Dataset Information

Transcriptome profiles in peripheral white blood cells at the time of artificial insemination discriminate beef heifers with different fertility potential.

ABSTRACT: 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.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).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.

SUBMITTER: Dickinson SE

PROVIDER: S-EPMC5807776 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Transcriptome profiles in peripheral white blood cells at the time of artificial insemination discriminate beef heifers with different fertility potential.

Dickinson Sarah E SE Griffin Brock A BA Elmore Michelle F MF Kriese-Anderson Lisa L Elmore Joshua B JB Dyce Paul W PW Rodning Soren P SP Biase Fernando H FH

BMC genomics 20180209 1

<h4>Background</h4>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 ...[more]

PMID: 29426285

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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.

Project description:Reproductive failure of replacement breeding animals is one of the leading causes of loss to the beef production industry. The losses are further increased due to the inability to diagnose the reproductive potential of the beef heifer prior to the breeding season until the pregnancy outcome. To overcome this problem, a system to discriminate beef heifers with varying reproductive potential as early and accurately as possible is demanded. The omics technologies, such as transcriptomics, could predict the future reproductive potential of beef heifers. Therefore, this manuscript provides the gene expression profile dataset using RNA-Seq identified from peripheral white blood cells (PWBC) of beef heifers at weaning. To accomplish this, the blood samples were collected at the time of weaning, processed to extract the PWBC pellet and stored at – 80 °C until further processing. After the breeding protocol (artificial insemination (AI) followed by natural bull service) and pregnancy diagnosis, the heifers that were pregnant to AI (n = 8) or remained open (n = 7) were utilized for this study. Total RNA was extracted from PWBC collected at the time of weaning from these samples and subjected to sequencing using the Illumina Nova-Seq platform. High-quality sequencing data was analyzed using a bioinformatic workflow based on FastQC and MultiQC for quality control, STAR for read alignment, and DESeq2 for differential expression analysis. Genes were considered significantly differentially expressed after adjustment with Bonferroni correction (padj ≤ 0.05) and absolute (log2 fold change) ≥ 0.5. Raw and processed RNA-Seq data were deposited and made publicly available on the gene expression omnibus database (GEO; GSE221903). To our knowledge, this is the first dataset investigating the change in the gene expression level as early as weaning to predict the future reproductive outcome in beef heifers. Interpretation of the main findings based on this data is reported in a research article titled “mRNA Signatures in Peripheral White Blood Cells Predicts Reproductive Potential in Beef Heifers at Weaning” [1].

Project description:The gene expression profiles were measured using RNA-Seq beef heifers sampled at weaning and retrospectively classified as pregnant through artificial insemination (AI-P, n = 8) or non-pregnant (NP, n = 7). In brief, crossbred heifers (Angus-Simmental) were used in this study. At the time of weaning (~238 days after birth), blood samples (10 mL) were collected from the jugular vein into vacutainers containing EDTA. The blood was processed to extract PWBC and was stored at -80 until further processing. During the breeding season, all the heifers followed the same breeding protocol, estrus synchronization, and fixed-time artificial insemination (AI). Fourteen days following the fixed-time AI, the non-pregnant heifers were exposed to fertile bulls for 60 days. Depending on the presence or absence of conceptus at 75 days following AI, heifers were classified as pregnant to AI (AI-P), pregnant to natural breeding (P-NB) or non-pregnant (NP). Heifers that were pregnant to AI (N = 8) and non-pregnant (N = 7) were considered for the study. Total RNA was extracted from peripheral white blood cells of 15 samples (AI-P and NP) and was subjected to sequencing. The RNA-Seq data were subjected to quality control and mapped with STAR aligner to Ensemble’s ARS UCD1.2 Bos taurus reference genome. Read quantification was performed using a STAR aligner to obtain raw counts per gene. Genes with low counts were filtered out. After filtering, the read counts were analyzed using DESeq2 v 1.26.0 to identify differentially expressed genes (DEGs). The DEGs with padj < 0.05 and absolute (log2 fold change) > 0.5 were considered significant. The NP heifers were considered as the reference group, and the DEGs up or downregulated were classified based on the log2 fold change direction. We identified 92 DEGs between AI-P and NP groups. The top pathways identified with DEGs were the immune system, cytokine production and defense response, hematopoietic cell lineage, positive regulation of reactive oxygen species metabolic process, and negative regulation of cysteine-type endopeptidase activity involved in the apoptotic process.

Project description:Subfertility in beef heifers leads to a substantial economic loss for producers and beef industry. To overcome this problem, producers require an efficient system to discriminate beef heifers with varying reproductive potential as early as possible. MicroRNAs are short non-coding RNAs that post-transcriptionally regulate gene expression. Herein, we profiled the miRNAs in peripheral white blood cells (PWBC) of beef heifers at weaning to investigate the differences in the beef heifers with varying reproductive outcomes. Blood samples from Angus-Simmental crossbred heifers were collected at weaning. The blood was processed to extract the PWBC pellet and was stored at -80 °C until further processing. After the synchronization of estrus and breeding protocol (artificial insemination (AI) followed by natural bull service) and pregnancy diagnosis, the heifers were categorized as fertile (pregnant to AI) or subfertile (not pregnant to AI or bull exposure). Total RNA was extracted from PWBC collected at the time of weaning from the fertile and subfertile heifers. After quality assessment, the total RNA was used to prepare libraries. The quality-checked libraries (n = 14; 7 samples per fertile and subfertile group) were pooled and sequenced (single-end 50 bp) using a NextSeq 500 platform. The raw sequence reads were analyzed using a bioinformatics workflow utilizing FastQC and MultiQC for quality control, Cutadapt for adapter trimming, miRDeep2 for alignment, and DESeq2 for differential expression analysis. The raw and normalized miRNA counts were deposited and made publicly available on the gene expression omnibus database (GEO; GSE225854). This is the first dataset investigating the miRNA expression level in PWBC at weaning in beef heifers to predict the future reproductive outcome. The results from the data presented here are reported in the research article titled "miRNA expression profiles of peripheral white blood cells from beef heifers with varying reproductive potential" [1].

Project description:The miRNA profiles were measured using small-RNA sequencing in beef heifers sampled at weaning that was retrospectively classified as fertile or subfertile following the breeding protocol. To accomplish this, the miRNA profiles were generated from the blood samples (10 mL) collected from crossbred heifers (Angus-Simmental) at the time of weaning (~238 days after birth). Peripheral white blood cells (PWBC) were extracted from the blood samples and stored at -80°C until further processing. During the breeding season, all the heifers followed the same breeding protocol, estrus synchronization, and fixed-time artificial insemination (AI). Fourteen days following the fixed-time AI, the non-pregnant heifers were exposed to fertile bulls for 60 days. Depending on the presence or absence of conceptus at 75 days following AI, heifers were classified as fertile for those who were pregnant through artificial insemination, pregnant to natural breeding (P-NB), or subfertile for those who were not pregnant. Heifers from fertile (n = 7) and subfertile (n = 7) groups were considered for the study. Total RNA was extracted from the PWBC of 14 samples and was subjected to small RNA library preparation and sequencing. After quality control, adapter trimming, and alignment, mature miRNAs were used for differential expression analysis. The read counts were transformed to counts per million (CPM), and raw counts with CPM < 1 in 50% of the samples were filtered out. The filtered raw counts were analyzed using DESeq2 v 1.26.0 to identify differentially expressed miRNAs (DEMIs). The DEMIs identified with a p-value < 0.05 and absolute (log2 fold change) > 0.5 were considered significant. With the subfertile heifers as the reference group, we identified 16 DEMIs between fertile and subfertile groups. To determine the genes targeting the DEMIs, we downloaded the target genes for each DEMI and retained only those genes expressed in the PWBCs. For the miRNA-gene correlation, we used the partial correlation and information theory (PCIT) approach to identify the significant gene-miRNA correlated pairs. The significant genes correlated with the miRNAs identified pathways including MAPK, ErbB, HIF-1, FoxO, p53, mTOR, T-cell receptor, insulin and GnRH signaling, apoptosis, and pathways regulating pluripotency of stem cells in the fertile group while cell cycle, p53 signaling pathway and apoptosis pathways in the subfertile group.

Dataset Information

Transcriptome profiles in peripheral white blood cells at the time of artificial insemination discriminate beef heifers with different fertility potential.

Publications

Transcriptome profiles in peripheral white blood cells at the time of artificial insemination discriminate beef heifers with different fertility potential.

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