1
|
Najm M, Martignetti L, Cornet M, Kelly-Aubert M, Sermet I, Calzone L, Stoven V. From CFTR to a CF signalling network: a systems biology approach to study Cystic Fibrosis. BMC Genomics 2024; 25:892. [PMID: 39342081 PMCID: PMC11438383 DOI: 10.1186/s12864-024-10752-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 08/30/2024] [Indexed: 10/01/2024] Open
Abstract
BACKGROUND Cystic Fibrosis (CF) is a monogenic disease caused by mutations in the gene coding the Cystic Fibrosis Transmembrane Regulator (CFTR) protein, but its overall physio-pathology cannot be solely explained by the loss of the CFTR chloride channel function. Indeed, CFTR belongs to a yet not fully deciphered network of proteins participating in various signalling pathways. METHODS We propose a systems biology approach to study how the absence of the CFTR protein at the membrane leads to perturbation of these pathways, resulting in a panel of deleterious CF cellular phenotypes. RESULTS Based on publicly available transcriptomic datasets, we built and analyzed a CF network that recapitulates signalling dysregulations. The CF network topology and its resulting phenotypes were found to be consistent with CF pathology. CONCLUSION Analysis of the network topology highlighted a few proteins that may initiate the propagation of dysregulations, those that trigger CF cellular phenotypes, and suggested several candidate therapeutic targets. Although our research is focused on CF, the global approach proposed in the present paper could also be followed to study other rare monogenic diseases.
Collapse
Affiliation(s)
- Matthieu Najm
- Center for Computational Biology (CBIO), Mines Paris-PSL, 75006, Paris, France.
- Institut Curie, Université PSL, 75005, Paris, France.
- INSERM U900, 75005, Paris, France.
| | - Loredana Martignetti
- Center for Computational Biology (CBIO), Mines Paris-PSL, 75006, Paris, France
- Institut Curie, Université PSL, 75005, Paris, France
- INSERM U900, 75005, Paris, France
| | - Matthieu Cornet
- Center for Computational Biology (CBIO), Mines Paris-PSL, 75006, Paris, France
- Institut Curie, Université PSL, 75005, Paris, France
- INSERM U900, 75005, Paris, France
- Institut Necker Enfants Malades, INSERM U1151, 75015, Paris, France
| | - Mairead Kelly-Aubert
- Institut Necker Enfants Malades, INSERM U1151, 75015, Paris, France
- Université Paris Cité, 75015, Paris, France
| | - Isabelle Sermet
- Institut Necker Enfants Malades, INSERM U1151, 75015, Paris, France
- Université Paris Cité, 75015, Paris, France
- Centre de Référence Maladies Rares, Mucoviscidose et Maladies Apparentées, Hôpital Necker Enfants Malades AP-HP Centre Paris Cité, 75015, Paris, France
| | - Laurence Calzone
- Center for Computational Biology (CBIO), Mines Paris-PSL, 75006, Paris, France.
- Institut Curie, Université PSL, 75005, Paris, France.
- INSERM U900, 75005, Paris, France.
| | - Véronique Stoven
- Center for Computational Biology (CBIO), Mines Paris-PSL, 75006, Paris, France.
- Institut Curie, Université PSL, 75005, Paris, France.
- INSERM U900, 75005, Paris, France.
| |
Collapse
|
2
|
Yang Z, Qi Y, Wang Y, Chen X, Wang Y, Zhang X. Identifying Network Biomarkers in Early Diagnosis of Hepatocellular Carcinoma via miRNA-Gene Interaction Network Analysis. Curr Issues Mol Biol 2023; 45:7374-7387. [PMID: 37754250 PMCID: PMC10529263 DOI: 10.3390/cimb45090466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 08/26/2023] [Accepted: 09/07/2023] [Indexed: 09/28/2023] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is a highly heterogeneous cancer at the histological level. Despite the emergence of new biological technology, advanced-stage HCC remains largely incurable. The prediction of a cancer biomarker is a key problem for targeted therapy in the disease. METHODS We performed a miRNA-gene integrated analysis to identify differentially expressed miRNAs (DEMs) and genes (DEGs) of HCC. The DEM-DEG interaction network was constructed and analyzed. Gene ontology enrichment and survival analyses were also performed in this study. RESULTS By the analysis of healthy and tumor samples, we found that 94 DEGs and 25 DEMs were significantly differentially expressed in different datasets. Gene ontology enrichment analysis showed that these 94 DEGs were significantly enriched in the term "Liver" with a statistical p-value of 1.71 × 10-26. Function enrichment analysis indicated that these genes were significantly overrepresented in the term "monocarboxylic acid metabolic process" with a p-value = 2.94 × 10-18. Two sets (fourteen genes and five miRNAs) were screened by a miRNA-gene integrated analysis of their interaction network. The statistical analysis of these molecules showed that five genes (CLEC4G, GLS2, H2AFZ, STMN1, TUBA1B) and two miRNAs (hsa-miR-326 and has-miR-331-5p) have significant effects on the survival prognosis of patients. CONCLUSION We believe that our study could provide critical clinical biomarkers for the targeted therapy of HCC.
Collapse
Affiliation(s)
- Zhiyuan Yang
- School of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, China (X.Z.)
| | - Yuanyuan Qi
- School of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, China (X.Z.)
| | - Yijing Wang
- School of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, China (X.Z.)
| | - Xiangyun Chen
- School of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, China (X.Z.)
| | - Yuerong Wang
- School of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, China (X.Z.)
| | - Xiaoli Zhang
- School of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, China (X.Z.)
- School of Physics and Key Laboratory of Molecular Biophysics of the Ministry of Education, Huazhong University of Science and Technology, Wuhan 430074, China
| |
Collapse
|
3
|
Singh V, Naldi A, Soliman S, Niarakis A. A large-scale Boolean model of the rheumatoid arthritis fibroblast-like synoviocytes predicts drug synergies in the arthritic joint. NPJ Syst Biol Appl 2023; 9:33. [PMID: 37454172 DOI: 10.1038/s41540-023-00294-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 06/29/2023] [Indexed: 07/18/2023] Open
Abstract
Rheumatoid arthritis (RA) is a complex autoimmune disease with an unknown aetiology. However, rheumatoid arthritis fibroblast-like synoviocytes (RA-FLS) play a significant role in initiating and perpetuating destructive joint inflammation by expressing immuno-modulating cytokines, adhesion molecules, and matrix remodelling enzymes. In addition, RA-FLS are primary drivers of inflammation, displaying high proliferative rates and an apoptosis-resistant phenotype. Thus, RA-FLS-directed therapies could become a complementary approach to immune-directed therapies by predicting the optimal conditions that would favour RA-FLS apoptosis, limit inflammation, slow the proliferation rate and minimise bone erosion and cartilage destruction. In this paper, we present a large-scale Boolean model for RA-FLS that consists of five submodels focusing on apoptosis, cell proliferation, matrix degradation, bone erosion and inflammation. The five-phenotype-specific submodels can be simulated independently or as a global model. In silico simulations and perturbations reproduced the expected biological behaviour of the system under defined initial conditions and input values. The model was then used to mimic the effect of mono or combined therapeutic treatments and predict novel targets and drug candidates through drug repurposing analysis.
Collapse
Affiliation(s)
- Vidisha Singh
- Université Paris-Saclay, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde-Genhotel, Univ Evry, Evry, France
| | - Aurelien Naldi
- Lifeware Group, Inria, Saclay-île de France, 91120, Palaiseau, France
| | - Sylvain Soliman
- Lifeware Group, Inria, Saclay-île de France, 91120, Palaiseau, France
| | - Anna Niarakis
- Université Paris-Saclay, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde-Genhotel, Univ Evry, Evry, France.
- Lifeware Group, Inria, Saclay-île de France, 91120, Palaiseau, France.
| |
Collapse
|
4
|
Expanding the Disease Network of Glioblastoma Multiforme via Topological Analysis. Int J Mol Sci 2023; 24:ijms24043075. [PMID: 36834486 PMCID: PMC9965660 DOI: 10.3390/ijms24043075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/27/2023] [Accepted: 01/30/2023] [Indexed: 02/09/2023] Open
Abstract
Glioblastoma multiforme (GBM), a grade IV glioma, is a challenging disease for patients and clinicians, with an extremely poor prognosis. These tumours manifest a high molecular heterogeneity, with limited therapeutic options for patients. Since GBM is a rare disease, sufficient statistically strong evidence is often not available to explore the roles of lesser-known GBM proteins. We present a network-based approach using centrality measures to explore some key, topologically strategic proteins for the analysis of GBM. Since network-based analyses are sensitive to changes in network topology, we analysed nine different GBM networks, and show that small but well-curated networks consistently highlight a set of proteins, indicating their likely involvement in the disease. We propose 18 novel candidates which, based on differential expression, mutation analysis, and survival analysis, indicate that they may play a role in GBM progression. These should be investigated further for their functional roles in GBM, their clinical prognostic relevance, and their potential as therapeutic targets.
Collapse
|
5
|
Zhou W, Ma X, Jia X, Zheng J, Yan L, Fu Y. Construction and comprehensive analysis of the biological network related to rheumatoid arthritis-related interstitial lung disease. Int J Rheum Dis 2023; 26:132-144. [PMID: 36261881 DOI: 10.1111/1756-185x.14466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 08/22/2022] [Accepted: 10/01/2022] [Indexed: 01/04/2023]
Abstract
OBJECTIVE Interstitial lung disease (ILD) is a severe manifestation of rheumatoid arthritis (RA), which is characterized by low survival time post-diagnosis. Thus, it is important to explore the role of gene regulation related with ILD. METHOD Constructed a RA-ILD-related long chain noncoding RNA - messenger RNA (lncRNA-mRNA) network (ILD-LMN), based on ILD- and RA-related genes. We analyzed the topological properties of the resulting network. RESULT The results for network modularization and functional analysis showed that ILD-LMN performed basic and specific functions in ILD pathology. Furthermore, differential expression and correlation analysis of hub nodes revealed highly correlated competitive endogenous RNA regulatory relationships with important roles in pathological regulation. Following this, statistical analysis of disease-related single nucleotide polymorphisms (SNPs) in hub lncRNAs revealed that some of transcription factor-related SNPs were significantly associated with the expression of lncRNA. In fact, these SNPs exhibited significant differential expression in disease and normal samples. CONCLUSION These results suggest that ILD-LMN has important implications in the study of disease. Altogether, the study of RA- and ILD-related lncRNA and genes on the basis of biological network would assist in providing better treatment opportunities for ILD patients. Additionally, it would promote further research on treatment of the disease.
Collapse
Affiliation(s)
- Wei Zhou
- Department of Rheumatology, Liaocheng People's Hospital, Liaocheng, China
| | - Xianghui Ma
- Department of Rheumatology, Dongying People's Hospital, Dongying, China
| | - Xiaodong Jia
- Department of the Key Laboratory of Ophthalmology, Liaocheng People's Hospital, Liaocheng, China
| | - Juan Zheng
- Department of the Key Laboratory of Ophthalmology, Liaocheng People's Hospital, Liaocheng, China
| | - Lili Yan
- Department of the Key Laboratory of Ophthalmology, Liaocheng People's Hospital, Liaocheng, China
| | - Yanfa Fu
- Department of Rheumatology, Liaocheng People's Hospital, Liaocheng, China
| |
Collapse
|
6
|
Gene Networks of Hyperglycemia, Diabetic Complications, and Human Proteins Targeted by SARS-CoV-2: What Is the Molecular Basis for Comorbidity? Int J Mol Sci 2022; 23:ijms23137247. [PMID: 35806251 PMCID: PMC9266766 DOI: 10.3390/ijms23137247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 06/25/2022] [Accepted: 06/27/2022] [Indexed: 12/10/2022] Open
Abstract
People with diabetes are more likely to have severe COVID-19 compared to the general population. Moreover, diabetes and COVID-19 demonstrate a certain parallelism in the mechanisms and organ damage. In this work, we applied bioinformatics analysis of associative molecular networks to identify key molecules and pathophysiological processes that determine SARS-CoV-2-induced disorders in patients with diabetes. Using text-mining-based approaches and ANDSystem as a bioinformatics tool, we reconstructed and matched networks related to hyperglycemia, diabetic complications, insulin resistance, and beta cell dysfunction with networks of SARS-CoV-2-targeted proteins. The latter included SARS-CoV-2 entry receptors (ACE2 and DPP4), SARS-CoV-2 entry associated proteases (TMPRSS2, CTSB, and CTSL), and 332 human intracellular proteins interacting with SARS-CoV-2. A number of genes/proteins targeted by SARS-CoV-2 (ACE2, BRD2, COMT, CTSB, CTSL, DNMT1, DPP4, ERP44, F2RL1, GDF15, GPX1, HDAC2, HMOX1, HYOU1, IDE, LOX, NUTF2, PCNT, PLAT, RAB10, RHOA, SCARB1, and SELENOS) were found in the networks of vascular diabetic complications and insulin resistance. According to the Gene Ontology enrichment analysis, the defined molecules are involved in the response to hypoxia, reactive oxygen species metabolism, immune and inflammatory response, regulation of angiogenesis, platelet degranulation, and other processes. The results expand the understanding of the molecular basis of diabetes and COVID-19 comorbidity.
Collapse
|
7
|
Stroggilos R, Frantzi M, Zoidakis J, Mokou M, Moulavasilis N, Mavrogeorgis E, Melidi A, Makridakis M, Stravodimos K, Roubelakis MG, Mischak H, Vlahou A. Gene Expression Monotonicity across Bladder Cancer Stages Informs on the Molecular Pathogenesis and Identifies a Prognostic Eight-Gene Signature. Cancers (Basel) 2022; 14:cancers14102542. [PMID: 35626146 PMCID: PMC9140126 DOI: 10.3390/cancers14102542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 05/14/2022] [Accepted: 05/17/2022] [Indexed: 01/27/2023] Open
Abstract
Despite advancements in molecular classification, tumor stage and grade still remain the most relevant prognosticators used by clinicians to decide on patient management. Here, we leverage publicly available data to characterize bladder cancer (BLCA)’s stage biology based on increased sample sizes, identify potential therapeutic targets, and extract putative biomarkers. A total of 1135 primary BLCA transcriptomes from 12 microarray studies were compiled in a meta-cohort and analyzed for monotonal alterations in pathway activities, gene expression, and co-expression patterns with increasing stage (Ta–T1–T2–T3–T4), starting from the non-malignant tumor-adjacent urothelium. The TCGA-2017 and IMvigor-210 RNA-Seq data were used to validate our findings. Wnt, MTORC1 signaling, and MYC activity were monotonically increased with increasing stage, while an opposite trend was detected for the catabolism of fatty acids, circadian clock genes, and the metabolism of heme. Co-expression network analysis highlighted stage- and cell-type-specific genes of potentially synergistic therapeutic value. An eight-gene signature, consisting of the genes AKAP7, ANLN, CBX7, CDC14B, ENO1, GTPBP4, MED19, and ZFP2, had independent prognostic value in both the discovery and validation sets. This novel eight-gene signature may increase the granularity of current risk-to-progression estimators.
Collapse
Affiliation(s)
- Rafael Stroggilos
- Systems Biology Center, Biomedical Research Foundation of the Academy of Athens, Soranou Efessiou 4, 11527 Athens, Greece; (R.S.); (J.Z.); (E.M.); (A.M.); (M.M.)
| | - Maria Frantzi
- Mosaiques Diagnostics GmbH, 30659 Hannover, Germany; (M.F.); (M.M.); (H.M.)
| | - Jerome Zoidakis
- Systems Biology Center, Biomedical Research Foundation of the Academy of Athens, Soranou Efessiou 4, 11527 Athens, Greece; (R.S.); (J.Z.); (E.M.); (A.M.); (M.M.)
| | - Marika Mokou
- Mosaiques Diagnostics GmbH, 30659 Hannover, Germany; (M.F.); (M.M.); (H.M.)
| | - Napoleon Moulavasilis
- 1st Department of Urology, Laiko Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece; (N.M.); (K.S.)
| | - Emmanouil Mavrogeorgis
- Systems Biology Center, Biomedical Research Foundation of the Academy of Athens, Soranou Efessiou 4, 11527 Athens, Greece; (R.S.); (J.Z.); (E.M.); (A.M.); (M.M.)
| | - Anna Melidi
- Systems Biology Center, Biomedical Research Foundation of the Academy of Athens, Soranou Efessiou 4, 11527 Athens, Greece; (R.S.); (J.Z.); (E.M.); (A.M.); (M.M.)
| | - Manousos Makridakis
- Systems Biology Center, Biomedical Research Foundation of the Academy of Athens, Soranou Efessiou 4, 11527 Athens, Greece; (R.S.); (J.Z.); (E.M.); (A.M.); (M.M.)
| | - Konstantinos Stravodimos
- 1st Department of Urology, Laiko Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece; (N.M.); (K.S.)
| | - Maria G. Roubelakis
- Laboratory of Biology, School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece;
- Cell and Gene Therapy Laboratory, Biomedical Research Foundation of the Academy of Athens, Soranou Efessiou 4, 11527 Athens, Greece
| | - Harald Mischak
- Mosaiques Diagnostics GmbH, 30659 Hannover, Germany; (M.F.); (M.M.); (H.M.)
| | - Antonia Vlahou
- Systems Biology Center, Biomedical Research Foundation of the Academy of Athens, Soranou Efessiou 4, 11527 Athens, Greece; (R.S.); (J.Z.); (E.M.); (A.M.); (M.M.)
- Correspondence: ; Tel.: +30-210-659-7506; Fax: +30-210-659-7545
| |
Collapse
|
8
|
Sagulkoo P, Suratanee A, Plaimas K. Immune-Related Protein Interaction Network in Severe COVID-19 Patients toward the Identification of Key Proteins and Drug Repurposing. Biomolecules 2022; 12:biom12050690. [PMID: 35625619 PMCID: PMC9138873 DOI: 10.3390/biom12050690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 05/07/2022] [Accepted: 05/09/2022] [Indexed: 02/05/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) is still an active global public health issue. Although vaccines and therapeutic options are available, some patients experience severe conditions and need critical care support. Hence, identifying key genes or proteins involved in immune-related severe COVID-19 is necessary to find or develop the targeted therapies. This study proposed a novel construction of an immune-related protein interaction network (IPIN) in severe cases with the use of a network diffusion technique on a human interactome network and transcriptomic data. Enrichment analysis revealed that the IPIN was mainly associated with antiviral, innate immune, apoptosis, cell division, and cell cycle regulation signaling pathways. Twenty-three proteins were identified as key proteins to find associated drugs. Finally, poly (I:C), mitomycin C, decitabine, gemcitabine, hydroxyurea, tamoxifen, and curcumin were the potential drugs interacting with the key proteins to heal severe COVID-19. In conclusion, IPIN can be a good representative network for the immune system that integrates the protein interaction network and transcriptomic data. Thus, the key proteins and target drugs in IPIN help to find a new treatment with the use of existing drugs to treat the disease apart from vaccination and conventional antiviral therapy.
Collapse
Affiliation(s)
- Pakorn Sagulkoo
- Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok 10330, Thailand;
- Center of Biomedical Informatics, Department of Family Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand
| | - Apichat Suratanee
- Department of Mathematics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand;
- Intelligent and Nonlinear Dynamics Innovations Research Center, Science and Technology Research Institute, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand
| | - Kitiporn Plaimas
- Advance Virtual and Intelligent Computing (AVIC) Center, Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand
- Omics Science and Bioinformatics Center, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand
- Correspondence:
| |
Collapse
|
9
|
Cucchiara F, Petrini I, Passaro A, Attili I, Crucitta S, Pardini E, de Marinis F, Danesi R, Re MD. Gene-Networks analyses define a subgroup of Small Cell Lung Cancers with short survival. Clin Lung Cancer 2022; 23:510-521. [DOI: 10.1016/j.cllc.2022.05.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 05/06/2022] [Accepted: 05/13/2022] [Indexed: 11/03/2022]
|
10
|
Bima AIH, Elsamanoudy AZ, Albaqami WF, Khan Z, Parambath SV, Al-Rayes N, Kaipa PR, Elango R, Banaganapalli B, Shaik NA. Integrative system biology and mathematical modeling of genetic networks identifies shared biomarkers for obesity and diabetes. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:2310-2329. [PMID: 35240786 DOI: 10.3934/mbe.2022107] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
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.
Collapse
Affiliation(s)
- Abdulhadi Ibrahim H Bima
- Department of Clinical Biochemistry, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Ayman Zaky Elsamanoudy
- Department of Clinical Biochemistry, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Medical Biochemistry and Molecular Biology, Faculty of Medicine, Mansoura University, Mansoura, Egypt
| | - Walaa F Albaqami
- Department of Science, Prince Sultan Military College of Health Sciences, Dhahran, Saudi Arabia
| | - Zeenath Khan
- Department of Science, Prince Sultan Military College of Health Sciences, Dhahran, Saudi Arabia
| | | | - Nuha Al-Rayes
- Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
- Princess Al-Jawhara Al-Brahim Center of Excellence in Research of Hereditary Disorders, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Prabhakar Rao Kaipa
- Department of Genetics, College of Science, Osmania University, Hyderabad, India
| | - Ramu Elango
- Princess Al-Jawhara Al-Brahim Center of Excellence in Research of Hereditary Disorders, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Genetic Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Babajan Banaganapalli
- Princess Al-Jawhara Al-Brahim Center of Excellence in Research of Hereditary Disorders, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Genetic Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Noor A Shaik
- Princess Al-Jawhara Al-Brahim Center of Excellence in Research of Hereditary Disorders, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Genetic Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| |
Collapse
|
11
|
|