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Record W4386168871 · doi:10.3390/antibiotics12091367

Horizontal Gene Transfer and Drug Resistance Involving Mycobacterium tuberculosis

2023· article· en· W4386168871 on OpenAlex
Xuhua Xia

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAntibiotics · 2023
Typearticle
Languageen
FieldMedicine
TopicMycobacterium research and diagnosis
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHorizontal gene transferGeneBiologyGenetics23S ribosomal RNAGenomeMycobacterium tuberculosisInsertion sequenceTransposable elementRNATuberculosisRibosome

Abstract

fetched live from OpenAlex

Mycobacterium tuberculosis (Mtb) acquires drug resistance at a rate comparable to that of bacterial pathogens that replicate much faster and have a higher mutation rate. One explanation for this rapid acquisition of drug resistance in Mtb is that drug resistance may evolve in other fast-replicating mycobacteria and then be transferred to Mtb through horizontal gene transfer (HGT). This paper aims to address three questions. First, does HGT occur between Mtb and other mycobacterial species? Second, what genes after HGT tend to survive in the recipient genome? Third, does HGT contribute to antibiotic resistance in Mtb? I present a conceptual framework for detecting HGT and analyze 39 ribosomal protein genes, 23S and 16S ribosomal RNA genes, as well as several genes targeted by antibiotics against Mtb, from 43 genomes representing all major groups within Mycobacterium. I also included mgtC and the insertion sequence IS6110 that were previously reported to be involved in HGT. The insertion sequence IS6110 shows clearly that the Mtb complex participates in HGT. However, the horizontal transferability of genes depends on gene function, as was previously hypothesized. HGT is not observed in functionally important genes such as ribosomal protein genes, rRNA genes, and other genes chosen as drug targets. This pattern can be explained by differential selection against functionally important and unimportant genes after HGT. Functionally unimportant genes such as IS6110 are not strongly selected against, so HGT events involving such genes are visible. For functionally important genes, a horizontally transferred diverged homologue from a different species may not work as well as the native counterpart, so the HGT event involving such genes is strongly selected against and eliminated, rendering them invisible to us. In short, while HGT involving the Mtb complex occurs, antibiotic resistance in the Mtb complex arose from mutations in those drug-targeted genes within the Mtb complex and was not gained through HGT.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.263
Threshold uncertainty score0.630

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.266
Teacher spread0.250 · 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