Dataset Information

Transcriptome analysis reveals pituitary lncRNA, circRNA and mRNA affecting fertility in high- and low-yielding goats.

ABSTRACT: The pituitary gland serves as the central endocrine regulator of growth, reproduction, and metabolism and plays a crucial role in the reproductive process of female animals. Transcriptome analysis was conducted using pituitary gland samples from Leizhou goats with varying levels of fecundity to investigate the effects of long noncoding RNA (lncRNA), circular RNA (circRNA), and mRNA regulation on pituitary hormone secretion and its association with goat fecundity. The analysis aimed to identify lncRNAs, circRNAs, and mRNAs that influence the fertility of Leizhou goats. GO and KEGG enrichment analyses were performed on differentially expressed lncRNAs, circRNAs, and mRNAs and revealed considerable enrichment in pathways, such as regulation of hormone secretion, germ cell development, and gonadotropin-releasing hormone secretion. The pituitary lncRNAs (ENSCHIT00000010293, ENSCHIT00000010304, ENSCHIT00000010306, ENSCHIT00000010290, ENSCHIT00000010298, ENSCHIT00000006769, ENSCHIT00000006767, ENSCHIT00000006921, and ENSCHIT00000001330) and circRNAs (chicirc_029285, chicirc_026618, chicirc_129655, chicirc_018248, chicirc_122554, chicirc_087101, and chicirc_078945) identified as differentially expressed regulated hormone secretion in the pituitary through their respective host genes. Additionally, differential mRNAs (GABBR2, SYCP1, HNF4A, CBLN1, and CDKN1A) influenced goat fecundity by affecting hormone secretion in the pituitary gland. These findings contribute to the understanding of the molecular mechanisms underlying pituitary regulation of fecundity in Leizhou goats.

SUBMITTER: Mao S

PROVIDER: S-EPMC10751935 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Transcriptome analysis reveals pituitary lncRNA, circRNA and mRNA affecting fertility in high- and low-yielding goats.

Mao Shuaixiang S Dong Shucan S Hou Biwei B Li Yaokun Y Sun Baoli B Guo Yongqing Y Deng Ming M Liu Dewu D Liu Guangbin G

Frontiers in genetics 20231212

The pituitary gland serves as the central endocrine regulator of growth, reproduction, and metabolism and plays a crucial role in the reproductive process of female animals. Transcriptome analysis was conducted using pituitary gland samples from Leizhou goats with varying levels of fecundity to investigate the effects of long noncoding RNA (lncRNA), circular RNA (circRNA), and mRNA regulation on pituitary hormone secretion and its association with goat fecundity. The analysis aimed to identify l ...[more]

PMID: 38152654

Similar Datasets

Project description:Background: There is growing interest in the genetic improvement of fertility traits in female goats. With high-throughput genotyping, single-cell RNA sequencing (scRNA-seq) is a powerful tool for measuring gene expression profiles. The primary objective was to investigate comparative transcriptome profiling of granulosa cells (GCs) of high- and low-fertility goats, using scRNA-seq. Methods: Thirty samples from Ji'ning Gray goats (n = 15 for high fertility and n = 15 for low fertility) were retrieved from publicly available scRNA-seq data. Functional enrichment analysis and a literature mining approach were applied to explore modules and hub genes related to fertility. Then, interactions between types of RNAs identified were predicted, and the ceRNA regulatory network was constructed by integrating these interactions with other gene regulatory networks (GRNs). Results and discussion: Comparative transcriptomics-related analyses identified 150 differentially expressed genes (DEGs) between high- and low-fertility groups, based on the fold change (≥5 and ≤-5) and false discovery rate (FDR <0.05). Among these genes, 80 were upregulated and 70 were downregulated. In addition, 81 mRNAs, 58 circRNAs, 8 lincRNAs, 19 lncRNAs, and 55 miRNAs were identified by literature mining. Furthermore, we identified 18 hub genes (SMAD1, SMAD2, SMAD3, SMAD4, TIMP1, ERBB2, BMP15, TGFB1, MAPK3, CTNNB1, BMPR2, AMHR2, TGFBR2, BMP4, ESR1, BMPR1B, AR, and TGFB2) involved in goat fertility. Identified biological networks and modules were mainly associated with ovary signature pathways. In addition, KEGG enrichment analysis identified regulating pluripotency of stem cells, cytokine-cytokine receptor interactions, ovarian steroidogenesis, oocyte meiosis, progesterone-mediated oocyte maturation, parathyroid and growth hormone synthesis, cortisol synthesis and secretion, and signaling pathways for prolactin, TGF-beta, Hippo, MAPK, PI3K-Akt, and FoxO. Functional annotation of identified DEGs implicated important biological pathways. These findings provided insights into the genetic basis of fertility in female goats and are an impetus to elucidate molecular ceRNA regulatory networks and functions of DEGs underlying ovarian follicular development.

Project description:Accumulating evidence has indicated that noncoding RNAs are involved in intervertebral disc degeneration (IDD); however, the competing endogenous RNA (ceRNA)‑mediated regulatory mechanisms in IDD remain rarely reported. The present study aimed to comprehensively investigate the alterations in expression levels of circular RNA (circRNA), long noncoding RNA (lncRNA), microRNA (miRNA/miR) and mRNA in the nucleus pulposus (NP) of patients with IDD. In addition, crucial lncRNA/circRNA‑miRNA‑mRNA ceRNA interaction axes were screened using the GSE67567 microarray dataset obtained from the Gene Expression Omnibus database. After data preprocessing, differentially expressed circRNAs (DECs), lncRNAs (DELs), miRNAs (DEMs) or genes (DEGs) between IDD and normal controls were identified using the Linear Models for Microarray data method. A protein‑protein interaction (PPI) network was constructed for DEGs based on protein databases, followed by module analysis. The ceRNA network was constructed based on the interaction between miRNAs and mRNAs, and lncRNAs/circRNAs and miRNAs. The underlying functions of mRNAs were predicted using the Database for Annotation, Visualization and Integrated Discovery database. The present study identified 636 DECs, 115 DELs, 84 DEMs and 1,040 DEGs between patients with IDD and control individuals. PPI network analysis demonstrated that Fos proto‑oncogene, AP‑1 transcription factor subunit (FOS), mitogen‑activated protein kinase 1 (MAPK1), hypoxia inducible factor 1 subunit α (HIF1A) and transforming growth factor β1 (TGFB1) were hub genes and enriched in modules. Metastasis‑associated lung adenocarcinoma transcript 1 (MALAT1)/hsa_circRNA_102348‑hsa‑miR‑185‑5p‑TGFB1/FOS, MALAT1‑hsa‑miR‑155‑5p‑HIF1A, hsa_circRNA_102399‑hsa‑miR‑302a‑3p‑HIF1A, MALAT1‑hsa‑miR‑519d‑3p‑MAPK1 and hsa_circRNA_100086‑hsa‑miR‑509‑3p‑MAPK1 ceRNA axes were obtained by constructing the ceRNA networks. In conclusion, these identified ceRNA interaction axes may be crucial targets for the treatment of IDD.

Project description:Background: Hepatocellular carcinoma (HCC) accounts for the majority of liver cancer, with the incidence and mortality rates increasing every year. Despite the improvement of clinical management, substantial challenges remain due to its high recurrence rates and short survival period. This study aimed to identify potential diagnostic and prognostic biomarkers in HCC through bioinformatic analysis. Methods: Datasets from GEO and TCGA databases were used for the bioinformatic analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were carried out by WebGestalt website and clusterProfiler package of R. The STRING database and Cytoscape software were used to establish the protein-protein interaction (PPI) network. The GEPIA website was used to perform expression analyses of the genes. The miRDB, miRWalk, and TargetScan were employed to predict miRNAs and the expression levels of the predicted miRNAs were explored via OncomiR database. LncRNAs were predicted in the StarBase and LncBase while circRNA prediction was performed by the circBank. ROC curve analysis and Kaplan-Meier (KM) survival analysis were performed to evaluate the diagnostic and prognostic value of the gene expression, respectively. Results: A total of 327 upregulated and 422 downregulated overlapping DEGs were identified between HCC tissues and noncancerous liver tissues. The PPI network was constructed with 89 nodes and 178 edges and eight hub genes were selected to predict upstream miRNAs and ceRNAs. A lncRNA/circRNA-miRNA-mRNA network was successfully constructed based on the ceRNA hypothesis, including five lncRNAs (DLGAP1-AS1, GAS5, LINC00665, TYMSOS, and ZFAS1), six circRNAs (hsa_circ_0003209, hsa_circ_0008128, hsa_circ_0020396, hsa_circ_0030051, hsa_circ_0034049, and hsa_circ_0082333), eight miRNAs (hsa-miR-150-5p, hsa-miR-19b-3p, hsa-miR-23b-3p, hsa-miR-26a-5p, hsa-miR-651-5p, hsa-miR-10a-5p, hsa-miR-214-5p and hsa-miR-486-5p), and five mRNAs (CDC6, GINS1, MCM4, MCM6, and MCM7). The ceRNA network can promote HCC progression via cell cycle, DNA replication, and other pathways. Clinical diagnostic and survival analyses demonstrated that the ZFAS1/hsa-miR-150-5p/GINS1 ceRNA regulatory axis had a high diagnostic and prognostic value. Conclusion: These results revealed that cell cycle and DNA replication pathway could be potential pathways to participate in HCC development. The ceRNA network is expected to provide potential biomarkers and therapeutic targets for HCC management, especially the ZFAS1/hsa-miR-150-5p/GINS1 regulatory axis.

Project description:BackgroundAlthough ischemic stroke exhibits a high prevalence in the elderly population, the involved genes and pathways are poorly understood. In this study, we proposed to identify differentially expressed genes (DEGs) and constructed a circular RAN (circRNA)/long noncoding RNA (lncRNA)/microRNA (miRNA)-mRNA network associated with the pathogenesis of ischemic stroke by using bioinformatics analysis.MethodsWe constructed a rat model of middle cerebral artery occlusion (MCAO) and conducted total RNA and microRNA sequencing in brain specimens from MCAO and normal rats. Transcriptome-wide expression patterns were analyzed and DEGs were defined by applying Ballgown and a cut of log2-transformed fold-change (log2FC) ≥ 1 (or ≤ -1) with a P value < 0.05. We exploited Pearson correlation analysis to determine the association between the circRNA/lncRNA/mRNA network and miRNAs (P < 0.05 and corr ≤ -0.6), and the competing endogenous RNAs (ceRNA) interaction network was visualized with Cytoscape software and separated into subnetworks using the Molecular Complex Detection (MCODE) algorithm. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were implemented for the pathway analysis of DEGs.ResultsUpregulated DEGs were significantly enhanced in positive regulation of cell migration, response to wounding, blood vessel morphogenesis, inflammatory response, and cell activation; Downregulated DEGs were associated with control of the modulation of chemical synaptic transmission, synapse organization, regulation of membrane potential, and regulation of ion transport. KEGG-pathway analysis showed that DEG-enhanced pathways were associated with the pathways of TNF signaling pathway, Fluid shear stress and atherosclerosis, NF-kappa B signaling pathway, Lipid and atherosclerosis, Human cytomegalovirus infection, Osteoclast differentiation, Chemokine signaling pathway, IL-17 signaling pathway, Viral protein interaction with cytokine and cytokine receptor, and Cytokine-cytokine receptor interaction. We uncovered several novel lncRNAs (lnc_00231, lnc_002239, lnc_004172; and a novel_circ0001704), five miRNAs (miR-200b-3p, miR-223-3p, miR-200c-3p, miR-3084a-3p, and miR-664-2-5p), and the top-10 mRNAs (upregulated mRNAs were Pdgfa, Il1b, Gdf15, Fosl1, and Cxcl2; downregulated mRNAs were Prkar2b, Olfm3, Lrrc73, Tmem38a, and Dlgap3) that were involved in ischemic stroke.ConclusionsThrough bioinformatic network analysis, we identified the underlying molecular mechanisms and key central genes that may contribute to an inflammatory response after cerebral infarction.

Dataset Information

Transcriptome analysis reveals pituitary lncRNA, circRNA and mRNA affecting fertility in high- and low-yielding goats.

Publications

Transcriptome analysis reveals pituitary lncRNA, circRNA and mRNA affecting fertility in high- and low-yielding goats.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets