Finnsheep RNA-Seq
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB34419 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR3522230_1.fastq.gz | Fastqsanger.gz | |||
| ERR3522230_2.fastq.gz | Fastqsanger.gz | |||
| ERR3522231_1.fastq.gz | Fastqsanger.gz | |||
| ERR3522231_2.fastq.gz | Fastqsanger.gz |
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Project description:miRNA data of two Finnsheep ovaries collected during the out-of-season breeding period.
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Project description:Here we used RNAseq to compare the transcriptomes of the medio-basal hypothalamus of goats killed at two different times of the year (end of January and mid-May) and under different hormonal conditions (follicular and luteal phase, in January only). The current experiment was designed to identify the impact of photoperiod upon the transcriptome and to compare it with that of the ewe. The current design also tested the potential impact of progesterone (P4) upon this transcriptome; ewes were treated with a vaginal fluogestone acetate sponge during the breeding season (January) and killed either 2 days (follicular phase) or 8 days (luteal phase) after estrumate treatment and sponge removal.
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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.
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