Protein-protein Network Research Articles

The potential mechanism of Astragali Radix in the treatment of children with nephrotic syndrome

BackgroundThe molecular mechanism of Astragali Radix in the treatment of children with nephrotic syndrome (NS) is unclear. This study aimed to use network pharmacology to explore this potential mechanism.MethodsThe Traditional Chinese Medicine Systems Pharmacology (TCMSP) database was used to identify the main active ingredients of Astragali Radix. The PharmMapper, Online Mendelian Inheritance in Man (OMIM), and GeneCards databases were then used to identify the active ingredients of Astragali Radix. The String database and Cytoscape software were used to construct the protein-protein network. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed using DAVID Database.ResultsIn the TCMSP Database, a total of 20 chemical constituents of Astragali Radix were screened. After removing the duplicates and false positive genes, 394 targets of these active ingredients were obtained from PharmMapper. By comparing the NS-related genes in the GeneCards and OMIM Databases, a total of 39 potential NS-related targets were ultimately identified. The protein-protein-interaction network included 39 nodes and 366 edges. The top 5 proteins were albumin (ALB), serine/threonine kinase (AKT1), epidermal growth factor receptor (EGFR), mitogen-activated protein kinase (MAPK), and matrix metallopeptidase 9 (MMP9). The GO analysis showed that the target genes were mainly involved in biological processes (e.g., signal transduction, the positive regulation of cell proliferation, and the positive regulation of migration). The cellular components included a plasma membrane, extracellular exosome, and extracellular space. The molecular functions included protein binding, zinc-ion binding, protein tyrosine kinase activity, and enzyme binding. The KEGG analysis showed that the treatment of NS by Astragali Radix mainly involved pathways in cancer, proteoglycans in cancer, the phosphatidylinositol 3-kinase and protein kinase B (PI3K-Akt) signaling pathway, the rennin-angiotensin-system (Ras) signaling pathways, and Forkhead box protein O1 (FoxO) signaling pathways.ConclusionsIn the present study, the network pharmacology method was used to explore the potential targets and pathways of Astragali Radix in the treatment of NS. We also provided future research directions for the treatment of NS with a complex pathogenesis.

Clarifying the pharmacological mechanisms of action of Shenfu Decoction on cardiovascular diseases using a network pharmacology approach.

Since the molecular mechanisms underlying in the pathogenesis of cardiovascular diseases (CVD) are extremely complex and have not yet been elucidated in detail, CVD remain the leading cause of death worldwide. Traditional Chinese medicine involves the treatment of disease from an overall perspective, and its therapeutic effects on CVD have been demonstrated. However, the mechanisms contributing to the multiscale treatment of cardiovascular diseases at the systematic level remain unclear. Network pharmacology methods and a gene chip data analysis were integrated and applied in the present study, which was conducted to investigate the potential target genes and related pathways of Shenfu Decoction (SFD) for the treatment of myocardial injury. The gene chip analysis was initially performed, followed by network pharmacology to identify differentially expressed genes (DEG) and a functional enrichment analysis. Protein-protein networks were constructed and a module analysis was conducted. A network analysis was used to identify the target genes of SFD. Regarding the results obtained, 1134 DEG were identified using the STRING website. The module analysis revealed that nine hub genes exhibited ubiquitin-protein ligase activity. Therefore, SFD significantly alters the expression of ubiquitination-related genes and, thus, plays an important therapeutic role in the treatment of heart failure. In conclusion, hub genes may provide a more detailed understanding of the molecular mechanisms of action of as well as candidate targets for SFD therapy.

Concentration-dependent transcriptome of zebrafish larvae for environmental bisphenol S assessment

The widespread use of bisphenol S (BPS) as an alternative to bisphenol A has captured attention due to its potential toxicity to aquatic organisms. In the present study, the zebrafish was used as a model to evaluate the toxicity of BPS and determine the underlying mechanisms. The environmental concentration-dependent (0, 0.1, 1, 10, 100, and 1000 μg/L BPS) transcriptome approach was employed in combination with toxicity assays to address the problem. Based on a weighted correlation network analysis, we speculated that excess reactive oxygen species (ROS) may initiate cellular events in BPS-exposed zebrafish, leading to multiple toxic effects. Furthermore, we used pathway enrichment analysis to identify key pathways (MAPK signalling pathway and metabolic pathways) that link the molecular mechanisms with different toxic effects. In addition, we performed protein-protein network and shortest path analyses to identify six hub genes (erbb2, rrm2, rps27a, his2h3c, cdk1, and mcm5) and their interactions. Moreover, we suggest that BPS may interact with erbb2 by molecular docking. Thus, the BPS-erbb2 interaction may activate the MAPK signalling and metabolic pathways, resulting in ROS production and then caused multiple toxic effects in zebrafish. This study provides information for characterising the mechanisms of BPS exposure in aquatic environments.

Identification of in vitro-in vivo components of Caoguo using accelerated solvent extraction combined with gas chromatography-mass spectrometry integrated with network pharmacology on indigestion

BackgroundCaoguo (Tsaoko Fructus), a traditional Chinese medicine, is widely used as medicine and dietary spices. Volatile components are among its important bioactive constituents used to treatment of abdominal distension and pain, but the mechanism is not clear up to now. The purpose of this study was to develop a simple, sensitive, and accurate method to analyze and identify components of Caoguo in vitro and in vivo, and further investigate the therapeutic mechanism of Caoguo on indigestion using network pharmacology.MethodsCaoguo were extracted by accelerated solvent extraction (ASE) and n-hexane:ethyl acetate (1:1, v/v) was selected as the extraction solvent. Gas chromatography-mass spectrometry (GC-MS) was adopted to analyze and identify the volatile components in vitro and in vivo. Network pharmacology including protein-protein network construction, Gene Ontology (GO) enrichment and pathway enrichment analysis and component-target-pathway network construction was applied.ResultsBy comparing the retention times and mass spectrometry data, as well as retrieving the reference literature, a total of 169 components were tentatively identified in Caoguo extract and 43 components were identified in rats plasma samples for the first time. The results of network pharmacology analysis indicated that the potential mechanism was mainly associated with regulation of lipolysis in adipocytes and serotonergic synapse signaling pathway, which might be responsible for the effect of indigestion.ConclusionsCaoguo was first extracted by ASE and the volatile chemical components in vivo were first identified by GC-MS. Coupled with network pharmacology analysis, a network of component-target-pathway was constructed to reveal the possible mechanism of Caoguo in treatment of indigestion. This study provided a new reference method for the extraction and analysis of Caoguo, laid a chemical basis for in-depth studies on pharmacodynamics and pharmacology, and revealed an updated understanding of the therapeutic effects of Caoguo on indigestion.

Towards a global investigation of transcriptomic signatures through co-expression networks and pathway knowledge for the identification of disease mechanisms.

We attempt to address a key question in the joint analysis of transcriptomic data: can we correlate the patterns we observe in transcriptomic datasets to known interactions and pathway knowledge to broaden our understanding of disease pathophysiology? We present a systematic approach that sheds light on the patterns observed in hundreds of transcriptomic datasets from over sixty indications by using pathways and molecular interactions as a template. Our analysis employs transcriptomic datasets to construct dozens of disease specific co-expression networks, alongside a human protein-protein interactome network. Leveraging the interoperability between these two network templates, we explore patterns both common and particular to these diseases on three different levels. Firstly, at the node-level, we identify most and least common proteins across diseases and evaluate their consistency against the interactome as a proxy for their prevalence in the scientific literature. Secondly, we overlay both network templates to analyze common correlations and interactions across diseases at the edge-level. Thirdly, we explore the similarity between patterns observed at the disease-level and pathway knowledge to identify signatures associated with specific diseases and indication areas. Finally, we present a case scenario in schizophrenia, where we show how our approach can be used to investigate disease pathophysiology.

The dual role of SrbA from Paracoccidioides lutzii: a hypoxic regulator.

The fungus Paracoccidioides lutzii is one of the species of the Paracoccidioides genus, responsible for a neglected human mycosis, endemic in Latin America, the paracoccidioidomycosis (PCM). In order to survive in the host, the fungus overcomes a hostile environment under low levels of oxygen (hypoxia) during the infectious process. The hypoxia adaptation mechanisms are variable among human pathogenic fungi and worthy to be investigated in Paracoccidoides spp. Previous proteomic results identified that P. lutzii responds to hypoxia and it has a functional homolog of the SrbA transcription factor, a well-described hypoxic regulator. However, the direct regulation of genes by SrbA and the biological processes it governs while performing protein interactions have not been revealed yet. The goal of this study was to demonstrate the potential of SrbA targets genes in P. lutzii. In addition, to show the SrbA three-dimensional aspects as well as a protein interaction map and important regions of interaction with predicted targets. The results show that SrbA-regulated genes were involved with several biological categories, such as metabolism, energy, basal processes for cell maintenance, fungal morphogenesis, defense, virulence, and signal transduction. Moreover, in order to investigate the SrbA's role as a protein, we performed a 3D simulation and also a protein-protein network linked to this hypoxic regulator. These in silico analyses revealed relevant aspects regarding the biology of this pathogen facing hypoxia and highlight the potential of SrbA as an antifungal target in the future.

Identification of Early Diagnostic and Prognostic Biomarkers via WGCNA in Stomach Adenocarcinoma.

Stomach adenocarcinoma (STAD) is a leading cause of cancer deaths, and the outcome of the patients remains dismal for the lack of effective biomarkers of early detection. Recent studies have elucidated the landscape of genomic alterations of gastric cancer and reveal some biomarkers of advanced-stage gastric cancer, however, information about early-stage biomarkers is limited. Here, we adopt Weighted Gene Co-expression Network Analysis (WGCNA) to screen potential biomarkers for early-stage STAD using RNA-Seq and clinical data from TCGA database. We find six gene clusters (or modules) are significantly correlated with the stage-I STADs. Among these, five hub genes, i.e., MS4A1, THBS2, VCAN, PDGFRB, and KCNA3 are identified and significantly de-regulated in the stage-I STADs compared with the normal stomach gland tissues, which suggests they can serve as potential early diagnostic biomarkers. Moreover, we show that high expression of VCAN and PDGFRB is associated with poor prognosis of STAD. VCAN encodes a large chondroitin sulfate proteoglycan that is the main component of the extracellular matrix, and PDGFRB encodes a cell surface tyrosine kinase receptor for members of the platelet-derived growth factor (PDGF) family. Consistently, Gene Ontology (GO) analysis of differentially expressed genes in the STADs indicates terms associated with extracellular matrix and receptor ligand activity are significantly enriched. Protein-protein network interaction analysis (PPI) and Gene Set Enrichment Analysis (GSEA) further support the core role of VCAN and PDGFRB in the tumorigenesis. Collectively, our study identifies the potential biomarkers for early detection and prognosis of STAD.

Translational Proteomics Analysis of Anthracycline-Induced Cardiotoxicity From Cardiac Microtissues to Human Heart Biopsies.

Anthracyclines, including doxorubicin, idarubicin, and epirubicin, are common antitumor drugs as well as well-known cardiotoxic agents. This study analyzed the proteomics alteration in cardiac tissues caused by these 3 anthracyclines analogs. The in vitro human cardiac microtissues were exposed to drugs in 2 weeks; the proteomic data were measured at 7 time points. The heart biopsy data were collected from heart failure patients, in which some patients underwent anthracycline treatment. The anthracyclines-affected proteins were separately identified in the in vitro and in vivo dataset using the WGCNA method. These proteins engage in different cellular pathways including translation, metabolism, mitochondrial function, muscle contraction, and signaling pathways. From proteins detected in 2 datasets, a protein-protein network was established with 4 hub proteins, and 7 weighted proteins from both cardiac microtissue and human biopsies data. These 11 proteins, which involve in mitochondrial functions and the NF-κB signaling pathway, could provide insights into the anthracycline toxic mechanism. Some of them, such as HSPA5, BAG3, and SH3BGRL, are cardiac therapy targets or cardiotoxicity biomarkers. Other proteins, such as ATP5F1B and EEF1D, showed similar responses in both the in vitro and in vivo data. This suggests that the in vitro outcomes could link to clinical phenomena in proteomic analysis.

Mass spectrometry and machine learning for the accurate diagnosis of benzylpenicillin and multidrug resistance of Staphylococcus aureus in bovine mastitis.

Staphylococcus aureus is a serious human and animal pathogen threat exhibiting extraordinary capacity for acquiring new antibiotic resistance traits in the pathogen population worldwide. The development of fast, affordable and effective diagnostic solutions capable of discriminating between antibiotic-resistant and susceptible S. aureus strains would be of huge benefit for effective disease detection and treatment. Here we develop a diagnostics solution that uses Matrix-Assisted Laser Desorption/Ionisation-Time of Flight Mass Spectrometry (MALDI-TOF) and machine learning, to identify signature profiles of antibiotic resistance to either multidrug or benzylpenicillin in S. aureus isolates. Using ten different supervised learning techniques, we have analysed a set of 82 S. aureus isolates collected from 67 cows diagnosed with bovine mastitis across 24 farms. For the multidrug phenotyping analysis, LDA, linear SVM, RBF SVM, logistic regression, naïve Bayes, MLP neural network and QDA had Cohen's kappa values over 85.00%. For the benzylpenicillin phenotyping analysis, RBF SVM, MLP neural network, naïve Bayes, logistic regression, linear SVM, QDA, LDA, and random forests had Cohen's kappa values over 85.00%. For the benzylpenicillin the diagnostic systems achieved up to (mean result ± standard deviation over 30 runs on the test set): accuracy = 97.54% ± 1.91%, sensitivity = 99.93% ± 0.25%, specificity = 95.04% ± 3.83%, and Cohen's kappa = 95.04% ± 3.83%. Moreover, the diagnostic platform complemented by a protein-protein network and 3D structural protein information framework allowed the identification of five molecular determinants underlying the susceptible and resistant profiles. Four proteins were able to classify multidrug-resistant and susceptible strains with 96.81% ± 0.43% accuracy. Five proteins, including the previous four, were able to classify benzylpenicillin resistant and susceptible strains with 97.54% ± 1.91% accuracy. Our approach may open up new avenues for the development of a fast, affordable and effective day-to-day diagnostic solution, which would offer new opportunities for targeting resistant bacteria.

Glycyrrhizic Acid for COVID-19: Findings of Targeting Pivotal Inflammatory Pathways Triggered by SARS-CoV-2.

Background: Coronavirus disease 2019 (COVID-19) is now a worldwide public health crisis. The causative pathogen is severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Novel therapeutic agents are desperately needed. Because of the frequent mutations in the virus and its ability to cause cytokine storms, targeting the viral proteins has some drawbacks. Targeting cellular factors or pivotal inflammatory pathways triggered by SARS-CoV-2 may produce a broader range of therapies. Glycyrrhizic acid (GA) might be beneficial against SARS-CoV-2 because of its anti-inflammatory and antiviral characteristics and possible ability to regulate crucial host factors. However, the mechanism underlying how GA regulates host factors remains to be determined. Methods: In our report, we conducted a bioinformatics analysis to identify possible GA targets, biological functions, protein-protein interactions, transcription-factor-gene interactions, transcription-factor-miRNA coregulatory networks, and the signaling pathways of GA against COVID-19. Results: Protein-protein interactions and network analysis showed that ICAM1, MMP9, TLR2, and SOCS3 had higher degree values, which may be key targets of GA for COVID-19. GO analysis indicated that the response to reactive oxygen species was significantly enriched. Pathway enrichment analysis showed that the IL-17, IL-6, TNF-α, IFN signals, complement system, and growth factor receptor signaling are the main pathways. The interactions of TF genes and miRNA with common targets and the activity of TFs were also recognized. Conclusions: GA may inhibit COVID-19 through its anti-oxidant, anti-viral, and anti-inflammatory effects, and its ability to activate the immune system, and targeted therapy for those pathways is a predominant strategy to inhibit the cytokine storms triggered by SARS-CoV-2 infection.

An integrated genomic approach to dissect the genetic landscape regulating the cell-to-cell transfer of α-synuclein.

SummaryNeuropathological and experimental evidence suggests that the cell-to-cell transfer of α-synuclein has an important role in the pathogenesis of Parkinson’s disease (PD). However, the mechanism underlying this phenomenon is not fully understood. We undertook a small interfering RNA (siRNA), genome-wide screen to identify genes regulating the cell-to-cell transfer of α-synuclein. A genetically encoded reporter, GFP-2A-αSynuclein-RFP, suitable for separating donor and recipient cells, was transiently transfected into HEK cells stably overexpressing α-synuclein. We find that 38 genes regulate the transfer of α-synuclein-RFP, one of which is ITGA8, a candidate gene identified through a recent PD genome-wide association study (GWAS). Weighted gene co-expression network analysis (WGCNA) and weighted protein-protein network interaction analysis (WPPNIA) show that those hits cluster in networks that include known PD genes more frequently than expected by random chance. The findings expand our understanding of the mechanism of α-synuclein spread.

Diagnostic Biomarker Hsa_circ_0126218 and Functioning Prediction in Peripheral Blood Monocular Cells of Female Patients With Major Depressive Disorder.

IntroductionAlthough major depressive diroder (MDD) has brought huge burden and challenges to society globally, effective and accurate diagnoses and treatments remain inadequate. The pathogenesis that for women are more likely to suffer from depression than men needs to be excavated as well. The function of circRNAs in pathological process of depression has not been widely investigated. This study aims to explore potential diagnostic biomarker circRNA of female patients with MDD and to investigate its role in pathogenesis.MethodsFirst, an expression profile of circRNAs in the peripheral blood monocular cells of MDD patients and healthy peripherals were established based on high-throughput sequencing analysis. In addition, the top 10 differentially expressed circRNAs were quantified by quantitative real-time PCR to explore diagnostic biomarkers. To further investigate the function of biomarkers in the pathogenesis of MDD, bioinformatics analysis on downstream target genes of the biomarkers was carried out.ResultsThere is a mass of dysregulated circRNAs in PBMCs between female MDD patients and healthy controls. Among the top 10 differentially expressed circRNAs, hsa_circ_0126218 is more feasible as a diagnostic biomarker. The expression level of hsa_circ_0126218 displayed upregulation in patients with MDD and the area under the operating characteristic curve of hsa_circ_0126218 was 0.801 (95% CI 0.7226–0.8791, p < 0.0001). To explain the competing endogenous RNA role of hsa_circ_0126218 in the pathogenesis of female MDD, a hsa_circ_0126218-miRNA-mRNA network was established. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses stated that some of the enriched pathways downstream of hsa_circ_0126218 are closely related to MDD. Moreover, we established a protein-protein network to further screen out the hub genes (PIK3CA, PTEN, MAPK1, CDC42, Lyn, YES1, EPHB2, SMAD2, STAT1, and ILK). The function of hsa_circ_0126218 was refined by constructing a verified circRNA-predicted miRNA-hub gene subnetwork.Conclusionhsa_circ_0126218 can be considered as a new female MDD biomarker, and the pathogenesis of female MDD by the downstream regulation of hsa_circ_0126218 has been predicted. These findings may help further improve the early detection, effective diagnosis, convenient monitoring of complications, precise treatment, and timely recurrence prevention of depression.

Identifying miRNAs in multiple sclerosis gray matter lesions that correlate with atrophy measures.

ObjectiveMultiple sclerosis (MS) is an inflammatory, demyelinating and neurodegenerative disease of the central nervous system (CNS). Though MS was initially considered to be a white matter demyelinating disease, myelin loss in cortical gray matter has been reported in all disease stages. We previously identified microRNAs (miRNAs) in white matter lesions (WMLs) that are detected in serum from MS patients. However, miRNA expression profiles in gray matter lesions (GMLs) from progressive MS brains are understudied.MethodsWe used a combination of global miRNAs and gene expression profiling of GMLs and independent validation using real‐time quantitative polymerase chain reaction (RT‐qPCR), immuno‐in situ hybridization, and immunohistochemistry.ResultsCompared to matched myelinated gray matter (GM) regions, we identified 82 miRNAs in GMLs, of which 10 were significantly upregulated and 17 were significantly downregulated. Among these 82 miRNAs, 13 were also detected in serum and importantly were associated with brain atrophy in MS patients. The predicted target mRNAs of these miRNAs belonged to pathways associated with axonal guidance, TGF‐β signaling, and FOXO signaling. Further, using state‐of‐the‐art human protein–protein interactome network analysis, we mapped the four key GM atrophy‐associated miRNAs (hsa‐miR‐149*, hsa‐miR‐20a, hsa‐miR‐29c, and hsa‐miR‐25) to their target mRNAs that were also changed in GMLs.InterpretationOur study identifies miRNAs altered in GMLs in progressive MS brains that correlate with atrophy measures. As these miRNAs were also detected in sera of MS patients, these could act as markers of GML demyelination in MS.

Prognostic value of CCR2 as an immune indicator in lung adenocarcinoma: A study based on tumor‐infiltrating immune cell analysis

BackgroundPrognostic indicators in lung adenocarcinoma (LUAD) have been seeking under database analysis, and remarkable advance is on the way.MethodsThis study calculated the scores of stromal and immune components of the tumor microenvironment (TME) in 551 LUAD samples using the ESTIMATE algorithm on The Cancer Genome Atlas (TCGA) database. R package ''limma'' was used to selected differentially expressed genes (DEG). We have analyzed the DEGs by means of Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichments. The protein‐protein network, univariate Cox analysis, and Lasso regression analysis were performed to selected survival‐related genes. Gene Set Enrichment Analysis (GSEA) represented the enriched pathway of CC chemokine receptor 2 (CCR2). The ratios of immune cells in the TME of each LUAD sample were obtained using the R package "limma" and CIBERSORT algorithm in R 4.0.2.ResultsThe ImmuneScore was positively correlated with prognosis regarding survival rate, T classification of TNM stages, and clinicopathological staging characteristics. GO and KEGG enrichments showed DEGs were associated with immune‐related activities. Three genes of LUAD were selected from the PPI network and Cox proportional hazards regression analysis. CCR2 was the most survival correlated gene by Lasso regression analysis. GSEA results showed that C2 kegg gene sets in the CCR2 high‐expression group were mainly enriched in the B cell or T cell receptor signaling pathway and natural killer cell‐mediated cytotoxicity. Correlation of CCR2 expression with prognosis was conducted, implicating a positive correlation with the prognosis of survival rate and M classification, negative correlation with the prognosis of T and N classifications. The correlation between CCR2 and tumor‐infiltrating immune cells (TICs) was analyzed, and 14 kinds of TICs were found closely correlated with CCR2 expression through difference analysis.ConclusionTherefore, CCR2 has prognostic value as an immune indicator in LUAD.

Identification of differentially expressed genes and pathways crosstalk analysis in Rheumatoid and Osteoarthritis using next-generation sequencing and protein-protein networks.

Osteoarthritis occurs when protective cartilage of bones worn out. Similarlty, cartilage damage occurs mainly in the pannus cartilage in rheumatoid arthritis. It is a potentially debilitating condition, affecting women two to three times more often than men. The cause and prognosis of rheumatoid and osteoarthritis are still poorly known. However, advances in the study of disease pathogenesis have encouraged the creation of new therapeutics with improved outcomes. The purpose of this study is to investigate the differentially expressed genes potentially involved in dysregulated rheumatoid arthritis (RA) and their association to other types of arthritis, including osteoarthritis (OA). Complete RNAs were isolated for RNA expression profiling using next-generation sequencing from human primary cultured normal and RA chondrocytes. From RNA sequencing results 250 differentially expressed genes were identified using bioinformatics analysis, of which 32 were found to be significantly playing role in RA pathogenesis and its associated diseases. Molecular ontologies of the identified genes showed they are connected to Innate immune response, Protein phosphorylation, Transcription initiation from RNA polymerase II promoter, Immune response, Neoplasms of bones, as well as osteorthritis, and Rheumatoid arthritis. Among the identified genes, TRAF1, TRAF2, BAMP, STX11, MEOX2, AES, REL, FHL3, PNMA1, SGTA, LZTS2, SIAH2, PNMA1, and TFCP2 were found to be highly enriched in the protein-protein interaction network. The significant cross talks were found in Hypertrophic cardiomyopathy, Small cell lung cancer, Proteasome, p53 signaling pathway, Arrhythmogenic right ventricular cardiomyopathy, Small cell lung cancer, SNARE interactions in vesicular transport, RIG-I-like receptor signaling pathway, and Hypertrophic cardiomyopathy pathways. The results offer new opportunities for target gene control in RA and OA cartilage destruction.

A New Framework for Discovering Protein Complex and Disease Association via Mining Multiple Databases.

One important challenge in the post-genomic era is to explore disease mechanisms by efficiently integrating different types of biological data. In fact, a single disease is usually caused through multiple genes products such as protein complexes rather than single gene. Therefore, it is meaningful for us to discover protein communities from the protein-protein interaction network and use them for inferring disease-disease associations. In this article, we propose a new framework including protein-protein networks, disease-gene associations and disease-complex pairs to cluster protein complexes and infer disease associations. Complexes discovered by our approach is superior in quality (Sn, PPV and ACC) and clustering quantity than other four popular methods on three PPI networks. A systematic analysis shows that disease pairs sharing more protein complexes (such as Glucose and Lipid Metabolic Disorders) are more similar and overlapping proteins may have different roles in different diseases. These findings can provide clinical scholars and medical practitioners with new ideas on disease identification and treatment.

Systemic sclerosis biomarkers detection in the secretome of TGFβ1-activated primary human lung fibroblasts

TGFβ1 is a profibrotic mediator that contributes to a broad spectrum of pathologies, including systemic sclerosis-associated pulmonary fibrosis (SSc-PF). However, the secretome of TGFβ1-stimulated primary human normal lung (NL) fibroblasts has not been well characterized. Using fluorescent 2-dimensional gel electrophoresis (2D-PAGE) and differential gel electrophoresis (DIGE) followed by Mass Spectrometry, we identified 37 differentially secreted proteins in the conditioned media of TGFβ1-activated NL fibroblasts and generated a protein-protein association network of the TGFβ1 secretome using STRING. Functional enrichment revealed that several biological processes and pathways characteristic of PF were enriched. Additionally, by comparing the TGFβ1 secretome of NL fibroblasts to proteomic biomarkers from biological fluids of systemic sclerosis (SSc) patients, we identified 11 overlapping proteins. Together our data validate the TGFβ1-induced secretome of NL fibroblasts as a valid in vitro model that reflects SSc biomarkers and identify potential therapeutic targets for SSc-PF. SignificanceAll proteins secreted by fibroblasts into the extracellular space, representing the secretome, promote cell-to-cell communication as well as tissue homeostasis, immune mechanisms, developmental regulation, proteolysis, development of the extracellular matrix (ECM) and cell adhesion. Therefore, it is crucial to understand how TGFβ1, a well-known profibrotic cytokine, modulates the secretome of pulmonary fibroblasts, and how the TGFβ1-induced secretome resembles biomarkers in SSc. Using functional enrichment analysis, key pathways and hub proteins can be identified and studied as potential therapeutic targets for pulmonary fibrosis.

Identification of VWF as a Novel Biomarker in Lung Adenocarcinoma by Comprehensive Analysis.

Lung adenocarcinoma (LUAD) is one of the most malignant tumors with high morbidity and mortality worldwide due to the lack of reliable methods for early diagnosis and effective treatment. It’s imperative to study the mechanism of its development and explore new biomarkers for early detection of LUAD. In this study, the Gene Expression Omnibus (GEO) dataset GSE43458 and The Cancer Genome Atlas (TCGA) were used to explore the differential co-expressed genes between LUAD and normal samples. Three hundred sixity-six co-expressed genes were identified by differential gene expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA) method. Those genes were mainly enriched in ameboidal-type cell migration (biological process), collagen-containing extracellular matrix (cell component), and extracellular matrix structure constituent (molecular function). The protein-protein network (PPI) was constructed and 10 hub genes were identified, including IL6, VWF, CDH5, PECAM1, EDN1, BDNF, CAV1, SPP1, TEK, and SELE. The expression level of hub genes was validated in the GEPIA database, compared with normal tissues, VWF is lowly expressed and SPP1 is upregulated in LUAD tissues. The survival analysis showed increased expression of SPP1 indicated unfavorable prognosis whereas high expression of VWF suggested favorable prognosis in LUAD (p < 0.05). Based on the immune infiltration analysis, the relationship between SPP1 and VWF expression and macrophage, neutrophil, and dendritic cell infiltration was weak in LUAD. Quantitative real-time PCR (qRT-PCR) and western blotting were used to validate the expression of VWF and SPP1 in normal human bronchial epithelial (HBE) cell and three LUAD cell lines, H1299, H1975, and A549. Immunohistochemistry (IHC) was further performed to detect the expression of VWF in 10 cases LUAD samples and matched normal tissues. In summary, the data suggest that VWF is a potential novel biomarker for prognosis of LUAD.

527 Expression of OPN3 correlating to tumor initiation and development in cutaneous melanoma

The function of OPN3 in melanoma remains largely unknown. To assess the biological role of OPN3 in the initiation and development of cutaneous melanoma, we first systematically investigated the expression patterns of OPN3 gene from human melanoma based on the available three independent gene expression datasets (GEO GSE3189, GSE8401 and GSE4587). We found that the OPN3 mRNA was upregulated in human melanoma when compared to normal skin and nevus tissue samples, especially in vertical growth phase melanoma. Additionally, no statistically significant difference was evident between the primary melanoma and metastasis of melanoma tissues. By the weighted correlation network and protein-protein association networks analysis, it suggested that OPN3 might play a role in the initiation and development of cutaneous melanoma. Next, to further illustrate the possible mechanisms by which OPN3 mediates melanoma cell tumorigenicity, RNA-seq analysis was performed on low expression of OPN3 and untreated M14 human skin melanoma cells. In this present study, our heatmap revealed that 5029 differentially expressed genes (DEGs) were upregulated, while 4495 DEGs were downregulated. Notably, in GO (Gene Ontology) term enrichment analysis our results indicated that the DEGs were evidently enriched in post-transcriptional gene regulation and cell cycle regulation, suggesting that OPN3 was mainly involved in proliferation and cell cycle regulation. The KEGG pathway analysis, OPN3 was dramatically enriched in cell cycle regulation process, neurotrophin signaling pathway and mRNA surveillance pathway. Taken together, we here showed that high expression of OPN3 can promote melanoma initiation and development by regulating cell cycle progression and tumor-associated signaling pathway.

Genome-wide transcriptional analysis unveils the molecular basis of organ-specific expression of isosteroidal alkaloids biosynthesis in critically endangered Fritillaria roylei Hook

Fritillaria roylei Hook. is a critically endangered high altitude Himalayan medicinal plant species with rich source of pharmaceutically active structurally diverse steroidal alkaloids. Nevertheless, except few marker compounds, the chemistry of the plant remains unexplored. Therefore, in the current study, transcriptome sequencing efforts were made to elucidate isosteroidal alkaloids biosynthesis by creating first organ-specific genomic resource using bulb, stem, and leaf tissues derived from natural populations of Indian Himalayan region. Overall, 349.9 million high quality paired-end reads obtained using NovaSeq 6000 platform were assembled (de novo) into 82,848 unigenes and 31,061 isoforms. Functional annotation and organ specific differential expression (DE) analysis identified 2488 significant DE transcripts, grouped into three potential sub-clusters (sub-cluster I: 728 transcripts; sub-cluster II: 446 transcripts and Sub-cluster III: 1314 transcripts). Subsequently, pathway enrichment (GO, KEGG) and protein-protein network analysis revealed significantly higher enrichment of phenyl-propanoid and steroid backbone including terpenoid, sesquiterpenoid and triterpenoid biosynthesis in bulb. Additionally, upregulated expression of cytochrome P450, UDP-dependent Glucuronosyltransferase families and key transcription factor families (FAR1, bHLH, GRAS, C2H2, TCP and MYB) suggests ‘bulb’ as a primary site of MVA mediated isosteroidal alkaloids biosynthesis. The comprehensive elucidation of molecular insights in this study is a first step towards the understanding of isosteroidal alkaloid biosynthesis pathway in F. roylei. Furthermore, key genes and regulators identified here can facilitate metabolic engineering of potential bioactive compounds at industrial scale.