PSXIII-B-12 Identifying Gene and Metabolite Signature Patterns by Mining co-Expression Networks for Fertility in Beef Heifers
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PROVIDER: S-EPMC9493603 | biostudies-literature | 2022 Sep
REPOSITORIES: biostudies-literature
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Project description:The ability of livestock to reproduce efficiently is critical to the sustainability of animal agriculture. Antral follicle count (AFC) and reproductive tract scores (RTS) can be used to estimate fertility in beef heifers, but the genetic mechanisms influencing variation in these measures are not well understood. Two genome-wide association studies (GWAS) were conducted to identify the significant loci associated with these traits. In total, 293 crossbred beef heifers were genotyped on the Bovine GGP 50K chip and genotypes were imputed to 836,121 markers. A GWAS was performed with the AFC phenotype for 217 heifers with a multi-locus mixed model, conducted using the year, age at time of sampling and principal component analysis groupings as the covariates. The RTS GWAS was performed with 289 heifers using an additive correlation/trend test comparing prepubertal to pubertal heifers. The loci on chromosomes 2, 3 and 23 were significant in the AFC GWAS and the loci on chromosomes 2, 8, 10 and 11 were significant in the RTS GWAS. The significant region on chromosome 2 was similar between both analyses. These regions contained genes associated with cell proliferation, transcription, apoptosis and development. This study proposes candidate genes for beef cattle fertility, although future research is needed to elucidate the precise mechanisms.
| S-EPMC7913221 | biostudies-literature
Project description:Reproductive failure remains a significant challenge to the beef industry. The omics technologies have provided opportunities to improve reproductive efficiency. We used a multistaged analysis from blood profiles to integrate metabolome (plasma) and transcriptome (peripheral white blood cells) in beef heifers. We used untargeted metabolomics and RNA-Seq paired data from six AI-pregnant (AI-P) and six nonpregnant (NP) Angus-Simmental crossbred heifers at artificial insemination (AI). Based on network co-expression analysis, we identified 17 and 37 hub genes in the AI-P and NP groups, respectively. Further, we identified TGM2, TMEM51, TAC3, NDRG4, and PDGFB as more connected in the NP heifers' network. The NP gene network showed a connectivity gain due to the rewiring of major regulators. The metabolomic analysis identified 18 and 15 hub metabolites in the AI-P and NP networks. Tryptophan and allantoic acid exhibited a connectivity gain in the NP and AI-P networks, respectively. The gene-metabolite integration identified tocopherol-a as positively correlated with ENSBTAG00000009943 in the AI-P group. Conversely, tocopherol-a was negatively correlated in the NP group with EXOSC2, TRNAUIAP, and SNX12. In the NP group, α-ketoglutarate-SMG8 and putrescine-HSD17B13 were positively correlated, whereas a-ketoglutarate-ALAS2 and tryptophan-MTMR1 were negatively correlated. These multiple interactions identified novel targets and pathways underlying fertility in bovines.
| S-EPMC9413342 | biostudies-literature
Project description:BackgroundInfertility 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.ResultsWe 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).ConclusionThe 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.
| S-EPMC5807776 | biostudies-literature
Project description:Chronic obstructive pulmonary disease (COPD) is a disease in which airflow obstruction in the lung makes it difficult for patients to breathe. Although COPD occurs predominantly in smokers, there are still deficits in our understanding of the additional risk factors in smokers. To gain a deeper understanding of the COPD molecular signatures, we used Sparse Multiple Canonical Correlation Network (SmCCNet), a recently developed tool that uses sparse multiple canonical correlation analysis, to integrate proteomic and metabolomic data from the blood of 1008 participants of the COPDGene study to identify novel protein-metabolite networks associated with lung function and emphysema. Our aim was to integrate -omic data through SmCCNet to build interpretable networks that could assist in the discovery of novel biomarkers that may have been overlooked in alternative biomarker discovery methods. We found a protein-metabolite network consisting of 13 proteins and 7 metabolites which had a -0.34 correlation (p-value = 2.5 × 10-28) to lung function. We also found a network of 13 proteins and 10 metabolites that had a -0.27 correlation (p-value = 2.6 × 10-17) to percent emphysema. Protein-metabolite networks can provide additional information on the progression of COPD that complements single biomarker or single -omic analyses.
| S-EPMC7241079 | biostudies-literature
Project description:BackgroundAdvances in metagenome sequencing data continue to enable new methods for analyzing biological systems. When handling microbial profile data, metagenome sequencing has proven to be far more comprehensive than traditional methods such as 16s rRNA data, which rely on partial sequences. Microbial community profiling can be used to obtain key biological insights that pave the way for more accurate understanding of complex systems that are critical for advancing biomedical research and healthcare. However, such attempts have mostly used partial or incomplete data to accurately capture those associations.MethodsThis study introduces a novel computational approach for the identification of co-occurring microbial communities using the abundance and functional roles of species-level microbiome data. The proposed approach is then used to identify signature pathways associated with inflammatory bowel disease (IBD). Furthermore, we developed a computational pipeline to identify microbial species co-occurrences from metagenome data at various granularity levels.ResultsWhen comparing the IBD group to a control group, we show that certain co-occurring communities of species are enriched for potential pathways. We also show that the identified co-occurring microbial species operate as a community to facilitate pathway enrichment.ConclusionsThe obtained findings suggest that the proposed network model, along with the computational pipeline, provide a valuable analytical tool to analyze complex biological systems and extract pathway signatures that can be used to diagnose certain health conditions.
| S-EPMC11580338 | biostudies-literature
Project description:Infertility and subfertility represent major problems in domestic animals and humans, and the majority of embryonic loss occurs during the first month of gestation that involves pregnancy recognition and conceptus implantation. The critical genes and physiological pathways in the endometrium that mediate pregnancy establishment and success are not well understood. In study one, predominantly Angus heifers were classified based on fertility using serial embryo transfer to select animals with intrinsic differences in pregnancy loss. In each of the four rounds, a single in vitro-produced, high-quality embryo was transferred into heifers on Day 7 postestrus and pregnancy was determined on Days 28 and 42 by ultrasound and then terminated. Heifers were classified based on pregnancy success as high fertile (HF), subfertile (SF), or infertile (IF). In study two, fertility-classified heifers were resynchronized and bred with semen from a single high-fertility bull. Blood samples were collected every other day from Days 0 to 36 postmating. Pregnancy rate was determined on Day 28 by ultrasound and was higher in HF (70.4%) than in heifers with low fertility (36.8%; SF and IF). Progesterone concentrations in serum during the first 20 days postestrus were not different in nonpregnant heifers and also not different in pregnant heifers among fertility groups. In study three, a single in vivo-produced embryo was transferred into fertility-classified heifers on Day 7 postestrus. The uteri were flushed on Day 14 to recover embryos, and endometrial biopsies were obtained from the ipsilateral uterine horn. Embryo recovery rate and conceptus length and area were not different among the heifer groups. RNA was sequenced from the Day 14 endometrial biopsies of pregnant HF, SF, and IF heifers (n = 5 per group) and analyzed by edgeR-robust analysis. There were 26 differentially expressed genes (DEGs) in the HF compared to SF endometrium, 12 DEGs for SF compared to IF endometrium, and three DEGs between the HF and IF endometrium. Several of the DEG-encoded proteins are involved in immune responses and are expressed in B cells. Results indicate that preimplantation conceptus survival and growth to Day 14 is not compromised in SF and IF heifers. Thus, the observed difference in capacity for pregnancy success in these fertility-classified heifers is manifest between Days 14 and 28 when pregnancy recognition signaling and conceptus elongation and implantation must occur for the establishment of pregnancy.
| S-EPMC5029478 | biostudies-literature
Project description:Although performance benefits of monensin have been extensively studied in finishing cattle, growing cattle, and dairy cows, considerably less published work is available evaluating response to monensin supplementation in cow-calf production systems. This meta-analysis investigated the impacts of monensin on performance of beef cows and developing replacement heifers. The replacement heifer analysis was conducted using data from 18 different peer-reviewed publications and experiment station reports. The mature cow analysis included 21 different publications and experiment station reports. The metaphor package (version 2.4-0; Viechtbauer, 2010) for R (version 4.0.3; www.r-project.org) was used to determine the overall effect size of monensin compared to a negative control. Each study's n, means, and SEM or P value was used to calculate the mean difference and estimate of within study variance for responses of interest. In replacement heifers, monensin treatment increased (P < 0.01); average daily gain (+0.03 ± 0.008 kg/d), feed efficiency (+0.013 ± 0.008 gain:feed), and percentage cycling before the breeding season (+15.9 ± 5.13%); while decreasing (P < 0.01): dry matter intake (0.293 ± 0.081 kg), and age at puberty (-8.9 ± 1.48 d). Six studies reporting ad libitum forage intake for mature cows showed decreased (P = 0.008) DMI by 0.85 ± 0.32 kg/d. Six studies reported milk yield and revealed an increase (P = 0.01) of 0.39 ± 0.15 kg/d when cows were supplemented with monensin. Monensin supplementation resulted in a reduction (P = 0.02) in days to first estrus by 18 ± 8.2 d and percentage of cows exhibiting estrus prior to the breeding season was increased by 19 ± 8% (P = 0.03). There were no differences in artificial insemination pregnancy nor total pregnancy for either the heifer or mature cow data sets. This analysis indicates potential for use of monensin in heifer development and beef cow production systems. Further research is needed to elucidate the effects on reproductive efficiency, DMI, milk production, weight, and body composition change.
| S-EPMC9290909 | biostudies-literature
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:Reproductive failure is still a challenge for beef producers and a significant cause of economic loss. The increased availability of transcriptomic data has shed light on the mechanisms modulating pregnancy success. Furthermore, new analytical tools, such as machine learning (ML), provide opportunities for data mining and uncovering new biological events that explain or predict reproductive outcomes. Herein, we identified potential biomarkers underlying pregnancy status and fertility-related networks by integrating gene expression profiles through ML and gene network modeling. We used public transcriptomic data from uterine luminal epithelial cells of cows retrospectively classified as pregnant (P, n = 25) and non-pregnant (NP, n = 18). First, we used a feature selection function from BioDiscML and identified SERPINE3, PDCD1, FNDC1, MRTFA, ARHGEF7, MEF2B, NAA16, ENSBTAG00000019474, and ENSBTAG00000054585 as candidate biomarker predictors of pregnancy status. Then, based on co-expression networks, we identified seven genes significantly rewired (gaining or losing connections) between the P and NP networks. These biomarkers were co-expressed with genes critical for uterine receptivity, including endometrial tissue remodeling, focal adhesion, and embryo development. We provided insights into the regulatory networks of fertility-related processes and demonstrated the potential of combining different analytical tools to prioritize candidate genes.
| S-EPMC9559512 | biostudies-literature
Project description:Nutrition imprinting carries consequences across generations. The effect of 55% vs. 65% of mature cow body weight (MBW; 545 kg) at breeding on the reproductive performance of heifers and their offspring was investigated. Angus-cross dam heifers were randomly fed to attain 55% (n = 1622) vs. 65% (n = 1578) of MBW, and offspring (F1) heifers born to dam heifers [55% (n = 1285) vs. 65% (n = 1324)] were fed to attain 65% of MBW. Bodyweight and reproductive indices were recorded throughout the study. In dam heifers, puberty (44% vs. 53%), breeding season pregnancy (86.4% vs. 90.6%) and 21-day calving rates (55.2% vs. 65.4%) did vary, but dystocia rate (8.7% vs. 9.0%) did not differ between 55% and 65% MBW groups. Puberty (49.2% vs. 58.2%), breeding season pregnancy (87.2% vs. 92.8%) and 21-day calving rates (53.8% vs. 64.1%) did differ (p < 0.05), but dystocia rate (8.4 vs. 9.2%) did not differ between F1 heifer groups. In conclusion, 55% of MBW at breeding negatively affected the reproductive performance of heifers and its offspring heifers. The recommendation is to feed heifers a balanced diet to reach 65% of MBW at breeding with consideration of production traits.
| S-EPMC8533008 | biostudies-literature