78 Effects of invitro production on the epigenome and transcriptome of bovine embryos determined through a multi-omics data integration approach

2021 ◽  
Vol 33 (2) ◽  
pp. 147
Author(s):  
M. Rabaglino ◽  
J. B.-M. Secher ◽  
P. Hyttel ◽  
H. Kadarmideen
Keyword(s):  
Survival Rates ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Receptor Interaction ◽  
P Value ◽  
Kegg Pathways ◽  
The Past ◽  
Before And After

In cattle, ovarian superovulation followed by invivo embryo collection and transfer (MOET), and the invitro production (IVP) of embryos are used all over the world to improve animal genetics. Application of MOET has resulted in the production of billions of healthy animals during the past 40 years, and IVP has evolved and given rise to significant numbers of calves during the past 10 years. Nevertheless, the use of MOET and IVP can affect the embryo epigenome, and therefore its transcriptome, before and after elongation, as shown by different studies. The integration of publicly available epigenome-transcriptome datasets generated by these studies could lead to a robust characterisation of the impacts of the application of MOET and IVP. The goal of this study was to integrate all publicly available data about MOET and IVP embryos to determine temporally differentially methylated regions (DMRs) and differentially expressed genes (DEGs) from blastocyst to elongation between IVP and MOET embryos. Datasets were downloaded from the Gene Expression Omnibus (GEO) database. Accession numbers were (1) for epigenomics: GSE69173, GSE97517, and GSE101895, plus one provided dataset from O’Doherty et al. (2018 BMC Genomics, 19, 438; https://doi.org/10.1186/s12864-018-4818-3), all hybridized to the EDMA platform GPL18384; (2) for transcriptomics: GSE12327, GSE21030, GSE24596, GSE24936, GSE27817, and GSE40101, all hybridized to the Affymetrix platform GPL2112. Both types of data were analysed with the limma package for R software, and functional enrichment analysis was done with the DAVID database. For DMRs, comparisons between IVP and MOET were made from spherical blastocysts (n=16 per group) on Day 7, to embryos on Day 15, specifically in the trophectoderm (TE) or embryonic disc (ED) regions (n=4 per region and per group). For DEGs, comparisons between IVP and MOET were made from spherical blastocysts (n=9 per group) to elongated blastocysts on Day 13 and embryos undergoing gastrulation on Day 16 (n=6 per group). Considering a P-value <0.05 and fold-change >2, there were 16 672 (TE) and 26 264 (ED) DMRs and 2236 DEGs that temporally differed between IVP and MOET. Most of the identified DMRs were found in intronic regions (around 36%) rather than exonic regions (8%). However, DMRs that were more methylated at IVP compared with MOET contained exons encoding for genes that enriched the Wnt signalling Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway in the ED, and focal adhesion and ECM-receptor interaction KEGG pathways (P<0.05) in the TE. Accordingly, DEGs with lower expression in elongated embryos (Day 13 and Day 16) at IVP as opposed to MOET were mainly associated with these three pathways. In conclusion, this multi-omics analysis demonstrates that even when embryos are produced under different conditions and experiments, the main changes imposed by IVP affected genes involved in embryonic development and adhesion to the endometrium, which could explain the lower survival rates at IVP compared with MOET.

scholarly journals Bioinformatic analysis identifies potential key genes of epilepsy

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0254326
Author(s):  
Yike Zhu ◽  
Dan Huang ◽  
Zhongyan Zhao ◽  
Chuansen Lu
Keyword(s):  
Network Analysis ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
P Value ◽  
Ppi Network ◽  
Functional Roles ◽  
Protein Protein Interaction

Background Epilepsy is one of the most common brain disorders worldwide. It is usually hard to be identified properly, and a third of patients are drug-resistant. Genes related to the progression and prognosis of epilepsy are particularly needed to be identified. Methods In our study, we downloaded the Gene Expression Omnibus (GEO) microarray expression profiling dataset GSE143272. Differentially expressed genes (DEGs) with a fold change (FC) >1.2 and a P-value <0.05 were identified by GEO2R and grouped in male, female and overlapping DEGs. Functional enrichment analysis and Protein-Protein Interaction (PPI) network analysis were performed. Results In total, 183 DEGs overlapped (77 ups and 106 downs), 302 DEGs (185 ups and 117 downs) in the male dataset, and 750 DEGs (464 ups and 286 downs) in the female dataset were obtained from the GSE143272 dataset. These DEGs were markedly enriched under various Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms. 16 following hub genes were identified based on PPI network analysis: ADCY7, C3AR1, DEGS1, CXCL1 in male-specific DEGs, TOLLIP, ORM1, ELANE, QPCT in female-specific DEGs and FCAR, CD3G, CLEC12A, MOSPD2, CD3D, ALDH3B1, GPR97, PLAUR in overlapping DEGs. Conclusion This discovery-driven study may be useful to provide a novel insight into the diagnosis and treatment of epilepsy. However, more experiments are needed in the future to study the functional roles of these genes in epilepsy.

scholarly journals Application of Bioinformatics Methods to Identify Key Genes and Functions in Chronic Pelvic Pain

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wenchao Sun ◽  
Qiji Ju
Keyword(s):  
Pelvic Pain ◽  
Chronic Pelvic Pain ◽  
Single Gene ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Receptor Interaction ◽  
Protein Protein Interaction ◽  
Key Genes

Neuropathologic pain (NPP) occurs in most patients with chronic pelvic pain (CPP), and the unique physiological characteristics of visceral sensory neurons make the current analgesic effect of CPP patients not optimistic. Therefore, this study explored the possible biological characteristics of key genes in CPP through the bioinformatics method. CPP-related dataset GSE131619 was downloaded from Gene Expression Omnibus to investigate the differentially expressed genes (DEGs) between lumbar dorsal root ganglia (DRG) and sacral DRG, and the functional enrichment analysis was performed. A protein-protein interaction (PPI) network was constructed to search subnet modules of specific biological processes, and then, the genes in the subnet were enriched by single gene set analysis. A CPP mouse model was established, and the expression of key genes were identified by qPCR. The results showed that 127 upregulated DEGs and 103 downregulated DEGs are identified. Functional enrichment analysis showed that most of the genes involved in signal transduction were involved in the pathway of receptor interaction. A subnet module related to neural signal regulation was identified in PPI, including CHRNB4, CHRNA3, and CHRNB2. All three genes were associated with neurological or inflammatory activity and are downregulated in the sacral spinal cord of CPP mice. This study provided three key candidate genes for CPP: CHRNB4, CHRNA3, and CHRNB2, which may be involved in the occurrence and development of CPP, and provided a powerful molecular target for the clinical diagnosis and treatment of CPP.

scholarly journals Identification of Common Oncogenic Genes and Pathways Both in Osteosarcoma and Ewing’s Sarcoma Using Bioinformatics Analysis

2021 ◽  
Author(s):  
Jingwei Zhang ◽  
Wenjun Liu ◽  
Liang Ding ◽  
Dongdong Cheng ◽  
Haijun Xiao
Keyword(s):  
Ewing’S Sarcoma ◽  
Ewing's Sarcoma ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
Hub Genes ◽  
Ppi Networks

Abstract Objective: This study aimed to explore common oncogenic genes and pathways both in osteosarcoma and Ewing’s sarcoma. Methods: Microarray data were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were respectively identified using the limma package, followed by intersection of common DEGs. Then, protein-protein interaction (PPI) networks were constructed and hub genes were identified. Furthermore, functional enrichment analysis was analyzed. The expression of common oncogenic genes was validated in 38 osteosarcoma and 17 Ewing’s sarcoma tissues by RT-qPCR and western blot. Results: 201 genes were differentially expressed. There were 121 nodes and 232 edges in the PPI network. 12 genes were considered as hub genes. Functional enrichment analysis results showed that hub genes FN1, COL1A1 and COL1A2 were all involved in extracellular matrix, protease binding and ECM-receptor interaction, which could be involved in the development of osteosarcoma and Ewing’s sarcoma. Among common oncogenic genes, FN1, COL1A1 and COL1A2 were lowly expressed both in osteosarcoma and Ewing’ s sarcoma tissues at mRNA and protein levels. Conclusion: Our findings revealed that common oncogenic genes such as FN1, COL1A1 and COL1A2 and pathways were both in osteosarcoma and Ewing’ s sarcoma.

scholarly journals Identification of Tumorigenic and Prognostic Biomarkers in Colorectal Cancer Based on microRNA Expression Profiles

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Yuntao Shi ◽  
Yingying Zhuang ◽  
Jialing Zhang ◽  
Mengxue Chen ◽  
Shangnong Wu
Keyword(s):  
Target Genes ◽  
Cox Regression ◽  
Expression Profiles ◽  
Survival Rates ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Risk Groups ◽  
Normal Mucosa ◽  
Microrna Expression ◽  
Functional Enrichment

Objective. Although noncoding RNAs, especially the microRNAs, have been found to play key roles in CRC development in intestinal tissue, the specific mechanism of these microRNAs has not been fully understood. Methods. GEO and TCGA database were used to explore the microRNA expression profiles of normal mucosa, adenoma, and carcinoma. And the differential expression genes were selected. Computationally, we built the SVM model and multivariable Cox regression model to evaluate the performance of tumorigenic microRNAs in discriminating the adenomas from normal tissues and risk prediction. Results. In this study, we identified 20 miRNA biomarkers dysregulated in the colon adenomas. The functional enrichment analysis showed that MAPK activity and MAPK cascade were highly enriched by these tumorigenic microRNAs. We also investigated the target genes of the tumorigenic microRNAs. Eleven genes, including PIGF, TPI1, KLF4, RARS, PCBP2, EIF5A, HK2, RAVER2, HMGN1, MAPK6, and NDUFA2, were identified to be frequently targeted by the tumorigenic microRNAs. The high AUC value and distinct overall survival rates between the two risk groups suggested that these tumorigenic microRNAs had the potential of diagnostic and prognostic value in CRC. Conclusions. The present study revealed possible mechanisms and pathways that may contribute to tumorigenesis of CRC, which could not only be used as CRC early detection biomarkers, but also be useful for tumorigenesis mechanism studies.

scholarly journals The Role of Circular RNA CDR1as/ciRS-7 in Regulating Tumor Microenvironment: A Pan-Cancer Analysis

Biomolecules ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 429 ◽  
Author(s):  
Zou ◽  
Zheng ◽  
Deng ◽  
Yang ◽  
Xie ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Signaling Pathway ◽  
Malignant Tumors ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Circular Rna ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Receptor Interaction

Circular RNA CDR1as/ciRS-7 functions as an oncogenic regulator in various cancers. However, there has been a lack of systematic and comprehensive analysis to further elucidate its underlying role in cancer. In the current study, we firstly performed a bioinformatics analysis of CDR1as among 868 cancer samples by using RNA-seq datasets of the MiOncoCirc database. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA), CIBERSORT, Estimating the Proportion of Immune and Cancer cells (EPIC), and the MAlignant Tumors using Expression data (ESTIMATE) algorithm were applied to investigate the underlying functions and pathways. Functional enrichment analysis suggested that CDR1as has roles associated with angiogenesis, extracellular matrix (ECM) organization, integrin binding, and collagen binding. Moreover, pathway analysis indicated that it may regulate the TGF-β signaling pathway and ECM-receptor interaction. Therefore, we used CIBERSORT, EPIC, and the ESTIMATE algorithm to investigate the association between CDR1as expression and the tumor microenvironment. Our data strongly suggest that CDR1as may play a specific role in immune and stromal cell infiltration in tumor tissue, especially those of CD8+ T cells, activated NK cells, M2 macrophages, cancer-associated fibroblasts (CAFs) and endothelial cells. Generally, systematic and comprehensive analyses of CDR1as were conducted to shed light on its underlying pro-cancerous mechanism. CDR1as regulates the TGF-β signaling pathway and ECM-receptor interaction to serve as a mediator in alteration of the tumor microenvironment.

scholarly journals Comparative Transcriptome Analysis Suggests Key Roles for 5-Hydroxytryptamlne Receptors in Control of Goose Egg Production

Genes ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 455 ◽  
Author(s):  
Qingyuan Ouyang ◽  
Shenqiang Hu ◽  
Guosong Wang ◽  
Jiwei Hu ◽  
Jiaman Zhang ◽  
...  
Keyword(s):  
Egg Production ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Production Performance ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
Anser Anser

To date, research on poultry egg production performance has only been conducted within inter or intra-breed groups, while those combining both inter- and intra-breed groups are lacking. Egg production performance is known to differ markedly between Sichuan white goose (Anser cygnoides) and Landes goose (Anser anser). In order to understand the mechanism of egg production performance in geese, we undertook this study. Here, 18 ovarian stromal samples from both Sichuan white goose and Landes goose at the age of 145 days (3 individuals before egg production initiation for each breed) and 730 days (3 high- and low egg production individuals during non-laying periods for each breed) were collected to reveal the genome-wide expression profiles of ovarian mRNAs and lncRNAs between these two geese breeds at different physiological stages. Briefly, 58, 347, 797, 777, and 881 differentially expressed genes (DEGs) and 56, 24, 154, 105, and 224 differentially expressed long non-coding RNAs (DElncRNAs) were found in LLD vs. HLD (low egg production Landes goose vs. high egg production Landes goose), LSC vs. HSC (low egg production Sichuan White goose vs. high egg production Sichuan white goose), YLD vs. YSC (young Landes goose vs. young Sichuan white goose), HLD vs. HSC (high egg production Landes goose vs. high egg production Sichuan white goose), and LLD vs. LSC (low egg production Landes goose vs. low egg production Sichuan white goose) groups, respectively. Functional enrichment analysis of these DEGs and DElncRNAs suggest that the “neuroactive ligand–receptor interaction pathway” is crucial for egg production, and particularly, members of the 5-hydroxytryptamine receptor (HTR) family affect egg production by regulating ovarian metabolic function. Furthermore, the big differences in the secondary structures among HTR1F and HTR1B, HTR2B, and HTR7 may lead to their different expression patterns in goose ovaries of both inter- and intra-breed groups. These results provide novel insights into the mechanisms regulating poultry egg production performance.

scholarly journals Identification and Validation of a Potential Prognostic 7-lncRNA Signature for Predicting Survival in Patients with Multiple Myeloma

2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Yun Zhong ◽  
Zhe Liu ◽  
Dangchi Li ◽  
Qinyuan Liao ◽  
Jingao Li
Keyword(s):  
Gene Expression ◽  
Risk Score ◽  
Cox Regression ◽  
External Validation ◽  
Survival Rates ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Test Set ◽  
Cox Regression Analysis

Background. An increasing number of studies have indicated that the abnormal expression of certain long noncoding RNAs (lncRNAs) is linked to the overall survival (OS) of patients with myeloma. Methods. Gene expression data of myeloma patients were downloaded from the Gene Expression Omnibus (GEO) database (GSE4581 and GSE57317). Cox regression analysis, Kaplan-Meier, and receiver operating characteristic (ROC) analysis were performed to construct and validate the prediction model. Single sample gene set enrichment (ssGSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to predict the function of a specified lncRNA. Results. In this study, a seven-lncRNA signature was identified and used to construct a risk score system for myeloma prognosis. This system was used to stratify patients with different survival rates in the training set into high-risk and low-risk groups. Test set, the entire test set, the external validation set, and the myeloma subtype achieved the authentication of the results. In addition, functional enrichment analysis indicated that 7 prognostic lncRNAs may be involved in the tumorigenesis of myeloma through cancer-related pathways and biological processes. The results of the immune score showed that IF_I was negatively correlated with the risk score. Compared with the published gene signature, the 7-lncRNA model has a higher C-index (above 0.8). Conclusion. In summary, our data provide evidence that seven lncRNAs could be used as independent biomarkers to predict the prognosis of myeloma, which also indicated that these 7 lncRNAs may be involved in the progression of myeloma.

scholarly journals Differential co-expression network analysis for identification of functional modules in HIV infection and tubercolosis-HIV co-infection

2016 ◽  
Vol 89 (2) ◽  
Author(s):  
Mohit Jha ◽  
Anvita Gupta ◽  
Sudha Singh ◽  
Khushhali Menaria Pandey
Keyword(s):  
Network Analysis ◽  
Hiv Infection ◽  
Enrichment Analysis ◽  
Gene Interaction ◽  
Gene Clusters ◽  
Functional Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Specific Gene ◽  
Diagnostic Strategies

Co-infection with tuberculosis (TB) is the preeminent cause of demise in human immunodeficiency virus (HIV) infected individuals. However, diagnosis of TB, particularly in the presence of an HIV co-infection, can be limiting owing to the high inaccuracy associated with conventional diagnostic strategies. Here we determine dysregulated pathways in TB-HIV co-infection and HIV infection utilizing coexpression networks. Primarily, we utilized preservation statistics to identify gene modules that exhibit a weak conservation of network topology within HIV infected and TB-HIV co-infected networks. Raw data was downloaded from Gene Expression Omnibus (GSE50834) and duly pre-processed. Co-expression networks for each condition (HIV infected and TB-HIV co-infected) were constructed independently. Preservation of HIV infected network edges was evaluated with respect to TB-HIV co-infected and vice versa using weighted correlation network analysis. Two out of the 22 modules were identified as exhibiting weak preservation in both conditions. Functional enrichment analysis identified that weakly preserved modules were pertinent to the condition under study. For instance, weakly preserved TBHIV co-infected module T1 enriched for genes associated with mitochondrion exhibited the highest fraction of gene interaction pairs exclusive to TB-HIV co-infection. Concisely, we illustrated the application of using preservation statistics to detect modules functionally linked with dysregulated pathways in disease, as exemplified by the mitochondrion module T1. Our analyses discovered gene clusters that are non-randomly linked with the disease. Highly specific gene pairs pointed to interactions between known markers of disease and favoured identification of possible markers that are likely to be associated with the disease.

KEGG-PATH: Kyoto encyclopedia of genes and genomes-based pathway analysis using a path analysis model

2014 ◽  
Vol 10 (9) ◽  
pp. 2441-2447 ◽  
Author(s):  
Junli Du ◽  
Zhifa Yuan ◽  
Ziwei Ma ◽  
Jiuzhou Song ◽  
Xiaoli Xie ◽  
...  
Keyword(s):  
Time Course ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Analysis Model ◽  
Kegg Pathways ◽  
Regulation Mechanisms ◽  
Complex Regulation ◽  
Multiple Treatments ◽  
Path Analysis Model

The KEGG-PATH approach, a kind of data mining through functional enrichment analysis of time-course experiments or those involving multiple treatments, can uncover the complex regulation mechanisms of KEGG pathways through the subdivision of total effect.

scholarly journals Comparative transcriptomic analysis of porcine peripheral blood reveals differentially expressed genes from the cytokine–cytokine receptor interaction pathway related to health status

Genome ◽  
2017 ◽  
Vol 60 (12) ◽  
pp. 1021-1028 ◽  
Author(s):  
M.H. Ye ◽  
H. Bao ◽  
Y. Meng ◽  
L.L. Guan ◽  
P. Stothard ◽  
...  
Keyword(s):  
Health Status ◽  
Differentially Expressed Genes ◽  
Peripheral Blood ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Cytokine Receptor ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
Marker Genes

While some research has looked into the host genetic response in pigs challenged with specific viruses or bacteria, few studies have explored the expression changes of transcripts in the peripheral blood of sick pigs that may be infected with multiple pathogens on farms. In this study, the architecture of the peripheral blood transcriptome of 64 Duroc sired commercial pigs, including 18 healthy animals at entry to a growing facility (set as a control) and 23 pairs of samples from healthy and sick pen mates, was generated using RNA-Seq technology. In total, 246 differentially expressed genes were identified to be specific to the sick animals. Functional enrichment analysis for those genes revealed that the over-represented gene ontology terms for the biological processes category were exclusively immune activity related. The cytokine–cytokine receptor interaction pathway was significantly enriched. Nine functional genes from this pathway encoding members (as well as their receptors) of the interleukins, chemokines, tumor necrosis factors, colony stimulating factors, activins, and interferons exhibited significant transcriptional alteration in sick animals. Our results suggest a subset of novel marker genes that may be useful candidate genes in the evaluation and prediction of health status in pigs under commercial production conditions.

Sign in / Sign up

Export Citation Format

Share Document