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Rewiring of gene expression in PWBC at artificial insemination is predictive of pregnancy outcome in cattle (Bos taurus)

ABSTRACT: Rewiring of gene expression in PWBC at artificial insemination is predictive of pregnancy outcome in cattle (Bos taurus)

PROVIDER: PRJNA609112 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR11194200.fastq.gz	Fastqsanger.gz
	SRR11194201.fastq.gz	Fastqsanger.gz
	SRR11194202.fastq.gz	Fastqsanger.gz
	SRR11194203.fastq.gz	Fastqsanger.gz
	SRR11194204.fastq.gz	Fastqsanger.gz

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Rewiring of gene expression in PWBC at artificial insemination is predictive of pregnancy outcome in cattle (Bos taurus)

Project description:Infertility is a disease that affects humans and cattle in similar ways. The resemblance includes complex genetic architecture, multiple etiology, low heritability of fertility related traits in females, and the frequency in the female population. Here, we used cattle as a biomedical model to test the hypothesis that gene expression profiles of protein-coding genes expressed in peripheral white blood cells (PWBCs), and circulating micro RNAs in plasma, are associated with female fertility, measured by pregnancy outcome. We drew blood samples from 17 female calves on the day of artificial insemination and analyzed transcript abundance for 10496 genes in PWBCs and 290 circulating micro RNAs. The females were later classified as pregnant to artificial insemination, pregnant to natural breeding or not pregnant. We identified 1860 genes producing significant differential coexpression (eFDR<0.002) based on pregnancy outcome. Additionally, 237 micro RNAs and 2274 genes in PWBCs presented differential coexpression based on pregnancy outcome. Furthermore, using a machine learning prediction algorithm we detected a subset of genes whose abundance could be used for blind categorization of pregnancy outcome. Our results provide strong evidence that bloodborne transcript abundance is highly associated with fertility in females.

2020-02-28 | GSE146041 | GEO

Machine learning analysis to identify endometrial transcriptomic biomarkers predictive of pregnancy success following artificial insemination in dairy cows

Project description:The objective of this research was to identify a set of genes whose transcript abundance is predictive of a cow’s ability to become pregnant following artificial insemination (AI). Endometrial epithelial cells from the uterine body were collected for RNA sequencing using the cytobrush method from 193 first-service Holstein cows at estrus prior to AI (day 0). A group of 253 first-service cows not used for cytobrush collection were controls. There was no effect of cytobrush collection on pregnancy outcomes at day 30 or 70, or on pregnancy loss between day 30 and 70. There were 2 upregulated and 214 downregulated genes (FDR < 0.05, absolute fold change > 2-fold) for cows pregnant at day 30 versus those that were not pregnant. Functional terms overrepresented in the downregulated genes included those related to immune and inflammatory responses. Machine learning for fertility biomarkers with the R package BORUTA resulted in identification of 57 biomarkers that predicted pregnancy outcome at day 30 with an average accuracy of 77%. Thus, machine learning can identify predictive biomarkers of pregnancy in endometrium with high accuracy. Moreover, sampling of endometrial epithelium using the cytobrush can help understand functional characteristics of the endometrium at AI without compromising cow fertility.

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Machine learning analysis to identify endometrial transcriptomic biomarkers predictive of pregnancy success following artificial insemination in dairy cows

Project description:Machine learning analysis to identify endometrial transcriptomic biomarkers predictive of pregnancy success following artificial insemination in dairy cows

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Sequencing of commercial boar semen use in artificial insemination

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Quantitative LC-MS study of compounds found predictive of COVID-19 severity and outcome

Project description:<h4><strong>INTRODUCTION: </strong>Since the beginning of the SARS-CoV-2 pandemic in December 2019 multiple metabolomics studies have proposed predictive biomarkers of infection severity and outcome. Whilst some trends have emerged, the findings remain intangible and uninformative when it comes to new patients.</h4><h4><strong>OBJECTIVES: </strong>In this study, we accurately quantitate a subset of compounds in patient serum that were found predictive of severity and outcome.</h4><h4><strong>METHODS: </strong>A targeted LC-MS method was used in 46 control and 95 acute COVID-19 patient samples to quantitate the selected metabolites. These compounds included tryptophan and its degradation products kynurenine and kynurenic acid (reflective of immune response), butyrylcarnitine and its isomer (reflective of energy metabolism) and finally 3’,4’-didehydro-3’-deoxycytidine, a deoxycytidine analogue, (reflective of host viral defence response). We subsequently examine changes in those markers by disease severity and outcome relative to those of control patients’ levels.</h4><h4><strong>RESULTS & CONCLUSION: </strong>Finally, we demonstrate the added value of the kynurenic acid / tryptophan ratio for severity and outcome prediction and highlight the viral detection potential of ddhC.</h4>

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Association between blood miR-26a levels following artificial insemination, and pregnancy outcome in dairy cattle.

Project description: Not available

| S-EPMC10420342 | biostudies-literature

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Effect of dietary n-3 PUFA supplementation and post-insemination plane of nutrition on global gene expression patterns in bovine uterine endometrial tissue

Project description:The objectives of this study were to examine the effect of (i) dietary supplementation with n-3 PUFA and (ii) post-insemination plane of nutrition on the global transcriptome of uterine endometrial tissue, with particular focus on key genes and pathways that may affect pregnancy outcome. A total of 60 estrous-synchronised continental crossbred heifers were offered a concentrate-based diet supplemented (n-3 PUFA; n=32) or not (Control; n=28) with an n-3 PUFA-enriched product for 30 days prior to insemination. Following insemination, animals were allocated, within treatment, to one of two post insemination diets, either remaining on their existing high plane of nutrition (Control + High (n = 14); n-3 PUFA + High (n = 15)), or fed a low plane of nutrition diet (Control + Low (n = 14); n-3 PUFA + Low (n = 17)). Uterine endometrial tissue was collected, RNA isolated and gene expression analysis conducted using RNAseq. Comparison of transcript abundance across groups, highlighted a significant effect of diet on the endometrial transcriptome. The mTOR signalling pathway as well as pathways involved in early pregnancy/fertility was significantly enriched as a consequence of diet.

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