Upload
others
View
1
Download
0
Embed Size (px)
Citation preview
Ben-Gurion University of the Negev The Jacob Blaustein Institutes for Desert Research
The Albert Katz International School for Desert Studies
Effect of heavy rainfall on desert soil bacterial community
composition and dynamics
Thesis submitted in partial fulfillment of the requirements for the degree of "Master
of Science"
By: Ani Azatyan
October, 2013
Ben-Gurion University of the Negev The Jacob Blaustein Institutes for Desert Research The Albert Katz International School for Desert Studies
Effect of heavy rainfall on desert soil bacterial community
composition and dynamics
Thesis submitted in partial fulfillment of the requirements for the degree of "Master
of Science"
By Ani Azatyan
Under the Supervision of Osnat Gillor
Department of Zuckerberg Institute for Water Research
Blaustein Institures for Desert Research
Ben Gurion University of the Negev
Author's Signature ……………….………………....… Date …………….
Approved by the Supervisor…………….……….….… Date …………….
Approved by the Director of the School ……………… Date ………….…
I
Abstract
The dogma in arid regions is that water strongly limits primary productivity and
therefore, arid ecosystems exhibit a pulse-dynamic response to rainfall, in which rain events
offer brief pulses of resource availability that can exert a strong influence on productivity
and function of plant and animal communities. However, the abundance, diversity and
structure of soil bacterial communities in arid ecosystems were seldom studied. It was
shown that soil bacteria in general are strongly influenced by soil temperature, moisture,
salinity, pH, or combinations of these parameters. In addition, seasonal variations of the
edaphic parameters often result in changes in the bacterial community structure, especially
in dry ecosystems. Investigations of temporal patterns of soil bacterial diversity in arid
environments, demonstrated that heavy rainfall is an important determinant of bacterial
activity variation, but overall changes in abundance and diversity were not elucidated. It is
not clear whether microorganisms in arid environments respond to rainfall events similarly
to macroorganisms. To answer this question the diversity and abundance of active bacterial
communities were studied in hot desert soil microcosms by closely following drought-rain-
drought cycles. The aim was to elucidate the bacterial response to the amount of rain (50
mm and 10 mm), the incubation temperature (25°C and 36°C), and diurnal cycles
(36/10°C) assuming that these parameters coalesce with the desiccation rate that will in turn
affect the bacterial community.
To that end, barren desert soil (directly below the crust) was collected and packed
into columns designed and constructed for the purpose of these experiments. Triplicate soil
columns were equipped with rain and drain simulators and were operated for one month
during each experiment. At constant intervals the soil was sampled from the columns for
II
physicochemical (including pH, salinity, water content, nitrite, nitrate and total carbon) and
bacterial analyses. The abundance, diversity and community composition of the bacterial
community were explored, with special focus on the dominant Actinobacteria phylum.
With an exception of water content, no major differences were observed in the
physicochemical parameters of the soil incubated under different rain regimes, temperature
or diurnal cycles. Interestingly, the biomass and diversity of the bacterial communities
including the Actinobacteria phylum, were unchanged under the various treatments.
However, the community composition was significantly altered within and between
experiments: the rain and temperature alterations yielded significantly different soil
bacterial communities, yet, diurnal cycles inflicted insignificant changes in the soil column
communities.
The obtained results suggest that unlike macroorganisms soil bacterial diversity and
abundance in hot desert environments are unaltered by hydration-desiccation cycles.
However, the community composition is markedly changed on a temporal scale following
rainfall and desiccation. Moreover, the community changes seem to be due to the amount of
rain and the desiccation temperature but not the diurnal cycles.
III
Acknowledgments
I want to express my deepest gratitude and appreciation to my supervisor Dr. Osnat
Gillor assisting and guiding me throughout my research project and the opportunities she
gave me to develop my skills and knowledge in molecular biology and environmental
microbiology. Thanks to Dr. Ines Soares for her generous support, and valuable advises. I
would like to thank also my student colleges and technicians in the Environmental
Hydrology and Microbiology Department for their help in the laboratory. My special
thanks to Lusine Ghazaryan for helping in the lab and introducing me to the technical skills
of molecular biology, and to Dr. Menachem Sklartz for his advises on statistical analyses.
I am thankful to Elena Morozovsky, secretary of ZIWR and Dorit Levin, assistant to
the AKIS Director, for their kind help and patience.
I would like to express my gratitude to the Albert Katz International School (AKIS)
for Desert Studies, and the Department of Environmental Hydrology and Microbiology, for
the financial support and for supplying me with the highest level of infrastructure and
technical facilities, and to everyone who helped me in the process of conducting my studies
and research.
And finally, my gratefulness to my family members and friends for giving me
strength and encouragement in each step of my life.
IV
Table of contents
Chapter 1. Introduction
1.1. Scientific background............................................................................................ 1
1.1.1. Bacterial activity and diversity in soil................................................................ 1
1.1.2. Bacterial diversity and abundance in arid soil.................................................... 2
1.1.3. Moisture effect on bacterial activity in arid soil................................................. 4
1.1.4. Temperature effect on bacterial growth and activity.......................................... 7
1.1.5. Actinobacteria..................................................................................................... 8
1.1.6. Firmicutes......................................................................................................... 10
Chapter 2. Research hypotheses and objectives................................................................ 12
Chapter 3. Materials and methods
3.1. Soil collecting from the field............................................................................... 13
3.2. Experimental setup.............................................................................................. 13
3.3. Soil sampling from the columns.......................................................................... 14
3.4. Soil physicochemical analyses............................................................................ 16
3.5. RNA extraction and cDNA synthesis.................................................................. 16
3.6. Bacterial abundance............................................................................................. 17
3.6.1. qPCR data analysis........................................................................................... 19
3.7. Community composition analysis……................................................................ 20
3.7.1. Terminal restriction fragment length polymorphism (T-RFLP) analysis......... 20
3.7.2. T-RFLP data analysis....................................................................................... 22
3.7.2.1. Shannon-Weaver species diversity index analysis........................................ 22
3.7.2.2. Stacked bar graphs......................................................................................... 23
V
3.7.2.3. Nonmetric multidimensional scaling............................................................. 23
3.7.2.4. Dissimilarity analysis..................................................................................... 25
Chapter 4. Results
4.1. Soil physicochemical characteristics during hydration-desiccation
experiments................................................................................................................. 26
4.2. Temporal dynamics of bacterial abundance during hydration-desiccation
experiments................................................................................................................. 29
4.3. Temporal dynamics of bacterial diversity during hydration-desiccation
experiments................................................................................................................. 31
4.4. Temporal dynamics in microbial community composition during hydration-
desiccation experiments.............................................................................................. 32
4.4.1. Bacterial community composition changes within experiments…………...... 32
4.4.2. Bacterial community composition changes between experiments ………...... 36
Chapter 5. Discussion
5.1. Soil physicochemical characteristics during hydration and desiccation.............. 42
5.2. Temporal dynamics of bacterial abundance during hydration and desiccation... 43
5.3. Temporal dynamics of bacterial diversity during hydration and desiccation...... 45
5.4. Temporal dynamics of bacterial community composition during hydration and
desiccation.................................................................................................................. 46
Chapter 6.
Conclusions................................................................................................................. 48
Future work................................................................................................................. 49
Chapter 7.
References....................................................................................................................50
VI
Supplementary data...................................................................................................65
Appendix 1. Recipe for RNAlater - RNA preservation medium..............................73
Appendix 2. Euclidean distance of the T-RFLP profiles of the samples..................74
VII
List of tables and figures
Table 1. Conditions applied to the soil columns and estimated average water loss during
the experiments ………….……................................................................................. 15
Table 2. Primers used in the study………….…................................................................ 18
Table 3. One-way ANOVA analysis of main soil physicochemical parameters upon
hydration-desiccation ………….…............................................................................ 26
Table 4. Main soil physicochemical parameters in all experiments.................................. 28
Table 5. Kruskal-Wallis non parametric test of the temporal changes in total abundance
of Bacteria, Actinobacteria and Firmicutes in soils under all treatments................... 30
Table 6. ANOVA test of the Shannon-Weaver diversity indices...................................... 31
Table 7. MRPP pairwise test between different experimental conditions......................... 38
Figure 1. Experimental setup............................................................................................. 15
Figure 2. Water content in soil columns incubated under different experimental
conditions.................................................................................................................... 29
Figure 3. Community size dynamics of Bacteria, Actinobacteria and Firmicutes upon
soil hydration and desiccation.................................................................................... 30
Figure 4. Shannon-Weaver diversity indices of Bacteria and Actinobacteria using
restriction enzymes TaqI and HapII, respectively...................................................... 32
Figure 5. Bacterial community composition dynamics within time in different
microcosms experiments.................................................................................................... 34
VIII
Figure 6. Actinobacterial community composition dynamics within time in different
microcosms
experiments................................................................................................................. 35
Figure 7. Non-metric multidimensional scaling (NMDS) ordinations of active Bacteria
communities upon hydration-desiccation................................................................... 37
Figure 8. Non-metric multidimensional scaling (NMDS) ordinations of active soil
Actinobacteria communities upon hydration and desiccation.................................... 40
Supplementary Table 1. MRPP pairwise test between the samples collected before and
after treatments........................................................................................................... 65
Supplementary Table 2. MRPP pairwise test between the different experimental
conditions.................................................................................................................... 65
Supplementary Figure 1. Soil WC (%), pH and EC (ds/m) at different sampling
depths.......................................................................................................................... 66
Supplementary Figure 2. Shannon-Weaver diversity indices of Bacteria and
Actinobacteria using restriction enzumes HpyCH4IV and HhaI, respectively.......... 67
Supplementary Figure 3. Non-metric multidimensional scaling (NMDS) ordinations of
bacterial and actinobacterial community changes before and after the rain events.... 68
Supplementary Figure 4. Bacterial community composition dynamics within time in
different microcosms experiments.............................................................................. 69
Supplementary Figure 5. Actinobacterial community composition dynamics within time
in different microcosms experiments.......................................................................... 70
Supplementary Figure 6. Non-metric multidimensional scaling (NMDS) ordinations of
active Bacteria communities upon soil hydration and desiccation............................. 71
IX
Supplementary Figure 7. Non-metric multidimensional scaling (NMDS) ordinations of
active Actinobacteria communities upon soil hydration and desiccation................... 72
X
List of abbreviations
ANOVA: Analysis of variance
cDNA: Complementary deoxyribonucleic acid
DDW: Double-distilled water
DNA: Desoxyribonucleic acid
DNTP: Deoxynucleotide triphosphate
EC: Electrical conductivity
HCL: Hydrogen chloride
KCL: Kalium chloride
LTER: Long term ecological research
MDS: Multidimensional scaling
MRPP: Multiresponse permutation procedure
NMDS: Nonmetric multidimensional scaling
OTU: Operational taxonomic unit
PCR: Polymerase chain reaction
PVC: Polyvinyl chloride
qPCR: Quantitative polymerase chain reaction
RDP: Ribosomal database
REs: Restriction enzymes
RNA: Ribosomal nucleic acid
SD: Standard deviation
SILVA: Comprehensive ribosomal RNA database
TC: Total carbon
T-RFLP: Terminal restriction fragment length polymorphism
T-RFs: Terminal restriction fragments
WC: Water content
ZIWR: Zuckerberg Institute for Water Research
1
Chapter 1. Introduction
1.1. Scientific background
1.1.1. Bacterial activity and diversity in soil
Prokaryotic organisms comprise a substantial proportion of Earth’s biota. Microbial
biomass carbon represents 0.6-1.1% of soil organic carbon and 1-20% of total plant
biomass carbon (Fierer et al., 2009). Prokaryotes contain about 10-fold more nitrogen and
phosphorus than plants, thus presenting the largest pool of macro-nutrients (Whitman et al.,
1998). The ability of prokaryotes to act as source-sink of nutrient cycling processes and as
regulators of organic matter transformations may determine many food webs structure and
function (Jordan et al., 1991; Vishnevetsky et al., 1997; Placella et al., 2012).
Bacterial diversity is of major interest due to the involvement of microorganisms in
biogeochemical cycles. Knowledge about bacterial community structure and diversity is
essential for understanding the relationship between environmental factors and the function
of ecosystems. The functional and genetic potential of microorganisms may exceed that of
higher organisms and provide a valuable source for novel products and technologies.
Despite their importance, at the present our knowledge is limited concerning the role of
bacteria in most ecosystems (Torsvik et al., 1996; Colwell, 1997; Hughes et al., 2001; Kirk
et al., 2004; Koeppel et al., 2013)
Soil is one of the most complex and ubiquitous habitats for microorganisms and is
considered to be a major reservoir of organic carbon. It has been estimated that the diversity
of prokaryotes in soil is orders of magnitude greater than in marine environments (Curtis et
al., 2002). Phylogenetic-based studies across different terrestrial biomes (from tropical
forest to desert) have shown that the general structure of bacterial communities was mostly
2
dominated by the same bacterial phyla - Acidobacteria, Actinobacteria, Proteobacteria and
Bacteroidetes (Fierer et al., 2009). Although these different biomes comprised similar
bacterial communities, the relative abundance of these groups varied, which could be
explained mostly by the soil pH (Fierer and Jackson, 2006; Fierer et al., 2009).
1.1.2. Bacterial diversity and abundance in arid soil
About a third of Earth's terrestrial environment is arid (mean annual precipitation <
250 mm), yet the bacterial communities of these ecosystems have not been thoroughly
studied. In the past decade studies conducted on the bacterial ecology of arid soil focused
on archiving soil bacteria, trying to determine what rules their diversity and how soil
bacterial communities are affected by specific changes and disturbances of the
environment. It was suggested that the structure of arid soil bacterial communities is
strongly influenced by soil temperature (Gestel et al., 2013), water availability (Williams
and Rice, 2007; Angel and Conrad, 2013; Placella et al., 2012; Barnard et al., 2013), pH
(Fierer and Jackson 2006; Lauber et al., 2009) or a combination of these parameters.
It was shown that bacterial community composition in arid soils is influenced by
fluctuations in temperature (Gestel et al., 2013), precipitation pulses (Huxman et al., 2004),
elevated UV radiation (Mattimore and Battista, 1996; Rainey et al., 2005) and nutrient
limitation (Placella et al., 2012; Gestel et al., 2013). Biotic factors, such as plant abundance
and grazing were also shown to have a role in shaping the composition of soil microbial
communities (Andrew et al., 2012). Yet, understanding of the factors that determine the
relationships between the bacterial community structure and the resources associated with
arid ecosystems is still limited (Zak et al., 2003; Bell et al., 2009). Moreover, in natural
environments these factors are interconnected and correlated, which in turn complicates the
3
uncoupling of these determinants. For example, Chihuahuan desert grassland soil bacterial
communities were responsive to rainfall, soil moisture pulses and subsequent nitrogen
availability during rain events (Bell et al., 2009). Several studies conducted in Israel arid,
semi-arid and Mediterranean sites revealed distinct spatial community clustering of
bacterial and archaeal domains, which abundance was correlated with soil water content
(Bachar et al., 2010; Angel et al., 2010). In semi-arid soils, bacterial and archaeal
community compositions were also affected by vegetation cover but not in arid and
Mediterranean soils (Angel et al., 2010).
Very few studies attempted to uncover the bacterial community structure of bulk
soils. In most of Israel soils Actinobacteria and Protebacteria are the most abundant, but in
arid soils Actinobacteria are dominant. Bulk soils in Israel’s Negev desert, like other arid
soils around the world, are dominated by the Actinobacteria that account for more than
45% of the total bacterial community (Bachar et al., 2010).
In the past, lack of adequate methods presented an obstacle to studies of soil bacterial
communities. The traditional cultivation methods in laboratory conditions studying
culturable strains provide little information on the ecological role and function of soil
bacteria since the vast majority (more than 99%) is unculturable or very difficult to culture
(Sait et al., 2002). In the past decade, molecular methods have become more common in
revealing the ecological characteristics of bacterial communities (Handelsman 2004; Fierer
et al., 2007; Placella et al., 2012; Angel and Conrad, 2013; Angel et al., 2013; Barnard et
al., 2013).
In order to understand the relationships between ecosystem function and bacterial
community structure one should accurately associate bacterial identity with its current
metabolic state. Bacterial studies commonly use the rRNA encoding gene for the
4
description of bacterial diversity in environmental samples, regardless of the
microorganisms’ metabolic state as soil extracted DNA is used as template for the
amplification of the genes. However, the rRNA itself could be used to portray the currently
active portion of microbes (Blazewicz et al., 2013; Barnard et al., 2013; Angel et al., 2013).
The use of the rRNA rather than DNA as template for amplification analysis has its
limitations: Dormant cells can contain high numbers of ribosomes synthesized to serve in
times when rapid response of the bacteria is required (Sukenik et al., 2012); The
relationship of rRNA concentration and growth rate are not always linearly correlated
(Worden and Binder, 2003); The presence of rRNA is indicative of potential protein
synthesis, but is not necessarily realized (Blazewicz et al., 2013); and lastly, rRNA is more
difficult to extract due to high degradability of the single strain rRNA molecule. Still,
analysis of bacterial community’s according to their RNA rather than DNA has potential
advantages as an indicator for characterization of its active members (Blazewicz et al.,
2013; Angel et al., 2013).
1.1.3. Moisture effect on bacterial activity in arid soil
In arid and semi-arid ecosystems plants net primary productivity depends strongly on
both total rainfall (Noy-Meir, 1973) and its variability (Le Houérou et al., 1988). Moreover,
these ecosystems can be affected by changes in the rainfall regime due to its interaction
with other limiting factors such as temperature, nutrient availability, soil texture and depth
(Seligman and van Keulen, 1989; Zaady, 2005). Moreover, plant species in hot desert
ecosystems often exhibit specific mechanisms that reduce vegetation in response to
draught, relying on seed banks to act during rain events (Harel et al., 2011) avoiding
5
demographic effects of reproductive failure (Evans and Cabin, 1995) or changing the
vegetation composition in time of abundance (Golodets et al., 2013).
It has been suggested that rainfall magnitude and frequency are likely to exert a
considerable influence on the physiological ecology and survivorship of biocrust mosses,
cyanobacteria and lichens (Pringoult and Garcia-Pichel, 2004; Coe et al., 2012; Rajeev et
al., 2013). Rainfall magnitude is typically a driver of resource availability; growth and
biomass increase in pulse-dynamic systems. Similarly, rainfall frequency is likely to
influence C balance because the timing of events is directly related to length of desiccation
period between events, which is probably related to recovery cost (Coe et al., 2012).
In microbial communities moisture is believed to be one of the main limiting factors
in arid soils, restraining growth to precipitation events (Waksman et al., 1931; Wen et al.,
2006; Placella et al., 2012; Barnard et al., 2013). Changes in soil water content were
suggested to impact the structure and physiology of soil bacterial communities by
influencing the osmotic potential (Kieft et al., 1987; Halverson et al., 2000; Placella et al.,
2012), nutrients transport and availability in the soil and cellular metabolism (Harris et al.,
1981; Williams et al., 2007), competitive interactions between microorganisms (Barnard et
al., 2013), and even by enhancing bacterial motility (Pringault et al., 2004; Rajeev et al.,
2013). Rain events in arid ecosystems may boost the metabolic activity of soil microbes
triggering mineralization processes that result in accumulation of nutrients, which
subsequently enhance the activity of soil macroorganisms (Huxman et al., 2004; Okoro et
al., 2009). In the Chihuahuan desert changes in soil moisture, coupled with changes in soil
temperatures and resource availability, were suggested to drive the soil-bacterial dynamics
(Bell et al., 2009). In this environment, drought and subsequent precipitation events
resulted in short-term changes in the abundance of the soil bacterial community, as
6
indicated by the slightly higher total bacterial and actinobacterial fatty acid methyl ester
levels detected in the winter compared to summer samples (Clark et al., 2009). The
diversity patterns of Bacteria in soil collected along Israel’s precipitation gradient (ranging
from 100 to 900 mm of annual rain) were constrained by precipitation and vegetation cover
(Angel et al., 2010), and the bacterial biomass in the arid soils was significantly lower
(Bachar et al., 2010).
A number of studies focused on the effects of moisture in desert soil crusts. Crusts
from the Las Bardenas Reales (Spain) desert region clearly showed evidence of
cyanobacterial migration (towards moisture) caused by change of water content, even in
dark conditions (Pringault et al., 2004). After a simulation of a rainfall event hydrating soil
crusts collected from different desert regions in the USA a rapid recovery of metabolism
was observed in cyanobacterial cells. Upon hydration of the crust biogeochemical changes
took place together with patterns of gene expression that were followed by bacterial exit
from dormancy (Brock et al., 1975; Rajeev et al., 2013).
The dynamics of the active bacterial communities (investigated by RNA-SIP) were
examined in biocrusts collected from arid and hyper-arid sites of the Negev Desert. The
crusts were incubated under dark-anoxic and light-oxic conditions after hydration with
heavy water (H218O). The results suggest that the biomass of the four major microbial
groups (Bacteria, Archaea, Cyanobacteria and Fungi) is unchanged, except for
Cyanobacteria in crust incubated under light conditions. However the community
composition changed significantly following hydration. Actinomicetales, a prominent (over
25% in relative abundance) bacterial component in dry crusts, collapsed to less than 1% of
the community following hydration (Angel and Conrad, 2013).
7
It has been suggested that Mediterranean soil bacteria can display anticipatory
strategies at seasonal scales: at the end of a summer dry-down period, the microbial
communities showed almost no measurable microbial activity (based on CO2 production),
yet, total extractable bacterial 16S rRNA was similar to that found after the first wet-up
event (Placella et al., 2012). On the other hand, soil bacteria show high resilience to the
drought conditions and different responses to wet up at the phyla and class level (Barnard et
al., 2013). Despite evidence indicating profound changes in bacterial diversity and
community composition in desert soil crusts following precipitation, to the best of my
knowledge, comprehensive studies monitoring changes in the active bacterial communities
and abundance in desert barren top soils during wet-dry cycles have not been conducted to
date.
1.1.4. Temperature effect on bacterial growth and activity
Another important factor controlling the activity of bacterial communities is
temperature; usually bacterial growth is enhanced at higher temperatures and inhibited at
low temperatures (Lloyd et al., 1994). The latter is mainly due to the inhibition of
enzymatic processes. However, high temperatures, above optimal level, are prone to cause
protein denaturation and cell death (Gestel et al., 2013). Thus, higher temperatures near or
above the optimum will result in an altered bacterial growth adapted to high temperatures,
while at the lower temperature the response will be less profound (Barcenas-Moreno et al.,
2009; Gestel et al., 2013).
Hot desert ecosystems with no or very poor vegetation coverage are known for high
soil temperatures, and wide annual and diurnal fluctuations. In such environments, even if
the mean annual temperature does not reach above optimum levels, the diurnal fluctuations
8
are large. The daytime summer temperatures can be very high in comparison with night
temperatures (Cable et al., 2011). Thus, sessile organisms living in these habitats should be
adapted not only to high temperatures but also to lower temperature, adjusting to wide
temperature range (Gestel et al., 2013).
Optimum soil respiration (linked to microbial growth) is usually found around 30°C
and is positively correlated to soil moisture, though these correlations are dependent on soil
type and the range of both temperature and soil moisture (Rosso et al., 1993; Qi et al.,
2002; Reichstein et al., 2002; Wen et al., 2006; Gestel et al., 2013). Temperature sensitivity
may be affected not only by soil moisture but also by other biotic and abiotic factors that
are spatially and temporally heterogeneous (Qi et al., 2002). At very high moisture, soil
particles are highly saturated with water and gas exchange between soil pores is limited,
which can lead to low concentration of oxygen and restrained aerobic respiration of the
microbial communities. Both dry and wet soil conditions can lower the sensitivity of
ecosystem respiration to temperature (Wen et al., 2006), and in soils where the water
content and availability are not in the optimal range, consideration of its effect on microbial
activity might be critical.
1.1.5. Actinobacteria
Actinobacteria comprise one of the largest phylum of the Bacterial domain,
characterized as Gram-positive Bacteria with a high guanine and cytosine content in their
DNA (Embley et al., 1994). They encompass a wide range of morphologies, from coccoid
to fragmenting hyphal forms with highly differentiated branched mycelium. Actinobacteria
may form spores in times of stress (Flärdh and Buttner, 2009; Swiercz et al., 2013).
Members of this group synthesize and excrete active extracellular secondary metabolites
9
(Bull et al., 2005; Fiedler et al., 2005) and are considered an unexhausted source for
bioactive products (e.g., antimicrobials, biopharmaceutins, agrichemicals and biocatalysts).
Although Actinobacteria have been studied extensively in the past, it is estimated that
only 3% of the natural-product potential of even the well-studied genus Streptomyces has
been realized (Watve et al., 2001), leaving ample opportunity for new discoveries. Bio-
discovery campaigns have been based on the premise that extreme environments, such as
deep seas and polar soils, are likely to contain novel microorganisms, which in turn may
produce novel metabolites. Several studies explored the abundance, diversity and
composition of biologically active natural products in Actinobacteria isolated from deep sea
hydrothermal vents (Feling et al., 2003; Bull et al., 2005), Antarctic soils (Lee et al., 2012)
and marine environments (Bull et al., 2005; Jensen et al., 2005; Lam, 2006).
Several surveys were reported to uncover bioactive natural products from
Actinobacteria isolated from the Atacama desert soil revealing high incidence of non-
ribosomal peptide synthases, indicative of the promising biotechnology opportunities of
these environments (Okoro et al., 2009). It was suggested that the relative abundance of
Actinobacteria in this soil increases at extreme drought conditions. Several studies have
shown Actinobacteria to be resilient to low levels of soil moisture and high temperatures
(Jiang et al., 1993; Zvyagintsev et al., 2007; Kurapova et al., 2012; Neilson et al., 2012;
Bull et al., 2005; Auche et al., 2013; Bull and Asenjo, 2013).
Desert soils present an under-researched biome in terms of its microbiotal secondary
metabolites, even more so in light of arid soil’s abundance and diversity of Actinobacteria.
For instance, this phylum represented 11.8% of the total bacterial diversity among the 48
genera of non-phototrophs detected in a study that combined molecular fingerprinting with
high throughput isolation to detect prominent bacterial communities in desert soil crusts
10
from the Colorado Plateau (Gundlapally et al., 2006). Recent bacterial community analyses
conducted in the Negev desert determined that the Actinobacteria consist over 45% of the
total bacterial community (Bachar et al., 2010). This phylum was found to be highly
prominent in other desert soils around the world: the Atacama desert where Actinobacteria,
comprise 94% of bacterial community (Connon et al., 2007). In the Tataouine (Chanal et
al., 2006), Arizona (Dunbar et al., 1999) and Australian (Holmes et al., 2000) deserts
Actinobacteria were also the dominating phylum.
Currently, the ecological function of Actinobacteria in arid ecosystems is unknown.
However, it may be inferred that the capacity of this phylum to form stress-resistant spores,
and to produce a wide array of natural extracellular metabolites, may give it a significant
competitive advantage. Actinobacteria may be able to out-compete their opponents by the
production of secondary metabolites. When resources are plentiful they may proliferate
undisturbed, yet once resources are depleted, they sporulate and thus persist, awaiting the
next rain event.
1.1.6. Firmicutes
The Firmicutes phylum mainly includes Gram-positive Bacteria with low GC
content. Many of them produce endospores, which are resistant to desiccation and survive
extreme conditions (Onyenwoke et al., 2004). Many microorganisms are capable of
resisting stress conditions such as temperature, desiccation, and antibiotics by entering
resting states or by forming spores (Roszak and Colwell, 1987; Onyenwoke et al., 2004). In
pure cultures of Bacillus, RNA is synthesized within minutes of spore transfer to favorable
conditions (Kennett and Sueoka, 1971). Bacterial dormancy is also thought to be important
in natural systems. Indeed, Firmicutes together with Actinobacteria were shown to be one
11
of the abundant groups in arid soils of Israel’s Negev desert consisting of about 10 and
45%, respectively in relative abundance (Bachar et al., 2010). Yet, in response to hydration
of dry crust samples from Israel’s Negev desert the relative abundance of Bacillales
increased sharply one day after hydration and decreased throughout the three subsequent
weeks of desiccation (Angel and Conrad, 2013).
In Mediterranean ecosystems increased activity of Bacilli was observed 3 to 24 hours
after wet-up, a timeframe that would have been sufficient for spore outgrowth (Placella et
al., 2012). In this study, Firmicutes were considered to be intermediate responders to
hydration, while Actinobactera responded rapidly with activity growth within 15 minutes to
1 hour. While in terms of C availability, Fierer et al. (2007) show that in the cross-site
study, neither Firmicutes nor Actinobacteria respond in any predictable manner to changes
in C availability.
A study conducted by Barnard et al. (2013) in California annual grasslands showed
that synthesis of Actinobacteria ribosomes was stimulated (from 62.5 to 82.9% relative
abundance) as summer dry-down progressed and slightly reduced (to 61.7%) after 2 hours
of rewetting, while the relative abundance of Firmicutes remained unchanged during dry-
down and wet up.
Spore forming strategy allows dormant cells to become potential seed banks and thus
contribute to the diversity and dynamics of communities in future generations. However,
very little is known about whether and to what extent dormancy influences the biodiversity
of bacterial communities (Jones and Lennon, 2010) and how they are affected by
environmental conditions, such as crowding, oxygen or temperature stress, and resource
limitation (Lewis, 2007).
12
Chapter 2. Research hypotheses and objectives
Given the accepted view that there exists a direct linkage between bursts of hydration
in arid environments and plants and animals’ growth, activity, structure and function, I
hypothesized that bacteria would follow the same patterns. In particular, I predicted that
arid soil bacterial communities would emulate plants pulse-dynamic response to rainfall
events exerting strongly on abundance and diversity. I further hypothesized that the rate of
desiccation would be influenced by rain amounts, temperature and diurnal cycles
questioning that all may play an important role in shaping the response of bacterial
biomass, diversity and community composition to the brief pulses of rain and the rate of
desiccation.
The aim of this study was to assess, under controlled laboratory conditions, the
dynamics of active (featuring the rRNA of the community) desert soil bacterial
communities during hydration-desiccation cycles, with special focus on Actinobacteria
(arid soil dominant bacterial phylum). To that end, continuous microcosoms experiments
were performed using bench-scale soil columns subjected to different intensities of rain,
different constant temperatures and night-day cycles. In particular, the current work was
aimed to assess the effects of these treatments on bacterial diversity, abundance and
community composition in arid soil.
13
Chapter 3. Materials and methods
3.1. Soil collecting from the field
The soil used in all experiments was collected from barren patches of an unmarked
plot at the long-term ecological research (LTER) station of Avdat (30°47' N, 34°46' E, 600-
700 m elevation). Eight randomly selected subsamples were taken from the top 5 cm of the
bulk soil (using ethanol-cleaned scoop), after the crust was removed. The soil samples were
collected into sterile bags (Whirl-Pack), transported to the Zuckerberg Institute for Water
Research (ZIWR) laboratory and kept at 4°C until homogenization (within 24 h of
sampling). Soil was homogenized by sieving trough an autoclave-sterilized sieve (2 mm
pore grid size), and 3.3 kg of the homogenate were packed in each of three replicate
columns (Figure 1).
3.2. Experimental setup
The experimental setup (Mishurov et al., 2008) consisted of three PVC (polyvinyl
chloride) cylinders (30 cm high, 10 cm in diameter) fitted at the bottom with a 4-8 m pore
size ceramic filter (Ace Glass Inc); an outlet tube at the base of the column led to a
continuously operating vacuum pump in order to simulate natural gravimetric forces in the
soil. Ten soil sampling ports were located around the perimeter of the top 12 cm of the
column (further details under 3.3). The sampling ports were fitted with Suba-Seal rubber
septa (Sigma-Aldrich).
To mimic rain events, a shower-like rain simulator was constructed (Figure 1A)
consisting of a PVC disk (10 cm in diameter) equipped with 21 syringe needles (0.4 x 13
mm) through which water dripped onto the soil surface by pumping (Gillson Minipuls 3
14
Peristaltic pump) double-distilled water (DDW) at a rate of 1.5 mL min-1. The volume of
water precipitating on the soil was 80 or 400 ml, corresponding to precipitation of 10 and
50 mm of rain, respectively according to the following formula:
𝐿 = (𝜋𝑟2) · 𝑚
where r is the radius of the soil column, 𝜋 = 3.14 and m is the precipitation amount in cm.
Following a single rain event, the columns were covered with aluminum foil to avoid
light penetration, so that photosynthetic microorganisms would be inhibited (Figure 1B),
and in incubators for 28 days under constant temperature of 25 or 36oC, or a day/night
cycle of 36/10oC (Table 1).
3.3. Soil sampling from the columns
The soil columns were sampled 0.5, 1.5 and 3 days intervals after the rain event, and
then once a week up to four weeks. Samples from the homogenate prior to packing into the
columns were considered as the baseline soil prior to treatment (time 0). Soil samples were
removed with a sterile spatula through the sampling ports (Figure 1A). At each sampling
time soil was collected from three levels of the column, at 4, 8 and 12 cm from the soil
surface. A total of 15-20 g of soil was removed from the sampling ports of each level and
mixed. For physicochemical analyses, the samples obtained from each level of the column
were combined and approximately 10 g of the mixture were used for all analyses, except
for TC determinations for which the three levels were combined. For molecular analyses, 7-
10 g soil from each level were suspended in RNA later (see Appendix 1) at 1:1 ratio, for
better stabilization of cellular RNA and stored at -80°C; prior to analysis, equal amounts of
the stored samples from the three different levels of each column were combined in one.
15
(A) (B)
Figure 1. (A) Soil column and rain simulator. 1- DDW; 2-peristaltic pump; 3- rain simulator; 4-
column; 5, 6 and 7- sampling ports at 4, 8 and 12 cm from the soil surface, respectively; 8- ceramic
filter (inside the column). (B) The three microcosm systems: 1, 2, 3- replicate columns; 4- water
trap; 5- vacuum pump.
Table 1. Conditions applied to the soil columns and estimated average water loss during the 28-day
experiments.
Experiment Temperature
(°C)
Rain event
(mm)
Field
collecting
date
Average
desiccation rate:
water loss per day
(%)
I 25 50 04.06.2012 3.7
II 25 10 15.07.2012 4.4
III 36 /10
(day / night) 50 14.09.2012 8.6
IV 36 50 19.10.2012 11.0
16
3.4. Soil physicochemical analyses
Soil physicochemical properties were determined according to standard methods
(SSSA, 1996) and are described in brief. Soil water content was determined by gravimetry.
Electrical conductivity (EC) and pH were measured with EC electrodes and pH meter,
respectively, in saturated soil extract. Nitrogen as nitrate and nitrite was determined in KCL
solution extract. Nitrite was determined by colorimetric method (HCL, sulfanilamide and
ethylenediamine dihydrochloride mix) using an Infinite M200, (Tecan) spectrophotometer.
Nitrate was measured by the second derivative method (APHA, 2005) using a BioMate 5
(Thermo Electron) spectrophotometer. Total carbon (TC) was measured in a CHNS/O
Elemental Analyzer (Flash 2000, Thermo).
Desiccation rates were calculated by the equation:
𝜔 = 𝜔0 × ℮−𝑘𝑡
where k is the desiccation rate, t is absolute temperature, and ω0 and ω are the initial and
the final water content, respectively (Kodikara et al., 2000). Here it is assumed that a)
desiccation follows a negative exponential curve, and b) desiccation rate is constant for a
given treatment.
3.5. RNA extraction and cDNA synthesis
Total nucleic acids were extracted by bead beating the soil in the presence of
phosphate buffer, 10% sodium dodecyl sulfate and phenol according to Angel et al. (2012)
(see http://www.nature.com/protocolexchange/protocols/2484 for the full protocol). The
obtained RNA was purified using RNA purification kit (Epicenter) according the
manufacturer’s instructions. The RNA was reverse-transcribed to cDNA using ImProm-
II™ Reverse Transcriptase (Promega) in the presence of Recombinant RNasin
17
Ribonuclease Inhibitor (Promega) following the manufacturer’s protocol; the resulting
cDNA was purified using PCR purification kit (Bioneer) and quantified
spectrophotometrically using Nanodrop.
3.6. Bacterial abundance
The abundance of the Bacteria, Actinobacteria and Firmicutes populations were
quantified using group specific qPCR assays targeting the 16S rRNA encoding gene. All
qPCR reactions were performed in an iCycler thermocycler equipped with a MyiQ
detection system (Bio-Rad) the data were processed using Bio-Rad CFX Manager 3.0
software (Bio-Rad). The quantifying assays were based on SYBR Green I quantification.
For all assays, standards containing known number of copies of the target gene were
used. Standards were serially diluted and used for construction of calibration curves for
each qPCR reaction plate. The standards used to determine the abundance were cloned
fragments of the 16S rRNA encoding genes obtained from amplifying and cloning the
DNA from Streptomices griseus to quantify the Bacteria and Actinobacteria. Amplified
fragments from Bacillus subtilis were used to quantify the Firmicutes. The primers used in
this study are detailed in Table 2.
18
Table 2. Primers used in this study.
Target Primer Sequence (5’-3’) Source
16S rRNA
(Bacteria)
S-D-Bact-0341-a-S-171 CCTACGGGAGGCAGCA(I)* Klindworth et
al. (2012)
S-*-Bact-0515-a-S-19 TTACCGCGGCTGCTGGCAC
S-D-Bact-0907-a-S-20 CCGTCAATTCMTTTGAGTTT
(I)*
16S rRNA
(Actinobacteria)
S-C-Act-235-a-201 CGCGGCCTATCAGCTTGTT
G
Stach et al.
(2003)
Act1200R TCRCCCCACCTTCCTCCG Bacchetti de
Gregoris et al.
(2011)
S-Bact-0515-a-S-19 TTACCGCGGCTGCTGGCAC Klindworth et
al. (2012)
Firmicutes 928F-Firm
TGAAACTYAAAGGAATTGA
CG
Bacchetti de
Gregoris et al.
(2011) 1040FirmR ACCATGCACCACCTGTC
1Primers used for amplification of T-RFLP-based amplicons were modified by the addition of inosine at the 3'
end in an attempt to broaden their target scope (Ben-Dov et al., 2006) and by the addition of FAM.
Preliminary PCR analysis were performed for all primer pairs used to insure the
specificity of the primers in polymerase chain reaction before performing the qPCR assays.
The primer pair used for amplifying Bacteria were S-D-Bact-0341-a-S-17 and S-*-Bact-
0515-a-S-19 (Klindworth et al., 2012). The primer pair used for amplifying Actinobacteria
were S-C-Act-235-a-20 and S-*-Bact-0515-a-S-19 (Stach et al., 2003). The primers used to
amplify the Firmicutes 16S rRNA were 928F-Firm and 1040FirmR (Bacchetti de Gregoris
et al., 2011). Primers were selected in accordance with RDP (http://rdp.cme.msu.edu/) and
SILVA (http://www.arb-silva.de/) databases.
19
For evaluating Bacteria and Actinobacteria each qPCR reaction contained the
following mixture: 10 μl of SYBR Absolute Blue qPCR Rox Mix (Thermo), 1 μl of 400
nM of each primer (Metabion), 5 μl of template cDNA and 3 μl of molecular grade water
(HyLab) assays. To quantify the Firmicutes population, slightly different reaction
conditions were used: duplicates of 25 μl were used for each qPCR reaction containing:
12.5 μl of SYBR Absolute Blue qPCR Rox Mix (Thermo), 0.5 μl of 250 nM of each primer
(Metabion), 5 μl of template and 6.5 μl of molecular grade water. Each reaction was
repeated at least twice.
Bacterial abundance estimation was performed under the following conditions: 95°C
for 15 min, followed by 35 cycles of 95°C for 10 sec, 60°C for 15 sec and 72°C for 30 sec
for extension. Actinobacteria abundance estimations conditions were: 95°C for 15 min,
followed by 35 cycles of 95°C for 45 sec, 63°C for 45sec and 72°C for 45 sec. Firmicutes
abundance estimation conditions were: 95°C for 15 min, followed by 35 cycles of 95°C for
10 sec, 65°C for 15 sec and 72°C for 45 sec. The reliability of quantification was evaluated
using a melting curve at 65-95°C.
3.6.1. qPCR data analysis
Temporal changes in abundance of Bacteria, Actinobacteria and Fermicutes
communities were assessed by ANOVA test with lm function in Stats package V.3.0.1. The
abundance measurements for all three communities were log-transformed to meet the
assumption of normality of the residuals and homogeneity of variances among groups
(defined as different time points). The assumptions of normality and homoscedasticity of
the residuals were tested by Shapiro-Wilks (Royston, 1995) and Bartlett tests (Bartlett,
1937) with shapiro.test and bartlett.test functions, respectively, in Stats package V.3.0.1.
20
Soil samples were independently taken from three replicate columns, thus the assumption
of independency of samples was not violated. Since after the data transformation the
assumption about the normality of the residuals was still violated, a non-parametric
equivalent Kruskal-Wallis test (Myles and Wolfe, 1973) was performed instead of ANOVA
in Stats package V.3.0.1 using kruskal.test function.
3.7. Community composition analysis
3.7.1. Terminal restriction fragment length polymorphism (T-RFLP) analysis
Bacteria and Actinobacteria community fingerprints of soil samples were obtained by
T-RFLP analysis. This method was first described by Liu et al. (1997) and is a high
throughput molecular technic frequently used for this purpose (Kirk et al., 2004). In the T-
RFLP method, fluorescent labeled PCR-amplicons of the target gene are generated and
subjected to a restriction reaction, normally using four-cutter restriction enzyme and
analyzed with a sequencer. Since the amplicons are labeled at their 5’ end, only the
terminal fragments of a restriction digests are detected by the sequencer (Tiedje et al.,
1999).
PCR amplification of the 16S rRNA-encoding gene using the cDNA as template was
performed following Angel et al. (2010). The primers that were used to target the 16S
rRNA encoding gene were selected in accordance with RDP (http://rdp.cme.msu.edu/) and
SILVA (http://www.arb-silva.de/) databases and are listed in Table 2.
The PCR reactions were carried in a thermocycler (Biometra) in triplicates to
minimize the reaction bias of the PCR. Each PCR mixture (50 μl) contained: 5 μl of Dream
Taq 10x buffer (Thermo), 2.5 mM MgCl2, 5 μl of bovine serum albumin solution (New
21
England Biolabs), 1000 nM of each PCR primer (HyLab) and 0.25 mM of each DNTP and
8 ng of cDNA as template.
The amplification reactions for Bacteria 16S rRNA encoding gene were carried under
the following conditions: 95°C for 5 min, followed by 30 cycles of 94°C for 45 sec, 45°C
for 45 sec and 72°C for 45 sec, and then 72°C for 10 min. For Actinobacteria the reactions
conditions were: 95°C for 5 min, followed by 30 cycles of 94°C for 45 sec, 58°C for 45
sec, 72°C for 1 min and 72°C for 45 sec. Aliquots of 5 μl of the PCR products were
visualized on 1% agarose gel (Sigma) using gel electrophoresis (Bio-Rad) to confirm
successful amplification.
Before the digestion with restriction enzymes, the amplified DNA samples were
combined and treated with Mung bean exonuclease (TaKara) according to the
manufacturer’s instructions, in order to eliminate the single-stranded amplicons that might
result in pseudo terminal restriction fragments (Egert et al., 2003). The purified PCR
products were digested with the restriction enzymes TaqI (TaKara) and HpyCH4IV (NEB)
for samples amplified using the bacterial primers S-D-Bact-0341-a-S-17-FAM and S-D-
Bact-0907-a-S-20 (Klindworth et al., 2012). The Actinobacteria rRNA-based amplicons
were digested with HhaI and HapII (TaKara) restriction enzymes for samples amplified
using primers S-C-Act-235-a-20-FAM and Act1200R (Table 2). Digestions were
performed according to the manufacturers’ instructions, and were followed by purification
using SigmaSpin™ Post-Reaction Clean-up Columns (Sigma). The samples were then
mixed with HiDi (Applied Biosystems) and MapMarker 1000 (Bioventures), and analyzed
with an ABI Prism® 3100 genetic analyzer (Applied Biosystems).
22
3.7.2. T-RFLP data analysis
Electropherograms were retrieved using Peak ScannerTM software V.1.0. (Applied
Biosystems). T-RFs with size below 50 bp and above 600 bp were removed due to the
ladder restrictions. Samples that had less than 18 (Bacteria) and 5 (Actinobacteria) such T-
RFs were removed from further statistical analyses. The true peaks were defined as those
with height standard deviation of more than 2 degrees from one another and T-RFLP
fragment sizes were rounded to the nearest integer; peak heights were expressed as
percentage of all the peaks present in a given sample.
3.7.2.1. Shannon-Weaver diversity index analysis
The bacterial community diversity at each time point within an experiment was
calculated using Shannon-Weaver (or Shannon-Wiener) diversity index (H’), which is a
measure of the amount of information (entropy) in the system and hence is a measure of the
difficulty in predicting the identity of the next individual sampled (Krebs, 1989).
H’ index was calculated using diversity function, Vegan package V.2.0.8. (Oksanen
et al., 2013) of the R Software V.3.0.1. H’ index takes into account both abundance and
evenness of species present in the given community and is expressed as:
𝐻’ = − ∑ 𝑝𝑖 (𝑙𝑜𝑔𝑏𝑝𝑖)
where pi is the proportional abundance of species i and b is the base of the logarithm. In this
study the H’ index was calculated taking b as 2.
One-way ANOVA test was performed to test the effect of time (having eight levels
as 0 (before the rain event) and 0.5, 1.5, 3, 7, 14, 21, 28 days after the single rain event) on
the Shannon-Weaver diversity index within each experiment. The test was performed with
23
lm function (Wilkinson et al., 1973; Chambers, 1992) of R programming environment
V.3.0.1. The assumptions of normality and homoscedasticity of the residuals were tested by
Shapiro-Wilks (Royston, 1995) and Bartlett (Bartlett, 1937) tests with shapiro.test and
bartlett.test functions, respectively, in Stats package V.3.0.1. Soil samples were
independently taken from three replicate columns, thus the assumption of independency of
samples was not violated.
Also, t-test (with t.test function in Stats package V.3.0.1.) pairwise test was
performed using each restriction enzyme separately to test the difference of bacterial and
actinobacterial communities’ diversity among experiments. The normality assumption was
checked by Shapiro-Wilks normality test and the equal variance assumption was tested
using var.test function, in Stats package V.3.0.1. Since the data violates the assumptions of
the t-test (mostly the normality assumption and in few cases the variance equality
assumption as well), the non-parametric analysis, Mann-Whitney test (Bauer, 1972), was
performed using wilcox.test function in Stats package V.3.0.1. of R software environment.
3.7.2.2. Stacked bar graphs
Stacked bars were portrayed to illustrate the bacterial community composition change
within each experiment, i.e. between time points of experiments. To that end barplot
function, Graphics package of the R Software V.3.0.1. was used (Murrell, 2005).
3.7.2.3. Nonmetric multidimensional scaling
Multivariate analysis was performed with metaMDS function, Vegan package
V.2.0.8. (Oksanen et al., 2013) of the R Software V.3.0.1. (http://www.r-project.org/).
Ordinations were generated with nonmetric multidimensional scaling (NMDS) (Taguchi et
24
al., 2004) using 500 iterations. In this method the similarity data is ordinated using an
iterative algorithm that takes the multidimensional data of a similarity matrix and presents
it in minimal dimensional space, typically two dimensions. The result of MDS ordination is
a map where the position of each sample is determined by its distance from all other points
analysed. Also, the NMDS plot can be rotated resulting the first axes to contain most of the
variance (Oksanen et al., 2013). Since MDS ordination is an iterative algorithm that
involves a ‘goodness of fit’ estimate, an important component of an MDS plot is a measure
of the goodness of fit of the final plot. In the case of an MDS ordination, the latter is termed
the ‘stress’ of the plot. A stress value greater than 0.2 indicates that the plot is close to
random, stress less than 0.2 indicates a useful 2 dimensional picture and less than 0.1
corresponds to an ideal ordination with no real prospect of misinterpretation (Clarke, 1993).
Significance of the environmental variables was assessed using Monte-Carlo
permutation against 999 random data sets. Differences between sample groups were
calculated by multi-response permutation procedure (MRPP) (Mielke et al., 1981) with
mrpp function, Vegan package V.2.0.8. (Oksanen et al., 2013) of the R Software V.3.0.1.
The MRPP test is based on the assumption that if two groups are different from each other,
the average within-group difference will be smaller than the average between-group
distance. The "effect size" of the difference between data groups is represented by the A-
statistic of the MRPP test (MRPP-A), while its significance is identified by the MRPP's P-
value. MRPP-A ranges from zero, meaning that data points are randomly distributed (i.e.
the two tested groups are homogenous), to one, meaning that data point distribution is
wholly determinate (i.e. the two groups are completely separate); therefore, we are able to
compare MRPP-A values obtained from different MRPP tests conducted with different data
sets.
25
3.7.2.4. Dissimilarity analysis
In NMDS analysis community dissimilarity was based on the Bray-Curtis index,
which includes the presence and relative abundance of T-RFs, which is defined as
𝐵𝐶𝑗𝑘 = 1 −∑ |𝑦𝑖𝑗 − 𝑦𝑖𝑘|𝑛
𝑖=1
∑ (𝑦𝑖𝑗 + 𝑦𝑖𝑘)𝑛𝑖=1
where 𝐵𝐶𝑗𝑘 is the Bray-Curtis dissimilarity between groups j and k, yij and yik are the height
of peak i in j and k groups, respectively, and n is the number of peaks summed over both
groups (Rees et al., 2004).
26
Chapter 4. Results
4.1. Soil physicochemical characteristics during hydration-desiccation
experiments
In each experiment the soil was sampled from three different depths of the columns
(4, 8 and 12 cm from the soil surface) and was tested for WC, EC, pH, NO2-N, NO3-N, and
TC. Then the results were analyzed to find whether samples collected concurrently from the
different depths were significantly different, using one-way ANOVA.
Table 3. One-way ANOVA analysis of water content (WC), pH, electrical conductivity (EC),
nitrate (NO3-N) and nitrite (NO2-N) measurements in soil samples collected from different depths
of the soil column (4, 8 and 12 cm) during the 28 days of hydration and desiccation. Assumptions of
the ANOVA test were not violated: independence of samples, normal distribution of the residuals
(Shapiro-Wilk test, p > 0.05), and homogeneity of variances among groups (Bartlett test, p > 0.05).
Rain: 50 mm
Temp: 25°C
Rain: 10 mm
Temp: 25°C
Rain: 50 mm
Temp: 36/10°C
Rain: 50 mm
Temp: 36°C
WC (%) F(2,21) = 0.02
p = 0.98
F(2,21) = 2.11
p = 0.15
F(2,21) = 0.15
p = 0.86
F(2,20) = 0.16
p = 0.85
pH F(2,21) = 0.48
p = 0.62
F(2,21) = 0.11
p = 0.8
F(2,21) = 0.52
p = 0.60
F(2,21) = 0.09
p = 0.92
EC (dS/m) F(2,21) = 0.13
p = 0.88
F(2,20) = 0.21
p = 0.82
F(2,21) = 0.09
p = 0.91
F(2,17) = 0.06
p = 0.94
NO2-N
(mg/kg soil)
F(2,21) = 0.02
p = 0.98
F(2,19) = 1.93
p = 0.17
F(2,12) = 0.16
p = 0.85
F(2,21) = 0.23
p = 0.79
NO3-N
(mg/kg soil)
F(2,18) = 0.13
p = 0.88
F(2,21) = 0.82
p = 0.46
F(2,12) = 0.42
p = 0.66
F(2,12) = 0.69
p = 0.52
*In the experiment where 10 mm of rain was applied, soil samples collected at 1.5 and 3 days from
the 4 cm depth were considered as outliers in order to meet the assumption of homogeneity of
variances.
The ANOVA results (Table 3) suggest that none of the physicochemical parameters
significantly differed (p > 0.05) due to column sampling level (Supplementary Figure 1).
27
Thus, the molecular analyses were performed on composites of the samples collected from
the three different depths considering the latter as biological replications. Moreover, as
there were no significant differences between the values of physicochemical parameters,
samples from the different depths of each soil column, collected at the same time point,
were pooled together. Thus, the results presented in Table 4 are the average measurements
of the three replicate columns ± SD (n = 9).
The results show that: (I) the simulated rain events had no marked effect on the soil
pH; the Negev soil is basic as it is rich in calcium carbonate (Brady and Weil, 2001), and
the pH values measured ranged between 7.8 and 8.5; (II) the EC of the soil was also
unaffected by the simulated rain and measures were all below 0.3 dS/m, with no major
differences between experiments; (III) measures of TC, nitrite and nitrate showed
fluctuations throughout the experiments (Table 4).
The initial water content in the soil was very low at 0.95% ± 0.21 and, as expected,
increased following rain simulation, to approximately 16 to 19% after 0.5 days with 50 mm
precipitation, and 5% after 10 mm (Table 4). The overall desiccation rate (Table 1) was
strongly dependent on the temperature, being much higher at 36°C than at 25°C (Figure 2,
Table 1). Another parameter that affected the overall desiccation rate was the amount of
water used for the rain simulation. With time, a decrease in water content was observed
under all the conditions tested, more pronounced during days 2 to 5, and more moderated
thereafter (Figure 2).
28
Table 4. Soil physicochemical parameters: water content (WC), pH, electrical conductivity (EC),
total carbon (TC), nitrite (NO2-N) and nitrate (NO3-N).
*NA- not available, **BD- below detection level.
29
4.2. Temporal dynamics of bacterial abundance during hydration-desiccation
experiments
Temporal changes in the abundance of Bacteria, Actinobacteria and Firmicutes were
assessed using amplification and detection of the 16S rRNA encoding gene by qPCR.
Figure 3 illustrates the results obtained in each experiment for Bacteria, Actinobacteria, and
Firmicutes, as well as the soil water content at each of the sampling times.
The results suggest that under all the conditions tested gene copies of Bacteria,
Actinobacteria and Firmicutes did not vary significantly with time (Figure 3; Table 5),
except for abundance of Firmicutes at 36°C temperature and 50 mm precipitation which
differed significantly with time (KW(6) = 0.19, p = 0.02) (Table 5).
Figure 2. Water content in the 4-12 cm
depth section of soil columns
incubated under different experimental
conditions. Values are the average of
independent replicates from combined
depths of the column for each time
point ± SD (n = 3).
30
Figure 3. Community size dynamics of Bacteria, Actinobacteria and Firmicutes upon hydration
and desiccation of soil columns. Values are average of independent biological replicates ± SD (n = 3
for Bacteria and Actinobacteria, and n = 2 for Firmicutes). Time 0 corresponds to soil prior to the
rain event.
Table 5. Kruskal-Wallis non parametric test of the temporal changes in total abundance of Bacteria,
Actinobacteria and Firmicutes in soil columns subjected to different treatments. Assumption of
independency of samples and homogeneity of variances among groups (Bartlett test, p > 0.05) were
not violated.
Rain: 50 mm
Temp: 25°C
Rain: 10 mm
Temp: 25°C
Rain: 50 mm
Temp: 36/10°C
Rain: 50 mm
Temp: 36°C
Bacteria KW(7) = 9.17
p = 0.24
KW(5) = 10.29
p = 0.07
KW(6) = 9.71
p = 0.13
KW(6) = 11.7
p = 0.07
Actinobacteria KW(7) = 6.84
p = 0.44
KW(6) = 12.35
p = 0.07
KW(5) = 12.00
p = 0.06
KW(6) = 13
p = 0.04
Firmicutes KW(7) = 14.4
p = 0.05
KW(7) = 9.77
p = 0.13
KW(6) = 11.79
p = 0.06
KW(6) = 0.19
p = 0.02
31
Although the overall abundance of the three bacterial groups was mostly stable
throughout each experiment, the higher hydration (50 mm rain) instigated a decrease in the
community size, which was detected at the first sampling time, 12 hours after the initial
hydration. This decrease was followed by an increase in abundance as the soil water
content decreased (Figure 3). Yet, when the amount of rain was low (i.e., 10 mm) the
changes in abundance were less pronounced (Figure 3B).
4.3. Temporal dynamics of bacterial diversity during hydration-desiccation
experiments
Shannon-Weaver diversity indices (H’) were calculated for Bacteria and
Actinobacteria for all samples collected during the experiments and for all the restriction
enzymes used (Figure 4). The community was diverse with estimated H’ values between 2
and 3, but only minor temporal changes were detected. The significance of the changes
observed was tested further using one-way ANOVA and was found to be insignificant both
for Bacteria (p > 0.05) and Actinobacteria (p > 0.05), indicating steady temporal diversity
which was unaffected by the hydration-desiccation events (Table 6).
Table 6. ANOVA test of the Shannon-Weaver diversity indices calculated for all soil samples of
each experiment. Assumptions of the ANOVA were not violated: independence of samples, normal
distribution of the residuals (Shapiro-Wilk test, p > 0.05) and homogeneity of variances among
groups (Bartlett test, p > 0.05).
Rain: 50 mm
Temp: 25°C
Rain: 10 mm
Temp: 25°C
Rain: 50 mm
Temp: 36/10°C
Rain: 50 mm
Temp: 36°C
Bacteria-
TaqI
F(7,17) = 0.93
p = 0.506
F(6,10) = 1.84
p = 0.188
F(6,13) = 0.32
p = 0.915
F(7,15) = 2.62
p = 0.056
Bacteria-
HpyCH4IV
F(6,16) = 1.00
p = 0.461
F(6,11) = 2.36
p = 0.103
F(6,12) = 0.52
p = 0.785
F(7,14) = 2.55
p = 0.064
Actinobacteria-
HapII
F(7,16) = 1.01
p = 0.458
F(6,13) = 1.20
p = 0.364
F(6,12) = 2. 783
p = 0.062
F(7,16) = 1.01
p = 0.458
Actinobacteria-
HhaI
F(7,14) = 0.77
p = 0.625
F(6,12) = 1.95
p = 0.153
F(5,9) = 0.62
p = 0.686
F(7,10) = 2.06
p = 0.145
32
The H’ values of the four experiments were plotted for each restriction enzyme
separately to detect the temporal dynamics of the diversity within and between the different
treatments. Little fluctuations but not major differences were observed between the
diversity of the Bacteria and Actinobacteria communities’ composition upon hydration and
desiccation of different experimental conditions (Figure 4; Supplementary Figure 2).
Figure 4. Shannon-Weaver diversity indices of (A) Bacteria using restriction enzyme TaqI and (B)
Actinobacteria, using restriction enzyme HapII
4.4. Temporal dynamics of bacterial community composition during hydration-
desiccation experiments
4.4.1. Bacterial community composition changes within experiments
NMDS analyses were performed to validate the evenness of the starting conditions.
To that end the community composition pattern of active bacterial and actinobacterial
fingerprints were compared between samples taken before and after the treatments were
applied. According to the NMDS graphs both Bacteria (Supplementary Figure 3A and B)
and Actinobacteria (Supplementary Figure 3C and D) communities were initially similar in
all experiments and changed following the applied treatments. The NMDS analyses were
33
confirmed by the MRPP tests for HpyCH4IV (A = 0.01758, p = 0.012) and HapII (A =
0.01088, p = 0.041) but not for TaqI (A = -2.295e-0.5, p = 0.442) and HhaI (A= 0.004511,
p = 0.182).
Stacked bar graphs of active Bacteria and Actinobacteria operational taxonomic units
(depicted by the TRFs) were plotted in order to demonstrate changes in their community
composition within each experiment (Figures 5 and 6; Supllementary Figures 4 and 5). As
mentioned above, the community composition of active bacterial and actinobacterial
fingerprints prior to the rain event mostly start with similar pattern in each of the
experiment and change throughout time after the rain event. In some cases (Figure 5B and
D; Figure 6B and D) the community composition at the end point resembled the initial
community (before hydration).
34
Figure 5. Bacterial community composition dynamics within time in different microcosms
experiments. (A) Rain 50 mm, Temp 25oC; (B) Rain 10 mm, Temp 25oC; (C) Rain 50 mm,
Temp 36/10oC; (D) Rain 50 mm, Temp 36oC. The segments represent abundance of each peak
(OTU) as percentage of all the peaks present in a given sample.
35
Figure 6. Actinobacterial community composition dynamics within time in different
microcosms experiments. (A) Rain 50 mm, Temp 25oC; (B) Rain 10 mm, Temp 25oC; (C) Rain
50 mm, Temp 36/10oC; (D) Rain 50 mm, Temp 36oC. The segments represent abundance of
each peak (OTU) as percentage of all the peaks present in a given sample.
36
4.4.2. Bacterial community composition changes between experiments
The fingerprint patterns of the Bacteria and Actinobacteria communities were
estimated using T-RFLP analysis. A fragment of the small subunit 16S rRNA encoding
gene was amplified and digested by two restriction enzymes for Bacteria and for
Actinobacteria. Figures 7 and 8 summarize the results obtained by digesting the amplicons
with the restriction enzymes TaqI for Bacteria and HhaII for Actinobacteria; the results for
the analyses conducted by digesting the amplicons with the restriction enzymes HpyCH4IV
for Bacteria and HhaI for Actinobacteria are presented in Supplementary Figures 6 and 7.
The statistical analyses of all T-RFs are presented in Table 7.
Non-metric multidimensional scaling (NMDS) analyses were performed to compare
the community composition pattern of the Bacteria and Actinobacteria communities under
the different environmental conditions. Pairs of experiments were compared for effects of
hydration intensity and temperature on the communities (Figures 7 and 8 for Bacteria and
Actinobacteria, respectively).
37
Figure 7. Non-metric multidimensional scaling (NMDS) ordinations of active Bacteria
communities in arid soil upon hydration and desiccation. The different set of conditions in the
microcosm experiments (see Table 1) are marked by color. Black- Rain 50 mm, Temp 25oC;
red- Rain 10 mm, Temp 25oC; green- Rain 50 mm, Temp 36/10oC; blue- Rain 50 mm, Temp
36oC. Axes represent distance with ideal (B, C) and almost ideal (A, D) ordination.
38
Table 7. MRPP pairwise test between different experimental conditions. P and A values are based
on Bray-Curtis distance measure of dissimilarity matrixes.
Temp: 25°C
Rain: 10 mm
vs. 50 mm
Rain: 50 mm
Temp: 25°C vs.
36/10°C
Rain: 50 mm
Temp: 25°C vs.
36°C
Rain: 50 mm
Temp: 36°C vs.
36/10°C
Bacteria-
TaqI
A** = 0.0723
p = 0.001
A** = 0.1167
p = 0.001
A* = 0.0383
p = 0.019
A = 0.0165
p = 0.055
Bacteria-
HpyCH4IV
A** = 0.0667
p = 0.001
A** = 0.0584
p = 0.001
A* = 0.0313
p = 0.009
A = 0.0141
p = 0.090
Actinobacteria-
HapII
A** = 0.0830
p = 0.001
A** = 0.0569
p = 0.003
A* = 0.0469
p = 0.005
A = 0.0162
p = 0.061
Actinobacteria-
HhaI
A* = 0.0830
p = 0.037
A** = 0.0571
p = 0.002
A* = 0.0388
p = 0.007
A* = 0.0290
p = 0.036
* Moderate significance (p < 0.05)
** High significance (p < 0.005)
The significance was based on MRPP analysis of active Bacteria and Actinobacteria
fingerprints, depending on the A-value that explains the separation level of compared
groups based on Bray-Curtis distance (Table 7). Very similar significances were obtained
also with MRPP test based on Euclidian distance (Supplementary Table 2; Appendix 2).
Bacteria fingerprint patterns were significantly different (Table 7) when soils were
differently hydrated but incubated at the same temperature (Figure 7A; Table 7). In the
soils treated with equivalent rain amounts (50 mm) significant differences were detected in
the Bacteria community composition patterns at different temperatures (Figure 7B and C;
Table 7). However, no significant differences were detected in the community composition
of Bacteria fingerprints due to the diurnal temperature cycle (Figure 7D; Table 7). The
higher A-values between the soil community patterns receiving 10 mm vs. 50 mm rain (A =
0.0723) and the ones in soils incubated at the different temperatures of 25°C and 36/10°C
(A = 0.1167) suggest a more significant separation between the compared community
patterns as shown in the cluster analyses (Figures 7A and B).
39
High significant differences were detected between the Actinobacteria fingerprint
patterns (Figure 8A; Table 7) when the soil microcosms were hydrated with 10 or 50 mm of
rain and incubated at the same temperature of 25°C. High significant differences were also
observed in soils receiving 50 mm rain and incubated at different temperatures of 25°C vs.
36/10°C (Figure 8B; Table 7). Lower but still significant differences were detected in soils
receiving 50 mm of rain but incubated at temperatures of 25°C vs. 36°C (Figure 8C; Table
7). Yet, no differences were detected when the microcosms were incubated with and
without the circadian cycle (Figure 8D; Table 7).
40
Figure 8. Non-metric multidimensional scaling (NMDS) ordinations of active Actinobacteria
communities in arid soil microcosms upon hydration and desiccation. The different set of
conditions in the microcosm experiments (see Table 1) are marked by color. Black- Rain 50
mm, Temp 25oC; red- Rain 10 mm, Temp 25oC; green- Rain 50 mm, Temp 36/10oC; blue- Rain
50 mm, Temp 36oC. Axes represent distance with almost ideal (A, B, C, D) ordination.
41
Chapter 5. Discussion
The factors shaping bacterial diversity and community composition in arid soils are
largely unknown. Surveys of soil bacteria community composition in general suggested
that pH (Lauber et al., 2008; Lauber et al., 2009; Fierer and Jackson, 2006) salinity
(Lozupone and Knight, 2007) and water content (Angel et al., 2010; Placella et al., 2012;
Barnard et al., 2013) shape the community composition, but would bacteria found in bulk
soils of arid environments obey the same rules? A spatial study of the bacterial composition
in arid and semi-arid soils suggested that precipitation plays an important role in the
community pattern (Angel et al., 2010). Moreover, a study comparing the patterns
portrayed by the total (DNA-based) and active (RNA-based) communities, suggested that
the latter better reflects fluctuation in the community than the former (Blazewitcz et al.,
2013). Could that suggest that in arid soils the main parameter shaping soil bacterial
community composition is precipitation?
The current work aimed to elucidate the effects of rain and desiccation rate on the
abundance and community composition of the soil bacteria in general, and of
Actinobacteria in particular. To this end, microcosms of desert soil were designed, in the
form of columns, which were constructed and packed with soil and subjected to different
amounts of rainfall and desiccation rate associated with temperature and diurnal cycle.
Subsamples from the microcosms’ soil were collected during the hydration-desiccation
cycle and the abundance, diversity and community composition of the soil bacterial
community as well as the physicochemical characteristics of the soil were monitored.
42
5.1. Soil physicochemical characteristics during hydration and desiccation
The Negev soil tested is not saline but it is basic and rich in calcium carbonate (Brady
and Weil, 2001). No major differences were observed in physicochemical parameters (pH,
salinity, total carbon, nitrite and nitrate) within and between experiments. None of the
tested physicochemical parameters were significantly affected by difference in sampling
depths at 4, 8 and 12 cm at the different conditions tested. Noticeable changes were
observed in water content throughout each experiment, especially with 50 mm initial rain
event. The dry soil sampled prior to the rain event had low water content (0.98% ± 0.21)
which increased following the rain simulation. The overall desiccation rate in each
experiment was strongly dependent on the temperature: it was much higher at 36°C than at
25°C (Table 1; Figure 2). Another parameter that controlled the overall desiccation rate was
the amount of water used for the rain simulation.
Thus, all physicochemical measurements in this study were in range of values that
may not be considered limiting to bacterial growth (Rietz and Haynes, 2003; Yuan et al.,
2007; Mavi et al., 2010). The fluctuations in the parameters values that were observed may
be attributed to stochasticity and probably have no effect on bacterial activity. The neutral
pH soils of deserts were shown to harbor higher number of bacterial taxa than the acidic
soils of tropical forests (Fierer and Jackson, 2006; Meklat et al., 2013). The pH values
measured in this study show little fluctuations and should have no effect on the bacterial
community.
Values of soil initial water content (prior to the rain event), pH, salinity, total carbon,
nitrite and nitrate are in line with previous studies conducted in the Avdat site at the Negev
43
desert (Angel et al., 2010; Bachar et al., 2010; Sher et al., 2013; Angel et al, 2013) (Tables
3 and 4).
5.2. Temporal dynamics of bacterial abundance during hydration and
desiccation
Under all the conditons tested the community sizes of Bacteria, Actinobacteria and
Fermicutes fluctuated slightly with time, but the changes were not significant throughout
each experiment (p > 0.05) (Figure 3; Table 5). This is in line with studies of Angel and
Conrad (2013), showing that hydration of the Negev soil crust did not affect the community
size of Bacteria, Archaea and Fungi throughout hydration-desiccation cycles. Only the
Firmicutes abundance significantly changed (p = 0.02) in soil incubated at 25°C and 36°C
and hydrated with 50 mm precipitation (Table 5). Although there were mostly no
significant differences throughout each experiment in the abundance of the three bacterial
groups following hydration with 50 mm rain, a slight decrease in the community size was
observed after the initial 12 hours, followed by a slight increase in abundance as the soil
water content decreased.
Extreme summer droughts, when soil water potentials commonly drop drastically
(Kieft et al., 1987), were suggested to cause a significant stress to soil microorganisms
(Potts, 1994) and were associated with minimal activity (Chou et al., 2008). Following a
hydration event and the sudden increase in water availability after the prolonged drought,
microbes were suggested to experience osmotic shock and die, serving as a potential
nutrient source for the cells that have passively equilibrated to the dry soil conditions and
survived (Kieft et al., 1987; Placella et al., 2012). Thus, the soil microbes that survived high
water potential conditions and accumulated organic and inorganic nutrients from dead cells
44
can now rapidly grow (Kieft et al., 1987). These observations are in line with our results:
the abundance of the Bacteria, Actinobacteria and Firmicutes had suddenly dropped during
the first 12 hours after the initial hydration (Figure 3), and then increased with the decrease
in soil water content. This pattern could also correlate with soil aeration. Once the soil is
over-hydrated it could inhibit aerobic microbes; later, desiccation leads to aeration of the
soil and oxygen is again accessible to the bacteria. Barnard et al. (2013) showed that in
California dry grasslands bacterial activity increased throughout the desiccation period.
Moreover, they could not detect significant changes in bacterial abundance 2 hours after
hydration, though it might have been too soon to detect any differences. However, when the
Actinobacteria activity was evaluated an increase in abundance was detected following the
desiccation and a reduction followed immediately (2 hours) after rewetting (Barnard et al.,
2013). It has been shown that these bacteria are able to grow under harsh dry conditions
(Goodfellow and Williams, 1983; Zvyagintsev et al., 2007). Actinobacetria may
accumulate ribosomes during the period of desiccation in anticipation for the next rain
event (Barnard et al., 2013). In contrast to relatively late response of Actinobacteria
described by Barnard et al. (2013), in the Mediterranean soil of California the relative
abundance of Actinobacteria was shown to increase rapidly within 15 minutes to 1 hour
after the hydration (Placella et al., 2012). The Firmicutes abundance did not change
throughout the desiccation period, albeit a slight decrease 2 hours after hydration (Barnard
et al., 2013). However, Placella et al. (2012) suggest that Firmicutes are intermediate
responders to soil hydration; increased activity of Bacilli was observed 3 to 24 hours after
hydration, a time which would have been sufficient for spore outgrowth. Concomitantly,
sequencing analysis of the arid and hyperarid soil crusts of the Negev desert suggested that
45
after one day of hydration the relative abundance of Bacilliales drastically increased and
then decreased over a period of 3 weeks (Angel and Conrad, 2013).
Here, we were unable to detect significant changes in total bacterial abundance
(Table 5; Figure 3), suggesting that although some taxa died, others grew, resulting in
reallocation of resources between bacterial response groups rather than an absolute increase
in bacterial biomass as was previously suggested (Placella et al., 2012).
5.3. Temporal dynamics of bacterial diversity during hydration and desiccation
Temporal fluctuations of soil Bacteria and Actinobacteria OUT’s diversities and their
relation to the experimental environmental factors (rain and temperature) were tested. The
Shannon-Weaver diversity index is a useful general diversity index that is influenced by
both species (OUT’s) richness and evenness, and is more sensitive to changes in abundance
of the rare groups (Hill et al., 2003).
The OTU diversity (H’) values obtained in our study are in the range of values
previously reported by Bachar et al. (2010) and Pereira e Silva et al. (2012) for arid soils.
No significant temporal changes were observed in the diversities of Bacteria nor in
Actinobacteria, in any of the experiments we had conducted (Figure 4). There were also no
noticeable changes in diversity between different experiments. The community composition
of Bacteria and Actinobacteria was experiencing temporal changes (Figures 7 and 8)
following changes in rain amount, incubation temperature and diurnal cycles, yet, the
diversity did not significantly change. This might indicate that there is a succession in the
community in contrast to the pulse-dynamics reported for desert plants during hydration-
desiccation cycles where plants wither and decline during desiccation and peak in biomass
and diversity following rainfall events (Coe et al., 2012). In the case of Bacteria and
46
Actinobacteria there is neither decline nor peak in OUT’s abundance or diversity following
the simulated rain events, which might suggest that the soil cannot support additional
species; as soon as one niche is vacated due to hydration another occupant immediately
takes its place.
5.4. Temporal dynamics of bacterial community composition during hydration
and desiccation
The NMDS analysis suggest that both bacteria and Actinobacteria communities
cluster differently before and after the applied treatments (Supplementary Figure 3) when
analyzed with the restriction enzymes HpyCH4IV and HapII. But when the comunity was
visualized following restriction with TaqI and HhaI no significantly differences were
detected. These might indicate that only members of the community differ due to the
applied treatments: HpyCH4IV and HapII spot these groups, while TaqI and HhaI cannot
point to members of the community that do not respond to the conditions employed in these
experiments. The identity or difference of said groups could be further elucidated by Next
Generation Sequencing type analysis.
Both the Bacteria and Actinobacteria community patterns showed temporal changes
in each of the experiments (Figures 5 and 6). NMDS analysis was performed to compare
the community composition of bacterial and actinobacterial communities following
hydration and desiccation in experiments which differed in desiccation rate resulting from
the amount of rain, temperature and diurnal cycles (Figures 7 and 8). According to the
results obtained the amount of rain significantly affected the community composition of
both Bacteria and Actinobacteria. The fingerprint patterns of both groups were also
significantly different when the soil columns were similarly hydrated but incubated under
47
different temperatures (Figures 7 and 8). Yet, no significant differences were found due to
diurnal temperature cycles.
Different studies conducted in arid and semi-arid environments have shown that
Bacteria, and especially Actinobacteria community patterns and their relative abundance
significantly change in response to hydration (Placella et al., 2012; Angel and Conrad,
2013; Angel et al., 2013; Barnard et al., 2013). As reported earlier, Actinobacteria are
among the groups having rapid response to hydration; their relative abundance increased
during desiccation and decreased 2 hours (Barnard et al., 2013) or 1 day (Angel and
Conrad, 2013) after the hydration. Alternatively, it was reported that Actinobacteria relative
abundance increases rapidly within 15 minutes to 1 hour after the hydration (Placella et al.,
2012). Our study suggests that the total abundance of Actinobacteria was unchanged during
hydration and desiccation, although their relative abundance may have changed.
Temperature directly affects water evaporation and thus the rate of soil desiccation,
yet diurnal cycles seem to have had marginal effect on desiccation. In arid environments
bacterial communities are well adapted to both high temperatures and diurnal fluctuations
in temperatures (Gestel et al., 2013). In this study temperature affected the overall
desiccation rate, especially in the first 2-5 days after the simulated rain event, when the
water content decreased faster at the higher temperature of 36°C. This is in agreement with
the results of Barnard et al. (2013) and indicates that the bacterial community composition
changes when a sudden rain event is applied on dry soil.
48
Chapter 6.
Conclusions
The current study assessed the effects of hydration-desiccation cycles on arid soil
bacterial community composition, diversity and abundance in controlled environments. The
aim of this study was to assess, under controlled laboratory conditions, the dynamics of
active (featuring the rRNA of the community) desert soil bacterial communities during
hydration-desiccation cycles, with special focus on Actinobacteria, the soil’s dominant
bacterial phylum. In addition we monitored some of the physicochemical features of the
soil including water content, pH, salinity, nitrate, nitrite and carbon content. We predicted
that arid soil bacterial communities would emulate plants pulse-dynamic response to
rainfall events exerting strong influence on abundance and diversity. The temporal changes
in arid soil Bacteria and Actinobacteria were monitored following the rain events and
different rates of desiccation.
None of the tested physicochemical parameters of the soil were significantly affected
by the different conditions tested, except for the water content. The bacterial and
actinobacterial diversity and abundance were also unaltered by hydration and desiccation
cycles, while the community composition was significantly changed after the rainfall.
Moreover, rain amount and incubation temperature altered the community, while diurnal
temperature cycles had no effect on the community composition.
The results suggest that the prediction that microorganisms respond to hydration
similarly to macroorganisms might not be valid. The bacterial community did not follow
the pulse-dynamic pattern but might have gone through succession-dynamics where
Bacteria and Actinobacteria died following the rain event, while other bacterial groups
49
grew, resulting in reallocation of resources between bacterial groups rather than an absolute
increase in bacterial biomass.
Future work
To test whether bacteria indeed go through succession in drought-rain-drought cycles,
high-throughput sequencing of the soil samples collected during the experiments should be
performed. The sequencing results will shed further light on the changes in the community
following hydration and desiccation.
50
Chapter 7.
References
Andrew DR, Fitak RR, Munguia-Vega A, Racolta A, Martinson VG, Dontsovag K. 2012.
“Abiotic factors shape microbial diversity in Sonoran Desert soils”. Applied and
Environmental Microbiology 78: 7527-1537.
Angel R, Pasternak X, Soares MIM, Conrad R, Gillor O. 2013. “Active and total
prokaryotic communities in dryland soils”. FEMS Microbiology Ecology 86: 130-
138.
Angel R, Soares MIM, Ungar ED, Gillor O. 2010. “Biogeography of soil Archaea and
Bacteria along a steep precipitation gradient”. ISME Journal 4: 553-563.
Angel R, Conrad R. 2013. “Elucidating the microbial resuscitation cascade in biological
soil crusts following a simulated rain event”. Environmental Microbiology 15: 1462-
2920.
Aouiche A, Bijani B, Zitouni A, Mathieu F, Sabaou N. 2013. “Antimicrobial activity of
saquayamycins produced by Streptomyces spp. PAL114 isolated from a Saharan
soil”. Journal of Medical Mycology doi: 10.1016/j.mycmed.2013.09.001
APHA. 2005. Standard methods for the examination of water and wastewater. 21st ed.
American Public Health Association, Washington, DC.
Bacchetti DGT, Aldred N, Clare AS, Burgess JG. 2011. “Improvement of phylum- and
class-specific primers for real-time PCR quantification of bacterial taxa”. Journal of
Microbiological Methods 86: 351-356.
51
Bachar A, Al-Ashhab A, Soares MIM, Sklarz MY, Angel R, Ungar ED, Gillor O. 2010.
“Soil microbial abundance and diversity along a low precipitation gradient”.
Microbial Ecology 60: 453-61.
Barcenas-Moreno G, Gomez-Brandon M, Rousk J, Baath E. 2009. “Adaptation of soil
microbial communities to temperature: comparison of fungi and bacteria in a
laboratory experiment”. Global Change Biology 15: 2950-2957.
Barnard RL, Osborne Catherine and Firestone MK. 2013. “Responses of soil bacterial and
fungal communities to extreme desiccation and rewetting”. ISME Journal 7: 2229-
2241.
Bartlett MS. 1937. “Properties of sufficiency and statistical tests”. Proceedings of the Royal
Society of London Series A 160: 268-282.
Bauer DF. 1972. “Constructing confidence sets using rank statistics”. Journal of the
American Statistical Association 67: 687-690.
Bell CW, McIntyre N, Cox S, Tissue DT, Zak JC. 2009. “Soil microbial responses to
temporal variations of moisture and temperature in a Chihuahuan Desert grassland”.
Microbial Ecology 56: 153-167.
Ben-Dov E, Shapiro O, Siboni N, Kushmaro A. 2006. “Advantage of using inosine at the 3’
termini of 16S rRNA gene universal primers for the study of microbial diversity”.
Applied Environmental Microbiology 72: 6902-6906.
Blazewicz SJ, Barnard RL, Daly RA, Firestone MK. 2013. “Evaluating rRNA as an
indicator of microbial activity in environmental communities: limitations and uses”.
ISME Journal 7: 2061-2068.
Brady NC and Weil RR. 2001. The Nature and Properties of Soils. 13th ed. Prentice Hall,
NJ.
52
Brock TD. 1975. “Effect of water potential on a Microcoleus (Cyanophyceae) from a desert
crust”. Journal of Phycology 11: 316-320.
Bull AT, Asenjo JA. 2013. “Microbiology of hyper-arid environments: recent insights from
the Atacama Desert, Chile”. Antonie Van Leeuwenhoek 103: 1173-1179.
Bull AT, Stach JEM, Ward AC, Goodfellow M. 2005. “Marine Actinobacteria:
perspectives, challenges, future directions”. Antonie Van Leeuwenhoek 87: 65-79.
Cable JM, Ogle K, Lucas RW, Huxman TE, Loik ME, Smith SD, Tissue DT, Ewers BE,
Pendall E, Welker JM, Charlet TN, Cleary M, Griffith A, Nowak RS, Rogers M,
Steltzer H, Sullivan PF, van Gestel NC. 2011. “The temperature responses of soil
respiration in deserts: a seven desert synthesis”. Biogeochemistry 103: 71-90.
Chambers JM. 1992. Linear models. Chapter 4 of Statistical Models in S eds J. M.
Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.
Chanal A, Chapon V, Benzerara K, Barakat M, Christen R, Achouak W, Barras F, Heulin
T. 2006. “The desert of Tataouine: an extreme environment that hosts a wide
diversity of microorganisms and radiotolerant bacteria”. Environmental Microbiology
8: 514-525.
Chou WW, Silver WL, Jackson RD, Thompson AW, Allen-Diaz B. 2008. “The sensitivity
of annual grassland carbon cycling to the quantity and timing of rainfall”. Global
Change Biology 14: 1382-1394.
Clark J, Campbell JH, Grizzle H, Acosta-Martinez V, Zak JC. 2009. “Soil microbial
community response to drought and precipitation variability in the Chihuahuan
desert”. Microbial Ecology 57: 248-260.
Clarke KR. 1993. “Non-parametric multivariate analyses of changes in community
structure”. Austral Ecology 18: 117-143.
53
Clauss MJ, Venable DL. 2000. “Seed germination in desert annuals: An empirical test of
adaptive bet hedging”. The American Naturalist 155: 168-186.
Coe KK, Belnap, J, Sparks JP. 2012. “Precipitation-driven carbon balance controls
survivorship of desert biocrust mosses”. Ecology 93: 1626-1636.
Colwell RR. 1997. “Microbial diversity: the importance of exploration and conservation”.
Journal of Industrial Microbiology and Biotechnology 18: 302-307.
Connon SA, Lester ED, Shafaat HS, Obenhuber DC, Ponce A. 2007. “Bacterial diversity in
hyperarid Atacama Desert soils”. Journal of Geophysical Research 112: doi
10.1029/2006JG000311.
Curtis TP, Sloan WT, Scannell JW. 2002. “Estimating prokaryotic diversity and its limits”.
Proceedings of the National Academy of Sciences of the United States of America
99: 10494-10499.
Davidson EA, Verchot LV, Cattanio JH, Ackerman IL, Carvalho JEM. 2000. “Effects of
soil water content on soil respiration in forests and cattle pastures of eastern
Amazonia”. Biogeochemistry 48: 53-69.
Dunbar JS, Takala S, Barns SM, Davis JA, Kuske CR. 1999. “Levels of bacterial
community diversity in four arid soils compared by cultivation and 16S rRNA gene
cloning”. Applied and Environmental Microbiology 65: 1662-1669.
Egert M, Friedrich MW. 2003. “Formation of pseudoterminal restriction fragments, a PCR-
related bias affecting terminal restriction fragment length polymorphism analysis of
microbial community structure”. Applied Environmental Microbiology 69: 2555-
2562.
Embley TM, Stackebrandt E. 1994. “The Molecular phylogeny and systematics of the
Actinomycetes”. Annual Review of Microbiology 48: 257-289.
54
Evans AS, Cabin RJ. 1995. “Can dormancy affect the evolution of post-germination traits-
The case of Lesquerella fendleri”. Ecology 76: 344-356.
Feling RH, Buchanan GO, Mincer TJ, Kauffman CA, Jensen PR, Fenical W. 2003.
“Salinosporamide A: a highly cytotoxic proteasome inhibitor from a novel microbial
source, a marine bacterium of the new genus Salinospora”. Angewandte Chemie-
International Edition 42: 355-357.
Fiedler HP, Bruntner C, Bull AT, Ward AC, Goodfellow M, Potterat O, Puder C, Mihm G.
2005. “Marine Actinomycetes as a source of novel secondary metabolites”. Antonie
Van Leeuwenhoek 87: 37-42.
Fierer N, Bradford MA, Jackson RB. 2007. “Toward an ecological classification of soil
bacteria”. Ecology 88: 1354-1364.
Fierer N, Jackson RB. 2006. “The diversity and biogeography of soil bacterial
communities”. Proceedings of the National Academy of Sciences of the United States
of America 103: 626-631.
Fierer N, Strickland MS, Liptzin D, Bradford MA, Cleveland CC. 2009. “Global patterns in
belowground communities”. Ecology Letters 12: 1238-1249.
Flärdh K, Buttner MJ. 2009. “Streptomyces morphogenetics: dissecting differentiation in a
filamentous bacterium”. Nature Reviews Microbiology 7: 36-49.
Gestel NC, Reischke S, Baath E. 2013. “Temperature sensitivity of bacterial growth in a
hot desert soil with large temperature fluctuations”. Soil Biology and Biochemistry
65: 180-185.
Girvan MS, Bullimore J, Pretty JN, Osborn AM, Ball AS. 2003. “Soil type is the primary
determinant of the composition of the total and active bacterial communities in arable
soils’. Applied and Environmental Microbiology 69: 1800-1809.
55
Goodfellow M, Williams ST. 1983. “Ecology of Actinomycetes”. Annual Reviews of
Microbiology 2: 75-77.
Golodets C, Sternberg M, Kigel J, Boeken, Henkin Z, Seligman NG, Ungar EG. 2013.
“From desert to Mediterranean rangelands: will increasing drought and inter-annual
rainfall variability affect herbaceous annual primary productivity?”. Climatic Change
doi: 10.1007/s10584-013-0758-8.
Gundlapally SR, Garcia-Pichel F. 2006. “The community and phylogenetic diversity of
biological soil crusts in the Colorado Plateau studied by molecular fingerprinting and
intensive cultivation”. Microbial Ecology 52: 345-357.
Halverson LJ, Jones TM, Firestone MK. 2000. “Release of intracellular solutes by four soil
bacteria exposed to dilution stress”. Soil Science Society of America 64: 1630-1637.
Handelsman, J. 2004. “Metagenomics: application of genomics to uncultured
microorganisms”. Microbiology and Molecular Biology Reviews 68: 669-685.
Harel D, Holzapfel C, Sternberg M. 2011. “Seed mass and dormancy of annual plant
populations and communities decreases with aridity and rainfall predictability”. Basic
Applied Ecology 12: 674-684.
Harris RF. 1981. “Effect of water potential on microbial growth and activity in soils in
water potential relations in soil microbiology” In: Parr JF, Gardner WR, Elliott LF.
(Eds.). Water Potential Relations in Soil Microbiology. Soil Science Society of
America, Madison, Wisconsin, pp 23-96.
Hill TCJ, Walsh KA, Harris JA, Moffett BF. 2003. “Using ecological diversity measures
with bacterial communities”. FEMS Microbiology Ecology 43: 1-11.
Holmes AJ, Bowyer J, Holley MP, O'Donoghue M, Montgomery M, Gillings MR. 2000.
“Diverse, yet-to-be-cultured members of the Rubrobacter subdivision of the
56
Actinobacteria are widespread in Australian arid soils”. FEMS Microbiology Ecology
33: 111-120.
Hughes JB, Hellmann JJ, Rickets TH, Bohannan JBM. 2001. “Counting the uncountable:
statistical approaches to estimating microbial diversity”. Applied and Environmental
Microbiology 67: 4399-4406.
Huxman TE, Snyder KA, Tissue D, Leffler AJ, Ogle K, Pockman W, Sandquist D, Potts
DL, Schwinning S. 2004. “Precipitation pulses and carbon fluxes in semiarid and arid
ecosystems”. Oecologia 141: 254-268.
Hyatt MT, Levinson HS. 1956. “Correlation of respiratory activity with phases of spore
germination and growth in Bacillus megaterium as influenced by manganese and
Lalanine”. Journal of Bacteriology 72: 176-183.
Jensen PR, Gontang E, Mafnas C, Mincer TJ, Fenical W. 2005. “Culturable marine
actinomycete diversity from tropical Pacific Ocean sediments”. Environmental
Microbiology 7: 1039-1048.
Jiang C, Xu L. 1993. “Actinomycete diversity in unusual habitats”. Actinomycetes 4: 47-
57.
Jones SE, Lennon JT. 2010. “Dormancy contributes to the maintenance of microbial
diversity”. PNAS 107: 5881-5886.
Jordan D, Beare MH. 1991. “A comparison of methods for estimating soil microbial
biomass carbon”. Agriculture, Ecosystems and Environments 34: 35-41.
Kennett RH, Sueoka N. 1971. “Gene expression during outgrowth of Bacillus subtilis
spores. The relationship between gene order on the chromosome and temporal
sequence of enzyme synthesis”. Journal of Molecular Biology 60: 31-44.
57
Kieft TE, Soroker E, Firestone MK. 1987. “Microbial biomass response to a rapid increase
in water potential when dry soil is wetted”. Soil Biological Biochemistry 19: 119-
126.
Kirk JL, Beaudette LA, Miranda H, Moutoglis P, Klironomos JN, Lee H, Trevors JT. 2004.
“Methods of studying soil microbial diversity”. Journal of Microbiological Methods
58: 169-188.
Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, Glockner FO. 2012.
“Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-
generation sequencing-based diversity studies” Nucleic Acids Research 41 pp. e1.
Kodikara JK, Barbour SL, Fredlund DG. 2000. “Desiccation cracking of soil layers”
presented at the Asian Cnference in Unsaturated Soils, UNSAT, Asia, Balkema,
Singapore, pp. 693-698.
Koeppel AF, Wertheim JO, Barone L, Gentile N, Krizanc D, Cohan FM. 2013. “Speedy
speciation in a bacterial microcosm: new species can arise as frequently as
adaptations within a species”. ISME Journal 7: 1080-1091.
Krebs CJ. 1989. Ecological Methodology. 654 pp. Harper Collins Inc., New York.
Kurapova AI, Zenova GM, Studnitsyna II, Kizilovab AK, Manucharovaa NA, Norovsurenc
Zh, Zvyagintseva DG. 2012. “Thermotolerant and thermophilic actinomycetes from
soils of Mongolia desert steppe zone”. Microbiology 81: 98-108.
Lam KS. 2006. “Discovery of novel metabolites from marine actinomycetes”. Current
Opinion in Microbiology 9: 245-251.
Lauber CL, Hamady M, Knight R, Fierer N. 2009. “Pyrosequencing-based assessment of
soil pH as a predictor of soil bacterial community structure at the continental scale”.
Applied and Environmental Microbiology 75: 5111-5120.
58
Lauber CL, Strickland MS, Bradford MA, Fierer N. 2008. “The influence of soil properties
on the structure of bacterial and fungal communities across land-use types”. Soil
Biology and Biochemistry 40: 2407-2415.
Le Houérou HN, Bingham RL, Skerbek W. 1988. “Relationship between the variability of
primary production and the variability of annual precipitation in world arid lands”.
Journal of Arid Environments 15: 1-18.
Lee LH, Cheah YK, Mohd Sidik S, Ab Mutalib NS, Tang YL, Lin HP, Hong K. 2012.
“Molecular characterization of Antarctic actinobacteria and screening for
antimicrobial metabolite production”. World Journal of Microbiology and
Biotechnology 28: 2125-2137.
Lewis K. 2007. “Persister cells, dormancy and infectious disease”. Nature Reviews of
Microbiology 5: 48-56.
Lloyd J, Taylor JA. 1994. “On the temperature dependence of soil respiration”. Functional
Ecology. 8: 315-323.
Lozupone CA, Knight R. 2007. “Global patterns in bacterial diversity”. Proceedings of the
National Academy of Sciences 104: 11436-11440.
Mattimore V, Battista JR. 1996. “Radioresistance of Deinococcus radiodurans: functions
necessary to survive ionizing radiation are also necessary to survive desiccation”.
Journal of Bacteriology 178: 633-637.
Mavi MS, Marschner P, Chittleborough DJ, Cox JW. 2010. “Microbial activity and
dissolved organic matter dynamics in the soils are affected by salinity and sodicity”.
19th World Congress of Soil Science, Soil Solutions for a Changing World.
McGarigal Kevin, Cushman Sam, Stafford Susan. 2000. “Multivariate statistics for wildlife
and ecology research”. NY 10013, USA.
59
Meklat A, Bouras N, Zitouni A, Mathieu F, Lebrihi A, Schumann P, Spröer C, Klenk HP,
Sabaou N. 2013. “Actinopolyspora saharensis sp. nov., a novel halophilic
actinomycete isolated from a Saharan soil of Algeria”. Antonie Van Leeuwenhoek
103: 771-776.
Meyer M, Kircher M. 2010. “Illumina sequencing library preparation for highly
multiplexed target capture and sequencing”. Cold Spring Harbor Protocols 6:
doi:10.1101/pdb.prot5448.
Mielke PW, Berry KJ, Brier GW. 1981. “Application of multi-response permutation
procedures for examining seasonal changes in monthly mean sea-level pressure
patterns”. Monthly Weather Review 109: 120-126.
Mishurov M, Yakirevich A, Weisbrod N. 2008. ‘Colloid transport in a heterogeneous
partially saturated sand column”. Environmental Science and Technology 42: 1066-
1071.
Murrell P. 2005. “R Graphics”. Chapman and Hall/CRC Press.
Myles Hollander and Douglas A. Wolfe. 1973. “Nonparametric Statistical Methods”. New
York: John Wiley & Sons. Pages 115-120.
Neilson JW, Quade J, Ortiz M, Nelson WM, Legatzki A, Tian F, LaComb M, Betancourt
JL, Wing RA, Soderlund CA, Maier RM. 2012. “Life at the hyperarid margin: novel
bacterial diversity in arid soils of the Atacama Desert, Chile”. Extremophiles 16: 553-
566.
Noy-Meir I. 1973. “Desert ecosystems: environment and producers”. Annual Review of
Ecology, Evolution and Systematics 4: 25-51.
60
Okoro ChK, Brown R, Jones AL, Andrews B, Asenjo JA, Goodfellow M, Bull AT. 2009.
“Diversity of culturable actinomycetes in hyper-arid soils of the Atacama desert,
Chile”. Antonie van Leeuwenhoek 95: 121-133.
Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O’Hara RB, Simpson GL,
Solymos P, Stevens MHH, Wagner H. 2013. “Community ecology package”. Version
2.0-8. http://cran.r-project.org/web/packages/vegan/vegan.pdf.
Onyenwoke RU, Brill OJA, Farahi K, Wiegel J. 2004. “Sporulation genes in members of
the low G+C Gram-type-positive phylogenetic branch (Firmicutes)”. Archives of
Microbiology 182: 182-192.
Pereira e Silva MC, Dias ACF, van Elsas JD, Salles JF. 2012. “Spatial and Temporal
Variation of Archaeal, Bacterial and Fungal Communities in Agricultural Soils”.
PLoS ONE 7: 515-554.
Placella SA, Brodie EL, Firestone MK. 2012. “Rainfall induced carbon dioxide pulses
result from sequential resuscitation of phylogenetically clustered microbial groups”.
Proceedings of National Academy of Science USA 109: 10931-10936.
Potts M. 1994. “Desiccation tolerance of prokaryotes”. Microbiological Reviews 58: 755-
805.
Pringault O, Garcia-Pichel F. 2004. “Hydrotaxis of cyanobacteria in desert crusts”.
Microbial Ecology 47: 366-373.
Qi Y, Xu M, Wu J. 2002. “Temperature sensitivity of soil respiration and its effects on
ecosystem carbon budget: nonlinearity begets surprise”. Ecological Modelling 153:
131-142.
Rainey FA, Ray K, Ferreira M, Gatz BZ, Nobre MF, Bagaley D, Rash BA, Park MJ, Earl
AM, Shank NC, Small AM, Henk MC, Battista JR, Kämpfer P, da Costa MS. 2005.
61
“Extensive Diversity of Ionizing-Radiation-Resistant Bacteria Recovered from
Sonoran Desert Soil and Description of Nine New Species of the Genus Deinococcus
Obtained from a Single Soil Sample”. Applied Environmental Microbiology 71:
5225-5235.
Rajeev L, Nunes da Rocha U, Klitgord NJ, Luning EG, Fortney J, Axen SD, Shih PM,
Bouskill NJ, Bowen B, Kerfeld CA, Garcia-Pichel F, Brodie EL, Northen TR,
Mukhopadhyay A. 2013. “Dynamic cyanobacterial response to hydration and
dehydration in a desert biological soil crust”. ISME Journal 7: 2178-2191.
Rees GN, Baldwin DS, Watson GO, Perryman S, Nielsen DL. 2004. “Ordination and
significance testing of microbial community composition derived from terminal
restriction fragment length polymorphisms: application of multivariate statistics”.
Antonie Van Leeuwenhoek 86: 339-347.
Reichstein M, Tenhunen JD, Roupsard O, Ourcival JM, Rambal S, Dore S, Valentini R.
2002. “Ecosystem respiration in two Mediterranean evergreen Holm Oak forests:
drought effects and decomposition dynamics”. Functional Ecology 16: 27-39.
Rietz DN, Haynes RJ. 2003. “Effects of irrigation induced salinity and sodicity on soil
microbial activity”. Soil Biology and Biochemistry 35: 845-854.
Rosso L, Lobry JR, Flandrois JP. 1993. “An unexpected correlation between cardinal
temperatures of microbial growth highlighted by a new model”. Journal of
Theoretical Biology 162: 447-463.
Roszak DB, Colwell RR. 1987. “Survival strategies of bacteria in the natural environment”.
Microbiological Reviews 51: 365-379.
Royston P. 1995. “Remark AS R94: A remark on algorithm AS 181: the W test for
normality”. Applied Statistics 44: 547-551.
62
Sait M, Hugenholtz P, Janssen P. 2002. “Cultivation of globally distributed soil bacteria
from phylogenetic lineages previously only detected in cultivation-independent
surveys”. Environmental Microbiology 4: 654-666.
Schwinning S, and Sala O. 2004. “Hierarchy of responses to resource pulses in arid and
semi-arid ecosystems”. Oecologia 141: 211-220.
Seligman NG, van Keulen H. 1989. “Herbage production of a Mediterranean grassland in
relation to soil depth, rainfall and nitrogen nutrition: a simulation study”. Ecological
Modeleing 47: 303-311.
Sher Y, Zaady E, Nejidat A. 2013. “Spatial and temporal diversity and abundance of
ammonia oxidizers in semi-arid and arid soils: indications for a differential seasonal
effect on archaeal and bacterial ammonia oxidizers”. FEMS Microbiology Ecology
doi: 10.1111/1574-6941.12180.
SSSA. 1996. Methods of Soil Analysis, Part 3, Soil Science Society of America. Madison,
WI, USA.
Stach JE, Maldonado LA,Ward AC, Goodfellow M, Bull AT. 2003. “New primers for the
class Actinobacteria: application to marine and terrestrial environments”.
Environmental Microbiology 5: 828-841.
Swiercz JP, Nanji T, Gloyd M, Guarné A, Elliot MA. 2013. “A novel nucleoid-associated
protein specific to the actinobacteria”. Nucleic Acit Research doi: 10.1093/nar/gkt095
Taguchi YH, Oono Y. 2004. “Relational patterns of gene expression via non-metric
multidimensional scaling analysis”. Bioinformatics 21: 730-740.
Torsvik V, Sorheim R, Golsoyr J. 1996. “Total bacterial diversity in soil and sediment
communities”. Journal of Industrial Microbiology 17: 170-178.
63
Ventura M, Canchaya C, Tauch A, Chandra G, Fitzgerald GF, Chater K, van Sinderen D.
2007. “Genomics of Actinobacteria: tracing the evolutionary history of an ancient
phylum”. Microbiology and Molecular Biology Reviews 71: 495-548.
Vishnevetsky S, Steinberger Y. 1997. “Bacterial and fungal dynamics and their
contribution to microbial biomass in desert soil”. Journal of Arid Environments 37:
83-90.
Waksman SA, Gerretsen FC. 1931. “Influence of temperature and moisture upon the nature
and extent of decomposition of plant residues by microorganisms”. Ecology 12: 33-
60.
Watve MG, Tickoo R, Jog MM, Bhole BD. 2001. “How many antibiotics are produced by
the genus Streptomyces?”. Archives of Microbiology 176: 386-390.
Wawrik B, Kudiev D, Abdivasievna UA, Kukor JJ, Zystra GJ, Kerkhof L. 2007.
“Biogeography of actinomycete communities and type II polyketide synthase genes
in soils collected in New Jersey and Central Asia”. Applied and Environmental
Microbiology 73: 2982-2989.
Wen XF, Yu GR, Sun XM, Li QK, Liu YF, Zhang LM, Ren CY, Fu YL, Li ZQ. 2006.
“Soil moisture effect on the temperature dependence of ecosystem respiration in a
subtropical Pinus plantation of southeastern China”. Agricultural and Forest
Meteorology 137: 166-175.
Whitford. 1996. “The importance of the biodiversity of soil biota in arid ecosystems”.
Biodiversity and Conservation 5: 185-195.
Whitman WB, Coleman DC, Wiebe WJ. 1998. “Prokaryotes: the unseen majority”.
Proceedings of the National Academy of Sciences of the USA 95: 6578-6583.
64
Wilkinson GN, Rogers CE. 1973. “Symbolic descriptions of factorial models for analysis
of variance”. Applied Statistics 22: 392-9.
Williams MA, Rice CW. 2007. “Seven years of enhanced water availability influences the
physiological, structural, and functional attributes of a soil microbial community”.
Applied Soil Ecology 35: 535-545.
Worden AZ, Binder BJ. 2003. “Growth regulation of rRNA content in Prochlorococcus and
Synechococcus (marine cyanobacteria) measured by whole-cell hybridization of
rRNA-targeted peptide nucleic acids”. Journal of Phycology 39: 527-534.
Yuan BC, Li ZZ, Liu H, Gao M, Zhang YY. 2007. “Microbial biomass and activity in salt
affected soils under arid conditions”. Applied Soil Ecology 35: 319-328.
Zaady E. 2005. “Seasonal change and nitrogen cycling in a patchy Negev desert: a review”.
Arid Land Research and Management 19: 111-124.
Zak DR, Holmes WE, White DC, Peacock AD, Tilman D. 2003. “Plant diversity, soil
microbial communities, and ecosystem function: are there any links?”. Ecology 84:
2042-2050.
Zak JC, Willig MR, Moorhead DL, Wildman HG. 1994. “Functional diversity of microbial
communities - a quantitative approach”. Soil Biology and Biochemistry 26: 1101-
1108.
Zvyagintsev DG, Zenova GM, Doroshenko EA, Gryadunova AA, Gracheva TA, Sudnitsyn
IJ. 2007. “Actinomycete growth in conditions of low moisture”. Biology Bulletin 34:
242-247.
65
Supplementary Data
Supplementary Table 1. MRPP pairwise test between samples collected before and after
treatments. P and A values are based on either Bray-Curtis or Euclidean distance measures of the
respective dissimilarity matrixes.
MRPP values
Bray-Curtis distance Euclidean distance
Bacteria- TaqI A = 0.004511
p = 0.182
A = 0.007031
p = 0.12
Bacteria- HpyCH4IV A* = 0.01758
p = 0.012
A* = 0.01553
P = 0.014
Actinobacteria-
HapII
A* = 0.01088
p = 0.041
A*= 0.01046
p = 0.047
Actinobacteria- HhaI A = 0.004511
p = 0.182
A = 0.007031
p = 0.12
* Moderate significance (p < 0.05)
Supplementary Table 2. MRPP pairwise analyses between different experimental conditions. P
and A values are based on Euclidean distance measured of the respective dissimilarity matrixes.
Temp: 25°C
Rain: 10 mm
vs. 50 mm
Rain: 50 mm
Temp: 25°C vs.
36/10°C
Rain: 50 mm
Temp: 25°C vs.
36°C
Rain: 50 mm
Temp: 36°C vs.
36/10°C
Bacteria-
TaqI
A** = 0.0835
p = 0.001
A** = 0.0998
p = 0.001
A* = 0.0347
p = 0.036
A** = 0.0254
p = 0.006
Bacteria-
HpyCH4IV
A** = 0.0693
p = 0.001
A** = 0.0511
p = 0.001
A* = 0.0266
p = 0.007
A = 0.0112
p = 0.124
Actinobacteria-
HapII
A** = 0.0994
p = 0.001
A** = 0.0784
p = 0.001
A* = 0.0450
p = 0.005
A = 0.0104
p = 0.099
Actinobacteria-
HhaI
A* = 0.0181
p = 0.072
A* = 0.0477
p = 0.005
A* = 0.0253
p = 0.038
A = 0.0185
p = 0.083
* Moderate significance (p < 0.05)
** High significance (p < 0.005)
66
Supplementary Figure 1. Values of (A) soil water content (%), (B) pH and (C) EC (ds/m) at the
depths of 4, 8 and 12 cm depth, under the different conditions of temperature and rain amount.
67
Supplementary Figure 2. Shannon-Weaver diversity indices of (A) Bacteria using restriction
enzume HpyCH4IV and (B) Actinobacteria, using restriction enzyme HhaI.
68
Supplementary Figure 3. Non-metric multidimensional scaling (NMDS) ordinations of active
bacterial (A and B) and actinobacterial (C and D) communities in arid soil microcosms before
and after the rain event. The different experimental conditions are marked by colors defining
the grouping of the samples by time-factor. NT- samples with no treatment which were collected
before the rain event, i.e. samples from the 0-time point. AR- samples collected from all four
experiments and all three columns after the rain event for each of the restriction enzyme used.
Axes represent distance with almost ideal (A, B, C, D) ordination.
69
Supplementary Figure 4. Bacterial community composition dynamics within time in different
microcosms experiments. (A) Rain 50 mm, Temp 25oC; (B) Rain 10 mm, Temp 25oC; (C) Rain
50 mm, Temp 36/10oC; (D) Rain 50 mm, Temp 36oC. The segments represent abundance of
each peak (OTU) as percentage of all the peaks present in a given sample.
70
Supplementary Figure 3. Actinobacterial community composition dynamics within time in
different microcosms experiments. (A) Rain 50 mm, Temp 25oC; (B) Rain 10 mm, Temp 25oC;
(C) Rain 50 mm, Temp 36/10oC; (D) Rain 50 mm, Temp 36oC. The segments represent
abundance of each peak (OTU) as percentage of all the peaks present in a given sample.
Supplementary Figure 5. Actinobacterial community composition dynamics within time in
different microcosms experiments. (A) Rain 50 mm, Temp 25oC; (B) Rain 10 mm, Temp 25oC;
(C) Rain 50 mm, Temp 36/10oC; (D) Rain 50 mm, Temp 36oC. The segments represent
abundance of each peak (OTU) as percentage of all the peaks present in a given sample.
71
1
Supplementary Figure 6. Non-metric multidimensional scaling (NMDS) ordinations of active
Bacteria communities in arid soil microcosms upon hydration and desiccation. The different
experimental conditions (see Table 1) are marked by color, Black- Rain 50 mm, Temp 25oC;
red- Rain 10 mm, Temp 25oC; green- Rain 50 mm, Temp 36/10oC; blue- Rain 50 mm, Temp
36oC. Axes represent distance with almost ideal (A, B, C, D) ordination.
72
Supplementary Figure 7. Non-metric multidimensional scaling (NMDS) ordinations of active
Actinobacteria communities in arid soil microcosms upon hydration and desiccation. The
different experimental conditions (see Table 1) are marked by color: black- Rain 50 mm, Temp
25oC; red- Rain 10 mm, Temp 25oC; green- Rain 50 mm, Temp 36/10oC; blue- Rain 50 mm,
Temp 36oC. Axes represent distance with ideal (A, D) and almost ideal (B, C) ordination.
73
Appendix 1.
Recipe for RNAlater - RNA preservation medium
Overview: This buffer mimics the RNAlater that can be bought from Ambion. Both
DNA and RNA are stable at room temperature in this buffer.
Materials: EDTA disodium, dehydrate; sodium citrate trisodium salt, dehydrate;
ammonium sulfate; ultrapure water; H2SO4 to adjust the pH, if necessary.
Preparation: 40 ml 0.5 M EDTA, 25 ml 1M sodium citrate, 700 g ammonium sulfate
and 935 ml of sterile distilled water were combined and stirred on a hot plate stirrer on low
heat until the ammonium sulfate completely dissolved. The solution was allowed to cool
and its pH was adjusted to 5.2 using 1M H2SO4. The ready solution was transferred to a
screw top sterile bottle and stored either at room temperature or refrigerated.
To make 40 mL of 0.5M EDTA: 7.44 g EDTA were added to 40 mL ultrapure water
and pH was adjusted to 8.
To make 25 mL of 1 M sodium citrate: 5.88 g EDTA were added to 25 mL ultrapure
water.
Precautions: All the flasks and bottles used for preparing or storing the solutions were
sterilized and baked in the oven at 180°C for at least 3 hours.
74
Appendix 2.
Euclidean distance of the T-RFLP profiles of the samples.
The Euclidian distance (ED) between two groups (j and k) based on n variables
(number of peaks) is defined as
𝐸𝐷𝑗𝑘 = √ ∑(𝑥𝑖𝑗 − 𝑥𝑖𝑘)2
𝑛
𝑖=1
where 𝑥𝑖𝑗 and 𝑥𝑖𝑘 are the height of peak i in j and k groups, respectively (McGarigal et al.,
2000).