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Record W4413439842 · doi:10.1016/j.jmb.2025.169403

Impact of Horizontal Gene Transfer on Adaptations to Extreme Environments

2025· review· en· W4413439842 on OpenAlexaff
Olga Zhaxybayeva, Camilla Nesbø

Bibliographic record

VenueJournal of Molecular Biology · 2025
Typereview
Languageen
FieldEnvironmental Science
TopicPolar Research and Ecology
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHorizontal gene transferExtreme environmentGene transferGeneEvolutionary biologyBiologyGeneticsComputational biologyGeographyPhylogenetics

Abstract

fetched live from OpenAlex

Horizontal (or lateral) gene transfer - an acquisition of genetic material not associated with the organismal reproduction - is known to alter genomes of most, if not all, living organisms. There is mounting evidence for the importance of gene exchange in organismal adaptations to new or changing environmental conditions. In comparison to accumulation of de novo mutations, acquisition of a gene already beneficial in the environment is fast and less costly, and thus an advantageous, way to adjust to survival and growth in new conditions. Adaptation to extreme environments at the boundaries of habitat conditions beyond which cellular integrity, metabolism and growth are not possible, is not an exception. Here we review the impact of horizontal gene transfer on organismal adaptations to natural and human-made extreme environments. This includes thermophiles living at high temperatures, psychrophiles found at low temperatures, acidophiles inhabiting high acidity environments, alkaliphiles thriving at high pH, halophiles found in high salt environments, xerophiles that can tolerate extremely low water availability, oligotrophes thriving at low nutrient availability, piezophiles inhabiting high pressure environments, and organisms that can withstand high levels of ionizing radiation. We also discuss the challenges and future directions for deciphering genetic determinants and horizontal gene transfer events of extremophiles' adaptations.

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 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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.977
Threshold uncertainty score0.875

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.043
GPT teacher head0.364
Teacher spread0.322 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations7
Published2025
Admission routes1
Has abstractyes

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