Exploring the differentially expressed genes in human lymphocytes upon response to ionizing radiation: a network biology approach

Sekaran, Tamizh Selvan Gnana; Kedilaya, Vishakh R; N, Suchetha Kumari; Shetty, Praveenkumar; Gollapalli, Pavan

doi:10.3857/roj.2021.00045

Cited by 12 publications

(6 citation statements)

References 44 publications

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“…ShinyGO is a user-friendly, graphical tool for enrichment analysis that helps researchers decipher the biological significance of a long list of genes [61]. This study used the KEGG database with a significance level of (p = 0.05) [62].…”

Section: Gene Set Enrichment and Pathway Analysismentioning

confidence: 99%

AI-Based Homology Modelling of Fatty Acid Transport Protein 1 Using AlphaFold: Structural Elucidation and Molecular Dynamics Exploration

Acharya,

Shetty,

Pavan

et al. 2023

Biomolecules

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Fatty acid transport protein 1 (FATP1) is an integral transmembrane protein that is involved in facilitating the translocation of long-chain fatty acids (LCFA) across the plasma membrane, thereby orchestrating the importation of LCFA into the cell. FATP1 also functions as an acyl-CoA ligase, catalyzing the ATP-dependent formation of fatty acyl-CoA using LCFA and VLCFA (very-long-chain fatty acids) as substrates. It is expressed in various types of tissues and is involved in the regulation of crucial signalling pathways, thus playing a vital role in numerous physiological and pathological conditions. Structural insight about FATP1 is, thus, extremely important for understanding the mechanism of action of this protein and developing efficient treatments against its anomalous expression and dysregulation, which are often associated with pathological conditions such as breast cancer. As of now, there has been no prior prediction or evaluation of the 3D configuration of the human FATP1 protein, hindering a comprehensive understanding of the distinct functional roles of its individual domains. In our pursuit to unravel the structure of the most commonly expressed isoforms of FATP1, we employed the cutting-edge ALPHAFOLD 2 model for an initial prediction of the entire protein’s structure. This prediction was complemented by molecular dynamics simulations, focusing on the most promising model. We predicted the structure of FATP1 in silico and thoroughly refined and validated it using coarse and molecular dynamics in the absence of the complete crystal structure. Their relative dynamics revealed the different properties of the characteristic FATP1.

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Section: Gene Set Enrichment and Pathway Analysismentioning

confidence: 99%

AI-Based Homology Modelling of Fatty Acid Transport Protein 1 Using AlphaFold: Structural Elucidation and Molecular Dynamics Exploration

Acharya,

Shetty,

Pavan

et al. 2023

Biomolecules

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“…A gene ontology (GO) functional enrichment analysis for the hub genes was carried out using ShinyGO (V0.76.1) to uncover dysregulated biological processes in radioresistant NSCLC cells. The GO biological processes were evaluated at 5% FDR stringency and restricted to Homo sapiens [22].…”

Section: Functional Enrichment Analysis Of Hub Genesmentioning

confidence: 99%

Small Volatile Inhibitors Targeting LncRNA in Radioresistant Non-small Cell Lung Cancer

Mukherjee

2022

Preprint

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Radiotherapy is the most crucial nonsurgical therapeutic method in the multidisciplinary care of non-small cell lung cancer (NSCLC) patients. However, radiation resistance continues to be a significant clinical issue, negatively affecting cancer prognosis in patients. The analysis of the RNA microarray dataset revealed the dysregulation of the cell cycle, evasion of apoptosis and cancer immune response. A co-expression analysis with a network pharmacology approach revealed a lncRNA (ENST00000605056) regulating three highly ranked hub genes, driving radioresistance in NSCLC cells. The small molecules that target these RNAs offer therapeutic modulation of multiple biological processes. The study comprises three volatile ligands due to their good pharmacokinetic profile to target ENST00000605056. The molecular interaction studies uncovered their high binding affinity to its binding pocket with a preponderance of non-covalent bond interactions between the ligand atoms and the nucleotides. The Molecular dynamics simulations revealed the binding stability of ligands to the long non-coding RNA (lncRNA) with a very low deviation compared to the control. This study demonstrated the ability of the small molecules to target lncRNA in overcoming the global concern of radioresistance among NSCLC patients and aid in future translational studies.

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“…The clusters were filtered using the features specified in the approach to ensure the efficiency of functional partners towards the core network of T2DM-TB DEGs. Based on the number of interactions between each node, the genes with most closely related interactors are grouped [29]. Seven closely connected groups emerged due to the clustering analysis of the genes in interaction network.…”

Section: Cluster Analysis Of Gene Interaction Networkmentioning

confidence: 99%

Exploring key molecular signatures of immune responses and pathways associated with tuberculosis in comorbid diabetes mellitus: a systems biology approach

Selvan

Gollapalli

Shetty

et al. 2022

Beni-Suef Univ J Basic Appl Sci

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Background Comorbid type 2 diabetes mellitus (T2DM) increases the risk for tuberculosis (TB) and its associated complications, although the pathological connections between T2DM and TB are unknown. The current research aims to identify shared molecular gene signatures and pathways that affirm the epidemiological association of T2DM and TB and afford clues on mechanistic basis of their association through integrative systems biology and bioinformatics approaches. Earlier research has found specific molecular markers linked to T2DM and TB, but, despite their importance, only offered a limited understanding of the genesis of this comorbidity. Our investigation used a network medicine method to find possible T2DM-TB molecular mediators. Results Functional annotation clustering, interaction networks, network cluster analysis, and network topology were part of our systematic investigation of T2DM-TB linked with 1603 differentially expressed genes (DEGs). The functional enrichment and gene interaction network analysis emphasized the importance of cytokine/chemokine signalling, T cell receptor signalling route, NF-kappa B signalling pathway and Jak-STAT signalling system. Furthermore, network analysis revealed significant DEGs such as ITGAM and STAT1, which may be necessary for T2DM-TB immune responses. Furthermore, these two genes are modulators in clusters C4 and C5, abundant in cytokine/chemokine signalling and Jak-STAT signalling pathways. Conclusions Our analyses highlight the role of ITGAM and STAT1 in T2DM-TB-associated pathways and advances our knowledge of the genetic processes driving this comorbidity.

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Exploring the differentially expressed genes in human lymphocytes upon response to ionizing radiation: a network biology approach

Cited by 12 publications

References 44 publications

AI-Based Homology Modelling of Fatty Acid Transport Protein 1 Using AlphaFold: Structural Elucidation and Molecular Dynamics Exploration

AI-Based Homology Modelling of Fatty Acid Transport Protein 1 Using AlphaFold: Structural Elucidation and Molecular Dynamics Exploration

Small Volatile Inhibitors Targeting LncRNA in Radioresistant Non-small Cell Lung Cancer

Exploring key molecular signatures of immune responses and pathways associated with tuberculosis in comorbid diabetes mellitus: a systems biology approach

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