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Record W2990232101 · doi:10.1186/s13059-019-1866-1

547 transcriptomes from 44 brain areas reveal features of the aging brain in non-human primates

2019· article· en· W2990232101 on OpenAlexaff
Mingli Li, Shihao Wu, Jin-Jin Zhang, Hang-Yu Tian, Yong Shao, Zhengbo Wang, David M. Irwin, Jiali Li, Xintian Hu, Dongdong Wu

Bibliographic record

VenueGenome biology · 2019
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsUniversity of Toronto
FundersChinese Academy of SciencesKunming Institute of Zoology, Chinese Academy of SciencesInstitute of Zoology, Chinese Academy of SciencesNational Natural Science Foundation of China
KeywordsBiologyHuman brainTranscriptomeNeuroscienceAlternative splicingAging brainBrain agingGene expressionGeneCerebral cortexRNA splicingEvolutionary biologyGeneticsCognitionMessenger RNARNA

Abstract

fetched live from OpenAlex

BACKGROUND: Brain aging is a complex process that depends on the precise regulation of multiple brain regions; however, the underlying molecular mechanisms behind this process remain to be clarified in non-human primates. RESULTS: Here, we explore non-human primate brain aging using 547 transcriptomes originating from 44 brain areas in rhesus macaques (Macaca mulatta). We show that expression connectivity between pairs of cerebral cortex areas as well as expression symmetry between the left and right hemispheres both decrease after aging. Although the aging mechanisms across different brain areas are largely convergent, changes in gene expression and alternative splicing vary at diverse genes, reinforcing the complex multifactorial basis of aging. Through gene co-expression network analysis, we identify nine modules that exhibit gain of connectivity in the aged brain and uncovered a hub gene, PGLS, underlying brain aging. We further confirm the functional significance of PGLS in mice at the gene transcription, molecular, and behavioral levels. CONCLUSIONS: Taken together, our study provides comprehensive transcriptomes on multiple brain regions in non-human primates and provides novel insights into the molecular mechanism of healthy brain aging.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
Threshold uncertainty score0.496

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.018
GPT teacher head0.274
Teacher spread0.256 · 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 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

Citations36
Published2019
Admission routes1
Has abstractyes

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