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Record W2803405459 · doi:10.3389/fmicb.2018.00812

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

2018· article· en· W2803405459 on OpenAlexafffund
Alfredo Pérez‐de‐Mora, Anna Lacourt, Michaye L. McMaster, Xiaoming Liang, Sandra Dworatzek, Elizabeth A. Edwards

Bibliographic record

VenueFrontiers in Microbiology · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Treatment and Disinfection
Canadian institutionsUniversity of Toronto
FundersFP7 People: Marie-Curie ActionsStrategic Environmental Research and Development ProgramNatural Sciences and Engineering Research Council of CanadaEuropean CommissionGovernment of OntarioSustainable Development Technology CanadaGenome CanadaOntario GenomicsLOEWE Zentrum AdRIAOntario Genomics InstituteU.S. Department of Defense
KeywordsAbundance (ecology)BiologyDehalococcoidesElectron acceptorMicrobiologyEcologyChemistryBiochemistry

Abstract

fetched live from OpenAlex

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 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 UK). Growth of the dominant D. mccartyi strain in KB-1 was detected at the UK 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.221
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations29
Published2018
Admission routes2
Has abstractyes

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