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Standards in Genomic Sciences

Open Access

Metagenomic analysis of intertidal hypersaline microbial mats from Elkhorn Slough, California, grown with and without molybdate

  • Patrik D’haeseleer1,
  • Jackson Z. Lee2Email author,
  • Leslie Prufert-Bebout2,
  • Luke C. Burow2, 3,
  • Angela M. Detweiler2, 4,
  • Peter K. Weber1,
  • Ulas Karaoz5,
  • Eoin L. Brodie5,
  • Tijana Glavina del Rio5, 6,
  • Susannah G. Tringe5, 6,
  • Brad M. Bebout2 and
  • Jennifer Pett-Ridge1
Contributed equally
Standards in Genomic Sciences201712:67

https://doi.org/10.1186/s40793-017-0279-6

Received: 14 July 2017

Accepted: 25 October 2017

Published: 15 November 2017

Abstract

Cyanobacterial mats are laminated microbial ecosystems which occur in highly diverse environments and which may provide a possible model for early life on Earth. Their ability to produce hydrogen also makes them of interest from a biotechnological and bioenergy perspective. Samples of an intertidal microbial mat from the Elkhorn Slough estuary in Monterey Bay, California, were transplanted to a greenhouse at NASA Ames Research Center to study a 24-h diel cycle, in the presence or absence of molybdate (which inhibits biohydrogen consumption by sulfate reducers). Here, we present metagenomic analyses of four samples that will be used as references for future metatranscriptomic analyses of this diel time series.

Keywords

Microbial matsHydrogenFermentationElkhorn sloughMetagenomics

Introduction

Microbial mats are amongst the most diverse microbial ecosystems on Earth, inhabiting some of the most inhospitable environments known, including hypersaline, dry, hot, cold, nutrient poor, and high UV environments. Photosynthetic microbial mats found in intertidal environments are stratified microbial communities. Microbial metabolism under anoxic conditions at night results in the generation of significant amounts of H2 and organic acids. The high microbial diversity of microbial mats makes possible a highly complex series of metabolic interactions between the microbes, the nature and extent of which are currently under investigation. To address this challenge, we are using a combination of metagenomics, metatranscriptomics, metaproteomics, iTags and naturally collected, as well as culture-based simplified microbial mats to study biogeochemical cycling (H2 production, N2 fixation, and fermentation) in mats collected from Elkhorn Slough, Monterey Bay, California. We present here the metagenome data, which will be used as a reference for metatranscriptomic analysis in a later paper.

Site information

Cyanobacterial mats are compact, laminated, and highly structured microbial communities (Fig. 1) that comprise great diversity at both the metabolic and phylogenetic level [1] and typically exist in highly saline environments such as lagoons and salterns. These mats notably have a suite of phototrophic organisms and photosynthetic lifestyles, from the dominant cyanobacterial phototroph Coleofasciculus chthonoplastes (basionym Microcoleus chthonoplastes) to purple sulfur and non-sulfur bacteria, and potentially other anoxygenic phototrophs. During the nighttime portion of the diel cycle, phototrophic organisms release fermentation byproducts which in turn help drive a shift from oxic to anoxic metabolism dominated by hydrogen consumption and sulfate reduction by sulfate reducing bacteria such as Desulfobacteriales [2]. Naturally occurring mats have a documented capacity to produce and liberate fermentation by-products (H2 and acetate primarily) [3, 4] and to consume them [5, 6] depending on the point in the diel cycle. Lastly, nitrogen assimilation is dominated by nitrogen fixation in these mats, typically by several members of the phylum Cyanobacteria such as ESFC-1 and Lyngbya sp. and by sulfate reducing bacteria [711]. The mats of Elkhorn Slough are situated in an estuary emptying into Monterey Bay, California and are located in a former salt production pond. The MIMS coding is shown in Table 1.
Fig. 1

a. Photograph of location of cores collected in the field from microbial mats at the Moss Landing Wildlife Area in Elkhorn Slough, Moss Landing, California on 07/11/11. Individual samples collected in core tubes were numbered and could be tracked throughout the diel experiment. b. Experimental apparatus used to incubate microbial mats throughout the diel period from 08/11/11 to 09/11/11. Incubation containers containing cores used for control and molybdate treatments are labeled

Table 1

Study information

Label

CD2A

CD6A

MD2A

MD6A

IMG/M ID

3,300,000,347

3,300,000,354

3,300,000,919

3,300,000,353

SRA ID

SRX2021703

SRX2021697

SRX2879537

SRX2021699

Study

Gs0067861

Gs0067861

Gs0067861

Gs0067861

GOLD ID (sequencing project)

Gp0053859

Gp0054619

Gp0054089

Gp0054045

GOLD ID (analysis project)

Ga0026496

Ga0026141

Ga0011764

Ga0026171

NCBI BIOPROJECT

PRJNA337838

PRJNA336658

PRJNA366469

PRJNA336698

Relevance

Biotechnological; hydrogen production

Biotechnological; hydrogen production

Biotechnological; hydrogen production

Biotechnological; hydrogen production

Microbial mats like the ones at Elkhorn Slough have long been studied as a model for early life and gained prominence with the discovery that hypersaline mats in Guerrero Negro, Baja California, represented one of the most highly species-diverse microbiomes ever studied [1]. Though not as diverse as the Lyngbya mats of the Guerrero Negro system, the Elkhorn Slough mat system captures a similar distribution of organisms observed in laminated seasonal microbial ecosystems [6, 12]. Several areas of microbial mat physiology research are on-going at the Elkhorn Slough site. The site has been used to isolate a novel nitrogen fixer [9] and to show that the majority of fixation is attributable to a Lyngbya sp. [10], and to identify the dominant SRB ( Desulfobacterales ) in the ecosystem [2]. Additionally, the site has been investigated for hydrogen cycling. Burow and colleagues [5], showed that hydrogen flux likely originates from the fermentation of photosynthate. This system has also been subjected to metatranscriptomics and metaproteomics analyses [12, 13].

Metagenome sequencing information

Metagenome project history

Building on previous work examining gene expression patterns associated with fermentation pathways in microbial mat systems [12], a 24-h study of Elkhorn Slough, CA microbial mats was conducted in 2011. Briefly, field-collected mats were incubated at NASA Ames in seawater media and repeatedly sampled over one diel cycle. In addition, to understand gene expression across the diel cycle, DNA and RNA were extracted from molybdate and control samples for metagenome and metatranscriptome sequencing. Study information is summarized in Table 1.

Sample information

To understand the variation in gene expression associated with the daytime oxygenic phototrophic and nighttime fermentation regimes in hypersaline microbial mats, a contiguous mat piece was sampled at regular intervals over a 24-h diel period. Additionally, to understand the impact of sulfate reduction on biohydrogen consumption and impacts on community-structure, molybdate was added as an inhibitor to a parallel experiment. Contiguous mat samples were incubated and sampled at regular intervals throughout a 24-h period (8 time points). Four metagenome samples (two time points 12 h apart, from mats with and without molybdate added to the overlying water) and 13 metratranscriptomes (including nine time points for the control time series, four for the molybdate time series, and duplicates for most time points) were sequenced using Illumina technology.

Sample preparation

Microbial mats used in the experiment were collected using 3 in. acrylic core tubes on the morning of 07/11/11 and transported to Ames Research Center (about one hour by car). The mats were collected from a single contiguous section of mat (Fig. 1a) and were not covered with water at the time of collection (low tide). The microbial mats were immediately transferred to temperature controlled water baths on a rooftop facility [14] (Fig. 1b) containing either seawater or seawater amended with 30 mM (final concentration) sodium molybdate to inhibit the activities of sulfate reducing bacteria. The seawater used was obtained from the boat launch in the Moss Landing harbor at the time of collection of the mats. Two replicate containers each were used for mat incubations: 1) seawater alone and 2) seawater with molybdate water baths.

Mat samples for metagenomic analysis were subsampled from the acrylic core tubes using smaller metal coring tubes (having an area of 1.15 cm2, and a depth of 0.5 cm) on 09/11/11 at 01:30 h and 13:30 h (PST), corresponding to the 2nd and 6th time point in the larger diel time series (one control and one molybdate sample at each time point). Samples were placed in liquid nitrogen immediately after collection and, after flash freezing, were stored in a − 80 °C freezer for later extraction.

The four samples, and resulting metagenomes presented here will be referred to by a 4-character code: CD2A (Control, DNA, time point 2, replicate A), CD6A (Control, DNA, time point 6, replicate A), MD2A (Molybdate, DNA, time point 2, replicate A), MD6A (Molybdate, DNA, time point 6, replicate A). Sample information is provided in Table 2 as per minimal information standards [15].
Table 2

Sample information

Label

CD2A

CD6A

MD2A

MD6A

GOLD ID (biosample)

Gb0053859

Gb0054619

Gb0054089

Gb0054045

Biome

Estuarine biome

Estuarine biome

Estuarine biome

Estuarine biome

Feature

Estuarine mud

Estuarine mud

Estuarine mud

Estuarine mud

Material

Microbial mat

Microbial mat

Microbial mat

Microbial mat

Latitude and Longitude

36.812947, −121.784692

36.812947, −121.784692

36.812947, −121.784692

36.812947, −121.784692

Vertical distance

1 m above sea level

1 m above sea level

1 m above sea level

1 m above sea level

Geographic location

Elkhorn Slough, Monterey Bay, California, USA

Elkhorn Slough, Monterey Bay, California, USA

Elkhorn Slough, Monterey Bay, California, USA

Elkhorn Slough, Monterey Bay, California, USA

Collection date and time

09/11/15, 01:30 h (PST)

09/11/15, 01:30 h (PST)

09/11/15, 13:30 h (PST)

09/11/15, 13:30 h (PST)

DNA extraction

Nucleic acids were extracted from the samples between 2/2/2012 and 24/3/12. For each time point and treatment, the top 2–2.5 mm (photosynthetic layer) of 4 1-cm diameter cores were extracted by initially placing each core in 2 ml tubes containing a mixture of 0.5 ml of RLT buffer (RNeasy Mini Elute Cleanup Kit #74204; Qiagen, Valencia, CA, USA) and 5 μl of 2-mercaptoethanol (cat. # 0482–100) (Amresco, Solon, OH, USA). Samples were homogenized using a rotor-stator homogenizer (Omni International, Kennesaw, GA, USA), followed by the addition of 0.5 mm zirconium beads (OPS Diagnostics, Lebanon, NJ, USA) and then bead-beaten for 40 s using a FastPrep FP120 Cell Disrupter (Qbiogene, Inc., Carlsbad, CA, USA). Samples were spun down and the supernatant for each tube was transferred into a new tube containing an equal volume of phenol:chloroform:isoamyl alcohol (25:24:1) (cat. # 0883–400) (Amresco, Solon, OH, USA). Samples were vortexed, incubated for 5 min at room temperature, and spun down. The supernatant from each tube was transferred to a new tube containing an equal volume of 100% ethanol (Fisher #BP2818, Waltham, MA, USA) and was vortexed. Replicates of supernatant and ethanol mix for each time point and treatment were pooled, run through a QIAmp spin column (QIAmp DNA mini kit #51304, Qiagen, Valencia, CA, USA), and further purified according to the QIAmp DNA mini kit protocol. DNA quality and concentration were measured using a QUBIT fluorometer model Q32857 (Invitrogen, Carlsbad, CA, USA). Samples were submitted to JGI for sequencing.

Library generation

500 ng of genomic DNA (2 μg for sample MD2A) was sheared using the Covaris E210 (Covaris) and size selected using Agencourt Ampure Beads (Beckman Coulter). The DNA fragments were treated with end repair, A-tailing, and adapter ligation using the TruSeq DNA Sample Prep Kit (Illumina) and purified using Agencourt Ampure Beads (Beckman Coulter). The prepared libraries were quantified using KAPA Biosystem’s next-generation sequencing library qPCR kit and run on a Roche LightCycler 480 real-time PCR instrument. The quantified libraries were then prepared for sequencing on the Illumina HiSeq sequencing platform utilizing a TruSeq paired-end cluster kit, v3, and Illumina’s cBot instrument to generate a clustered flowcell for sequencing. The library information is summarized in Table 3.
Table 3

Library information

Label

IUTO

IUTP

HCZO

IUTS

Sample Label(s)

CD2A

CD6A

MD2A

MD6A

Sample prep method

Illumina TruSeq DNA Sample Prep Kit

Illumina TruSeq DNA Sample Prep Kit

Illumina TruSeq DNA Sample Prep Kit

Illumina TruSeq DNA Sample Prep Kit

Library prep method(s)

Illumina TruSeq paired-end cluster kit, v3

Illumina TruSeq paired-end cluster kit, v3

Illumina TruSeq paired-end cluster kit, v3

Illumina TruSeq paired-end cluster kit, v3

Sequencing platform(s)

Illumina HiSeq 2000

Illumina HiSeq 2000

Illumina HiSeq 2000

Illumina HiSeq 2000

Sequencing chemistry

V3 SBS Kit

V3 SBS Kit

V3 SBS Kit

V3 SBS Kit

Sequence size (GBp)

19.6

14.8

13.8

17

Number of reads

130,503,566

98,760,526

91,877,294

113,089,944

Single-read or paired-end sequencing?

Paired-end

Paired-end

Paired-end

Paired-end

Sequencing library insert size

0.27 kb

0.27 kb

0.27 kb

0.27 kb

Average read length

150

150

150

150

Standard deviation for read length

0

0

0

0

Sequencing technology

Sequencing of the flowcell was performed on the Illumina HiSeq2000 sequencer using a TruSeq SBS sequencing kit 200 cycles, v3, following a 2 × 150 indexed run recipe. All sequencing was performed by the Joint Genome Institute in Walnut Creek, CA, USA.

Sequence processing, annotation, and data analysis

Sequence processing

Raw Illumina metagenomic reads were screened against Illumina artifacts with a sliding window with a kmer size of 28, step size of 1. Screened reads were trimmed from both ends using a minimum quality cutoff of 3, reads with 3 or more N’s or with average quality score of less than Q20 were removed. In addition, reads with a minimum sequence length of <50 bps were removed. The sequence processing is summarized in Table 4.
Table 4

Sequence processing

Label

IUTO

IUTP

HCZO

IUTS

Tool(s) used for quality control

IMG/M (default)

IMG/M (default)

IMG/M (default)

IMG/M (default)

Number of sequences removed by quality control procedures

5,710,382

4,026,834

2589,674

4,659,580

Number of sequences that passed quality control procedures

124,793,184

94,733,692

89,287,620

108,430,364

Number of artificial duplicate reads

NA

NA

NA

NA

Metagenome processing

Trimmed, screened, paired-end Illumina reads were assembled using SOAPdenovo v1.05 [16] at a range of Kmers (85, 89, 93, 97, 101, 105). Default settings for all SOAPdenovo assemblies were used (options "-K 81 -p 32 -R -d 1"). Contigs generated by each assembly (6 total contig sets), were de-replicated using in-house Perl scripts. Contigs were then sorted into two pools based on length. Contigs smaller than 1800 bp were assembled using Newbler [17] in attempt to generate larger contigs (flags: -tr, −rip, −mi 98, −ml 80). All assembled contigs larger than 1800 bp, as well as, the contigs generated from the final Newbler run were combined using minimus 2 (flags: -D MINID = 98 -D OVERLAP = 80) [18]. Read depths were estimated based on read mapping with BWA [19]. These sequences are currently available to the public at IMG/M and the JGI genome portals. Metagenome statistics are summarized in Table 5.
Table 5

Metagenome statistics

Label

CD2A

CD6A

MD2A

MD6A

Libraries used

IUTO

IUTP

HCZO

IUTS

Assembly tool(s) used

SOAPdenovo v1.05 (default)

SOAPdenovo v1.05 (default)

SOAPdenovo v1.05 (default)

SOAPdenovo v1.05 (default)

Number of contigs after assembly

247,547

141,229

292,231

257,101

Number of singletons after assembly

1,568,087

83,272

1,166,131

1,565,449

minimal contig length

200

200

200

200

Total bases assembled

152,203,650

90,602,774

173,570,670

178,522,206

Contig n50

749

906

695

1.1 kb

% of Sequences assembled

38%

29%

38%

38%

Measure for % assembled

reads mapped to contigs using BWA

reads mapped to contigs using BWA

reads mapped to contigs using BWA

reads mapped to contigs using BWA

Metagenome annotation

Prior to annotation, all sequences were trimmed to remove low quality regions falling below a minimum quality of Q13, and stretches of undetermined sequences at the ends of contigs were removed. Low complexity regions were masked using the dust algorithm from the NCBI toolkit and very similar sequences (similarity >95%) with identical 5′ pentanucleotides were replaced by one representative, typically the longest, using uclust [20]. The gene prediction pipeline included the detection of non-coding RNA genes (tRNA and rRNA) and CRISPRs, followed by prediction of protein coding genes.

Identification of tRNAs was performed using tRNAScan-SE-1.23 [21]. In case of conflicting predictions, the best scoring predictions were selected. Since the program cannot detect fragmented tRNAs at the end of the sequences, we also checked the last 150 nt of the sequences by comparing these to a database of nt sequences of tRNAs identified in the isolate genomes using blastn [22]. Hits with high similarity were kept; all other parameters were set to default values. Ribosomal RNA genes were predicted using hmmsearch [23] with internally developed models for the three types of RNAs for the domains of life. Identification of CRISPR elements was performed using the programs CRT [24] and PILERCR [25]. The predictions from both programs were concatenated and, in case of overlapping predictions, the shorter prediction was removed.

Identification of protein-coding genes was performed using four different gene calling tools, GeneMark (v. 2.8) [26],Metagene (v. 1.0) [27], Prodigal (V2.50: November, 2010) [28] and FragGenescan (v. 1.16) [29] all of which are ab initio gene prediction programs. We typically followed a majority rule based decision scheme to select the gene calls. When there was a tie, we selected genes based on an order of gene callers determined by runs on simulated metagenomic datasets (Genemark > Prodigal > Metagene > FragGeneScan). At the last step, CDS and other feature predictions were consolidated. The regions identified previously as RNA genes and CRISPRs were preferred over protein-coding genes. Functional prediction followed and involved comparison of predicted protein sequences to the public IMG database using the usearch algorithm [20], the COG database using the NCBI developed PSSMs [30], the Pfam database [31] using hmmsearch. Assignment to KEGG Ortholog protein families was performed using the algorithm described in [32]. Annotation parameters are summarized in Table 6.
Table 6

Annotation parameters

Label

CD2A

CD6A

MD2A

MD6A

Annotation system

IMG/M

IMG/M

IMG/M

IMG/M

Gene calling program

FragGeneScan version 1.16, prokaryotic GeneMark.hmm version 2.8, Metagene Annotator version 1.0, Prodigal V2.50: November, 2010

FragGeneScan version 1.16, prokaryotic GeneMark.hmm version 2.8, Metagene Annotator version 1.0, Prodigal V2.50: November, 2010

FragGeneScan version 1.16, prokaryotic GeneMark.hmm version 2.8, Metagene Annotator version 1.0, Prodigal V2.50: November, 2010

FragGeneScan version 1.16, prokaryotic GeneMark.hmm version 2.8, Metagene Annotator version 1.0, Prodigal V2.50: November, 2010

Annotation algorithm

    

Database(s) used

IMG, COG, Pfam, KEGG

IMG, COG, Pfam, KEGG

IMG, COG, Pfam, KEGG

IMG, COG, Pfam, KEGG

Metagenome properties

Metagenomes were sequenced and assembled into 141,229 (CD6A) to 292,231 (MD2A) contigs, covering 90.6 to 173.6Mbp. GC content of the metagenomes ranged from 46% to 52%. These metagenomes include between 206,164 and 399,161 genes each. More than 99% of these are protein coding, and around 40% have some level of function annotation. Metagenome properties are summarized in Table 7.
Table 7

Metagenome properties

Label

CD2A

CD6A

MD2A

MD6A

Number of contigs

247,547

141,229

292,231

257,101

GBp

152,203,650

90,602,774

173,570,670

178,522,206

Number of features identified

354,269

206,164

399,161

389,398

CDS

351,921

204,616

396,301

386,642

rRNA

673

577

834

805

others

1675

971

2026

1951

CDSs with COG

156,087

86,041

199,065

173,132

CDSs with Pfam

157,748

88,969

186,210

178,182

CDS with SEED subsystem

NA

NA

NA

NA

Alpha diversity

NA

NA

NA

NA

Taxonomic diversity

The taxonomic diversity and phylogenetic structure of the metagenomes was determined based on the best BLASTp hits of assembled protein-coding genes with 60% or more identity to protein in the listed phyla, as calculated by the Phylogenetic Distribution of Genes feature in IMG/M. The phylogeny reported is the one in use in IMG/M [33], which uses the phylogeny described as part of the genomic encyclopedia of Bacteria and Archaea (GEBA) project [34]. Taxonomic composition is summarized in Table 8. Gene copies are estimated based on the number of genes in the assembled metagenome, multiplied by the average read depth of each gene. This provides a better estimate for the total number of reads coming from each taxon, which is proportional to the abundance of those taxa in the microbial mats. Across the assembled metagenomes, the fraction of annotated genes (not accounting for gene copies) that are unassigned at the 60% sequence identity level ranges between 64% and 67%, with 7–13% mapping to phylum Bacteroidetes , 8–13% phylum Cyanobacteria , and 9–16% phylum Proteobacteria . However the estimated gene copies show that these samples are in fact dominated by Cyanobacteria sequences (27–49% of estimated gene copies), with smaller contributions from Proteobacteria , Bacteroidetes , and a variety of other bacterial phyla, and only 34–44% unassigned. The majority of cyanobacterial sequences map to Coleofasciculus chthonoplastes (19–39% of the total estimated gene copies) and Lyngbya sp. PCC 8106 (3.5–5.5% of estimated gene copies). Other individual bacterial species that capture a large fraction of estimated gene copies at 60% identity include Erythrobacter sp. NAP1 ( Alphaproteobacteria ; up to 3.6% in MD6A), Allochromatium vinosum ( Gammaproteobacteria ; up to 3.3% in CD6A), and Marivirga tractuosa ( Cytophagia ; up to 2% in MD6A).
Table 8

Taxonomic composition

Phylum

CD2A

CD6A

MD2A

MD6A

Cyanobacteria

2,886,834

1,682,393

1,341,178

1,831,579

Proteobacteria

844,689

368,701

757,946

701,003

Bacteroidetes

279,447

117,112

512,734

645,277

Chloroflexi

11,158

7671

84,811

7443

Planctomycetes

32,641

3990

19,619

19,417

Firmicutes

14,252

7592

17,425

13,233

Verrucomicrobia

10,189

3125

7299

22,666

Gemmatimonadetes

13,305

7096

4257

7385

Chlorobi

8996

5188

6181

8539

Actinobacteria

8964

3794

8707

6873

Deinococcus-Thermus

4724

1281

6013

2722

Unassigned

2,133,807

1,191,276

2,206,260

2,140,978

There are noticeable differences in taxonomic composition among the four metagenomes. For example, the molybdate treated samples MD2A and MD6A contain fewer sequences from phylum Cyanobacteria and more from phylum Bacteroidetes than the control samples. Some of these differences may be due to spatial heterogeneity in the mat from which the samples were collected.

Functional diversity

The distribution of COG functional categories is very similar between the four genomes (Table 9), with Pearson correlation of the log of the number of genes assigned to each category ranging from 0.986 (CD2A vs. CD6A) to 0.999 (CD2A vs. MD6A), suggesting a broad functional similarity between the samples, despite differences in species composition.
Table 9

Functional diversity

COG Category

CD2A

CD6A

MD2A

MD6A

Translation, ribosomal structure and biogenesis

9405

5221

12,469

11,311

RNA processing and modification

74

26

206

39

Transcription

9669

5290

12,476

10,739

Replication, recombination and repair

11,830

6833

14,356

12,322

Chromatin structure and dynamics

107

62

179

101

Cell cycle control, Cell division, chromosome partitioning

1782

988

2408

1907

Nuclear structure

0

1

4

0

Defense mechanisms

3970

2122

4878

4433

Signal transduction mechanisms

13,275

7589

16,709

13,770

Cell wall/membrane biogenesis

11,461

6586

15,115

13,860

Cell motility

3020

1469

3728

2589

Cytoskeleton

48

12

80

27

Extracellular structures

0

0

2

0

Intracellular trafficking and secretion

4536

2401

6057

4509

Posttranslational modification, protein turnover, chaperones

7137

3962

9349

7808

Energy production and conversion

11,737

6252

15,089

12,719

Carbohydrate transport and metabolism

8698

4741

11,199

9685

Amino acid transport and metabolism

14,099

7254

17,462

15,088

Nucleotide transport and metabolism

3830

2069

5089

4469

Coenzyme transport and metabolism

7489

4104

9368

8213

Lipid transport and metabolism

5603

2666

7504

6460

Inorganic ion transport and metabolism

8887

4635

11,353

10,081

Secondary metabolites biosynthesis, transport and catabolism

4011

2040

4818

4185

General function prediction only

20,092

11,257

26,360

22,338

Function unknown

13,560

7933

18,351

15,032

Not in COGs

198,182

120,123

200,096

216,266

Conclusions

We sequenced and assembled metagenomes for four samples of microbial mat from the Elkhorn Slough estuary in Monterey Bay, California, to be used as reference data for a diel metatranscriptomic study in the presence or absence of molybdate. All four metagenomes were dominated by cyanobacterial sequences, primarily Coleofasciculus chthonoplastes . Despite some differences in community composition between the four metagenomes (which may be partly due to spatial heterogeneity in the mat), their functional composition in terms of COG functional categories is quite similar.

Abbreviations

BLAST: 

Basic local alignment search tool

COG: 

Clusters of orthologous groups

IMG: 

Integrated Microbial Genomes

Pfam: 

Protein families

SRB: 

Sulfate reducing bacteria

Declarations

Acknowledgements

We thank Jeff Cann, Associate Wildlife Biologist, Central Region, California Department of Fish and Wildlife, for coordinating our access to the Moss Landing Wildlife Area.

Funding

This research was supported by the U.S. Department of Energy Office of Science, Office of Biological and Environmental Research Genomic Science program under the LLNL Biofuels SFA, FWP SCW1039, and by JGI Community Sequencing Program award #701. Work at LLNL was performed under the auspices of the U.S. Department of Energy under Contract DE-AC52-07NA27344. Work at LBNL, the National Energy Research Scientific Computing Center (NERSC), and the DOE Joint Genome Institute (JGI) was performed under the auspices of the U.S. Department of Energy Office of Science under Contract No. DE-AC02-05CH11231.

Authors’ contributions

BMB and LPB collected samples; LPB, LCB, AMD, PKW, BMB, and JPR designed and conducted the experiment; LCB, AMD, TGR and SGT generated and processed data; JZL, UK, ELB, PD, BMB, and JPR worked on data analysis and interpretation; PD, JZL, BMB, AMD and JPR drafted the article; PD, JZL, BMB, UK, PKW, TGR, SGT, AMD and JPR made final revisions to the manuscript. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

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Authors’ Affiliations

(1)
Lawrence Livermore National Laboratory, Livermore, USA
(2)
NASA Ames Research Center, Moffett Field, USA
(3)
Stanford University, Stanford, USA
(4)
Bay Area Environmental Research Institute, Petaluma, USA
(5)
Lawrence Berkeley National Laboratory, Berkeley, USA
(6)
Department of Energy Joint Genome Institute, Walnut Creek, USA

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© The Author(s). 2017

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