Evidence map›Paper›PMID 42650082›Full record

ArticleGenes2026

Integrated Gene-Metabolite Network Analysis Identifies Pathways Associated with Immune Regulation and Metabolic Remodeling in Subfertile Beef Heifers.

Priyanka Banerjee, Rachel Phillips, Anna G Holliman, Soren P Rodning, Wellison J S Diniz, Paul W Dyce

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genes, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Priyanka BanerjeeDepartment of Animal Sciences, College of Agriculture, Auburn University, Auburn, AL 36849, USA.ORCID 0000-0001-5054-3095
Rachel PhillipsDepartment of Animal Sciences, College of Agriculture, Auburn University, Auburn, AL 36849, USA.ORCID 0009-0000-2177-1355
Anna G HollimanDepartment of Animal Sciences, College of Agriculture, Auburn University, Auburn, AL 36849, USA.
Soren P RodningDepartment of Animal Sciences, College of Agriculture, Auburn University, Auburn, AL 36849, USA.ORCID 0000-0003-2128-9512
Wellison J S DinizDepartment of Animal Sciences, College of Agriculture, Auburn University, Auburn, AL 36849, USA.ORCID 0000-0003-1082-5535
Paul W DyceDepartment of Animal Sciences, College of Agriculture, Auburn University, Auburn, AL 36849, USA.ORCID 0000-0002-4574-3321

Funding

Agricultural Research Service 58-6010-1-005Foundation for Food and Agriculture Research FF-NIA19-0000000048
6 · The paper itself

Abstract

BACKGROUND/

objectivesReproductive inefficiency remains a major contributor to heifer culling and reduced herd longevity in beef systems. This study examined the molecular basis of fertility by analyzing granulosa cells and follicular fluid from Angus-Simmental crossbred heifers classified as fertile or subfertile.

methodsGranulosa cells and follicular fluid were collected for RNA sequencing and metabolomic analysis. Differential expression, network, and gene-metabolite integration analyses identified genes, metabolites, and pathways associated with fertility differences between groups.

resultsWe identified 90 differentially expressed genes from the granulosa cells, including

conclusionsThese signatures provide novel targets and pathways underlying beef heifer fertility.

Indexed as

Gene Regulatory NetworksInfertility, FemaleMetabolomeAnimalsCattleFemaleFertilityFollicular FluidGene Expression RegulationGranulosa CellsMetabolic Networks and Pathwaysbeef heifergene–metabolite integrationmetabolomenetwork analysisreproductive efficiencysubfertilitytranscriptome

Identifiers

PMID42650082
PMCPMC13512160

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.