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. Author manuscript; available in PMC: 2018 Nov 1.
Published in final edited form as: Environ Sci Technol. 2018 Apr 24;52(10):5691–5699. doi: 10.1021/acs.est.7b06574

Dual Role of Humic Substances As Electron Donor and Shuttle for Dissimilatory Iron Reduction

Noah Stern †, Jacqueline Mejia †, Shaomei He ‡,§, Yu Yang ‖, Matthew Ginder-Vogel †, Eric E Roden ‡,*
PMCID: PMC6211804  NIHMSID: NIHMS987600  PMID: 29658273

Abstract

Dissimilatory iron-reducing bacteria (DIRB) are known to use humic substances (HS) as electron shuttles for dissimilatory iron reduction (DIR) by transferring electrons to HS-quinone moieties, which in turn rapidly reduce Fe(III) oxides. However, the potential for HS to serve as a source of organic carbon (OC) that can donate electrons for DIR is unknown. We studied whether humic acids (HA) and humins (HM) recovered from peat soil by sodium pyrophosphate extraction could serve as both electron shuttles and electron donors for DIR by freshwater sediment microorganisms. Both HA and HM served as electron shuttles in cultures amended with glucose. However, only HA served as an electron donor for DIR. Metagenomes from HA-containing cultures had an overrepresentation of genes involved in polysaccharide and to a lesser extent aromatic compound degradation, suggesting complex OC metabolism. Genomic searches for the porin-cytochrome complex involved in DIR resulted in matches to Ignavibacterium/Melioribacter, DIRB capable of polymeric OC metabolism. These results indicate that such taxa may have played a role in both DIR and decomposition of complex OC. Our results suggest that decomposition of HS coupled to DIR and other anaerobic pathways could play an important role in soil and sediment OC metabolism.

Graphical Abstract

graphic file with name nihms-987600-f0001.jpg

INTRODUCTION

Soil organic carbon (SOC) formation involves the biological, chemical, and physical transformation of plant and microbial matter into organic products that vary in size at different stages of decomposition and is comprised of a heterogeneous mixture of cellulose, hemicellulose, lignin, lipids, tannins, proteins, sugar monomers, and other organic products.1,2 Since all of these forms of organic carbon are in principle susceptible to microbial degradation, a combination of matrix protection, microbial ecology, enzyme kinetics and geochemical conditions control how SOC is formed and preserved.3 Materials produced through the degradation of these compounds have been commonly referred to as “humic substances” (HS), organic compounds that have undergone transformation such that they no longer resemble their original physicochemical state.2,4 “Humic substances” have traditionally been recovered from soils via alkali extraction, which upon further processing leads to operationally defined fractions including humic acid (referred to here as HA), fulvic acid, and humin (referred to here as HM).5 The term “alkali-extracts” has been recently introduced to explicitly acknowledge that these forms of organic carbon are the result of standardized chemical extraction procedures and not necessarily representative of a native organic materials in soils or sediments.6

The significance of SOC in the global carbon cycle is obvious as this reservoir contains more carbon than global vegetation and the atmosphere combined.7 In addition to serving as a large reservoir of organic carbon (OC), under anoxic conditions microorganisms can respire by transferring electrons to dissolved or solid phase quinone moieties in HS.8,9 Microbial reduction of quinone moieties accelerates reduction of Fe(III) oxides via a process called electron shuttling, whereby microbially reduced quinones rapidly react to transfer electrons onto Fe(III) oxides.8–13 This process has received a great deal of attention owing to the broad impact of dissimilatory iron reduction (DIR) on the geochemistry of anoxic soils and sediments.14 The same pool of HS that contains quinone moieties also represents a large reservoir of OC that can potentially donate electrons to DIR itself. However, the metabolism of HS under Fe(III)-reducing conditions has not been previously explored. Rather, virtually all previous studies of DIR in the presence of HS have focused on the role of HS as electron shuttles in the presence of excess labile OC.8,9,15 Coupling of HS degradation to DIR has the potential to create complexity in SOC degradation pathways, since it has recently been shown that DIR can lead to the release of dissolved organic carbon (DOC) associated with Fe(III) oxide surfaces, with important implications for Fe–C associations in soils and sediments.16,17

It is well-known that hydrolytic and fermentative microorganisms are responsible for breaking down complex OC into organic compounds that can then be utilized by dissimilatory iron reducing bacteria (DIRB).18 However, the metabolic activity of DIRB such as Geobacter is more complex, as such taxa are able to utilize monoaromatic compounds for growth.19–21 In addition, DIRB from the Ignavibacteriales (Ignavibacterium/Melioribacter) are capable of degrading complex polysaccharides.22,23 Thus, the potential exists for DIRB to participate directly in the metabolism of HS, in addition to oxidizing the products of hydrolytic and fermentative metabolism.

Through a combination of geochemical and metagenomic sequencing data this study sought to disentangle electron shuttling and electron donating interactions involving HS and Fe(III)-reducing microbial communities from freshwater sediments. The potential for microbial communities to metabolize HS was determined by quantifying the presence of genes coding for carbohydrate active enzymes (CAZymes) such as glycoside hydrolases, polysaccharide lyases, glycosyl transferases, carbon binding modules and carbohydrate esterases, and genes involved in the degradation of aromatic carbon.24 The presence of extracellular electron transfer (EET) systems that are involved in DIR was based on the normalized abundance of porin-cytochrome complex (PCC) and PilA genes first discovered in Geobacter.25–28 Investigating the reduction of Fe(III) oxides and the degradation of HS under controlled conditions provides insight into the microbial community structure and the metabolic pathways involved in coupled Fe–C cycling in soils and sediments.

MATERIALS AND METHODS

Humic Substances.

Two different HS obtained from an organic rich histosol from Amherst, Massachusetts were employed in our experiments: a humic acid (HA) fraction representing the combination of two extractions with 0.1 M Na4P2O7; and the final nonextractable humin (HM) fraction. The chemical properties of the isolated HS used in this study have been previously described.29,30 The Na4P207 solution used to recover HA differs from the classical pH 13 sodium hydroxide extraction,31 targeting materials associated with organo–metal complexes32 as opposed to bulk alkali-soluble SOC.

DIR Experiments.

DIR experiments were conducted in anoxic 10 mM PIPES (1,4-piperazinediethanesulfonic acid) buffered medium containing 100 μM NH4Cl and 10 μM KH2PO4. Duplicate reactors were amended with 100 mmol L−1 of reagent-grade hematite (Fe2O3, Fisher Scientific) as the Fe(III) oxide mineral phase and 180 mg L−1 of either HA or HM as a source of SOC, with or without the presence of 100 μM glucose as an auxiliary electron donor to test the potential for electron shuttling. Note that the HA and HM were added in particulate form to mimic solid-phase SOC. Sterile control reactors containing only 100 μM NH4Cl, 10 μM KH2PO4 and HA/HM showed DOC values below 0.3 ppm after a 72 days. Hematite was chosen as the Fe(III) oxide phase for the sake of consistency with recent studies of biochar-accelerated Fe(III) oxide reduction.33 Previous studies have demonstrated its reducibility by freshwater DIRB,34 and unlike ferrihydrite and other Fe(III) oxides, hematite does not undergo reductive phase transformations, which can have a confounding effect on oxide reducibility.35 The inoculum used for the experiment was obtained from a freshwater pond in Middleton, WI. DIR was determined by monitoring the accumulation of aqueous and 0.5 M HCl-extractable Fe(II) using the ferrozine assay.36

Metagenome Sequencing and Assembly.

Shotgun Illumina sequencing was performed on six metagenomes. On average, 10 Gbp of raw sequences were obtained for each metagenome. Metagenome assembly was performed using the CLC Genomics workbench (version 6.02; CLC bio, Inc., Cambridge, MA). The raw reads were merged to obtain paired-end reads; quality trimmed and filtered by length; and assembled using a k-mer of 63 with scaffolding (Supporting Information (SI) Table S1). After assembly, the average fold coverage of each contig was estimated and all contigs were uploaded to the Integrated Microbial Genomes with Microbiomes (IMG/MER) database (http://img.jgi.doe.gov/mer) for gene prediction and functional annotation.37 Genome reconstruction (i.e., binning) from metagenomes was carried out by a coverage-composition algorithm using MaxBin.38 The completeness and potential contamination of the bins was assessed using CheckM39 and the resulting high quality bins of interest were further evaluated.

Microbial Community Analysis.

Conserved phylogenetic marker and 16S rRNA genes from metagenomes were used to estimate microbial community composition. Phylogenetic marker genes from Phylosift40 that are present in >99% bacteria and archaea were chosen for this analysis. These genes are represented by 32 COGs (cluster of orthologous groups) listed in SI Table S2. Genes annotated by these 32 COGs were extracted and BLASTP was conducted against the Ref_Seq protein database with an e-value of 1e−5, with the top 20 hits being retained. MEGAN441 was used for taxonomic classification of the BLASTP results using the lowest common ancestor algorithm.41 As these phylogenetic marker genes have different lengths due to different gene completeness and different COG families that they represent, the abundance of each phylogenetic marker gene was estimated using its coverage depth weighted by its recovered length and then normalized by its expected full length (i.e., the consensus length of each COG listed in SI Table S2) according to He et al.42 The relative abundance of each phylum within the total community was calculated using the normalized abundance of all phylogenetic marker genes.

Identification of OC Degradation Systems.

The metagenomes were compared based on functional units of carbohydrate active enzymes (CAZymes) or KEGG Orthology (KO). CAZymes are divided into five classes: glycoside hydrolases (GHs); glycosyl transferases (GTs); polysaccharide lyases (PLs); and auxiliary activities (AA) enzymes. The database also includes modules such as the cohesin module, dockerin, the S-layer homology (SLH) module and carbon binding modules (CBMs). CAZyme genes were determined by uploading the annotated metagenomes to a database for automated CAZyme annotation (dbCAN) and using a 1e−6 cutoff E-value and a 30% cutoff coverage value.43 When redundancies were detected, classification was determined based on the lowest E-value or highest coverage value. KEGG-KO assignments were performed during IMG/MER gene annotation. We focused on 140 KOs belonging to 22 KEGG modules within the aromatics degradation (AD) category. 44–47 Comparison across all six metagenomes was possible by multiplying the scaffold coverage (i.e., scaffold read depth) by a normalization factor, which was computed by dividing 8 420 617 307 bases by the number of bases of raw reads that matched after the assembly of each sample (SI Table S1). The abundance of genes reported from here on, represents normalized gene copy numbers. To determine the taxonomic distribution of genes involved in OC degradation, all reads were subjected to BLASTP searches against the NCBI nr database with an E-value cutoff of 1e−5. BLASTP results were input to MEGAN4 was used for taxonomic classification using the lowest common ancestor algorithm.

Identification of PCC and PilA Genes.

PCC gene systems involved in DIRB are composed of one beta-barrel protein and adjacent multiheme cytochromes.25,26 These systems were identified by a hidden Markov model (HMM)48 based on 29 PCC porin gene homologues26 and created using the hmmbuild function in HMMER 3.0.49 Confirmation that the identified porin gene contained a beta-barrel protein structure was preformed using PRED-TMBB.50 Adjacent cytochrome genes and their respective heme-binding sites were searched manually in IMG.51 The cellular location (periplasmic, outer membrane or extracellular) of all genes was determined by Cello2go.38 PilA genes27,28,52 involved in DIRB were identified by IMG protein BLAST using 15 known Geobacter specific PilA gene sequences and a maximum E-value of 10−5. The cellular location of the PilA genes was confirmed by Cello2go38 and gene sequence alignment was performed using PROMALS3D multiple sequence and structure alignment server.53 Taxonomic assignments of all identified EET gene systems was carried out by BLASTP as described in the previous section.

RESULTS AND DISCUSSION

DIR in the Presence and Absence of HS.

The initial rate and long-term extent of hematite reduction were both enhanced by the presence of HA (Figure 1A). In the presence of HA the addition of 0.1 mM glucose stimulated a further increase in DIR reduction; however, the rate and extent of Fe(III) oxide reduction was greater in the presence of HA only compared to glucose only. These results clearly indicate that HA served as a source of electrons for DIRB. In addition, the higher initial rate of DIR in the presence of HA compared to the glucose only reactors is suggestive of an electron shuttling effect analogous to previous studies with solid-phase humic substances.9 Based on the measured electron-accepting capacity of the HA (2.4 mmol e− mol C−1 16) and the amount of OC added with that material (ca. 6.4 mmol C L−1; see other calculations below), the bulk concentration of electron shuttles was on the order of 0.015 mmol e− L−1. Previous studies with HAs that have comparable electron-accepting/donating capacity suggest that this amount of electron-shuttling capacity is sufficient to accelerate microbial Fe(III) oxide reduction.10 These calculations also constrain the possible extent of abiotic Fe(III) reduction by residual reducing capacity of the HA (which was not evaluated in this study) to values less than 5% of observed Fe(III) reduction activity in the HA experiments. The total amount of Fe(II) produced in the HA+glucose reactors was equivalent, within experimental error, to the sum of Fe(II) production in the HA-only and glucose-only treatments. The apparent inability of glucose to promote HA metabolism cannot be attributed to an electron acceptor limitation of OC metabolism, because the long-term extent of hematite reduction was 2-fold lower than in previous studies where natural freshwater bacteria were provided with greater amounts of labile OC.34 This observation suggests that DIR was electron donor limited in our experiments, a conclusion supported by the virtual absence of methane in the headspace of the cultures at the end of the experiment (data not shown).\

Figure 1.

Figure 1.

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0.5 M HCI-exractable Fe(II) production for the HA (A) and HM (B) hematie reduction experiments. Data points represent the mean ± range of dublicate reactors.

DIR in the presence of HM alone was similar to that in the no HS addition treatments (Figure 1B, HM only vs the inoculated control (-HM, -Glu)). As discussed below, the minor amount of reduction in these treatments was likely driven by metabolism of small amounts of OC in the inoculum. The fact that HM in the glucose-amended reactors increased the initial rate of DIR but did not serve as a source of electrons suggests this HM served only as an electron shuttle. Collectively our results show the ability of chemically extracted HS to function as electron shuttles as previously recognized,54 but also in the case of HA as a source of electrons for DIRB.

Microbial Community Composition.

Shotgun metagenomic sequencing was carried out to analyze the microbial communities in the cultures and their potential to metabolize complex OC. Conserved phylogenetic marker genes from the metagenomes were used to estimate microbial community composition (SI Table S3). Reactors containing HA were dominated by a diverse group of microorganisms including Spirochaetes, Proteobacteria, Euryarchaeota, Bacteroidetes, Ignavibacteriaae, Acidobacteria, Firmicutes, and Chloroflexi. Conversely, Proteobacteria and Acidobacteria were dominant taxa in both the HM and glucose-only reactors, with the abundance of Acidobacteria being higher in the presence of glucose. Overall, HA promoted the development of a more diverse microbial community compared to HM and/or glucose. In particular the high abundance of organisms from phyla such as Spirochaetes, Euryarchaeota, Bacteroidetes, Ignavibacteriaae, Firmicutes, and Chloroflexi, all of which are known to degrade complex SOC materials,55–58 implies that these taxa played a role in HA metabolism. Of course, taxa from the Proteobacteria and Acidobacteria were also substantial components of the HA microbial community and were likely also involved in organic carbon metabolism. In contrast to the HA reactors, HM did not stimulate DIR activity, and did not substantially alter microbial community composition relative to the glucose-only treatments, Based on these results, we infer that HM did not stimulate the growth of any particular phyla thought to participate in the metabolism of HS.

DIR Gene Systems.

Organisms responsible for DIR in the cultures were assessed by searching for PCC (Figure 2) and PilA (SI Figure S2) gene systems. Contrary to expectations, the reactors with highest rates of DIR did not show significant increases in PCC or PilA gene abundance. Experiments with HM and/or glucose had PCC genes mainly assigned to Geobacter and Geothrix, whereas HA reactors had increases in Ignavibacterium/Melioribacter related taxa. The latter taxa are known to be capable of fermentative growth with polysaccharides as well as DIR;22,23,59 thus the presence of both PCC and CAZyme genes (see Figure 3) attributable to these organisms in the HA cultures suggests that they played a role both in DIR and upstream production of fermentation end-products that served as substrates for DIR. It is notable that our findings reveal for the first time that the previously described DIRB Geothrix60 contains PCC gene systems for reduction of extracellular electron acceptors (see SI Figure S1 and Supporting Text 1).

Figure 2.

Figure 2.

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Abundance and taxonomic assingnments of porin-cytochrome complex (PCC) genes found in the HA and HM cultures. The gene abundance was normalized by the total assempled base pairsAbbrevations: HA = humic acid; G = glucose; HM = humin. G1 and G2 represent replicate glucose-only cultures.

Figure 3.

Figure 3.

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Abundance of genes categorized winthin a CAZyme class (GH, GT, CBM, CE, PL, and AA) and KEGG aromatics degradation (AD) category, in the HA and HM data sets. The gene abundance was normalized by the assembled base pairs.

General Trends in Organic Carbon Metabolism Gene Abundance.

The potential for HA and HM degradation to provide electrons for DIRB was evaluated by determining if genes coding for CAZymes were differentially present in cultures with or without HS at the end of the experiments.24 A total of 42 312 genes across all treatments were classified as one of 258 different CAZyme families. The metagenomes amended with HA had more sequences coding for GHs, GTs, CBMs, CEs, PLs, cohesin, dockerin, and SLH compared to the metagenomes from cultures containing HM and/or glucose (Figure 3). Additionally, GHs, GTs, and PLs showed slightly higher gene abundance in treatments containing HM compared to those with only glucose (Figure 3). The increase in CAZymes in the HA experiments implies that these enzymes played an important role in the degradation of HS. CAZyme family gene abundance was similar in both HM-containing and glucose only cultures. These results indicate that HM did not undergo significant enzymatic transformation, which is consistent with the finding that HM did not serve as an electron donor for DIR (Figure 1B). The significant abundance of CAZymes in HM cultures can be attributed to metabolism of small amounts of complex OC in the inoculum.

The taxonomic origin of each CAZyme was determined by BLASTP. Bacteroidetes, Firmicutes, Euryarchaeota, Chlorobi, Chloroflexi, Spirochaetes, Planctomycetes, and unclassified bacteria dominated the taxonomic classification of CAZymes present in HA experiments (Figure 4A). On the contrary, the CAZymes detected in treatments containing HM and/or glucose were dominated by Proteobacteria, Acidobacteria, and unclassified bacteria. GHs, CBMs, CEs, and PLs were also dominated by Euryarchaeota and Bacteroidetes when HM and glucose were present (Figure 4B). Overall, higher diversity in CAZymes taxonomic assignments present in the HA experiments was expected since the phylogenetic marker genes revealed that these microbial communities were much more diverse (SI Table S3). It should also be noted that the abundance of unclassified taxa was greater in the HA experiments, suggesting the presence of novel microorganisms capable of complex OC degradation.

Figure 4.

Figure 4.

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Taxonomic assignment of genes classfied within a CAZyme class (GH, GT, CBM, CE, PL, and AA) and KEGG Aromics degradation (AD) category, in the HA (A) and HM (B) data sets. The y-axis represents the sum of the gene abundances within a specific category and for a specific experiments. Abbrevations: HA = humic acid; G = glucose; HM = humin. G1 and G2 represent replicate glugose-only cultures.

The high abundance of GHs, GTs, CBMs, CEs, PLs, cohesin, dockerin, and SLH in HA experiments indicates that a large portion of the HA resembled polysaccharides and was in fact biodegradable. This is in agreement with previous geochemical results and the higher (oxygen + nitrogen)/carbon ratio found in HA (0.94) compared to HM (0.56).29 Additionally, many of these CAZymes were assigned to Bacteroidetes, Firmicutes, Chloroflexi, and Spirochaetes, which are important contributors to the degradation of lignocellulosic material in various environments.61–64 Planctomycetes and Euryarchaeota were also detected in the presence of HAs and have been associated with cellulose decomposition with GTs,65,66 and metabolism of carbohydrates with GHs,42,63 respectively.

Key Enzymatic Systems Involved in Humic Substance Metabolism.

To determine which enzymes had the most important role in the metabolism of HS, we determined which CAZyme families and KEGG modules had gene copy numbers at least two times higher in HS metagenomes compared to glucose only metagenomes. 129 and 72 CAZyme families and, 13 and 6 KEGG KO assignments with this criterion were identified in the HA and HM metagenomes, respectively (see SI Supporting Text 2 and Tables S4–S7). The CAZyme classes with the highest gene copy numbers were GHs and CBMs, followed by CEs, GTs, and PLs (Figure 5). AAs and ADs under this criterion were negligible. The most abundant GHs in HA treatments were categorized as cellulases and endohemicellulases and debranching and oligosaccharide degrading enzymes.33,42,57,62,67,68 Identical analysis of the HM treatments revealed cellulases, endohemicellulases, and debranching, and oligosaccharide degrading enzymes, but also included pectinases and fungal cell wall degrading enzymes. The higher abundance of these CAZyme families in the presence of HA compared to HM and/or glucose only treatments is another indication that chemically-extracted SOC contains polysaccharides that are bioavailable for microbial degradation into simpler organic acids utilized by DIRB.18,20

Figure 5.

Figure 5.

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Sum of CAZyme and AD genes that had abundance values at least two times higher in metagnomes amended with HA or HM compared to those with only glucose.

Aromatic Carbon Degradation.

Aromatic carbon makes up more than 10% of the bulk carbon in the HA and HM used in this study.29 This component of HS includes quinone moieties69 that were likely involved in electron shuttling for DIR, yet these same compounds could undergo degradation coupled to DIR.3 The abundance of redox enzymes that act in conjunction with CAZymes in aromatic carbon degradation (referred to as AAs) was similar in all metagenomes, but highest when HAs were the only carbon source added (Figure 3). By looking at these trends, one might conclude that AAs were important in the degradation of HAs when glucose was not present. However, AAs were also found in the metagenomes amended with only glucose (Figure 3), suggesting that the inoculum contained aromatic carbon compounds, in addition to other complex OC components as described above, that were subject to degradation. The taxonomic origin of AAs found in the HA treatments (primarily Firmicutes, Proteobacteria, and Euryarchaeota) differed from those in the glucose-only and/or HM cultures, which were dominated by Proteobacteria (Figure 4). These results demonstrate that diverse taxa with the capability of aromatic carbon degradation were enriched in the HA cultures.

Since AAs were not predominant in the CAZyme gene inventories, a search for genes involved in the degradation of smaller aromatic compounds using the KEGG Orthology (KO) database was carried out. A total of 2222 genes across all metagenomes were assigned to the aromatics degradation (AD) category,44–47 and their abundance was much lower compared to that of GHs, GTs, CBMs, and CEs (Figure 3). Most of the AAs and ADs require oxygen to function and their low abundance in our anoxic incubations is not surprising. Moreover, the preferred OC substrate for microorganisms is usually composed of carbohydrates, not aromatic structures.70 Therefore, the low abundance of AAs and ADs can be explained if the metabolism of more labile OC (i.e., carbohydrates) happened prior to the metabolism of aromatic moieties.

We have illustrated that HS can be broken down by hydrolytic and fermentative microorganisms. However, DIRB such as Geobacter and Ignavibacter also have the metabolic capacity to utilize monoaromatic compounds for growth.19–23 Fifteen draft genomes containing a complete EET system were recovered from the metagenomes, and were identified as Geobacter, Geothrix, Holophaga, Ignavibacteriales, Melioribacter, Anaeromyxobacter, and Prolixibacter. Thirteen genomes within the same taxonomic categories were downloaded from the IMG database in order to have at least one reference for all taxa. All genomes were screened (see SI Supporting Text 3) for ADs, and while it was confirmed that the Geobacter metallireducens reference genome has a complete pathway for toluene degradation and benzoyl-CoA degradation, no such AD degradation pathways were observed in any other genome (SI Tables S8–S12). Overall there was no correlation between DIR and AD gene abundance (SI Tables S6 and S7); thus, it seems unlikely that aromatic carbon degradation was a major pathway for HS metabolism in this experiment. However, the large increase in Ignavibacterium/Melioribacter related PCC gene systems in the HA vs HM cultures (Figure 2) does suggest that these organisms played a role in upstream decomposition of complex OC.

Environmental Implications.

Previous studies of the influence of HS on DIR in soils and sediments have focused on electron shuttling effects rather than coupling between DIR and HS (e.g., as a surrogate for SOC) decomposition.8,9,15 Through a combination of DIR incubation experiments and metagenomic sequence gene counts this study separated the simultaneous processes of electron shuttling and electron donating by HS recovered from peat soil by sodium pyrophosphate extraction. The analysis revealed that metabolism of the HA fraction recovered from the soil was directly coupled to DIR. After a 30 day period roughly 3% of the HA electron equivalents present in the reactors was coupled to DIRB metabolism (see calculation below). Evidence of this phenomenon was provided by the overrepresentation of putative genes coding for enzymes that break down complex lignocellulosic material. For example, 129 CAZy families were at least two times more abundant in experiments containing HA compared to those with glucose only. The potential for microorganisms to utilize a small but significant portion of HA (see below) is consistent with the emerging view of SOC as a continuum of variably decomposable organic compounds.6

Limited information on the biological degradation of HS under anoxic conditions is currently available,71 and the potential contribution of HS degradation to overall SOC metabolism under Fe(III)-reducing and other anaerobic respiratory conditions remains an open question. The amount of DIR driven by HA degradation in our experiments was ca. 0.7 mmol Fe(III) L−1 in the HA-only treatments (Figure 1A), equivalent to oxidation of ca. 0.175 mmol C L−1. This represents 2.8% of the total OC added to the reactors, based on the amount of HA added (180 mg L−1) and the OC content of the HA (42.4%72). Although this may seem like only a minor amount of OC degradation, it is important to consider how much SOC this could represent in a typical OC-rich soil or sediment. Consider a soil with 15% dry weight SOC and a bulk density of 0.4 g dry mass cm−3, typical values for histosols with a significant mineral content73 such as the one from which the HA used in this study was extracted.72 The HA fraction of this histosol represented ca. 10% of total SOC.74 Based on these values, the total HA content of the soil was on the order of 12 000 μmol per cm3 bulk soil. Assuming, based on our results, that ca. 3% of this material is subject to microbial degradation under anaerobic conditions, this value translates into a biodegradable SOC pool of over 300 μmol cm−3. This represents a surprisingly large pool of labile OC that could drive DIR while at the same time promoting that process via electron shuttling. In situations where Fe(III) oxides are not abundant, this large pool of labile OC would alternatively drive other anaerobic metabolic pathways such as sulfate reduction or methanogenesis. Although, due to the extraction process HS do not directly represent SOC,6 our own and other recent results71 affirm that chemically extracted HS represent a pool of SOC that is indeed biodegradable. In addition, recent studies indicate that DIR can liberate OC compounds associated with Fe(III) oxide surfaces16,17 that are thought to represent a significant pool of preserved SOC in soils and sediments.75,76 This raises the possibility of a positive feedback loop where DIR driven by SOC depolymerization/fermentation and accelerated by electron shuttling leads to destabilization of Fe-associated OC which in turn further accelerates anaerobic carbon metabolism.

Supplementary Material

Supporting Information

ACKNOWLEDGMENTS

This research was supported by the U.S. Department of Energy, Office of Biological and Environmental Research, Subsurface Biogeochemical Research (SBR) program through grants DE-SC0014275 and DE-SC0016217, and by the SBR Scientific Focus Area at the Pacific Northwest National Laboratory (PNNL). Jacqueline Mejia was supported by the Graduate Engineering Research Scholars and the Biotechnology Training Program (NIH 5T32-GM08349) from the University of Wisconsin-Madison. Funds for the metagenomic sequencing were provided by Office of the Vice Chancellor for Research and Graduate Education at UW-Madison.

Footnotes

Supporting Information

The Supporting Information contains 3 Supporting Text sections, 4 Figures, and 12 Tables that complement the metagenomic results presented in the manuscript, including a description of the porin-cytochrome complex (PCC) system found in Geothrix fermentans (Supplemental Text 1 and Figure S1); results of the PilA gene search (Figure S2); detailed information about the assembly of raw reads using CLC Genomics workbench (Table S1); a list of the phylogenetic marker genes (Table S2) used to determine the microbial community composition (Table S3); an illustration of the presence of carbohydrate active enzyme (CAZyme) modules and their taxonomic assignments (Figures S3 and S4); the abundance and known activity of CAZyme families and KEGG aromatic degradation genes that had an abundance at least two times higher in the presence of HA or HM compared to the glucose only treatments (Supporting Text 2 and Tables S4–S7); and a description and results of screening PCC-containing draft genomes for KEGG aromatic degradation pathways (Supporting Text 3 and Tables S8–S12). The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.7b06574.

(PDF)

The authors declare no competing financial interest.

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