scholarly journals Unraveling proteome changes and potential regulatory proteins of bovine follicular Granulosa cells by mass spectrometry and multi-omics analysis

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Shuning Hou ◽  
Qingling Hao ◽  
Zhiwei Zhu ◽  
Dongmei Xu ◽  
Wenzhong Liu ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Data Analysis ◽  
Pathway Analysis ◽  
Kegg Pathway ◽  
Follicular Development ◽  
Regulatory Proteins ◽  
Differentially Expressed ◽  
Omics Data ◽  
Kegg Pathway Analysis ◽  
Omics Data Analysis

Abstract Background In previous study, we performed next-gene sequencing to investigate the differentially expressed transcripts of bovine follicular granulosa cells (GCs) at dominant follicle (DF) and subordinate follicle (SF) stages during first follicular wave. Present study is designed to further identify the key regulatory proteins and signaling pathways associated with follicular development using label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) and multi-omics data analysis approach. Methods DF and SF from three cattle were collected by daily ultrasonography. The GCs were isolated from each follicle, total proteins were digested by trypsin, and then proteomic analyzed via LC-MS/MS, respectively. Proteins identified were retrieved from Uniprot-COW fasta database, and differentially expressed proteins were used to functional enrichment and KEGG pathway analysis. Proteome data and transcriptome data obtained from previous studies were integrated. Results Total 3409 proteins were identified from 30,321 peptides (FDR ≤0.01) obtained from LC-MS/MS analysis and 259 of them were found to be differentially expressed at different stage of follicular development (fold Change > 2, P < 0.05). KEGG pathway analysis of proteome data revealed important signaling pathways associated with follicular development, multi-omics data analysis results showed 13 proteins were identified as being differentially expressed in DF versus SF. Conclusions This study represents the first investigation of transcriptome and proteome of bovine follicles and offers essential information for future investigation of DF and SF in cattle. It also will enrich the theory of animal follicular development.

scholarly journals Bioinformatic Analysis of Circular RNA-Associated ceRNA Network Associated with Hepatocellular Carcinoma

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Jiacheng Wu ◽  
Shui Liu ◽  
Yien Xiang ◽  
Xianzhi Qu ◽  
Yingjun Xie ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Pathway Analysis ◽  
Target Genes ◽  
Kegg Pathway ◽  
Interaction Network ◽  
Bioinformatic Analysis ◽  
Differentially Expressed ◽  
Qrt Pcr ◽  
Cerna Network ◽  
Kegg Pathway Analysis

Hepatocellular carcinoma (HCC) is the sixth most common cancer worldwide and is associated with a high mortality rate and poor treatment efficacy. In an attempt to investigate the mechanisms involved in the pathogenesis of HCC, bioinformatic analysis and validation by qRT-PCR were performed. Three circRNA GEO datasets and one miRNA GEO dataset were selected for this purpose. Upon combined biological prediction, a total of 11 differentially expressed circRNAs, 15 differentially expressed miRNAs, and 560 target genes were screened to construct a circRNA-related ceRNA network. GO analysis and KEGG pathway analysis were performed for the 560 target genes. To further screen key genes, a protein-protein interaction network of the target genes was constructed using STRING, and the genes and modules with higher degree were identified by MCODE and CytoHubba plugins of Cytoscape. Subsequently, a module was screened out and subjected to GO enrichment analysis and KEGG pathway analysis. This module included eight genes, which were further screened using TCGA. Finally, UBE2L3 was selected as a key gene and the hsa_circ_0009910–miR-1261–UBE2L3 regulatory axis was established. The relative expression of the regulatory axis members was confirmed by qRT-PCR in 30 pairs of samples, including HCC tissues and adjacent nontumor tissues. The results suggested that hsa_circ_0009910, which was upregulated in HCC tissues, participates in the pathogenesis of HCC by acting as a sponge of miR-1261 to regulate the expression of UBE2L3. Overall, this study provides support for the possible mechanisms of progression in HCC.

scholarly journals Bioinformatics analysis for the identification of differentially expressed genes and related signaling pathways in H. pylori-CagA transfected gastric cancer cells

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11203
Author(s):  
Dingyu Chen ◽  
Chao Li ◽  
Yan Zhao ◽  
Jianjiang Zhou ◽  
Qinrong Wang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Helicobacter Pylori ◽  
Differentially Expressed Genes ◽  
Pathway Analysis ◽  
Kegg Pathway ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Ags Cells ◽  
Kegg Pathway Analysis ◽  
Key Genes

Aim Helicobacter pylori cytotoxin-associated protein A (CagA) is an important virulence factor known to induce gastric cancer development. However, the cause and the underlying molecular events of CagA induction remain unclear. Here, we applied integrated bioinformatics to identify the key genes involved in the process of CagA-induced gastric epithelial cell inflammation and can ceration to comprehend the potential molecular mechanisms involved. Materials and Methods AGS cells were transected with pcDNA3.1 and pcDNA3.1::CagA for 24 h. The transfected cells were subjected to transcriptome sequencing to obtain the expressed genes. Differentially expressed genes (DEG) with adjusted P value < 0.05, — logFC —> 2 were screened, and the R package was applied for gene ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The differential gene protein–protein interaction (PPI) network was constructed using the STRING Cytoscape application, which conducted visual analysis to create the key function networks and identify the key genes. Next, the Kaplan–Meier plotter survival analysis tool was employed to analyze the survival of the key genes derived from the PPI network. Further analysis of the key gene expressions in gastric cancer and normal tissues were performed based on The Cancer Genome Atlas (TCGA) database and RT-qPCR verification. Results After transfection of AGS cells, the cell morphology changes in a hummingbird shape and causes the level of CagA phosphorylation to increase. Transcriptomics identified 6882 DEG, of which 4052 were upregulated and 2830 were downregulated, among which q-value < 0.05, FC > 2, and FC under the condition of ≤2. Accordingly, 1062 DEG were screened, of which 594 were upregulated and 468 were downregulated. The DEG participated in a total of 151 biological processes, 56 cell components, and 40 molecular functions. The KEGG pathway analysis revealed that the DEG were involved in 21 pathways. The PPI network analysis revealed three highly interconnected clusters. In addition, 30 DEG with the highest degree were analyzed in the TCGA database. As a result, 12 DEG were found to be highly expressed in gastric cancer, while seven DEG were related to the poor prognosis of gastric cancer. RT-qPCR verification results showed that Helicobacter pylori CagA caused up-regulation of BPTF, caspase3, CDH1, CTNNB1, and POLR2A expression. Conclusion The current comprehensive analysis provides new insights for exploring the effect of CagA in human gastric cancer, which could help us understand the molecular mechanism underlying the occurrence and development of gastric cancer caused by Helicobacter pylori.

scholarly journals GAIT-GM: Galaxy tools for modeling metabolite changes as a function of gene expression

2020 ◽  
Author(s):  
Lauren M. McIntyre ◽  
Francisco Huertas ◽  
Olexander Moskalenko ◽  
Marta Llansola ◽  
Vicente Felipo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Text Mining ◽  
Kegg Pathway ◽  
Annotation Tool ◽  
Omics Data ◽  
Metabolomics Data ◽  
Pathway Data ◽  
User Friendly ◽  
Omics Data Analysis

AbstractGalaxy is a user-friendly platform with a strong development community and a rich set of tools for omics data analysis. While multi-omics experiments are becoming popular, tools for multi-omics data analysis are poorly represented in this platform. Here we present GAIT-GM, a set of new Galaxy tools for integrative analysis of gene expression and metabolomics data. In the Annotation Tool, features are mapped to KEGG pathway using a text mining approach to increase the number of mapped metabolites. Several interconnected databases are used to maximally map gene IDs across species. In the Integration Tool, changes in metabolite levels are modelled as a function of gene expression in a flexible manner. Both unbiased exploration of relationships between genes and metabolites and biologically informed models based on pathway data are enabled. The GAIT-GM tools are freely available at https://github.com/SECIMTools/gait-gm.

scholarly journals Identification of major depressive disorder disease-related genes and functional pathways based on system dynamic changes of network connectivity

2020 ◽  
Author(s):  
Ruijie Geng ◽  
Xiao Huang
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Pathway Analysis ◽  
Healthy Subjects ◽  
Kegg Pathway ◽  
Network Connectivity ◽  
Differentially Expressed ◽  
Therapeutic Drug ◽  
Major Depressive ◽  
Kegg Pathway Analysis

Abstract Objective: Major depressive disorder (MDD) is a leading psychiatric disorder that involves complex abnormal biological functions and neural networks. This study aimed to compare the changes in the network connectivity of different brain tissues under different pathological conditions, analyzed the biological pathways and genes that are significantly related to disease progression, and further predicted the potential therapeutic drug targets.Methods: Expression of differentially expressed genes (DEGs) were analyzed with postmortem cingulate cortex (ACC) and prefrontal cortex (PFC) mRNA expression profile datasets downloaded from the Gene Expression Omnibus (GEO) database, including 76 MDD patients and 76 healthy subjects in ACC and 63 MDD patients and 63 healthy subjects in PFC. The co-expression network construction was based on system network analysis. The function of the genes was annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Human Protein Reference Database (HPRD, http://www.hprd.org/) was used for gene interaction relationship mapping.Results: We filtered 586 DEGs in ACC and 616 DEGs in PFC for further analysis. By constructing the Co-expression network, we found that the gene connectivity was significantly reduced under disease conditions (P=0.04 in PFC and P=1.227e-09 in ACC). Crosstalk analysis showed that CD19, PTDSS2 and NDST2 were significantly differentially expressed in ACC and PFC of MDD patients. Among them, CD19 and PTDSS2 have been targeted by several drugs in the Drugbank database. KEGG pathway analysis demonstrated that the function of CD19 and PTDSS2 were enriched with the pathway of Glycerophospholipid metabolism and T cell receptor signaling pathway. Conclusion: Co-expression network and tissue comparing analysis can identify signaling pathways and cross talk genes related to MDD, which may provide novel insight for understanding the molecular mechanisms of MDD.

Ouabain rescues rat nephrogenesis during intrauterine growth restriction by regulating the complement and coagulation cascades and calcium signaling pathway

2015 ◽  
Vol 7 (1) ◽  
pp. 91-101 ◽  
Author(s):  
L. Chen ◽  
J. Yue ◽  
X. Han ◽  
J. Li ◽  
Y. Hu
Keyword(s):  
Calcium Signaling ◽  
Signaling Pathway ◽  
Pathway Analysis ◽  
Intrauterine Growth Restriction ◽  
Kegg Pathway ◽  
Growth Restriction ◽  
Differentially Expressed ◽  
Intrauterine Growth ◽  
Kegg Pathway Analysis ◽  
Calcium Signaling Pathway

Intrauterine growth restriction (IUGR) is associated with a reduction in the numbers of nephrons in neonates, which increases the risk of hypertension. Our previous study showed that ouabain protects the development of the embryonic kidney during IUGR. To explore this molecular mechanism, IUGR rats were induced by protein and calorie restriction throughout pregnancy, and ouabain was delivered using a mini osmotic pump. RNA sequencing technology was used to identify the differentially expressed genes (DEGs) of the embryonic kidneys. DEGs were submitted to the Database for Annotation and Visualization and Integrated Discovery, and gene ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted. Maternal malnutrition significantly reduced fetal weight, but ouabain treatment had no significant effect on body weight. A total of 322 (177 upregulated and 145 downregulated) DEGs were detected between control and the IUGR group. Meanwhile, 318 DEGs were found to be differentially expressed (180 increased and 138 decreased) between the IUGR group and the ouabain-treated group. KEGG pathway analysis indicated that maternal undernutrition mainly disrupts the complement and coagulation cascades and the calcium signaling pathway, which could be protected by ouabain treatment. Taken together, these two biological pathways may play an important role in nephrogenesis, indicating potential novel therapeutic targets against the unfavorable effects of IUGR.

scholarly journals Transcriptome Analysis of Bovine Ovarian Follicles at Predeviation and Onset of Deviation Stages of a Follicular Wave

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Pengfei Li ◽  
Jinzhu Meng ◽  
Wenzhong Liu ◽  
George W. Smith ◽  
Jianbo Yao ◽  
...  
Keyword(s):  
Pathway Analysis ◽  
Kegg Pathway ◽  
Steroid Hormone ◽  
Follicular Development ◽  
Regulatory Mechanisms ◽  
Biosynthesis Pathway ◽  
Follicular Wave ◽  
Kegg Pathway Analysis ◽  
Hormone Biosynthesis ◽  
Upregulated Genes

For two libraries (PDF1 and ODF1) using Illumina sequencing 44,082,301 and 43,708,132 clean reads were obtained, respectively. After being mapped to the bovine RefSeq database, 15,533 genes were identified to be expressed in both types of follicles (cut-off RPKM > 0.5), of which 719 were highly expressed in bovine follicles (cut-off RPKM > 100). Furthermore, 83 genes were identified as being differentially expressed in ODF1 versus PDF1, where 42 genes were upregulated and 41 genes were downregulated. KEGG pathway analysis revealed two upregulated genes in ODF1 versus PDF1, CYP11A1, and CYP19A1, which are important genes in the steroid hormone biosynthesis pathway. This study represents the first investigation of transcriptome of bovine follicles at predeviation and onset of deviation stages and provides a foundation for future investigation of the regulatory mechanisms involved in follicular development in cattle.

Identification of differentially expressed genes, biological pathways and prognostic signature in bladder cancer

2021 ◽  
Author(s):  
Fucai Tang ◽  
Xiayan Qian ◽  
Zeguang Lu ◽  
Yongchang Lai ◽  
Zhibiao Li ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Differentially Expressed Genes ◽  
Pathway Analysis ◽  
Kegg Pathway ◽  
Biological Pathways ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Go Analysis ◽  
Ppi Networks ◽  
Kegg Pathway Analysis

Abstract Background Bladder cancer (BC) is one of the most common malignant cancer of urinary system in the worldwide. The purpose of the present study was to analysis differentially expressed genes (DEGs), biological pathways and prognostic significance BC by bioinformatics analysis. Methods The gene expression dataset GSE7476 and the mRNA Seq sequencing data were downloaded respectively from GEO and TCGA. A total of 220 DEGs were obtained in BC. GO analysis and KEGG pathway analysis were performed for up- and down-regulated DEGs. Then, a protein-protein interaction (PPI) networks and module were constructed by Cytoscape software. Survival analysis of hub genes was performed. Results The result of GO analysis revealed that the up-regulated DEGs were enriched mainly in sister chromatid segregation, while the down-regulated DEGs were enriched mainly in muscle contraction. The result of KEGG pathway analysis showed that up-regulated DEGs were enriched mainly in cell cycle, while down-regulated DEGs enriched in IL-17 signaling pathway. 41 hub gene and 3 crucial modules were identified in the PPI network. 15 genes significantly associated with patient prognosis in BC were obtained by Kaplan-Meier analysis. Conclusions In summary, the present study identified hub genes, crucial pathways and provide possible the molecular targets and prognostic biomarkers for targeted therapy and prognostic assessment of BC.

scholarly journals Identification of major depressive disorder disease-related genes and functional pathways based on system dynamic changes of network connectivity

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Ruijie Geng ◽  
Xiao Huang
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Pathway Analysis ◽  
Healthy Subjects ◽  
Kegg Pathway ◽  
Network Connectivity ◽  
Differentially Expressed ◽  
Therapeutic Drug ◽  
Major Depressive ◽  
Kegg Pathway Analysis

Abstract Background Major depressive disorder (MDD) is a leading psychiatric disorder that involves complex abnormal biological functions and neural networks. This study aimed to compare the changes in the network connectivity of different brain tissues under different pathological conditions, analyzed the biological pathways and genes that are significantly related to disease progression, and further predicted the potential therapeutic drug targets. Methods Expression of differentially expressed genes (DEGs) were analyzed with postmortem cingulate cortex (ACC) and prefrontal cortex (PFC) mRNA expression profile datasets downloaded from the Gene Expression Omnibus (GEO) database, including 76 MDD patients and 76 healthy subjects in ACC and 63 MDD patients and 63 healthy subjects in PFC. The co-expression network construction was based on system network analysis. The function of the genes was annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Human Protein Reference Database (HPRD, http://www.hprd.org/) was used for gene interaction relationship mapping. Results We filtered 586 DEGs in ACC and 616 DEGs in PFC for further analysis. By constructing the co-expression network, we found that the gene connectivity was significantly reduced under disease conditions (P = 0.04 in PFC and P = 1.227e−09 in ACC). Crosstalk analysis showed that CD19, PTDSS2 and NDST2 were significantly differentially expressed in ACC and PFC of MDD patients. Among them, CD19 and PTDSS2 have been targeted by several drugs in the Drugbank database. KEGG pathway analysis demonstrated that the function of CD19 and PTDSS2 were enriched with the pathway of Glycerophospholipid metabolism and T cell receptor signaling pathway. Conclusion Co-expression network and tissue comparing analysis can identify signaling pathways and cross talk genes related to MDD, which may provide novel insight for understanding the molecular mechanisms of MDD.

Transcriptional identification of differentially expressed genes during the prepupal–pupal transition in the oriental armyworm, Mythimna separata (Walker) (Lepidoptera: Noctuidae)

2021 ◽  
pp. 1-14
Author(s):  
Peirong Li ◽  
Xinru Li ◽  
Wei Wang ◽  
Xiaoling Tan ◽  
Xiaoqi Wang ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Metabolic Pathways ◽  
Developmental Stages ◽  
Kegg Pathway ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Mythimna Separata ◽  
Kegg Pathway Analysis ◽  
Oriental Armyworm ◽  
Lepidoptera Noctuidae

Abstract The oriental armyworm, Mythimna separata (Walker) is a serious pest of agriculture that does particular damage to Gramineae crops in Asia, Europe, and Oceania. Metamorphosis is a key developmental stage in insects, although the genes underlying the metamorphic transition in M. separata remain largely unknown. Here, we sequenced the transcriptomes of five stages; mature larvae (ML), wandering (W), and pupation (1, 5, and 10 days after pupation, designated P1, P5, and P10) to identify transition-associated genes. Four libraries were generated, with 22,884, 23,534, 26,643, and 33,238 differentially expressed genes (DEGs) for the ML-vs-W, W-vs-P1, P1-vs-P5, and P5-vs-P10, respectively. Gene ontology enrichment analysis of DEGs showed that genes regulating the biosynthesis of the membrane and integral components of the membrane, which includes the cuticular protein (CP), 20-hydroxyecdysone (20E), and juvenile hormone (JH) biosynthesis, were enriched. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that DEGs were enriched in the metabolic pathways. Of these DEGs, thirty CP, seventeen 20E, and seven JH genes were differentially expressed across the developmental stages. For transcriptome validation, ten CP, 20E, and JH-related genes were selected and verified by real-time PCR quantitative. Collectively, our results provided a basis for further studies of the molecular mechanism of metamorphosis in M. separata.

scholarly journals POS0399 TMT-BASED QUANTITATIVE PROTEOMICS ANALYSIS OF SYNOVIAL FLUID-DERIVED EXOSOMES IN RHEUMATOID ARTHRITIS, AXIAL SPONDYLOARTHRITIS, GOUT AND OSTEOARTHRITIS

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 428.3-429
Author(s):  
Y. Liu ◽  
Y. Huang ◽  
Q. Huang ◽  
Z. Huang ◽  
Z. Li ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Cluster Analysis ◽  
Synovial Fluid ◽  
Pathway Analysis ◽  
Quantitative Proteomics ◽  
Axial Spondyloarthritis ◽  
Kegg Pathway ◽  
Hierarchical Cluster ◽  
Kegg Pathway Analysis ◽  
Differential Proteins

Background:The pathogeneses of the joint diseases rheumatoid arthritis (RA), axial spondyloarthritis (axSpA), gout, and osteoarthritis (OA) are still not fully elucidated. Exosomes in synovial fluid (SF) has a critical role in the pathogenesis of arthritis. None of study has compared the proteomics of SF-derived exosomes in RA, axSpA, gout and OA.Objectives:To compare the proteomics of SF-derived exosomes in RA, axSpA, gout and OA based on tandem mass tags (TMT) labeled quantitative proteomics technique.Methods:SF-derived exosomes was isolated from RA, axSpA, gout and OA patients by the Exoquick kit combined ultracentrifugation method. TMT labeled quantitative proteomics technique was used to compare the proteomics of SF-derived exosomes. Volcano plot, hierarchical cluster, Gene Ontologies (GO), Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted.Results:A total of 1678 credible proteins were detected. With the cut off criteria of |log2 (fold-change)| ≥1.2 and p-value <0.05, 267 (140 up-regulated and 127 down-regulated)differential proteins were found in OA vs gout, 291 (179 and 112) in axSpA vs OA, 515 (109 and 406) in RA vs axSpA, 298 (191 and 107) in axSpA vs gout, 462 (160 and 302) in RA vs gout, 536 (170 and 366) in RA vs OA. GO analysis showed that the biological progress of differential proteins were mainly enriched in the “immune response”. Regarding the molecular function, the differential proteins mainly mediated “antigen binding”. GO analysis of the cellular components indicated that most proteins were annotated as “extracellular exosomes”. KEGG pathway analysis demonstrated differential proteins were significantly enriched in “complement and coagulation cascades”. The hierarchical cluster analysis of the differential proteins in the four groups showed that Lysozyme C and Keratin were more abundant in gout, Hemoglobin and Actin-related protein 2/3 complex subunit 3 in OA, Sodium/potassium-transporting ATPase subunit alpha-1 and Immunoglobulin heavy constant delta in axSpA, Pregnancy zone protein and Stromelysin-1 in RA.Conclusion:The protein profiles of SF-derived exosomes in RA, axSpA, gout and OA patients were different. The differential proteins were the potential biomarkers of RA, axSpA, gout and OA.References:[1]Cretu D, Diamandis E P, Chandran V. Delineating the synovial fluid proteome: recent advancements and ongoing challenges in biomarker research.[J]. Critical reviews in clinical laboratory sciences, 2013,50(2):51-63.[2]McArdle A J, Menikou S. What is proteomics?[J]. Archives of disease in childhood. Education and practice edition, 2020.Figure 1.The hierarchical cluster analysis of differential proteins in axSpA, OA, Gout and RA.Disclosure of Interests:None declared

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