scholarly journals Transcriptional Reprogramming in Rumen Epithelium during the Developmental Transition of Pre-Ruminant to the Ruminant in Cattle

Animals ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 2870
Author(s):  
Ransom L. Baldwin Baldwin ◽  
Mei Liu ◽  
Erin E. Connor ◽  
Timothy G. Ramsay ◽  
George E. Liu ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Bioinformatic Analysis ◽  
Gene Activity ◽  
Differentially Expressed ◽  
Critical Pathways ◽  
Dietary Shift ◽  
Rumen Epithelium ◽  
Transcriptional Reprogramming ◽  
Ruminal Epithelium ◽  
And Function

We present an analysis of transcriptomic dynamics in rumen epithelium of 18 Holstein calves during the transition from pre-rumination to rumination in cattle-fed hay or concentrated diets at weaning. Three calves each were euthanized at 14 and 42 d of age to exemplify preweaning, and six calves each were provided diets of either milk replacer and grass hay or calf starter to introduce weaning. The two distinct phases of rumen development and function in cattle are tightly regulated by a series of signaling events and clusters of effectors on critical pathways. The dietary shift from liquid to solid feeds prompted the shifting of gene activity. The number of differentially expressed genes increased significantly after weaning. Bioinformatic analysis revealed gene activity shifts underline the functional transitions in the ruminal epithelium and signify the transcriptomic reprogramming. Gene ontogeny (GO) term enrichment shows extensively activated biological functions of differentially expressed genes in the ruminal epithelium after weaning were predominant metabolic functions. The transcriptomic reprogramming signifies a correlation between gene activity and changes in metabolism and energy production in the rumen epithelium, which occur at weaning when transitioning from glucose use to VFA use by epithelium during the weaning.

PSVIII-25 Transcriptional reprogramming in rumen epithelium during the developmental transition of pre-ruminant to the ruminant in cattle

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 355-356
Author(s):  
Ransom L Baldwin ◽  
Erin E Connor ◽  
Timothy G Ramsay ◽  
George E Liu ◽  
Cong-Jun Li
Keyword(s):  
Animal Feed ◽  
Expression Patterns ◽  
Principal Component ◽  
Epithelial Tissue ◽  
Dietary Shift ◽  
Rumen Epithelium ◽  
Transcriptional Reprogramming ◽  
Rumen Development ◽  
Signaling Events ◽  
Ruminal Epithelium

Abstract The rumen is a critical organ mediating nutrient uptake and use in cattle. Healthy rumen development is essential to ensure animal feed efficiency. In this work, we present an analysis of transcriptomic dynamics in rumen epithelium during the transition from pre-rumination to rumination in cattle-fed hay or concentrated diets at weaning (eighteen Holstein bull calves, 3 X 6 groups). These two distinct phases of rumen development and function in cattle are tightly regulated by a series of signaling events and clusters of effectors on key pathways. Our analysis identifies putative signaling events and effectors. Gene activity shifts indicated the transcriptomic reprogramming required to induce developmental changes in ruminal epithelium and functional transitions. A principal component analysis distinguished the temporal expression patterns that clustered separately between pre- and post-weaning groups. A GO-term enrichment analysis reflected functional (physical and metabolic) development of ruminal epithelium and revealed the greatest number of DEGs were enriched in biological processes related to energy metabolism. Canonical pathway and upstream regulator analyses revealed transcription reprogramming with clusters of critical pathways and upstream regulators controlling functional and developmental transitions with no significant differences between hay- and concentrate-fed groups at weaning. The most highly activated transcription factors expressed during the weaning transition were PPARGC1A, INSR, NFE2L2, MYC, MYCN, and PPARA. Overall, the dietary shift from liquid to solid feeds prompted transcriptional reprogramming in rumen epithelial tissue reflecting critical nutrient-gene interactions occurring during the developmental progression of ruminant digestion.

scholarly journals Assessment of Embryo-Induced Transcriptomic Changes in Hamster Uterus Using RNA-Seq

2018 ◽  
Vol 46 (5) ◽  
pp. 1868-1878 ◽  
Author(s):  
Ming-Yu Huang ◽  
Wen-Qian Zhang ◽  
Miao Zhao ◽  
Can Zhu ◽  
Jia-Peng He ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Embryo Implantation ◽  
Bioinformatic Analysis ◽  
Differentially Expressed ◽  
Implantation Site ◽  
Rna Seq ◽  
Promoter Regions ◽  
Functional Clustering ◽  
Gene Network Analysis ◽  
Transcriptomic Changes

Background/Aims: The mouse is widely used as an animal model for studying human embryo implantation. However, the mouse is unique in that both ovarian progesterone and estrogen are critical to implantation, whereas in the majority of species (e.g. human and hamster) implantation can occur in the presence of progesterone alone. Methods: In this study, we analyzed embryo-induced transcriptomic changes in the hamster uterus during embryo implantation by using RNA-seq. Differentially expressed genes were characterized by bioinformatic analysis. Results: We identified a total of 781 differentially expressed genes, of which 367 genes were up-regulated and 414 genes were down-regulated at the implantation site compared to the inter-implantation site. Functional clustering and gene network analysis highlighted the cell cycle process in uterus upon embryo implantation. By examining of the promoter regions of differentially expressed genes, we identified 7 causal transcription factors. Additionally, through connectivity map (CMap) analysis, multiple compounds were identified to have potential anti-implantation effects due to their ability to reverse embryo-induced transcriptomic changes. Conclusion: Our study provides a valuable resource for in-depth understanding of the mechanism underlying embryo implantation.

Bioinformatic Analysis of Differentially Expressed Genes and Screening of Hub Genes in Uveal Melanoma Cells with BRCA1-Associated Protein 1 Related Protein 1 Depletion

2020 ◽  
Vol 16 (8) ◽  
pp. 1205-1218
Author(s):  
Wei Li ◽  
Aiqin Nie ◽  
Qiang Li ◽  
He Cao ◽  
Yinwei Song ◽  
...  
Keyword(s):  
Cell Adhesion ◽  
Differentially Expressed Genes ◽  
Uveal Melanoma ◽  
Wnt Signaling Pathway ◽  
Tumor Development ◽  
Bioinformatic Analysis ◽  
Melanoma Cells ◽  
Differentially Expressed ◽  
Data Sets ◽  
And Migration

Recent studies have found that chromosome 3 is frequently mutated in metastatic uveal melanoma (UVM), which leads to the loss of BAP1 expression or the weakening of BRCA1-associated protein 1 (BAP1) function and promotes metastasis of uveal melanoma cells. However, the specific signaling pathways that are affected by BAP1 depletion in uveal melanoma remain unclear. Our aim in this study was to verify the effect and regulatory mechanism of BAP1 on uveal melanoma. RT-qPCR and western blotting results showed that BAP1 was significantly down-regulated in OCM-1A cells treated with a BAP1 shRNA vector. MTT, cell scratch and transwell migration assays showed that low expression of BAP1 significantly promoted the proliferation and migration of UVM cells. A total of 269 up-regulated and 807 down-regulated genes were identified from the combined GSE110193 and GSE48863 data sets. These differentially expressed genes are mainly involved in the composition of extracellular matrix and the regulation of the Wnt signaling pathway and are closely related to the cell adhesion pathway. CXCL8, COL5A3, COL11A1, and COL12A1 were among the differentially expressed genes and are closely related to the prognosis of UVM. Therefore, the deletion of BAP1 is closely related to poor prognosis of UVM and is a risk factor for UVM metastasis. The potential targets of BAP1 include CXCL8, COL5A3, COL11A1, and COL12A1. It is believed that BAP1 regulates UVM cell adhesion through these four genes and ultimately regulates tumor development and migration.

scholarly journals Differentially expressed genes and interacting pathways in bladder cancer revealed by bioinformatic analysis

2014 ◽  
Vol 10 (4) ◽  
pp. 1746-1752 ◽  
Author(s):  
YINZHOU SHEN ◽  
XUELEI WANG ◽  
YONGCHAO JIN ◽  
JIASUN LU ◽  
GUANGMING QIU ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Differentially Expressed Genes ◽  
Bioinformatic Analysis ◽  
Differentially Expressed

scholarly journals Identification of Methylated Differentially Expressed Hub Genes and Immune Infiltration in Diabetic Retinopathy

2021 ◽  
Author(s):  
Qi Zhou ◽  
Xin Xiong ◽  
Min Tang ◽  
Yingqing Lei ◽  
Hongbin Lv
Keyword(s):  
Gene Expression ◽  
Extracellular Matrix ◽  
Immune Cell ◽  
Interaction Network ◽  
Bioinformatic Analysis ◽  
Differentially Expressed ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Hub Genes ◽  
Critical Pathways

Abstract BackgroundDiabetic retinopathy (DR), a severe complication of diabetes mellitus (DM), is a global social and economic burden. However, the pathological mechanisms mediating DR are not well-understood. This study aimed to identify differentially methylated and differentially expressed hub genes (DMGs and DEGs, respectively) and associated signaling pathways, and to evaluate immune cell infiltration involved in DR. MethodsTwo publicly available datasets were downloaded from the Gene Expression Omnibus database. Transcriptome and epigenome microarray data and multi-component weighted gene coexpression network analysis (WGCNA) were utilized to determine hub genes within DR. One dataset was utilized to screen DEGs and to further explore their potential biological functions using functional annotation analysis. A protein-protein interaction network was constructed. Gene set enrichment and variation analyses (GSVA and GSEA, respectively) were utilized to identify the potential mechanisms mediating the function of hub genes in DR. Infiltrating immune cells were evaluated in one dataset using CIBERSORT. The Connectivity Map (CMap) database was used to predict potential therapeutic agents. ResultsIn total, 673 DEGs (151 upregulated and 522 downregulated genes) were detected. Gene expression was significantly enriched in the extracellular matrix and sensory organ development, extracellular matrix organization, and glial cell differentiation pathways. Through WGCNA, one module was found to be significantly related with DR (r=0.34, P =0.002), and 979 hub genes were identified. By comparing DMGs, DEGs, and genes in WGCNA, we identified eight hub genes in DR ( AKAP13, BOC, ACSS1, ARNT2, TGFB2, LHFPL2, GFPT2, TNFRSF1A ), which were significantly enriched in critical pathways involving coagulation, angiogenesis, TGF-β, and TNF-α-NF-κB signaling via GSVA and GSEA. Immune cell infiltration analysis revealed that activated natural killer cells, M0 macrophages, resting mast cells, and CD8 + T cells may be involved in DR. ARNT2, TGFB2, LHFPL2 , and AKAP13 expression were correlated with immune cell processes, and ZG-10, JNK-9L, chromomycin-a3, and calyculin were identified as potential drugs against DR. Finally, TNFRSF1A , GFPT2 , and LHFPL2 expression levels were consistent with the bioinformatic analysis. ConclusionsOur results are informative with respect to correlations between differentially methylated and expressed hub genes and immune cell infiltration in DR, providing new insight towards DR drug development and treatment.

scholarly journals Bioinformatic Analysis: Screening and Identification of Differentially Expressed Genes Modified by m6A in Ovarian Cancer

2020 ◽  
Author(s):  
Huidong Liu ◽  
Wen-wen Zhang ◽  
Ge Lou
Keyword(s):  
Ovarian Cancer ◽  
Differentially Expressed Genes ◽  
Clinical Sample ◽  
Therapeutic Targets ◽  
Enrichment Analysis ◽  
Underlying Mechanism ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Kegg Pathways

Abstract Background: N6-methyladenosine(m6A) is one of the most common RNA modifications that occurs at the nitrogen-6 position of adenine. Emerging evidence has revealed that regulatory functions of m6A play an essential role in the development of cancer. However the study of m6A in ovarian cancer(OC) is still in our infancy. In this work ,we aimed to identify and analysis the differentially expressed genes(DEGs） modified by m6A which can provide new therapeutic targets and key biomarkers in OC.Methods: We downloaded Microarray datasets GSE146553 and GSE124766 from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified by GEO2R analysis tools. Subsequently, The DAVID database was used to construct Enrichment analysis of GO and KEGG pathways. Next, the DEGs modified by m6A were identified by m6AVar database. Finally, the functional analysis and clinical sample validation of these genes were verified by ONCOMINE, GEPIA, cBioPortal online platform and Kaplan-Meier Plotter.Results:152 DEGs were selected ,and the DEGs were mainly enriched in extracellular exosome, spindle microtubule, response to hypoxia and cell cycle .And we identified 15 DEGs which were modified by m6A:MAPK10、MXRA5、CHD7、MECOM、SCN7A、GREB、PRUNE2、MX2、TOP2A、JAM2、DST、LAPTM5、CDKN2A、GATM and ANGPTL1. After statistical analysis, two DEGs (SCN7A and GAMT) were selected for detailed study. We revealed that SCN7A and GAMT were expressed at a low level in OC. Afterwards, Survival analysis showed that SCN7A and GAMT expression were correlated with OC overall survival. And the expression of SCN7A and GAMT mRNA decreasing in different TNM stages. Finally, we presumed that the modification of m6A spongs GAMT via EIF4A3 or FUS to participate in the occcurrence and the development of OC.Conclusion: Altogether, the current study identified and analysised the DEGs modified by m6A in OC. It will help us to investigate the underlying mechanism and progression of OC. In addition, it can provide new diagnostic markers and potential therapeutic targets in OC.

scholarly journals Bioinformatic analysis of differentially expressed genes and identification of key genes in EBV-transformed lymphoblasts

2019 ◽  
Vol 116 ◽  
pp. 108984
Author(s):  
Yunlian Tang ◽  
Yating Zhong ◽  
Ting Fu ◽  
Yang Zhang ◽  
Ailan Cheng ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Bioinformatic Analysis ◽  
Differentially Expressed ◽  
Key Genes
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