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Machine learning approach to integrated endometrial transcriptomic datasets reveals biomarkers predicting uterine receptivity in cattle at seven days after estrous.

ABSTRACT: The main goal was to apply machine learning (ML) methods on integrated multi-transcriptomic data, to identify endometrial genes capable of predicting uterine receptivity according to their expression patterns in the cow. Public data from five studies were re-analyzed. In all of them, endometrial samples were obtained at day 6-7 of the estrous cycle, from cows or heifers of four different European breeds, classified as pregnant (n?=?26) or not (n?=?26). First, gene selection was performed through supervised and unsupervised ML algorithms. Then, the predictive ability of potential key genes was evaluated through support vector machine as classifier, using the expression levels of the samples from all the breeds but one, to train the model, and the samples from that one breed, to test it. Finally, the biological meaning of the key genes was explored. Fifty genes were identified, and they could predict uterine receptivity with an overall 96.1% accuracy, despite the animal's breed and category. Genes with higher expression in the pregnant cows were related to circadian rhythm, Wnt receptor signaling pathway, and embryonic development. This novel and robust combination of computational tools allowed the identification of a group of biologically relevant endometrial genes that could support pregnancy in the cattle.

SUBMITTER: Rabaglino MB

PROVIDER: S-EPMC7550564 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Machine learning approach to integrated endometrial transcriptomic datasets reveals biomarkers predicting uterine receptivity in cattle at seven days after estrous.

Rabaglino Maria B MB Kadarmideen Haja N HN

Scientific reports 20201012 1

The main goal was to apply machine learning (ML) methods on integrated multi-transcriptomic data, to identify endometrial genes capable of predicting uterine receptivity according to their expression patterns in the cow. Public data from five studies were re-analyzed. In all of them, endometrial samples were obtained at day 6-7 of the estrous cycle, from cows or heifers of four different European breeds, classified as pregnant (n = 26) or not (n = 26). First, gene selection was performed through ...[more]

PMID: 33046742

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Project description:BackgroundThe establishment of uterine receptivity is essential for embryo implantation initiation and involves a significant morphological transformation in the endometrial epithelial cells (EECs). The remodeling of junctional complexes and membrane-associated cytoskeleton is crucial for epithelial transformation. However, little is known about how this process is regulated in EECs during the receptive phase. ARHGAP19 is a Rho GTPase-activating protein that participates in various cytoskeletal-related events, including epithelial morphogenesis. Here, we investigated the role of ARHGAP19 in endometrial epithelial transformation during the establishment of uterine receptivity. The upstream regulator of ARHGAP19 was also investigated.MethodsARHGAP19 expression was examined in mouse uteri during early pregnancy and in human EEC lines. The role of ARHGAP19 was investigated by manipulating its expression in EECs. The effect of ARHGAP19 on junctional proteins in EECs was examined by western blotting and immunofluorescence. The effect of ARHGAP19 on microvilli was examined by scanning electron microscopy. The upstream microRNA (miRNA) was predicted using online databases and validated by the dual-luciferase assay. The in vivo and in vitro effect of miRNA on endogenous ARHGAP19 was examined by uterine injection of miRNA agomirs and transfection of miRNA mimics or inhibitors.ResultsARHGAP19 was upregulated in the receptive mouse uteri and human EECs. Overexpression of ARHGAP19 in non-receptive EECs downregulated the expression of junctional proteins and resulted in their redistribution. Meanwhile, upregulating ARHGAP19 reorganized the cytoskeletal structure of EECs, leading to a decline of microvilli and changes in cell configuration. These changes weakened epithelial cell polarity and promoted the transition of non-receptive EECs to a receptive phenotype. Besides, miR-192-5p, a miRNA that plays a key role in maintaining epithelial properties, was validated as an upstream regulator of ARHGAP19.ConclusionThese results suggested that ARHGAP19 may contribute to the transition of EECs from a non-receptive to a receptive state by regulating the remodeling of junctional proteins and membrane-associated cytoskeleton.

Project description:BACKGROUND:In beef cattle, changes in the periovulatory endocrine milieu are associated with fertility and conceptus growth. A large preovulatory follicle (POF) and the resulting elevated concentrations of progesterone (P4) during diestrus positively affect pregnancy rates. Amino acids (AA) are important components of maternally derived secretions that are crucial for embryonic survival before implantation. The hypothesis is that the size of the POF and the concentration of P4 in early diestrus modulate the endometrial abundance of SLC transcripts related to AA transport and metabolism and subsequently impact luminal concentrations of AA. The follicle growth of Nelore cows was manipulated to produce two experimental groups: large POF and CL (LF-LCL group) and small POF and CL (SF-SCL group). On Day 4 (D4; Experiment 1) and Day 7 (D7; Experiment 2) after GnRH-induced ovulation (GnRH treatment?=?D0), the animals were slaughtered and uterine tissues and uterine washings were collected. qRT-PCR was used to evaluate the expression levels of AA transporters in D4 and D7 endometrial tissues. The concentrations of AA were quantified in D4 and D7 uterine washings by HPLC. RESULTS:Transcript results show that, on D4, SLC6A6, SLC7A4, SLC17A5, SLC38A1, SLC38A7 and SCLY and on D7 SLC1A4, SLC6A1, SLC6A14, SLC7A4, SLC7A7, SLC7A8, SLC17A5, SLC38A1, SLC38A7, SLC43A2 and DDO were more abundant in the endometria of cows from the LF-LCL group (P?<?0.05). In addition, concentrations of AA in the uterine lumen were influenced by the endocrine profiles of the mother. In this context, D4 uterine washings revealed that greater concentrations of taurine, alanine and ?-aminobutyric acid were present in SF-SCL (P?<?0.05). In contrast, lower concentrations of valine and cystathionine were quantified on D7 uterine washings from SF-SCL cows (P?<?0.05). CONCLUSION:The present study revealed an association between the abundance of transcripts related to AA transport and metabolism in the endometrium and specific periovulatory endocrine profiles related to the receptive status of the mother. Such insights suggest that AAs are involved in uterine function to support embryo development.

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Machine learning approach to integrated endometrial transcriptomic datasets reveals biomarkers predicting uterine receptivity in cattle at seven days after estrous.

Publications

Machine learning approach to integrated endometrial transcriptomic datasets reveals biomarkers predicting uterine receptivity in cattle at seven days after estrous.

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