scholarly journals ECR-MAPK Regulation in Liver Early Development

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Xiu-Ju Zhao ◽  
Hexian Zhuo
Keyword(s):  
Early Development ◽  
Tyrosine Kinases ◽  
Infant Development ◽  
Expression Profiles ◽  
Interaction Network ◽  
Mapk Signaling ◽  
Individual Development ◽  
Male Rats ◽  
Gene Interaction Network ◽  
Extracellular Region

Early growth is connected to a key link between embryonic development and aging. In this paper, liver gene expression profiles were assayed at postnatal day 22 and week 16 of age. Meanwhile another independent animal experiment and cell culture were carried out for validation. Significance analysis of microarrays, qPCR verification, drug induction/inhibition assays, and metabonomics indicated thatalpha-2u globulin(extracellular region)-socs2(-SH2-containing signals/receptor tyrosine kinases)-ppp2r2a/pik3c3(MAPK signaling)-hsd3b5/cav2(metabolism/organization) plays a vital role in early development. Taken together, early development of male rats is ECR and MAPK-mediated coordination of cancer-like growth and negative regulations. Our data represent the first comprehensive description of early individual development, which could be a valuable basis for understanding the functioning of the gene interaction network of infant development.

scholarly journals Network-based cancer genomic data integration for pattern discovery

2021 ◽  
Vol 22 (S1) ◽  
Author(s):  
Fangfang Zhu ◽  
Jiang Li ◽  
Juan Liu ◽  
Wenwen Min
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Profiles ◽  
Mirna Gene ◽  
Interaction Network ◽  
Gene Interaction ◽  
Functional Modules ◽  
Expression Data ◽  
Gene Interaction Network ◽  
Gene Modules

Abstract Background Since genes involved in the same biological modules usually present correlated expression profiles, lots of computational methods have been proposed to identify gene functional modules based on the expression profiles data. Recently, Sparse Singular Value Decomposition (SSVD) method has been proposed to bicluster gene expression data to identify gene modules. However, this model can only handle the gene expression data where no gene interaction information is integrated. Ignoring the prior gene interaction information may produce the identified gene modules hard to be biologically interpreted. Results In this paper, we develop a Sparse Network-regularized SVD (SNSVD) method that integrates a prior gene interaction network from a protein protein interaction network and gene expression data to identify underlying gene functional modules. The results on a set of simulated data show that SNSVD is more effective than the traditional SVD-based methods. The further experiment results on real cancer genomic data show that most co-expressed modules are not only significantly enriched on GO/KEGG pathways, but also correspond to dense sub-networks in the prior gene interaction network. Besides, we also use our method to identify ten differentially co-expressed miRNA-gene modules by integrating matched miRNA and mRNA expression data of breast cancer from The Cancer Genome Atlas (TCGA). Several important breast cancer related miRNA-gene modules are discovered. Conclusions All the results demonstrate that SNSVD can overcome the drawbacks of SSVD and capture more biologically relevant functional modules by incorporating a prior gene interaction network. These identified functional modules may provide a new perspective to understand the diagnostics, occurrence and progression of cancer.

scholarly journals A Bioinformatics Approach to Identifying Potential Biomarkers for Cryptosporidium parvum: A Coccidian Parasite Associated with Fetal Diarrhea

Vaccines ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1427
Author(s):  
Mumdooh J. Sabir ◽  
Ross Low ◽  
Neil Hall ◽  
Majid Rasool Kamli ◽  
Md. Zubbair Malik
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Cryptosporidium Parvum ◽  
Early Stage ◽  
Expression Profiles ◽  
Interaction Network ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Data Sets ◽  
Gene Interaction Network

Cryptosporidium parvum (C. parvum) is a protozoan parasite known for cryptosporidiosis in pre-weaned calves. Animals and patients with immunosuppression are at risk of developing the disease, which can cause potentially fatal diarrhoea. The present study aimed to construct a network biology framework based on the differentially expressed genes (DEGs) of C. parvum infected subjects. In this way, the gene expression profiling analysis of C. parvum infected individuals can give us a snapshot of actively expressed genes and transcripts under infection conditions. In the present study, we have analyzed microarray data sets and compared the gene expression profiles of the patients with the different data sets of the healthy control. Using a network medicine approach to identify the most influential genes in the gene interaction network, we uncovered essential genes and pathways related to C. parvum infection. We identified 164 differentially expressed genes (109 up- and 54 down-regulated DEGs) and allocated them to pathway and gene set enrichment analysis. The results underpin the identification of seven significant hub genes with high centrality values: ISG15, MX1, IFI44L, STAT1, IFIT1, OAS1, IFIT3, RSAD2, IFITM1, and IFI44. These genes are associated with diverse biological processes not limited to host interaction, type 1 interferon production, or response to IL-gamma. Furthermore, four genes (IFI44, IFIT3, IFITM1, and MX1) were also discovered to be involved in innate immunity, inflammation, apoptosis, phosphorylation, cell proliferation, and cell signaling. In conclusion, these results reinforce the development and implementation of tools based on gene profiles to identify and treat Cryptosporidium parvum-related diseases at an early stage.

scholarly journals Identification of key regulators in parathyroid adenoma using an integrative gene network analysis

2020 ◽  
Vol 16 (11) ◽  
pp. 910-922
Author(s):  
Nikhat Imam ◽  
Keyword(s):  
Gene Expression ◽  
Parathyroid Adenoma ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Interaction Network ◽  
Gene Interaction ◽  
Structural Features ◽  
Gene Interaction Network ◽  
Parathyroid Adenomas

Parathyroid adenoma (PA) is marked by a certain benign outgrowth in the surface of parathyroid glands. The transcriptome analysis of parathyroid adenomas can provide a deep insight into actively expressed genes and transcripts. Hence, we analyzed and compared the gene expression profiles of parathyroid adenomas and healthy parathyroid gland tissues from database name. We identified a total of 280 differentially expressed genes (196 up-regulated, 84 down-regulated), which are involved in a wide array of biological processes. We further constructed a gene interaction network and analyzed its topological properties to know the network structure and its hidden mechanism. This will help to understand the molecular mechanisms underlying parathyroid adenoma development. We thus identified 13 key regulators (PRPF19, SMC3, POSTN, SNIP1, EBF1, MEIS2, PAX9, SCUBE2, WNT4, ARHGAP10, DOCK5, CAV1 and VSIR), which are deep-rooted from top to bottom in the gene interaction network forming a backbone for the network. The structural features of the network are probably maintained by crosstalk between important genes within the network along with associated functional modules. Thus, gene-expression profiling and network approach could be used to provide an independent platform to glen insights from available clinical data.

scholarly journals Sample-specific perturbation of gene interactions identifies breast cancer subtypes

2020 ◽  
Author(s):  
Yuanyuan Chen ◽  
Yu Gu ◽  
Zixi Hu ◽  
Xiao Sun
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Biological Networks ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Interaction Network ◽  
Gene Interaction ◽  
Breast Cancer Subtypes ◽  
Gene Interaction Network ◽  
Cancer Subtypes

Abstract Breast cancer is a highly heterogeneous disease, and there are many forms of categorization for breast cancer based on gene expression profiles. Gene expression profiles are variables and may show differences if measured at different time points or under different conditions. In contrast, biological networks are relatively stable over time and under different conditions. In this study, we used a gene interaction network from a new point of view to explore the subtypes of breast cancer based on individual-specific edge perturbations measured by relative gene expression value. Our study reveals that there are four breast cancer subtypes based on gene interaction perturbations at the individual level. The new network-based subtypes of breast cancer show strong heterogeneity in prognosis, somatic mutations, phenotypic changes and enriched pathways. The network-based subtypes are closely related to the PAM50 subtypes and immunohistochemistry index. This work helps us to better understand the heterogeneity and mechanisms of breast cancer from a network perspective.

Genome-Wide Identification of differently expressed lncRNAs, mRNAs, and circRNAs in patients with osteoarthritis

2020 ◽  
Vol 15 ◽  
Author(s):  
Yeqing Sun ◽  
Lei Chen ◽  
Yingqi Zhang ◽  
Jincheng Zhang ◽  
Shashi Ranjan Tiwari
Keyword(s):  
Regulatory Networks ◽  
Expression Profiles ◽  
Interleukin 1 ◽  
Interaction Network ◽  
Circular Rna ◽  
Negative Regulation ◽  
Differentially Expressed ◽  
Positive Regulation ◽  
Proteinprotein Interaction ◽  
Differently Expressed Genes

Background: Osteoarthritis (OA), one of the most important causes leading to joint disability, was considered as an untreatable disease. A series of genes were reported to regulate the pathogenesis of OA, including microRNAs, Long non-coding RNAs and Circular RNA. So far, the expression profiles and functions of lncRNAs, mRNAs, and circRNAs in OA are not fully understood. Objective: The present study aimed to identify differently expressed genes in OA. Methods: The present study conducted RNA-seq to identify differently expressed genes in OA. Ontology (GO) analysis was used to analysis the Molecular Function and Biological Process. KEGG pathway analysis was used to perform the differentially expressed lncRNAs in biological pathways. Results: Hierarchical clustering revealed a total of 943 mRNAs, 518 lncRNAs, and 300 circRNAs were dysregulated in OA compared to normal samples. Furthermore, we constructed differentially expressed mRNAs mediated proteinprotein interaction network, differentially expressed lncRNAs mediated trans regulatory networks, and competitive endogenous RNA (ceRNA) to reveal the interaction among these genes in OA. Bioinformatics analysis revealed these dysregulated genes were involved in regulating multiple biological processes, such as wound healing, negative regulation of ossification, sister chromatid cohesion, positive regulation of interleukin-1 alpha production, sodium ion transmembrane transport, positive regulation of cell migration, and negative regulation of inflammatory response. To the best of our knowledge, this study for the first time revealed the expression pattern of mRNAs, lncRNAs and circRNAs in OA. Conclusion: This study provided novel information to validate these differentially expressed RNAs may be as possible biomarkers and targets in OA.

scholarly journals Identification of MicroRNA as Sepsis Biomarker Based on miRNAs Regulatory Network Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jie Huang ◽  
Zhandong Sun ◽  
Wenying Yan ◽  
Yujie Zhu ◽  
Yuxin Lin ◽  
...  
Keyword(s):  
Regulatory Network ◽  
Expression Profiles ◽  
Delayed Diagnosis ◽  
Interaction Network ◽  
High Sensitivity ◽  
Regulate Gene Expression ◽  
Protein Protein Interaction ◽  
Small Noncoding Rnas ◽  
Mirna Expression Profiles ◽  
Pathways Analysis

Sepsis is regarded as arising from an unusual systemic response to infection but the physiopathology of sepsis remains elusive. At present, sepsis is still a fatal condition with delayed diagnosis and a poor outcome. Many biomarkers have been reported in clinical application for patients with sepsis, and claimed to improve the diagnosis and treatment. Because of the difficulty in the interpreting of clinical features of sepsis, some biomarkers do not show high sensitivity and specificity. MicroRNAs (miRNAs) are small noncoding RNAs which pair the sites in mRNAs to regulate gene expression in eukaryotes. They play a key role in inflammatory response, and have been validated to be potential sepsis biomarker recently. In the present work, we apply a miRNA regulatory network based method to identify novel microRNA biomarkers associated with the early diagnosis of sepsis. By analyzing the miRNA expression profiles and the miRNA regulatory network, we obtained novel miRNAs associated with sepsis. Pathways analysis, disease ontology analysis, and protein-protein interaction network (PIN) analysis, as well as ROC curve, were exploited to testify the reliability of the predicted miRNAs. We finally identified 8 novel miRNAs which have the potential to be sepsis biomarkers.

scholarly journals Analyses of long non-coding RNA and mRNA profiling in the spleen of diarrheic piglets caused by Clostridium perfringens type C

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5997 ◽  
Author(s):  
Zunqiang Yan ◽  
Xiaoyu Huang ◽  
Wenyang Sun ◽  
Qiaoli Yang ◽  
Hairen Shi ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Clostridium Perfringens ◽  
Diarrheal Disease ◽  
Inflammatory Responses ◽  
Expression Profiles ◽  
Mapk Signaling ◽  
Non Coding Rna ◽  
Receptor Signaling Pathway ◽  
Type C ◽  
Long Non Coding Rna

Background Clostridium perfringens (C. perfringens) type C is the most common bacteria causing piglet diarrheal disease and it greatly affects the economy of the global pig industry. The spleen is an important immune organ in mammals; it plays an irreplaceable role in resisting and eradicating pathogenic microorganisms. Based on different immune capacity in piglets, individuals display the resistance and susceptibility to diarrhea caused by C. perfringens type C. Recently, long non-coding RNA (lncRNA) and mRNA have been found to be involved in host immune and inflammatory responses to pathogenic infections. However, little is known about spleen transcriptome information in piglet diarrhea caused by C. perfringens type C. Methods Hence, we infected 7-day-old piglets with C. perfringens type C to lead to diarrhea. Then, we investigated lncRNA and mRNA expression profiles in spleens of piglets, including control (SC), susceptible (SS), and resistant (SR) groups. Results As a result, 2,056 novel lncRNAs and 2,417 differentially expressed genes were found. These lncRNAs shared the same characteristics of fewer exons and shorter length. Bioinformatics analysis identified that two lncRNAs (ALDBSSCT0000006918 and ALDBSSCT0000007366) may be involved in five immune/inflammation-related pathways (such as Toll-like receptor signaling pathway, MAPK signaling pathway, and Jak-STAT signaling pathway), which were associated with resistance and susceptibility to C. perfringens type C infection. This study contributes to the understanding of potential mechanisms involved in the immune response of piglets infected with C. perfringens type C.

scholarly journals Comparative analysis of gene expression profiles in differentiated subcutaneous adipocytes between Jiaxing Black and Large White Pigs

2020 ◽  
Author(s):  
Dawei Zhang ◽  
Wenjing Wu ◽  
Xin Huang ◽  
Ke Xu ◽  
Cheng Zheng ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Expression Profiles ◽  
Mapk Signaling ◽  
Fat Deposition ◽  
Mapk Signaling Pathway ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Domestic Pig ◽  
Large White ◽  
Pig Breeds

Abstract Background: Chinese domestic pig breeds are reputed for pork quality, but their low ratio of lean-to-fat carcass weight decreases production efficiency. A better understanding of the genetic regulation network of SC fat tissue is necessary for the rational selection of Chinese domestic pig breeds. In the present study, SC adipocytes were isolated from Jiaxing Black pigs (a Chinese indigenous pig breed with redundant SC fat deposition) and Large White pigs (a lean-type pig breed with relatively low SC fat deposition) and the expression profiles of mRNAs and lncRNAs were compared by RNA-seq analysis to identify biomarkers correlated with the differences of SC fat deposition between the two breeds.Results: A total of 3,371 differentially expressed genes (DEGs) and 1,182 differentially expressed lncRNAs (DELs) were identified in SC adipocytes between Jiaxing Black (JX) and Large White (LW) pigs, which included 797 upregulated mRNAs, 2,574 downregulated mRNAs, 461 upregulated lncRNAs and 721 downregulated lncRNAs. Gene Ontology and KEGG pathway analyses revealed that the DEGs and DELs were mainly involved in the immune response, cell fate determination, PI3K-Akt signaling pathway and MAPK signaling pathway, which are known to be related to adipogenesis and lipid metabolism. The expression levels of DEGs and DELs according to the RNA-seq data were verified by quantitative PCR, which showed 81.8% consistency. The differences in MAPK pathway activity between JX and LW pigs was confirmed by western blot analysis, with <100-fold elevated p38 phosphorylation in JX pigs.Conclusions: This study offers a detailed characterization of mRNAs and lncRNAs in fat- and lean-type pig breeds. The activity of the MAPK signaling pathway was found to be associated with subcutaneous adipogenesis. These results greatly enhance our understanding of the molecular mechanisms regulating SC fat deposition in pigs.

scholarly journals Community Detection in Large-Scale Bipartite Biological Networks

2021 ◽  
Vol 12 ◽  
Author(s):  
Genís Calderer ◽  
Marieke L. Kuijjer
Keyword(s):  
Biological Networks ◽  
Large Scale ◽  
Interaction Network ◽  
Gene Interaction ◽  
Community Structures ◽  
Gene Interaction Network ◽  
Structure Detection ◽  
Wide Range ◽  
Disease Associations ◽  
Or Gene

Networks are useful tools to represent and analyze interactions on a large, or genome-wide scale and have therefore been widely used in biology. Many biological networks—such as those that represent regulatory interactions, drug-gene, or gene-disease associations—are of a bipartite nature, meaning they consist of two different types of nodes, with connections only forming between the different node sets. Analysis of such networks requires methodologies that are specifically designed to handle their bipartite nature. Community structure detection is a method used to identify clusters of nodes in a network. This approach is especially helpful in large-scale biological network analysis, as it can find structure in networks that often resemble a “hairball” of interactions in visualizations. Often, the communities identified in biological networks are enriched for specific biological processes and thus allow one to assign drugs, regulatory molecules, or diseases to such processes. In addition, comparison of community structures between different biological conditions can help to identify how network rewiring may lead to tissue development or disease, for example. In this mini review, we give a theoretical basis of different methods that can be applied to detect communities in bipartite biological networks. We introduce and discuss different scores that can be used to assess the quality of these community structures. We then apply a wide range of methods to a drug-gene interaction network to highlight the strengths and weaknesses of these methods in their application to large-scale, bipartite biological networks.

scholarly journals Red nucleus IL-33 facilitates the early development of mononeuropathic pain in male rats by inducing TNF-α through activating ERK, p38 MAPK, and JAK2/STAT3

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Hao-Nan Li ◽  
Qing-Qing Yang ◽  
Wen-Tao Wang ◽  
Xue Tian ◽  
Fan Feng ◽  
...  
Keyword(s):  
P38 Mapk ◽  
Early Development ◽  
Red Nucleus ◽  
Neutralizing Antibody ◽  
Male Rats ◽  
Post Injury ◽  
Pain Model ◽  
Mechanical Hypersensitivity ◽  
Downstream Signaling ◽  
Tnf Α

Abstract Background Our recent studies have identified that the red nucleus (RN) dual-directionally modulates the development and maintenance of mononeuropathic pain through secreting proinflammatory and anti-inflammatory cytokines. Here, we further explored the action of red nucleus IL-33 in the early development of mononeuropathic pain. Methods In this study, male rats with spared nerve injury (SNI) were used as mononeuropathic pain model. Immunohistochemistry, Western blotting, and behavioral testing were used to assess the expressions, cellular distributions, and actions of red nucleus IL-33 and its related downstream signaling molecules. Results IL-33 and its receptor ST2 were constitutively expressed in the RN in naive rats. After SNI, both IL-33 and ST2 were upregulated significantly at 3 days and peaked at 1 week post-injury, especially in RN neurons, oligodendrocytes, and microglia. Blockade of red nucleus IL-33 with anti-IL-33 neutralizing antibody attenuated SNI-induced mononeuropathic pain, while intrarubral administration of exogenous IL-33 evoked mechanical hypersensitivity in naive rats. Red nucleus IL-33 generated an algesic effect in the early development of SNI-induced mononeuropathic pain through activating NF-κB, ERK, p38 MAPK, and JAK2/STAT3, suppression of NF-κB, ERK, p38 MAPK, and JAK2/STAT3 with corresponding inhibitors markedly attenuated SNI-induced mononeuropathic pain or IL-33-evoked mechanical hypersensitivity in naive rats. Red nucleus IL-33 contributed to SNI-induced mononeuropathic pain by stimulating TNF-α expression, which could be abolished by administration of inhibitors against ERK, p38 MAPK, and JAK2/STAT3, but not NF-κB. Conclusions These results suggest that red nucleus IL-33 facilitates the early development of mononeuropathic pain through activating NF-κB, ERK, p38 MAPK, and JAK2/STAT3. IL-33 mediates algesic effect partly by inducing TNF-α through activating ERK, p38 MAPK and JAK2/STAT3.

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