Functional Enrichment Analysis Tool Research Articles

G:Profiler-interoperable web service for functional enrichment analysis and gene identifier mapping (2023 update).

g:Profiler is a reliable and up-to-date functional enrichment analysis tool that supports various evidence types, identifier types and organisms. The toolset integrates many databases, including Gene Ontology, KEGG and TRANSFAC, to provide a comprehensive and in-depth analysis of gene lists. It also provides interactive and intuitive user interfaces and supports ordered queries and custom statistical backgrounds, among other settings. g:Profiler provides multiple programmatic interfaces to access its functionality. These can be easily integrated into custom workflows and external tools, making them valuable resources for researchers who want to develop their own solutions. g:Profiler has been available since 2007 and is used to analyse millions of queries. Research reproducibility and transparency are achieved by maintaining working versions of all past database releases since 2015. g:Profiler supports 849 species, including vertebrates, plants, fungi, insects and parasites, and can analyse any organism through user-uploaded custom annotation files. In this update article, we introduce a novel filtering method highlighting Gene Ontology driver terms, accompanied by new graph visualizations providing a broader context for significant Gene Ontology terms. As a leading enrichment analysis and gene list interoperability service, g:Profiler offers a valuable resource for genetics, biology and medical researchers. It is freely accessible at https://biit.cs.ut.ee/gprofiler.

Integrative Analysis Reveals the Expression Pattern of SOX9 in Satellite Glial Cells after Sciatic Nerve Injury

Several complex cellular and gene regulatory processes are involved in peripheral nerve repair. This study uses bioinformatics to analyze the differentially expressed genes (DEGs) in the satellite glial cells of mice following sciatic nerve injury. R software screens differentially expressed genes, and the WebGestalt functional enrichment analysis tool conducts Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomics (KEGG) pathway analysis. The Search Tool for the Retrieval of Interacting Genes/Proteins constructs protein interaction networks, and the cytoHubba plug-in in the Cytoscape software predicts core genes. Subsequently, the sciatic nerve injury model of mice was established and the dorsal root ganglion satellite glial cells were isolated and cultured. Satellite glial cells-related markers were verified by immunofluorescence staining. Real-time polymerase chain reaction assay and Western blotting assay were used to detect the mRNA and protein expression of Sox9 in satellite glial cells. A total of 991 DEGs were screened, of which 383 were upregulated, and 508 were downregulated. The GO analysis revealed the processes of biosynthesis, negative regulation of cell development, PDZ domain binding, and other biological processes were enriched in DEGs. According to the KEGG pathway analysis, DEGs are primarily involved in steroid biosynthesis, hedgehog signaling pathway, terpenoid backbone biosynthesis, American lateral skeleton, and melanoma pathways. According to various cytoHubba algorithms, the common core genes in the protein-protein interaction network are Atf3, Mmp2, and Sox9. Among these, Sox9 was reported to be involved in the central nervous system and the generation and development of astrocytes and could mediate the transformation between neurogenic and glial cells. The experimental results showed that satellite glial cell marker GS were co-labeled with Sox9; stem cell characteristic markers Nestin and p75NTR were labeled satellite glial cells. The mRNA and protein expression of Sox9 in satellite glial cells were increased after sciatic nerve injury. In this study, bioinformatics was used to analyze the DEGs of satellite glial cells after sciatic nerve injury, and transcription factors related to satellite glial cells were screened, among which Sox9 may be associated with the fate of satellite glial cells.

Differential gene expression analysis of papillary thyroid carcinoma reveals important genes for lymph node metastasis

Background: Papillary thyroid carcinoma (PTC) is the most prevalent thyroid cancer. We explored the differential gene expression (DEG) of the transcriptional regulation in thyroid cancer with the main aim of determining the genes involved in lymph node metastasis (LNM) in PTC. Methods: We employed a bioinformatics pipeline for RNA-Seq analysis in PTC with and without LNM at the gene expression level. We performed read mapping, read quantitation, and DEG analysis using STAR, Cufflinks, and Cuffdiff, respectively. Subsequently, functional annotation and pathway enrichment were carried out using FunRich (functional enrichment analysis tool). Results: Expression profiling revealed changes in the PTC with LNM (33 genes at p-value < 0.05 and log2 fold change |1.0|) compared to the adjacent normal thyroid, whereas 69 genes showed differential expressionsin the PTC with lymph node negative (LNN) versus adjacent normal thyroid. We identified 31 significant DEGs in PTC LNM versus PTC LNN; and 44 significant DEGs between adjacent normal thyroid tissues from PTC LNM and PTC LNN. The biological processes of genes expressed at higher levels in PTC LNM compared to PTC LNN were involved in cell communication, energy pathway, and metabolism, whereas ion transport, energy pathways, and regulation of nucleobase, nucleoside, nucleotide, and nucleic acid metabolisms, were downregulated. Conclusion: These findings provide further evidence for the role of cellular transport regulatory processes in metastatic disease.

Pharmacological target and the biological mechanism of gallic acid for anticataract effect: A network analysis

BackgroundThis study aimed to identify the pharmacological targets and mechanisms of action of the traditional Indian plant, Saraca asoca bioactive, gallic acid, in the treatment of cataract using bioinformatics tools i.e., network pharmacology. Material/MethodsTargets of cataract were obtained by using DisGeNET gene discovery web-based database. The herbal ingredients target (HIT), the SuperPred, and the SwissTargetPrediction database were used for compound target prediction i.e., targets of gallic acid. Pathophysiological and therapeutic targets were imported to the STRING database, and the Cytoscape network integration software was used to construct component-target and disease-target interaction networks as a part of protein-protein interaction (PPI). Core targets were identified by network topological parameters and were further tested to identify the biological process and the signaling pathways by using functional enrichment analysis tool, FunRich. ResultsKey targets genes for gallic acid in the treatment of patients with cataract were identified, including indoleamine-2,3-dioxygenase-1 (IDO1), serum albumin (ALB), estrogen receptor (ESR1), prostaglandin G/H synthase-2 (PTGS2), epidermal growth factor receptor (EGFR), plasminogen activator inhibitor-1 (SERPINE1), aromatase (CYP19A1), neutrophil elastase (ELANE), and catechol-O-methyltransferase (COMT), apoptosis regulator (BCL2), carbonic anhydrase-4 (CA4), tyrosine-protein kinase receptor UFO (AXL), tyrosinase (TYR), kallikrein-7 (KLK7), and serine/threonine-protein kinase (NEK2). The signaling pathways of core targets mainly involved signaling events mediated by BMP receptors, integrins, proteoglycan syndecan, glypican, TRAIL, S1P (sphingosine-1-phosphate), LKB1, endothelins, VEGF, VEGFR, estrogen receptors, INF-γ, IL-5, mTOR, biological oxidation, kynurenines formation, TGF-β, etc. ConclusionPharmacological network analysis was used to identify the gene targets and mechanisms of gallic acid for the treatment of patient with cataract.

Network centrality approaches used to uncover and classify most influential nodes with their related miRNAs in cardiovascular diseases

Cardiovascular diseases (CVDs) is the diseases of the heart and blood vessels such as hypertension, coronary artery disease, peripheral artery disease, stroke, heart disease congenital, cardiac arrest and heart failure. CVDs is the leading cause of death worldwide, with around 17.9 million fatalities in 2019. In this study we identified hub genes (which could be used as new biomarkers or therapeutic targets in CVD) and pathways associated with CVD in infants based on gene expression profiles. Despite the discovery of a number of potential biomarkers, it is unlikely that a single biomarker can help definitively classify CVD. A total 24 Differentially expressed genes (DEGs) between CVD and normal (controls) infants were identified based on linear modeling of the microarray data using Limma package in GEO2R. A protein-protein interaction (PPI) network (with 222 nodes and 2992 interaction/edges) was constructed using the STRING (available online, at https://string-db.org/). Based on primary measures of centrality, four significant genes Osteoglycin (OGN), Toll-like receptor 3 (TLR3)s, C3 (Complement component 3), and Nicotinamide Phosphoribosyl transferase (NAMPT) were revealed using Cytoscape's plugin (Cytohubba, CytoNCA, Centiscape, Network Analyzer) and IVI graph packages in R. Topological centrality was applied to characterize the biological importance of genes in the network. in order to identify the biological functions and enrichment signaling pathways of DEGs, ToppFun (https://toppgene.cchmc.org/enrichment.jsp) and Funrich (Functional Enrichment analysis tool http://funrich.org/) were used. Further, these hub genes were uploaded to the miRNet database to find their association with microRNAs (A network with 47 nodes and 85 edges). Finally, four core miRNAs, has-miR-210-3p, has-miR-133a-3p, has-miR-129-2-3p, and has-miR-124-3p, were employed in mienturnet for disease ontology, with three key genes in common between two centralities (Degree and Betweenness). Finally, these hub genes were uploaded to the DGIdb4.0 database to find their association with Drugs. The resultant molecular studies found TLR3 interaction with rintatolimod. The goal of this study is to uncover important genes linked to CVD and further investigate their prognostic significance for its early detection and effective therapies.

Identification of dysregulated pathways through <i>SLC30A8</i> protein interaction in type 1 diabetes mellitus

<abstract><sec> <title>Objective</title> <p>The aim of the current study was to explore the gene enrichment and dysregulated pathways on the basis of interaction network analysis of <italic>SLC30A8</italic> in type 1 diabetes mellitus (T1DM). <italic>SLC30A8</italic> polymorphism could be characterized as a beneficial tool to identify the interacting gene in developing T1DM.</p> </sec><sec> <title>Materials and methods</title> <p><italic>SLC30A8</italic> interacting protein interaction network was obtained by String Interaction network Version 11.0. Ten proteins were identified interacting with <italic>SLC30A8</italic> and were analysed by protein-protein interaction and enrichment network analysis along with Functional Enrichment analysis tool (FunRich 3.1.3) to map the gene data sets. In entire analysis, FunRich database was used as background against all annotated gene/protein list. Protein-protein interaction (PPI) and enrichment network analysis of the selected protein: <italic>SLC30A8</italic> gene along with gene mapping and pathway enrichment were performed using FunRich 3.1.3 and String Interaction network Version 11.0.</p> </sec><sec> <title>Results</title> <p>Biological pathway grouping displayed enriched proteins in TRAIL signalling pathway (<italic>p</italic> < 0.001). <italic>PTPRN, GAD2</italic> and <italic>TCF7L2</italic> were enriched in TRAIL Signalling pathway when <italic>INS</italic> was made focused gene and directly interacting with <italic>SLC30A8</italic>.</p> </sec><sec> <title>Conclusions</title> <p>TRAIL signalling pathways were enriched in T1DM. Therefore, <italic>SLC30A8</italic> along with <italic>PTPRN, GAD2</italic> and <italic>TCF7L2</italic> involved in TRAIL pathway must be further explored to understand their in vivo role in T1DM.</p> </sec></abstract>

Bioinformatics Analysis Reveals Diagnostic Markers and Vital Pathways Involved in Acute Coronary Syndrome

Background Acute coronary syndrome (ACS) has a high incidence and mortality rate. Early detection and intervention would provide clinical benefits. This study aimed to reveal hub genes, transcription factors (TFs), and microRNAs (miRNAs) that affect plaque stability and provide the possibility for the early diagnosis and treatment of ACS. Methods We obtained gene expression matrix GSE19339 for ACS patients and healthy subjects from public database. The differentially expressed genes (DEGs) were screened using Limma package in R software. The biological functions of DEGs were shown by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). Protein-protein interaction (PPI) network was mapped in Cytoscape, followed by screening of hub genes based on the Molecular Complex Detection (MCODE) plug-in. Functional Enrichment analysis tool (FunRich) and Database for Annotation, Visualization and Integrated Discovery (DAVID) were used to predict miRNAs and TFs, respectively. Finally, GSE60993 expression matrix was chosen to plot receiver operating characteristic (ROC) curves with the aim of further assessing the reliability of our findings. Results We obtained 176 DEGs and further identified 16 hub genes by MCODE. The results of functional enrichment analysis showed that DEGs mediated inflammatory response and immune-related pathways. Among the predicted miRNAs, hsa-miR-4770, hsa-miR-5195, and hsa-miR-6088 all possessed two target genes, which might be closely related to the development of ACS. Moreover, we identified 11 TFs regulating hub gene transcriptional processes. Finally, ROC curves confirmed three genes with high confidence (area under the curve > 0.9), including VEGFA, SPP1, and VCAM1. Conclusion This study suggests that three genes (VEGFA, SPP1, and VCAM1) were involved in the molecular mechanisms of ACS pathogenesis and could serve as biomarkers of disease progression.

Cytokines Explored in Saliva and Tears from Radiated Cancer Patients Correlate with Clinical Manifestations, Influencing Important Immunoregulatory Cellular Pathways.

Although radiotherapy is a common form of treatment for head and neck cancer, it may lead to tissue damage in the salivary and lacrimal glands, possibly affecting cytokine expression in the gland fluid of treated individuals. Cytokine profiles in saliva and tear fluid of 29 radiated head and neck cancer patients and 20 controls were screened using a multiplex assay. Correlations between cytokine expression and clinical oral and ocular manifestations were examined, and cellular pathways influenced by these cytokines were assessed using the Functional Enrichment Analysis Tool. Significantly elevated cytokines identified in patient saliva were CCL21, IL-4, CX3CL1, CCL2, CXCL1 and CCL15. Many of these cytokines correlated positively with objective signs of oral dryness, and reduced saliva production in the patients. Although CCL21 and IL-4 levels were significantly lower in patient tear fluid, they correlated with subjective ocular symptoms. These increased salivary cytokines affected pro-inflammatory and apoptotic cellular pathways, including T cell signalling, several interleukin signalling pathways, TNF and TGF-β receptor signalling, and the apoptotic p53 pathway. In conclusion, the upregulated salivary cytokines identified suggest an interplay between innate and adaptive immunity, affecting immunoregulatory cellular pathways. Whether this is due to late effects of radiotherapy or tissue repair remains to be investigated.

Proteomic Signatures of Healthy Intervertebral Discs From Organ Donors: A Comparison With Previous Studies on Discs From Scoliosis, Animals, and Trauma.

ObjectiveTo catalog and characterize the proteome of normal human intervertebral disc (IVD). MethodsNine magnetic resonance imaging (MRI) normal IVDs were harvested from 9 different brain dead yet alive voluntary organ donors and were subjected to electrospray ionization-liquid chromatography tandem mass spectrometry (ESI-LC-MS/MS) acquisition. ResultsA total of 1,116 proteins were identified. Functional enrichment analysis tool DAVID ver. 6.8 categorized: extracellular proteins (38%), intracellular (31%), protein-containing complex (13%), organelle (9%), membrane proteins (6%), supramolecular complex (2%), and 1% in the cell junction. Molecular function revealed: binding activity (42%), catalytic activity (31%), regulatory activity (14%), and structural activity (7%). Molecular transducer, transporter, and transcription regulator activity together contributed to 6%. A comparison of the proteins obtained from this study to others in the literature showed a wide variation in content with only 3% of bovine, 5% of murine, 54% of human scoliotic discs, and 10.2% of discs adjacent to lumbar burst fractures common to our study of organ donors. Between proteins reported in scoliosis and lumbar fracture patients, only 13.51% were common, further signifying the contrast amongst the various MRI normal IVD samples. ConclusionThe proteome of “healthy” human IVDs has been defined, and our results show that proteomic data on IVDs obtained from scoliosis, fracture patients, and cadavers lack normal physiological conditions and should not be used as biological controls despite normal MRI findings. This questions the validity of previous studies that have used such discs as controls for analyzing the pathomechanisms of disc degeneration.

MiR-596 down regulates SOX4 expression and is a potential novel biomarker for gastric cancer.

BackgroundGastric cancer (GC) is one of the most commonly diagnosed malignancies of the human digestive tract, and currently there is a dearth of effective biomarkers for this disease.MethodsMiR-598 expression levels were analyzed by the cancer genome atlas (TCGA database) mining and verified in GC patient plasma using real-time reverse transcription polymerase chain reaction (RT-PCR) assay. We used the GEPIA and UALCAN databases and immunohistochemistry (IHC) to analyze SOX4 expression. The MTT assay assessed MNK28 and SGC7901 cell proliferation after transfection with miR-596 plasmids. The analytical tools, Functional Enrichment Analysis Tool (FunRich), Database of Immune Cell Expression (DICE) and Tumor IMmune Estimation Resource (TIMER) were used to analyze correlations between SOX4 and immune responses. Furthermore, a Kaplan Meier plotter database explored correlations between miR-596, SOX4 and overall patient survival.ResultsData from TCGA and RT-PCR indicated that miR-598 was lowly expressed in GC patients. The miRWalk database showed that SOX4 was the target genes of miR-596 and also revealed that miR-596 bound directly to SOX4. MiR-596 over-expression further depressed GC cell proliferation. In addition, the FunRich database showed that SOX4 was involved in immune responses, and was further shown to be differentially expressed in CD4+ T cells by DICE. Specifically, TIMER indicated that high expression of SOX4 was negatively correlated with infiltrating CD4+ T cells in stomach adenocarcinoma (STAD). Moreover, high expression of miR-596 and low expression of SOX4 prolonged the overall survival (OS) of GC patients.ConclusionsOur study reveals a crucial role for miR-596 in tumor-associated immune infiltration and predicting prognoses in GC patients.

Proteomic and histopathological characterisation of sicca subjects and primary Sj\xf6gren\u2019s syndrome patients reveals promising tear, saliva and extracellular vesicle disease biomarkers

BackgroundMononuclear cell infiltration of exocrine glands, production of Ro/SSA and La/SSB autoantibodies, along with oral and ocular dryness, are characteristic features of primary Sjögren’s syndrome (pSS). Non-SS sicca subjects, an underexplored group in relation to pSS, display similar sicca symptoms, with possible mild signs of inflammation in their salivary glands, yet with no serological detection of autoantibody production. In this study, we investigated inflammatory manifestations in the salivary gland tissue, tear fluid and saliva of non-SS subjects, as compared to pSS patients and healthy individuals.MethodsFifteen non-SS, 10 pSS and 10 healthy subjects were included in the analyses. Histological evaluation of salivary gland biopsies was performed. Liquid chromatography-mass spectrometry (LC-MS) was conducted on tear fluid and stimulated whole saliva, and proteomic biomarker profiles were generated. Extracellular vesicle (EVs) isolation and characterisation from both fluids were also combined with LC-MS. The LC-MS data were analysed for quantitative differences between patient and control groups using Scaffold. Database for Annotation, Visualization and Integrated Discovery (DAVID) and Functional Enrichment Analysis Tool (FunRich) were applied for functional analyses.ResultsHistopathological evaluation of salivary gland biopsies showed implications of milder inflammation in non-SS subjects through mononuclear cell infiltration, fibrosis and fatty replacement, as compared to pSS patients. Although unaffected in the non-SS group, upregulation of proinflammatory pathways and proteins involved in ubiquitination (LMO7 and HUWE1) and B cell differentiation (TPD52) were detected in tear fluid of pSS patients. Moreover, overexpression of proteins STOM, ANXA4 and ANXA1, regulating cellular innate and adaptive immunological pathways, were further identified in EVs from tear fluid of pSS patients. Finally, whole saliva and EVs isolated from whole saliva of pSS patients expressed proteins vital for innate MHC class I cellular regulation (NGAL) and T cell activation (CD44).ConclusionsNon-SS sicca subjects may show implications of mild inflammation in their glandular tissue, while their protein profile was strikingly more similar to healthy controls than to pSS patients. Hence, the tear and salivary biomarkers identified could be implemented as potential non-invasive diagnostic tools that may aid in increasing diagnostic accuracy when evaluating non-SS subjects and pSS patients and monitoring disease progression.

The Effects of Plumbagin on Pancreatic Cancer: A Mechanistic Network Pharmacology Approach.

BackgroundThis study aimed to use a network pharmacology approach to establish the effects of plumbagin on pancreatic cancer (PC) and to predict core targets and biological functions, pathways, and mechanisms of action.Material/MethodsGenes associated with the pathogenesis of PC were obtained from a database of gene-disease associations (DisGeNET). Putative genes associated with plumbagin were identified from the databases of drug target identification (PharmMapper), target prediction of bioactive components (SwissTargetPrediction), and comprehensive drug target information (DrugBank). PC targets of plumbagin were harvested by using a functional enrichment analysis tool (FunRich). The data of function-related protein-protein interactions (PPIs) with a confidence score >0.9 were obtained by using functional protein association networks (STRING). The network interactions of plumbagin and PC targets and function-related proteins were constructed through complex network analysis and visualization (Cytoscape). The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis were used to identify the effects of plumbagin.ResultsThe most important biotargets for plumbagin in PC were identified as TP53, MAPK1, BCL2, and IL6. A total of 1,731 annotations and 121 enriched pathways for plumbagin and PC were identified by KEGG and GO analysis. The top 10 signaling pathways of plumbagin and PC were screened, followed by identification of biological components and functions.ConclusionsNetwork pharmacology established the effects of plumbagin on PC, predicted core targets, biological functions, pathways, and mechanisms of action. Further studies are needed to validate these predictive biotargets in PC.

The Functional Analysis of Selenium-Related Genes and Magnesium-Related Genes in the Gene Expression Profile Microarray in the Peripheral Blood Mononuclear Cells of Keshan Disease.

Keshan disease (KD) is an endemic cardiomyopathy with high mortality. Selenium (Se) deficiency is closely related to KD, while magnesium (Mg) plays many critical roles in the cardiovascular function. The molecular mechanism of KD pathogenesis is still unclear. Until now, we have not found any studies investigating the association between Se- or Mg-related genes and KD. In this study, oligonucleotide microarray analysis was used to identify the differentially expressed genes in the peripheral blood mononuclear cells between KD patients and normal controls. Next, human metabolome database (HMDB) was used to screen Se- and Mg-related genes. Function classification, gene pathway, and interaction network of Se- and Mg-related genes in KD peripheral blood mononuclear cells were defined by FunRich (functional enrichment analysis tool). Among 83 differentially expressed genes, five Se-related (DIO2, GPX1, GPX2, GPX4, and GPX7) and five Mg-related (ACSL6, EYA4, IDH2, PPM1A, and STK11) genes were recognized from HMDB. Two significant biological processes (energy pathways and metabolism), one molecular function (peroxidase activity), one biological pathway (glutathione redox reactions I), and one gene interaction network were constituted from Se-related and Mg-related genes. Se-related gene DIO2 and Mg-related genes STK11 and IDH2 may have key roles in the myocardial dysfunction of KD. However, we still have not obtained any interaction between Se-related gene and Mg-related gene. The interactions between RPS6KB1, PTEN, ATM, HSP90AA1, SNRK, PRKAA2, SMARCA4, HSPA1A, and STK11 may play important roles in the abnormal cardiac function of KD.

Computational Drug Discovery in Chemotherapy-induced Alopecia via Text Mining and Biomedical Databases

PurposeChemotherapy-induced alopecia (CIA) is a common and often stressful adverse effect associated with chemotherapy. CIA can cause more psychosocial pressure in patients, including effects on sexuality, self-esteem, and social relationships. We analyzed publicly available data to identify drugs formulated for topical use targeting the relevant CIA molecular pathways by using computational tools. MethodsThe genes associated with CIA were determined by text mining, and the gene ontology of the gene set was studied using the Functional Enrichment analysis tool. Protein–protein interaction network analysis was performed using the String database. Enriched gene sets belonging to the identified pathways were queried against the Drug–Gene Interaction database to find drug candidates for topical use in CIA. FindingsOur analysis identified 427 genes common to CIA text-mining concepts. Gene enrichment analysis and protein–protein interaction analysis yielded 19 genes potentially targetable by a total of 29 drugs that could possibly be formulated for topical application. ImplicationsThe findings from the present analysis would give a new thought to help discover more effective agents, and present tremendous opportunities to study novel target pharmacology and facilitate drug repositioning efforts in the pharmaceutical industry.

NeVOmics: An Enrichment Tool for Gene Ontology and Functional Network Analysis and Visualization of Data from OMICs Technologies.

The increasing number of OMICs studies demands bioinformatic tools that aid in the analysis of large sets of genes or proteins to understand their roles in the cell and establish functional networks and pathways. In the last decade, over-representation or enrichment tools have played a successful role in the functional analysis of large gene/protein lists, which is evidenced by thousands of publications citing these tools. However, in most cases the results of these analyses are long lists of biological terms associated to proteins that are difficult to digest and interpret. Here we present NeVOmics, Network-based Visualization for Omics, a functional enrichment analysis tool that identifies statistically over-represented biological terms within a given gene/protein set. This tool provides a hypergeometric distribution test to calculate significantly enriched biological terms, and facilitates analysis on cluster distribution and relationship of proteins to processes and pathways. NeVOmics is adapted to use updated information from the two main annotation databases: Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG). NeVOmics compares favorably to other Gene Ontology and enrichment tools regarding coverage in the identification of biological terms. NeVOmics can also build different network-based graphical representations from the enrichment results, which makes it an integrative tool that greatly facilitates interpretation of results obtained by OMICs approaches. NeVOmics is freely accessible at https://github.com/bioinfproject/bioinfo/.

The prognostic roles of mRNAs of the exosomes derived from bone marrow stromal cells in common malignancies: a bioinformatic study.

BackgroundThe inclusion of exosomes enters the recipient cells by means of endocytosis or direct fusion for information exchange between cells and cells. The inclusion of BMSCs-exo helps to guide the diagnosis and prognosis of cancer, especially cancer.PurposeThis research was to systematically elucidate the prognostic value of mRNAs of the exosomes derived from bone marrow stromal cells (BMSCs) in common malignant neoplasms, such as breast cancer, ovarian cancer, lung cancer, and gastric cancer.MethodsGene expression data (GSE78235) for the exosomes derived from BMSCs were extracted from the Gene Expression Omnibus database. Firstly, the differentially expressed genes were detected by comparing the RNA expression from exosomes derived from BMSCs between four tumor patients and two healthy controls using the limma package. Subsequently, functional enrichment analysis, including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes of differentially expressed genes, was performed using the Functional Enrichment analysis tool (FunRich v3.1.3) software followed by the construction of a protein–protein interaction (PPI) network via STRING v3.6.0. Molecular Complex Detection was used to screen the hub proteins by setting up the following threshold score ≥4 and nodes ≥10. Cytoscape v3.6.1 was used to for visualizing PPI network. Finally, Kaplan–Meier analysis for hub proteins was performed by Kaplan–Meier plotter online platform.ResultsA total of 386 genes originating from the exosomes derived from BMSCs were identifed as statistically signifcant (P < 0.05, FDR <0.05), which consisted of 150 upregulated genes and 236 downregulated genes. Also, 32 pathways were identifed as signifcant (P < 0.05, FDR < 0.05). The PPI network of exosomes derived from BMSC proteins included 100 protein nodes with 579 interaction edges. The hub proteins, including PODN, ZNF521, and CFI, which interacted with ten or more other proteins, were indicated as the hub proteins of PPN of exosomes derived from BMSCs.ConclusionTaken together, our findings revealed the prognostic roles of mRNAs of exosomes derived from BMSCs and provided implications for targeted therapy for common malignant neoplasms. However, further studies require large samples and experimental verification.

A novel community driven software for functional enrichment analysis of extracellular vesicles data

ABSTRACTBioinformatics tools are imperative for the in depth analysis of heterogeneous high-throughput data. Most of the software tools are developed by specific laboratories or groups or companies wherein they are designed to perform the required analysis for the group. However, such software tools may fail to capture “what the community needs in a tool”. Here, we describe a novel community-driven approach to build a comprehensive functional enrichment analysis tool. Using the existing FunRich tool as a template, we invited researchers to request additional features and/or changes. Remarkably, with the enthusiastic participation of the community, we were able to implement 90% of the requested features. FunRich enables plugin for extracellular vesicles wherein users can download and analyse data from Vesiclepedia database. By involving researchers early through community needs software development, we believe that comprehensive analysis tools can be developed in various scientific disciplines.

Pro-angiogenic capacities of microvesicles produced by skin wound myofibroblasts.

Wound healing is a very highly organized process where numerous cell types are tightly regulated to restore injured tissue. Myofibroblasts are cells that produce new extracellular matrix and contract wound edges. We previously reported that the human myofibroblasts isolated from normal wound (WMyos) produced microvesicles (MVs) in the presence of the serum. In this study, MVs were further characterized using a proteomic strategy and potential functions of the MVs were determined. MV proteins isolated from six WMyo populations were separated using two-dimensional differential gel electrophoresis. Highly conserved spots were selected and analyzed using mass spectrometry resulting in the identification of 381 different human proteins. Using the DAVID database, clusters of proteins involved in cell motion, apoptosis and adhesion, but also in extracellular matrix production (21 proteins, enrichment score: 3.32) and in blood vessel development/angiogenesis (19 proteins, enrichment score: 2.66) were identified. Another analysis using the functional enrichment analysis tool FunRich was consistent with these results. While the action of the myofibroblasts on extracellular matrix formation is well known, their angiogenic potential is less studied. To further characterize the angiogenic activity of the MVs, they were added to cultured microvascular endothelial cells to evaluate their influence on cell growth and migration using scratch test and capillary-like structure formation in Matrigel®. The addition of a MV-enriched preparation significantly increased endothelial cell growth, migration and capillary formation compared with controls. The release of microvesicles by the wound myofibroblasts brings new perspectives to the field of communication between cells during the normal healing process.

Tox21 Enricher: Web‐based chemical and functional enrichment analysis tool for Tox21 toxicity screening platform

Humans are exposed to tens of thousands of chemicals that are used in our daily life, some at levels that may pose a health risk. For many of these chemicals, there are limited toxicological information which makes risk assessment impossible. The United States Toxicology Testing in the 21st Century (Tox21) program was established to develop more efficient and human relevant toxicity assessment methods. The Tox21 program is currently screening over 10,000 chemicals, the Tox21 10K library, using quantitative high‐throughput screening (qHTS) of assays that measure effects on toxicity pathways. To date, more than 70 assays have yielded &gt;12 million concentration‐response curves by Tox21 researchers. One approach to using this data is to perform chemical‐relational analysis based on the patterns of activity across Tox21 assays and then to use nearest neighbor‐based prediction to infer the toxicological properties of untested chemicals via their association with tested chemicals. One approach to inferring the specific properties is to perform chemical annotation enrichment of chemical neighborhoods. Here, we present Tox21 Enricher, a web‐based chemical annotation enrichment tool for Tox21 assay data. Tox21 Enricher identifies significantly over‐represented chemical and biological annotations among lists of chemicals (neighborhoods) facilitating the identification of the toxicological properties or mechanisms in the chemical set. Currently, Tox21 Enricher supports 34 chemical annotation categories, including KEGG pathway ChemDisease, and Gene Ontology from Comparative Toxicogenomics Database; known target and Anatomical Therapeutic Chemical codes from DrugBank; predicted in vivo and in vitro toxicity; Chemical substructures from ToxPrint; a number of clinical and toxicological classifications from DrugMatrix; PubChem PharmAction and MeSH terms. Tox21 Enricher uses a modified Fisher's exact test, a widely used statistical test in gene set enrichment analysis, to assess the significance of enrichment. Tox21 Enricher allows users to submit lists of chemicals using their CAS Registry Numbers as input. Enriched chemical and biological annotations are presented in multiple formats: matrices in Excel file, heatmap images visualizing the level of similarity and dissimilarity of enriched annotations across the input chemical sets, and annotation network views illustrating the associations among the significant annotations. Tox21 Enricher also allows users to search using chemical structures (SMILES strings) that are not contained in the Tox21 10K library. This feature allows researchers to identify list of chemicals that structurally similar to chemicals in the library and then perform chemical annotation enrichment to infer the potential toxicological properties on chemicals not in the Tox21 library.Tox21 Enricher is freely available at http://hurlab.med.und.edu/Tox21Enricher. Powered with advanced features like concept‐network visualization and structure‐based query, Tox21 Enricher is expected to enhance the toxicity research.

BMDExpress Data Viewer - a visualization tool to analyze BMDExpress datasets.

Regulatory agencies increasingly apply benchmark dose (BMD) modeling to determine points of departure for risk assessment. BMDExpress applies BMD modeling to transcriptomic datasets to identify transcriptional BMDs. However, graphing and analytical capabilities within BMDExpress are limited, and the analysis of output files is challenging. We developed a web‐based application, BMDExpress Data Viewer (http://apps.sciome.com:8082/BMDX_Viewer/), for visualizing and graphing BMDExpress output files. The application consists of “Summary Visualization” and “Dataset Exploratory” tools. Through analysis of transcriptomic datasets of the toxicants furan and 4,4′‐methylenebis(N,N‐dimethyl)benzenamine, we demonstrate that the “Summary Visualization Tools” can be used to examine distributions of gene and pathway BMD values, and to derive a potential point of departure value based on summary statistics. By applying filters on enrichment P‐values and minimum number of significant genes, the “Functional Enrichment Analysis” tool enables the user to select biological processes or pathways that are selectively perturbed by chemical exposure and identify the related BMD. The “Multiple Dataset Comparison” tool enables comparison of gene and pathway BMD values across multiple experiments (e.g., across timepoints or tissues). The “BMDL‐BMD Range Plotter” tool facilitates the observation of BMD trends across biological processes or pathways. Through our case studies, we demonstrate that BMDExpress Data Viewer is a useful tool to visualize, explore and analyze BMDExpress output files. Visualizing the data in this manner enables rapid assessment of data quality, model fit, doses of peak activity, most sensitive pathway perturbations and other metrics that will be useful in applying toxicogenomics in risk assessment. © 2015 Her Majesty the Queen in Right of Canada. Journal of Applied Toxicology published by John Wiley & Sons, Ltd.