New insights into Dehalococcoides mccartyi metabolism from a reconstructed metabolic network-based systems-level analysis of D. mccartyi transcriptomes

Transcriptional regulation of organohalide pollutant utilisation in bacteria

Organohalides are organic molecules formed biotically and abiotically, both naturally and through industrial production. They are usually toxic and represent a health risk for living organisms, including humans. Bacteria capable of degrading organohalides for growth express dehalogenase genes encoding enzymes that cleave carbon-halogen bonds. Such bacteria are of potential high interest for bioremediation of contaminated sites. Dehalogenase genes are often part of gene clusters that may include regulators, accessory genes and genes for transporters and other enzymes of organohalide degradation pathways. Organohalides and their degradation products affect the activity of regulatory factors, and extensive genome-wide modulation of gene expression helps dehalogenating bacteria to cope with stresses associated with dehalogenation, such as intracellular increase of halides, dehalogenase-dependent acid production, organohalide toxicity and misrouting and bottlenecks in metabolic fluxes. This review focuses on transcriptional regulation of gene clusters for dehalogenation in bacteria, as studied in laboratory experiments and in situ. The diversity in gene content, organization and regulation of such gene clusters is highlighted for representative organohalide-degrading bacteria. Selected examples illustrate a key, overlooked role of regulatory processes, often strain-specific, for efficient dehalogenation and productive growth in presence of organohalides.

Establishing the relationship between molecular biomarkers and biotransformation rates: Extension of knowledge for dechlorination of polychlorinated dibenzo-p-dioxins and furans (PCDD/Fs)

Anaerobic reductive treatment technologies offer cost-effective and large-scale treatment of chlorinated compounds, including polychlorinated dibenzo-p-dioxins and furans (PCDD/Fs). The information about the degradation rates of these compounds in natural settings is critical but difficult to obtain because of slow degradation processes. Establishing a relationship between biotransformation rate and abundance of biomarkers is one of the most critical challenges faced by the bioremediation industry. When solved for a given contaminant, it may result in significant cost savings because of serving as a basis for action. In the current review, we have summarized the studies highlighting the use of biomarkers, particularly DNA and RNA, as a proxy for reductive dechlorination of chlorinated ethenes. As the use of biomarkers for predicting biotransformation rates has not yet been executed for PCDD/Fs, we propose the extension of the same knowledge for dioxins, where slow degradation rates further necessitate the need for developing the biomarker-rate relationship. For this, we have first retrieved and calculated the bioremediation rates of different PCDD/Fs and then highlighted the key sequences that can be used as potential biomarkers. We have also discussed the implications and hurdles in developing such a relationship. Improvements in current techniques and collaboration with some other fields, such as biokinetic modeling, can improve the predictive capability of the biomarkers so that they can be used for effectively predicting biotransformation rates of dioxins and related compounds. In the future, a valid and established relationship between biomarkers and biotransformation rates of dioxin may result in significant cost savings, whilst also serving as a basis for action.

Regulation of organohalide respiration

Organohalide respiration (OHR) is an anaerobic metabolism by which bacteria conserve energy with the use of halogenated compounds as terminal electron acceptors. Genes involved in OHR are organized in reductive dehalogenase (rdh) gene clusters and can be found in relatively high copy numbers in the genomes of organohalide-respiring bacteria (OHRB). The minimal rdh gene set is composed by rdhA and rdhB, encoding the catalytic enzyme involved in reductive dehalogenation and its putative membrane anchor, respectively. In this chapter, we present the major findings concerning the regulatory strategies developed by OHRB to control the expression of the rdh gene clusters. The first section focuses on the description of regulation patterns obtained from targeted transcriptional analyses, and from transcriptomic and proteomic studies, while the second section offers a detailed overview of the biochemically characterized OHR regulatory proteins identified so far. Depending on OHRB, transcriptional regulators belonging to three different protein families are found in the direct vicinity of rdh gene clusters, suggesting that they activate the transcription of their cognate gene cluster. In this chapter, strong emphasis was laid on the family of CRP/FNR-type RdhK regulators which belong to members of the genera Dehalobacter and Desulfitobacterium. Whereas only chlorophenols have been identified as effectors for RdhK regulators, the protein sequence diversity suggests a broader organohalide spectrum. Thus, effector identification of new regulators offers a promising alternative to elucidate the substrates of yet uncharacterized reductive dehalogenases. Future work investigating the possible cross-talk between OHR regulators and their possible use as biosensors is discussed.

Chlorinated Electron Acceptor Abundance Drives Selection of Dehalococcoides mccartyi (D. mccartyi) Strains in Dechlorinating Enrichment Cultures and Groundwater Environments

Dehalococcoides mccartyi (D. mccartyi) strains differ primarily from one another by the number and identity of the reductive dehalogenase homologous catalytic subunit A (rdhA) genes within their respective genomes. While multiple rdhA genes have been sequenced, the activity of the corresponding proteins has been identified in only a few cases. Examples include the enzymes whose substrates are groundwater contaminants such as trichloroethene (TCE), cis-dichloroethene (cDCE) and vinyl chloride (VC). The associated rdhA genes, namely tceA, bvcA, and vcrA, along with the D. mccartyi 16S rRNA gene are often used as biomarkers of growth in field samples. In this study, we monitored an additional 12 uncharacterized rdhA sequences identified in the metagenome in the mixed D. mccartyi-containing culture KB-1 to monitor population shifts in more detail. Quantitative PCR (qPCR) assays were developed for 15 D. mccartyi rdhA genes and used to measure population diversity in 11 different sub-cultures of KB-1, each enriched on different chlorinated ethenes and ethanes. The proportion of rdhA gene copies relative to D. mccartyi 16S rRNA gene copies revealed the presence of multiple distinct D. mccartyi strains in each culture, many more than the two strains inferred from 16S rRNA analysis. The specific electron acceptor amended to each culture had a major influence on the distribution of D. mccartyi strains and their associated rdhA genes. We also surveyed the abundance of rdhA genes in samples from two bioaugmented field sites (Canada and United Kingdom). Growth of the dominant D. mccartyi strain in KB-1 was detected at the United Kingdom site. At both field sites, the measurement of relative rdhA abundances revealed D. mccartyi population shifts over time as dechlorination progressed from TCE through cDCE to VC and ethene. These shifts indicate a selective pressure of the most abundant chlorinated electron acceptor, as was also observed in lab cultures. These results also suggest that reductive dechlorination at contaminated sites is brought about by multiple strains of D. mccartyi whether or not the site is bioaugmented. Understanding the driving forces behind D. mccartyi population selection and activity is improving predictability of remediation performance at chlorinated solvent contaminated sites.

Elucidation of complexity and prediction of interactions in microbial communities

Microorganisms engage in complex interactions with other members of the microbial community, higher organisms as well as their environment. However, determining the exact nature of these interactions can be challenging due to the large number of members in these communities and the manifold of interactions they can engage in. Various omic data, such as 16S rRNA gene sequencing, shotgun metagenomics, metatranscriptomics, metaproteomics and metabolomics, have been deployed to unravel the community structure, interactions and resulting community dynamics in situ. Interpretation of these multi-omic data often requires advanced computational methods. Modelling approaches are powerful tools to integrate, contextualize and interpret experimental data, thus shedding light on the underlying processes shaping the microbiome. Here, we review current methods and approaches, both experimental and computational, to elucidate interactions in microbial communities and to predict their responses to perturbations.

Microbial degradation of chloroethenes: a review

Contamination by chloroethenes has a severe negative effect on both the environment and human health. This has prompted intensive remediation activity in recent years, along with research into the efficacy of natural microbial communities for degrading toxic chloroethenes into less harmful compounds. Microbial degradation of chloroethenes can take place either through anaerobic organohalide respiration, where chloroethenes serve as electron acceptors; anaerobic and aerobic metabolic degradation, where chloroethenes are used as electron donors; or anaerobic and aerobic co-metabolic degradation, with chloroethene degradation occurring as a by-product during microbial metabolism of other growth substrates, without energy or carbon benefit. Recent research has focused on optimising these natural processes to serve as effective bioremediation technologies, with particular emphasis on (a) the diversity and role of bacterial groups involved in dechlorination microbial processes, and (b) detection of bacterial enzymes and genes connected with dehalogenation activity. In this review, we summarise the different mechanisms of chloroethene bacterial degradation suitable for bioremediation and provide a list of dechlorinating bacteria. We also provide an up-to-date summary of primers available for detecting functional genes in anaerobic and aerobic bacteria degrading chloroethenes metabolically or co-metabolically.

Inferring Gene Networks for Strains of Dehalococcoides Highlights Conserved Relationships between Genes Encoding Core Catabolic and Cell-Wall Structural Proteins

The interpretation of high-throughput gene expression data for non-model microorganisms remains obscured because of the high fraction of hypothetical genes and the limited number of methods for the robust inference of gene networks. Therefore, to elucidate gene-gene and gene-condition linkages in the bioremediation-important genus Dehalococcoides, we applied a Bayesian inference strategy called Reverse Engineering/Forward Simulation (REFS^™) on transcriptomic data collected from two organohalide-respiring communities containing different Dehalococcoides mccartyi strains: the Cornell University mixed community D2 and the commercially available KB-1^® bioaugmentation culture. In total, 49 and 24 microarray datasets were included in the REFS^™ analysis to generate an ensemble of 1,000 networks for the Dehalococcoides population in the Cornell D2 and KB-1^® culture, respectively. Considering only linkages that appeared in the consensus network for each culture (exceeding the determined frequency cutoff of ≥ 60%), the resulting Cornell D2 and KB-1^® consensus networks maintained 1,105 nodes (genes or conditions) with 974 edges and 1,714 nodes with 1,455 edges, respectively. These consensus networks captured multiple strong and biologically informative relationships. One of the main highlighted relationships shared between these two cultures was a direct edge between the transcript encoding for the major reductive dehalogenase (tceA (D2) or vcrA (KB-1^®)) and the transcript for the putative S-layer cell wall protein (DET1407 (D2) or KB1_1396 (KB-1^®)). Additionally, transcripts for two key oxidoreductases (a [Ni Fe] hydrogenase, Hup, and a protein with similarity to a formate dehydrogenase, “Fdh”) were strongly linked, generalizing a strong relationship noted previously for Dehalococcoides mccartyi strain 195 to multiple strains of Dehalococcoides. Notably, the pangenome array utilized when monitoring the KB-1^® culture was capable of resolving signals from multiple strains, and the network inference engine was able to reconstruct gene networks in the distinct strain populations.

Experimental validation of in silico model-predicted isocitrate dehydrogenase and phosphomannose isomerase from Dehalococcoides mccartyi

Gene sequences annotated as proteins of unknown or non‐specific function and hypothetical proteins account for a large fraction of most genomes. In the strictly anaerobic and organohalide respiring Dehalococcoides mccartyi, this lack of annotation plagues almost half the genome. Using a combination of bioinformatics analyses and genome‐wide metabolic modelling, new or more specific annotations were proposed for about 80 of these poorly annotated genes in previous investigations of D. mccartyi metabolism. Herein, we report the experimental validation of the proposed reannotations for two such genes (KB1_0495 and KB1_0553) from D. mccartyi strains in the KB‐1 community. KB1_0495 or DmIDH was originally annotated as an NAD⁺‐dependent isocitrate dehydrogenase, but biochemical assays revealed its activity primarily with NADP⁺ as a cofactor. KB1_0553, also denoted as DmPMI, was originally annotated as a hypothetical protein/sugar isomerase domain protein. We previously proposed that it was a bifunctional phosphoglucose isomerase/phosphomannose isomerase, but only phosphomannose isomerase activity was identified and confirmed experimentally. Further bioinformatics analyses of these two protein sequences suggest their affiliation to potentially novel enzyme families within their respective larger enzyme super families.