Integrative system biology and mathematical modeling of genetic networks identifies shared biomarkers for obesity and diabetes

Abdulhadi Ibrahim H. Bima; Ayman Zaky Elsamanoudy; Walaa F Albaqami; Zeenath Khan; Snijesh Valiya Parambath; Nuha Al-Rayes; Prabhakar Rao Kaipa; Ramu Elango; Babajan Banaganapalli; Noor A. Shaik; Abdulhadi Ibrahim H. Bima; Ayman Zaky Elsamanoudy; Walaa F Albaqami; Zeenath Khan; Snijesh Valiya Parambath; Nuha Al-Rayes; Prabhakar Rao Kaipa; Ramu Elango; Babajan Banaganapalli; Noor A. Shaik

doi:10.3934/mbe.2022107

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 3: 2310-2329. doi: 10.3934/mbe.2022107

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Research article

Integrative system biology and mathematical modeling of genetic networks identifies shared biomarkers for obesity and diabetes

1.
Department of Clinical Biochemistry, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
2.
Department of Medical Biochemistry and Molecular Biology, Faculty of Medicine, Mansoura University, Mansoura, Egypt
3.
Department of Science, Prince Sultan Military College of Health Sciences, Dhahran, Saudi Arabia
4.
Division of Molecular Medicine, St. John's Research Institute, Bangalore, Karnataka, India
5.
Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
6.
Princess Al-Jawhara Al-Brahim Center of Excellence in Research of Hereditary Disorders, King Abdulaziz University, Jeddah, Saudi Arabia
7.
Department of Genetics, College of Science, Osmania University, Hyderabad, India
8.
Department of Genetic Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
+ Equal first authors

Received: 20 October 2021 Revised: 08 December 2021 Accepted: 21 December 2021 Published: 04 January 2022

Obesity and type 2 and diabetes mellitus (T2D) are two dual epidemics whose shared genetic pathological mechanisms are still far from being fully understood. Therefore, this study is aimed at discovering key genes, molecular mechanisms, and new drug targets for obesity and T2D by analyzing the genome wide gene expression data with different computational biology approaches. In this study, the RNA-sequencing data of isolated primary human adipocytes from individuals who are lean, obese, and T2D was analyzed by an integrated framework consisting of gene expression, protein interaction network (PIN), tissue specificity, and druggability approaches. Our findings show a total of 1932 unique differentially expressed genes (DEGs) across the diabetes versus obese group comparison (p≤0.05). The PIN analysis of these 1932 DEGs identified 190 high centrality network (HCN) genes, which were annotated against 3367 GO terms and functional pathways, like response to insulin signaling, phosphorylation, lipid metabolism, glucose metabolism, etc. (p≤0.05). By applying additional PIN and topological parameters to 190 HCN genes, we further mapped 25 high confidence genes, functionally connected with diabetes and obesity traits. Interestingly, ERBB2, FN1, FYN, HSPA1A, HBA1, and ITGB1 genes were found to be tractable by small chemicals, antibodies, and/or enzyme molecules. In conclusion, our study highlights the potential of computational biology methods in correlating expression data to topological parameters, functional relationships, and druggability characteristics of the candidate genes involved in complex metabolic disorders with a common etiological basis.
- obesity,
- diabetes mellitus,
- DEGs,
- PPI,
- gene network
Citation: Abdulhadi Ibrahim H. Bima, Ayman Zaky Elsamanoudy, Walaa F Albaqami, Zeenath Khan, Snijesh Valiya Parambath, Nuha Al-Rayes, Prabhakar Rao Kaipa, Ramu Elango, Babajan Banaganapalli, Noor A. Shaik. Integrative system biology and mathematical modeling of genetic networks identifies shared biomarkers for obesity and diabetes[J]. Mathematical Biosciences and Engineering, 2022, 19(3): 2310-2329. doi: 10.3934/mbe.2022107

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Abstract

Obesity and type 2 and diabetes mellitus (T2D) are two dual epidemics whose shared genetic pathological mechanisms are still far from being fully understood. Therefore, this study is aimed at discovering key genes, molecular mechanisms, and new drug targets for obesity and T2D by analyzing the genome wide gene expression data with different computational biology approaches. In this study, the RNA-sequencing data of isolated primary human adipocytes from individuals who are lean, obese, and T2D was analyzed by an integrated framework consisting of gene expression, protein interaction network (PIN), tissue specificity, and druggability approaches. Our findings show a total of 1932 unique differentially expressed genes (DEGs) across the diabetes versus obese group comparison (p≤0.05). The PIN analysis of these 1932 DEGs identified 190 high centrality network (HCN) genes, which were annotated against 3367 GO terms and functional pathways, like response to insulin signaling, phosphorylation, lipid metabolism, glucose metabolism, etc. (p≤0.05). By applying additional PIN and topological parameters to 190 HCN genes, we further mapped 25 high confidence genes, functionally connected with diabetes and obesity traits. Interestingly, ERBB2, FN1, FYN, HSPA1A, HBA1, and ITGB1 genes were found to be tractable by small chemicals, antibodies, and/or enzyme molecules. In conclusion, our study highlights the potential of computational biology methods in correlating expression data to topological parameters, functional relationships, and druggability characteristics of the candidate genes involved in complex metabolic disorders with a common etiological basis.

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