Login Register Cart 0
Generic placeholder image

Current Computer-Aided Drug Design

Editor-in-Chief

ISSN (Print): 1573-4099
ISSN (Online): 1875-6697

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Unearthing Insights into Metabolic Syndrome by Linking Drugs, Targets, and Gene Expressions Using Similarity Measures and Graph Theory

Author(s): Alwaz Zafar, Bilal Wajid*, Ans Shabbir, Fahim Gohar Awan, Momina Ahsan, Sarfraz Ahmad, Imran Wajid, Faria Anwar and Fazeelat Mazhar

Volume 20, Issue 6, 2024

Published on: 06 September, 2023

Page: [773 - 783] Pages: 11

DOI: 10.2174/1573409920666230817101913

Price: $65

Abstract

Aims and Objectives: Metabolic syndrome (MetS) is a group of metabolic disorders that includes obesity in combination with at least any two of the following conditions, i.e., insulin resistance, high blood pressure, low HDL cholesterol, and high triglycerides level. Treatment of this syndrome is challenging because of the multiple interlinked factors that lead to increased risks of type-2 diabetes and cardiovascular diseases. This study aims to conduct extensive in silico analysis to (i) find central genes that play a pivotal role in MetS and (ii) propose suitable drugs for therapy. Our objective is to first create a drug-disease network and then identify novel genes in the drug-disease network with strong associations to drug targets, which can help in increasing the therapeutical effects of different drugs. In the future, these novel genes can be used to calculate drug synergy and propose new drugs for the effective treatment of MetS.

Methods: For this purpose, we (i) investigated associated drugs and pathways for MetS, (ii) employed eight different similarity measures to construct eight gene regulatory networks, (iii) chose an optimal network, where a maximum number of drug targets were central, (iv) determined central genes exhibiting strong associations with these drug targets and associated disease-causing pathways, and lastly (v) employed these candidate genes to propose suitable drugs.

Results: Our results indicated (i) a novel drug-disease network complex, with (ii) novel genes associated with MetS.

Conclusion: Our developed drug-disease network complex closely represents MetS with associated novel findings and markers for an improved understanding of the disease and suggested therapy.

« Previous Next »
Graphical Abstract

[1]
Silveira Rossi, J.L.; Barbalho, S.M.; Reverete de Araujo, R.; Bechara, M.D.; Sloan, K.P.; Sloan, L.A. Metabolic syndrome and cardiovascular diseases: Going beyond traditional risk factors. Diabetes Metab. Res. Rev., 2022, 38(3), e3502.
[http://dx.doi.org/10.1002/dmrr.3502] [PMID: 34614543]
[2]
Kaur, J. A comprehensive review on metabolic syndrome. Cardiol. Res. Pract., 2014, 2014, 1-21.
[http://dx.doi.org/10.1155/2014/943162] [PMID: 24711954]
[3]
Scuteri, A.; Laurent, S.; Cucca, F.; Cockcroft, J.; Cunha, P.G.; Mañas, L.R.; Raso, F.U.M.; Muiesan, M.L.; Ryliškytė, L.; Rietzschel, E.; Strait, J.; Vlachopoulos, C.; Völzke, H.; Lakatta, E.G.; Nilsson, P.M. Metabolic syndrome across Europe: Different clusters of risk factors. Eur. J. Prev. Cardiol., 2015, 22(4), 486-491.
[http://dx.doi.org/10.1177/2047487314525529] [PMID: 24647805]
[4]
Ansarimoghaddam, A.; Adineh, H.A.; Zareban, I.; Iranpour, S.; HosseinZadeh, A.; Kh, F. Prevalence of metabolic syndrome in Middle-East countries: Meta-analysis of cross-sectional studies. Diabetes Metab. Syndr., 2018, 12(2), 195-201.
[http://dx.doi.org/10.1016/j.dsx.2017.11.004] [PMID: 29203060]
[5]
Mezhal, F.; Ahmad, A.; Abdulle, A.; Leinberger-Jabari, A.; Oulhaj, A.; AlJunaibi, A.; Alnaeemi, A.; Al Dhaheri, A.S.; AlZaabi, E.; Al-Maskari, F.; AlAnouti, F.; Alsafar, H.; Alkaabi, J.; Wareth, L.A.; Aljaber, M.; Kazim, M.; Alblooshi, M.; Al-Houqani, M.; Hag Ali, M.; Oumeziane, N.; El-Shahawy, O.; Al-Rifai, R.H.; Sherman, S.; Shah, S.M.; Loney, T.; Almahmeed, W.; Idaghdour, Y.; Ahmed, L.A.; Ali, R. Metabolic syndrome in fasting and non-fasting participants: The UAE healthy future study. Int. J. Environ. Res. Public Health, 2022, 19(21), 13757.
[http://dx.doi.org/10.3390/ijerph192113757] [PMID: 36360639]
[6]
Lee, S.B.; Kwon, H.C.; Kang, M.I.; Park, Y.B.; Park, J.Y.; Lee, S.W. Increased prevalence rate of metabolic syndrome is an independent predictor of cardiovascular disease in patients with antineutrophil cytoplasmic antibody-associated vasculitis. Rheumatol. Int., 2022, 42(2), 291-302.
[http://dx.doi.org/10.1007/s00296-021-04908-1] [PMID: 34086074]
[7]
Kovalkova, N.A.; Ragino, Y.I.; Travnikova, N.Y.; Denisova, D.V.; Shcherbakova, L.V.; Voevoda, M.I. Associations between metabolic syndrome and reduced lung function in young people. Ter. Arkh., 2017, 89(10), 54-61.
[http://dx.doi.org/10.17116/terarkh2017891054-61] [PMID: 29171471]
[8]
Medeiros, M.M.C.; Xavier de Oliveira, Í.M.A.; Ribeiro, Á.T.M. Prevalence of metabolic syndrome in a cohort of systemic lupus erythematosus patients from Northeastern Brazil: Association with disease activity, nephritis, smoking, and age. Rheumatol. Int., 2016, 36(1), 117-124.
[http://dx.doi.org/10.1007/s00296-015-3316-z] [PMID: 26149124]
[9]
Moore, J.X.; Chaudhary, N.; Akinyemiju, T. Metabolic syndrome prevalence by race/ethnicity and sex in the United States, National health and nutrition examination survey, 1988-2012. Prev. Chronic Dis., 2017, 14(14), 160287.
[http://dx.doi.org/10.5888/pcd14.160287] [PMID: 28301314]
[10]
Yang, C.; Jia, X.; Wang, Y.; Fan, J.; Zhao, C.; Yang, Y.; Shi, X.; Chen, Y.; Sun, Y.; Yu, Y.; Guo, X.; Li, Y.; He, J.; Xu, X.; Xiong, Y.; Hu, D. Trends and influence factors in the prevalence, intervention, and control of metabolic syndrome among US adults, 1999-2018. BMC Geriatr., 2022, 22(1), 979.
[http://dx.doi.org/10.1186/s12877-022-03672-6] [PMID: 36536296]
[11]
Voss, J.D.; Masuoka, P.; Webber, B.J.; Scher, A.I.; Atkinson, R.L. Association of elevation, urbanization and ambient temperature with obesity prevalence in the United States. Int. J. Obes., 2013, 37(10), 1407-1412.
[http://dx.doi.org/10.1038/ijo.2013.5] [PMID: 23357956]
[12]
Slack, T.; Myers, C.A.; Martin, C.K.; Heymsfield, S.B. The geographic concentration of us adult obesity prevalence and associated social, economic, and environmental factors. Obesity, 2014, 22(3), 868-874.
[http://dx.doi.org/10.1002/oby.20502] [PMID: 23630100]
[13]
Krijnen, H.K.; Hoveling, L.A.; Liefbroer, A.C.; Bültmann, U.; Smidt, N. Socioeconomic differences in metabolic syndrome development among males and females, and the mediating role of health literacy and self-management skills. Prev. Med., 2022, 161, 107140.
[http://dx.doi.org/10.1016/j.ypmed.2022.107140] [PMID: 35803357]
[14]
Beltrán-Sánchez, H.; Harhay, M.O.; Harhay, M.M.; McElligott, S. Prevalence and trends of metabolic syndrome in the adult U.S. population, 1999-2010. J. Am. Coll. Cardiol., 2013, 62(8), 697-703.
[http://dx.doi.org/10.1016/j.jacc.2013.05.064] [PMID: 23810877]
[15]
Andrew J., K.; Angelo, S. Insulin resistance and the metabolic syndrome. In: Diabetes in Old Age; Wiley Online Library, 2017; pp. 177-212.
[http://dx.doi.org/10.1002/9781118954621.ch15]
[16]
Alberti, K.G.M.M.; Zimmet, P.; Shaw, J. Metabolic syndrome-a new world-wide definition. A consensus statement from the international diabetes federation. Diabet. Med., 2006, 23(5), 469-480.
[http://dx.doi.org/10.1111/j.1464-5491.2006.01858.x] [PMID: 16681555]
[17]
Han, J.M.; Levings, M.K. Immune regulation in obesity-associated adipose inflammation. J. Immunol., 2013, 191(2), 527-532.
[http://dx.doi.org/10.4049/jimmunol.1301035] [PMID: 23825387]
[18]
Olefsky, J.M.; Glass, C.K. Macrophages, inflammation, and insulin resistance. Annu. Rev. Physiol., 2010, 72(1), 219-246.
[http://dx.doi.org/10.1146/annurev-physiol-021909-135846] [PMID: 20148674]
[19]
Shoelson, S.E.; Lee, J.; Goldfine, A.B. Inflammation and insulin resistance. J. Clin. Invest., 2006, 116(7), 1793-1801.
[http://dx.doi.org/10.1172/JCI29069] [PMID: 16823477]
[20]
Grundy, S.M.; Hansen, B.; Smith, S.C., Jr; Cleeman, J.I.; Kahn, R.A. Clinical management of metabolic syndrome: report of the american heart association/national heart, lung, and blood institute/american diabetes association conference on scientific issues related to management. Circulation, 2004, 109(4), 551-556.
[http://dx.doi.org/10.1161/01.CIR.0000112379.88385.67] [PMID: 14757684]
[21]
Metwaly, A.; Reitmeier, S.; Haller, D. Microbiome risk profiles as biomarkers for inflammatory and metabolic disorders. Nat. Rev. Gastroenterol. Hepatol., 2022, 19(6), 383-397.
[http://dx.doi.org/10.1038/s41575-022-00581-2] [PMID: 35190727]
[22]
Salleh, M.; Hani, F. Reconstructing gene regulatory networks from knock-out data using gaussian noise model and pearson correlation coefficient. Comput. Biol. Chem., 2015, 59(Pt B), 3-14.
[http://dx.doi.org/10.1016/j.compbiolchem.2015.04.012]
[23]
Pripp, Are Hugo Pearson's or Spearman's correlation coefficients. Tidsskr. Nor. Laegeforen., 2018, 138(8), (10).
[http://dx.doi.org/10.4045/tidsskr.18.0042]
[24]
Ma, Y. On inference for kendall's τ within a longitudinal data setting. J. Appl. Stat., 2012, 39(11), 2441-2452.
[http://dx.doi.org/10.1080/02664763.2012.712954] [PMID: 23554542]
[25]
Yan, Xiting; Anqi, L; Jose, G A novel pathway-based distance score enhances assessment of disease heterogeneity in gene expression. BMC Bioinformatics., 2017, 18(1), 309.
[http://dx.doi.org/10.1186/s12859-017-1727-4]
[26]
Kirişci, M. New cosine similarity and distance measures for Fermatean fuzzy sets and TOPSIS approach. Knowl. Inf. Syst., 2023, 65(2), 855-868.
[http://dx.doi.org/10.1007/s10115-022-01776-4] [PMID: 36373008]
[27]
Rao Kakita, V.M.; Ramakrishna, V.H. Mahalanobis distance correlation: A novel approach for quantitating changes in multidimensional NMR spectra in biological applications. J. Magn. Reson., 2022, 337, 107165.
[http://dx.doi.org/10.1016/j.jmr.2022.107165]
[28]
Xu, H.; Zeng, W.; Zeng, X.; Yen, G.G. An evolutionary algorithm based on minkowski distance for many-objective optimization. IEEE Trans. Cybern., 2019, 49(11), 3968-3979.
[http://dx.doi.org/10.1109/TCYB.2018.2856208] [PMID: 30059330]
[29]
Lesk, A.M. Extraction of geometrically similar substructures: Least-squares and Chebyshev fitting and the difference distance matrix. Proteins, 1998, 33(3), 320-328.
[http://dx.doi.org/10.1002/(SICI)1097-0134(19981115)33:3<320::AID-PROT2>3.0.CO;2-Q] [PMID: 9829692]
[30]
Xu, X.M.; Liu, Y.; Feng, Y.; Xu, J.J.; Gao, J.; Salvi, R.; Wu, Y.; Yin, X.; Chen, Y.C. Degree centrality and functional connections in presbycusis with and without cognitive impairments. Brain Imaging Behav., 2022, 16(6), 2725-2734.
[http://dx.doi.org/10.1007/s11682-022-00734-6] [PMID: 36327020]
[31]
Li, G.; Li, M.; Wang, J.; Li, Y.; Pan, Y. United neighborhood closeness centrality and orthology for predicting essential proteins. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 2018, 17(4), 1.
[http://dx.doi.org/10.1109/TCBB.2018.2889978] [PMID: 30596582]
[32]
Rungta, Pranay Deep Identifying nodal properties that are crucial for the dynamical robustness of multistable networks. Phys. Rev. E., 2018, 98((2-1)), 022314.
[http://dx.doi.org/10.1103/PhysRevE.98.022314]
[33]
Taylor, D.; Myers, S.A.; Clauset, A.; Porter, M.A.; & Mucha, P.J. Eigenvector-based centrality measures for temporal networks. Multiscale Model. Simul., 2017, 15(1), 537-574.
[http://dx.doi.org/10.1137/16M1066142]
[34]
Higham, D.J.; Higham, N.J. MATLAB guide. In: Philadelphia; SIAM: PA, USA, 2016; 150, .
[35]
Kamburov, A.; Herwig, R. ConsensusPathDB 2022: Molecular interactions update as a resource for network biology. Nucleic Acids Res., 2022, 50(D1), D587-D595.
[http://dx.doi.org/10.1093/nar/gkab1128] [PMID: 34850110]
[36]
Huang, D.W.; Sherman, B.T.; Lempicki, R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc., 2009, 4(1), 44-57.
[http://dx.doi.org/10.1038/nprot.2008.211] [PMID: 19131956]
[37]
Kanehisa, M.; Furumichi, M.; Tanabe, M.; Sato, Y.; Morishima, K. KEGG: New perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res., 2017, 45(D1), D353-D361.
[http://dx.doi.org/10.1093/nar/gkw1092] [PMID: 27899662]
[38]
Zeng, X.; Tu, X.; Liu, Y.; Fu, X.; Su, Y.; Ruan, Z.; Cui, F.; Jiang, H.; Zhou, Y.; Hu, H. Toward better drug discovery with knowledge graph. Curr. Opin. Struct. Biol., 2022, 72, 114-126.
[http://dx.doi.org/10.1016/j.sbi.2021.09.003] [PMID: 34649044]
[39]
Kleinbongard, P.; Lieder, H.R.; Skyschally, A.; Alloosh, M.; Gödecke, A.; Rahmann, S.; Sturek, M.; Heusch, G. Non-responsiveness to cardioprotection by ischaemic preconditioning in Ossabaw minipigs with genetic predisposition to, but without the phenotype of the metabolic syndrome. Basic Res. Cardiol., 2022, 117(1), 58.
[http://dx.doi.org/10.1007/s00395-022-00965-0] [PMID: 36374343]
[40]
Szklarczyk, D.; Gable, A.L.; Lyon, D.; Junge, A.; Wyder, S.; Huerta-Cepas, J.; Simonovic, M.; Doncheva, N.T.; Morris, J.H.; Bork, P.; Jensen, L.J.; Mering, C. STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res., 2019, 47(D1), D607-D613.
[http://dx.doi.org/10.1093/nar/gky1131] [PMID: 30476243]
[41]
GEO Accession viewer. Available from: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE44000
[42]
KEGG DISEASE: Genetic Obesity. Available from: www.genome.jp/entry/H02106.
[43]
KEGG PATHWAY: Insulin Resistance - Homo Sapiens (Human). Available from: www.genome.jp/kegg-bin/show_pathway?hsa0493
[44]
KEGG PATHWAY: Type I Diabetes Mellitus - Homo Sapiens (Human). Available from: www.genome.jp/kegg-bin/show_pathway?hsa04940.
[45]
KEGG PATHWAY: Type II Diabetes Mellitus - Homo Sapiens (Human). Available from: www.genome.jp/kegg-bin/show_pathway?hsa04930
[46]
KEGG PATHWAY: Fluid Shear Stress and Atherosclerosis - Homo Sapiens (Human). Available from: www.genome.jp/kegg-bin/show_pathway?hsa05418.
[47]
Yin, Z.; Deng, T.; Peterson, L.E.; Yu, R.; Lin, J.; Hamilton, D.J.; Reardon, P.R.; Sherman, V.; Winnier, G.E.; Zhan, M.; Lyon, C.J.; Wong, S.T.C.; Hsueh, W.A. Transcriptome analysis of human adipocytes implicates the NOD-like receptor pathway in obesity-induced adipose inflammation. Mol. Cell. Endocrinol., 2014, 394(1-2), 80-87.
[http://dx.doi.org/10.1016/j.mce.2014.06.018] [PMID: 25011057]
[48]
Brazil, D.P.; Hemmings, B.A. Ten years of protein kinase B signalling: A hard Akt to follow. Trends Biochem. Sci., 2001, 26(11), 657-664.
[http://dx.doi.org/10.1016/S0968-0004(01)01958-2] [PMID: 11701324]
[49]
Keshet, Y.; Seger, R. The MAP kinase signaling cascades: A system of hundreds of components regulates a diverse array of physiological functions. Methods Mol. Biol., 2010, 661, 3-38.
[http://dx.doi.org/10.1007/978-1-60761-795-2_1] [PMID: 20811974]
[50]
Zhang, W.; Liu, H.T. MAPK signal pathways in the regulation of cell proliferation in mammalian cells. Cell Res., 2002, 12(1), 9-18.
[http://dx.doi.org/10.1038/sj.cr.7290105] [PMID: 11942415]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy