Assessment of key regulatory genes and identification of possible drug targets for Leprosy (Hansen's disease) using network-based approach.

Leprosy is a major health concern and continues to be a source of fear and stigma among people worldwide. Despite remarkable achievements in the treatment, understanding of pathogenesis and transmission, epidemiology of leprosy still remains inadequate. The prolonged incubation period, slow rates of occurrence in those exposed and deceptive clinical presentation pose challenges to develop reliable strategies to stop transmission. Hence, there is a need for improved diagnostics and therapies to prevent mortality caused by leprosy. The objectives of this study are to identify significant genes from protein-protein interactions (PPIs) network of leprosy and to choose the most effective therapeutic targets. Fifty genes related with leprosy were discovered by literature mining. These genes were used to construct a primary network. Leading Eigen Vector method was used to break down the primary network into various sub-networks or communities. It was found that the primary network was divided into many sub-networks at the 6 levels. Seed genes were traced at each level till key regulatory genes were identified. Three seed genes, namely, GNAI3, NOTCH1, and HIF1A, were able to make their way till the final motif stage. These genes along with their interacting partners were considered key regulators of the leprosy network. This study provides leprosy-associated key genes which can lead to improved diagnosis and therapies for leprosy patients.

Identification of the Key miRNAs and Genes Associated with the Regulation of Non-Small Cell Lung Cancer: A Network-Based Approach.

Lung cancer is the major cause of cancer-associated deaths across the world in both men and women. Lung cancer consists of two major clinicopathological categories, i.e., small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). Lack of diagnosis of NSCLC at an early stage in addition to poor prognosis results in ineffective treatment, thus, biomarkers for appropriate diagnosis and exact prognosis of NSCLC need urgent attention. The proposed study aimed to reveal essential microRNAs (miRNAs) involved in the carcinogenesis of NSCLC that probably could act as potential biomarkers. The NSCLC-associated expression datasets revealed 12 differentially expressed miRNAs (DEMs). MiRNA-mRNA network identified key miRNAs and their associated genes, for which functional enrichment analysis was applied. Further, survival and validation analysis for key genes was performed and consequently transcription factors (TFs) were predicted. We obtained twelve miRNAs as common DEMs after assessment of all datasets. Further, four key miRNAs and nine key genes were extracted from significant modules based on the centrality approach. The key genes and miRNAs reported in our study might provide some information for potential biomarkers profitable to increased prognosis and diagnosis of lung cancer.

Identification of pathogenic genes associated with CKD: An integrated bioinformatics approach.

Chronic kidney disease (CKD) is defined as a persistent abnormality in the structure and function of kidneys and leads to high morbidity and mortality in individuals across the world. Globally, approximately 8%-16% of the population is affected by CKD. Proper screening, staging, diagnosis, and the appropriate management of CKD by primary care clinicians are essential in preventing the adverse outcomes associated with CKD worldwide. In light of this, the identification of biomarkers for the appropriate management of CKD is urgently required. Growing evidence has suggested the role of mRNAs and microRNAs in CKD, however, the gene expression profile of CKD is presently uncertain. The present study aimed to identify diagnostic biomarkers and therapeutic targets for patients with CKD. The human microarray profile datasets, consisting of normal samples and treated samples were analyzed thoroughly to unveil the differentially expressed genes (DEGs). After selection, the interrelationship among DEGs was carried out to identify the overlapping DEGs, which were visualized using the Cytoscape program. Furthermore, the PPI network was constructed from the String database using the selected DEGs. Then, from the PPI network, significant modules and sub-networks were extracted by applying the different centralities methods (closeness, betweenness, stress, etc.) using MCODE, Cytohubba, and Centiserver. After sub-network analysis we identified six overlapped hub genes (RPS5, RPL37A, RPLP0, CXCL8, HLA-A, and ANXA1). Additionally, the enrichment analysis was undertaken on hub genes to determine their significant functions. Furthermore, these six genes were used to find their associated miRNAs and targeted drugs. Finally, two genes CXCL8 and HLA-A were common for Ribavirin drug (the gene-drug interaction), after docking studies HLA-A was selected for further investigation. To conclude our findings, we can say that the identified hub genes and their related miRNAs can serve as potential diagnostic biomarkers and therapeutic targets for CKD treatment strategies.

Identification and Validation of Pathogenic Genes in Sepsis and Associated Diseases by Integrated Bioinformatics Approach.

Sepsis is a clinical syndrome with high mortality and morbidity rates. In sepsis, the abrupt release of cytokines by the innate immune system may cause multiorgan failure, leading to septic shock and associated complications. In the presence of a number of systemic disorders, such as sepsis, infections, diabetes, and systemic lupus erythematosus (SLE), cardiorenal syndrome (CRS) type 5 is defined by concomitant cardiac and renal dysfunctions Thus, our study suggests that certain mRNAs and unexplored pathways may pave a way to unravel critical therapeutic targets in three debilitating and interrelated illnesses, namely, sepsis, SLE, and CRS. Sepsis, SLE, and CRS are closely interrelated complex diseases likely sharing an overlapping pathogenesis caused by erroneous gene network activities. We sought to identify the shared gene networks and the key genes for sepsis, SLE, and CRS by completing an integrative analysis. Initially, 868 DEGs were identified in 16 GSE datasets. Based on degree centrality, 27 hub genes were revealed. The gProfiler webtool was used to perform functional annotations and enriched molecular pathway analyses. Finally, core hub genes (EGR1, MMP9, and CD44) were validated using RT-PCR analysis. Our comprehensive multiplex network approach to hub gene discovery is effective, as evidenced by the findings. This work provides a novel research path for a new research direction in multi-omics biological data analysis.

Identification of key regulators in Sarcoidosis through multidimensional systems biological approach.

Sarcoidosis is a multi-organ disorder where immunology, genetic and environmental factors play a key role in causing Sarcoidosis, but its molecular mechanism remains unclear. Identification of its genetics profiling that regulates the Sarcoidosis network will be one of the main challenges to understand its aetiology. We have identified differentially expressed genes (DEGs) by analyzing the gene expression profiling of Sarcoidosis and compared it with healthy control. Gene set enrichment analysis showed that these DEGs were mainly enriched in the inflammatory response, immune system, and pathways in cancer. Sarcoidosis protein interaction network was constructed by a total of 877 DEGs (up-down) and calculated its network topological properties, which follow hierarchical scale-free fractal nature up to six levels of the organization. We identified a large number of leading hubs that contain six key regulators (KRs) including ICOS, CTLA4, FLT3LG, CD33, GPR29 and ITGA4 are deeply rooted in the network from top to bottom, considering a backbone of the network. We identified the transcriptional factors (TFs) which are closely interacted with KRs. These genes and their TFs regulating the Sarcoidosis network are expected to be the main target for the therapeutic approaches and potential biomarkers. However, experimental validations of KRs needed to confirm their efficacy.

Human gene expression profiling identifies key therapeutic targets in tuberculosis infection: A systematic network meta-analysis.

Tuberculosis (TB) is one of the deadliest diseases since ancient times and is still a global health problem. So, there is a need to develop new approaches for early detection of TB and understand the host-pathogen relationship. In the present study, we have analyzed microarray data sets and compared the transcriptome profiling of the healthy individual with latent infection (LTBI) and active TB (TB) patients, and identified the differentially expressed genes (DEGs). Next, we performed the systematic network meta-analysis of the DEGs, which identified the seven most influencing hub genes (IL6, IL1B, TNF, NFKB1, STAT1, JAK2, and MAPK8) as the potential therapeutic target in the tuberculosis disease. These target genes are involved in many biological processes like cell cycle control, apoptosis, complement signalling, enhanced cytokine & chemokine signalling, pro-inflammatory responses, and host immune responses. Additionally, we also identified 22 inferred genes that are mainly engaged in the induction of innate immune response, cellular response to interleukin-6, inflammatory response, apoptotic process, I-kappaB-phosphorylation, JAK-STAT signalling pathway, macrophage activation, cell growth, and cell signalling. The proper attention of these inferred genes may open up a new horizon to understand the defensive mechanisms of TB disease. The transcriptome profiling and network approach can enhance the understanding of the molecular pathogenesis of tuberculosis infection and have implications for the plan and execution of mRNA expression tools to support early diagnostics and treatment of Mycobacterium tuberculosis (M.tb).

Deciphering key genes in cardio-renal syndrome using network analysis.

Cardio-renal syndrome (CRS) is a rapidly recognized clinical entity which refers to the inextricably connection between heart and renal impairment, whereby abnormality to one organ directly promotes deterioration of the other one. Biological markers help to gain insight into the pathological processes for early diagnosis with higher accuracy of CRS using known clinical findings. Therefore, it is of interest to identify target genes in associated pathways implicated linked to CRS. Hence, 119 CRS genes were extracted from the literature to construct the PPIN network. We used the MCODE tool to generate modules from network so as to select the top 10 modules from 23 available modules. The modules were further analyzed to identify 12 essential genes in the network. These biomarkers are potential emerging tools for understanding the pathophysiologic mechanisms for the early diagnosis of CRS. Ontological analysis shows that they are rich in MF protease binding and endo-peptidase inhibitor activity. Thus, this data help increase our knowledge on CRS to improve clinical management of the disease.

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.

Network-Based Approach and IVI Methodologies, a Combined Data Investigation Identified Probable Key Genes in Cardiovascular Disease and Chronic Kidney Disease.

In fact, the risk of dying from CVD is significant when compared to the risk of developing end-stage renal disease (ESRD). Moreover, patients with severe CKD are often excluded from randomized controlled trials, making evidence-based therapy of comorbidities like CVD complicated. Thus, the goal of this study was to use an integrated bioinformatics approach to not only uncover Differentially Expressed Genes (DEGs), their associated functions, and pathways but also give a glimpse of how these two conditions are related at the molecular level. We started with GEO2R/R program (version 3.6.3, 64 bit) to get DEGs by comparing gene expression microarray data from CVD and CKD. Thereafter, the online STRING version 11.1 program was used to look for any correlations between all these common and/or overlapping DEGs, and the results were visualized using Cytoscape (version 3.8.0). Further, we used MCODE, a cytoscape plugin, and identified a total of 15 modules/clusters of the primary network. Interestingly, 10 of these modules contained our genes of interest (key genes). Out of these 10 modules that consist of 19 key genes (11 downregulated and 8 up-regulated), Module 1 (RPL13, RPLP0, RPS24, and RPS2) and module 5 (MYC, COX7B, and SOCS3) had the highest number of these genes. Then we used ClueGO to add a layer of GO terms with pathways to get a functionally ordered network. Finally, to identify the most influential nodes, we employed a novel technique called Integrated Value of Influence (IVI) by combining the network's most critical topological attributes. This method suggests that the nodes with many connections (calculated by hubness score) and high spreading potential (the spreader nodes are intended to have the most impact on the information flow in the network) are the most influential or essential nodes in a network. Thus, based on IVI values, hubness score, and spreading score, top 20 nodes were extracted, in which RPS27A non-seed gene and RPS2, a seed gene, came out to be the important node in the network.

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

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

Identification and Validation of Potential miRNAs, as Biomarkers for Sepsis and Associated Lung Injury: A Network-Based Approach.

Sepsis is a dysregulated immune response disease affecting millions worldwide. Delayed diagnosis, poor prognosis, and disease heterogeneity make its treatment ineffective. miRNAs are imposingly involved in personalized medicine such as therapeutics, due to their high sensitivity and accuracy. Our study aimed to reveal the biomarkers that may be involved in the dysregulated immune response in sepsis and lung injury using a computational approach and in vivo validation studies. A sepsis miRNA Gene Expression Omnibus (GEO) dataset based on the former analysis of blood samples was used to identify differentially expressed miRNAs (DEMs) and associated hub genes. Sepsis-associated genes from the Comparative Toxicogenomics Database (CTD) that overlapped with identified DEM targets were utilized for network construction. In total, 317 genes were found to be regulated by 10 DEMs (three upregulated, namely miR-4634, miR-4638-5p, and miR-4769-5p, and seven downregulated, namely miR-4299, miR-451a, miR181a-2-3p, miR-16-5p, miR-5704, miR-144-3p, and miR-1290). Overall hub genes (HIP1, GJC1, MDM4, IL6R, and ERC1) and for miR-16-5p (SYNRG, TNRC6B, and LAMTOR3) were identified based on centrality measures (degree, betweenness, and closeness). In vivo validation of miRNAs in lung tissue showed significantly downregulated expression of miR-16-5p corroborating with our computational findings, whereas expression of miR-181a-2-3p and miR-451a were found to be upregulated in contrast to the computational approach. In conclusion, the differential expression pattern of miRNAs and hub genes reported in this study may help to unravel many unexplored regulatory pathways, leading to the identification of critical molecular targets for increased prognosis, diagnosis, and drug efficacy in sepsis and associated organ injuries.

Identification and Classification of Differentially Expressed Genes and Network Meta-Analysis Reveals Potential Molecular Signatures Associated With Tuberculosis.

Tuberculosis (TB) is one of deadly transmissible disease that causes death worldwide; however, only 10% of people infected with Mycobacterium tuberculosis develop disease, indicating that host genetic factors may play key role in determining susceptibility to TB disease. In this way, the analysis of gene expression profiling of TB infected individuals can give us a snapshot of actively expressed genes and transcripts under various conditions. In the present study, we have analyzed microarray data set and compared the gene expression profiles of patients with different datasets of healthy control, latent infection, and active TB. We observed the transition of genes from normal condition to different stages of the TB and identified and annotated those genes/pathways/processes that have important roles in TB disease during its cyclic interventions in the human body. We identified 488 genes that were differentially expressed at various stages of TB and allocated to pathways and gene set enrichment analysis. These pathways as well as GSEA's importance were evaluated according to the number of DEGs presents in both. In addition, we studied the gene regulatory networks that may help to further understand the molecular mechanism of immune response against the TB infection and provide us a new angle for future biomarker and therapeutic targets. In this study, we identified 26 leading hubs which are deeply rooted from top to bottom in the gene regulatory network and work as the backbone of the network. These leading hubs contains 31 key regulator genes, of which 14 genes were up-regulated and 17 genes were down-regulated. The proposed approach is based on gene-expression profiling, and network analysis approaches predict some unknown TB-associated genes, which can be considered (or can be tested) as reliable candidates for further (in vivo/in vitro) studies.

Assessment of the key regulatory genes and their Interologs for Turner Syndrome employing network approach.

Turner Syndrome (TS) is a condition where several genes are affected but the molecular mechanism remains unknown. Identifying the genes that regulate the TS network is one of the main challenges in understanding its aetiology. Here, we studied the regulatory network from manually curated genes reported in the literature and identified essential proteins involved in TS. The power-law distribution analysis showed that TS network carries scale-free hierarchical fractal attributes. This organization of the network maintained the self-ruled constitution of nodes at various levels without having centrality-lethality control systems. Out of twenty-seven genes culminating into leading hubs in the network, we identified two key regulators (KRs) i.e. KDM6A and BDNF. These KRs serve as the backbone for all the network activities. Removal of KRs does not cause its breakdown, rather a change in the topological properties was observed. Since essential proteins are evolutionarily conserved, the orthologs of selected interacting proteins in C. elegans, cat and macaque monkey (lower to higher level organisms) were identified. We deciphered three important interologs i.e. KDM6A-WDR5, KDM6A-ASH2L and WDR5-ASH2L that form a triangular motif. In conclusion, these KRs and identified interologs are expected to regulate the TS network signifying their biological importance.