Merit Research Journal of Microbiology and Biological Sciences (ISSN: 2408Available online http://www.meritresearchjournals.org/mbs/index.htm Copyright © 2016 Merit Research Journals

Original Research Article

Prokaryotic diversity from the culturetaxonomic analysis of

Ramón Alberto Batista-García1,

Jackson4, Alan D. W. Dobson

1Centro de Investigación en Dinámica Celular, Universidad Autónoma del Estado de Morelos. Cuernavaca,

Morelos, Mexico.

2Centro de Investigación en

Biotecnología, Universidad Autónoma del Estado de Morelos. Cuernavaca,

Morelos, Mexico.

3Instituto Tecnológico de Zacatepec.

Zacatepec, Morelos, Mexico.

4Marine Biotechnology Centre,

Environmental Research Institute, University College Cork. Cork, Ireland.

5School of Microbiology, University

College Cork. Cork, Ireland.

*Corresponding Author’s E-mail: rbatista25@yahoo.com,

rabg@uaem.mx +(52) 777-3297057 Ext. 4550 +(52) 7773297057 Ext. 3457

A 16S rDNA metagenome library was constructed from sugar bagasse samples and a phylogenetic analysis was conducted. This allowed us to explore the biodiversity present in the bagasse samples and identify novbacterial groups inhabiting this lignocellulosic rich substrate. The most abundant phyla were division TM7 was the minor phyla. Overall, the prokaryotic microbial diversity revealed the presence of 13 families and 17 genera, with Burkholderiaceae

observed, respectively. Since we were also bacterial genera, we could identify some specific metabolic pathways that may be important in lignocellulose degradation. Microbial communities inhabiting natural lignocellulosic substrates have to date received little attention, genera present in these substrates resulting in a lack of a comprehensive knowledge about the microbial process involved in lignocellulosic degradation. This study provides some insight intocommunity structure in sugarcane bagasse from Meso Americ (the most important sugar producing region in the world) and is the first such study to characterize these bagasse inhabiting populations. Keywords: environments, bacterial communities.

INTRODUCTION Plant biomass is the most important, abundant and widespread source of carbon on earth. For many years, plant biomass was considered as a waste and as a result its use was totally under exploited. However, more recently there has been an increased interestintegrated management of biomass, and especially of sugarcane bagasse (Li et al., 2009). A range of applications, such as the production of ethanol, methanol, biodiesel, oil, hydrogen, plastics, paper, polymers, resins, electricity and other raw materials, have driven the use of biomass (Demirbas, 2005). Particularly, sugarcane bagasse obtained from sugar mills is currently widely

Merit Research Journal of Microbiology and Biological Sciences (ISSN: 2408-7076) Vol. 4(1) pp. 022-038Available online http://www.meritresearchjournals.org/mbs/index.htm

Prokaryotic diversity from the culture-independent taxonomic analysis of a sugarcane bagasse

metagenome

1,2*, Rocío Casasanero2, Alberto Alvárez-Castillo, Alan D. W. Dobson4,5 and Jorge Luis Folch-Mallol

Abstract

A 16S rDNA metagenome library was constructed from sugar bagasse samples and a phylogenetic analysis was conducted. This allowed us to explore the biodiversity present in the bagasse samples and identify novbacterial groups inhabiting this lignocellulosic rich substrate. The most abundant phyla were Proteobacteria and Acidobacteria

division TM7 was the minor phyla. Overall, the prokaryotic microbial diversity revealed the presence of 13 families and 17 genera, with Burkholderiaceae and Burkholderia being the major family and genus observed, respectively. Since we were also able to detect some known bacterial genera, we could identify some specific metabolic pathways that may be important in lignocellulose degradation. Microbial communities inhabiting natural lignocellulosic substrates have to date received little attention, subsequently there is little information about the main bacterial genera present in these substrates resulting in a lack of a comprehensive knowledge about the microbial process involved in lignocellulosic degradation. This study provides some insight intocommunity structure in sugarcane bagasse from Meso Americ (the most important sugar producing region in the world) and is the first such study to characterize these bagasse inhabiting populations.

Keywords: Metagenomic approaches, 16S rDNA biodiversity, cellulolytic environments, bacterial communities.

Plant biomass is the most important, abundant and widespread source of carbon on earth. For many years, plant biomass was considered as a waste and as a result its use was totally under exploited. However, more recently there has been an increased interest in the integrated management of biomass, and especially of

, 2009). A range of applications, such as the production of ethanol, methanol, biodiesel, oil, hydrogen, plastics, paper, polymers, resins,

terials, have driven the use of biomass (Demirbas, 2005). Particularly, sugarcane bagasse obtained from sugar mills is currently widely

used in both paper and energy production (Guimarães al., 2012).

Biomass-based biorefineries have gained immense popularity in recent years. Challenges still exist however, in particularly with respect to the lack of costbreak-through technologies to allow the conversion bagasse into alcohol or other products (Himmel, 2008). The main problem is the low yields osaccharification of the bagasse. This is a complex process and it is often defined as a critical step, depending on the chemical composition of the biomass (Chen et al., 2014; Geng et al., 2014).

038, January, 2016

independent a sugarcane bagasse

Castillo3, Stephen A. Mallol2

A 16S rDNA metagenome library was constructed from sugar bagasse samples and a phylogenetic analysis was conducted. This allowed us to explore the biodiversity present in the bagasse samples and identify novel bacterial groups inhabiting this lignocellulosic rich substrate. The most

Acidobacteria, while Candidate division TM7 was the minor phyla. Overall, the prokaryotic microbial diversity revealed the presence of 13 families and 17 genera, with

being the major family and genus able to detect some known

bacterial genera, we could identify some specific metabolic pathways that may be important in lignocellulose degradation. Microbial communities inhabiting natural lignocellulosic substrates have to date received little

about the main bacterial genera present in these substrates resulting in a lack of a comprehensive knowledge about the microbial process involved in lignocellulosic degradation. This study provides some insight into the unique microbial community structure in sugarcane bagasse from Meso Americ (the most important sugar producing region in the world) and is the first such study to

S rDNA biodiversity, cellulolytic

used in both paper and energy production (Guimarães et

based biorefineries have gained immense arity in recent years. Challenges still exist however,

in particularly with respect to the lack of cost-effective through technologies to allow the conversion

bagasse into alcohol or other products (Himmel, 2008). The main problem is the low yields obtained from saccharification of the bagasse. This is a complex process and it is often defined as a critical step, depending on the chemical composition of the biomass

, 2014).

Sugarcane bagasse is predominantly composed of three polysaccharides: cellulose, hemicellulose and pectin and the heterogeneous aromatic polymer (lignin) (Rezende et al., 2011; Liu et al., 2012). Cellulose is a homopolymer, the major reservoir of glucose in nature; while pectin, lignin and hemicellulose are complex heteropolymers of organic acids, cyclic phenolic compounds and sugars, respectively. Different molecular interactions result in tight complexes being formed between these polymers. Hemicellulose, for example, has a β-(1→4)-linked backbone made up of xyloglucans, xylans, mannans and glucomannans, and β-(1→3, 1→4)-glucans (Scheller and Ulvskov, 2010). Different branching monomers (D-galactose, D-xylose, L-arabinose, D-glucuronic acid) result in various types of hemicelluloses being formed. Moreover several acids (ferulic and acetic, mainly) also confer a higher level of structural complexity because they can esterify with hemicellulose (Brink and Vries, 2011).

Recalcitrant crystalline and amorphous structures of these polymers limit sugarcane bagasse conversion into fermentable sugars for further use in biorefineries (Liu et al., 2012; Geng et al., 2014). Current understanding of how to efficiently transform this material through technologically scalable, replicable and sustainable processes is as yet largely incomplete.

On average the quantitative composition of sugarcane bagasse is generally 35 – 50% cellulose with 20 – 30% each of hemicellulose and lignin, and usually also contains aliphatic acids (1 – 3%) (acetic, formic, and levulinic acids), furan derivatives (furfural and hydroxymethylfurfural) and phenolic compounds (Chandel et al., 2014). Its chemical nature (qualitative and quantitative) and structure define it as one of the most recalcitrant wastes in the agriculture industry (Rattanachomsri et al., 2011).

With one ton of processed sugarcane generating on average 250 kg of bagasse (Guimarães et al., 2012), and the annual increase in demand for sugar the production of bagasse continues to increase. Thus there is an increased interest in the lignocellulose-based biorefineries area with a particular focus on the use of cultured-independent metagenomic based taxonomic analysis of microbial communities present in the lignocellulosic rich bagasse ecological niche. A more comprehensive knowledge of the sugarcane bagasse microbiome will provide further insights into the microbial community structure and metabolic potential and help define their potential role as lignocellulose degraders, thereby increasing our understanding of the natural degradation processes that occur in lignocellulolytic rich environments such as bagasse. Moreover, it may ultimately help facilitate a better and more optimal use of bagasse as an industrial resource for biofuels and other products.

Bacteria are the most abundant, diverse and widespread microorganisms on earth. They colonize a

Batista-Garcia et al. 023 huge variety of natural environments, including very specialized ecological niches such as lignocellulosic rich habitats; which is largely possible due to the immense metabolic diversity and genetic adaptability of microbes (Leis et al., 2013). Given that only an estimated <1% of microorganisms are cultivable under laboratory conditions (Simon and Daniel, 2011), metagenomic based tools have been successfully employed to conduct comprehensive ecological studies of bacterial communities in a wide variety of ecosystems. Structural metagenomics allows both the composition and structure of microbial communities to be assessed by describing the major genera and species that inhabit an ecosystem (Simon and Daniel, 2011). It can also provide information about the role of microorganisms in the biogeochemical cycles, propose ecological interactions between members of different microbial communities and postulate hypotheses about evolutionary aspects of specific ecosystems (Simon and Daniel, 2009).

While a wide range of quite diverse metagenomes have to date been extensively analysed (Rasheed et al., 2013; Curson et al., 2014; Jadeja et al., 2014; Kanwar et al., 2014; Patel et al., 2014; Tseng and Tang, 2014; Xu et al., 2014) very few metagenomic studies have been performed on the microbiomes of lignocellulosic based substrates (Rattanachomsri et al., 2011; Kanokratana et al., 2013). Sugarcane bagasse piles stored near sugar mills are an excellent cellulolytic environment and represent a unique habitat in which to study lignocellulosic and metabolically versatile microbial communities. The prokaryotic populations colonizing sugarcane bagasse in particular and other cellulosic materials in general have surprisingly been quite poorly studied to date and consequently there is lack of a comprehensive understanding of their microbial ecology.

The objective of our work was to analyse the prokaryotic diversity of a sugarcane bagasse metagenome, and subsequently, the structural composition of the bacterial communities was obtained by employing cultured-independent methods. The V3 – V6 region of the 16S rDNA gene was used to analyse the bacterial populations, and a sequence dataset was analysed with strong algorithms using two taxonomic classifiers. Our groups are interested in biological pre treatments of lignocellulosic materials for biorefinery purposes and by exploring the biodiversity profiles, this work provides new insights to better understand the microbial populations present in sugarcane bagasse piles and thus may provide insights into new strategies for more efficient lignocellulose degradation. MATERIALS AND METHODS Sugarcane bagasse sampling Decaying humid sugarcane bagasse sampling was

024 Merit Res. J. Microbiol. Biol. Sci.

Figure 1. Sugarcane bagasse sampling. (A) Location of the collection site at Zacatepec municipality (Morelos State), Mexico. Cartesian coordinates were estimated by measuring satellite instruments (GPS: global positioning system, Model eTrex 20).(B) General scheme of sampling for one pile. Three samples in triplicates (R1, R2 and R3) of sugarcane bagasse were taken at different levels (surface, core and bottom) of the column of eachsampled pile. A compost sample was obtained pooling R1, R2 and R3 from each sampling level (CSS: surface compost sample, CSC: core compost sample and CSB: bottom compost sample). A final compost sample (CSF) for further experiments was obtained pooling CSS, CSC and CSB.

performed at the beginning of spring (April) 2008 in the bagasse yard of a Sugar Mill (N 18°39'15.9", W 99°11'10.1") located in Zacatepec, a municipality in the central-south region in the state of Morelos in Mexico (Figure 1, A). Bagasse samples were collected from large open-air piles (10 - 12 m height), which had been standing in the field for approximately five months prior to the time of collection.

Three triplicate samples (1 kg) from three different piles were taken. These were collected from different positions in each pile, namely from the surface (20 cm below the surface), the core (10 m) and the bottom (20 cm above the soil) of the pile. The samples were then mixed to homogeneity prior to DNA extraction as shown in Figure 1, B. This strategy was employed to ensure samples were taken from potentially different microenvironments within the piles (oxygen, temperature, humidity, radiation, pH). Subsequently, a composted sample for further experiments was generated, and consequently a representative sample was obtained (Figure 1, B). The bagasse samples were then placed in sterile plastic bags and stored on dry ice for transport to the laboratory where they were subsequently frozen at -80

oC.

The sampling was performed during the dry season to avoid ecological succession due to rain washing. The meteorological station in the Zacatepec´s locality registered, an average temperature of 28

oC and 0.8 mm

of accumulated rainfall in April 2008. The rainy season is from July until September of each year, and the average

annual rainfall in this locality is typically 800 – 1200 mm. The probability of rain in the dry season is very infrequent. The temperature in the bagasse pile surface was 30 ± 1

oC, while in the core it was 40 ± 1

oC.

Chemical sugarcane bagasse characterization The lignin, cellulose and hemicellulose composition of the bagasse sample was determined according to Dominguez-Dominguez et al. (2012). The bagasse was ground and dried at 70

oC for 24 h. To determine the

cellulose composition, 15 mL of acetic acid (80%) and 1.5 mL of concentrated nitric acid were added to 1 g of milled bagasse. The sample was treated at reflux for 20 min and filtered thereafter. The residue was washed with absolute ethanol, baked dried at 100

oC and finally weighted

(material A). It was later burned at 540 oC, cooled to room

temperature in a desiccator and weighted (material B). The cellulose percentage was calculated using the following equation:

%��������� =��� ���� − ��� ����

���������ℎ�∗ 100

To determine the lignin composition, 70 mL of sulphuric acid (1.25%) was added to 1 g of milled bagasse. The sample was treated at reflux for 120 min, filtered thereafter and then washed with water. Subsequently 30 mL of sulphuric acid (72%) was added and the material

was agitated at 100 rpm for 4 h. The solids were then recovered, filtrated, washed, dried (100

oC) and weighted

(material C). The material was cremated at 540 oC,

cooled to room temperature in a desiccator and weighted (material D). The lignin percentage was calculated using the following equation:

%������ =��� ���� − ��� ����

���������ℎ�∗ 100

The percentage of hemicellulose was calculated considering the cellulose and lignin compositions: %ℎ����������� = 100% − (%��������� + %������) The residual reducing sugars were calculated using the DNS (3,5-dinitrosalicylic acid) method with a glucose standard curve (Miller et al., 1959). The total reducing sugars were expressed per g of bagasse. Metagenomic DNA extraction from sugarcane bagasse 10 g of sugarcane bagasse was suspended in 100 mL of lysis buffer: 0.1 M Tris-HCl pH 8, 0.1 M EDTA (ethylenediaminetetraacetic acid) pH 8, 0.1 M Na2PO4 pH 8, 1.5 M NaCl, 2% SDS (sodiumdodecyl sulfate) and 3% CTAB (cetyltrimethylammonium bromide). The mixture was frozen on dry ice and later defrosted at 80

oC to

promote cell lysis. This procedure was repeated three times. Bagasse fibres and suspended particles were removed by centrifugation at 12000 rpm and subsequent filtration (0.45 µm) (Fermentas Ltd., Mexico). The resulting solution was treated with phenol-chloroform-isoamyl alcohol (25:24:1) to remove humic acids and other co-extracted contaminating substances. Finally it was treated with chloroform to remove phenol traces. DNA was precipitated with ethanol, recovered in 50 µL of Mili-Q water, analysed by gel electrophoresis and quantified using a spectrophotometer (Nanodrop ND-1000). Finally, an additional purification step was performed using the Fermentas DNA extraction kit (Fermentas Ltd., Mexico). DNA was extracted from three independent samples, pooled and then stored at -20

oC.

PCR amplicon library preparation for sequencing A 16S rRNA PCR amplicon gene library of the full-length V3 – V6 region (1300 bp: highly conserved region) was prepared using metagenomic DNA from sugarcane bagasse as a template. The PCR primers used were (i) forward: U968-GC [5´-(GC-clamp)-AAC GCG AAG AAC CTT AC-3´] and (ii) reverse: L1401 (5´-CGG TGT GTA CAA GAC CC-3´] (Felske et al., 1996). Cycle conditions comprised initial denaturation at 94

oC for 5 min followed

by 30 cycles of denaturation at 92 oC for 60 s, primer

annealing at 60 oC for 60 s and extension at 72

oC for

Batista-Garcia et al. 025 45 s. A final extension at 72

oC for 10 min followed. Three

individual PCR reactions were performed. Amplicons were purified by agarose gel

electrophoresis using the Fermentas gel purification kit (Fermentas Ltd., Mexico) as per the manufacturer’s instructions and ligated to pGEM-T Easy vector (PROMEGA Ltd., Mexico). Clones derived from electrotransformed Escherichia coli DH5α were selected on Luria-Bertani agar plates containing isopropyl-β-D-thiogalactopyranoside (IPTG)/5-bromo-4-chloro-3-indolyl-β-D-galactopyranoside (X-Gal) (3 and 15 µg/mL, respectively) and ampicillin (100 µg/mL). Representative colonies were randomly selected and subsequently, sequenced in the Langebio’s Genomic Services Unit (Cinvestav-Irapuato, Mexico). Two sequences from each clone were obtained. In this order, rounds of sequencing using (i) PCR forward primer and vector reverse primer, and (ii) PCR reverse primer and vector forward primer were performed. Finally, sequences were assembled in the DNA Baser edit program version 4.16.0. Sequencing data analysis All sequence reads were processed by two platforms: (i) Ribosomal Database Project (RDP) pipeline (http://rdp.cme.msu.edu/) and (ii) NGS analysis pipeline of the SILVA rRNA gene database project (SILVAngs 1.1) (http://www.arb-silva.de/) (Quast et al., 2013). Both allow analysis of large scale small- and large subunit ribosomal (rRNA) gene tag sequencing projects.

The sequencing dataset was quality-filtered using the RDP (Release 11.2, 2014) and SILVAngs (Release 115, 2014) pipelines. During initial processing (in both platforms) reads with more than 2% of ambiguities or 2% of homopolymers, shorter than 100 bases, sequences of low alignment quality (50 alignment identity, 40 alignment score) and identical reads were identified and excluded from downstream analysis. Primer portions were trimmed, and the orientation of reads and chimera formation were checked.

Unique sequences were aligned using the secondary-structure INFERNAL aligner procedure in the RDP pipeline (Nawrocki and Eddy, 2007) and the SILVA Incremental Aligner (Pruesse et al., 2012). Furthermore, (OTUs: operational taxonomic units) sequences were clustered by complete-linkage clustering. The RDP Classifier allowed the assignment of taxa using naïve Bayesian algorithm with a confidence threshold of 95% (Wang et al., 2007; Cole et al., 2013); while SILVA Classifier classified each OTU using cd-hit-est (version 3.1.2; http://bioinformatic.org/cd-hit) (Li and Godzik, 2006) running in accurate mode, ignoring overhangs, and applying identity criteria of 1.00 and 0.98, respectively. The classification was performed by a local nucleotide BLAST search against the non-redundant version of the

026 Merit Res. J. Microbiol. Biol. Sci. SILVA SSU Ref dataset (release 115; http://www.arb-silva.de) using blastn (version 2.2.28+; http://blast.ncbi. nlm.nih.gov/Blast.cgi) with default settings (Camacho et al., 2009). According with the function “[(% sequence identity + % alignment coverage)/2]”, reads without any BLAST hits or reads with weak BLAST hits showing values lower than 93, remain unclassified (Ondov et al., 2011). Subsequently, phylum and full-taxonomic fingerprints were constructed (Ionescu et al., 2012; Klindworth et al., 2013). The classification and taxonomic nomenclature considered in this analysis are in accordance with the last edition of Bergey’s Manual of Systematic Bacteriology (Garrity and Hold, 2012). Rarefaction curves, Shannon – Weaver index, the Chao1 richness estimator and the abundance-base coverage estimator (ACE) were obtained using RDP tools.

Phylogenetic trees were prepared using the server Phylogeny.fr (http://www.phylogeny.fr/). MUSCLE and ClustalW for the multiple alignments, Gblocks for the alignment curation and neighbour-joining method were used in the analysis. Phylogeny.fr considers various bioinformatics algorithms to construct a robust phylogenetic tree from a set of sequences (Deereper et al., 2008).

An OTUs network visualization showing OTUs interaction was generated to compare the prokaryotic diversity derived of our bagasse sample with a dataset obtained from the Thai bagasse deposited in GenBank under accession numbers HM362440-HM362617 (Rattanachomsri et al., 2011). The network map was prepared in Qiime (Quantitative Insights Into Microbial Ecology) version 1.8.0 (Caporaso et al., 2010) and was visualised in Cytoscape program version 3.1.1 (Shannon et al., 2013). Silva and RDP classifiers were used to prepare the network files in Qiime.

The 16S rRNA gene dataset was deposited in GenBank-NCBI (National Center for Biotechnology Information) under accession numbers: KM882649-KM882821. RESULTS AND DISCUSSION It is clear that lignocellulosic rich environments contain microbial diversity that remains as yet largely understudied. The increasing demand of bagasse-based biorefineries to use sugarcane bagasse as a raw material to obtain products of associated high-value supports our approach of attempting to gain a better understanding of the overall structures of the microbial communities present in the bagasse (Lima et al., 2014; Silva et al., 2014). Thus a more comprehensive knowledge of the microorganisms present within sugarcane bagasse may allow greater potential access to their metabolic capabilities which are important in allowing them inhabit this particular lignocellulose rich ecosystem. With this in mind this study focused on analysing the prokaryotic

diversity of a sugarcane bagasse metagenome, using cultured-independent methods. Chemical sugarcane bagasse composition The chemical composition revealed that the bagasse from the Zacatepec’s Sugar Mill used in this work was composed of 53.73% cellulose, 26.37% lignin and 19.9% hemicellulose. The residual reducing sugar analysis showed 0.18 mg/g bagasse. This composition is similar to levels previously reported in other studies (Rattanachomsri et al., 2011; Rezende et al., 2011; Siqueira et al., 2013) and confirms the recalcitrant nature of this agricultural waste. The polymeric composition both qualitative and quantitative of the bagasse, suggest that quite unique microbial diversity is likely to be present in this lignocellulosic rich environment. Analysis of these bacterial population should provide valuable insights into not only overall population structures but also potentially of methods or approaches to control the microbial processes within these populations for optimal utilization of the bagasse for bio-fuel applications. Metagenomic DNA isolated from sugarcane bagasse For metagenomic analysis, the quantity of DNA isolated is important to ensure adequate representation of the microbial communities in the sample. Isolation and purification of metagenomic DNA from lignocellulosic rich substrates has a number of drawbacks including the fact that there are no standardized methods available for nucleic acid extraction and no commercially available extraction kits are available. In this study an average of 0.5 µg metagenomic DNA per 1 g of bagasse was isolated from each sample. This yield of metagenomic DNA is low however when compared with previously described yields from soils; where DNA yields around 15 - 50 µg of DNA per 1 g of soil have been reported (Henne et al., 1999; Yun et al., 2004; Amorim et al., 2008). As previously stated sugarcane bagasse is a very recalcitrant lignocellulosic waste, primarily due to its chemical structure and composition (Chandel et al., 2014). This recalcitrance is likely not to allow the development of a wide variety of microbial communities (Silva et al., 2009); which may explain the low yields of DNA obtained from the sugarcane bagasse samples. Furthermore, the fibrous nature of the bagasse also constitutes a limitation for DNA extraction, while the granularity of a sample is also known to have a marked effect on the yield of DNA (Brady et al., 2007).

Crude DNA solutions prepared from the sugarcane bagasse were extremely dark and very viscous, and additionally showed a brownish colour indicating the presence of contaminants (Brady et al., 2007). Sugarcane bagasses samples are known to contain

Batista-Garcia et al. 027

Figure 2. Isolation and purification ofsugarcane bagasse metagenomic DNA. (A) Extraction of metagenomic DNA from three independent samples of bagasse. Lane 1: 1 kb ladder. Lanes 2, 3 and 4: Crude metagenomic DNA. (B) Purification of metagenomic DNA. Lane 1: 1 kb ladder. Lane 2: Bagasse metagenomic DNA before additional purification step. Lane 3: Bagasse metagenomic DNA after additional purification step. (C) PCR reaction to amplify approximately 1300 bp of the gene coding for 16S rRNA from sugarcane bagasse metagenomic DNA. Lane 1: 1 kb ladder. Lane 2 and 3: Bands corresponding to gene fragments coding for 16S rRNA from metagenomic DNA and Rhizobium meliloti (PCR control) respectively.

humic and aliphatic acids (acetic, formic, and levulinic acid), furan derivatives (furfural and hydroxymethylfurfural) and phenolic compounds (Chandel et al., 2014). These common compounds are derived from the production of molasses, which are concomitantly extracted with metagenomic DNA and can be observed as a large smear following agarose gel electrophoresis (Figure 2, A). They are considered as strong pollutants in DNA solutions because they contribute to the denaturation of nucleic acids, can affect their stability, inhibit numerous enzymes such as DNA polymerases and moreover, negatively interfere with PCR reactions (Tebbe and Vajhen, 1993; Lim et al., 2005). The isolation of DNA from bagasse resulted in co-extraction of other undesirable compounds, thus this aforementioned dark colour is most likely explained by the fact that co-extracted substances are known to cause a blackish colour in crude DNA solutions (Tebbe and Vajhen, 1993) and the observed physical properties of the DNA suggest that co-extracted contaminants were not completely removed during the purification step (Lim et al., 2005). Repeated DNA purification steps were therefore undertaken to ensure the removal of these contaminants, with the DNA being purified until complete decolouration was observed. The final gel electrophoresis analyses showed a defined DNA band of high molecular weight (> 10000 bp) (Figure 2, B).

Overall DNA concentration and quality was assessed by reading absorbance (A) of the bagasse samples at 260 and 280 nm using Nanodrop, with a ratio

value of 1.79 being obtained, confirming that DNA purity was suitable for further metagenomic analyses. Prokaryotic microbial diversity A library of 500 clones with amplicons derived from 16S rDNA PCR (approximately 1300 bp; Fig. 2, C) was obtained, and 200 of these clones were subsequently analysed. Following quality filtering, 177 reads with an average length of 1305 bp were analysed. The minimal and maximum sequence length was 513 and 1355 respectively. During the quality control we identified and discarded: five sequences that showed more than 2% of ambiguities, nine that were shorter than 100 bases, four that revealed low alignment quality and five that were classified as duplicated reads. A histogram from the aligned reads displaying the number of sequences vs. the position (start and end positions) was generated during the alignment process (Supplementary Figure 1).

Three bacterial phyla (Acidobacteria, Candidate division TM7 and Proteobacteria) were observed in bagasse-derived reads. Comparison of OTUs against RDP and SILVA shows that the Proteobacteria and Acidobacteria were the largely predominant bacterial lineages constituting 60 and 34% of the gene amplicon sequences respectively, while Candidate division TM7 was the minor phylum (Figure 3, A). These three phyla have previously been reported to be dominant in lignocellulosic rich environments such as native forest

028 Merit Res. J. Microbiol. Biol. Sci.

Supplementary Figure 1

Figure 3. Taxonomic fingerprint. (A) Phylum fingerprint. (B) Full-taxonomic fingerprint. Fingerprints were derived from SILVA pipeline analysis.

soil enriched with cellulose (Ghio et al., 2012); the gut of Holotrichia parallela larvae (Huang et al., 2012) and cellulose rich soil from the Chaco region of South America (Talia et al., 2012). According to the SILVA classifier the taxonomic classification resulted in 100% of the sequences being classified (phylum level) in more

than 40 OTUs, and subsequently the full-taxonomic fingerprint was obtained (Figure 3, B). Overall, the prokaryotic microbial diversity revealed the presence of 13 families and 17 genera, with Burkholderiaceae and Burkholderia being the major family and genus respectively. On the other hand, the taxonomic analysis

Batista-Garcia et al. 029

Figure 4. Relativetaxonomic representation. (A) Classified genera in the phylum Acidobacteria and its relative abundance. (B) Relative abundance of Proteobacterialclasses. (C, D, E and F) Classified genera and relative abundance of Alphaproteobacteria, Betaproteobacteria, Deltaproteobacteria and Gammaproteobacteria respectively. All values are shown according to the total sequences analysed. This classification was obtained from SILVA.

based on the RDP classifier also resulted in 100% of the sequences being classified at the phylum level. However, this classifier (with similar confidence threshold as that of the SILVA classifier) identified 48 hits as unclassified sequenced at the division-class-genera-species levels; with a large number of acidobacterial sequences (78.7%) not being classified below the phylum level. Similarly one sequence from the phylum Proteobacteria could not be classified below the phylum level. This information strongly suggests that sugarcane bagasse is an interesting lignocellulosic rich habitat in which to identify new strains, species and even genera of bacteria. Although all sequences were assigned to phyla, 27.1% of bacterial reads were not classified at genus level. A notable proportion of sequence reads without classification to phylum level or below the phylum level has previously been found in structural metagenomic studies from cellulolytic and non-cellulolytic ecosystems (Engelbrektson et al., 2010; Kanokratana et al., 2013; Xu et al., 2014).

Five genera (Acidobacteriaceae family) belonging to

the phylum Acidobacteria phylum were identified: Granulicella, Telmatobacter, Terriglobus, Edaphobacter and Acidobacterium (Figure 4, A). The phylum Proteobacteria was also highly represented. A relative abundance analysis with the full-taxonomic fingerprint shows the following distribution: Betaproteobacteria (35%), Alphaproteobacteria (18%), Gammaproteobacteria (5%) and Deltaproteobacteria (2%) (Figure 4, B). Taxonomic classification revealed that the best represented genus in the class Alphaproteobacteria is Hyphomicrobium, with an additional four genera being identified (Methylovirgula, Bradyrhizobium, Rhizomicrobium and Pseudolabrys) (Figure 4, C). Nine reads which classified in the Caulobactereaceae and Beijerinckiaceae families were related to uncultured bacteria. Two (Achromobacter and Burkholderia) and one (Haliangium) genera respectively represent Betaproteobacteria and Deltaproteobacteria classes (Figure 4, D and E). Based on their 16S rDNA, 62 sequences were grouped in the genus Bulkholderia (Figure 3, B and Figure 4, D). Nine reads were classified

030 Merit Res. J. Microbiol. Biol. Sci.

Figure 5. Culture-independent taxonomic analysis reveals significant differences in the microbial composition derived from cellulolytic (box enclosed with solid lines) and non-cellulolytic (box enclosed with dashed lines)environmental metagenomic sequence sets.

as Gammaproteobacteria and related to different genera (Escherichia-Shigella, Pseudomonas and Dyella). Three sequences belonging to the Xanthomonadaceae family also showed similarity with uncultured bacteria (Figure 4, F).

A large number of environmental metagenomes (rumen, rivers, seas, oceans, lakes, soils, foods, faecal materials, sediments, gut insects and sludge) have to date been structurally and functionally analysed (Rasheed et al., 2013; Curson et al., 2014; Jadeja et al., 2014; Kanwar et al., 2014; Patel et al., 2014; Tseng and Tang, 2014; Xu et al., 2014). However, the composition of microbial communities inhabiting lignocellulosic environments and in particular lignocellulosic rich wastes remains largely under-studied. Only two other studies have to date undertaken a phylogenetic analysis of the complex microbial community structure in industrial bagasse feedstock piles. Both were undertaken by the Champreda group and reported much higher levels of diversity than had previously been anticipated (Rattanachomsri et al., 2011; Kanokratana et al., 2013). Those studies identified between ten and seven bacterial phyla associated with sugarcane bagasse in Thailand. Rattanachomsri et al. (2011) also report the prevalence of Proteobacteria in their taxonomic classification, with Proteobacteria, Firmicutes, Bacteroides, Acidobacteria, Actinobacteria and TM7 as the major represented phyla.

On other hand, Kanokratana et al. (2013) found a differential microbial distribution among different samples of sugarcane bagasse, depending on the location of the sample within the bagasse; but again Proteobacteria represent the major phylum in many of these samples. It should also be noted that a widespread bacterial diversity was not observed in these samples with Firmicutes, Proteobacteria and Bacteroides primarily predominating. However, structural metagenomic analyses from other environmental metagenomes (soils, sediments, marine sponge) show a vast microbial biodiversity (Webster et al., 2010; Lee et al., 2011; Valverde and Mellado, 2013; Xu et al., 2014), with some studies describing more than 40 phyla (Valverde and Mellado, 2013; Xu et al., 2014).

The low microbial diversity present in the samples is more than likely related to the structural complexity and recalcitrant nature of bagasse, resulting in a somewhat limited number of bacterial genera being capable of colonizing this ecosystem. Notwithstanding this, clear differences were observed in the structural composition of microbial communities in our bagasse samples when comparing community structures reported for other lignocellulosic and non-lignocellulosic environments (Figure 5). Talia et al. (2012) found 10 phyla of cellulolytic bacteria inhabiting soil samples; while the rumen microbiome analysis of both ruminants, Odocoites virginianus and Rangifer tarandus, revealed seven and

Batista-Garcia et al. 031

Figure 6. Sample-basedrarefaction curves of 16S rDNA sequences amplified from sugarcane bagasse metagenome. Rarefaction curves were calculated in RDP pipeline. Assignments of OTUs at different genetic distance levels were obtained: 0.01 (strain), 0.03 (species), 0.05 (genus), 0.15 (class) and 0.28 (phylum).

four different phyla (Pope et al., 2012; Gruninger et al., 2014). Only four phyla were described from the metagenome analysis of the gut of Holotrichia parallela (larve) (Huang et al., 2012). The previous studies are consistent that Proteobacteria and Bacteroides are the main phyla identified in these lignocellulosic environments. Proteobacteria have also been identified as the main phylum in non-lignocellulosic metagenomes isolated from Amazon River water (Ghai et al., 2011), mangrove sediment, desert and artic soil (Xu et al., 2014). Only the sediment sample collected from mangrove showed a low bacterial diversity with four phyla, while the Amazon River water revealed high microbial diversity levels representing more than ten phyla.

Rarefaction curves were obtained at different genetic distance levels in order to compare the richness of the genetic diversity as a function of number of sequences (Figure 6). A typical trend was observed at higher genetic distance level (0.01, 0.03, 0.05, 0.15 and 0.28). The curve at a genetic distance level of 0.28 (phylum level) showed a rapid asymptotic behaviour according to the low phylum diversity found in the bagasse metagenome. Curves representing the genus and class levels (0.05 and 0.15) demonstrated a sigmoid trend up to saturation. Gradual increases observed in the rarefaction curves at different levels (strain, species, genus) support the taxonomic analysis proposed by the RDP and SILVA´s

classifier algorithms. Rarefaction curves for the sugarcane bagasse samples showed some levelling off indicating that the library was representative and that the estimations of microbial diversity were likely to be accurate (Jackson et al., 2012).

The number of OTUs in the sample was determined with Shannon and Chao1 diversity indexes being calculated (Table 1). Identification of unique phylotypes using RDP and SILVA pipelines showed 42 OTUs from the metagenomic dataset analysis. Chao1 species

richness estimates predict 59.21 OTUs at ≥98% sequence identity for the sugarcane bagasse sample suggesting that 29% of the diversity was not sampled, while on the other hand, Chao1 phylum and class richness show that 100% of the phylum and class level diversities were sampled. Notwithstanding this, our results are comparable with other metagenomic studies which report effective sampling values close to 70%, while contrasting with those published results by authors that only recovered sequences representing between 50-60% of the metagenomic biodiversity (Rattanachomsri et al., 2011; Jackson et al., 2012; Kanokratana et al., 2013).

Finally, we compared our results with those obtained by the Thai group since these are the only ones currently available. OTUs-level comparisons using a network map was prepared in Qiime and its visualization showed that there is a close relationships between the identified OTUs in both sets of samples (our bagasse sample and the

032 Merit Res. J. Microbiol. Biol. Sci.

Table 1. Analysis of 16S rRNA sequences from sugarcane bagasse. Chao1 species richness and Shannon diversity indexes were calculated at different genetic distance levels.

Figure 7. OTU network map showing OTU interaction between a sugarcane bagasse of this study (red network) and Thai sugarcane bagasse (green network) (Rattanachomsri et al., 2011).

Thai bagasse sample) (Figure 7). The network map did not reveal a limited degree of shared OTUs between both bagasse samples. This suggests that the microbiome of the bagasses was very similar. In addition while a high convergence in network interaction was apparent, it should be noted that no relationships between some branches of the networks were observed.This visualization is in accordance with the 16S rDNA-based taxonomic classification obtained for the two sugarcane bagasse samples. Linking phylogenetic taxonomy to functional capability The composition of microbial communities within a given ecosystem is known to reflect the metabolic roles of these bacteria (Kassen & Rainey 2004; Achtman & Wagner, 2008; Jackson et al., 2012; Prosser, 2012). All the genera identified in the bagasse samples contain at least one species with the potential to degrade lignocellulosic materials. Sugarcane bagasse is known to

contain very low levels of available nitrogen and accessible organic matter. Its enriched composition of lignin, cellulose and hemicellulose together with the low free water content and other factors such as its acidic pH, largely limits the ability of many bacteria to colonize the bagasse resulting in somewhat limited microbial diversity coupled with quite long natural degradation processes (Goldbeck et al., 2014).

There was a predominance of aerobic, microaerophilic and facultative anaerobic genera, while 16S rDNA gene sequences related to strict anaerobic genera were not observed. Bacterial genera which are involved in key steps of biogeochemical nitrogen cyclling: N2 fixation (Pseudomonas, Burkholderia, Rhizomicrobium), amomonia oxidation (Burkholderia, Achromobacter), nitrite and nitrate reduction (Bradyrhizobium, Dyella, Acidobacterium) were also identified in this study (Torres et al., 2011; Kundu et al., 2012; Kim et al., 2014; Zuleta et al., 2014). Moreover, some of the species were related to genera that have been shown to possess thermotolerant properties (Faoro et al., 2012; Tajima et al., 2012).

Genetic distance Richness Chao1 richness Shannon index

0.02 42

61.37

2.88

0.03 35

50.00

2.70

0.05 17

36.00

2.47

0.15 10

10.00

0.77

0.3 3

3.00

0.61

Batista-Garcia et al. 033

The phylum Acidobacteria comprises two classes: Acidobacteria and Holophagae, ofwhich, Acidobacteria comprises 19 species in seven genera. In this study we only found sequences related with the Acidobacteria class. Acidobacteria is one of the most widespread bacterial phyla found in nature with members being detected in soil, freshwater, marine, extreme and polluted environments (Ward et al., 2009). Species of the Subdivision 1 (Acidobacteria class) grow very slowly and can often take weeks to form visible colonies. They also can grow under conditions of nutrient deprivation and over quite a broad pH range (Koch et al., 2008;

Pankratov & Dedysh, 2010; Männistö et al., 2012). Sugarcane bagasse is a moderately acidic and low-nutrient environment and therefore not surprisingly, perhaps around 34% of the analysed sequences were identified as Acidobacterium, Terroglobus, Edaphobacter, Granulicella and Telmatobacter (all genera belonging to the Acidobacteria class). Representatives of the phylum Acidobacteria have previously been reported to possess a high potential to degrade plant biomass materials, being able to metabolise complex carbon sources such as xylan, crystalline and amorphous cellulose, hemicellulose, pectin, starch and chitin (Ward et al., 2009). Polymeric carbon is in fact a useful substrate for the cultivation of Acidobacteria (Joseph et al., 2003; Schoenborn et al., 2004). This explains the high percentage of Acidobacteria, which we observed in the sugarcane bagasse samples.

The phylum Acidobacteria is a non-autotrophic phylum since enzymes related to autotrophic metabolism have not been found in their members. Acidobacteria have been reported to oxidize carbon monoxide (CO) and are believed to significantly contribute to the removal of CO present in the atmosphere (King et al., 2007; Ward et al., 2009). This capability together with their capacity to degrade complex lignocellulosic materials, suggests a potential active role in carbon cycling by Acidobacteria within the sugarcane bagasse. Indeed Acidobacteria are known to be adapted to growth in environments with low substrate availability and that as slow growing oligotrophs their overall abundance within a microbial community is strongly regulated by pH (Naethern et al., 2014).

Similarly, Acidobacteria are known to play a significant role in nitrogen metabolism, with genes encoding nitrogen transporters and regulatory proteins involved in nitrogen metabolism, enzymes such as nitrate and nitrite reductases, ammonium oxidase together with enzymes involved in denitrification and enzymes involved in nitrogen metabolism have been reported (Ward et al., 2009; Lin et al., 2014). Thus, the metabolic plasticity of members of the Acidobacteria phylum helps to ensure their survival in low-nitrogen environments such as sugarcane bagasse.

Acidobacteria also possess the capacity to produce extracellular cellulose, in both microfibril and filamentous forms; with cellulose synthesis being implicated in the

molecular mechanisms involved in survival under stressful conditions, such as those

likely to be encountered in lignocellulosic rich ecosystems. In addition the ability to synthesise extracellular cellulose suggests that at least some members of the Acidobacteria possess the ability to survive repeated cycles of rehydration and drying (Ward et al., 2009). The production of extracellular cellulose in bacteria is also known to enhance biofilm production, thereby promoting aeration and hydrations of microenvironments by retaining water under dry conditions (Ude et al., 2006; White et al., 2006). In addition cellulose derived from acidobacterial metabolism (cellulose synthesis and/or bagasse degradation) is believed to promote colonization due to enhanced adherence to the substrate, again through biofilm-formation; thereby providing a loose network for nutrient and water retention. Thus these physiological aspects of extracellular cellulose production are likely to be important for survival of the Acidobacteria in the sugarcane bagasse piles.

A phylogenetic reconstruction was conducted involving 60 sugarcane bagasse derived sequences from the phylum Acidobacteria (class Acidobacteria) (Figure 8). A 16S rDNA sequence subset of members of all species of this class was collected from NCBI. Overall, the phylogenic analysis reveals that the majority of the 16S rDNA reads are not grouped with previously described acidobacteria. The RR168 sequence shows no phylogenetic relationship with the rest of the analysed sequences (Figure 8). Only three 16S rDNA sequences were directly grouped with species of the genera Telmatobacter, Acidobacterium and Edaphobacter. These results indicate the existence of a substantial number of new ribotypes from the 16S rDNA sequences obtained in this study. They may represent novel genera, species and even strains given that the aforementioned phylogenetic reconstruction shows a distant relationship with the previously reported genera of the Acidobacteria class.

Betaproteobacteria was the most represented class in the phylum Proteobacteria, with the genus Burkholderia (62 sequences) being the most representative Betaproteobacteria present in the 16S rDNA library from the bagasse. This genus comprises more than 70 described species (Mathew et al., 2014) with a wide and intriguing biological versatility displayed across a broad range of lifestyles (Compant et al., 2008; Via et al., 2011). The genus has a worldwide distribution, it has been described from diverse environments and has been implicated in the degradation of xenobiotics, in plant growth promotion, in biocontrol strategies in agriculture and in symbiotic and non-symbiotic biological nitrogen fixation (Suarez-Moreno et al., 2012; Zuleta et al., 2014). Several species have also been described which possess lignocellulolytic potential (Maki et al., 2011; Fujii et al., 2012).

034 Merit Res. J. Microbiol. Biol. Sci.

Figure 8. Radial phylogenetic reconstruction for the phylum Acidobacteria. The relationship between 16s rDNA gene dataset derived from sugarcane bagasse (only reads classified in Acidobacteria phylum) and 19 sequences of 16S rDNA belonging at 19 species of the class are showed.

One of the most distinctive physiological characteristics of Burkholderia is their ability to produce siderophores with the primary role of iron (Fe

2+) acquisition/chelation

(Vandamme et al., 2007). Siderophore-mediated nutrient acquisition has been identified as an important survival factor in members of the genus Burkholderia particularly in metal-poor environments (Thomas et al., 2007). While siderophore production is typically induced under iron-limiting conditions, siderophores can form complexes with other essential metals (e.g. Mo

2+, Mn

2+, Co

2+, Ni

2+)

(Ahmen and Holmstrom, 2014). Moreover in various bacteria, they have also been reported to be involved in metal homeostasis and transport of other biologically relevant metals such as Cd

2+,Cu

2+,Ni

2+,Pb

2+,Zn

2+ (Schalk

et al., 2011). Thus siderophore production is a likely strategy employed by Burkholderia to successfully colonize low-metal environments such as sugarcane bagasse, to not only allow metal acquisition but to also promote mineral dissolution from fibrous materials within the bagasse (Shirvani and Nourbakhsh, 2010). Thus siderophore production by Burkholderia could play an important role in bagasse degradation by facilitating Fe and other metal uptake by the degrader microbial populations (e.g. Acidobacteria and Burkholderia species) under Fe

2+-limiting conditions.

In addition there are other important links between microbial siderophore production and lignocelloluse. Different species of Burkholderia have been reported to

produce catecholate and hydroxamate (Barelmann et al., 1996; Kvitko et al., 2012; de los Santos-Villalobos et al., 2012). Some of these siderophores have been implicated in the redox speciation of Fe

2+, by facilitating the

reduction of Fe3+

. The reaction between hydrogen peroxide and reduced Fe generates oxygen radicals, which are known to play an important role in the depolymerization of lignin, hemicellulose and cellulose (Xu and Goodell, 2001; Arantes and Milagres, 2007). While Burkholderia are known to protect themselves from H2O2 through the production of catalases/peroxidases (Lefebre et al., 2005).

It has been observed that siderophores (with the same chemical nature that catecholate and hydroxamate) are capable of decreasing lignocellulosic viscosity and promote its transformation (Milagres et al., 2012). Thus siderophore production by Burkholderia sp. within the sugarcane bagasse would clearly facilitate an important survival strategy both from a nutrient uptake and a lignocellulose biotransformation perspective.

Other minority taxonomic classes and subdivisions were found to represent approximately 29% of the analysed sequences. Cellulolytic activities have previously been described in some of the identified genera within this population (Lednick et al., 2000; Wadell and Bang, 2008; Huang et al., 2012; Song et al., 2013). For example a read classified as Methylovirgula

Batista-Garcia et al. 035

Figure 9. Radial phylogenetic reconstruction for the phylum Proteobacteria. The relationship between 16S rDNA gene dataset derived from sugarcane bagasse (only reads classified inthe phylumProteobacteria) and 43 sequences of the 16S rDNA belonging to 43 type species and/or genera are shown.

sp. was found in the 16S rDNA library. This genus contains only one described species to date namely Methylovirgula ligni, an obligate acidophile with a pH optimum between 4.5 and 5 (Vorob´er et al., 2009), consistent with the moderate pH of bagasse.

A radial phylogenetic visualization shows the relationships of the sequences classified as Proteobacteria (60% of the total) derived from sugarcane bagasse metagenomic DNA (Figure 9). 16S rDNA sequences (37 in total) belonging to type species of the genera and classes identified in the full-taxonomic fingerprint were collected in the NCBI. The phylogenic analyses revealed that only four 16S rDNA bagasse sequences were directly grouped with species, Burkholderia caribiensis, Pseudomonas aeruginosa,

Methylovirgula ligni and Beijerinckia derxii. The phylogenetic visualization for the bagasse sequences identified as Proteobacteria demonstrates once again that there is a clear predominance of bacteria showing a truly distant relationship with the type species.

To the best of our knowledge, this is the first study in Mexico and indeed in Central or South America describing the complex composition of the microbial populations inhabiting sugarcane bagasse feedstock piles from sugar mills. Sugar mills in Mexico and in Central and South America, represent an important economic activity and bagasse generation is around 350 million metric tons per year on a worldwide basis (http://www.fas.usda.gov). These production levels generate serious pollution problems and demand the

036 Merit Res. J. Microbiol. Biol. Sci. need for proper waste management strategies (Kiatkittipong et al., 2009). These reasons support this type of study, whereby exploration of microbial diversity can shed further light on not only the likely metabolic activities of the microbial populations present, but of their potential in facilitating further degradation of this quite recalacitrant biomass. ACKNOWLEDGMENTS This work was funded by grant CB-153789-Q from the National Council for Science and Technology (CONACyT) from the Mexican government: http://www.conacyt.gob.mx, and “Proyecto Biomasa” (Acuerdo 13/2007) of UAM-Cuajimalpa. RB-G and R-C received a scholarship from CONACyT. Additionally RB-G received a short-term fellowship from the Europe Molecular Biology Organization (EMBO): ASTF503-2013 to perform part of this work at University College Cork, in Ireland. We are grateful to Dr. Verawat Champreda (Enzyme Technology Laboratory, Bioresources Technology Unit, National Center for Genetic Engineering and Biotechnology, Thailand) who shared with us his dataset derived from the structural metagenome analysis from sugarcane bagasse and Jamie Fitzgerald for his help with the network visualization. REFERENCES Achtman M, Wagner M (2008). Microbial diversity and the genetic

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