Open Access

Metagenomes of tropical soil-derived anaerobic switchgrass-adapted consortia with and without iron

  • Kristen M. DeAngelis1, 2Email author,
  • Patrik D’Haeseleer2, 3,
  • Dylan Chivian4, 5,
  • Blake Simmons2, 6,
  • Adam P. Arkin4, 5,
  • Konstantinos Mavromatis7,
  • Stephanie Malfatti7,
  • Susannah Tringe7 and
  • Terry C. Hazen2, 8, 9, 10
Standards in Genomic Sciences20137:7030382

https://doi.org/10.4056/sigs.3377516

Published: 25 February 2013

Abstract

Tropical forest soils decompose litter rapidly with frequent episodes of anoxia, making it likely that bacteria using alternate terminal electron acceptors (TEAs) such as iron play a large role in supporting decomposition under these conditions. The prevalence of many types of metabolism in litter deconstruction makes these soils useful templates for improving biofuel production. To investigate how iron availability affects decomposition, we cultivated feedstock-adapted consortia (FACs) derived from iron-rich tropical forest soils accustomed to experiencing frequent episodes of anaerobic conditions and frequently fluctuating redox. One consortium was propagated under fermenting conditions, with switchgrass as the sole carbon source in minimal media (SG only FACs), and the other consortium was treated the same way but received poorly crystalline iron as an additional terminal electron acceptor (SG + Fe FACs). We sequenced the metagenomes of both consortia to a depth of about 150 Mb each, resulting in a coverage of 26× for the more diverse SG + Fe FACs, and 81× for the relatively less diverse SG only FACs. Both consortia were able to quickly grow on switchgrass, and the iron-amended consortium exhibited significantly higher microbial diversity than the unamended consortium. We found evidence of higher stress in the unamended FACs and increased sugar transport and utilization in the iron-amended FACs. This work provides metagenomic evidence that supplementation of alternative TEAs may improve feedstock deconstruction in biofuel production.

Keywords

Anaerobic decomposition switchgrass Panicum virgatum tropical forest soil feedstock-adapted consortia bacteria archaea metagenomics

Introduction

Development of renewable, sustainable biofuels from plant feedstock material has emerged as a key goal of the US Department of Energy. The use of lignocellulose as a renewable energy source has many advantages, above all that lignocellulose is the most abundant biopolymer on earth, with its production independent of food agriculture [1]. The deconstruction of plant biomass is a key first step in the conversion of plant sugars to biofuels, though this step has posed a great challenge to making biofuels economically viable. The major hurdles involve both lignin occlusion of cellulose and lignin derivatives that inhibit lignocellulose deconstruction and fuel synthesis [1]. Lignin is also a potentially valuable waste stream that is currently burned to produce energy as heat [2]. Part of the impact of this work is the discovery of enzymes and pathways in natural ecosystems that function to liberate lignin from cellulose. These discoveries promise to both provide insight into the natural processes of plant lignin decomposition, as well as improve efficiency of biofuels production.

The microbial communities present in the wet tropical soils of Puerto Rican rain forests are promising in providing pathways to overcome the challenges of lignocellulose deconstruction. These tropical soil communities are responsible for near complete decomposition of leaf plant litter in as little as eighteen months [3], which is interesting considering that the soils experience strong fluctuations in redox potential, switching from a completely oxic state to an anoxic state on a daily or weekly basis [4,5]. We have also observed considerable microbial activity and plant litter decomposition under anaerobic conditions in the lab and field [69]. This is at odds with the current paradigm of the “enzyme latch hypothesis,” which posits that oxidative enzyme activities are the rate-limiting steps of plant litter decomposition [1012]. Understanding the enzymes employed by native tropical soil microbes to deconstruct lignocellulose has the potential to illuminate the mechanisms of fast anaerobic lignocellulose decomposition.

Classification and features

Two metagenomes were generated from feedstock-adapted consortia, originally derived from tropical forest soil communities [Table 1]. Soil samples were collected from a wet subtropical lower montane forest in the Luquillo Experimental Forest, which is part of the NSF-sponsored Long-Term Ecological Research program in Puerto Rico (18°18′N, 65°50′W). The fieldwork was conducted and samples collected and transported under USDA permit number P526P-08-00634. Soils are acidic (pH 5.5), clayey ultisols with high iron and aluminum content and characterized by a fluctuating redox that ranges from oxic to anoxic on a timescale of weeks [4,5,15]. Soils were collected from the Bisley watershed, 250 meters above sea level (masl) from the 0–10 cm depth, using a 2.5-cm diameter soil corer. Cores were stored intact in Ziploc bags at ambient temperature and immediately transported to the lab, where they were used for growth inoculum.
Table 1.

Classification and general features of the four metagenome data sets according to the Minimum Information about Genomes and Metagenomes (MIMS) standards [13].

MIMS ID

Property

Term

Evidence codea

 

Current classification

Metagenome ecological metagenome terrestrial metagenome

TAS [5]

 

Carbon source

Switchgrass

IDA

 

Energy source

Switchgrass

IDA

 

Terminal electron receptor

Iron reduction or fermentation

TAS [8]

MIGS-6

Habitat

Consortia (mixed community) derived from wet tropical forest soils

TAS [8]

MIGS-14

Pathogenicity

none

NAS

MIGS-4

Geographic location

Wet tropical forest, Puerto Rico, USA

 

MIGS-5

Sample collection time

April, 2009

 

MIGS-4.1

Latitude

18°18□N

 

MIGS-4.2

Longitude

65°50□W

 

MIGS-4.3

Depth

0–10 cm

TAS [8]

MIGS-4.4

Altitude

250 masl

TAS [8]

a Evidence codes - IDA: Inferred from Direct Assay; TAS: Traceable Author Statement (i.e., a direct report exists in the literature); NAS: Non-traceable Author Statement (i.e., not directly observed for the living, isolated sample, but based on a generally accepted property for the species, or anecdotal evidence). These evidence codes are from the Gene Ontology project [14].

For adaptation to growth on feed-stocks as sole carbon source, tropical forest soils were homogenized then used to inoculate basal salts minimal medium (BMM) [16] containing trace minerals [17,18], vitamins [19], and buffered to pH 5.5 to match the measured soil pH using MES. Soils were added at a rate of 0.5 g (wet weight) per 200 mL BMM, and the resulting mixture was incubated anaerobically at ambient temperatures for 8 weeks with 10 g L-1 dried, ground switchgrass as the sole carbon source. Samples of switchgrass (MPV 2 cultivar) were kindly provided by the laboratory of Dr. Ken Vogel (USDA, ARS, Lincoln, NE). Soluble iron was added to a final concentration of 5 mM. A stock solution of soluble iron was obtained by adding ferric chloride hexahydrate [Fe(III)] to a solution of nitrilotriacetic acid disodium salt and sodium bicarbonate. Dinitrogen gas was bubbled through media to remove any dissolved O2, and containers were quickly sealed with airtight stoppers to maintain anaerobic conditions. Containers were autoclaved for 20 min at 121°C. Anaerobic switchgrass-adapted consortia were enriched from tropical forest soils by passaging the communities two times for ten weeks each, with switchgrass as the sole carbon source, under anaerobic conditions with and without supplemental iron.

Metagenome sequencing information

Metagenome project history

These metagenomes were selected based on the ability of the consortia to mineralize switchgrass as the sole C source anaerobically, and represented two distinct metabolisms for deconstructing switchgrass that are both likely to be prevalent under natural field conditions. Sequence analysis of the small subunit ribosomal RNA genes revealed that growth on switchgrass as the sole carbon source resulted in a richness of 84 taxa, while inclusion of iron in the consortia growth media resulted in a richness of 336 taxa; this was in comparison to the richness of the original soil sample which was 1,339 taxa [20] based on 97% identity.

Growth conditions and DNA isolation

Consortia were grown for metagenomic DNA sequencing in the same manner as described for the cultivation of communities, as above. DNA was extracted using a CTAB extraction method, which is the standard operating procedure recommended by the Joint Genome Institute. Cells from the consortia were pelleted by centrifugation and reconstituted in TE to an equivalent OD (600 nm) of about 1.0 using direct counts. Lysozyme was added (final concentration 1.3 mg per ml) and incubated for 5 minutes at room temperature, then 10% SDS (33 µl per ml) and proteinase K (final concentration 5.5µl per ml) was added and incubated at 37oC for 1 hour. Sodium chloride (5M stock added to final concentration 0.22 M) was added, then the CTAB/NaCl buffer was added both at 0.075 ml per ml starting volume. This mix was incubated at 65oC for 10 minutes. Chloroform:isoamyl alcohol (24:1) was added at 0.2 vol, then centrifuged at 14,000 x g for 10 minutes at room temperature. DNA in the aqueous phase was extracted again with phenol:chloroform:isoamyl alcohol (25:24:1), subjected to an ethanol precipitation, and the DNA pellet finally reconstituted at 37oC for 20 minutes in TE plus RNAse. The quantity and quality of the extraction were checked by gel electrophoresis using JGI standards.

Metagenome sequencing and assembly

The metagenomes were sequenced using the Illumina GaII sequencing platform. Two types of short-insert (300 bp) paired-end libraries were generated, with and without PCR amplification after adapter ligation. All general aspects of the library construction process can be accessed via the DOE Joint Genome Institute website [21]. 16.2 Gb of Illumina GaII sequence data were generated for the PR soil-derived Feedstock-adapted consortia SG + Fe sample and 33.9 Gb Illumina GaII sequence data were generated for the PR soil-derived Feedstock-adapted consortia SG only sample. Raw Illumina metagenomic reads were trimmed using a minimum quality score cutoff of 10. Trimmed, paired-end Illumina reads were assembled using SOAPdenovo v1.05 [22] with a range of Kmers (85, 89, 93, 97, 101, 105). Default settings for all SOAPdenovo assemblies were used (flags: -d 1 and -R). Contigs generated by each assembly (6 total contig sets) were sorted into two pools based on length. Contigs smaller than 1,800 bp were assembled using Newbler (Life Technologies, Carlsbad, CA) in an attempt to generate larger contigs (flags: -tr, -rip, -mi 98, -ml 80). All assembled contigs larger than 1,800 bp, as well as the contigs generated from the final Newbler run, were combined using minimus 2 (flags: -D MINID=98 -D OVERLAP=80)(AMOS [23]). The assembly was a result of two rounds of sequencing in this manner, with and without amplification. Table 2 presents the project information and its association with MIGS version 2.0 compliance [13]. These sequences are currently available to the public at IMG/M.
Table 2.

Project information

MIGS ID

Property

Term

MIGS-31

Finishing quality

Standard Draft

MIGS-28

Libraries used

Illumina standard paired-end library (0.3 kb insert size)

MIGS-29

Sequencing platforms

Illumina GaIIX

MIGS-31.2

Fold coverage

26.1466 × (PR soil-derived FAC SG + Fe) 81.1761 × (PR soil-derived FAC SG only)

MIGS-30

Assemblers

SOAPdenovo v1.05, Newbler v2.5, minimus2

MIGS-32

Gene calling method

Glimmer

 

GOLD ID

Gm00278

 

IMG Project ID

18182

 

Project relevance

biotechnological

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 are removed. Low complexity regions are masked using the dust algorithm from the NCBI toolkit and very similar sequences (similarity > 95%) with identical 5′ pentanucleotides are replaced by one representative, typically the longest, using uclust [24]. 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 [25]. 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 70 nt of the sequences by comparing these to a database of nt sequences of tRNAs identified in the isolate genomes using blastn [26]. Hits with high similarity were kept; all other parameters are set to default values. Ribosomal RNA genes (tsu, ssu, lsu) were predicted using the hmmsearch [27] 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 [28] and PILERCR [29]. 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.2.6r) [29] or Metagene (v. Aug08) [30], prodigal [31] and FragGenescan [32] 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 [24], the COG db using the NCBI developed PSSMs [33], the pfam db [34] using hmmsearch. Assignment to KEGG Ortholog protein families was performed using the algorithm described in [35].

Metagenome properties

The metagenomes were sequenced at a total size of 152,660,070 bp for the SG only FACS and 154,120,208 bp for the SG + Fe FACS. The GC content of these metagenomes was 41.18% for SG only and 46.02% for SG + Fe FACs. This sequencing included 197,271 and 193,491 predicted genes with 98.85% and 99.62% predicted protein-coding genes for SG only and SG + Fe FACs, respectively. A total of 127,406 and 129,389 of the protein coding genes, or 64.58% and 66.87% of the total predicted protein-coding genes, were assigned to a putative function with the remaining annotated as hypothetical proteins for SG only and SG + Fe FACs, respectively.

The iron-amended consortia (SG + Fe) was significantly enriched in protein-coding genes with putative function for 11 of the 25 general COG categories that genes were assigned. The largest differences were observed for genes associated with amino acid transport and metabolism (E), carbohydrate transport and metabolism (G), and secondary metabolites biosynthesis, transport, and catabolism (Q), which were all enriched in the iron-amended compared to the unamended FACs. Genes assigned to translation, ribosomal structure and biogenesis (J) and transcription (K) were significantly depleted in the iron-amended compared to the unamended consortia. The properties and the statistics of the genome are summarized in Table 3, Table 4 and Table 5.
Table 3.

Summary of metagenomes

Metagenome

Size (Mb)

# scaffolds

GOLD ID

GOLD sample idID

SG only

152.66

57,147

Gm00278

Gs0000888

SG + Fe

154.12

65,160

Gm00278

Gs0000889

Table 4.

Nucleotide content and gene count levels of the metagenomes

 

SG only Metagenome

SG + Fe Metagenome

 

Number

% of Total

Number

% of Total

DNA, total number of bases

152,660,070

100.00%

154,120,208

100.00%

DNA coding number of bases

130,438,005

85.44%

136,080,382

88.29%

DNA G+C number of bases

62,858,797

41.18%*

70,930,796

46.02%*

DNA scaffolds

57,147

100.00%

65,160

100.00%

CRISPR Count

51

 

4

 

Genes total number

197,271

100.00%

193,491

100.00%

Protein coding genes

195,006

98.85%

192,751

99.62%

RNA genes

2,265

1.15%

740

0.38%

rRNA genes

294

0.15%

16

0.01%

5S rRNA

106

0.05%

9

0.00%

16S rRNA

75

0.04%

3

0.00%

18S rRNA

1

0.00%

4

0.00%

tRNA genes

1,971

1.00%

724

0.37%

Protein coding genes with function prediction

127,406

64.58%

129,389

66.87%

without function prediction

67,600

34.27%

63,362

32.75%

not connected to SwissProt Protein Product

195,006

98.85%

192,751

99.62%

Protein coding genes with enzymes

33,383

16.92%

30,632

15.83%

w/o enzymes but with candidate KO based enzymes

26,793

13.58%

32,919

17.01%

Protein coding genes connected to KEGG pathways3

37,533

19.03%

34,348

17.75%

not connected to KEGG pathways

157,473

79.83%

15,8403

81.87%

Protein coding genes connected to KEGG Orthology (KO)

63,949

32.42%

57,111

29.52%

not connected to KEGG Orthology (KO)

131,057

66.44%

135,640

70.10%

Protein coding genes connected to MetaCyc pathways

32,243

16.34%

29,552

15.27%

not connected to MetaCyc pathways

162,763

82.51%

163,199

84.34%

Protein coding genes with COGs3

121,020

61.35%

123,077

63.61%

with Pfam3

115,645

58.62%

118,589

61.29%

with TIGRfam3

33,743

17.10%

33,969

17.56%

in internal clusters

77,655

39.36%

73,856

38.17%

Protein coding genes coding signal peptides

48,556

24.61%

49,644

25.66%

Protein coding genes coding transmembrane proteins

43,693

22.15%

43,726

22.60%

COG clusters

4,125

84.65%

3,974

81.55%

KOG clusters

0

0.00%

0

0.00%

Pfam clusters

4,447

37.33%

4,293

36.04%

TIGRfam clusters

2,580

64.13%

2,489

61.87%

* GC percentage shown as count of G’s and C’s divided by a total number of G’s, C’s, A’s, and T’s. This is not necessarily synonymous with the total number of bases.

Table 5.

Number of genes associated with the 25 general COG functional categories.

ID

Name

SG only

SG + Fe

R

P-value

J

Translation, ribosomal structure and biogenesis

6,659

6,055

4.57

*** 0.000

A

RNA processing and modification

21

22

0

n.s.

K

RNA processing and modification

21

22

0

n.s.

L

Replication, recombination and repair

6,248

6,103

1.36

0.086

B

Chromatin structure and dynamics

49

35

0.21

n.s.

D

Cell cycle control, cell division, chromosome partitioning

1,457

1,68

1.35

0.089

Y

Nuclear structure

V

Defense mechanisms

2,884

3,232

−2.11

* 0.018

T

Signal transduction mechanisms

10,430

10,271

2.01

* 0.022

M

Cell wall/membrane/envelope biogenesis

8,396

8,753

−1.67

* 0.047

N

Cell motility

3,150

3,146

−1.2

n.s.

Z

Cytoskeleton

39

43

−0.29

n.s.

W

Extracellular structures

2

1

0

n.s.

U

Intracellular trafficking, secretion, and vesicular transport

2,438

2,525

−0.56

n.s.

O

Posttranslational modification, protein turnover, chaperones

3,893

3,914

0.14

n.s.

C

Energy production and conversion

8,221

8,426

0.08

n.s.

G

Carbohydrate transport and metabolism

13,038

14,361

−3.69

*** 0.000

E

Amino acid transport and metabolism

9,571

10,682

−5.33

*** 0.000

F

Nucleotide transport and metabolism

2,808

3,022

−2.21

* 0.014

H

Coenzyme transport and metabolism

5,193

5,080

2.17

* 0.015

I

Lipid transport and metabolism

3,034

3,375

−1.9

* 0.029

P

Inorganic ion transport and metabolism

5,914

6,171

0.12

n.s.

Q

Secondary metabolites biosynthesis, transport and catabolism

1,608

1,916

−2.51

** 0.006

R

General function prediction only

15,442

15,796

−0.87

n.s.

S

Function unknown

10,667

10,106

0.6

n.s.

aP-value symbols denote * P<0.05, ** P<0.01, *** P<0.001, and n.s. indicates not significant.

Taxonomic diversity

The taxonomic diversity and phylogenetic structure of the two metagenomes was determined based on all genes, classifying at a minimum 60% identity to members of the listed phyla. The phylogeny reported is the one in use in IMG/M [36], which uses the phylogeny described as part of the genomic encyclopedia of Bacteria and Archaea (GEBA) project [37].

Both consortia were dominated by representative genes belonging to the Firmicutes, which accounted for 20 and 23% of the counts in the SG only and SG + Fe FACs, respectively. In terms of relative abundance, the next most dominant genes belonged to the phylum Bacteroidetes, accounting for 7% of the counts, and Proteobacteria, accounting for 6% of the counts. Members of the archaeal phylum Euryarchaeota accounted for 2.6% and 1.34% of the SG only and SG + Fe FACs gene counts, respectively. There were very few documented members of the Eukaryota, accounting for less than one-tenth of one percent. Plasmid population-associated genes were dominated by those associated with Firmicutes and Proteobacteria, and these were outnumbered by double-stranded DNA viruses by about two to one.

Differences were observed in abundance of genes in many phyla, which was expected given the much higher richness observed by pyrosequencing the small subunit ribosomal RNA genes in consortia amended with iron as terminal electron acceptor. To visualize which phyla were over- or under-represented in gene counts for each metagenome, we chose to present the phyla that were at least two-fold (double) differentially represented in the iron-amended consortia compared to the unamended consortia. Fold-differences were calculated by dividing the counts detected in SG+Fe FACs divided by counts detected in SG only FACs, and the log2 of fold differences are presented in Figure 1. Phyla that were over-represented in the iron-amended FACs included Acidobacteria (5.17-fold enriched), Actinobacteria (2.5-fold), Elusimicrobia (2-fold), and Chlamydiae (2-fold), which are all Bacteria; also over-represented in the SG + Fe were Firmicutes plasmids (2-fold). Of these, only the Acidobacteria and Actinobacteria were abundant (>2% relative abundance), with the rest detected in the tens of counts per metagenome. Phyla that were under-represented in the iron-amended FACs compared to the SG only FACs included Basidiomycota (domain Eukaryota, 0.625-fold), Euryarchaeota (domain Archaea, 0.52-fold), Chordata (domain Eukaryota, 0.5-fold), Proteobacteria Plasmids (0.5-fold), and Crenarchaeota (domain Archaea, 0.17-fold) [Table 6].
Figure 1.

Phyla that are at least 2-fold differentially represented in one metagenome compared to the other, and had greater than one representative detected. Phyla with gene counts over-represented in the iron-amended consortium (SG + Fe) are colored brown, while phyla with gene counts over-represented in the unamended consortium (SG only) are colored light green.

Table 6.

Overview of taxonomic diversity in metagenomes.

Domain

Phylum

SG only Count

%

SG + Fe Count

%

Archaea

     
 

Crenarchaeota

575

0.29

88

0.05

 

Euryarchaeota

5,089

2.58

2,590

1.34

 

Nanoarchaeota

1

0

0

0

Bacteria

     
 

Acidobacteria

478

0.24

2,392

1.24

 

Actinobacteria

1,113

0.56

2,756

1.42

 

Aquificae

100

0.05

99

0.05

 

Bacteroidetes

14,680

7.44

14,937

7.72

 

Chlamydiae

16

0.01

35

0.02

 

Chlorobi

460

0.23

530

0.27

 

Chloroflexi

1,073

0.54

2042

1.06

 

Chrysiogenetes

13

0.01

18

0.01

 

Cyanobacteria

742

0.38

823

0.43

 

Deferribacteres

117

0.06

121

0.06

 

Deinococcus-Thermus

158

0.08

275

0.14

 

Dictyoglomi

132

0.07

144

0.07

 

Elusimicrobia

17

0.01

35

0.02

 

Fibrobacteres

30

0.02

36

0.02

 

Firmicutes

38,958

19.75

44,858

23.18

 

Fusobacteria

533

0.27

534

0.28

 

Gemmatimonadetes

11

0.01

29

0.01

 

Lentisphaerae

113

0.06

165

0.09

 

Nitrospirae

70

0.04

80

0.04

 

Planctomycetes

375

0.19

448

0.23

 

Proteobacteria

11,289

5.72

11,803

6.1

 

Spirochaetes

2,460

1.25

3,795

1.96

 

Synergistetes

314

0.16

460

0.24

 

Tenericutes

30

0.02

34

0.02

 

Thermodesulfobacteria

36

0.02

43

0.02

 

Thermotogae

330

0.17

414

0.21

 

Verrucomicrobia

450

0.23

611

0.32

Eukaryota

     
 

Apicomplexa

25

0.01

19

0.01

 

Arthropoda

32

0.02

30

0.02

 

Ascomycota

64

0.03

59

0.03

 

Bacillariophyta

7

0

8

0

 

Basidiomycota

8

0

5

0

 

Chlorophyta

5

0

5

0

 

Chordata

32

0.02

29

0.01

 

Microsporidia

1

0

0

0

 

Nematoda

7

0

3

0

 

Streptophyta

73

0.04

66

0.03

Plasmid:Archaea

     
 

Crenarchaeota

5

0

2

0

 

Euryarchaeota

4

0

4

0

Plasmid:Bacteria

     
 

Actinobacteria

3

0

3

0

 

Bacteroidetes

2

0

1

0

 

Firmicutes

23

0.01

32

0.02

 

Proteobacteria

32

0.02

29

0.01

Viruses

     
 

ds DNA viruses, no RNA stage

113

0.06

97

0.05

 

ss DNA viruses

1

0

0

0

While gene counts of representative phyla suggest phylogenetic differences, these data are certainly biased towards phyla that have more sequenced representatives. Additionally, phyla that are included in coverage of popular universal small subunit rRNA primers are also may be over-represented in these analyses because of their over-representation in the databases. While the relative abundances of between-phyla comparisons may be questionable based on differential representation in the database, the relative abundances of taxa within a phyla is reflective of the distinct metabolic conditions afforded by growth of consortia with lignocellulose as sole C source either with or without iron as an additional terminal electron acceptor.

In an additional, separate experiment, we tested the effects of additional terminal electron acceptors on the ability of feedstock-adapted consortia to degrade switchgrass, which included iron as well as sulfate and nitrate, with switchgrass-only as a control. In this additional experiment, we analyzed the resulting microbial communities by the taxonomic marker 16S ribosomal RNA gene sequence libraries [8]. These communities were grown from the SG only FACs whose metagenomic sequences are presented here. Further passages were made before community analysis, making these consortia from this additional experiment less rich and characterized by fewer dominant species. Because these communities are simpler, we are able to more closely examine the relationships among taxa and co-occurrences under varying availability of terminal electron acceptors.

We observed some differences in taxon occurrence and functional gene abundance between the iron-amended and iron-unamended metagenomes, and used network analysis to illustrate the phylogenetic basis of taxon co-occurrence among differences in availability of terminal electron acceptors. Network analyses were constructed by calculating all possible correlations between taxa using Pearson’s correlation coefficient, then discarding any pairwise correlations that did not meet the criteria for a “connection”, which was a minimum r value of 0.9 and minimum P-value of 0.01. Network analysis was conducted based on the methods presented in Barberán et al. [38] in R using the packages igraph [39], Hmisc [40], multtest [41], doMC [42], and foreach [43].

These networks were strongly dominated by a few taxa, as evidenced by the large number of singletons (661 taxa, 62%) and doubletons (167 taxa, 16%) detected among the total taxa detected (1,060). These singleton and doubleton taxa were not included in the network analysis, leaving 232 taxa for analyzing co-occurrences. Of the 1,060 taxa included in this analysis, 170 taxa met the minimum criteria for a significant connection, with 579 connections between them. Taxa are mapped in Figure 2 with colors corresponding to phylum (left) as well as by generalist or specialist (right), where generalists were defined as taxa detected in all TEA treatments, while specialists were defined as taxa detected in only one treatment.
Figure 2.

Network analysis of feedstock adapted consortia grown on switchgrass only (SG only), SG plus iron oxides (FeOx), SG plus nitrate (NO3-), or SG plus sulfate (SO3-). Each point represents one taxon, and the size of the point corresponds to the number of connections (edges) associated with the taxon. Edges (grey lines) indicate a minimum correlation of Pearson r = 0.9 as well as statistical significance (P<0.01). On the left, taxa are colored by taxonomy according to their assigned phylum; on the right, taxa are colored based on whether they are generalists (present in all four treatments) or specialists (present in one treatment only and absent in the rest).

As expected due to the static anaerobic conditions, networked communities are dominated by Firmicutes, which are prevalent in all clusters. Firmicutes also dominated the SG only and SG + Fe FACs, accounting for 20 and 23% of total richness, respectively. The Firmicutes contain the Clostridiales, which are fast-growing obligate anaerobes, fermenters, and well-known lignocellulolytic microbes [4446]. In our consortia networks, the Firmicutes tended to either be generalists or switchgrass-only specialists, which may also explain their prevalence in our metagenomes. The specialists were dominated by Firmicutes, with the notable observation that there were no nitrate specialists detected by this method. Of the remaining specialists, there were more sulfate-specialists than any other kind, followed by switchgrass-only specialists, then iron specialists. All iron specialists were Firmicutes; this was somewhat surprising considering that the best-known iron reducers are in the phylum Proteobacteria, including Geobacter and Shewanella [47,48]. However, these taxa were notably absent in previous phylogenetic and metagenomic analyses of wet tropical forest soils of Puerto Rico [9,20], and there are actually a wide diversity of iron-reducing bacteria within the Firmicutes. In the network, Firmicutes also tended to co-occur either with each other, forming large cliques, or with taxa from diverse phyla. Generalists were mostly Firmicutes, but also included representatives from the phyla Proteobacteria and Methanomicrobia (of the Euryarchaeota). These phyla are known to accommodate some well-known K-selected species; taxa that are not fast growers but have persistent growth and are able to survive under a range of conditions [12,49].

Functional genetic diversity

Analysis of pfams that were significantly different between iron-amended FACs (SG+Fe) and unamended FACs (SG only) suggested that there were strong differences between the function and metabolic status of the two consortia (Table 7, Figure 3). There were 15 pfams that were significantly enriched in the SG + Fe FACs, compared to 23 pfams enriched in SG only compared to SG + Fe. The pfams enriched in the SG + Fe suggested that the addition of iron caused the consortia to be overall more efficient at transporting xylose and other nutrients, as evidenced by the large number of ABC transporters and other bacterial transporter systems. Transporters made up the bulk of the identified pfams, representing the most abundant pfam domains differentially detected in the SG + Fe compared to the SG only FACs. There was also evidence that the increased transport of carbon and other nutrients resulted in increased biosynthesis of biomass and secondary metabolites, with pfams such as S-layer homology domain, oxidoreductase family domains, [Fe-S] binding domains, and polyketide synthesis domains. The SG + Fe FACs were also significantly enriched in glycosyl-hydrolase family 65 domains compared to the switchgrass only FACs, suggesting that this community had more members that were able to utilize the switchgrass for energy and microbial biomass.
Figure 3.

Illustration of pfams that were differentially represented in SG only compared to SG + Fe. On the left, pfams are listed for the consortium grown in switchgrass only with no iron (SG only), and on the right, pfams are listed for the consortium grown in switchgrass with iron (SG + Fe). This illustration is based on data from Table 7.

Table 7.

Report of pfams that were significantly enriched

ID

SG only

R

SG + Fe

 

Description

Enriched in SG+Fe FACs

     

pfam00005

2,409

−7.38

8.10e-14

3,025

ABC transpoerter

pfam00528

1,817

−7.36

9.02e-14

2,348

Bacterial binding protein-dependent transport systems

pfam02653

394

−6.24

2.23e-10

605

Bacterial binding protein-dependent transport systems

pfam00106

420

−4.88

5.38e-07

589

short-chain dehydrogenases/reductases family

pfam01979

163

−5.55

1.44e-08

287

large metal dependent hydrolase superfamily

pfam08352

99

−6.43

6.20e-11

218

C-terminus of oligopeptide ABC transporter ATP binding proteins

pfam02894

146

−4.1

2.03e-05

231

Oxidoreductase family, C-terminal alpha/beta domain

pfam02782

113

−4.33

7.56e-06

193

FGGY carbohydrate kinase family

pfam00395

130

−3.98

3.39e-05

208

S-layer homology domain

pfam01266

93

−3.77

8.17e-05

156

FAD dependent oxidoreductase family

pfam02801

47

−4.13

1.79e-05

99

Beta-ketoacyl-ACP synthase (fatty acid synthesis)

pfam00404

2

−6.02

8.86e-10

43

Dockerin: protein domain in cellulosome cellular structure

pfam01799

24

−3.71

1.02e-04

59

[2Fe-2S] binding domain

pfam03632

19

−3.8

7.32e-05

52

glycoside hydrolase family 65

pfam08659

1

−5.41

3.23e-08

33

polyketide synthase domain, catalyses the first step in the reductive modification of the beta-carbonyl centers in the growing polyketide chain

Enriched in SG only FACs

     

pfam00990

697

5.95

1.33e-09

508

GGDEF domain, cyclic di-GMP synthesis involved in intracellular signaling

pfam03466

499

6.29

1.60e-10

330

LysR substrate binding domain, similar to periplasmic binding protein

pfam00126

476

5.71

5.71e-09

326

Helix-turn-helix DNA binding domain

pfam00583

1,048

3.88

5.26e-05

905

Acetyltransferase (or transacetylase)

pfam00989

569

3.89

4.92e-05

459

PAS domain, signal sensor

pfam00563

262

5.43

2.89e-08

157

EAL domain, possible diguanylate phosphodiesterase with metal-binding site

pfam01473

135

8.26

1.11e-16

31

Putative cell wall binding repeat

pfam02311

405

3.78

7.82e-05

314

rabinose-binding and dimerization domain of the AraC regulatory protein

pfam00665

201

4.53

2.94e-06

124

Retroviral integrase

pfam02378

145

4.25

1.08e-05

84

Phosphotransferase system, EIIC, part of a sugar-specific permease system

pfam00801

163

3.71

1.03e-04

106

Polycystic-kidney disease domain, usually involved in mediating protein-protein interactions

pfam01797

119

3.84

6.07e-05

69

Transposase IS200, for transposition of insertion elements

pfam01609

119

3.76

8.44e-05

70

Transposase DDE domain, for transposition of insertion elements

pfam01011

75

4.54

2.83e-06

30

beta propeller, found in several enzymes which utilize pyrrolo-quinoline quinone as a prosthetic group

pfam00367

80

4.35

6.80e-06

35

phosphotransferase system, EIIB

pfam02302

105

3.69

1.13e-04

60

phosphoenolpyruvate: sugar phosphotransferase system (PTS) system, Lactose/Cellobiose specific IIB subunit

pfam09681

47

5.93

1.53e-09

5

N-terminal phage replisome organizer, origin of phage replication

pfam01978

83

3.83

6.47e-05

42

sugar-specific transcriptional regulator of the trehalose/maltose ABC transporter

pfam03143

52

3.75

8.81e-05

21

GTP-binding elongation factor family

pfam09820

35

4.41

5.09e-06

7

predicted AAA-ATPase domain

pfam08350

33

4

3.22e-05

8

domain of unknown function, so far found only at the C-terminus of archaean proteins

pfam08495

36

3.74

9.02e-05

11

FIST N domain: novel sensory domain present in signal transduction proteins

pfam09373

22

4.45

4.34e-06

1

Pseudomurein-binding repeat, pseudomurein being a cell-wall structure

pfam08004

18

3.96

3.71e-05

1

domain of unknown function, so far found only among archaeal proteins

in either the iron-amended consortia (SG + Fe FAC, upper half of table) or the unamended feedstock-adapted consortia (SG only FAC, lower half of table).

In contrast, the pfams detected in the SG only FACs that were significantly enriched compared to the SG + Fe FACs hinted at stressful conditions in survival of the community without the addition of the exogenous terminal electron acceptor iron. There were a number of intracellular signaling domains that were enriched, suggesting that there were more interactions among remaining community members that grow under these conditions. There was also evidence of enrichment for mobile genetic elements and viral DNA transfer, evidenced by increased detection of transposase domains, retroviral integrases, and phage replication domains. It has been demonstrated that communities under stress have higher transfer rate of mobile genetic elements, potentially as a mechanism to induce better survival strategies [50]. These differences in detected pfams at the DNA level suggest that the metagenomic sequencing of the SG only FACs occurred prior to the community adapting to the lack of exogenous terminal electron acceptors. That is, our sequencing was performed before the community had arrived at a new equilibrium, and over the course of selection for anaerobic growth of tropical soil communities on switchgrass as sole C source, the communities were unable to adapt to the lack of iron as terminal electron acceptor.

Functional Genes Related to Feedstock Deconstruction

To recover genes that were specifically involved in switchgrass deconstruction, we used blastp to pull out sequences from the annotated, assembled metagenomes that had E-value of 1e-20 or better. The target list contained 101 proteins, consisting of glycosyl hydrolases, lignases, and other proposed lignocellulose-degrading enzymes based on genome analysis of the isolate Enterobacter lignolyticus SCF1 [51], which originated from these same soils. This resulted in 1,001 hits from both the SG only and SG + Fe FACs, but 54 and 198 targets on scaffolds longer than 10kb from the SG only and SG + Fe FACs, respectively. These results are summarized in Table 8, where we report the number of genes clustered by COG ID number. There were 13 COGs that contain genes detected in both FACs, three COGs with genes detected in the SG only FAC but not the SG + Fe FAC, and 24 COGs with genes detected in the SG + Fe FAC but not the SG only FAC. This imbalance in target lignocellulolytic genes, with many more genes detected with iron amendment than without the TEA amendment, supports our conclusion that iron addition improves lignocellulose decomposition among these FACs.
Table 8.

Count of genes in COGs that bear protein sequence homology to target lignocellulolytic genes of interest.

COG ID

SG only

SG + Fe

COG Description

COG1472

11

23

Beta-glucosidase-related glycosidases

COG3250

11

7

Beta-galactosidase/beta-glucuronidase

COG5001

9

9

Predicted signal transduction protein containing a membrane domain, an EAL and a GGDEF domain

COG1028

4

33

Dehydrogenases with different specificities (related to short-chain alcohol dehydrogenases)

COG0300

3

2

Short-chain dehydrogenases of various substrate specificities

COG4221

3

2

Short-chain alcohol dehydrogenase of unknown specificity

COG1012

2

11

NAD-dependent aldehyde dehydrogenases

COG0677

2

3

UDP-N-acetyl-D-mannosaminuronate dehydrogenase

COG3384

2

3

Uncharacterized conserved protein

COG0280

1

3

Phosphotransacetylase

COG1344

1

3

Flagellin and related hook-associated proteins

COG3325

1

2

Chitinase

COG0277

1

1

FAD/FMN-containing dehydrogenases

COG0179

1

 

2-keto-4-pentenoate hydratase/2-oxohepta-3-ene-1,7-dioic acid hydratase (catechol pathway)

COG1874

1

 

Beta-galactosidase

COG2132

1

 

Putative multicopper oxidases

COG1129

 

20

ABC-type sugar transport system, ATPase component

COG2723

 

11

Beta-glucosidase/6-phospho-beta-glucosidase/beta-galactosidase

COG0411

 

9

ABC-type branched-chain amino acid transport systems, ATPase component

COG0673

 

8

Predicted dehydrogenases and related proteins

COG0036

 

4

Pentose-5-phosphate-3-epimerase

COG1455

 

4

Phosphotransferase system cellobiose-specific component IIC

COG1486

 

4

Alpha-galactosidases/6-phospho-beta-glucosidases, family 4 of glycosyl hydrolases

COG2200

 

4

FOG: EAL domain

COG0366

 

3

Glycosidases

COG1004

 

3

Predicted UDP-glucose 6-dehydrogenase

COG3842

 

3

ABC-type spermidine/putrescine transport systems, ATPase components

COG3845

 

3

ABC-type uncharacterized transport systems, ATPase components

COG0435

 

2

Predicted glutathione S-transferase

COG0583

 

2

Transcriptional regulator

COG0812

 

2

UDP-N-acetylmuramate dehydrogenase

COG3836

 

2

2,4-dihydroxyhept-2-ene-1,7-dioic acid aldolase

COG4213

 

2

ABC-type xylose transport system, periplasmic component

COG4214

 

2

ABC-type xylose transport system, permease component

COG0376

 

1

Catalase (peroxidase I)

COG0410

 

1

ABC-type branched-chain amino acid transport systems, ATPase component

COG1640

 

1

4-alpha-glucanotransferase

COG1921

 

1

Selenocysteine synthase [seryl-tRNASer selenium transferase]

COG1960

 

1

Acyl-CoA dehydrogenases

COG2368

 

1

Aromatic ring hydroxylase

COG2373

 

1

Large extracellular alpha-helical protein

Total Result

54

197

 

Conclusion

Metagenome sequencing of iron-amended and unamended feedstock-adapted consortia suggests that iron amendment results in microbial communities that are more active or more efficient at lignocellulose degradation. This is evidenced by the increased abundance of genes associated carbohydrate transport and decreased abundance of genes associated with cell maintenance and growth. The iron amendment was only applied after one generation of anaerobic growth, so it is possible that further generations of growth in the presence of iron would result in consortia better able to degrade lignocellulosic feedstocks. This research also supports the possibility that anaerobic lignocellulose deconstruction could benefit from metabolism supplemented by additional TEAs.

Abbreviations

EMBL: 

European Molecular Biology Laboratory

NCBI: 

National Center for Biotechnology Information (Bethesda, MD, USA)

RDP: 

Ribosomal Database Project (East Lan-sing, MI, USA)

Declarations

Acknowledgements

The work conducted in part by the US Department of Energy Joint Genome Institute and in part by the Joint BioEnergy Institute (http://www.jbei.org) supported by the US Department of Energy, Office of Science, Office of Biological and Environmental Research, under Contract No. DE-AC02-05CH11231. We would like to thank Dr. Ken Vogel (USDA, ARS, Lincoln, NE) for providing samples of switchgrass (MPV 2 cultivar) for use in these studies. We are also grateful to Albert Barberán for guidance in constructing the community networks.

Authors’ Affiliations

(1)
Microbiology Department, University of Massachusetts
(2)
Microbial Communities Group, Deconstruction Division, Joint BioEnergy Institute
(3)
Lawrence Livermore National Laboratory
(4)
Physical Biosciences Division, Lawrence Berkeley National Laboratory
(5)
Technologies Division, Joint BioEnergy Institute
(6)
Sandia National Lab
(7)
Department of Energy Joint Genome Institute
(8)
Ecology Department, Earth Sciences Division, Lawrence Berkeley National Laboratory
(9)
Department of Civil and Environmental Engineering, University of Tennessee
(10)
Biosciences Division, Oak Ridge National Laboratory

References

  1. Lee SK, Chou H, Ham TS, Lee TS, Keasling JD. Metabolic engineering of microorganisms for biofuels production: from bugs to synthetic biology to fuels. Curr Opin Biotechnol 2008; 19:556–563. PubMed http://dx.doi.org/10.1016/j.copbio.2008.10.014View ArticlePubMedGoogle Scholar
  2. Jaeger KE, Eggert T. Lipases for biotechnology. Curr Opin Biotechnol 2002; 13:390–397. PubMed http://dx.doi.org/10.1016/S0958-1669(02)00341-5View ArticlePubMedGoogle Scholar
  3. Parton W, Silver WL, Burke IC, Grassens L, Harmon ME, Currie WS, King JY, Adair EC, Brandt LA, Hart SC, et al. Global-scale similarities in nitrogen release patterns during long-term decomposition. Science 2007; 315:361–364. PubMed http://dx.doi.org/10.1126/science.1134853View ArticlePubMedGoogle Scholar
  4. Silver WL, Lugo A, Keller M. Soil oxygen availability and biogeochemistry along rainfall and topographic gradients in upland wet tropical forest soils. Biogeochemistry 1999; 44:301–328. http://dx.doi.org/10.1007/BF00996995Google Scholar
  5. Silver WL, Liptzin D, Almaraz M. Soil redox dynamics and biogeochemistry alonga tropical elevational gradient. Ecol Bull 2013; (In press).Google Scholar
  6. Pett-Ridge J, Firestone MK. Redox fluctuation structures microbial communities in a wet tropical soil. Appl Environ Microbiol 2005; 71:6998–7007. PubMed http://dx.doi.org/10.1128/AEM.71.11.6998-7007.2005PubMed CentralView ArticlePubMedGoogle Scholar
  7. DeAngelis KM, Allgaier M, Chavarria Y, Fortney JL, Hugenholtz P, Simmons B, Sublette K, Silver WL, Hazen TC. Characterization of Trapped Lignin-Degrading Microbes in Tropical Forest Soil. PLoS ONE 2011; 6:e19306. PubMed http://dx.doi.org/10.1371/journal.pone.0019306PubMed CentralView ArticlePubMedGoogle Scholar
  8. DeAngelis, K. M. et al. Anaerobic decomposition of switchgrass by tropical soil-derived feedstock-adapted consortia. MBio 3, (2012).Google Scholar
  9. DeAngelis KM, Silver WL, Thompson AW, Firestone MK. Microbial communities acclimate to recurring changes in soil redox potential status. Environ Microbiol 2010; 12:3137–3149. PubMed http://dx.doi.org/10.1111/j.1462-2920.2010.02286.xView ArticlePubMedGoogle Scholar
  10. Paul EA, Clark FE. Soil microbiology, ecology, and biochemistry. (Academic Pr, 1996).Google Scholar
  11. Freeman C, Ostle N, Kang H. An enzymic ‘latch’ on a global carbon store. Nature 2001; 409:149. PubMed http://dx.doi.org/10.1038/35051650View ArticlePubMedGoogle Scholar
  12. Fierer N, Grandy AS, Six J, Paul EA. Searching for unifying principles in soil ecology. Soil Biol Biochem 2009; 41:2249–2256. http://dx.doi.org/10.1016/j.soilbio.2009.06.009View ArticleGoogle Scholar
  13. Field D, Garrity G, Gray T, Morrison N, Selengut J, Sterk P, Tatusova T, Thomson N, Allen MJ, Angiuoli SV, et al. The minimum information about a genome sequence (MIGS) specification. Nat Biotechnol 2008; 26:541–547. PubMed http://dx.doi.org/10.1038/nbt1360PubMed CentralView ArticlePubMedGoogle Scholar
  14. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT. Gene Ontology: tool for the unification of biology. Nat Genet 2000; 25:25–29. PubMed http://dx.doi.org/10.1038/75556PubMed CentralView ArticlePubMedGoogle Scholar
  15. McGroddy M, Silver WL. Variations in Below-ground Carbon Storage and Soil CO2 Flux Rates along a Wet Tropical Climate Gradient1. Biotropica 2000; 32:614–624. http://dx.doi.org/10.1646/0006-3606(2000)032[0614:VIBCSA]2.0.CO;2View ArticleGoogle Scholar
  16. Tanner, R.S. et al. Cultivation of bacteria and fungi. 69–78 (2007).Google Scholar
  17. Widdel F, Kohring GW, Mayer F. Studies on dissimilatory sulfate-reducing bacteria that decompose fatty acids. Arch Microbiol 1981; 129:395–400. PubMed http://dx.doi.org/10.1007/BF00406470View ArticlePubMedGoogle Scholar
  18. Tschech A, Pfennig N. Growth yield increase linked to caffeate reduction in Acetobacterium woodii. Arch Microbiol 1984; 137:163–167. http://dx.doi.org/10.1007/BF00414460View ArticleGoogle Scholar
  19. Janssen PH, Schuhmann A, Morschel E, Rainey FA. Novel anaerobic ultramicrobacteria belonging to the Verrucomicrobiales lineage of bacterial descent isolated by dilution culture from anoxic rice paddy soil. Appl Environ Microbiol 1997; 63:1382–1388. PubMedPubMed CentralPubMedGoogle Scholar
  20. DeAngelis KM, Gladden JM, Allgaier M, D’haeseleer P. Strategies for enhancing the effectiveness of metagenomic-based enzyme discovery in lignocellulolytic microbial communities. Bio-Energy Research 2010; 3:146–158. http://dx.doi.org/10.1007/s12155-010-9089-zGoogle Scholar
  21. DOE Joint Genome Institute. http://www.jgi.doe.gov
  22. SOAPdenovo v1.05. http://soap.genomics.org.cn/soapdenovo.html
  23. AMOS. http://sourceforge.net/projects/amos
  24. Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 2010; 26:2460–2461. PubMed http://dx.doi.org/10.1093/bioinformatics/btq461View ArticlePubMedGoogle Scholar
  25. Lowe, T. M. & Eddy, S. R. tRNAscan-SE: A Program for Improved Detection of Transfer RNA Genes in Genomic Sequence. Nucl. Acids Res. 25, 0955–964 (1997).View ArticleGoogle Scholar
  26. Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 1997; 25:3389–3402. PubMed http://dx.doi.org/10.1093/nar/25.17.3389PubMed CentralView ArticlePubMedGoogle Scholar
  27. Eddy SR. Accelerated Profile HMM Searches. PLOS Comput Biol 2011; 7:e1002195. PubMed http://dx.doi.org/10.1371/journal.pcbi.1002195PubMed CentralView ArticlePubMedGoogle Scholar
  28. Bland C, Ramsey TL, Sabree F, Lowe M, Brown K, Kyrpides NC, Hugenholtz P. CRISPR Recognition Tool (CRT): a tool for automatic detection of clustered regularly interspaced palindromic repeats. BMC Bioinformatics 2007; 8:209. PubMed http://dx.doi.org/10.1186/1471-2105-8-209PubMed CentralView ArticlePubMedGoogle Scholar
  29. Edgar RC. PILER-CR: Fast and accurate identification of CRISPR repeats. BMC Bioinformatics 2007; 8:18. PubMed http://dx.doi.org/10.1186/1471-2105-8-18PubMed CentralView ArticlePubMedGoogle Scholar
  30. Noguchi H, Park J, Takagi T. MetaGene: prokaryotic gene finding from environmental genome shotgun sequences. Nucleic Acids Res 2006; 34:5623–5630. PubMed http://dx.doi.org/10.1093/nar/gkl723PubMed CentralView ArticlePubMedGoogle Scholar
  31. Hyatt D, Chen GL, Locascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 2010; 11:119. PubMed http://dx.doi.org/10.1186/1471-2105-11-119PubMed CentralView ArticlePubMedGoogle Scholar
  32. Rho M, Tang H, Ye Y. FragGeneScan: predicting genes in short and error-prone reads. Nucleic Acids Res 2010; 38:e191. PubMed http://dx.doi.org/10.1093/nar/gkq747PubMed CentralView ArticlePubMedGoogle Scholar
  33. Tatusov RL, Fedorova ND, Jackson JD, Jacobs AR, Kiryutin B, Koonin EV, Krylov DM, Mazumder R, Mekhedov SL, Nikolskaya AN, et al. The COG database: an updated version includes eukaryotes. BMC Bioinformatics 2003; 4:41. PubMed http://dx.doi.org/10.1186/1471-2105-4-41PubMed CentralView ArticlePubMedGoogle Scholar
  34. Finn RD, Mistry J, Tate J, Coggill P, Heger A, Pollington JE, Gavin OL, Gunasekaran P, Ceric G, Forslund K. The Pfam protein families database. Nucleic Acids Res 2010; 38:D211–D222. PubMed http://dx.doi.org/10.1093/nar/gkp985PubMed CentralView ArticlePubMedGoogle Scholar
  35. Mao X, Cai T, Olyarchuk JG, Wei L. Automated genome annotation and pathway identification using the KEGG Orthology (KO) as a controlled vocabulary. Bioinformatics 2005; 21:3787–3793. PubMed http://dx.doi.org/10.1093/bioinformatics/bti430View ArticlePubMedGoogle Scholar
  36. Markowitz VM, Chen IM, Chu K, Szeto E, Palaniappan K, Grechkin Y, Ratner A, Jacob B, Pati A, Huntemann M. IMG/M: the integrated metagenome data management and comparative analysis system. Nucleic Acids Res 2012; 40:D123–D129. PubMed http://dx.doi.org/10.1093/nar/gkr975PubMed CentralView ArticlePubMedGoogle Scholar
  37. Wu D, Hugenholtz P, Mavromatis K, Pukall R, Dalin E, Ivanova NN, Kunin V, Goodwin L, Wu M, Tindall BJ. A phylogeny-driven genomic encyclopaedia of Bacteria and Archaea. Nature 2009; 462:1056–1060. PubMed http://dx.doi.org/10.1038/nature08656PubMed CentralView ArticlePubMedGoogle Scholar
  38. Barberán A, Bates ST, Casamayor EO, Fierer N. Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J 2012; 6:343–351. PubMed http://dx.doi.org/10.1038/ismej.2011.119PubMed CentralView ArticlePubMedGoogle Scholar
  39. Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJournal, Complex Systems 1695 (2006).Google Scholar
  40. Harrell, F. E. & And with contributions from many other users Hmisc (Harrell Miscellaneous). R package version 3.9-3 (2012). at http://biostat.mc.vanderbilt.edu/trac/Hmisc
  41. Pollard KS, Gilbert HN, Ge Y, Taylor S, Dudoit S. multtest: Resampling-based multiple hypothesis testing. R package version 2.12.0 (2012). at http://www.bioconductor.org/packages/2.10/bioc/html/multtest.html
  42. Revol doMC: Foreach parallel adaptor for the multicore package. R package version 1.2.5 (2012). at http://CRAN.R-project.org/package=doMC
  43. Revolution Analytics foreach: Foreach looping construct for R. R package version 1.4.0 (2012). at http://CRAN.R-project.org/package=foreach
  44. Leschine SB, Canale-Parola E. Mesophilic cellulolytic clostridia from freshwater environments. Appl Environ Microbiol 1983; 46:728–737. PubMedPubMed CentralPubMedGoogle Scholar
  45. Calusinska, M., Happe, T., Joris, B. & Wilmotte, A. The surprising diversity of clostridial hydrogenases: a comparative genomic perspective. Microbiology (Reading, Engl.) 156, 1575–1588 (2010).Google Scholar
  46. Schimel JP, Schaeffer SM. Microbial control over carbon cycling in soil. Front Microbiol 2012; 3:348. PubMedPubMed CentralView ArticlePubMedGoogle Scholar
  47. Lovley DR, Holmes DE, Nevin KP. Dissimilatory fe (iii) and mn (iv) reduction. Adv Microb Physiol 2004; 49:219–286. PubMed http://dx.doi.org/10.1016/S0065-2911(04)49005-5View ArticlePubMedGoogle Scholar
  48. Weber KA, Achenbach LA, Coates JD. Microorganisms pumping iron: anaerobic microbial iron oxidation and reduction. Nat Rev Microbiol 2006; 4:752–764. PubMed http://dx.doi.org/10.1038/nrmicro1490View ArticlePubMedGoogle Scholar
  49. Stevenson BS, Eichorst SA, Wertz JT, Schmidt TM, Breznak JA. New Strategies for Cultivation and Detection of Previously Uncultured Microbes. Appl Environ Microbiol 2004; 70:4748–4755. PubMed http://dx.doi.org/10.1128/AEM.70.8.4748-4755.2004PubMed CentralView ArticlePubMedGoogle Scholar
  50. Heuer, H. & Smalla, K. Plasmids foster diversification and adaptation of bacterial populations in soil. FEMS Microbiology Reviews n/a-n/a (2012).Google Scholar
  51. DeAngelis KM, D’Haeseleer P, Chivian D, Fortney JL, Khudyakov J, Simmons B, Woo H, Arkin AP, Davenport KW, Goodwin L. Complete genome sequence of ‘Enterobacter lignolyticus’ SCF1. Stand Genomic Sci 2011; 5:69–85. PubMed http://dx.doi.org/10.4056/sigs.2104875PubMed CentralView ArticlePubMedGoogle Scholar

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