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Record W2889750280 · doi:10.1101/422303

The molecular mechanisms underlying hidden phenotypic variation among metallo-ß-lactamases

2018· preprint· en· W2889750280 on OpenAlexafffund
Raymond D. Socha, Nobuhiko Tokuriki

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2018
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
FundersCanadian Institutes of Health ResearchMichael Smith Health Research BC
KeywordsBiologyPeriplasmic spaceGenePhenotypeGeneticsGenetic variationHorizontal gene transferGenomeEscherichia coli

Abstract

fetched live from OpenAlex

Abstract Genetic variation among orthologous genes has been largely formed through neutral genetic drift to maintain the same functional role. In some circumstances, however, this genetic variation can create critical phenotypic variation, particularly when genes are transferred to a new host by horizontal gene transfer (HGT). Unveiling “hidden phenotypic variation” through HGT is especially important for genes that confer resistance to antibiotics, which continue to disseminate to new organisms through HGT. Despite this biomedical importance, our understanding of the molecular mechanisms that underlie hidden phenotypic variation remains limited. Here we sought to determine the extent of hidden phenotypic variation in the B1 metallo-β-lactamase (MBL) family, as well as to determine its molecular basis by systematically characterizing eight MBL orthologs when they are expressed in three different organisms ( E. coli, P. aeruginosa, and K. pneumoniae ). We found that these MBLs confer diverse levels of resistance in each organism, which cannot be explained by variation in catalytic efficiency alone; rather, it is the combination of the catalytic efficiency and abundance of functional periplasmic enzyme that best predicts the observed variation in resistance. The level of functional periplasmic expression varied dramatically between MBL orthologs and between hosts. This was the result changes at multiple levels of each enzyme’s functional: 1) the quantity of mRNA; 2) the amount of MBL expressed; and 3) the efficacy of functional enzyme translocation to the periplasm. Overall, we see that it is the interaction between each gene and the host’s underlying cellular processes (transcription, translation, and translocation) that determines MBL genetic incompatibility thorough HGT. These host-specific processes may constrain the effective spread and deployment of MBLs to certain host species, and could explain the current observed distribution bias. Author Summary Orthologous genes spread among different organisms, typically maintaining the same functional role within the cell while accumulating some, presumably functionally-inert, genetic variation over time. However, these seemingly neutral gene sequence changes among orthologs can be revealed to have substantial difference in protein phenotypes, and thus, organismal fitness, when they are transferred to other host species. This so-called “hidden phenotypic variation” through horizontal gene transfer may play an important role in dissemination of antibiotic resistance genes, in particular. In this work, we systematically investigated the extent of phenotypic variation in eight orthologous antibiotic resistant genes from the metallo-β-lactamases family (MBLs), and identified the molecular causes underlying the observed phenotypic variation. We found that functional protein expression varied substantially among MBLs (causing significant variation in the level of antibiotic resistance conferred), and that this could not be explained by variation in catalytic efficiency alone. Instead, we see that functional variation is caused by multiple steps in the protein production, transcription, translation and translocation, that are necessary to provide functional enzymes in the bacterial periplasm. Thus, the successful gene transfer and dissemination of antibiotic resistance genes can be determined by complex interactions between the gene and host underlying cellular processes.

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 score0.000
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.013
GPT teacher head0.228
Teacher spread0.215 · 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

Citations0
Published2018
Admission routes2
Has abstractyes

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