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Record W4221059947 · doi:10.1126/sciadv.abm6494

Gene losses in the common vampire bat illuminate molecular adaptations to blood feeding

2022· article· en· W4221059947 on OpenAlexaff
Moritz Blumer, Tom Brown, Mariella Bontempo Freitas, Ana Luiza Fonseca Destro, Juraci Alves de Oliveira, Ariadna E. Morales, Tilman Schell, Carola Greve, Martin Pippel, David Jebb, Nikolai Hecker, Alexis-Walid Ahmed, Bogdan Kirilenko, Maddy Foote, Axel Janke, Burton K. Lim, Michael Hiller

Bibliographic record

VenueScience Advances · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsRoyal Ontario MuseumToronto Zoo
FundersMax-Planck-Institut für Physik Komplexer SystemeVlaamse regeringRadboud Universitair Medisch CentrumMax-Planck-GesellschaftConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsVampireDesmodus rotundusBiologyGeneGeneticsEvolutionary biologyComputer scienceVirology

Abstract

fetched live from OpenAlex

Vampire bats are the only mammals that feed exclusively on blood. To uncover genomic changes associated with this dietary adaptation, we generated a haplotype-resolved genome of the common vampire bat and screened 27 bat species for genes that were specifically lost in the vampire bat lineage. We found previously unknown gene losses that relate to reduced insulin secretion ( FFAR1 and SLC30A8 ), limited glycogen stores ( PPP1R3E ), and a unique gastric physiology ( CTSE ). Other gene losses likely reflect the biased nutrient composition ( ERN2 and CTRL ) and distinct pathogen diversity of blood ( RNASE7 ) and predict the complete lack of cone-based vision in these strictly nocturnal bats ( PDE6H and PDE6C ). Notably, REP15 loss likely helped vampire bats adapt to high dietary iron levels by enhancing iron excretion, and the loss of CYP39A1 could have contributed to their exceptional cognitive abilities. These findings enhance our understanding of vampire bat biology and the genomic underpinnings of adaptations to blood feeding.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.840
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.021
GPT teacher head0.257
Teacher spread0.236 · 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.

Study designObservational
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

Citations71
Published2022
Admission routes1
Has abstractyes

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