Identification of key proteins in host–pathogen interactions between Mycobacterium tuberculosis and Homo sapiens: A systematic network theoretical approach

Verma, Ram Nayan; Malik, Md. Zubbair; Singh, Gajendra Pratap; Subbarao, Naidu

doi:10.1016/j.health.2022.100052

Cited by 14 publications

(8 citation statements)

References 44 publications

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“…The STRING database integrates known and predicted associations between proteins encompassing physical interactions and functional associations. Similarly, the pathogen proteins interacting with host DEG were mined from various literature sources ( Rapanoel et al., 2013 ; Penn et al., 2018 ; Augenstreich and Briken, 2020 ; Verma et al., 2022 ). These data were used to construct human protein – Mtb protein interaction network (hmPPI).…”

Section: Methodsmentioning

confidence: 99%

Meta-analysis of active tuberculosis gene expression ascertains host directed drug targets

Ponnusamy

Arumugam

2022

Front. Cell. Infect. Microbiol.

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Multi-drug resistant tuberculosis still remains a major public health crisis globally. With the emergence of newer active tuberculosis disease, the requirement of prolonged treatment time and adherence to therapy till its completion necessitates the search of newer therapeutics, targeting human host factors. The current work utilized statistical meta-analysis of human gene transcriptomes of active pulmonary tuberculosis disease obtained from six public datasets. The meta-analysis resulted in the identification of 2038 significantly differentially expressed genes (DEGs) in the active tuberculosis disease. The gene ontology (GO) analysis revealed that these genes were major contributors in immune responses. The pathway enrichment analyses identified from various human canonical pathways are related to other infectious diseases. In addition, the comparison of the DEGs with the tuberculosis genome wide association study (GWAS) datasets revealed the presence of few genetic variants in their proximity. The analysis of protein interaction networks (human and Mycobacterium tuberculosis) and host directed drug-target interaction network led to new candidate drug targets for drug repurposing studies. The current work sheds light on host genes and pathways enriched in active tuberculosis disease and suggest potential drug repurposing targets for host-directed therapies.

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Section: Methodsmentioning

confidence: 99%

Meta-analysis of active tuberculosis gene expression ascertains host directed drug targets

Ponnusamy

Arumugam

2022

Front. Cell. Infect. Microbiol.

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“…Therefore, further study is needed using other mathematical techniques to explore the critical role of lncRNAs and their potential roles as a therapy [38]. Host-pathogen protein-protein interaction can be studied to get more insight [39].…”

Section: Limitation Of Studymentioning

confidence: 99%

A two-step approach-machine learning, variational autoencoder, and weighted gene co-expression network analysis identify key signature genes and pathways implicated in active visceral leishmaniasis

Verma

Subbarao

Singh

2022

Preprint

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Leishmania donovani, a kinetoplastid parasite causing leishmaniasis, is an opportunistic parasitic pathogen that affects immunocompromised individuals and is a common cause of Kala-azar. Specific parasite molecules can be delivered into host epithelial cells and may act as effector molecules for intracellular parasite development. So, there is a need to develop new approaches to understanding the interaction between the host and the pathogen. In our study, we built a weighted gene co-expression network using differentially expressed genes obtained through analysis of leishmaniasis-infected patients. Our goal was to identify key signature genes and pathways associated with visceral leishmaniasis infection by network biology analysis which can identify the most influential genes in the gene co-expression interaction network. We identified five prominent genes, IFNG, SC5D, LSM1, CMC2, and SAR1B, with higher interamodular connectivity, as the key signature genes. A deep neural network model- variational autoencoder was utilized to create new features, and a support vector machine validated the key signature genes. These key signature genes are involved in various biological processes like cytokine-cytokine receptor interaction, TGF-beta signaling pathway, antigen processing and presentation, IL-17 signaling pathway, Th1 and Th2 cell differentiation, and T-cell receptor signaling pathway. Besides, we also identified 04 significant miRNAs targeted with key signature genes, including hsa-miR-340-5p, hsa-miR-325-3p, hsa-miR-182-5p, hsa-miR-1271-5p/hsa-miR-96-5p. Further, analysis of the differentially expressed genes revealed that many critical cellular responses were triggered by visceral leishmaniasis infection, including immune responses and inflammatory and cell apoptosis. We get FDA-approved anti-inflammatory agents Emapalumab and Methylprednisolone as a re-proposed drug for leishmaniasis cure. Our study can enhance the understanding of the molecular pathogenesis of visceral leishmaniasis infection and have implications for the plan and execution of mRNA expression tools to support early diagnostics and treatment of visceral leishmaniasis infection.

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“…In this study, the protein interaction maps are analyzed through graph/network theory to get insights about the theoretical aspect of complex networks (24). According to the graph theory, analysis of the topological structure of a network (PPI network in our study) provides important information of the network (25) through which novel disease genes and pathways, biomarkers and drug targets for complex diseases can be identified (26).…”

Section: Introductionmentioning

confidence: 99%

Identification of Key Regulators in Pancreatic Ductal Adenocarcinoma using Network theoretical Approach

Bhattacharjee,

Ghosh

2024

Preprint

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Pancreatic Ductal Adenocarcinoma (PDAC) is a devastating disease with poor clinical outcomes which is mainly because of delayed disease detection resistance to chemotherapy and lack of specific targeted therapies. The disease's development involves complex interactions among immunological, genetic, and environmental factors, yet its molecular mechanism remains elusive. A major challenge in understanding PDAC etiology lies in unraveling the genetic profiling that governs the PDAC network. To address this, we examined the gene expression profile of PDAC and compared it with that of healthy controls, identifying differentially expressed genes (DEGs). These DEGs formed the basis for constructing the PDAC protein interaction network, and their network topological properties were calculated. It was found that the PDAC network self-organizes into a scale-free fractal state with weakly hierarchical organization. Newman and Girvan algorithm (leading eigenvector (LEV) method) of community detection enumerated four communities leading to at least one motif. Our analysis revealed 33 key regulators were predominantly enriched in neuroactive ligand-receptor interaction, Cell adhesion molecules, Leukocyte transendothelial migration pathways; positive regulation of cell proliferation, positive regulation of protein kinase B signaling biological functions; G-protein beta-subunit binding, receptor binding molecular functions etc. Transcription Factor and mi-RNA of the key regulators were obtained. Recognizing the therapeutic potential and biomarker significance of PDAC Key regulators, we also identified approved drugs for specific genes. However, it is imperative to subject Key regulators to experimental validation to establish their efficacy in the context of PDAC.

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Identification of key proteins in host–pathogen interactions between Mycobacterium tuberculosis and Homo sapiens: A systematic network theoretical approach

Cited by 14 publications

References 44 publications

Meta-analysis of active tuberculosis gene expression ascertains host directed drug targets

Meta-analysis of active tuberculosis gene expression ascertains host directed drug targets

A two-step approach-machine learning, variational autoencoder, and weighted gene co-expression network analysis identify key signature genes and pathways implicated in active visceral leishmaniasis

Identification of Key Regulators in Pancreatic Ductal Adenocarcinoma using Network theoretical Approach

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