Pre‐ and post‐puberty expression of genes and proteins in the uterus of <i>Bos indicus</i> heifers: the luteal phase effect post‐puberty

Fortes, M. R. S.; Zacchi, Lucía F.; Nguyen, Loan; Raidan, Fernanda Santos Silva; Weller, M.M.D.C.A.; Choo, Jovin J. Y.; Reverter, Antônio; Rêgo, João Paulo Arcelino do; Boe-Hansen, G.; Porto-Neto, Laércio R.; Lehnert, S. A.; Cánovas, Ángela; Schulz, Benjamin L.; Islas‐Trejo, Alma; Medrano, Juan F.; Thomas, M. G.; Moore, S. S.

doi:10.1111/age.12721

Cited by 20 publications

(20 citation statements)

References 51 publications

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“…Lists of differentially expressed genes and proteins that emerged from the comparison of transcriptomics and proteomics data of hypothalamus, pituitary and ovaries of pre- and post-pubertal Brahman heifers were available from previous work of our group [ 43 , 44 , 45 ]. In these previous studies, the comparative transcriptomics and proteomics of the hypothalamus, pituitary and ovarian tissues of pre- vs. post-pubertal cattle was done by RNA sequencing and tandem mass spectrometry (MS/MS), respectively.…”

Section: Methodsmentioning

confidence: 99%

Meta-Analysis of Heifer Traits Identified Reproductive Pathways in Bos indicus Cattle

Tahir

Porto-Neto

Gondro

et al. 2021

Genes

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Fertility traits measured early in life define the reproductive potential of heifers. Knowledge of genetics and biology can help devise genomic selection methods to improve heifer fertility. In this study, we used ~2400 Brahman cattle to perform GWAS and multi-trait meta-analysis to determine genomic regions associated with heifer fertility. Heifer traits measured were pregnancy at first mating opportunity (PREG1, a binary trait), first conception score (FCS, score 1 to 3) and rebreeding score (REB, score 1 to 3.5). The heritability estimates were 0.17 (0.03) for PREG1, 0.11 (0.05) for FCS and 0.28 (0.05) for REB. The three traits were highly genetically correlated (0.75–0.83) as expected. Meta-analysis was performed using SNP effects estimated for each of the three traits, adjusted for standard error. We identified 1359 significant SNPs (p-value < 9.9 × 10−6 at FDR < 0.0001) in the multi-trait meta-analysis. Genomic regions of 0.5 Mb around each significant SNP from the meta-analysis were annotated to create a list of 2560 positional candidate genes. The most significant SNP was in the vicinity of a genomic region on chromosome 8, encompassing the genes SLC44A1, FSD1L, FKTN, TAL2 and TMEM38B. The genomic region in humans that contains homologs of these genes is associated with age at puberty in girls. Top significant SNPs pointed to additional fertility-related genes, again within a 0.5 Mb region, including ESR2, ITPR1, GNG2, RGS9BP, ANKRD27, TDRD12, GRM1, MTHFD1, PTGDR and NTNG1. Functional pathway enrichment analysis resulted in many positional candidate genes relating to known fertility pathways, including GnRH signaling, estrogen signaling, progesterone mediated oocyte maturation, cAMP signaling, calcium signaling, glutamatergic signaling, focal adhesion, PI3K-AKT signaling and ovarian steroidogenesis pathway. The comparison of results from this study with previous transcriptomics and proteomics studies on puberty of the same cattle breed (Brahman) but in a different population identified 392 genes in common from which some genes—BRAF, GABRA2, GABR1B, GAD1, FSHR, CNGA3, PDE10A, SNAP25, ESR2, GRIA2, ORAI1, EGFR, CHRNA5, VDAC2, ACVR2B, ORAI3, CYP11A1, GRIN2A, ATP2B3, CAMK2A, PLA2G, CAMK2D and MAPK3—are also part of the above-mentioned pathways. The biological functions of the positional candidate genes and their annotation to known pathways allowed integrating the results into a bigger picture of molecular mechanisms related to puberty in the hypothalamus–pituitary–ovarian axis. A reasonable number of genes, common between previous puberty studies and this study on early reproductive traits, corroborates the proposed molecular mechanisms. This study identified the polymorphism associated with early reproductive traits, and candidate genes that provided a visualization of the proposed mechanisms, coordinating the hypothalamic, pituitary, and ovarian functions for reproductive performance in Brahman cattle.

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Section: Methodsmentioning

confidence: 99%

Meta-Analysis of Heifer Traits Identified Reproductive Pathways in Bos indicus Cattle

Tahir

Porto-Neto

Gondro

et al. 2021

Genes

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“…It is comprised of five tissues (hypothalamus, pituitary, ovary, uterus and liver) from 6 pre- and 6 post-pubertal Brahman ( Bos indicus ) heifers, totalizing 60 samples. More details about animal management and data generation can be found in Fortes et al (2016), Nguyen et al (2017), Fortes et al (2018) and Nguyen et al (2018). In short, libraries were paired-end sequenced using an Illumina HiSeq200 equipment and reads were aligned to the bovine reference genome (UMD3.1).…”

Section: Methodsmentioning

confidence: 99%

Genome-wide co-expression distributions as a metric to prioritize genes of functional importance

Hudson¹,

Lehnert

Fortes

et al. 2020

Preprint

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Genome-wide gene expression is routinely used as a tool to gain a systems-level understanding of complex, biological processes. Numerical approaches that have been used to highlight influential genes include abundance, differential expression, differential variation, network connectivity and differential connectivity. Network connectivity tends to be built on a small subset of extremely high co-expression signals that are deemed significant, but this overlooks the vast majority of pairwise signals. Here, we aimed to assess a complementary strategy, namely whether the entire shape of the distribution of genome-wide co-expression values contains a meaningful biological signal that has hitherto remained hidden from view. We have developed a computational pipeline to assign one of 8 distributions (including normal, skewed, bimodal, kurtotic, inverted) to every gene. We then used a hypergeometric enrichment process to determine if particular genes (regulators versus non-regulators) and properties (differentially expressed or not) tend to be associated with particular distributions greater than would be expected by chance. Examination of several distinct data sets spanning 4 species indicates that there is indeed an additional biological signal present in the genome-wide distribution of co-expression values which would be overlooked by currently adopted approaches.Author summaryHigh-throughput technologies, such as RNA-Seq, enables access to a vast amount of data. Here, we describe a new approach to interrogate these data and extract further information to help researchers to understand complex phenotypes. Our method is based on gene-level co-expression distributions which were compared to eight possible template shapes to group genes with similar behaviours. The method was tested using five different datasets and the consistency of the results indicate it can be used as a complementary strategy to analyse transcriptomic data.

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“…The Cattle Puberty dataset is comprised of five tissues (hypothalamus, pituitary, ovary, uterus, and liver) from 6 pre- and 6 post-pubertal Brahman heifers, corresponding to Reference [ 11 , 12 , 13 , 14 ]. A total of 16,978 genes that presented average FPKM ≥ 0.2 in at least 1 tissue were used for analysis.…”

Section: Methodsmentioning

confidence: 99%

Genome-Wide Co-Expression Distributions as a Metric to Prioritize Genes of Functional Importance

Hudson

Lehnert

Fortes

et al. 2020

Genes

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Genome-wide gene expression analysis are routinely used to gain a systems-level understanding of complex processes, including network connectivity. Network connectivity tends to be built on a small subset of extremely high co-expression signals that are deemed significant, but this overlooks the vast majority of pairwise signals. Here, we developed a computational pipeline to assign to every gene its pair-wise genome-wide co-expression distribution to one of 8 template distributions shapes varying between unimodal, bimodal, skewed, or symmetrical, representing different proportions of positive and negative correlations. We then used a hypergeometric test to determine if specific genes (regulators versus non-regulators) and properties (differentially expressed or not) are associated with a particular distribution shape. We applied our methodology to five publicly available RNA sequencing (RNA-seq) datasets from four organisms in different physiological conditions and tissues. Our results suggest that genes can be assigned consistently to pre-defined distribution shapes, regarding the enrichment of differential expression and regulatory genes, in situations involving contrasting phenotypes, time-series, or physiological baseline data. There is indeed a striking additional biological signal present in the genome-wide distribution of co-expression values which would be overlooked by currently adopted approaches. Our method can be applied to extract further information from transcriptomic data and help uncover the molecular mechanisms involved in the regulation of complex biological process and phenotypes.

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Pre‐ and post‐puberty expression of genes and proteins in the uterus of Bos indicus heifers: the luteal phase effect post‐puberty

Cited by 20 publications

References 51 publications

Meta-Analysis of Heifer Traits Identified Reproductive Pathways in Bos indicus Cattle

Meta-Analysis of Heifer Traits Identified Reproductive Pathways in Bos indicus Cattle

Genome-wide co-expression distributions as a metric to prioritize genes of functional importance

Genome-Wide Co-Expression Distributions as a Metric to Prioritize Genes of Functional Importance

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