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      Anaerobic Microbial Metabolism of Dichloroacetate

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          Abstract

          Dichloroacetate (DCA) is ubiquitous in the environment due to natural formation via biological and abiotic chlorination processes and the turnover of chlorinated organic materials (e.g., humic substances). Additional sources include DCA usage as a chemical feedstock and cancer drug and its unintentional formation during drinking water disinfection by chlorination.

          ABSTRACT

          Dichloroacetate (DCA) commonly occurs in the environment due to natural production and anthropogenic releases, but its fate under anoxic conditions is uncertain. Mixed culture RM comprising “ Candidatus Dichloromethanomonas elyunquensis” strain RM utilizes DCA as an energy source, and the transient formation of formate, H 2, and carbon monoxide (CO) was observed during growth. Only about half of the DCA was recovered as acetate, suggesting a fermentative catabolic route rather than a reductive dechlorination pathway. Sequencing of 16S rRNA gene amplicons and 16S rRNA gene-targeted quantitative real-time PCR (qPCR) implicated “ Candidatus Dichloromethanomonas elyunquensis” strain RM in DCA degradation. An ( S)-2-haloacid dehalogenase (HAD) encoded on the genome of strain RM was heterologously expressed, and the purified HAD demonstrated the cofactor-independent stoichiometric conversion of DCA to glyoxylate at a rate of 90 ± 4.6 nkat mg −1 protein. Differential protein expression analysis identified enzymes catalyzing the conversion of DCA to acetyl coenzyme A (acetyl-CoA) via glyoxylate as well as enzymes of the Wood-Ljungdahl pathway. Glyoxylate carboligase, which catalyzes the condensation of two molecules of glyoxylate to form tartronate semialdehyde, was highly abundant in DCA-grown cells. The physiological, biochemical, and proteogenomic data demonstrate the involvement of an HAD and the Wood-Ljungdahl pathway in the anaerobic fermentation of DCA, which has implications for DCA turnover in natural and engineered environments, as well as the metabolism of the cancer drug DCA by gut microbiota.

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          QIIME allows analysis of high-throughput community sequencing data.

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            Search and clustering orders of magnitude faster than BLAST.

            Biological sequence data is accumulating rapidly, motivating the development of improved high-throughput methods for sequence classification. UBLAST and USEARCH are new algorithms enabling sensitive local and global search of large sequence databases at exceptionally high speeds. They are often orders of magnitude faster than BLAST in practical applications, though sensitivity to distant protein relationships is lower. UCLUST is a new clustering method that exploits USEARCH to assign sequences to clusters. UCLUST offers several advantages over the widely used program CD-HIT, including higher speed, lower memory use, improved sensitivity, clustering at lower identities and classification of much larger datasets. Binaries are available at no charge for non-commercial use at http://www.drive5.com/usearch.
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              Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample.

              The ongoing revolution in high-throughput sequencing continues to democratize the ability of small groups of investigators to map the microbial component of the biosphere. In particular, the coevolution of new sequencing platforms and new software tools allows data acquisition and analysis on an unprecedented scale. Here we report the next stage in this coevolutionary arms race, using the Illumina GAIIx platform to sequence a diverse array of 25 environmental samples and three known "mock communities" at a depth averaging 3.1 million reads per sample. We demonstrate excellent consistency in taxonomic recovery and recapture diversity patterns that were previously reported on the basis of metaanalysis of many studies from the literature (notably, the saline/nonsaline split in environmental samples and the split between host-associated and free-living communities). We also demonstrate that 2,000 Illumina single-end reads are sufficient to recapture the same relationships among samples that we observe with the full dataset. The results thus open up the possibility of conducting large-scale studies analyzing thousands of samples simultaneously to survey microbial communities at an unprecedented spatial and temporal resolution.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                mBio
                mBio
                mbio
                mbio
                mBio
                mBio
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                2150-7511
                27 April 2021
                Mar-Apr 2021
                : 12
                : 2
                : e00537-21
                Affiliations
                [a ]Center for Environmental Biotechnology, University of Tennessee, Knoxville, Tennessee, USA
                [b ]Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, Tennessee, USA
                [c ]Department of Microbiology, University of Tennessee, Knoxville, Tennessee, USA
                [d ]Department of Biosystems Engineering & Soil Science, University of Tennessee, Knoxville, Tennessee, USA
                [e ]Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, Tennessee, USA
                [f ]Genome Science and Technology, University of Tennessee, Knoxville, Tennessee, USA
                [g ]University of Tennessee and Oak Ridge National Laboratory (UT-ORNL) Joint Institute for Biological Sciences (JIBS), Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
                [h ]Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
                University of Massachusetts Amherst
                Author notes
                Address correspondence to Frank E. Löffler, frank.loeffler@ 123456utk.edu .
                Author information
                https://orcid.org/0000-0003-3375-4805
                https://orcid.org/0000-0001-7708-786X
                https://orcid.org/0000-0002-9797-4279
                Article
                mBio00537-21
                10.1128/mBio.00537-21
                8092247
                33906923
                8599ea39-eaa4-4636-9f2e-dac388491daf
                Copyright © 2021 Chen et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

                History
                : 11 March 2021
                : 17 March 2021
                Page count
                supplementary-material: 10, Figures: 6, Tables: 0, Equations: 3, References: 89, Pages: 16, Words: 10116
                Categories
                Research Article
                Custom metadata
                March/April 2021

                Life sciences
                dichloroacetate,haloacid dehalogenase,fermentation,comparative proteomics,anaerobic catabolic pathways

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