scholarly journals Integrated transcriptomic and proteomic study on the different molecular mechanisms of PC12 cell growth on chitosan and collagen/chitosan films

2020 ◽  
Vol 7 (6) ◽  
pp. 553-565
Author(s):  
Xiaoying Lü ◽  
Yan Huang ◽  
Yayun Qu ◽  
Yiwen Zhang ◽  
Zequn Zhang
Keyword(s):  
Cell Adhesion ◽  
Cell Growth ◽  
Cell Viability ◽  
Pc12 Cells ◽  
Differentially Expressed Genes ◽  
Pc12 Cell ◽  
Molecular Mechanisms ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
Chitosan Films

Abstract The purpose of this article is to integrate the transcriptomic analysis and the proteomic profiles and to reveal and compare the different molecular mechanisms of PC12 cell growth on the surface of chitosan films and collagen/chitosan films. First, the chitosan films and the collagen/chitosan films were prepared. Subsequently, the cell viability assay was performed; the cell viability of the PC12 cells cultured on the collagen/chitosan films for 24 h was significantly higher than that on the chitosan films. Then, with cDNA microarray, the numbers of differentially expressed genes of PC12 cells on the surface of chitosan and collagen/chitosan films were 13349 and 5165, respectively. Next, the biological pathway analysis indicated that the differentially expressed genes were involved in 40 pathways directly related to cell adhesion and growth. The integrated transcriptomic and our previous proteomic analysis revealed that three biological pathways—extracellular matrix–receptor interaction, focal adhesion and regulation of actin cytoskeleton—were regulated in the processes of protein adsorption, cell adhesion and growth. The adsorbed proteins on the material surfaces further influenced the expression of important downstream genes by regulating the expression of related receptor genes in these three pathways. In comparison, chitosan films had a strong inhibitory effect on PC12 cell adhesion and growth, resulting in the significantly lower cell viability on its surface; on the contrary, collagen/chitosan films were more conducive to promoting PC12 cell adhesion and growth, resulting in higher cell viability.

scholarly journals Identification of Differentially Expressed Genes in Porcine Ovaries at Proestrus and Estrus Stages Using RNA-Seq Technique

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Songbai Yang ◽  
Xiaolong Zhou ◽  
Yue Pei ◽  
Han Wang ◽  
Ke He ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Expression Patterns ◽  
Hormone Secretion ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
The Novel ◽  
Kegg Pathways ◽  
Go Enrichment ◽  
Single Organism

Estrus is an important factor for the fecundity of sows, and it is involved in ovulation and hormone secretion in ovaries. To better understand the molecular mechanisms of porcine estrus, the expression patterns of ovarian mRNA at proestrus and estrus stages were analyzed using RNA sequencing technology. A total of 2,167 differentially expressed genes (DEGs) were identified (P≤0.05, log2  Ratio≥1), of which 784 were upregulated and 1,383 were downregulated in the estrus compared with the proestrus group. Gene Ontology (GO) enrichment indicated that these DEGs were mainly involved in the cellular process, single-organism process, cell and cell part, and binding and metabolic process. In addition, a pathway analysis showed that these DEGs were significantly enriched in 33 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, including cell adhesion molecules, ECM-receptor interaction, and cytokine-cytokine receptor interaction. Quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR) confirmed the differential expression of 10 selected DEGs. Many of the novel candidate genes identified in this study will be valuable for understanding the molecular mechanisms of the sow estrous cycle.

scholarly journals Transcriptome analysis reveals modulation of the STAT family in PEDV-infected IPEC-J2 cells

BMC Genomics ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhengzheng Hu ◽  
Yuchen Li ◽  
Heng Du ◽  
Junxiao Ren ◽  
Xianrui Zheng ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Differentially Expressed Genes ◽  
Influenza A ◽  
Molecular Mechanisms ◽  
Host Cells ◽  
Differentially Expressed ◽  
Economic Losses ◽  
Receptor Interaction ◽  
Epithelial Cell Line ◽  
Diarrhea Virus

Abstract Background Porcine epidemic diarrhea virus (PEDV) is a causative agent of serious viral enteric disease in suckling pigs. Such diseases cause considerable economic losses in the global swine industry. Enhancing our knowledge of PEDV-induced transcriptomic responses in host cells is imperative to understanding the molecular mechanisms involved in the immune response. Here, we analyzed the transcriptomic profile of intestinal porcine epithelial cell line J2 (IPEC-J2) after infection with a classical strain of PEDV to explore the host response. Results In total, 854 genes were significantly differentially expressed after PEDV infection, including 716 upregulated and 138 downregulated genes. Functional annotation analysis revealed that the differentially expressed genes were mainly enriched in the influenza A, TNF signaling, inflammatory response, cytokine receptor interaction, and other immune-related pathways. Next, the putative promoter regions of the 854 differentially expressed genes were examined for the presence of transcription factor binding sites using the MEME tool. As a result, 504 sequences (59.02%) were identified as possessing at least one binding site of signal transducer and activator of transcription (STAT), and five STAT transcription factors were significantly induced by PEDV infection. Furthermore, we revealed the regulatory network induced by STAT members in the process of PEDV infection. Conclusion Our transcriptomic analysis described the host genetic response to PEDV infection in detail in IPEC-J2 cells, and suggested that STAT transcription factors may serve as key regulators in the response to PEDV infection. These results further our understanding of the pathogenesis of PEDV.

scholarly journals Identification of differentially expressed genes and signaling pathways in rheumatoid arthritis by integrated bioinformatics analysis

2020 ◽  
Author(s):  
Yanzhi Ge ◽  
Li Zhou ◽  
Zuxiang Chen ◽  
Yingying Mao ◽  
Ting Li ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Signaling Pathways ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Kegg Pathway ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
Effective Prevention ◽  
Ppi Networks

Abstract Background The disability rate associated with rheumatoid arthritis (RA) ranks high among inflammatory joint diseases. However, the cause and potential molecular events are as yet not clear. Here, we aimed to identify key genes and pathways involved in RA utilizing integrated bioinformatics analysis and uncover underlying molecular mechanisms. Materials and methods The expression profiles of GSE55235, GSE55457, GSE55584 and GSE77298 were downloaded from the Gene Expression Omnibus database, which contained 76 synovial membrane samples, including 49 RA samples and 27 controls. The microarray datasets were consolidated and differentially expressed genes (DEGs) were acquired and further analyzed by bioinformatics techniques. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were performed using R (version 3.6.1), respectively. The protein-protein interaction (PPI) networks of DEGs were developed utilizing the STRING database. Results A total of 828 DEGs were recognized, with 758 up-regulated and 70 down-regulated. GO and KEGG pathway analyses demonstrated that these DEGs focused primarily on multifactorial binding, transcription activity, cytokin-cytokin receptor interaction and relevant signaling pathways. The 30 most firmly related genes among DEGs were identified from the PPI network. Conclusion This study shows that screening for DEGs and pathways utilizing integrated bioinformatics analyses could aid in the comprehension of the molecular mechanisms involved in RA development. In addition, our study provides valuable data for the effective prevention, diagnosis, treatment and rehabilitation of RA patients as well as providing potential targets for the treatment of RA.

scholarly journals Network Analyses of Differentially Expressed Genes in Osteoarthritis to Identify Hub Genes

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Zhaoyan Li ◽  
Lei Zhong ◽  
Zhenwu Du ◽  
Gaoyang Chen ◽  
Jing Shang ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
Hub Genes ◽  
Expression Levels ◽  
Network Analyses ◽  
Synovial Membranes

Background. Osteoarthritis (OA) is the most common degenerative disease in orthopedics. However, the cause and underlying molecular mechanisms are not clear. This study aims to identify the hub genes and pathways involved in the occurrence of osteoarthritis. Methods. The raw data of GSE89408 were downloaded from the Gene Expression Omnibus (GEO) database, and the differentially expressed genes (DEGs) were identified by R software. The DAVID database was used for pathway and gene ontology analysis, and p<0.05 and gene count >2 were set as the cut-off point. Moreover, protein-protein interaction (PPI) network construction was applied for exploring the hub genes in osteoarthritis. The expression levels of the top ten hub genes in knee osteoarthritis synovial membranes and controls were detected by quantitative real-time PCR system. Results. A total of 229 DEGs were identified in osteoarthritis synovial membranes compared with normal synovial membranes, including 145 upregulated and 84 downregulated differentially expressed genes. The KEGG pathway analysis results showed that up-DEGs were enriched in proteoglycans in cytokine-cytokine receptor interaction, chemokine signaling pathway, rheumatoid arthritis, and TNF signaling pathway, whereas down-DEGs were enriched in the PPAR signaling pathway and AMPK signaling pathway. The qRT-PCR results showed that the expression levels of ADIPOQ, IL6, and CXCR1 in the synovium of osteoarthritis were significantly increased (p <0.05).

scholarly journals Identification of Differentially Expressed Genes and Enriched Pathways in SARS-CoV-2/ COVID-19 using Bioinformatics Analysis

2020 ◽  
Author(s):  
Ali Mohamed Alshabi ◽  
Ibrahim Ahmed Shaikh ◽  
Basavaraj Mallikarjunayya Vastrad ◽  
Chanabasayya Mallikarjunayya Vastrad
Keyword(s):  
Differentially Expressed Genes ◽  
Messenger Rna ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
Diagnostic Efficiency ◽  
Current Investigation ◽  
Roc Curve Analysis

Abstract BackgroundThe exact molecular mechanisms of the progression of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection remain unclear. The current investigation strived to understand and functionally analyze the differentially expressed genes (DEGs) between SARS-CoV-2 infection and mock samples applying extensive bioinformatics analyses.MethodsGSE148729 dataset was downloaded from the Gene Expression Omnibus (GEO) and investigated utilising the limma package in R software to identify DEGs. Pathway and gene ontology (GO) enrichment analysis of the up and down-regulated genes were performed in ToppGene. The HIPPIE database was applied to estimate the interactions of up and down-regulated genes and to construct a protein-protein interaction (PPI) network using Cytoscape software. Receiver operating characteristic (ROC) was utilized for validation.ResultsA total of 928 DEGs (461 up-regulated genes and 467 down-regulated genes) were identified between SARS-CoV-2 infection and mock samples. The up and down-regulated genes were significantly enriched in cytokine-cytokine receptor interaction, and ascorbate and aldarate metabolism. Several significant GO terms, including the response to biotic stimulus and oxoacid metabolic process, were identified. The top hub genes and target genes included JUN, FBXO6, PCLAF, CFTR, TXNIP, PMAIP1, BRI3BP, FAHD1, PROX1, CXCL11, SERHL2 and CFI. ROC curve analysis showed that messenger RNA levels of these ten genes (DDX58, IFITM2, IRF1, PML, SAMHD1, ACSS1, CYP2U1, DDC, PNMT and UGT2A3) exhibited better diagnostic efficiency between SARS-CoV-2 infection and mock.ConclusionsThe current investigation distinguished vital genes and pathways that may be implicated in the progression of SARS-CoV-2 infection, providing a new understanding of the underlying molecular mechanisms of SARS-CoV-2 infection.

scholarly journals Transcriptomic Analyses of the Hypothalamic-Pituitary-Gonadal Axis Identify Candidate Genes Related to Egg Production in Xinjiang Yili Geese

Animals ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 90
Author(s):  
Yingping Wu ◽  
Xiaoyu Zhao ◽  
Li Chen ◽  
Junhua Wang ◽  
Yuqing Duan ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Differentially Expressed Genes ◽  
Egg Production ◽  
Molecular Mechanisms ◽  
Egg Laying ◽  
Differentially Expressed ◽  
Cdna Libraries ◽  
Receptor Interaction ◽  
Sequencing Data ◽  
Pituitary Glands

The study was conducted to investigate the transcriptomic differences of the hypothalamic-pituitary-gonadal axis between Xinjiang Yili geese with high and low egg production and to find candidate genes regulating the egg production of Xinjiang Yili geese. The 8 selected Xinjiang Yili Geese with high or low egg production (4 for each group) were 3 years old, with good health, and under the same feeding condition. High-throughput sequencing technology was used to sequence cDNA libraries of the hypothalami, pituitary glands, and ovaries. The sequencing data were compared and analyzed, and the transcripts with significant differences were identified and analyzed with bioinformatics. The study showed that the transcriptome sequencing data of the 24 samples contained a total of 1,176,496,146 valid reads and 176.47 gigabase data. Differential expression analyses identified 135, 56, and 331 genes in the hypothalami, pituitary glands, and ovaries of Xinjiang Yili geese with high and low egg production. Further annotation of these differentially expressed genes in the non-redundant protein sequence database (Nr) revealed that 98, 52, and 309 genes were annotated, respectively. Through the annotations of GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) databases, 30 candidate genes related to the egg production of Xinjiang Yili geese were preliminarily selected. The gap junction, focal adhesion, and ECM-receptor interaction signaling pathways were enriched with the hypothalamic, pituitary, and ovarian differentially expressed genes, and the calcium signaling pathway was enriched with the pituitary and ovarian differentially expressed genes. Thus, these pathways in the hypothalamic-pituitary-gonadal axis may play an important role in regulating egg production of Xinjiang Yili geese. The results provided the transcriptomic information of the hypothalamic-pituitary-gonadal axis of Xinjiang Yili geese and laid the theoretical basis for revealing the molecular mechanisms regulating the egg-laying traits of Xinjiang Yili geese.

Identification of Differentially Expressed Genes Associated with Papillary Thyroid Carcinoma

2020 ◽  
Vol 23 (6) ◽  
pp. 546-553
Author(s):  
Hongyuan Cui ◽  
Mingwei Zhu ◽  
Junhua Zhang ◽  
Wenqin Li ◽  
Lihui Zou ◽  
...  
Keyword(s):  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Cellular Proliferation ◽  
Mrna Level ◽  
Thyroid Tissue ◽  
Differentially Expressed ◽  
Thyroid Tumors ◽  
Papillary Thyroid

Objective: Next-generation sequencing (NGS) was performed to identify genes that were differentially expressed between normal thyroid tissue and papillary thyroid carcinoma (PTC). Materials & Methods: Six candidate genes were selected and further confirmed with quantitative real-time polymerase chain reaction (qRT-PCR), and immunohistochemistry in samples from 24 fresh thyroid tumors and adjacent normal tissues. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was used to investigate signal transduction pathways of the differentially expressed genes. Results: In total, 1690 genes were differentially expressed between samples from patients with PTC and the adjacent normal tissue. Among these, SFRP4, ZNF90, and DCN were the top three upregulated genes, whereas KIRREL3, TRIM36, and GABBR2 were downregulated with the smallest p values. Several pathways were associated with the differentially expressed genes and involved in cellular proliferation, cell migration, and endocrine system tumor progression, which may contribute to the pathogenesis of PTC. Upregulation of SFRP4, ZNF90, and DCN at the mRNA level was further validated with RT-PCR, and DCN expression was further confirmed with immunostaining of PTC samples. Conclusion: These results provide new insights into the molecular mechanisms of PTC. Identification of differentially expressed genes should not only improve the tumor signature for thyroid tumors as a diagnostic biomarker but also reveal potential targets for thyroid tumor treatment.

scholarly journals AB0005 IDENTIFICATION OF KEY GENES AND PATHWAYS FOR PSORIASIS BASED ON GEO DATABASES BY BIOINFORMATICS ANALYSIS

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 1037.2-1038
Author(s):  
X. Sun ◽  
S. X. Zhang ◽  
S. Song ◽  
T. Kong ◽  
C. Zheng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Signaling Pathways ◽  
Differentially Expressed Genes ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Shanxi Province ◽  
Receptor Interaction ◽  
Term Therapy ◽  
Pathway Enrichment Analysis ◽  
Key Genes

Background:Psoriasis is an immune-mediated, genetic disease manifesting in the skin or joints or both, and also has a strong genetic predisposition and autoimmune pathogenic traits1. The hallmark of psoriasis is sustained inflammation that leads to uncontrolled keratinocyte proliferation and dysfunctional differentiation. And it’s also a chronic relapsing disease, which often necessitates a long-term therapy2.Objectives:To investigate the molecular mechanisms of psoriasis and find the potential gene targets for diagnosis and treating psoriasis.Methods:Total 334 gene expression data of patients with psoriasis research (GSE13355 GSE14905 and GSE30999) were obtained from the Gene Expression Omnibus database. After data preprocessing and screening of differentially expressed genes (DEGs) by R software. Online toll Metascape3 was used to analyze Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs. Interactions of proteins encoded by DEGs were discovered by Protein-protein interaction network (PPI) using STRING online software. Cytoscape software was utilized to visualize PPI and the degree of each DEGs was obtained by analyzing the topological structure of the PPI network.Results:A total of 611 DEGs were found to be differentially expressed in psoriasis. GO analysis revealed that up-regulated DEGs were mostly associated with defense and response to external stimulus while down-regulated DEGs were mostly associated with metabolism and synthesis of lipids. KEGG enrichment analysis suggested they were mainly enriched in IL-17 signaling, Toll-like receptor signaling and PPAR signaling pathways, Cytokine-cytokine receptor interaction and lipid metabolism. In addition, top 9 key genes (CXCL10, OASL, IFIT1, IFIT3, RSAD2, MX1, OAS1, IFI44 and OAS2) were identified through Cytoscape.Conclusion:DEGs of psoriasis may play an essential role in disease development and may be potential pathogeneses of psoriasis.References:[1]Boehncke WH, Schon MP. Psoriasis. Lancet 2015;386(9997):983-94. doi: 10.1016/S0140-6736(14)61909-7 [published Online First: 2015/05/31].[2]Zhang YJ, Sun YZ, Gao XH, et al. Integrated bioinformatic analysis of differentially expressed genes and signaling pathways in plaque psoriasis. Mol Med Rep 2019;20(1):225-35. doi: 10.3892/mmr.2019.10241 [published Online First: 2019/05/23].[3]Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 2019;10(1):1523. doi: 10.1038/s41467-019-09234-6 [published Online First: 2019/04/05].Acknowledgements:This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared

scholarly journals Comparative Transcriptomic Analysis of Embryo Implantation in Mice and Rats

2018 ◽  
Vol 50 (2) ◽  
pp. 668-678 ◽  
Author(s):  
Wen-Qian Zhang ◽  
Miao Zhao ◽  
Ming-Yu Huang ◽  
Ji-Long Liu
Keyword(s):  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Embryo Implantation ◽  
Differentially Expressed ◽  
Rat Uterus ◽  
Mouse Uterus ◽  
Pathway Network ◽  
Implantation Sites ◽  
High Selection ◽  
Transcriptomic Changes

Background/Aims: Embryo implantation is an essential process for eutherian pregnancy, but this process varies across eutherians. The genomic mechanisms that led to the emergence and diversification of embryo implantation are largely unknown. Methods: In this study, we analyzed transcriptomic changes during embryo implantation in mice and rats by using RNA-seq. Bioinformatics and evolutionary analyses were performed to characterize implantation-associated genes in these two species. Results: We identified a total of 518 differentially expressed genes in mouse uterus during implantation, of which 253 genes were up-regulated and 265 genes were down-regulated at the implantation sites compared with the inter-implantation sites. In rat uterus, there were 374 differentially expressed genes, of which 284 genes were up-regulated and 90 genes were down-regulated. A cross-species comparison revealed that 92 up-regulated genes and 20 down-regulated genes were shared. The differences and similarities between mice and rats were investigated further at the gene ontology, pathway, network, and causal transcription factor levels. Additionally, we found that embryo implantation might have evolved through the recruitment of ancient genes into uterine expression. The evolutionary rates of the differentially expressed genes in mouse and rat uterus were significantly lower than those of the non-changed genes, indicating that implantation-related genes are evolutionary conserved due to high selection pressure. Conclusion: Our study provides insights into the molecular mechanisms involved in the evolution of embryo implantation.

scholarly journals Bioinformatics analysis of differentially expressed genes and identification of an miRNA–mRNA network associated with entorhinal cortex and hippocampus in Alzheimer’s disease

Hereditas ◽  
2021 ◽  
Vol 158 (1) ◽  
Author(s):  
Haoming Li ◽  
Linqing Zou ◽  
Jinhong Shi ◽  
Xiao Han
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Differentially Expressed Genes ◽  
Entorhinal Cortex ◽  
Molecular Mechanisms ◽  
Kegg Pathway ◽  
Neurodegenerative Disorder ◽  
Early Stage ◽  
Differentially Expressed ◽  
Hub Genes

Abstract Background Alzheimer’s disease (AD) is a fatal neurodegenerative disorder, and the lesions originate in the entorhinal cortex (EC) and hippocampus (HIP) at the early stage of AD progression. Gaining insight into the molecular mechanisms underlying AD is critical for the diagnosis and treatment of this disorder. Recent discoveries have uncovered the essential roles of microRNAs (miRNAs) in aging and have identified the potential of miRNAs serving as biomarkers in AD diagnosis. Methods We sought to apply bioinformatics tools to investigate microarray profiles and characterize differentially expressed genes (DEGs) in both EC and HIP and identify specific candidate genes and pathways that might be implicated in AD for further analysis. Furthermore, we considered that DEGs might be dysregulated by miRNAs. Therefore, we investigated patients with AD and healthy controls by studying the gene profiling of their brain and blood samples to identify AD-related DEGs, differentially expressed miRNAs (DEmiRNAs), along with gene ontology (GO) analysis, KEGG pathway analysis, and construction of an AD-specific miRNA–mRNA interaction network. Results Our analysis identified 10 key hub genes in the EC and HIP of patients with AD, and these hub genes were focused on energy metabolism, suggesting that metabolic dyshomeostasis contributed to the progression of the early AD pathology. Moreover, after the construction of an miRNA–mRNA network, we identified 9 blood-related DEmiRNAs, which regulated 10 target genes in the KEGG pathway. Conclusions Our findings indicated these DEmiRNAs having the potential to act as diagnostic biomarkers at an early stage of AD.

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