Identification of Key Pathways and Genes in Obesity Using Bioinformatics Analysis and Molecular Docking Studies

Joshi, Harish; Vastrad, Basavaraj; Joshi, Nidhi; Vastrad, Chanabasayya; Tengli, Anandkumar; Kotturshetti, Iranna

doi:10.3389/fendo.2021.628907

Cited by 11 publications

(7 citation statements)

References 190 publications

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“…Moreover, a total of four down-regulated DEGs exclusive to BPD-DS also showed an association with adipocyte size change. Among them, CORO1C , a gene recently identified to be up-regulated in the SAT of individuals with obesity [ 69 ], was found to be closely linked to %TWL and was also identified as hypomethylated. From a broader perspective, methylomic changes observed in this study were mostly exclusive to BPD-DS, which points to an epigenetic-mediated mechanism by which gene expression changes in SAT may occur in a greater extent in patients undergoing this type of surgery.…”

Section: Discussionmentioning

confidence: 99%

Molecular remodeling of adipose tissue is associated with metabolic recovery after weight loss surgery

et al. 2022

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Background Bariatric surgery is an effective therapy for individuals with severe obesity to achieve sustainable weight loss and to reduce comorbidities. Examining the molecular signature of subcutaneous adipose tissue (SAT) following different types of bariatric surgery may help in gaining further insight into their distinct metabolic impact. Results Subjects undergoing biliopancreatic diversion with duodenal switch (BPD-DS) showed a significantly higher percentage of total weight loss than those undergoing gastric bypass or sleeve gastrectomy (RYGB + SG) (41.7 ± 4.6 vs 28.2 ± 6.8%; p = 0.00005). Individuals losing more weight were also significantly more prone to achieve both type 2 diabetes and dyslipidemia remission (OR = 0.75; 95%CI = 0.51–0.91; p = 0.03). Whole transcriptome and methylome profiling showed that bariatric surgery induced a profound molecular remodeling of SAT at 12 months postoperative, mainly through gene down-regulation and hypermethylation. The extent of changes observed was greater following BPD-DS, with 61.1% and 49.8% of up- and down-regulated genes, as well as 85.7% and 70.4% of hyper- and hypomethylated genes being exclusive to this procedure, and mostly associated with a marked decrease of immune and inflammatory responses. Weight loss was strongly associated with genes being simultaneously differentially expressed and methylated in BPD-DS, with the strongest association being observed for GPD1L (r2 = 0.83; p = 1.4 × 10–6). Conclusions Present findings point to the greater SAT molecular remodeling following BPD-DS as potentially linked with higher metabolic remission rates. These results will contribute to a better understanding of the metabolic pathways involved in the response to bariatric surgery and will eventually lead to the development of gene targets for the treatment of obesity. Trial registration ClinicalTrials.gov NCT02390973.

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Section: Discussionmentioning

confidence: 99%

Molecular remodeling of adipose tissue is associated with metabolic recovery after weight loss surgery

et al. 2022

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“…Other genes in MAPK signaling that are not significant according to the gene scores ( RPS6KA4, DUSP4, MAPKAPK5 ) have also been associated with obesity ( Ow and Kuznetsov 2015 ). EEF2K is also predicted to be a novel target for obesity ( Joshi et al 2021 ). However, this module also contains genes (e.g.…”

Section: Resultsmentioning

confidence: 99%

Identification of disease modules using higher-order network structure

Singh,

Kuder,

Ritz

2023

Bioinformatics Advances

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Motivation Higher-order interaction patterns among proteins have the potential to reveal mechanisms behind molecular processes and diseases. While clustering methods are used to identify functional groups within molecular interaction networks, these methods largely focus on edge density and do not explicitly take into consideration higher-order interactions. Disease genes in these networks have been shown to exhibit rich higher-order structure in their vicinity, and considering these higher-order interaction patterns in network clustering have the potential to reveal new disease-associated modules. Results We propose a higher-order community detection method which identifies community structure in networks with respect to specific higher-order connectivity patterns beyond edges. Higher-order community detection on four different protein-protein interaction networks identifies biologically significant modules and disease modules that conventional edge-based clustering methods fail to discover. Higher-order clusters also identify disease modules from GWAS data, including new modules that were not discovered by top-performing approaches in a Disease Module DREAM Challenge. Our approach provides a more comprehensive view of community structure that enables us to predict new disease-gene associations. Availability https://github.com/Reed-CompBio/graphlet-clustering Supplementary Information Supplementary data are available at Bioinformatics Advances online.

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“…Other genes in MAPK signaling that are not significant according to the gene scores ( RPS6KA4, DUSP4, MAPKAPK5 ) have also been associated with obesity (Ow and Kuznetsov, 2015). EEF2K is also predicted to be a novel target for obesity (Joshi et al, 2021). However, this module also contains genes (e.g., ELK1, MAPKAPK2 ) with no gene scores that could potentially be associated with BMI.…”

Section: Resultsmentioning

confidence: 99%

Identification of Disease Modules Using Higher-Order Network Structure

Singh

Kuder

Ritz

2022

Preprint

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Motivation:Higher-order interaction patterns among proteins have the potential to reveal mechanisms behind molecular processes and diseases. While clustering methods are used to identify functional groups within molecular interaction networks, these methods largely focus on edge density and do not explicitly take into consideration higher-order interactions. Disease genes in these networks have been shown to exhibit rich higher-order structure in their vicinity, and considering these higher-order interaction patterns in network clustering have the potential to reveal new disease-associated modules.Results:We propose a higher-order community detection method which identifies community structure in networks with respect to specific higher-order connectivity patterns beyond edges. Higher-order community detection on four different protein-protein interaction networks identifies biologically significant modules and disease modules that conventional edge-based clustering methods fail to discover. Higher-order clusters also identify disease modules from GWAS data, including new modules that were not discovered by top-performing approaches in a Disease Module DREAM Challenge. Our approach provides a more comprehensive view of community structure that enables us to predict new disease-gene associations.Availability:https://github.com/Reed-CompBio/graphlet-clustering

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Identification of Key Pathways and Genes in Obesity Using Bioinformatics Analysis and Molecular Docking Studies

Cited by 11 publications

References 190 publications

Molecular remodeling of adipose tissue is associated with metabolic recovery after weight loss surgery

Molecular remodeling of adipose tissue is associated with metabolic recovery after weight loss surgery

Identification of disease modules using higher-order network structure

Identification of Disease Modules Using Higher-Order Network Structure

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