addition, U: Urea and P addition. Low and High Dust are different ammounts of Saharan Dust.
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Some mesocosms experiments had been performed before to evaluate the changes of bacterial
community structure due to changes in nutrient availability and predators (e.g. Lebaron et al. 1999
and Schäfer et al. 2001). Previous to the studies in this thesis two mesocosms experiments were
performed with coastal waters of the NW Mediterranean (Pinhassi et al. 2004, Allers et al. 2007).
All of these experiments show differences between treatments and control in Chla concentration,
bacterial abundance and bacterial production (Fig 2, 3 and 4) that can be inspected further.
Hence, the utilization of mesocosms as an approach to study the effect of different nutrients additions
can be very useful. However, we also show, in the oil-spill simulation experiments in the Galician
coast (Chapter 2) that sometimes there may not be any bacterial response because, as shown by the
Fig.3.- Bacterial abundance (cells ml-1) values along the incubation of four different mesocosms experiments with NW Mediterranean waters. Panels as in Figure 1.
106
107
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Synthesis and dicussion
concentration dependent experiment, the concentrations of the compound added in the experiment
were not high enough to generate a response. The complexity of mesocosms experiments implies that
not many experimental situations can be analyzed at once. However, the comparison (meta-analysis)
of similar mesocosms experiments performed in different environments or settings allows for an
increase in the signi cance of the conclusions over what would be possible to know from just an
experiment alone. It allows generalizing some conclusions to situations other than the one assayed,
but also allows seeing differences between experiments (Olsen et al. 2006).
These results indicate a bacterial community uesd to the oil concentration tested. From the transplant
experiments done in North Atlantic Ocean, the bags with the same origin as the incubation tank water
had the same pattern in bacterial abundance, bacterial production and community structure, suggesting
Fig.4.- Bacterial production (µgC l-1 d-1) along the incubation of four different mesocosms experiments with NW Mediterranean waters. Panels as in Figure 1.
1
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Synthesis and dicussion
no effect of enclosure in the evolution of the bags. All together con rms that enclosure often did not
mask the effects on bacterial communities due to changes in the environment.
ii) THE STATUS OF THE INITIAL COMMUNITIES
Inspection of the effect of oil on plankton community structure, as we did in Chapter 2, running
similarly designed mesocosms experiments at different times of the seasonal cycle in the planktonic
ecosystem of Ría de Vigo allowed us to detect differences in the response of the microbial community
depending on the initial situation. In those experiments, we started a mesocosms with a decaying
phytoplankton community and another one with a blooming community. However, the community
that was more sensitive to oil seemed to be the summer one, something that we assigned to the fact
that the water in the Ría at htat time of the year comes mainly from the upwelling of oceanic deep
water that enters the Ría and fertilizes the planktonic community. The bacteria in these waters are
likely not to be naturally exposed to oil components nor to other allochthonous C sources and might
thus be more sensitive to stressors such as the polycyclic aromatic hydrocarbons added.
The response of the NW Mediterranean communities to nutrient additions was also varied
depending in part on the time of the year when the experiment was done, but also dependoing on
the experimental conditions used. For example, chlorophyll decreased in the control treatment of the
experiment reported in Fig. 2B (Allers et al. 2007), but this experiment was run with very low light.
Chlorophyll also decreased in the Saharan Dust addition experiment (Chapter 4), but this was due to
the fact that the starting community was the one with the largest Chla value (ca. 3 mg m-3), during a
natural bloom situation, and the experiment evolved during the decline of that bloom.
A few other things are interesting and can be seen in Figs. 2-5. For example, bacteria seem to
develop a small initial bloom (except in the Saharan Dust experiment), before there is any noticeable
development in Chla. This can be related to the very important P-limitation of bacteria in the area
(Pinhassi et al. 2006). The effect of P on bacteria (note that all treatments in Chapters 3 and 4 had
received P except the controls) is much stronger than this effect on phytoplankton, as can also be seen
comparing Fig. 2 (Chla) and Fig. 2 (bacterial abundance). Finally, the chlorophyll maximum yield in
these experiments is ca. 2 orders of magnitude (Chapter 3, addition of Si + P), while the max. bacterial
yield did not go over one order of magnitude (in the experiment reported by Pinhassi et al. 2004, and
in the addition of a high concentration of Saharan Dust). This can probably be traced back again to
the well known lack of proportionality between primary producers and bacteria when their natural
changes are compared through regression lines (Gasol & Duarte 2000), but can also be masked by the
fact that bacterial numbers might hide increases in bacterial cell size and, thus, in bacterial biomass.
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The role of agellates as grazers of bacteria is very apparent in these experiments. In almost all
of them bacteria present an initial peak before algal development, then there is a through, and a
second peak of bacteria. In one of these studies (Allers et al. 2007) we could identify most bacteria
developing in the rst peak as Alteromonadaceae, while most of the bacteria in the second peak were
Rhodobacteraceae. The rst group of sequences were much more homogeneous between treatments
than the second group which differed according to the treatment. In fact, it appears as if the initial
bacterial peak, particularly when P is added, is rather independent of the treatment, and only the second
bacterial peak has to do with the treatment applied (see Chapter 3). The rst bacteria developing
would be oportunistic phylotypes that would then be grazed down by agellates.
iii) MEASURING BACTERIAL COMMUNITY STRUCTURE WITH DGGE
For the comparison of bacterial community structure in the different treatments, we used the
ngerprinting technique DGGE (denaturing gradient gel electrophoresis, Muyzer et al. 1997). The
level of diversity detection of this method is quite low (<30 sequences) compared to other molecular
techniques such as clone libraries (which were used in chapter one with a target of 100 sequence
types per sample) and much lower compared with the newer shotgun techniques, which are able to
detect thousand of sequences. However, this is a very useful methodology for comparative analyses of
microbial communities and their response to changing environments. (Kasai et al. 2001, Labbé et al.
2007, Brakstad et al. 2006, Castle et al. 2006, Ogino et al. 2001, Macnaughton et al. 1999, Casamayor
et al. 2002, Sánchez et al. 2007).
In Chapter 1 we presented a comparative study of the potential of sequence detection by culturing
and culture-independent techniques. It was observed that the molecular techniques differed substantially
from the cultivation techniques and, therefore, it was the combination of all the techniques what
allowed to enlarge our view of the bacterial biodiversity present at the investigated location (Donachie
et al. 2007). In Figure 5 we show a review of the different studies presented in Donachie et al. (2007)
with the addition of the results of our study. It is a plot of the percentage of sequences retrieved by
clone libraries in comparison with the sequences from isolates and to the sequences detected by
both techniques. We found similar overlap as in the other marine study (Eilers et al. 2000), and non-
overlapping detection by the two sets of techniques, suggests that the isolate sequences do not represent
the diversity of the environment. The concept of biodiversity has been explained by Pedrós-Alió
(2006) as a pool of very abundant species and a tail (of unknown length) of rare/occasional species.
This author proposes a framework in which each type can be studied with different strategies. PCR
primers will predominantly hybridize and represent the common taxa, and the rare ones will only be
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retrieved by PCR-independent techniques such as cultivation or metagenomics techniques. Certainly,
there is an increasing interest in cultivation (for example, the Moore Foundation initiative http://www.
moore.org/marine-micro.aspx) because it provides the complete genome of the isolate and is a way to
test the hypotheses that emerge from metagenomic data, since procedures for reconstructing genomes
from whole-genome shotgun sequences are not yet very reliable.
Apart from differences from culture and culture independent techniques, differences in the
diversity detected between molecular procedures have been described (e.g. Alonso-Sáez et al. 2007).
In particular, this study showed that the dominant group present in Blanes Bay (SAR11) was not well
detected by the DGGE technique. Castle & Kirchman (2004) carried out a comparative study between
DGGE and FISH, and also showed that DGGE failed to detect the most abundant phylogenetic group
Fracti
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Fig. 5.- Comparative analysis of diversity detected by culture-dependent and culture-independent approaches in the same habitat (taken from Donachie et al. 2007 with the addition of our data). Percentages of unique sequence groups reported in libraries done using molecular analyses (PCR-based 16S rRNA clones), in cultivation, or by both techniques (if a phylotype is detected by both methods it is listed separately: ´overlap´). The habitats shown are (1) Blanes Bay Microbial Observatory (Lekunberri et al. chapter 1); (3) endodontic pathogenic oral bio lms (Munson et al., 2002); (4) exodontic oral bio lms (Munson et al. 2004); (5) Hawaiian lakes (Donaiche et al. 2004); (6) rhizoplane (Kaiser et al. 2001); (7) bird feathers (Shawkey et al. 2005); (8) North Sea (Eilers et al., 2000); (9) hypersaline salterns (Maturrano et al. 2006).
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detected by FISH in some samples (Betaproteobacteria in their samples). Besides primer speci city,
these authors argued that high richness within groups could lead to an underestimation as compared
with FISH, because different sequences would appear as different faint bands, which could be dif cult
to excise from the gels for sequencing. Indeed, Sánchez et al. (submitted) have shown that the likely
reason for the lack of detection of SAR11 in DGGEs of Blanes Bay is the high evenness of multiple
microdiverse phyloypes, individually non-dominant in the area.
iv) DIFFERENT FERTILIZING EFFECTS OF THE DIFFERENT ADDITIONS
Mesocosms experiments have the dif culty of nding the right balance between realistic conditions
and controlled changes. The different studies presented in the thesis are in a scale from more control
to more realistic approaches (Fig. 1). The transplant experiment (Chapter 5) represent the most
realistic and less controlled approach, where the microbial community was transplanted to another
conditions that we did not modify. In a second step of complexity, we tried to simulate realistic events
occurring to natural communities. The real concentrations of PAHs are very dif cult to accurately
determine and in addressing the effects of an oil spill, in doing the experiments reported in Chapter
2, we concluded that we should better create a concentration-dependent experiment to determine the
threshold concentration that affected the communities. In the experiment mimicking a Saharan dust
deposition event, the problem was in the dif culty of determining the dust composition (Chapter
4). We decided to use two different concentrations and we observed very different whole microbial
metabolism effects with each concentration. The most controlled approach is the addition of simple
compounds, adding well known concentrations as we did in Chapter 3 where we tried to induce
different phytoplankton blooms.
In the experiments done in the Ría de Vigo, and using concentrations similar to those measured after
the Prestige accident (20-30 μg PAH L-1) we did not get an effect on bacteria. Not seeing a signi cant
response to the realistic addition of oil suggests that the natural communities are used to a certain
level of these substances in the water and maybe even the addition remains below the concentration of
available DOC. Our results contrasted to the measurements of bacterial production after the accident
in the A Coruña area that were higher as compared to previous years (Bode et al. 2006) indicating an
effect of oil. In contrast, in the concentration dependent experiment the concentration of 40 μg PAH
L-1 indicated a detectable effect and the 80 μg PAH L-1 a clear detrimental effect on phytoplankton,
and a large development of Gammaproteobacteria. These results coincided with Nayar et al. (2004)
that reported toxicity to the autotrophs and stimulatory effect to the bacteria with the addition of high
oil concentrations.
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Synthesis and dicussion
The addition of Saharan dust only generated signi cant increases in bacterial abundance at the
highest concentration, suggesting a high contribution of the DOC content of the dust on the bacterial
community, and a limitation in phytoplankton growth by other nutrients such as silicate or nitrogen.
Previous studies have shown that addition of glucose alone as a C source does not necessarily stimulate
bacterial development (Pinhassi et al. 2006, Allers et al. 2007).
Finally, our results indicate a very strong effect of phosphorus on bacterial activity and abundance
that seems to supersede the effects of Carbon. Figures 3 and 4 indicate that all the treatments with
added P generated similar levels of bacterial biomass and production: treatments P and CP in Allers
et al. (2007), treatments P, Si, and U in Chapter 3, and treatments DH, DL and P in the Chapter 4
experiment.
v) FACTORS CONTROLLING BACTERIAL COMMUNITY STRUCTURE
In all ecosystems there is a ux of energy and material from autotrophic to heterotrophic organisms.
Phytoplankton organisms are the major producers of new production in the open ocean, while
bacteria utilise DOM as an energy source, and this is mainly of phytoplankton origin (5-50%, Nagata
2000). Bacteria also compete for nutrients with phytoplankton. At the same time the heterotrophic
nano agellates are effective bacterivores in the sea (e.g. Sorokin et al. 1979, Sierburth et al. 1982,
Unrein et al. 2007). Bacteria have been regarded as remineralizers, responsible for converting organic
matter to inorganic and recycling nutrients to primary produces and predation is known to provide a
feedback of some material ow to the food webs. Thus, the nutrient ow within the microbial loop is
tightly coupled. And the microbial loop concept originated from the idea that bacteria and agellates
recirculated the carbon that was previously though to be lost from the system. Grazers are important
in regulating bacterial abundance and production, and also seem to be a key factor controlling the
recycling of nutrients accumulated as bacteria (Caron & Goldman 1990).
It is also well known that nutrient additions cause changes on phytoplankton and these changes
are followed by shifts in bacterial community composition (Pinhassi et al. 2006, Allers et al. 2007),
in biomass and in bacterial function (production) at different stages of phytoplankton blooms (Smith
et al. 1995). Pernthaler et al. (1997) suggested that there was disagreement to whether the abundance
and productivity of bacteria are determined mainly by predators or nutrients. In a recent study Allers
184
Synthesis and dicussion
et al. (2007) considered a combination of both factors (bottom-up and top-down) controlling bacterial
succession and growth. This was in agreement with Lebaron et al. (1999) that suggested that nutrients
and grazing play a key role in shaping the morphologic, genotypic and phenotypic composition of
bacterial communities.
In our experiments we focused on the effect of the different additions on the bacterial community.
Besides we measure the HNF abundance in all the experiments except in the transplant experiment
(Chapter 5), where we controlled grazing pressure by ltration. From our results we can conclude the
importance of the predators on determining bacterial community structure. In Chapter 2, where we
correlated the changes in bacterial community structure with the changes in bacterial production (as no
change in structure is possible without production), we observed that this correlation was only signi cant
before the peak of HNF but not after that peak, suggesting a change in bacterial community structure,
maybe towards grazing-resistant forms (Pernthaler 2005). In the phytoplankton bloom stimulation
experiment (Chapter 3), the differences in bacterial activity and abundance between treatments were
found in the second bacterial peak, after the appearance of the HNF. Hence, there seems to be a shift
in bacterial community structure caused by the grazing pressure. In the experiments of transplant
of bacterial communities to different depths in the water column (Chapter 5) we observed that the
factors that determined a speci c community structure were mainly the environmental conditions,
although grazing pressure appeared to also play a role in determining bacterial abundance.
When analyzing the relative effects of nutrients or predators on community structure, the problem
of comparison between experiments arises. Did bacteria change more in the Sahara dust addition
experiment? or when we added nutrients? When we detect that community structure is different in the
ltered (no predators) than in the un ltered samples because the communities are not placed exactly
in the same position in the NMDS plot (Fig 6 in Chapter 5), how can we evaluate the signi cance of
this difference? To approach answering these questions, we decide to evaluate each of the experiments
presented in this thesis by averaging taking each DGGE analysis, and computing the average similitude
in each experiment. If, in an experiment, all treatments were to evolve equally, the average Bray-
Curtis similarity index would be 100. If the samples were to immediately change and no single band
would be repeated from treatment to treatment, then average similarity would be 0. Figure 6 compares
the values that we have measured during this thesis, with the typical seasonal evolution recorded in
Blanes Bay.
In the experiments where we detected little change, such as those in Ría de Vigo in spring, fall
and winter, the value of the average Bray-Curtis was about 65. In the summer experiment when we
did see differences, the value was 45. The seasonal changes in Blanes Bay provided values of 35-
45. And the experiments reported values ranging from 35 (transplant experiments, chapter 5) to 65
185
Synthesis and dicussion
(Saharan dust addition, Chapter 4), with intermediate values in the nutrient addition experiment. This
comparison allows us to quantitatively say that the changes produced by the transplant were of the
same magnitude than those occurring on average along the seasonal cycle in the NW Mediterranean,
but that the changes produced by Saharan dust were as modest as those produced by oil additions in
Ría de Vigo communities.
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Fig. 6.- Comparison of the average (± SE) similarity indices (Bray-Curtis) in each of the studies for which we had analyzed the variability of bacterial community structure using the DGGE technique. We included the seasonal variability at the Blanes Bay Microbial Observatory during years 1997-1998 (in Schauer et al. 2003), and years 2003-2004 (Alonso-Sáez et al. 2007).
186
Synthesis and dicussion
vi) WHICH BACTERIA ARE SELECTED IN THESE TYPE OF ADDITION EXPERIMENTS?
Almost every ecophysiological parameter in the oceans is thought to have an impact on the diversity
of microbial communities. Recent studies show that some bacterial taxa exhibit biogeographical
patterns, which are traced to the changes in the environmental characteristics (Pace 2003). Previous
studies have shown that the different conditions of the different times of the year affect the bacterial
assemblage in our study site (Schauer et al. 2003, Alonso-Sáez et al. 2007), i.e. there is seasonality. The
bacterial community structure of our study area was also investigated previously by two mesocoms
approaches. In the rst one, Pinhassi et al. (2004) found the presence of Roseobacter genera in all
treatments and a remarkable predominance of Bacteroidetes (in particular Flavobacteria) following
the phytoplankton blooms. Later on Allers et al. (2007) showed a clear contribution of the Roseobacter
genera (Alfaproteobacteria subclass) dependent on the Chla concentrations and an until increase in
Alteromodaceae (Gammaproteobacteria) at the start of the blooms. Other studies carried out in the
NW Mediterranean also found similar patterns, Schäfer et al. (2001), e.g. in an enrichment experiment
found particular dominance of genera Ruegeria belonging to the Alphaproteobacteria subclass, and
members of the Bacteroidetes. Lebaron et al. (1999) also found that phylotype Alteromonas macleodii
was dominant at the end of the incubation.
Langenhender (2005) suggests that bacterial community composition and bacterial functioning do
not need to be very tightly linked being however both mostly in uenced by the environment. There
are some bacteria that have speci c functionalities and only become dominant in speci c situations.
For example, the Gammaproteobacteria are generally the dominant bacterial group after oil additions
(Grossman et al. 1999, Kasai et al. 2001). There are two key organisms identi ed with the major roles in
the degradation in the petroleum hydrocarbons. Alcanivorax is responsible for alkane biodegradation,
whereas Cycloclasticus degrades various aromatic hydrocarbons (Harayama et al. 2004). In our oil
spill stimulation experiment we found a clear shift towards Gammaproteobacteria in the 80 μg L-1
treatment, where Cycloclasticus ended up being a large faction of the total Gammaproteobacteria.
Not in all experiments we decided to explore phylogenetically the bacteria generating relevant
bands in the DGGE. We did so only in the Saharan Dust experiment (Chapter 4), where we obtained
bands belonging to Bacteroidetes (known to have a life-style of attachment to particles), Within
the Alphaproteobacteria identi ed, bacteria of the Ruegeria/Silicibacter cluster appeared in several
treatments, as well as several Rhodobacteraceae. These are all common organisms in Blanes Bay,
easy to isolate in plates, and common in mesocosms experiments (Chapter 1). In fact, addition of
sequences retrieved (in the past) from enrichment experiments allowed to nd quite some phylotypes
appearing both in culture, and in the natural environment, but when the natural environment had been
modi ed by the different allochthonous enrichments studied here.
187
Conclusions
1.- Combination of culturing and culture independent approaches has increased substantially the diversity detected in Blanes Bay. We analyzed 136 isolates which belonged mainly to the Gammaproteobacteria, Alphaproteobacteria, or the Bacteroidetes. Addition of these organisms increased the estimates of richness in ca. two times the OTUs richness previously described with molecular tools alone.
2.- Nine cases of complete or nearly complete identity between isolates and in situ sequences were found: ve in the Gammaproteobacteria, three in the Alphaproteobacteria and one in the Bacteroidetes. This suggests that culturing to some extent provides representative organisms for physiological response experiments for various taxa detected by molecular methods.
3.- Around 30% of the environmental clones and isolates formed microdiversity clusters constrained at 99% 16S rRNA gene sequence similarity. The nding of microdiversity clusters among isolate and clone sequences alike, suggests that this type of microdiversity is not an artifact and that similar evolutionary processes act on cultured and uncultured bacteria.
4.- The bacterioplankton communities of coastal areas where there is an intense impact of human activities, such as Ría de Vigo, are used to certain concentrations of PAHs dissolved in the water. However, during the summer when in that area there is an entrance of oceanic water oil additions affected much more the function and community structure of the resident microbial communities.
5.- Development of agellate populations in the experiments performed in diverse marine areas was seen to explain some of the patterns in bacterial function and community structure seen. In the mesocosm experiments performed in Ría de Vigo, for example, we detected a signi cant effect of
agellates on bacterial community structure.
6.- Addition of different types of inorganic nutrient to NW Mediterranean waters generated responses in bacterial function and community structure mainly because of the P-limited nature of bacterial growth in the area. An initial bacterial peak appeared in all samples to which P was added, independently of the phytoplankton species selected by the addition. A second bacterial peak later on during the experiment was more diverse, showing effects of the different phytoplankton that developed.
7.- Besides the fertilization produced by the phosphorus content of Saharan dust we found that an addition of dust also stimulated directly bacterial activity and community respiration.Dust addition increased bacterial abundance by ca. 2-fold and bacterial production by 5-fold. Primary production and community respiration were stimulated by dust and by P, but the net result of the addition of low amounts of dust was a heterotrophic system, while the net result of the high dust and P additions were net autotrophic communities.
CONCLUSIONS
188
Conclusions
8.- Bacterial community structure changed little between the P-addition and a low addition of dust (0.05 g L-1), but the resulting communities were very different from the control with no additions, and from the communities that developed following a large dust addition (0.5 g L-1),
9.- The incubation of bacterial communities in different environmental conditions (water from surface transplanted to the mesopelagic or the DCM, and viceversa) led to bacterial function and growth to mainly follow the conditions of the incubation tank, with little effects of the initial community and of the presence or absence of predators. Predator presence had a small effect on bacterial production but not on nal bacterial yield.
10.- The incubation of bacterial communities in different environmental conditions (water from surface transplanted to the mesopelagic and viceversa) lead to changes in bacterial community structure, indicating bottom-up forces to be much more important than top-down regulation.
11.- Comparison of different experimental approaches ranging from extremely modi ed nutrient elds (in Chapter 1) and little touched environmental transplants (chapter 5) show how each approach illustrates different aspects of the dynamic interplay between bacterial community structure, diversity and function in the ocean. The comparison also allows detection of general patterns in bacterial development in experimental mesocosms, which vary depending on the initial conditions of the samples.
12.- Comparison of the changes in bacterial community structure induced by the different experimental treatments presented in this work allows the discrimination of large induced changes (e.g. the transplant experiments of Chapter 5) from low induced changes (Saharan dust addition). These can, in turn, be compared to the seasonal variability do discriminate “large” from “small” effects on bacterial community struture.
189
Future perspectives
FUTURE PERSPECTIVES
In the different enrichment experiments presented, we used the ngerprinting technique DGGE that permits the comparison between samples but, however, has a limitation and seldom ever presents more than 20 bands. We suggest that should promote a technique such as ARISA (Ampli ed Ribosomal Intergenic Spacer Anlysis, Brown et al. 2005), which is a technique from which a number of around 100 sequences can be retrieved. The RNA section utilized by this technique is from 16S to 23S, including the ITS fragment that has large variability in length and sequence. These products are run on automated sequencer generating an electropherogram where the sequences are differentiated. Standards (e.g. from clones) can be run and used to identify the peaks (Brown et al. 2005, Hewson & Fuhrman 2006).
Besides the culture and molecular techniques used in this thesis to study the diversity of a speci c
environment, we suggest the use of PCR-independent approaches such as uorescence in situ
hybridization (FISH) to complement the analyses of biodiversity of the ecosystem and to follow the
temporal dynamics of bacterial populations in enrichment experiments. We used this approach in
Teira et al. (2007) to detect the dynamics of small contributors to community structure (such as the
Gammaproteobacteria Cycloclasticus) in the oil-spill simulation experiment. In Chapter 2 we show
how, this technique complements well what we can see in DGGE analyses.
There is an increasing interest in relating the ecological function of microbes and diversity. We, thus,
suggest that more effort in this issue would provide a better understanding of ecosystem functioning.
Different approaches could be used to address this question:
i) One approach would be the isolation of bacteria that allow the sequencing of the complete
genome and the subsequent physiological analysis of the capacity of the isolates (e.g. González et al.
2008) specially during enrichment experiment where the concentration of the addition is more or less
controlled.
ii) The combination of FISH with microautoradiography provides the information of the single-
cell activity of speci c bacterial groups on carbon processing. This technique utilizes radio labelled
compounds, and thus labelling of the allochthonous compounds to determine the bacteria taking up
these substrates isn’t an easy task. We tried hard with 14C-labelled hydrocarbons, without success.
190
Future perspectives
iii) Another approach to determine which are the organisms growing because of a speci c substrate
addition, is a recently developed technique that consists in the incubation with compounds labelled
with Bromodioxidouridine (BrdU), that is an halogenated nucleoside that works as an analoge to
thymidine and as an alternative to [H3] TdR. It is an alternative to radioactive substrates and can be
applied to determine bacterial production if one follows a subsequent molecular analysis. It is used
as a speci c ngerprinting technique (BUMP-DGGE) that reveals the phylogenetic af liations of
the presumably actively growing bacteria that have incorporated this compound (Urbach et al. 1999;
Hamasaki et al. 2007). The method has been used simultaneously to FISH (Pernthaler et al. 2002).
193
Annexes
ANNEX I:
Traditional methods for bacterial isolation and cultivation
Bacteria play signi cant roles in marine food webs and in most marine biogeochemical cycles
(Fenchel 1988). The large biomass of bacteria is believed to be a repository of relatively unknown
diversity. Despite the overwhelming bacterial diversity present in the world’s oceans, the majority
of marine bacteria can not be easily cultivated in the laboratory (Giovannoni & Rappé 2000).
The discrepancy between direct microscopic enumeration and plate counts of bacteria was rst
pointed by Jannasch & Jones (1959). They attributed it to the presence of bacteria in aggregates, to
selective effects of the used media, and to the presence of inactive cells. Since values estimated by
epi uorescence direct counts are orders of magnitude higher than those estimated by plate counts (the
so-called CFU or colony forming units), this has been referred to as “the great plate count anomaly”
(Staley & Konopka 1985). Comparison of direct cell counts and classic cultivation approaches, have
shown that only a minor fraction, <1% of total bacterial numbers, can be cultivated in common media
(Staley & Konopka 1985)
In contrast, based on DNA-DNA hybridation of the genomic DNAs of isolates obtained with the
traditional ZoBell medium run against community DNA, it has been suggested that readily culturable
bacteria can be abundant in the marine water column (Rehnstam et al. 1993, González et al. 1996,
González & Moran 1997, Pinhassi et al. 1997).
The great challenge in microbial ecology is to relate diversity to the ecological function and
biogeochemical activities of speci c bacteria (Hamasaki et al. 2007). To correlate the activity of
speci c microorganisms with de ned environmental or physiological parameters
Among the different ways for associating diversity and functioning, one possibility is the isolation
of relevant members of the assemblage and their study in the laboratory. If we know that a given
organism does a given function, we might be able to search for its presence to better know about
that linkage. But currently, the general opinion is that the isolates tend not to be representatives of
the indigenous bacteria. Lack of knowledge about the dominant uncultured microorganisms can be
attributed directly to their low cultivability by standard microbiological techniques.
194
Annexes
Progress has been made in recent years at cultivating members of the numerically abundant
clades; for example, the SAR11 clade using sterile marine water supplemented with either phosphate,
ammonium and de ned mixture of organic carbon compounds (Rappé et al. 2002), the marine group
I Crenarchaeota was grown aerobically at 28ºC in Synthetic Crenarchaeota Media amended with
autoclaved vitamin solution, KH2PO
4 solution, selenite-tungstate solution, bicarbonate solution
and ammonium chloride per litre of media (Konneke et al. 2005) and the OM43 clade (Connon &
Giovannoni, 2002) were obtained in pure cultures. Cultivation allows a comprehensive physiological
characterization of relevant members of bacterial communities. Therefore, is important to complement
the culture-independent ndings of the microbial communities in aquatic and other environments
(Stevens et al. 2005).
The interest on the characterization of new species is increasing, particularly since it is now possible
to know their genomes through initiatives such as the Gordon & Betty Moore Foundation Marine
Microbial Initiative. Knowing genomic composition allows us to determine the function of microbial
organisms in speci c environments. Rush et al. (2007) published part of the Sorcereer II Global
Ocean Sampling Expedition and suggested the utility of having isolates from a well characterized
biogeochemical site to be used as scafolds for reconstruction of the genomes.
Using traditional cultivation techniques we obtained isolates collected offshore at the Blanes Bay
Microbial Observatory (32º 80’N 34º 93’E) Some of these isolates were described as novel. Of some,
whole-genome sequencing was carried out by the J.Craig Venter Institute (USA) through the Gordon
and Betty Moore Foundation initiative in Marine Microbiology (https://www.moore.org/marine-
micro.aspx).
195
Annexes
A.- PROTOCOL OF ISOLATION
1.- Petri dish preparation
- 4 g of Peptone (Bacto TM Peptone DIFCO 211677)
- 0.8 g of yeast extract (Bacto Yeast Extract DIFCO 212750)
- 12.5 g of Agar (Bacto TM Agar DIFCO 214010)
- Add 600 ml seawater ltrated by 0.2 μm (sterivex Millipore SVBV010RS
or Membrane lters GVWP02500 or GVWP04700)
- Add 200 ml of sterile Milli Q water
- Mix until is dissolved (with a magnetic stirrer)
- Autoclave 20 minutes at 121ºC (1 kg/cm2)
- Mix the bottle and autoclave again 20 minutes
- Let it stand until is warm and can be touched
1.1-Prepare Petri dishes
- Leave the Petri dishes in the laminar ux chamber for 30 minutes with ultraviolet light on
- Dispense the liquid into the Petri dishes (40 ml)
- Close the plates
- Leave for 24 h to solidify
- Turn upside-down the Petri dishes (To prevent agar from drying too much)
- Leave standing for 2 days (To completely dry out)
- Close each Petri dish with para lm. (To avoid any contamination)
- Label them with the preparation date.
196
Annexes
1.2- Spread out sample onto the Petri dishes
- Prepare different dilutions of the sample (with sterile seawater): 1x, 10x and 100x
- Add 100 μl with a pipette and spread with a digralsky spreader
- Leave the Petri dishes growing for one week at in situ temperature.
1.3- Isolation of the colonies
- Pick the colonies (with an inoculating loop) and spread them in a new Petri dish.
- Divide the Petri dish in four slices
- Spread each colony in one square of the Petri dish.
- Label each square with the number or name of colony and date of the inoculation.
Fig.1.- Colony forming bacteria from Blanes Bay growing on agar plates.
197
Annexes
1.4- Growth in liquid media
Preparation of 1 L of the Zobell liquid media:
- 4 g of Peptone
- 0.8 g of yeast extract
- Add 600 ml seawater ltrated by 0.2 μm
- Add 200 ml of sterile MilliQ water
- Mix until everything is dissolved
- Dispense 20 ml in 100 ml sterile polycarbonate Nalgene bottles.
- Autoclave for 20 minutes at 121ºC
- Pick the chosen colony from the Petri dish
- Place it in the liquid media
- Leave one or two days until the liquid is opaque.
1.5- DNA collection
- Place all the liquid media in a 15 ml centrifuge tube (DELTALAB) and centrifugate for 10
minutes at 4500 rpm
- Remove the supernatant with a pipette.
- Maintain the pellet at -80ºC
1.6- Check for contamination
- Take 15 μl of the liquid media and spread them on a Petri dish.
- When the drop is dry, spread in the Petri dish with an inoculation loop.
- If the growing colonies are only of one type we can presume that there is no contamination.
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Annexes
1.7- Glicerination
- Mix 900 μl of sterile glycerol (SIGMA G5516) at 50% with 900 μl of the liquid media
- Keep at –80ºC.
Fig.2.- Two different strains growing on agar plates.
199
Annexes
ANNEX II:
Scanning Electronic Microscopy (SEM) images of bacterial
cultures
An electron microscope is a type of microscope that uses an electron beam to produce and enlarged
an image. Electron microscopes have much greater resolving power and can obtain much higher
magni cations than light microscopes. The two important contributions to electronic microscopy
were the Broglie (1924) theory about the wave proprieties of electron movement and the Bush (1926)
discovery of the magnetic or electrostatic elds.
There are two kinds of electron microscopes: the transmission (TEM) and scanning (SEM), or the
combination of both (METB). TEM has to be used in ultra-thin cuts (from 10 to 200 nm) depending
on the sample. The electrons pass through the sample to form the image. The electron beam scan
over the surface, and the signals produced include secondary electrons, backscattered electrons,
characteristic X rays, Auger electrons, and photons of various energies. The interactions that the
beam electrons undergo in a specimen reveal information on the specimen’s composition, topography,
crystallography and other properties.
Samples preparation is a critical process. The main steps are, the selection of the sample, cleaning,
xation, deshydratation, placement on the supports and coverage with a conductive product or a
metal. All these have to be gently done to avoid the cells to shrink or to break in their surface.
200
Annexes
2- PROTOCOL SEM
2.1- Sample preparation
- Counts of bacteria should not exceed 10 6 cells ml-1
- Prepare different dilutions of the liquid media: 1x, 10x and 100x in 10 ml
2.2- Sample xation
- Add Glutaraldehyde at 2-3% nal concentration to 10 ml of the different dilutions.
- Leave xing during 2-4 h.
2.3- Poli-L-Lysine treatment: to adhere the sample to the lter
- Cover the lter with a drop of Poli-L-Lysine 0.1% w/v solution (SIGMA)
- Wait 1 minute and submerge the lters in Milli Q water for 30 seconds.
2.4- Filtration
Filtrate the samples with 0.2 μm polycarbonate lter previously prepared with Poly-L-Lysine
(Millipore).
After the ltration, place the lters in a metal net capsule to avoid coiling of the lter.
2.5- Dehydratation
Deshydratate in an alcoholic series with Ethanol and esterile MilliQ water:
Leave for 15 minutes at 30%, 15 min. at 50 %, 15 min at 70 %, 15 min at 80 %, 15 minutes at
90 % (two times) and 15 minutes at 100 % (two times)
2.6- Dry by critical point
Critical point dryer (BAL-TEC, CPD 0309). is used to end the dry process without any surface tension on the samples.
1º- Place the sample in a cage of the dryer.2º- Add ethanol just to cover the sample 3º- Lower temperature until 4-7ºC4º- Fill the rest of the cage with liquid CO
2
5º-Remove half of the total liquid every 15 minutes around 7-8 times until is all CO2
6º- Increase temperature until 37ºC to avoid liquifying of the CO2.( The critical point of CO
2 is
reached at a temperature of 31ºC and a pressure of 73,8 bar)7º- Decrease pressure 6 bar per minute to avoid liquifying of the CO
2
201
Annexes
Images of Blanes Bay Isolates: (check Chapter 1 - Table 4 for details of the strains).
Fig.1 M134 : Dokdonia donghensis
Fig.2 M193: Phaeobacter sp.
Fig3 M222: Vibrio sp.
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Annexes
Fig.4 M121: Marinomonas blandensis
Fig.5 M217: Leeuwenhoekiella blandensis
Pinhassi P, Bowman JP, Nedashkovskaya OI, Lekunberri I, Gomez-Consarnau L & Pedrós-Alió C (2006). Leeuwenhoekiella blandensis, sp.nov., a novel marine member of the family Flavobacteriaceae. Inter J Syst Evol Microb 56: 1489-1493.
Pedrós-Alió, C. 2007. Dipping into the rare biosphere. Science 315:192-193
203
Annexes
Fig.6 M297: Reinekea blandensis
Pinhassi J, Pujalte MJ, Macián MC, Lekunberri I, González JM, Pedros-Alió C & Arahal DR (2007). Reinekea blandensis sp. nov., a marine genome-sequenced gammaproteobacterium. Inter J Syst and Evol Microb 57: 2370-2375
Fig.7 M152: Polaribacter dokdonensis
González JM, Fernández-Gómez B, Fernàndez-Guerra A, Sánchez O, Gómez-Consarnau L, Coll-Lladó M, del Campo J, Escudero L, Rodríguez-Martínez R, Alonso-Sáez L, Latasa M, Paulsen I, Nedashkovskaya O, Lekunberri I, Pinhassi J, & Pedrós-Alió C (2008) Genome analysis of the proteorhodopsin-containing marine bacterium Polaribacter sp. MED152 (Flavobacteria): a tale of two environments. PNAS doi: 10.1073/pnas.0712027105
204
Annexes
Fig.8 RED 65:Bermanella marisrubri
Pinhassi J, Pujalte MJ, Pascual J, González JM, Lekunberri I, Pedrós-Alió C & Arahal DR (2008) Bermanella marisrubri gen. nov., sp. nov., a novel genome-sequenced Gammaproteobacterium from the Red Sea. Inter J Syst Evol Microb (in press)
Fig.9 SCB36 Winogradskyella sp.
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Annexes
Fig.10 MED92: Neuptuniibacter caesariensi
Arahal DA, Lekunberri I, Gonzalez JM, Pascual J, Pujalte MJ, Pedrós-Alió C, & Pinhassi J (2007) Neuptuniibacter caesariensis gen. nov., sp. nov., a novel marine genome-sequenced gammaproteobacterium.Inter J Syst Evol Microb 57: 1000-1006
209
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Aunque aún no me lo crea parece que ya llego el nal de esta tesis, donde queda re ejado el trabajo que he conseguido realizar gracias al apoyo y ayuda de tantísima gente que día a día han estado ahí para compartir cada momento y ofrecerme la mejor de sus sonrisas, pero también gracias a esa otra parte de mi que a pesar de la distancia siempre han estado tan cerca.
Primero de todo agradecer a mi director de tesis sin quién no habría sido posible este trabajo. Gracias por haberme apoyado desde el primer momento en el que me presenté en tu despacho (con un día de plazo para pedir la beca) y por todo lo que me has enseñado. Sobretodo gracias por tener esa actitud positiva para afrontar cada problema que tanto me ha motivado para realizar mi trabajo. Muchísimas gracias por darme la oportunidad de conocer el mar (tan lejano para mí) desde su punto de vista más pequeñito hasta el más grande en las campañas oceanográ cas. Mil gracias en este último periodo por todo el esfuerzo dedicado.
Mis primeros pasitos en la biología molecular fueron dados de la mano de Jarone, a quien agradezco enormemente que me transmitiera su gran pasión por la ciencia, pero también agradezco haber sido siempre tan cercano para mí. Muchas gracias por valorar siempre mi trabajo y seguir mis pasos aún desde Suecia.
En esta tesis han sido cruciales los consejos y correcciones de Carles, muchas gracias por toda la ayuda. A Silvia Acinas agradezco muy especialmente la ayuda y el toque tan especial que dio a mi trabajo. A Emilio por transmitirme sus conocimientos de ARB. A Jose González por contar con mi colaboración y por los varios favores de última hora. A Dolors por los días tan divertidos en Vigo y las aventurillas varias que lo rodearon (con parada en Lerma) y por irradiar siempre alegría. Mila esker horrela izateagatik! A Cèlia por los animos y ayuda especialmente esta última temporada. A Ramon, Cesc y Rafel por sus ánimos y comentarios. Y a todos los que habéis conseguido transmitir tan buen ambiente a todas las generaciones, mil gracias por hacer del ICM un lugar tan especial, de donde cuesta tanto marchar!
Muy especialmente quiero dar las gracias a Andrés que ha sido mi compañero en este viaje desde su inicio, sobre todo en los últimos años, con él que he compartido el estrés, la alegría, las penas, el hambre, el sueño... cada día. Como darte las gracias por haber sido siempre tan compresivo, animarme incluso en tus peores momentos y intentar hacerme ver los problemas de otra manera. Mil gracias por ser el mejor compañero y un gran amigo. Askenian lortu dugu! Orain ospatzeko.. Argentinara!
A Laura Gómez quiero agradecer su gran ayuda en todo momento y disponibilidad. Muchísimas gracias por darme tan buenos consejos y por estar ahí en momentos difíciles y transmitirme tranquilidad. Aunque te marcharas a Suecia (no te lo tendré en cuenta) ha sido un lujo que siempre consiguiéramos el encuentro. Quedan muchos otros!
Gracias a mis chicas por haber compartido conmigo las mejores sobremesas y hacer que volviese mi sonrisa incluso en la peor de las situaciones. A Vane por dar un toque tan especial a cada momento y conseguir sacarme de la monotonía, a Irene por transmitirme siempre mucha energía y optimismo, a Clarita por tantos y tantos favores y por haberme entendido siempre, a Dacha por haber cuidado también de sus niños, a la María que aunque ya marchó siempre alegró nuestras comidas, a Pilar
AGRADECIMIENTOS
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por su agradable compañía y por su puesto no se me olvida el chico Javier que siempre tiene los comentarios más oportunos y me hace reír tanto. A todas y cada uno gracias por tan buenos consejos y por haber compartido conmigo los mejores momentos en el Instituto.
Gracias a Laurita por todo lo que me has enseñado y todo lo que hemos compartido juntas(desde jefe, viajes a piso) y espero que algún día nos volvamos a encontrar, quien sabe quizá Plentzian?
A Imelda que (aunque de Burgos) siempre consiguió hacerme reír y que tanto la eché de menos cuando se marcho, pero a pesar de todo siempre estuvo muy cerquita. Aún nos queda mucho por celebrar!!
A Fer que cuando estaba en Barcelona tanto me ayudo incluso acogiéndome en su casa, que también consiguió hacer más amena mi estancia en Holanda. Preparate boludo que en breve nos volveremos a ver!
A Pati por todos esos momentos compartidos y las interminables charlas que durante este último año tanto me ayudaron.
A Georgios por descubrirme lo fascinante de las profundidades del mar y estar siempre dispuesto a ayudarme. Gracias por dejarme el legado de enseñar el magni co programa de enmaquetación. A Cris por transmitirme siempre alegría y cuidar de mÍ esta última temporada y Lorenzo por sacarme siempre una sonrisa. A mis compis de piso Meri e Inma quienes siempre me hicieron reír. A Ale y Vero por la celebración tan especial del txupinazo en las calles de la Barceloneta. A mis venios italianos Ida y Danielle por las estupendas cenas compartidas.
A Jose Manuel sin quien no habrían dado lugar esas maravillosas fotos del SEM (a pesar de encontrar tan aburridas a las bacterias) Olga por su ayuda en molecular, a Enric por sus consejos de estadística, a Mickhail por su ayuda con las conversión de unidades, a Silvia también por su ayuda con la estadística, a Hugo por resolverme tantas dudas y a muchos otros que en momentos cruciales me echaron un cable. A Susana y Vicky las estudiantes que estuvieron de prácticas y tanto me ayudaron .
El trabajo que se realizó en Vigo fue gracias a la colaboración de mucha gente, gracias a todos por los esfuerzos que hicieron posible que saliera a delante, muy especialmente a Paco y Pepe por todas las sugerencias y ayuda. A todo el resto de gente involucrada Luisa, Belen, Mar, Isabel, Oscar, Pili, Eva, Jose, Maria y muchos otros. Por supuesto fue indispensable la ayuda de los asturianos expertos en citometría Xelu y Ale, muchas gracias a los dos por lo bien que lo pasamos juntos y por haber estado ahí en cada momento para lo que fuera.
A toda la gente que me ha ayudado en mi estancia en Holanda. Eva y Vero que tan bien me cuidaron y hicieron mis estancias más divertidas y emocionantes con la colaboración el de Bilbo y Goa. A mi compañero de bungalow Pedro que consiguió que cada día fuera un aventura en la pequeña isla. A Txetxu que justo marchaba cuando llegue y con quien pasé ratos tan divertidos, gracias lo compartido en Texel y en el Hesperides. Special thanks to Gerhard for all the help in my stays, for looking after me so well in any situation, promise next time I will not drive in Texel!
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En mi estancia en Kalmar agradecer muy especialmente la hospitalidad de Neus y Jarone y a Nuria y la perrita: Twiggy que tanta compañía me hizo. A Anna por llevarme a conocer un Julefrokost en un granja donde conocí la esencia de la sociedad escandinava.
De las campañas realizadas quisiera agradecer a todos mis compañeros con los que compartí esta aventura tan especial para mí. Especialmente a Javier, Fede y Ale que los volví un poco loquitos con mis experimentos y a Nahiara, Eva, Nacho, Marc, Xavi, Ramón, Kim...mil gracias por tantas risas compartidas. También quiero agradecer al O cial Alfonso que tan bien me cuido.
A toda la gente que siempre ha tenido una sonrisa que ofrecer y tan bien me han hecho sentir, algunos que ya marcharon, algunas nuevas incorporaciones y a los de siempre MIL GRACIAS: Violeta, Gemma Oscar, Mariona, Ana Sabata, Estela, Hugo, Montse Sala, Albert René, Silvia Anglés, Nagore, Laura Arin, Elisa, Nuria Gómez, Sofía, Sonia, Ester, Montse, Raquel, Lorena, Bea, Elisabet, Arantza, , Clara, Juancho, Thomas, Ero, Mireia, Bego, Lidia, Andrea, Jordi, Xavi Leal, Rodrigo, Julia, Evarist, Albert Calbet, Eva Calvo, Carles, Eva Flo, Enrique Isla, Josep Maria, Cristina Linares, Cova, Sergi Rossi, Elisabeta...
Agradecer a Sara por toda la ayuda que me prestó para que viniese a Barcelona y a Jordi Dach por sus consejos de venir al ICM.
Toda la gente que estuvieron ahí pero más alejados de la ciencia:
A Antonia por buscarme el mejor hogar (“Parallel”) donde encontré una gran familia, y tuve un inversión total en la cultura catalana, gracias a todos y muy especialmente a Miquel, Pau, Jordi, Cunci y Serrano, por haber cuidado también de vuestra “chiqueta” en sus primeras andanzas por la gran ciudad, y por esas riquísimas paellas, arroz al forn y por todos los rincones que descubrimos juntos de Barcelona. Muy especialmente a Alfred por todo su cariño y apoyo en los inicios de esta aventura.
A Blanca y a Mateo por contar conmigo y entenderme, y a todos los del Bitacora, especialmente al Arnau y Juantxo.
A toda la cuadrrrrrrilla de Iruña: Maite, Izaskun, Sandra, Eva, Isa y Nerea, que han seguido toda la vida mis pasos día a día, sin perder detalle, para las que nunca la distancia fue un problema. Gracias por todas la visitas y por la amistad incondicional. Por ese sentimiento de cuadrilla que siempre nos ha mantenido juntas. Mila esker por todo lo vivido por esperar siempre son ansia mi llegada. A Maitetxu especialmente por su gran imaginación, sus buenos consejos de diseño y por las largas conversaciones de Skype que tanto me han ayudado. A todas gracias por escucharme siempre y hacerme sentir tan especial. Mil gracias por pedir que Sanfermin me echara un capote y conseguirlo! A pesar de que no veáis llegar el día de mi vuelta: Benetan laster itzuliko naiz!
A mis primicos: Mikel, Edurne, Amaia, Mirentxu, Lander, Iosu por venirme a visitar con tanta ilusión, por animarme siempre, por tantas conversaciones con las que hemos intentado arreglar el mundo, por
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hacer que el hecho de estar juntos fuera tan especial. Por incluso alguno veniros a vivir a Barcelona y estar ahí siempre que ha hecho falta.
A mi tío Joaquim que rápidamente captó mi idea, muchas gracias por el diseño.
A Maria que durante todos estos años (y muchos más) estuviera donde estuviera nunca se separó de mí, que siempre me entendió. Por esos ratos compartidos en Leoz, Barcelona, Etxaleku... y por supuesto a Nuria muduaren onen amatxoa! Por comprenderme y mostrarme la vida de otra manera.
A Arantza que siempre estuvo ahí me fuera donde me fuera mil gracias por la amistad y con anza de saber que puedo contar contigo. Gracias a Dani quien me animó a salir de la vieja Iruña, a Felix y Amaia que fueron mi familia danesa y me animaron a venir. A Unai: dudarik gabe lagundu zenidan urte asko pasa izan arren, mila esker denagatik!
A toda la gente de Iruña que cada vez que voy me reciben con tanta ilusión Jon, Maite Aginaga, David, Monica, Mikel Goñi, Mikel de Goñi, Iosu, Maider, Eloisa...y muy especialmente a Aritz por todo el cariño.
Finalmente agradecer a mis padres y Maitanica que siempre con aron en mí y me dieron las energías, la tranquilidad y el cariño incondicional que me ha motivado cada día.
MUCHAS GRACIAS A TODOS!!!MOLTES GRACIES A TOTS!!!MILA ESKER DENORI!!!THANKS TO EVERYONE!!!