Bat genomes illuminate adaptations to viral tolerance and disease resistance
Bibliographic record
Abstract
Zoonoses are infectious diseases transmitted from animals to humans. Bats have been suggested to harbour more zoonotic viruses than any other mammalian order1. Infections in bats are largely asymptomatic2,3, indicating limited tissue-damaging inflammation and immunopathology. To investigate the genomic basis of disease resistance, the Bat1K project generated reference-quality genomes of ten bat species, including potential viral reservoirs. Here we describe a systematic analysis covering 115 mammalian genomes that revealed that signatures of selection in immune genes are more prevalent in bats than in other mammalian orders. We found an excess of immune gene adaptations in the ancestral chiropteran branch and in many descending bat lineages, highlighting viral entry and detection factors, and regulators of antiviral and inflammatory responses. ISG15, which is an antiviral gene contributing to hyperinflammation during COVID-19 (refs. 4,5), exhibits key residue changes in rhinolophid and hipposiderid bats. Cellular infection experiments show species-specific antiviral differences and an essential role of protein conjugation in antiviral function of bat ISG15, separate from its role in secretion and inflammation in humans. Furthermore, in contrast to humans, ISG15 in most rhinolophid and hipposiderid bats has strong anti-SARS-CoV-2 activity. Our work reveals molecular mechanisms that contribute to viral tolerance and disease resistance in bats. A systematic analysis of 115 mammalian genomes, including 10 new bat genomes, reveals prevalent positive selection in immune genes in bats and shows key adaptations in the antiviral gene ISG15 that aid disease resistance in bats, including to coronaviruses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".