Metagenomes of tropical soil-derived anaerobic switchgrass-adapted consortia with and without iron
© The Author(s) 2013
Published: 25 February 2013
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.
KeywordsAnaerobic decomposition switchgrass Panicum virgatum tropical forest soil feedstock-adapted consortia bacteria archaea metagenomics
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 . 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 . Lignin is also a potentially valuable waste stream that is currently burned to produce energy as heat . 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 , 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 [6–9]. 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 [10–12]. 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
Classification and general features of the four metagenome data sets according to the Minimum Information about Genomes and Metagenomes (MIMS) standards .
Metagenome ecological metagenome terrestrial metagenome
Terminal electron receptor
Iron reduction or fermentation
Consortia (mixed community) derived from wet tropical forest soils
Wet tropical forest, Puerto Rico, USA
Sample collection time
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)  containing trace minerals [17,18], vitamins , 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  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
Illumina standard paired-end library (0.3 kb insert size)
26.1466 × (PR soil-derived FAC SG + Fe) 81.1761 × (PR soil-derived FAC SG only)
SOAPdenovo v1.05, Newbler v2.5, minimus2
Gene calling method
IMG Project ID
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 . 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 . 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 . 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  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  and PILERCR . 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)  or Metagene (v. Aug08) , prodigal  and FragGenescan  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 , the COG db using the NCBI developed PSSMs , the pfam db  using hmmsearch. Assignment to KEGG Ortholog protein families was performed using the algorithm described in .
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.
Summary of metagenomes
GOLD sample idID
SG + Fe
Nucleotide content and gene count levels of the metagenomes
SG only Metagenome
SG + Fe Metagenome
% of Total
% of Total
DNA, total number of bases
DNA coding number of bases
DNA G+C number of bases
Genes total number
Protein coding genes
Protein coding genes with function prediction
without function prediction
not connected to SwissProt Protein Product
Protein coding genes with enzymes
w/o enzymes but with candidate KO based enzymes
Protein coding genes connected to KEGG pathways3
not connected to KEGG pathways
Protein coding genes connected to KEGG Orthology (KO)
not connected to KEGG Orthology (KO)
Protein coding genes connected to MetaCyc pathways
not connected to MetaCyc pathways
Protein coding genes with COGs3
in internal clusters
Protein coding genes coding signal peptides
Protein coding genes coding transmembrane proteins
Number of genes associated with the 25 general COG functional categories.
SG + Fe
Translation, ribosomal structure and biogenesis
RNA processing and modification
RNA processing and modification
Replication, recombination and repair
Chromatin structure and dynamics
Cell cycle control, cell division, chromosome partitioning
Signal transduction mechanisms
Cell wall/membrane/envelope biogenesis
Intracellular trafficking, secretion, and vesicular transport
Posttranslational modification, protein turnover, chaperones
Energy production and conversion
Carbohydrate transport and metabolism
Amino acid transport and metabolism
Nucleotide transport and metabolism
Coenzyme transport and metabolism
Lipid transport and metabolism
Inorganic ion transport and metabolism
Secondary metabolites biosynthesis, transport and catabolism
General function prediction only
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 , which uses the phylogeny described as part of the genomic encyclopedia of Bacteria and Archaea (GEBA) project .
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.
Overview of taxonomic diversity in metagenomes.
SG only Count
SG + Fe Count
ds DNA viruses, no RNA stage
ss DNA viruses
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 . 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.  in R using the packages igraph , Hmisc , multtest , doMC , and foreach .
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 [44–46]. 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
Report of pfams that were significantly enriched†
SG + Fe
Enriched in SG+Fe FACs
Bacterial binding protein-dependent transport systems
Bacterial binding protein-dependent transport systems
short-chain dehydrogenases/reductases family
large metal dependent hydrolase superfamily
C-terminus of oligopeptide ABC transporter ATP binding proteins
Oxidoreductase family, C-terminal alpha/beta domain
FGGY carbohydrate kinase family
S-layer homology domain
FAD dependent oxidoreductase family
Beta-ketoacyl-ACP synthase (fatty acid synthesis)
Dockerin: protein domain in cellulosome cellular structure
[2Fe-2S] binding domain
glycoside hydrolase family 65
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
GGDEF domain, cyclic di-GMP synthesis involved in intracellular signaling
LysR substrate binding domain, similar to periplasmic binding protein
Helix-turn-helix DNA binding domain
Acetyltransferase (or transacetylase)
PAS domain, signal sensor
EAL domain, possible diguanylate phosphodiesterase with metal-binding site
Putative cell wall binding repeat
rabinose-binding and dimerization domain of the AraC regulatory protein
Phosphotransferase system, EIIC, part of a sugar-specific permease system
Polycystic-kidney disease domain, usually involved in mediating protein-protein interactions
Transposase IS200, for transposition of insertion elements
Transposase DDE domain, for transposition of insertion elements
beta propeller, found in several enzymes which utilize pyrrolo-quinoline quinone as a prosthetic group
phosphotransferase system, EIIB
phosphoenolpyruvate: sugar phosphotransferase system (PTS) system, Lactose/Cellobiose specific IIB subunit
N-terminal phage replisome organizer, origin of phage replication
sugar-specific transcriptional regulator of the trehalose/maltose ABC transporter
GTP-binding elongation factor family
predicted AAA-ATPase domain
domain of unknown function, so far found only at the C-terminus of archaean proteins
FIST N domain: novel sensory domain present in signal transduction proteins
Pseudomurein-binding repeat, pseudomurein being a cell-wall structure
domain of unknown function, so far found only among archaeal proteins
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 . 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
Count of genes in COGs that bear protein sequence homology to target lignocellulolytic genes of interest.
SG + Fe
Predicted signal transduction protein containing a membrane domain, an EAL and a GGDEF domain
Dehydrogenases with different specificities (related to short-chain alcohol dehydrogenases)
Short-chain dehydrogenases of various substrate specificities
Short-chain alcohol dehydrogenase of unknown specificity
NAD-dependent aldehyde dehydrogenases
Uncharacterized conserved protein
Flagellin and related hook-associated proteins
2-keto-4-pentenoate hydratase/2-oxohepta-3-ene-1,7-dioic acid hydratase (catechol pathway)
Putative multicopper oxidases
ABC-type sugar transport system, ATPase component
ABC-type branched-chain amino acid transport systems, ATPase component
Predicted dehydrogenases and related proteins
Phosphotransferase system cellobiose-specific component IIC
Alpha-galactosidases/6-phospho-beta-glucosidases, family 4 of glycosyl hydrolases
FOG: EAL domain
Predicted UDP-glucose 6-dehydrogenase
ABC-type spermidine/putrescine transport systems, ATPase components
ABC-type uncharacterized transport systems, ATPase components
Predicted glutathione S-transferase
2,4-dihydroxyhept-2-ene-1,7-dioic acid aldolase
ABC-type xylose transport system, periplasmic component
ABC-type xylose transport system, permease component
Catalase (peroxidase I)
ABC-type branched-chain amino acid transport systems, ATPase component
Selenocysteine synthase [seryl-tRNASer selenium transferase]
Aromatic ring hydroxylase
Large extracellular alpha-helical protein
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.
European Molecular Biology Laboratory
National Center for Biotechnology Information (Bethesda, MD, USA)
Ribosomal Database Project (East Lan-sing, MI, USA)
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.
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