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Record W3109196395 · doi:10.1101/366112

The evolution of ecological flexibility, large brains, and long lives: capuchin monkey genomics revealed with fecalFACS

2018· preprint· en· W3109196395 on OpenAlexaff
Joseph D. Orkin, Michael J. Montague, Daniela Tejada-Martínez, Marc de Manuel, Javier del Campo, Saúl Cheves Hernández, Anthony Di Fiore, Claudia Fontsere, Jason A. Hodgson, Mareike C. Janiak, Lukas F. K. Kuderna, Esther Lizano, Maria Pia Martin, Yoshihito Niimura, George H. Perry, Carmen Soto Valverde, Jia Tang, Wesley C. Warren, João Pedro de Magalhães, Shoji Kawamura, Tomàs Marquès‐Bonet, Roman Krawetz, Amanda Melin

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2018
Typepreprint
Languageen
FieldPsychology
TopicPrimate Behavior and Ecology
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
Fundersnot available
KeywordsBiologyComparative genomicsGenomicsEvolutionary biologyAdaptation (eye)GenomePopulationBrain sizeLongevityGeneGeneticsNeuroscience

Abstract

fetched live from OpenAlex

ABSTRACT Ecological flexibility, extended lifespans, and large brains, have long intrigued evolutionary biologists, and comparative genomics offers an efficient and effective tool for generating new insights into the evolution of such traits. Studies of capuchin monkeys are particularly well situated to shed light on the selective pressures and genetic underpinnings of local adaptation to diverse habitats, longevity, and brain development. Distributed widely across Central and South America, they are inventive and extractive foragers, known for their sensorimotor intelligence. Capuchins have the largest relative brain size of any monkey and a lifespan that exceeds 50 years, despite their small (3-5 kg) body size. We assemble a de novo reference genome for Cebus imitator and provide the first genome annotation of a capuchin monkey. Through high-depth sequencing of DNA derived from blood, various tissues and feces via fluorescence activated cell sorting (fecalFACS) to isolate monkey epithelial cells, we compared genomes of capuchin populations from tropical dry forests and lowland rainforests and identified population divergence in genes involved in water balance, kidney function, and metabolism. Through a comparative genomics approach spanning a wide diversity of mammals, we identified genes under positive selection associated with longevity and brain development. Additionally, we provide a technological advancement in the use of non-invasive genomics for studies of free-ranging mammals. Our intra- and interspecific comparative study of capuchin genomics provides new insights into processes underlying local adaptation to diverse and physiologically challenging environments, as well as the molecular basis of brain evolution and longevity. SIGNIFICANCE Surviving challenging environments, living long lives, and engaging in complex cognitive processes are hallmark characteristics of human evolution. Similar traits have evolved in parallel in capuchin monkeys, but their genetic underpinnings remain unexplored. We developed and annotated a reference assembly for white-faced capuchin monkeys to explore the evolution of these phenotypes. By comparing populations of capuchins inhabiting rainforest versus dry forests with seasonal droughts, we detected selection in genes associated with kidney function, muscular wasting, and metabolism, suggesting adaptation to periodic resource scarcity. When comparing capuchins to other mammals, we identified evidence of selection in multiple genes implicated in longevity and brain development. Our research was facilitated by our new method to generate high- and low-coverage genomes from non-invasive biomaterials.

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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.021
GPT teacher head0.267
Teacher spread0.246 · 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

Citations7
Published2018
Admission routes1
Has abstractyes

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