Rewiring of gene expression in PWBC at artificial insemination is predictive of pregnancy outcome in cattle (Bos taurus)
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA609112 | ENA |
REPOSITORIES: ENA
Dataset's files
| 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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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
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.
2024-04-10 | GSE248266 | GEO
Project description:Machine learning analysis to identify endometrial transcriptomic biomarkers predictive of pregnancy success following artificial insemination in dairy cows
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Project description:Sequencing of commercial boar semen use in artificial insemination
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Project description:Early pregnancy diagnosis is key to maximise productivity of dairy herds. We previously showed that an increase in the levels of miR-26 could be detected as soon as day 8 of pregnancy in heifers. The aims of this study were to determine whether 1) plasma miR-26 levels would be distinctly elevated, retrospectively, early after artificial insemination in lactating cows with successful compared to failed pregnancies, 2) the early increase in miRNA levels in cows with successful pregnancy could be accounted for by changes in miRNA expression in white blood cells (WBCs), presumably induced by the effects of embryo-derived interferon tau (IFNt), and 3) plasma miRNA levels may provide a reliable early predictor of pregnancy that could be used at a herd level. Blood samples were taken from a total of 34 dairy cows (lactation number 1 to 4) before (D0) and 9 and 18 days after artificial insemination at oestrus, followed by confirmation of pregnancy status by ultrasound on D32. In addition, WBCs collected from non-pregnant cows (n = 4) were stimulated in vitro with recombinant ovine IFNt (0-100 pg/ml). Levels of miRNAs and ISG15, a known IFNt-induced gene, were quantified by qPCR. Relative to D0, a larger increase in plasma miR-26a (P = 0.04) occurred on D9 in cows later confirmed to be pregnant (n = 12) than in cows with a failed pregnancy (n = 22). Expression of miR-26a in WBCs was not affected (P>0.1) by pregnancy status or IFNt stimulation in vitro, in contrast to ISG15 expression which increased markedly (P<0.0001) both in WBC samples collected on D18 from animals later confirmed to be pregnant, and in WBCs after stimulation with IFNt in vitro. Finally, ROC analyses revealed that miR-26a on D9 or D18 could predict pregnancy outcome with much lower accuracy than WBC ISG15 on D18 (Likelihood ratio, 2.3 vs 15.4). In summary, a modest increase in plasma miR-26a levels occurs during early pregnancy in mature dairy cows which may not accounted for by changes in miRNA levels in WBCs or the effects of IFNt. Moreover, compared to ISG15, changes in miR-26a levels may not provide an accurate test for early diagnosis of pregnancy in cows.
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Project description:The seminal plasma (SP) is the liquid component of semen that facilitates sperm transport through the female genital tract. SP modulates the activity of the ovary, oviductal environment and uterine function during the periovulatory and early pregnancy period. Extracellular vesicles (EVs) secreted in the oviduct (oEVs) and uterus (uEVs) have been shown to influence the expression of endometrial genes that regulate fertilization and early embryo development. In some species, semen is composed of well-separated fractions that vary in concentration of spermatozoa and SP composition and volume. This study aimed to investigate the impact of different accumulative fractions of the porcine ejaculate (F1, composed of the sperm-rich fraction (SRF); F2, composed of F1 plus the intermediate fraction; F3, composed of F2 plus the post-SRF) on oEVs and uEVs protein cargo. Six days after the onset of estrus, we determined the oEVs and uEVs size and protein concentration in pregnant sows by artificial insemination (AI-sows) and in non-inseminated sows as control (C-sows). We also identified the main proteins in oEVs and uEVs, in AI-F1, AI-F2, AI-F3, and C-sows. Our results indicated that although the size of EVs is similar between AI- and C-sows, the protein concentration of both oEVs and uEVs was significantly lower in AI-sows (p < 0.05). Proteomic analysis identified 38 unique proteins in oEVs from AI-sows, mainly involved in protein stabilization, glycolytic and carbohydrate processes. The uEVs from AI-sows showed the presence of 43 unique proteins, including already-known fertility-related proteins (EZR, HSPAA901, PDS). We also demonstrated that the protein composition of oEVs and uEVs differed depending on the seminal fraction(s) inseminated (F1, F2, or F3). In conclusion, we have found a specific protein cargo in uterine and oviductal EVs depending on the type of semen fraction the sow was inseminated with, and these insemination with different seminal fractions results in the oviductal and uterine secretion of specific EVs proteins are closely associated with reproductive processes.
2024-01-26 | PXD044639 | Pride
Project description:Infertility remains the most prevalent reason for cattle being removed from production environments. We utilized metabolomic profiling to identify metabolites in the blood plasma that may be useful in identifying infertile heifers at the time of artificial insemination (AI). Prior to AI, phenotypic parameters including body condition, weight, and reproductive organ measurements were collected. These were determined not effective at differentiating between fertile and infertile heifers. Analysis of the resulting metabolomic profiles revealed 15 metabolites at significantly different levels (T-test P ≤ 0.05), with seven metabolites having a greater than 2-fold difference (T-test P ≤ 0.05, fold change ≥2, ROC-AUC ≥ 0.80) between infertile and fertile heifers. We further characterized the utility of using the levels of these metabolites in the blood plasma to discriminate between fertile and infertile heifers. Finally, we investigated the potential role inflammation may play by comparing the expression of inflammatory cytokines in the white blood cells of infertile heifers to that of fertile heifers. We found significantly higher expression in infertile heifers of the proinflammatory markers tumor necrosis factor alpha (TNFα), interleukin 6 (IL6), and the C-X-C motif chemokine 5 (CXCL5). Our work offers potentially valuable information regarding the diagnosis of fertility problems in heifers undergoing AI.
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Project description:Background: Infertility is a longstanding limitation in livestock production with important economic impact for the cattle industry. Female reproductive traits are polygenic and lowly heritable in nature, thus selection for fertility is challenging. Beef cattle operations leverage estrous synchronization in combination with artificial insemination (AI) to breed heifers and benefit from an early and uniform calving season. A couple of weeks following AI, heifers are exposed to bulls for an opportunity to become pregnant by natural breeding (NB), but they may also not become pregnant during this time period. Focusing on beef heifers, in their first breeding season, we hypothesized that: a- at the time of AI, the transcriptome of peripheral white blood cells (PWBC) differs between heifers that become pregnant to AI and heifers that become pregnant late in the breeding season by NB or do not become pregnant during the breeding season; and b- the ratio of transcript abundance between genes in PWBC classifies heifers according to pregnancy by AI, NB, or failure to become pregnant. Results: We generated RNA-sequencing data from 23 heifers from two locations (A: six AI-pregnant and five NB-pregnant; and B: six AI-pregnant and six non-pregnant). After filtering out lowly expressed genes, we quantified transcript abundance for 12,538 genes. The comparison of gene expression levels between AI-pregnant and NB-pregnant heifers yielded 18 differentially expressed genes (DEGs) (ADAM20, ALDH5A1, ANG, BOLA-DQB, DMBT1, FCER1A, GSTM3, KIR3DL1, LOC107131247, LOC618633, LYZ, MNS1, P2RY12, PPP1R1B, SIGLEC14, TPPP, TTLL1, UGT8, eFDR≤0.02). The comparison of gene expression levels between AI-pregnant and non-pregnant heifers yielded six DEGs (ALAS2, CNKSR3, LOC522763, SAXO2, TAC3, TFF2, eFDR≤0.05). We calculated the ratio of expression levels between all gene pairs and assessed their potential to classify samples according to experimental groups. Considering all samples, relative expression from two gene pairs correctly classified 10 out of 12 AI-pregnant heifers (P=0.0028) separately from the other 11 heifers (NB-pregnant, or non-pregnant). Conclusion: The transcriptome profile in PWBC, at the time of AI, is associated with the fertility potential of beef heifers. Transcript levels of specific genes may be further explored as potential classifiers, and thus selection tools, of heifer fertility.
2018-01-13 | GSE103628 | GEO
Project description:IntroductionThe low pregnancy rate by artificial insemination in sheep represents a fundamental challenge for breeding programs. In this species, oestrus synchronization is carried out by manipulating hormonal regimens through the insertion of progestogen intravaginal devices. This reproductive strategy may alter the vaginal microbiota affecting the artificial insemination outcome.MethodsIn this study, we analyzed the vaginal microbiome of 94 vaginal swabs collected from 47 ewes with alternative treatments applied to the progesterone-releasing intravaginal devices (probiotic, maltodextrin, antibiotic and control), in two sample periods (before placing and after removing the devices). To our knowledge, this is the first study using nanopore-based metagenome sequencing for vaginal microbiome characterization in livestock.ResultsOur results revealed a significant lower abundance of the genera Oenococcus (Firmicutes) and Neisseria (Proteobacteria) in pregnant compared to non-pregnant ewes. We also detected a significant lower abundance of Campylobacter in the group of samples treated with the probiotic.DiscussionAlthough the use of probiotics represents a promising practice to improve insemination results, the election of the suitable species and concentration requires further investigation. In addition, the use of progestogen in the synchronization devices seemed to increase the alpha-diversity and decrease the abundance of harmful microorganisms belonging to Gammaproteobacteria and Fusobacteriia classes, suggesting a beneficial effect of their use.
| S-EPMC10076614 | biostudies-literature
Project description:The causes of variation in the success of laparoscopic artificial insemination (AI) in sheep are not well understood. As such, this study incorporated the contributions of multiple male and female factors relevant to the success of AI into a comprehensive prediction model for pregnancy success. Data from Merino ewes (N = 30 254) including age, uterine tone (1; pale/flaccid-5; turgid/pink), intra-abdominal fat (1; little to no fat present-5; high fat), time of insemination and sire used, were recorded during AI. A subset of semen per sire (N = 388) was thawed and assessed for volume, subjective motility, sperm concentration, and morphology. Sperm motility (CASA), viability and acrosome integrity (FITC-PNA/PI), membrane fluidity (M540/Yo-Pro), mitochondrial superoxide production (Mitosox Red/Sytox Green), lipid peroxidation (Bodipy C11), level of intracellular reactive oxygen species (H2DCFDA) and DNA fragmentation (Acridine Orange) were also assessed 0, 3 and 6 h post-thaw. Logistic binomial regression revealed sperm concentration (P < 0.001), CASA parameters at 0 h (PCA3; P = 0.03), viable acrosome intact sperm at 6 h (P = 0.02), abnormal morphology (P < 0.001), uterine tone (P < 0.001) and intra-abdominal fat (P = 0.03) of ewes influenced likelihood of pregnancy. Results generated will help standardise the pre-screening and selection of semen and ewes prior to artificial breeding programs, reducing variation in the success of sheep AI.
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