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Record W2255054576 · doi:10.1038/srep10372

Roles of small RNAs in the effects of nutrition on apoptosis and spermatogenesis in the adult testis

2015· article· en· W2255054576 on OpenAlexaff
Yongjuan Guan, Guanxiang Liang, Penelope A.R. Hawken, Irek Malecki, Greg Cozens, Philip E. Vercoe, Graeme B. Martin, Le Luo Guan

Bibliographic record

VenueScientific Reports · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsUniversity of Alberta
FundersZhejiang UniversityCommonwealth Scientific and Industrial Research Organisation
KeywordsSpermatogenesisBiologyApoptosisSertoli cellGerm cellmicroRNAAndrologyGeneGametogenesisSpermInternal medicineEndocrinologyCell biologyGeneticsEmbryogenesisMedicine

Abstract

fetched live from OpenAlex

We tested whether reductions in spermatozoal quality induced by under-nutrition are associated with increased germ cell apoptosis and disrupted spermatogenesis, and whether these effects are mediated by small RNAs. Groups of 8 male sheep were fed for a 10% increase or 10% decrease in body mass over 65 days. Underfeeding increased the number of apoptotic germ cells (P < 0.05) and increased the expression of apoptosis-related genes (P < 0.05) in testicular tissue. We identified 44 miRNAs and 35 putative piRNAs that were differentially expressed in well-fed and underfed males (FDR < 0.05). Some were related to reproductive system development, apoptosis (miRNAs), and sperm production and quality (piRNAs). Novel-miR-144 (miR-98), was found to target three apoptotic genes (TP53, CASP3, FASL). The proportion of miRNAs as a total of small RNAs was greater in well-fed males than in underfed males (P < 0.05) and was correlated (r = 0.8, P < 0.05) with the proportion of piRNAs in well-fed and underfed males. In conclusion, the reductions in spermatozoal quality induced by under-nutrition are caused, at least partly, by disruptions to Sertoli cell function and increased germ cell apoptosis, mediated by changes in the expression of miRNAs and piRNAs.

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 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 score0.015
Threshold uncertainty score0.155

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.230
Teacher spread0.220 · 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

Citations48
Published2015
Admission routes1
Has abstractyes

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