Metagenomic Sequence Data Research Articles

An equation‐free method reveals the ecological interaction networks within complex microbial ecosystems

Abstract Mapping the network of ecological interactions is a key to understanding the composition, stability, function and dynamics of microbial communities. In recent years various approaches have been used to reveal microbial interaction networks from metagenomic sequencing data, such as time‐series analysis, machine learning and statistical techniques. Despite these efforts it is still not possible to capture details of the ecological interactions behind complex microbial dynamics. We developed the sparse S‐map method (SSM), which generates a sparse interaction network from a multivariate ecological time series without presuming any mathematical formulation for the underlying microbial processes. The advantage of the SSM over alternative methodologies is that it fully utilizes the observed data using a framework of empirical dynamic modelling. This makes the SSM robust to non‐equilibrium dynamics and underlying complexity (nonlinearity) in microbial processes. We showed that an increase in dataset size or a decrease in observational error improved the accuracy of SSM However, the accuracy of a comparative equation‐based method was almost unchanged for both cases and equivalent to the SSM at best. Hence, the SSM outperformed a comparative equation‐based method when datasets were large and the magnitude of observational errors was small. The results were robust to the magnitude of process noise and the functional forms of interspecific interactions that we tested. We applied the method to a microbiome data of six mice and found that there were different microbial interaction regimes between young to middle age (4–40 week‐old) and middle to old age (36–72 week‐old) mice. The complexity of microbial relationships impedes detailed equation‐based modelling. Our method provides a powerful alternative framework to infer ecological interaction networks of microbial communities in various environments and will be improved by further developments in metagenomics sequencing technologies leading to increased dataset size and improved accuracy and precision.

1,003 reference genomes of bacterial and archaeal isolates expand coverage of the tree of life

Metagenomic and microbial sequence data are made easier to interpret with the addition of 1,003 genomes to the Genomic Encyclopedia of Bacteria and Archaea.

A Model-Based Approach For Species Abundance Quantification Based On Shotgun Metagenomic Data.

The human microbiome, which includes the collective microbes residing in or on the human body, has a profound influence on the human health. DNA sequencing technology has made the large-scale human microbiome studies possible by using shotgun metagenomic sequencing. One important aspect of data analysis of such metagenomic data is to quantify the bacterial abundances based on the metagenomic sequencing data. Existing methods almost always quantify such abundances one sample at a time, which ignore certain systematic differences in read coverage along the genomes due to GC contents, copy number variation and the bacterial origin of replication. In order to account for such differences in read counts, we propose a multi-sample Poisson model to quantify microbial abundances based on read counts that are assigned to species-specific taxonomic markers. Our model takes into account the marker-specific effects when normalizing the sequencing count data in order to obtain more accurate quantification of the species abundances. Compared to currently available methods on simulated data and real data sets, our method has demonstrated an improved accuracy in bacterial abundance quantification, which leads to more biologically interesting results from downstream data analysis.

From the Cover: Aloin, a Component of the Aloe Vera Plant Leaf, Induces Pathological Changes and Modulates the Composition of Microbiota in the Large Intestines of F344/N Male Rats.

In a previous study, the oral administration of an Aloe vera whole leaf extract induced dose-related mucosal and goblet cell hyperplasia in the rat colon after 13 weeks and colon cancer after 2 years. The primary goal of this study was to determine whether or not the administration of aloin, a component of the Aloe vera plant leaf, would replicate the pathophysiological effects that were observed in rats in the previous study with an Aloe vera whole leaf extract. Groups of 10 male F344/N rats were administered aloin at 0, 6.95, 13.9, 27.8, 55.7, 111, 223, and 446 mg/kg drinking water for 13 weeks. At the end of study, rat feces were collected, and the composition of fecal bacteria was investigated by next generation sequencing of the PCR-amplified V3/V4 region of the 16S rRNA gene. At necropsy, blood was collected by cardiac puncture and organs and sections of the large intestine were collected for histopathology. Aloin induced dose-related increased incidences and severities of mucosal and goblet cell hyperplasia that extended from the cecum to the rectum, with increased incidences and severities detected at aloin doses ≥55.7 mg/kg drinking water. Analysis of the 16S rRNA metagenomics sequencing data revealed marked shifts in the structure of the gut microbiota in aloin-treated rats at each taxonomic rank. This study highlights the similarities in effects observed for aloin and the Aloe vera whole leaf extract, and points to a potential mechanism of action to explain the observed pathological changes via modulation of the gut microbiota composition.

RNA-Dependent Cysteine Biosynthesis in Bacteria and Archaea.

ABSTRACTThe diversity of the genetic code systems used by microbes on earth is yet to be elucidated. It is known that certain methanogenic archaea employ an alternative system for cysteine (Cys) biosynthesis and encoding; tRNACys is first acylated with phosphoserine (Sep) by O-phosphoseryl-tRNA synthetase (SepRS) and then converted to Cys-tRNACys by Sep-tRNA:Cys-tRNA synthase (SepCysS). In this study, we searched all genomic and metagenomic protein sequence data in the Integrated Microbial Genomes (IMG) system and at the NCBI to reveal new clades of SepRS and SepCysS proteins belonging to diverse archaea in the four major groups (DPANN, Euryarchaeota, TACK, and Asgard) and two groups of bacteria (“Candidatus Parcubacteria” and Chloroflexi). Bacterial SepRS and SepCysS charged bacterial tRNACys species with cysteine in vitro. Homologs of SepCysE, a scaffold protein facilitating SepRS⋅SepCysS complex assembly in Euryarchaeota class I methanogens, are found in a few groups of TACK and Asgard archaea, whereas the C-terminally truncated homologs exist fused or genetically coupled with diverse SepCysS species. Investigation of the selenocysteine (Sec)- and pyrrolysine (Pyl)-utilizing traits in SepRS-utilizing archaea and bacteria revealed that the archaea carrying full-length SepCysE employ Sec and that SepRS is often found in Pyl-utilizing archaea and Chloroflexi bacteria. We discuss possible contributions of the SepRS-SepCysS system for sulfur assimilation, methanogenesis, and other metabolic processes requiring large amounts of iron-sulfur enzymes or Pyl-containing enzymes.

PARTIE: a partition engine to separate metagenomic and amplicon projects in the Sequence Read Archive.

MotivationThe Sequence Read Archive (SRA) contains raw data from many different types of sequence projects. As of 2017, the SRA contained approximately ten petabases of DNA sequence (1016 bp). Annotations of the data are provided by the submitter, and mining the data in the SRA is complicated by both the amount of data and the detail within those annotations. Here, we introduce PARTIE, a partition engine optimized to differentiate sequence read data into metagenomic (random) and amplicon (targeted) sequence data sets.ResultsPARTIE subsamples reads from the sequencing file and calculates four different statistics: k-mer frequency, 16S abundance, prokaryotic- and viral-read abundance. These metrics are used to create a RandomForest decision tree to classify the sequencing data, and PARTIE provides mechanisms for both supervised and unsupervised classification. We demonstrate the accuracy of PARTIE for classifying SRA data, discuss the probable error rates in the SRA annotations and introduce a resource assessing SRA data.Availability and ImplementationPARTIE and reclassified metagenome SRA entries are available from https://github.com/linsalrob/partieSupplementary information Supplementary data are available at Bioinformatics online.

A Review of Bioinformatics Tools for Bio-Prospecting from Metagenomic Sequence Data.

The microbiome can be defined as the community of microorganisms that live in a particular environment. Metagenomics is the practice of sequencing DNA from the genomes of all organisms present in a particular sample, and has become a common method for the study of microbiome population structure and function. Increasingly, researchers are finding novel genes encoded within metagenomes, many of which may be of interest to the biotechnology and pharmaceutical industries. However, such “bioprospecting” requires a suite of sophisticated bioinformatics tools to make sense of the data. This review summarizes the most commonly used bioinformatics tools for the assembly and annotation of metagenomic sequence data with the aim of discovering novel genes.

Bioinformatic Analysis Reveals Archaeal tRNATyr and tRNATrp Identities in Bacteria

The tRNA identity elements for some amino acids are distinct between the bacterial and archaeal domains. Searching in recent genomic and metagenomic sequence data, we found some candidate phyla radiation (CPR) bacteria with archaeal tRNA identity for Tyr-tRNA and Trp-tRNA synthesis. These bacteria possess genes for tyrosyl-tRNA synthetase (TyrRS) and tryptophanyl-tRNA synthetase (TrpRS) predicted to be derived from DPANN superphylum archaea, while the cognate tRNATyr and tRNATrp genes reveal bacterial or archaeal origins. We identified a trace of domain fusion and swapping in the archaeal-type TyrRS gene of a bacterial lineage, suggesting that CPR bacteria may have used this mechanism to create diverse proteins. Archaeal-type TrpRS of bacteria and a few TrpRS species of DPANN archaea represent a new phylogenetic clade (named TrpRS-A). The TrpRS-A open reading frames (ORFs) are always associated with another ORF (named ORF1) encoding an unknown protein without global sequence identity to any known protein. However, our protein structure prediction identified a putative HIGH-motif and KMSKS-motif as well as many α-helices that are characteristic of class I aminoacyl-tRNA synthetase (aaRS) homologs. These results provide another example of the diversity of molecular components that implement the genetic code and provide a clue to the early evolution of life and the genetic code.

Metagenomic assessment of methane production-oxidation and nitrogen metabolism of long term manured systems in lowland rice paddy

Biochemical pathways of methanogenesis and methanotrophy coupled with carbon (C)-nitrogen (N) metabolism were studied in long term (13years) manured systems in lowland rice paddy through metagenomics approach. Manured systems included in this study were, control (exclusion of application of any manure), farm yard manure (FYM, @5Mgha−1yr−1) and green manuring (GM with Sesbania aculeata). Metagenomic sequence data revealed the dominance of C decomposing bacterial communities' like Proteobacteria, Planctomycetes, Actinobacteria, Firmicutes, Acidobacteria, in manure amended soils as compared to control. Diversities for assimilatory and dissimilatory N-fixing microorganisms at phylum level were found higher under GM as compared to rest. Two genera responsible for methanogenesis, viz. Methanolobus and Methanotorris were absent in manured systems as compared to control. The acetoclastic and serine pathway was found as the predominant pathway for methanogenesis and methanotrophy, respectively, in tropical lowland rice paddy. Abundance reads of enzymes were in the range of 254–445 in the acetoclastic methanogenesis pathway. On the other hand, these were varied from 165 to 216 in serine pathway of methanotrophy. Lowland paddy soil exhibited higher functional and structural diversities in manured systems as compared to unamended control in respect to labile C pools and CH4 production. Methane (CH4) emission was 31% higher in FYM system than GM. However, nitrous oxide (N2O) emission was found 25% higher in GM as compared to FYM. As a whole, bacterial diversities were higher under FYM system in tropical lowland rice paddy as compared to GM and unamended systems.

Large-scale 16S gene assembly using metagenomics shotgun sequences.

Combining a 16S rRNA (16S) gene database with metagenomic shotgun sequences promises unbiased identification of known and novel microbes. To achieve this, we herein report reference-based ribosome assembly (RAMBL), a computational pipeline, which integrates taxonomic tree search and Dirichlet process clustering to reconstruct full-length 16S gene sequences from metagenomic sequencing data with high accuracy. By benchmarking against the synthetic and real shotgun sequences, we demonstrated that full-length 16S gene assemblies of RAMBL were a good proxy for known and putative microbes, including Candidate Phyla Radiation. We found that 30-40% of bacteria genera in the terrestrial and intestinal biomes have no closely related genome sequences. We also observed that RAMBL was able to generate a more accurate determination of environmental microbial diversity and yield better disease classification, suggesting that full-length 16S gene assemblies are a powerful alternative to marker gene set and 16S short reads. RAMBL first realizes the access to full-length 16S gene sequences in the near-terabase-scale metagenomic shotgun sequences, which markedly improve metagenomic data analysis and interpretation. RAMBL is available at https://github.com/homopolymer/RAMBL for academic use. zengfeng@xmu.edu.cn. Supplementary data are available at Bioinformatics online.

Protein structure determination using metagenome sequence data.

Despite decades of work by structural biologists, there are still ~5200 protein families with unknown structure outside the range of comparative modeling. We show that Rosetta structure prediction guided by residue-residue contacts inferred from evolutionary information can accurately model proteins that belong to large families and that metagenome sequence data more than triple the number of protein families with sufficient sequences for accurate modeling. We then integrate metagenome data, contact-based structure matching, and Rosetta structure calculations to generate models for 614 protein families with currently unknown structures; 206 are membrane proteins and 137 have folds not represented in the Protein Data Bank. This approach provides the representative models for large protein families originally envisioned as the goal of the Protein Structure Initiative at a fraction of the cost.

An informative approach on differential abundance analysis for time-course metagenomic sequencing data.

The advent of high-throughput next generation sequencing technology has greatly promoted the field of metagenomics where previously unattainable information about microbial communities can be discovered. Detecting differentially abundant features (e.g. species or genes) plays a critical role in revealing the contributors (i.e. pathogens) to the biological or medical status of microbial samples. However, currently available statistical methods lack power in detecting differentially abundant features contrasting different biological or medical conditions, in particular, for time series metagenomic sequencing data. We have proposed a novel procedure, metaDprof, which is built upon a spline-based method assuming heterogeneous error, to meet the challenges of detecting differentially abundant features from metagenomic samples by comparing different biological/medical conditions across time. It contains two stages: (i) global detection on features and (ii) time interval detection for significant features. The detection procedures in both stages are based on sound statistical support. Compared with existing methods the new method metaDprof shows the best performance in comprehensive simulation studies. Not only can it accurately detect features relating to the biological condition or disease status of samples but it also can accurately detect the starting and ending time points when the differences arise. The proposed method is also applied to a real metagenomic dataset and the results provide an interesting angle to understand the relationship between the microbiota in mouse gut and diet type. R code and an example dataset are available at https://cals.arizona.edu/∼anling/sbg/software.htm. anling@email.arizona.edu. Supplementary data are available at Bioinformatics online.

Negative binomial mixed models for analyzing microbiome count data

BackgroundRecent advances in next-generation sequencing (NGS) technology enable researchers to collect a large volume of metagenomic sequencing data. These data provide valuable resources for investigating interactions between the microbiome and host environmental/clinical factors. In addition to the well-known properties of microbiome count measurements, for example, varied total sequence reads across samples, over-dispersion and zero-inflation, microbiome studies usually collect samples with hierarchical structures, which introduce correlation among the samples and thus further complicate the analysis and interpretation of microbiome count data.ResultsIn this article, we propose negative binomial mixed models (NBMMs) for detecting the association between the microbiome and host environmental/clinical factors for correlated microbiome count data. Although having not dealt with zero-inflation, the proposed mixed-effects models account for correlation among the samples by incorporating random effects into the commonly used fixed-effects negative binomial model, and can efficiently handle over-dispersion and varying total reads. We have developed a flexible and efficient IWLS (Iterative Weighted Least Squares) algorithm to fit the proposed NBMMs by taking advantage of the standard procedure for fitting the linear mixed models.ConclusionsWe evaluate and demonstrate the proposed method via extensive simulation studies and the application to mouse gut microbiome data. The results show that the proposed method has desirable properties and outperform the previously used methods in terms of both empirical power and Type I error. The method has been incorporated into the freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/ and http://github.com/abbyyan3/BhGLM), providing a useful tool for analyzing microbiome data.

From Gene Annotation to Function Prediction for Metagenomics.

Microbes play important roles in almost every aspect of life, including human health and diseases. Facilitated by the rapid development of sequencing technologies, metagenomics research has accelerated the accumulation of genomic sequences of microbial species that had been inaccessible before. Analysis of the metagenomic sequencing data can reveal not only the species but also the functional composition of microbial communities. Here, we report a pipeline for functional annotation of metagenomic datasets. The pipeline is built from several programs that we have developed for metagenomic sequence analysis including a protein-coding gene predictor for short reads (or contigs) and a fast similarity search tool. Given a metagenomic dataset, the pipeline reports putative protein-coding genes (or gene fragments) and functional annotations of the genes in Gene Ontology (GO) terms and Enzyme Commission (EC) numbers, and potential metabolic pathways that are likely encoded by the metagenome. Fun4Me is available for download at https://sourceforge.net/projects/fun4me .

Integrated metagenomic data analysis demonstrates that a loss of diversity in oral microbiota is associated with periodontitis

BackgroundPeriodontitis is an inflammatory disease affecting the tissues supporting teeth (periodontium). Integrative analysis of metagenomic samples from multiple periodontitis studies is a powerful way to examine microbiota diversity and interactions within host oral cavity.MethodsA total of 43 subjects were recruited to participate in two previous studies profiling the microbial community of human subgingival plaque samples using shotgun metagenomic sequencing. We integrated metagenomic sequence data from those two studies, including six healthy controls, 14 sites representative of stable periodontitis, 16 sites representative of progressing periodontitis, and seven periodontal sites of unknown status. We applied phylogenetic diversity, differential abundance, and network analyses, as well as clustering, to the integrated dataset to compare microbiological community profiles among the different disease states.ResultsWe found alpha-diversity, i.e., mean species diversity in sites or habitats at a local scale, to be the single strongest predictor of subjects’ periodontitis status (P < 0.011). More specifically, healthy subjects had the highest alpha-diversity, while subjects with stable sites had the lowest alpha-diversity. From these results, we developed an alpha-diversity logistic model-based naive classifier able to perfectly predict the disease status of the seven subjects with unknown periodontal status (not used in training). Phylogenetic profiling resulted in the discovery of nine marker microbes, and these species are able to differentiate between stable and progressing periodontitis, achieving an accuracy of 94.4%. Finally, we found that the reduction of negatively correlated species is a notable signature of disease progression.ConclusionsOur results consistently show a strong association between the loss of oral microbiota diversity and the progression of periodontitis, suggesting that metagenomics sequencing and phylogenetic profiling are predictive of early periodontitis, leading to potential therapeutic intervention. Our results also support a keystone pathogen-mediated polymicrobial synergy and dysbiosis (PSD) model to explain the etiology of periodontitis. Apart from P. gingivalis, we identified three additional keystone species potentially mediating the progression of periodontitis progression based on pathogenic characteristics similar to those of known keystone pathogens.

Inference of Environmental Factor-Microbe and Microbe-Microbe Associations from Metagenomic Data Using a Hierarchical Bayesian Statistical Model

The inference of associations between environmental factors and microbes and among microbes is critical to interpreting metagenomic data, but compositional bias, indirect associations resulting from common factors, and variance within metagenomic sequencing data limit the discovery of associations. To account for these problems, we propose metagenomic Lognormal-Dirichlet-Multinomial (mLDM), a hierarchical Bayesian model with sparsity constraints, to estimate absolute microbial abundance and simultaneously infer both conditionally dependent associations among microbes and direct associations between microbes and environmental factors. We empirically show the effectiveness of the mLDM model using synthetic data, data from the TARA Oceans project, and a colorectal cancer dataset. Finally, we apply mLDM to 16S sequencing data from the western English Channel and report several associations. Our model can be used on both natural environmental and human metagenomic datasets, promoting the understanding of associations in the microbial community.

Reconstruction of the Metabolic Potential of Acidophilic Sideroxydans Strains from the Metagenome of an Microaerophilic Enrichment Culture of Acidophilic Iron-Oxidizing Bacteria from a Pilot Plant for the Treatment of Acid Mine Drainage Reveals Metabolic Versatility and Adaptation to Life at Low pH.

Bacterial community analyses of samples from a pilot plant for the treatment of acid mine drainage (AMD) from the lignite-mining district in Lusatia (East Germany) had previously demonstrated the dominance of two groups of acidophilic iron oxidizers: the novel candidate genus “Ferrovum” and a group comprising Gallionella-like strains. Since pure culture had proven difficult, previous studies have used genome analyses of co-cultures consisting of “Ferrovum” and a strain of the heterotrophic acidophile Acidiphilium in order to obtain insight into the life style of these novel bacteria. Here we report on attempts to undertake a similar study on Gallionella-like acidophiles from AMD. Isolates belonging to the family Gallionellaceae are still restricted to the microaerophilic and neutrophilic iron oxidizers Sideroxydans and Gallionella. Availability of genomic or metagenomic sequence data of acidophilic strains of these genera should, therefore, be relevant for defining adaptive strategies in pH homeostasis. This is particularly the case since complete genome sequences of the neutrophilic strains G. capsiferriformans ES-2 and S. lithotrophicus ES-1 permit the direct comparison of the metabolic capacity of neutrophilic and acidophilic members of the same genus and, thus, the detection of biochemical features that are specific to acidophilic strains to support life under acidic conditions. Isolation attempts undertaken in this study resulted in the microaerophilic enrichment culture ADE-12-1 which, based on 16S rRNA gene sequence analysis, consisted of at least three to four distinct Gallionellaceae strains that appear to be closely related to the neutrophilic iron oxidizer S. lithotrophicus ES-1. Key hypotheses inferred from the metabolic reconstruction of the metagenomic sequence data of these acidophilic Sideroxydans strains include the putative role of urea hydrolysis, formate oxidation and cyanophycin decarboxylation in pH homeostasis.

Strain-Level Discrimination of Shiga Toxin-Producing Escherichia coli in Spinach Using Metagenomic Sequencing.

Consumption of fresh bagged spinach contaminated with Shiga toxin-producing Escherichia coli (STEC) has led to severe illness and death; however current culture-based methods to detect foodborne STEC are time consuming. Since not all STEC strains are considered pathogenic to humans, it is crucial to incorporate virulence characterization of STEC in the detection method. In this study, we assess the comprehensiveness of utilizing a shotgun metagenomics approach for detection and strain-level identification by spiking spinach with a variety of genomically disparate STEC strains at a low contamination level of 0.1 CFU/g. Molecular serotyping, virulence gene characterization, microbial community analysis, and E. coli core gene single nucleotide polymorphism (SNP) analysis were performed on metagenomic sequence data from enriched samples. It was determined from bacterial community analysis that E. coli, which was classified at the phylogroup level, was a major component of the population in most samples. However, in over half the samples, molecular serotyping revealed the presence of indigenous E. coli which also contributed to the percent abundance of E. coli. Despite the presence of additional E. coli strains, the serotype and virulence genes of the spiked STEC, including correct Shiga toxin subtype, were detected in 94% of the samples with a total number of reads per sample averaging 2.4 million. Variation in STEC abundance and/or detection was observed in replicate spiked samples, indicating an effect from the indigenous microbiota during enrichment. SNP analysis of the metagenomic data correctly placed the spiked STEC in a phylogeny of related strains in cases where the indigenous E. coli did not predominate in the enriched sample. Also, for these samples, our analysis demonstrates that strain-level phylogenetic resolution is possible using shotgun metagenomic data for determining the genomic relatedness of a contaminating STEC strain to other closely related E. coli.

A Graph-Centric Approach for Metagenome-Guided Peptide and Protein Identification in Metaproteomics.

Metaproteomic studies adopt the common bottom-up proteomics approach to investigate the protein composition and the dynamics of protein expression in microbial communities. When matched metagenomic and/or metatranscriptomic data of the microbial communities are available, metaproteomic data analyses often employ a metagenome-guided approach, in which complete or fragmental protein-coding genes are first directly predicted from metagenomic (and/or metatranscriptomic) sequences or from their assemblies, and the resulting protein sequences are then used as the reference database for peptide/protein identification from MS/MS spectra. This approach is often limited because protein coding genes predicted from metagenomes are incomplete and fragmental. In this paper, we present a graph-centric approach to improving metagenome-guided peptide and protein identification in metaproteomics. Our method exploits the de Bruijn graph structure reported by metagenome assembly algorithms to generate a comprehensive database of protein sequences encoded in the community. We tested our method using several public metaproteomic datasets with matched metagenomic and metatranscriptomic sequencing data acquired from complex microbial communities in a biological wastewater treatment plant. The results showed that many more peptides and proteins can be identified when assembly graphs were utilized, improving the characterization of the proteins expressed in the microbial communities. The additional proteins we identified contribute to the characterization of important pathways such as those involved in degradation of chemical hazards. Our tools are released as open-source software on github at https://github.com/COL-IU/Graph2Pro.

Refined experimental annotation reveals conserved corrinoid autotrophy in chloroform-respiring Dehalobacter isolates.

Two novel chlorinated alkane-respiring Dehalobacter restrictus strains CF and DCA were isolated from the same enrichment culture, ACT-3, and characterized. The closed genomes of these highly similar sister strains were previously assembled from metagenomic sequence data and annotated. The isolation of the strains enabled experimental verification of predicted annotations, particularly focusing on irregularities or predicted gaps in central metabolic pathways and cofactor biosynthesis. Similar to D. restrictus strain PER-K23, strains CF and DCA require arginine, histidine and threonine for growth, although the corresponding biosynthesis pathways are predicted to be functional. Using strain CF to experimentally verify annotations, we determined that the predicted defective serine biosynthesis pathway can be rescued with a promiscuous serine hydroxymethyltransferase. Strain CF grew without added thiamine although the thiamine biosynthesis pathway is predicted to be absent; intracellular thiamine diphosphate, the cofactor of carboxylases in central metabolism, was not detected in cell extracts. Thus, strain CF may use amino acids to replenish central metabolites, portending entangled metabolite exchanges in ACT-3. Consistent with annotation, strain CF possesses a functional corrinoid biosynthesis pathway, demonstrated by increasing corrinoid content during growth and guided cobalamin biosynthesis in corrinoid-free medium. Chloroform toxicity to corrinoid-producing methanogens and acetogens may drive the conservation of corrinoid autotrophy in Dehalobacter strains. Heme detection in strain CF cell extracts suggests the 'archaeal' heme biosynthesis pathway also functions in anaerobic Firmicutes. This study reinforces the importance of incorporating enzyme promiscuity and cofactor availability in genome-scale functional predictions and identifies essential nutrient interdependencies in anaerobic dechlorinating microbial communities.