Discovery and Characterization of Fluopipamine, a Putative Cellulose Synthase 1 Antagonist within Arabidopsis.

Herbicide-resistant weeds are increasingly a problem in crop fields when exposed to similar chemistry over time. To avoid future yield losses, identifying herbicidal chemistry needs to be accelerated. We screened 50,000 small molecules using a liquid-handling robot and light microscopy focusing on pre-emergent herbicides in the family of cellulose biosynthesis inhibitors. Through phenotypic, chemical, genetic, and in silico methods we uncovered 6-{[4-(2-fluorophenyl)-1-piperazinyl]methyl}-N-(2-methoxy-5-methylphenyl)-1,3,5-triazine-2,4-diamine (fluopipamine). Symptomologies support fluopipamine as a putative antagonist of cellulose synthase enzyme 1 (CESA1) from Arabidopsis (Arabidopsis thaliana). Ectopic lignification, inhibition of etiolation, phenotypes including loss of anisotropic cellular expansion, swollen roots, and live cell imaging link fluopipamine to cellulose biosynthesis inhibition. Radiolabeled glucose incorporation of cellulose decreased in short-duration experiments when seedlings were incubated in fluopipamine. To elucidate the mechanism, ethylmethanesulfonate mutagenized M2 seedlings were screened for fluopipamine resistance. Two loci of genetic resistance were linked to CESA1. In silico docking of fluopipamine, quinoxyphen, and flupoxam against various CESA1 mutations suggests that an alternative binding site at the interface between CESA proteins is necessary to preserve cellulose polymerization in compound presence. These data uncovered potential fundamental mechanisms of cellulose biosynthesis in plants along with feasible leads for herbicidal uses.

Exploring the role of plant lysin motif receptor-like kinases in regulating plant-microbe interactions in the bioenergy crop Populus.

For plants, distinguishing between mutualistic and pathogenic microbes is a matter of survival. All microbes contain microbe-associated molecular patterns (MAMPs) that are perceived by plant pattern recognition receptors (PRRs). Lysin motif receptor-like kinases (LysM-RLKs) are PRRs attuned for binding and triggering a response to specific MAMPs, including chitin oligomers (COs) in fungi, lipo-chitooligosaccharides (LCOs), which are produced by mycorrhizal fungi and nitrogen-fixing rhizobial bacteria, and peptidoglycan in bacteria. The identification and characterization of LysM-RLKs in candidate bioenergy crops including Populus are limited compared to other model plant species, thus inhibiting our ability to both understand and engineer microbe-mediated gains in plant productivity. As such, we performed a sequence analysis of LysM-RLKs in the Populus genome and predicted their function based on phylogenetic analysis with known LysM-RLKs. Then, using predictive models, molecular dynamics simulations, and comparative structural analysis with previously characterized CO and LCO plant receptors, we identified probable ligand-binding sites in Populus LysM-RLKs. Using several machine learning models, we predicted remarkably consistent binding affinity rankings of Populus proteins to CO. In addition, we used a modified Random Walk with Restart network-topology based approach to identify a subset of Populus LysM-RLKs that are functionally related and propose a corresponding signal transduction cascade. Our findings provide the first look into the role of LysM-RLKs in Populus-microbe interactions and establish a crucial jumping-off point for future research efforts to understand specificity and redundancy in microbial perception mechanisms.

Evaluating the performance of random forest and iterative random forest based methods when applied to gene expression data.

Gene-to-gene networks, such as Gene Regulatory Networks (GRN) and Predictive Expression Networks (PEN) capture relationships between genes and are beneficial for use in downstream biological analyses. There exists multiple network inference tools to produce these gene-to-gene networks from matrices of gene expression data. Random Forest-Leave One Out Prediction (RF-LOOP) is a method that has been shown to be efficient at producing these gene-to-gene networks, frequently known as GEne Network Inference with Ensemble of trees (GENIE3). Random Forest can be replaced in this process by iterative Random Forest (iRF), which performs variable selection and boosting. Here we validate that iterative Random Forest-Leave One Out Prediction (iRF-LOOP) produces higher quality networks than GENIE3 (RF-LOOP). We use both synthetic and empirical networks from the Dialogue for Reverse Engineering Assessment and Methods (DREAM) Challenges by Sage Bionetworks, as well as two additional empirical networks created from Arabidopsis thaliana and Populus trichocarpa expression data.

Antiviral Strategies Against SARS-CoV-2: A Systems Biology Approach.

The unprecedented scientific achievements in combating the COVID-19 pandemic reflect a global response informed by unprecedented access to data. We now have the ability to rapidly generate a diversity of information on an emerging pathogen and, by using high-performance computing and a systems biology approach, we can mine this wealth of information to understand the complexities of viral pathogenesis and contagion like never before. These efforts will aid in the development of vaccines, antiviral medications, and inform policymakers and clinicians. Here we detail computational protocols developed as SARS-CoV-2 began to spread across the globe. They include pathogen detection, comparative structural proteomics, evolutionary adaptation analysis via network and artificial intelligence methodologies, and multiomic integration. These protocols constitute a core framework on which to build a systems-level infrastructure that can be quickly brought to bear on future pathogens before they evolve into pandemic proportions.

Potential Pathogenicity Determinants Identified from Structural Proteomics of SARS-CoV and SARS-CoV-2.

Despite SARS-CoV and SARS-CoV-2 being equipped with highly similar protein arsenals, the corresponding zoonoses have spread among humans at extremely different rates. The specific characteristics of these viruses that led to such distinct outcomes remain unclear. Here, we apply proteome-wide comparative structural analysis aiming to identify the unique molecular elements in the SARS-CoV-2 proteome that may explain the differing consequences. By combining protein modeling and molecular dynamics simulations, we suggest nonconservative substitutions in functional regions of the spike glycoprotein (S), nsp1, and nsp3 that are contributing to differences in virulence. Particularly, we explain why the substitutions at the receptor-binding domain of S affect the structure-dynamics behavior in complexes with putative host receptors. Conservation of functional protein regions within the two taxa is also noteworthy. We suggest that the highly conserved main protease, nsp5, of SARS-CoV and SARS-CoV-2 is part of their mechanism of circumventing the host interferon antiviral response. Overall, most substitutions occur on the protein surfaces and may be modulating their antigenic properties and interactions with other macromolecules. Our results imply that the striking difference in the pervasiveness of SARS-CoV-2 and SARS-CoV among humans seems to significantly derive from molecular features that modulate the efficiency of viral particles in entering the host cells and blocking the host immune response.

Potentially adaptive SARS-CoV-2 mutations discovered with novel spatiotemporal and explainable AI models.

BACKGROUND: A mechanistic understanding of the spread of SARS-CoV-2 and diligent tracking of ongoing mutagenesis are of key importance to plan robust strategies for confining its transmission. Large numbers of available sequences and their dates of transmission provide an unprecedented opportunity to analyze evolutionary adaptation in novel ways. Addition of high-resolution structural information can reveal the functional basis of these processes at the molecular level. Integrated systems biology-directed analyses of these data layers afford valuable insights to build a global understanding of the COVID-19 pandemic. RESULTS: Here we identify globally distributed haplotypes from 15,789 SARS-CoV-2 genomes and model their success based on their duration, dispersal, and frequency in the host population. Our models identify mutations that are likely compensatory adaptive changes that allowed for rapid expansion of the virus. Functional predictions from structural analyses indicate that, contrary to previous reports, the Asp614Gly mutation in the spike glycoprotein (S) likely reduced transmission and the subsequent Pro323Leu mutation in the RNA-dependent RNA polymerase led to the precipitous spread of the virus. Our model also suggests that two mutations in the nsp13 helicase allowed for the adaptation of the virus to the Pacific Northwest of the USA. Finally, our explainable artificial intelligence algorithm identified a mutational hotspot in the sequence of S that also displays a signature of positive selection and may have implications for tissue or cell-specific expression of the virus. CONCLUSIONS: These results provide valuable insights for the development of drugs and surveillance strategies to combat the current and future pandemics.

Network Modeling of Complex Data Sets.

We demonstrate a selection of network and machine learning techniques useful in the analysis of complex datasets, including 2-way similarity networks, Markov clustering, enrichment statistical networks, FCROS differential analysis, and random forests. We demonstrate each of these techniques on the Populus trichocarpa gene expression atlas.

Finding New Cell Wall Regulatory Genes in Populus trichocarpa Using Multiple Lines of Evidence.

Understanding the regulatory network controlling cell wall biosynthesis is of great interest in Populus trichocarpa, both because of its status as a model woody perennial and its importance for lignocellulosic products. We searched for genes with putatively unknown roles in regulating cell wall biosynthesis using an extended network-based Lines of Evidence (LOE) pipeline to combine multiple omics data sets in P. trichocarpa, including gene coexpression, gene comethylation, population level pairwise SNP correlations, and two distinct SNP-metabolite Genome Wide Association Study (GWAS) layers. By incorporating validation, ranking, and filtering approaches we produced a list of nine high priority gene candidates for involvement in the regulation of cell wall biosynthesis. We subsequently performed a detailed investigation of candidate gene GROWTH-REGULATING FACTOR 9 (PtGRF9). To investigate the role of PtGRF9 in regulating cell wall biosynthesis, we assessed the genome-wide connections of PtGRF9 and a paralog across data layers with functional enrichment analyses, predictive transcription factor binding site analysis, and an independent comparison to eQTN data. Our findings indicate that PtGRF9 likely affects the cell wall by directly repressing genes involved in cell wall biosynthesis, such as PtCCoAOMT and PtMYB.41, and indirectly by regulating homeobox genes. Furthermore, evidence suggests that PtGRF9 paralogs may act as transcriptional co-regulators that direct the global energy usage of the plant. Using our extended pipeline, we show multiple lines of evidence implicating the involvement of these genes in cell wall regulatory functions and demonstrate the value of this method for prioritizing candidate genes for experimental validation.

Wavelet-Based Genomic Signal Processing for Centromere Identification and Hypothesis Generation.

Various 'omics data types have been generated for Populus trichocarpa, each providing a layer of information which can be represented as a density signal across a chromosome. We make use of genome sequence data, variants data across a population as well as methylation data across 10 different tissues, combined with wavelet-based signal processing to perform a comprehensive analysis of the signature of the centromere in these different data signals, and successfully identify putative centromeric regions in P. trichocarpa from these signals. Furthermore, using SNP (single nucleotide polymorphism) correlations across a natural population of P. trichocarpa, we find evidence for the co-evolution of the centromeric histone CENH3 with the sequence of the newly identified centromeric regions, and identify a new CENH3 candidate in P. trichocarpa.

Multi-Phenotype Association Decomposition: Unraveling Complex Gene-Phenotype Relationships.

Various patterns of multi-phenotype associations (MPAs) exist in the results of Genome Wide Association Studies (GWAS) involving different topologies of single nucleotide polymorphism (SNP)-phenotype associations. These can provide interesting information about the different impacts of a gene on closely related phenotypes or disparate phenotypes (pleiotropy). In this work we present MPA Decomposition, a new network-based approach which decomposes the results of a multi-phenotype GWAS study into three bipartite networks, which, when used together, unravel the multi-phenotype signatures of genes on a genome-wide scale. The decomposition involves the construction of a phenotype powerset space, and subsequent mapping of genes into this new space. Clustering of genes in this powerset space groups genes based on their detailed MPA signatures. We show that this method allows us to find multiple different MPA and pleiotropic signatures within individual genes and to classify and cluster genes based on these SNP-phenotype association topologies. We demonstrate the use of this approach on a GWAS analysis of a large population of 882 Populus trichocarpa genotypes using untargeted metabolomics phenotypes. This method should prove invaluable in the interpretation of large GWAS datasets and aid in future synthetic biology efforts designed to optimize phenotypes of interest.

Improved growth rate in Clostridium thermocellum hydrogenase mutant via perturbed sulfur metabolism.

BACKGROUND: Metabolic engineering is a commonly used approach to develop organisms for an industrial function, but engineering aimed at improving one phenotype can negatively impact other phenotypes. This lack of robustness can prove problematic. Cellulolytic bacterium Clostridium thermocellum is able to rapidly ferment cellulose to ethanol and other products. Recently, genes involved in H2 production, including the hydrogenase maturase hydG and NiFe hydrogenase ech, were deleted from the chromosome of C. thermocellum. While ethanol yield increased, the growth rate of ΔhydG decreased substantially compared to wild type. RESULTS: Addition of 5 mM acetate to the growth medium improved the growth rate in C. thermocellum ∆hydG, whereas wild type remained unaffected. Transcriptomic analysis of the wild type showed essentially no response to the addition of acetate. However, in C. thermocellum ΔhydG, 204 and 56 genes were significantly differentially regulated relative to wild type in the absence and presence of acetate, respectively. Genes, Clo1313_0108-0125, which are predicted to encode a sulfate transport system and sulfate assimilatory pathway, were drastically upregulated in C. thermocellum ΔhydG in the presence of added acetate. A similar pattern was seen with proteomics. Further physiological characterization demonstrated an increase in sulfide synthesis and elimination of cysteine consumption in C. thermocellum ΔhydG. Clostridium thermocellum ΔhydGΔech had a higher growth rate than ΔhydG in the absence of added acetate, and a similar but less pronounced transcriptional and physiological effect was seen in this strain upon addition of acetate. CONCLUSIONS: Sulfur metabolism is perturbed in C. thermocellum ΔhydG strains, likely to increase flux through sulfate reduction to act either as an electron sink to balance redox reactions or to offset an unknown deficiency in sulfur assimilation.

Multi-omics of permafrost, active layer and thermokarst bog soil microbiomes.

Over 20% of Earth's terrestrial surface is underlain by permafrost with vast stores of carbon that, once thawed, may represent the largest future transfer of carbon from the biosphere to the atmosphere. This process is largely dependent on microbial responses, but we know little about microbial activity in intact, let alone in thawing, permafrost. Molecular approaches have recently revealed the identities and functional gene composition of microorganisms in some permafrost soils and a rapid shift in functional gene composition during short-term thaw experiments. However, the fate of permafrost carbon depends on climatic, hydrological and microbial responses to thaw at decadal scales. Here we use the combination of several molecular 'omics' approaches to determine the phylogenetic composition of the microbial communities, including several draft genomes of novel species, their functional potential and activity in soils representing different states of thaw: intact permafrost, seasonally thawed active layer and thermokarst bog. The multi-omics strategy reveals a good correlation of process rates to omics data for dominant processes, such as methanogenesis in the bog, as well as novel survival strategies for potentially active microbes in permafrost.

Proteomic identification of mammalian cell surface derived glycosylphosphatidylinositol-anchored proteins through selective glycan enrichment.

Glycosylphosphatidylinositol-anchored proteins (GPI-APs) are an important class of glycoproteins that are tethered to the surface of mammalian cells via the lipid GPI. GPI-APs have been implicated in many important cellular functions including cell adhesion, cell signaling, and immune regulation. Proteomic identification of mammalian GPI-APs en masse has been limited technically by poor sensitivity for these low abundance proteins and the use of methods that destroy cell integrity. Here, we present methodology that permits identification of GPI-APs liberated directly from the surface of intact mammalian cells through exploitation of their appended glycans to enrich for these proteins ahead of LC-MS/MS analyses. We validate our approach in HeLa cells, identifying a greater number of GPI-APs from intact cells than has been previously identified from isolated HeLa membranes and a lipid raft preparation. We further apply our approach to define the cohort of endogenous GPI-APs that populate the distinct apical and basolateral membrane surfaces of polarized epithelial cell monolayers. Our approach provides a new method to achieve greater sensitivity in the identification of low abundance GPI-APs from the surface of live cells and the nondestructive nature of the method provides new opportunities for the temporal or spatial analysis of cellular GPI-AP expression and dynamics.

Colonization state influences the hemocyte proteome in a beneficial squid-Vibrio symbiosis.

The squid Euprymna scolopes and the luminescent bacterium Vibrio fischeri form a highly specific beneficial light organ symbiosis. Not only does the host have to select V. fischeri from the environment, but it must also prevent subsequent colonization by non-symbiotic microorganisms. Host macrophage-like hemocytes are believed to play a role in mediating the symbiosis with V. fischeri. Previous studies have shown that the colonization state of the light organ influences the host's hemocyte response to the symbiont. To further understand the molecular mechanisms behind this process, we used two quantitative mass-spectrometry-based proteomic techniques, isobaric tags for relative and absolute quantification (iTRAQ) and label-free spectral counting, to compare and quantify the adult hemocyte proteomes from colonized (sym) and uncolonized (antibiotic-treated/cured) squid. Overall, iTRAQ allowed for the quantification of 1,024 proteins with two or more peptides. Thirty-seven unique proteins were determined to be significantly different between sym and cured hemocytes (p value < 0.05), with 20 more abundant proteins and 17 less abundant in sym hemocytes. The label-free approach resulted in 1,241 proteins that were identified in all replicates. Of 185 unique proteins present at significantly different amounts in sym hemocytes (as determined by spectral counting), 92 were more abundant and 93 were less abundant. Comparisons between iTRAQ and spectral counting revealed that 30 of the 37 proteins quantified via iTRAQ exhibited trends similar to those identified by the label-free method. Both proteomic techniques mutually identified 16 proteins that were significantly different between the two groups of hemocytes (p value < 0.05). The presence of V. fischeri in the host light organ influenced the abundance of proteins associated with the cytoskeleton, adhesion, lysosomes, proteolysis, and the innate immune response. These data provide evidence that colonization by V. fischeri alters the hemocyte proteome and reveals proteins that may be important for maintaining host-symbiont specificity.

Twelve previously unknown phage genera are ubiquitous in global oceans.

Viruses are fundamental to ecosystems ranging from oceans to humans, yet our ability to study them is bottlenecked by the lack of ecologically relevant isolates, resulting in "unknowns" dominating culture-independent surveys. Here we present genomes from 31 phages infecting multiple strains of the aquatic bacterium Cellulophaga baltica (Bacteroidetes) to provide data for an underrepresented and environmentally abundant bacterial lineage. Comparative genomics delineated 12 phage groups that (i) each represent a new genus, and (ii) represent one novel and four well-known viral families. This diversity contrasts the few well-studied marine phage systems, but parallels the diversity of phages infecting human-associated bacteria. Although all 12 Cellulophaga phages represent new genera, the podoviruses and icosahedral, nontailed ssDNA phages were exceptional, with genomes up to twice as large as those previously observed for each phage type. Structural novelty was also substantial, requiring experimental phage proteomics to identify 83% of the structural proteins. The presence of uncommon nucleotide metabolism genes in four genera likely underscores the importance of scavenging nutrient-rich molecules as previously seen for phages in marine environments. Metagenomic recruitment analyses suggest that these particular Cellulophaga phages are rare and may represent a first glimpse into the phage side of the rare biosphere. However, these analyses also revealed that these phage genera are widespread, occurring in 94% of 137 investigated metagenomes. Together, this diverse and novel collection of phages identifies a small but ubiquitous fraction of unknown marine viral diversity and provides numerous environmentally relevant phage-host systems for experimental hypothesis testing.

Environmental proteomics reveals early microbial community responses to biostimulation at a uranium- and nitrate-contaminated site.

High-performance MS instrumentation coupled with improved protein extraction techniques enables metaproteomics to identify active members of soil and groundwater microbial communities. Metaproteomics workflows were applied to study the initial responses (i.e. 4 days post treatment) of the indigenous aquifer microbiota to biostimulation with emulsified vegetable oil (EVO) at a uranium-contaminated site. Members of the Betaproteobacteria (i.e. Dechloromonas, Ralstonia, Rhodoferax, Polaromonas, Delftia, Chromobacterium) and the Firmicutes dominated the biostimulated aquifer community. Proteome characterization revealed distinct differences between the microbial biomass collected from groundwater influenced by biostimulation and groundwater collected upgradient of the EVO injection points. In particular, proteins involved in ammonium assimilation, EVO degradation, and polyhydroxybutyrate granule formation were prominent following biostimulation. Interestingly, the atypical NosZ of Dechloromonas spp. was highly abundant, suggesting active nitrous oxide (N2 O) respiration. c-Type cytochromes were barely detected, as was citrate synthase, a biomarker for hexavalent uranium reduction activity, suggesting that uranium reduction has not commenced 4 days post EVO amendment. Environmental metaproteomics identified microbial community responses to biostimulation and elucidated active pathways demonstrating the value of this technique as a monitoring tool and for complementing nucleic acid-based approaches.

Metaproteomics reveals abundant transposase expression in mutualistic endosymbionts.

Transposases, enzymes that catalyze the movement of mobile genetic elements, are the most abundant genes in nature. While many bacteria encode an abundance of transposases in their genomes, the current paradigm is that the expression of transposase genes is tightly regulated and generally low due to its severe mutagenic effects. In the current study, we detected the highest number of transposase proteins ever reported in bacteria, in symbionts of the gutless marine worm Olavius algarvensis with metaproteomics. At least 26 different transposases from 12 different families were detected, and genomic and proteomic analyses suggest that many of these are active. This high expression of transposases indicates that the mechanisms for their tight regulation have been disabled or no longer exist.

New group in the Leptospirillum clade: cultivation-independent community genomics, proteomics, and transcriptomics of the new species "Leptospirillum group IV UBA BS".

Leptospirillum spp. are widespread members of acidophilic microbial communities that catalyze ferrous iron oxidation, thereby increasing sulfide mineral dissolution rates. These bacteria play important roles in environmental acidification and are harnessed for bioleaching-based metal recovery. Known members of the Leptospirillum clade of the Nitrospira phylum are Leptospirillum ferrooxidans (group I), Leptospirillum ferriphilum and "Leptospirillum rubarum" (group II), and Leptospirillum ferrodiazotrophum (group III). In the Richmond Mine acid mine drainage (AMD) system, biofilm formation is initiated by L. rubarum; L. ferrodiazotrophum appears in later developmental stages. Here we used community metagenomic data from unusual, thick floating biofilms to identify distinguishing metabolic traits in a rare and uncultivated community member, the new species "Leptospirillum group IV UBA BS." These biofilms typically also contain a variety of Archaea, Actinobacteria, and a few other Leptospirillum spp. The Leptospirillum group IV UBA BS species shares 98% 16S rRNA sequence identity and 70% average amino acid identity between orthologs with its closest relative, L. ferrodiazotrophum. The presence of nitrogen fixation and reverse tricarboxylic acid (TCA) cycle proteins suggest an autotrophic metabolism similar to that of L. ferrodiazotrophum, while hydrogenase proteins suggest anaerobic metabolism. Community transcriptomic and proteomic analyses demonstrate expression of a multicopper oxidase unique to this species, as well as hydrogenases and core metabolic genes. Results suggest that the Leptospirillum group IV UBA BS species might play important roles in carbon fixation, nitrogen fixation, hydrogen metabolism, and iron oxidation in some acidic environments.

Insights into the structure and metabolic function of microbes that shape pelagic iron-rich aggregates ("iron snow").

Microbial ferrous iron [Fe(II)] oxidation leads to the formation of iron-rich macroscopic aggregates ("iron snow") at the redoxcline in a stratified lignite mine lake in east-central Germany. We aimed to identify the abundant Fe-oxidizing and Fe-reducing microorganisms likely to be involved in the formation and transformation of iron snow present in the redoxcline in two basins of the lake that differ in their pH values. Nucleic acid- and lipid-stained microbial cells of various morphologies detected by confocal laser scanning microscopy were homogeneously distributed in all iron snow samples. The dominant iron mineral appeared to be schwertmannite, with shorter needles in the northern than in the central basin samples. Total bacterial 16S rRNA gene copies ranged from 5.0 × 10(8) copies g (dry weight)(-1) in the acidic central lake basin (pH 3.3) to 4.0 × 10(10) copies g (dry weight)(-1) in the less acidic (pH 5.9) northern basin. Total RNA-based quantitative PCR assigned up to 61% of metabolically active microbial communities to Fe-oxidizing- and Fe-reducing-related bacteria, indicating that iron metabolism was an important metabolic strategy. Molecular identification of abundant groups suggested that iron snow surfaces were formed by chemoautotrophic iron oxidizers, such as Acidimicrobium, Ferrovum, Acidithiobacillus, Thiobacillus, and Chlorobium, in the redoxcline and were rapidly colonized by heterotrophic iron reducers, such as Acidiphilium, Albidiferax-like, and Geobacter-like groups. Metaproteomics yielded 283 different proteins from northern basin iron snow samples, and protein identification provided a glimpse into some of their in situ metabolic processes, such as primary production (CO2 fixation), respiration, motility, and survival strategies.

Biostimulation induces syntrophic interactions that impact C, S and N cycling in a sediment microbial community.

Stimulation of subsurface microorganisms to induce reductive immobilization of metals is a promising approach for bioremediation, yet the overall microbial community response is typically poorly understood. Here we used proteogenomics to test the hypothesis that excess input of acetate activates complex community functioning and syntrophic interactions among autotrophs and heterotrophs. A flow-through sediment column was incubated in a groundwater well of an acetate-amended aquifer and recovered during microbial sulfate reduction. De novo reconstruction of community sequences yielded near-complete genomes of Desulfobacter (Deltaproteobacteria), Sulfurovum- and Sulfurimonas-like Epsilonproteobacteria and Bacteroidetes. Partial genomes were obtained for Clostridiales (Firmicutes) and Desulfuromonadales-like Deltaproteobacteria. The majority of proteins identified by mass spectrometry corresponded to Desulfobacter-like species, and demonstrate the role of this organism in sulfate reduction (Dsr and APS), nitrogen fixation and acetate oxidation to CO2 during amendment. Results indicate less abundant Desulfuromonadales, and possibly Bacteroidetes, also actively contributed to CO2 production via the tricarboxylic acid (TCA) cycle. Proteomic data indicate that sulfide was partially re-oxidized by Epsilonproteobacteria through nitrate-dependent sulfide oxidation (using Nap, Nir, Nos, SQR and Sox), with CO2 fixed using the reverse TCA cycle. We infer that high acetate concentrations, aimed at stimulating anaerobic heterotrophy, led to the co-enrichment of, and carbon fixation in Epsilonproteobacteria. Results give an insight into ecosystem behavior following addition of simple organic carbon to the subsurface, and demonstrate a range of biological processes and community interactions were stimulated.