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Record W2944076458 · doi:10.3389/fmicb.2019.00888

Identification of Resistance Genes and Response to Arsenic in Rhodococcus aetherivorans BCP1

2019· article· en· W2944076458 on OpenAlexaff
Andrea Firrincieli, Alessandro Presentato, Giusi Favoino, Rosita Marabottini, Enrica Allevato, Silvia Rita Stazi, Giuseppe Scarascia Mugnozza, Antoine Harfouche, Maurizio Petruccioli, Raymond J. Turner, Davide Zannoni, Martina Cappelletti

Bibliographic record

VenueFrontiers in Microbiology · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsUniversity of Calgary
FundersUniversità di BolognaMinistero dell’Istruzione, dell’Università e della RicercaUniversità degli Studi della Tuscia
KeywordsRhodococcusArsenateArsenicActinobacteriaThioredoxin reductaseBiologyGeneArseniteBacteriaBiochemistryGlutaredoxinStreptomycesMicrobiologyGeneticsThioredoxinChemistry16S ribosomal RNA

Abstract

fetched live from OpenAlex

Arsenic (As) ranks among the priority metal(loid)s that are of public health concern. In the environment, arsenic is present in different forms, organic or inorganic, featured by various toxicity levels. Bacteria have developed different strategies to deal with this toxicity involving different resistance genetic determinants. Bacterial strains of Rhodococcus genus, and more in general Actinobacteria phylum, have the ability to cope with high concentrations of toxic metalloids, although little is known on the molecular and genetic bases of these metabolic features. Here we show that Rhodococcus aetherivorans BCP1, an extremophilic actinobacterial strain able to tolerate high concentrations of organic solvents and toxic metalloids, can grow in the presence of high concentrations of As(V) (> 240 mM) under aerobic growth conditions using glucose as sole carbon and energy source. Notably, by increasing the As(V) concentration in the growth medium, BCP1 cells improved their growth performance as well as their capacity of reducing As(V) into As(III). Genomic analysis of BCP1 compared to other actinobacterial strains revealed the presence of three gene clusters responsible for organic and inorganic arsenic resistance. In particular, two adjacent and divergently oriented ars gene clusters include three arsenate reductase genes (arsC1/2/3) involved in resistance mechanisms against As(V). A sequence similarity network and phylogenetic analysis of these arsenate reductase genes indicated that two of them (ArsC2/3) are functionally related to thioredoxin (Trx)/thioredoxin reductase (TrxR)-dependent class and one of them (ArsC1) to the mycothiol (MSH)/mycoredoxin (Mrx)-dependent class. A targeted transcriptomic analysis performed by RT-qPCR indicated that the arsenate reductase genes as well as other genes included in the ars gene cluster (possible regulator gene, arsR, and arsenite extrusion genes, arsA, acr3 and arsD) are transcriptionally induced when BCP1 cells were exposed to As(V) supplied at two different sub-lethal concentrations. This work provides for the first time insights into the arsenic resistance mechanisms of a Rhodococcus strain, revealing some of the unique metabolic requirements for the environmental persistence of this bacterial genus and its possible use in bioremediation procedures of toxic metal 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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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

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 head0.003
GPT teacher head0.208
Teacher spread0.204 · 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 designBench or experimental
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

Citations241
Published2019
Admission routes1
Has abstractyes

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