Epigenetic alternations and targeted therapy in intrauterine adhesion: A comparative study.

Although intrauterine adhesion (IUA) has been well recognized as a critical factor in infertility, little information is available regarding the molecular mechanisms. We performed a high-throughput RNA sequencing in the endometrium of three IUA patients and three normal controls. And another two gene expression profiles (PMID34968168 and GSE160365) were analyzed together. A total of 252 DEGs were identified. Cell cycle, E2F target, G2M checkpoint, integrin3 pathway and H1F1 signaling were aberrantly regulated in the IUA endometrium. 10 hub genes (CCL2, TFRC, THY1, IGF1, CTGF, SELL, SERPINE1, HBB, HBA1, LYZ) were exhibited in PPI analysis. FOXM1, IKBKB and MYC were three common transcription factors of DEGs. Five chemicals (MK-1775, PAC-1, TW-37, BIX-01294, 3-matida) were identified as putative therapeutic agents for IUA. Collectively, a series of DEGs associated with IUA were disclosed. Five chemicals and ten hub genes may be further explored as potential drugs and targets for IUA treatment.

Chandipura virus changes cellular miRNome in human microglial cells.

Chandipura virus (CHPV) is a neurotropic virus, known to cause encephalitis in humans. The microRNAs (miRNA/miR) play an important role in the pathogenesis of viral infection. The present study is focused on the role of miRNAs during CHPV (strain 1653514) infection in human microglial cells. The deep sequencing of CHPV-infected human microglial cells identified a total of 12 differentially expressed miRNA (DEMs). To elucidate the role of DEMs, the target gene prediction, Gene Ontology term (GO Term), pathway enrichment analysis, and miRNA-messenger RNA (mRNA) interaction network analysis was performed. The GO terms and pathway enrichment analysis provided 146 enriched genes; which were involved in interferon response, cytokine and chemokine signaling. Further, the WGCNA (weighted gene coexpression network analysis) of the enriched genes were discretely categorized into three modules (blue, brown, and turquoise). The hub genes in the blue module may correlate to CHPV induced neuroinflammation. Altogether, the miRNA-mRNA interaction network and WGCNA study revealed the following pairs, hsa-miR-542-3p and FAF1, hsa-miR-92a-1-5p and MYD88, and hsa-miR-3187-3p and TNFRSF21, which may contribute to neuroinflammation during CHPV infection in human microglial cells.

[Comparative analysis of the role of CD4(+) and CD8(+) T cells in severe asthma development].

The role of CD8^(+) T cells in asthma has not been fully discussed. The mechanisms of CD4^(+) and CD8^(+) cells in severe asthma (SA) development were compared. The microarray data (GSE31773) was downloaded from the Gene Expression Omnibus (GEO) database, including 20 samples of CD4^(+) and CD8^(+) T cells, which were collected from 8 health controls (HC), 4 non-severe asthma (NSA) and 8 SA patients. DEGs of CD4^(+) and CD8^(+) T cells in the HC vs. NSA and HC vs. SA groups were identified using the limma package in R. GO and pathway enrichment analysis of the common DEGs between the two groups were analyzed using DAVID. The interactive network of DEGs and significant modules were further explored. In CD4^(+) cells, there were 168 DEGs in HC vs. NSA group and 685 DEGs in HC vs. SA group, while for CD8^(+) T cells there were 719 DEGs in the HC vs. NSA groups and 1255 DEGs in the HC vs. SA groups. Besides, 80 common DEGs from CD4^(+) samples were enriched in the MAPKKK cascade and molecular metabolism, and 385 common DEGs of CD8^(+) T cells were significantly related with cell apoptosis and transformation. Moreover, two significant modules of DEGs in CD4^(+) were found to be involved with MPO and BPI. One module of CD8^(+) T cells containing PDHA1 and MRPL42 was identified to be related with glycolysis. In conclusion, MPO and BPI in CD4^(+), and PDHA1 and MRPL42 in CD8^(+) T cells might be used as specific biomarkers of SA progression. Therapy targeting the functions of CD4^(+) and CD8^(+) T cells may provide a novel perspective for SA treatment.

The putative maintaining mechanism of gut bacterial ecosystem in giant pandas and its potential application in conservation.

Animals living in captivity and the wild show differences in the internal structure of their gut microbiomes. Here, we performed a meta-analysis of the microbial data of about 494 fecal samples obtained from giant pandas (captive and wild giant pandas). Our results show that the modular structures and topological features of the captive giant panda gut microbiome differ from those of the wild populations. The co-occurrence network of wild giant pandas also contained more nodes and edges, indicating a higher complexity and stability compared to that of captive giant pandas. Keystone species analysis revealed the differences between geographically different wild populations, indicating the potential effect of geography on the internal modular structure. When combining all the giant panda samples for module analysis, we found that the abundant taxa (e.g., belonged to Flavobacterium, Herbaspirillum, and Escherichia-Shigella) usually acted as module hubs to stabilize the modular structure, while the rare taxa usually acted as connectors of different modules. We conclude that abundant and rare taxa play different roles in the gut bacterial ecosystem. The conservation of some key bacterial species is essential for promoting the development of the gut microbiome in pandas. The living environment of the giant pandas can influence the internal structure, topological features, and strength of interrelationships in the gut microbiome. This study provides new insights into the conservation and management of giant panda populations.

Application of network composite module analysis and verification to explore the bidirectional immunomodulatory effect of Zukamu granules on Th1 / Th2 cytokines in lung injury.

ETHNOPHARMACOLOGICAL RELEVANCE: Zukamu granules (ZKMG), as the preferred drug for the treatment of colds in Uygur medical theory, has been used for 1500 years. It is also widely used in China and included in the National Essential Drugs List (2018 edition). It has unique anti-inflammatory, antitussive and analgesic effects. AIM OF THE STUDY: Aiming at the research of traditional Chinese medicine (TCM) with the characteristics of overall regulation of body diseases and the immune regulation mechanism with the concept of integrity, this paper put forward the integrated application of network composite module analysis and animal experiment verification to study the immune regulation mechanism of TCM. MATERIALS AND METHODS: The active components and targets of ZKMG were predicted, and network module analysis was performed to explore their potential immunomodulatory mechanisms. Then acute lung injury (ALI) mice and idiopathic pulmonary fibrosis (IPF) rats were used as pathological models to observe the effects of ZKMG on the pathological conditions of infected ALI and IPF rats, determine the contents of Th1, Th2 characteristic cytokines and immunoglobulins, and study the intervention of GATA3/STAT6 signal pathway. RESULTS: The results of network composite module analysis showed that ZKMG contained 173 pharmacodynamic components and 249 potential targets, and four key modules were obtained. The immunomodulatory effects of ZKMG were related to T cell receptor signaling pathway. The validation results of bioeffects that ZKMG could carry out bidirectional immune regulation on Th1/Th2 cytokines in the stage of ALI and IPF, so as to play the role of regulating immune homeostasis and organ protection. CONCLUSIONS: The network composite module analysis and verification method is an exploration to study the immune regulation mechanism of TCM by combining the network module prediction analysis with animal experiments, which provides a reference for subsequent research.

Insight into the potential candidate genes and signaling pathways involved in lymphoma disease in dogs using a comprehensive whole blood transcriptome analysis.

Lymphoma is one of the most prevalent hematological cancers, accounting for 15-20 % of new cancer diagnoses in dogs. Therefore, this study aims to explore the important genes and pathways involved in canine lymphoma progression and understand the underlying molecular mechanisms using RNA sequencing. In this study, RNAs acquired from seven pairs of lymphoma and non-lymphoma blood samples were sequenced from different breeds of dogs. Sequencing reads were preprocessed, aligned with the reference genome, assembled and expressions were estimated through bioinformatics approaches. At a false discovery rate (FDR) < 0.05 and fold change (FC) ≥ 1.5, a total of 625 differentially expressed genes (DEGs) were identified between lymphoma and non-lymphoma samples, including 347 up-regulated DEGs such as SLC38A11, SCN3A, ZIC5 etc. and 278 down-regulated DEGs such as LOC475937, CSMD1, KRT14 etc. GO enrichment analysis showed that these DEGs were highly enriched for molecular function of ATP binding and calcium ion binding, cellular process of focal adhesion, and biological process of immune response, and defense response to virus. Similarly, KEGG pathways analysis revealed 11 significantly enriched pathways such as ECM-receptor interaction, cell cycle, PI3K-Akt signaling pathway, ABC transporters etc. In the protein-protein interaction (PPI) network, CDK1 was found to be a top hub gene with highest degree of connectivity. Three modules selected from the PPI network showed that canine lymphoma was highly associated with cell cycle, ECM-receptor interaction, hypertrophic cardiomyopathy, dilated cardiomyopathy and RIG-I-like receptor signaling pathway. Overall, our findings highlighted new candidate therapeutic targets for further testing in canine lymphoma and facilitate the understanding of molecular mechanism of lymphoma's progression in dogs.

Identifying potential novel insights for COVID-19 pathogenesis and therapeutics using an integrated bioinformatics analysis of host transcriptome.

The molecular mechanisms underlying the pathogenesis of COVID-19 have not been fully discovered. This study aims to decipher potentially hidden parts of the pathogenesis of COVID-19, potential novel drug targets, and identify potential drug candidates. Two gene expression profiles were analyzed, and overlapping differentially expressed genes (DEGs) were selected for which top enriched transcription factors and kinases were identified, and pathway analysis was performed. Protein-protein interaction (PPI) of DEGs was constructed, hub genes were identified, and module analysis was also performed. DGIdb database was used to identify drugs for the potential targets (hub genes and the most enriched transcription factors and kinases for DEGs). A drug-potential target network was constructed, and drugs were ranked according to the degree. L1000FDW was used to identify drugs that can reverse transcriptional profiles of COVID-19. We identified drugs currently in clinical trials, others predicted by different methods, and novel potential drug candidates Entrectinib, Omeprazole, and Exemestane for combating COVID-19. Besides the well-known pathogenic pathways, it was found that axon guidance is a potential pathogenic pathway. Sema7A, which may exacerbate hypercytokinemia, is considered a potential novel drug target. Another potential novel pathway is related to TINF2 overexpression, which may induce potential telomere dysfunction and damage DNA that may exacerbate lung fibrosis. This study identified new potential insights regarding COVID-19 pathogenesis and treatment, which might help us improve our understanding of the mechanisms of COVID-19.

In Silico Integrative Approach Revealed Key MicroRNAs and Associated Target Genes in Cardiorenal Syndrome.

Cardiorenal syndromes constellate primary dysfunction of either heart or kidney whereby one organ dysfunction leads to the dysfunction of another. The role of several microRNAs (miRNAs) has been implicated in number of diseases, including hypertension, heart failure, and kidney diseases. Wide range of miRNAs has been identified as ideal candidate biomarkers due to their stable expression. Current study was aimed to identify crucial miRNAs and their target genes associated with cardiorenal syndrome and to explore their interaction analysis. Three differentially expressed microRNAs (DEMs), namely, hsa-miR-4476, hsa-miR-345-3p, and hsa-miR-371a-5p, were obtained from GSE89699 and GSE87885 microRNA data sets, using R/GEO2R tools. Furthermore, literature mining resulted in the retrieval of 15 miRNAs from scientific research and review articles. The miRNAs-gene networks were constructed using miRNet (a Web platform of miRNA-centric network visual analytics). CytoHubba (Cytoscape plugin) was adopted to identify the modules and the top-ranked nodes in the network based on Degree centrality, Closeness centrality, Betweenness centrality, and Stress centrality. The overlapped miRNAs were further used in pathway enrichment analysis. We found that hsa-miR-21-5p was common in 8 pathways out of the top 10. Based on the degree, 5 miRNAs, namely, hsa-mir-122-5p, hsa-mir-222-3p, hsa-mir-21-5p, hsa-mir-146a-5p, and hsa-mir-29b-3p, are considered as key influencing nodes in a network. We suggest that the identified miRNAs and their target genes may have pathological relevance in cardiorenal syndrome (CRS) and may emerge as potential diagnostic biomarkers.

Agent Repurposing for the Treatment of Advanced Stage Diffuse Large B-Cell Lymphoma Based on Gene Expression and Network Perturbation Analysis.

Over 50% of diffuse large B-cell lymphoma (DLBCL) patients are diagnosed at an advanced stage. Although there are a few therapeutic strategies for DLBCL, most of them are more effective in limited-stage cancer patients. The prognosis of patients with advanced-stage DLBCL is usually poor with frequent recurrence and metastasis. In this study, we aimed to identify gene expression and network differences between limited- and advanced-stage DLBCL patients, with the goal of identifying potential agents that could be used to relieve the severity of DLBCL. Specifically, RNA sequencing data of DLBCL patients at different clinical stages were collected from the cancer genome atlas (TCGA). Differentially expressed genes were identified using DESeq2, and then, weighted gene correlation network analysis (WGCNA) and differential module analysis were performed to find variations between different stages. In addition, important genes were extracted by key driver analysis, and potential agents for DLBCL were identified according to gene-expression perturbations and the Crowd Extracted Expression of Differential Signatures (CREEDS) drug signature database. As a result, 20 up-regulated and 73 down-regulated genes were identified and 79 gene co-expression modules were found using WGCNA, among which, the thistle1 module was highly related to the clinical stage of DLBCL. KEGG pathway and GO enrichment analyses of genes in the thistle1 module indicated that DLBCL progression was mainly related to the NOD-like receptor signaling pathway, neutrophil activation, secretory granule membrane, and carboxylic acid binding. A total of 47 key drivers were identified through key driver analysis with 11 up-regulated key driver genes and 36 down-regulated key diver genes in advanced-stage DLBCL patients. Five genes (MMP1, RAB6C, ACCSL, RGS21 and MOCOS) appeared as hub genes, being closely related to the occurrence and development of DLBCL. Finally, both differentially expressed genes and key driver genes were subjected to CREEDS analysis, and 10 potential agents were predicted to have the potential for application in advanced-stage DLBCL patients. In conclusion, we propose a novel pipeline to utilize perturbed gene-expression signatures during DLBCL progression for identifying agents, and we successfully utilized this approach to generate a list of promising compounds.

Identification of biomarkers and mechanisms of diabetic cardiomyopathy using microarray data.

BACKGROUND: The study aimed to uncover the regulation mechanisms of diabetic cardiomyopathy (DCM) and provide novel prognostic biomarkers. METHODS: The dataset GSE62203 downloaded from the Gene Expression Omnibus database was utilized in the present study. After pretreatment using the Affy package, differentially expressed genes (DEGs) were identified by the limma package, followed by functional enrichment analysis and protein- protein interaction (PPI) network analysis. Furthermore, module analysis was conducted using MCODE plug-in of Cytoscape, and functional enrichment analysis was also performed for genes in the modules. RESULTS: A set of 560 DEGs were screened, mainly enriched in the metabolic process and cell cycle related process. Hub nodes in the PPI network were LDHA (lactate dehydrogenase A), ALDOC (aldolase C, fructose-bisphosphate) and ABCE1 (ATP Binding Cassette Subfamily E Member 1), which were also highlighted in Module 1 or Module 2 and predominantly enriched in the processes of glycolysis and ribosome biogenesis. Additionally, LDHA were linked with ALDOC in the PPI network. Besides, activating transcription factor 4 (ATF4) was prominent in Module 3; while myosin heavy chain 6 (MYH6) was highlighted in Module 4 and was mainly involved in muscle cells related biological processes. CONCLUSIONS: Five potential biomarkers including LDHA, ALDOC, ABCE1, ATF4 and MYH6 were identified for DCM prognosis.

Module Analysis Using Single-Patient Differential Expression Signatures Improves the Power of Association Studies for Alzheimer's Disease.

The causal mechanism of Alzheimer's disease is extremely complex. Achieving great statistical power in association studies usually requires a large number of samples. In this work, we illustrated a different strategy to identify AD risk genes by clustering AD patients into modules based on their single-patient differential expression signatures. The evaluation suggested that our method could enrich AD patients with similar clinical manifestations. Applying this to a cohort of only 310 AD patients, we identified 174 AD risk loci at a strict threshold of empirical p < 0.05, while only two loci were identified using all the AD patients. As an evaluation, we collected 23 AD risk genes reported in a recent large-scale meta-analysis and found that 18 of them were rediscovered by association studies using clustered AD patients, while only three of them were rediscovered using all AD patients. Functional annotation suggested that AD-associated genetic variants mainly disturbed neuronal/synaptic function. Our results suggested module analysis helped to enrich AD patients affected by the common risk variants.

Analysis of the pharmacological mechanism of Banxia Xiexin decoction in treating depression and ulcerative colitis based on a biological network module.

BACKGROUND: The network pharmacology method was used to predict the active components of Banxia Xiexin decoction, its targets and the key signalling pathways that are activated in the treatment of depression and ulcerative colitis to explore the common mechanism. METHODS: The active components and targets of Banxia Xiexin decoction were obtained by searching the ETCM,TCMSP and TCMIP database. The disease targets of depression and ulcerative colitis were obtained by combining the following the DisGeNET, OMIM,Drugbank,CTD and PharmGKB disease databases. The drug and disease target genes were obtained from the intersection of the herbal medicine targets and the disease targets and were imported into the STRING platform for the analysis of PPI network. The network modules were constructed using Cytoscape software. An analysis of the functional annotations of GO terms and KEGG signalling pathways was performed for each network module. Then, the tissue distribution, sub-cellular distribution and protein attributes of the key targets in the pathway were analysed by the BioGPS, Genecards and DisGeNET databases. RESULTS: The mechanism of Banxia Xiexin Decoction in the treatment of depression and ulcerative colitis is related to drug reaction, steroid metabolism, lipid metabolism, inflammatory response, oxidative stress response, cell response to lipopolysaccharide, insulin secretion regulation, estradiol response and other biological functions, mainly through the regulation of 5-hydroxytryptamine synaptic, arachidonic acid metabolism, HIF-1 signaling pathway and NF-kappa B signaling pathway can achieve the effect of same treatment for different diseases. CONCLUSIONS: The mechanism of Banxia Xiexin Decoction in treating different diseases involves direct or indirect correlation of multiple signal pathways, mainly involved in drug metabolism and lipid metabolism, but also through comprehensive intervention of the body's nervous system, immune system, digestive system and other systems. The effective components of Banxia Xiexin Decoction are mainly act on eight key target proteins (such as ALB, IL6, VEGFA, TNF, PTGS2, MAPK1, STAT3, EGFR) to carry out multi-target effect mechanism, biological mechanism of treating different diseases with the same treatment, and related mechanism of overall treatment, which provide theoretical reference for further research on the material basis and mechanism of Banxiaxiexin decoction on antidepressant and prevention and treatment of ulcerative colitis.

Screening and Interaction Analysis of Key Genes in miR-542-3p Over- Expressed Osteosarcoma Cells by Bioinformatics.

BACKGROUND: Osteosarcoma is one of the most serious primary malignant bone tumors that threaten the lives of children and adolescents. However, the mechanism underlying and how to prevent or treat the disease have not been well understood. AIMS AND OBJECTIVE: This aim of the present study was to identify the key genes and explore novel insights into the molecular mechanism of miR-542-3p over-expressed Osteosarcoma. MATERIALS AND METHODS: Gene expression profile data GDS5367 was downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were screened using GEO2R, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the DAVID database. And protein-protein interaction (PPI) network was constructed by the STRING database. In addition, the most highly connected module was screened by plugin MCODE and hub genes by plugin CytoHubba. Furthermore, UALCAN and The Cancer Genome Atlas were performed for survival analysis. RESULT: In total, 1421 DEGs were identified, including 598 genes were up-regulated and 823 genes were down-regulated. GO analysis showed that DEGs were classified into three groups and DEGs mainly enriched in Steroid biosynthesis, Ubiquitin mediated proteolysis and p53 signaling pathway. Six hub genes (UBA52, RNF114, UBE2H, TRIP12, HNRNPC, and PTBP1) may be key genes with the progression of osteosarcoma. CONCLUSION: The results could better understand the mechanism of osteosarcoma, which may facilitate a novel insight into treatment targets.

Identification of hub genes and pathways in glioblastoma by bioinformatics analysis.

Glioblastoma (GBM) is the most common type of malignant brain tumor, and is associated with poor patient prognosis. A comprehensive understanding of the molecular mechanism underlying GBM may help to guide the identification of novel diagnoses and treatment targets. The gene expression profile of the GSE4290 GBM dataset was analyzed in order to identify differentially expressed genes (DEGs). Enriched pathways were identified through Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes analyses. A protein-protein interaction network was constructed in order to identify hub genes and for module analysis. Expression and survival analyses were conducted in order to screen and validate critical genes. A total of 1,801 DEGs were recorded, including 620 upregulated and 1,181 downregulated genes. Upregulated DEGs were enriched in the terms 'mitotic cell cycle process', 'mitotic cell cycle' and 'cell cycle process'. Downregulated genes were enriched in 'transsynaptic signaling', 'anterograde transsynaptic signaling' and 'synaptic signaling'. A total of 15 hub genes, which displayed a high degree of connectivity, were selected. These genes included vascular endothelial growth factor A, cyclin-dependent kinase 1 (CDK1), cell-division cycle protein 20 (CDC20), aurora kinase A (AURKA), and budding uninhibited by benzimidazoles 1 (BUB1). The identified DEGs and hub genes may help guide investigations on the mechanisms underlying the development and progression of GBM. CDK1, CDC20, AURKA and BUB1, which are involved in cell cycle pathways, may be potential targets in the diagnosis and therapy of GBM.

Anaerobic/aerobic conditions determine antibiotic resistance genes removal patterns from leachate by affecting bacteria taxa-genes co-occurrence modules.

Landfill treatment of municipal solid waste treatment produces a large amount of leachate, which has been an important hotspot of ARGs. This study aimed to investigate the ARGs removal potential, kinetics and mechanism from leachate in aerobic and anaerobic conditions. Simulated landfill reactors showed the efficacy in reducing ARGs, and the removal efficiencies depended on ARGs types and aerobic/anaerobic conditions. The ARGs tetQ and blaCTX-M were more likely to attenuate with the log-removal efficiencies of 1.50-3 order of magnitude. The ARGs removal kinetic was well fitted by modified Collins-Selleck model, and aerobic condition showed better removal capacities and kinetics than anaerobic condition. Among the ARGs with great removal performance, sul2, aadA1and blaCTX-M were eliminated from leachate and refuse simultaneously, but tetM, ermB, and mefA were removed from leachate but enriched in refuse. Aerobic/anaerobic states might drive the bacterial community shift of leachate and refuse, and topology property comparison of co-occurrence networks suggested that refuse had a closer non-random host relationship between ARGs and microbial taxa than leachate. Further module analyses revealed that ARGs removal efficiencies depended on the taxonomy of host bacteria in leachate, while the refuse taxa-ARGs correlation determined ARGs removal patterns. By selecting distinct bacteria cluster in different conditions, aerobic treatment benefited ARGs reduction in leachate and refuse, while anaerobic treatment enhanced the enrichment of ARGs in refuse. These findings can potentially foster the understanding of ARGs removal mechanism in biological treatment processes.

Identify differential gene expressions in fatty infiltration process in rotator cuff.

BACKGROUND: Rotator cuff tears are one of the most frequent upper extremity injuries and lead to pain and disability. Recent studies have implicated fatty infiltration in rotator cuff is a key failure element with the higher re-tear rates and poorer functional prognosis. Therefore, we investigated the differential expression of key genes in each stage of rotator cuff tear. METHODS: A published expression profile was downloaded from the Gene Expression Omnibus database and analyzed using the Linear Models for Microarray Data (LIMMA) package in R language to identify differentially expressed genes (DEGs) in different stages of injured rotator cuff muscles. Gene ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to annotate the function of the DEGs. Finally, PPI network and module analysis were used to identify hub genes. RESULTS: A total of 1089 fatty infiltration-related DEGs were identified, including 733 upregulated and 356 downregulated genes, and GO analyses confirmed that fatty infiltration was strongly associated with inflammatory response, aging, response to lipopolysaccharide, and immune response. Significantly enriched KEGG pathways associated with these DEGs included the phagosome, cell adhesion molecules, tuberculosis, and osteoclast differentiation. Further analyses via a PPI network and module analysis identified a total of 259 hub genes. Among these, Tmprss11d, Ptprc, Itgam, Mmp9, Tlr2, Il1b, Il18, Ccl5, Cxcl10, and Ccr7 were the top ten hub genes. CONCLUSIONS: Our findings indicated the potential key genes and pathways involved in fatty degeneration in the development of fatty infiltration and supplied underlying therapeutic targets in the future.

Intermodule Coupling Analysis of Huang-Lian-Jie-Du Decoction on Stroke.

Huang-Lian-Jie-Du Decoction (HLJDD) is a "Fangji" made up of well-designed Chinese herb array and widely used to treat ischemic stroke. Here we aimed to investigate pharmacological mechanism by introducing an inter-module analysis to identify an overarching view of target profile and action mode of HLJDD. Stroke-related genes were obtained from OMIM (Online Mendelian Inheritance in Man). And the potential target proteins of HLJDD were identified according to TCMsp (Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform). The two sets of molecules related to stroke and HLJDD were respectively imported into STRING database to construct the stroke network and HLJDD network, which were dissected into modules through MCODE, respectively. We analyzed the inter-module connectivity by quantify "coupling score" (CS) between HLJDD-modules (H-modules) and stroke-modules (S-module) to explore the pharmacological acting pattern of HLJDD on stroke. A total of 267 stroke-related proteins and 15 S-modules, 335 HLJDD putative targeting proteins, and 13 H-modules were identified, respectively. HLJDD directly targeted 28 proteins in stroke network, majority (16, 57.14%) of which were in S-modules 1 and 4. According to the modular map based on inter-module CS analysis, H-modules 1, 2, and 8 densely connected with S-modules 1, 3, and 4 to constitute a module-to-module bridgeness, and the enriched pathways of this bridgeness with top significance were TNF signaling pathway, HIF signaling pathway, and PI3K-Akt signaling pathway. Furthermore, through this bridgeness, H-modules 2 and 4 cooperatively work together to regulate mitochondrial apoptosis against the ischemia injury. Finally, the core protein in H-module 4 account for mitochondrial apoptosis was validated by an in vivo experiment. This study has developed an integrative approach by inter-modular analysis for elucidating the "shotgun-like" pharmacological mechanism of HLJDD for stroke.

Identification of differentially expressed genes, associated functional terms pathways, and candidate diagnostic biomarkers in inflammatory bowel diseases by bioinformatics analysis.

Inflammatory bowel diseases (IBDs), including ulcerative colitis (UC) and Crohn's disease (CD), are chronic inflammatory disorders caused by genetic influences, the immune system and environmental factors. However, the underlying pathogenesis of IBDs and the pivotal molecular interactions remain to be fully elucidated. The aim of the present study was to identify genetic signatures in patients with IBDs and elucidate the potential molecular mechanisms underlying IBD subtypes. The gene expression profiles of the GSE75214 datasets were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified in UC and CD patients compared with controls using the GEO2R tool. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of DEGs were performed using DAVID. Furthermore, protein-protein interaction (PPI) networks of the DEGs were constructed using Cytoscape software. Subsequently, significant modules were selected and the hub genes were identified. In the GO and KEGG pathway analysis, the top enriched pathways in UC and CD included Staphylococcus aureus infection, rheumatoid arthritis, complement and coagulation cascades, PI3K/Akt signaling pathway and osteoclast differentiation. In addition, the GO terms in the category biological process significantly enriched by these genes were inflammatory response, immune response, leukocyte migration, cell adhesion, response to molecules of bacterial origin and extracellular matrix (ECM) organization. However, several other biological processes (GO terms) and pathways (e.g., 'chemotaxis', 'collagen catabolic process' and 'ECM-receptor interaction') exhibited significant differences between the two subtypes of IBD. The top 10 hub genes were identified from the PPI network using respective DEGs. Of note, the hub genes G protein subunit gamma 11 (GNG11), G protein subunit beta 4 (GNB4), Angiotensinogen (AGT), Phosphoinositide-3-kinase regulatory subunit 3 (PIK3R3) and C-C motif chemokine receptor 7 (CCR7) are disease-specific and may be used as biomarkers for differentiating UC from CD. Furthermore, module analysis further confirmed that common significant pathways involved in the pathogenesis of IBD subtypes were associated with chemokine-induced inflammation, innate immunity, adapted immunity and infectious microbes. In conclusion, the present study identified DEGs, key target genes, functional pathways and enrichment analysis of IBDs, enhancing the understanding of the pathogenesis of IBDs and also advancing the clarification of the underlying molecular mechanisms of UC and CD. Furthermore, these results may provide potential molecular targets and diagnostic biomarkers for UC and CD.

Comprehensive Analysis of Gene Expression Profiles of Sepsis-Induced Multiorgan Failure Identified Its Valuable Biomarkers.

Sepsis is an inflammatory-related disease, and severe sepsis would induce multiorgan dysfunction, which is the most common cause of death of patients in noncoronary intensive care units. Progression of novel therapeutic strategies has proven to be of little impact on the mortality of severe sepsis, and unfortunately, its mechanisms still remain poorly understood. In this study, we analyzed gene expression profiles of severe sepsis with failure of lung, kidney, and liver for the identification of potential biomarkers. We first downloaded the gene expression profiles from the Gene Expression Omnibus and performed preprocessing of raw microarray data sets and identification of differential expression genes (DEGs) through the R programming software; then, significantly enriched functions of DEGs in lung, kidney, and liver failure sepsis samples were obtained from the Database for Annotation, Visualization, and Integrated Discovery; finally, protein-protein interaction network was constructed for DEGs based on the STRING database, and network modules were also obtained through the MCODE cluster method. As a result, lung failure sepsis has the highest number of DEGs of 859, whereas the number of DEGs in kidney and liver failure sepsis samples is 178 and 175, respectively. In addition, 17 overlaps were obtained among the three lists of DEGs. Biological processes related to immune and inflammatory response were found to be significantly enriched in DEGs. Network and module analysis identified four gene clusters in which all or most of genes were upregulated. The expression changes of Icam1 and Socs3 were further validated through quantitative PCR analysis. This study should shed light on the development of sepsis and provide potential therapeutic targets for sepsis-induced multiorgan failure.

Identification of Critical Genes and miRNAs Associated with the Development of Parkinson's Disease.

The purpose of this study was to explore the key mechanism involved in the pathogenesis of Parkinson's disease (PD) based on microarray analysis. The expression profile data of GSE7621, which contained 9 substantia nigra tissues isolated from normals and 16 substantia nigra tissues isolated from PD patients, was obtained from Gene Expression Omnibus. The differentially expressed genes (DEGs) were screened, followed by functional enrichment analysis and protein-protein interaction (PPI) network construction. After the miRNAs regulating the DEGs were predicted, the miRNA-DEG regulatory network was then constructed. Besides, the 6-hydroxydopamine rat model of PD was established and the expression of key DEGs and miRNA was detected. A total of 388 DEGs were identified, including 218 upregulated genes and 170 downregulated ones. Tyrosine hydroxylase (TH) and solute carrier family 6 member 3 (SLC6A3) were significantly related to the functional terms of catecholamine biosynthetic process and dopamine biosynthetic process. TH and SLC6A3 were hub nodes in the PPI network. EBF3 could be targeted by miR-218. Moreover, TH and SLC6A3 were found downregulated in the 6-OHDA rat model of PD, while miR-218 was markedly upregulated. Our results reveal that SLC6A3, TH, and EBF3 targeted by miR-218 could be involved in PD. These molecules might provide a new insight into the development of therapeutic strategies for PD.