Analysis of topological properties and drug discovery for bipolar disorder and associated diseases: A bioinformatics approach

Khairul Alam Shadhin; Md. Rakibul Hasan; Bikash Kumar Paul; Kawsar Ahmed; Mohammad Ali Moni; Danial Kahrizi; Touhid Bhuyian; Sobhy M. Ibrahim

doi:10.14715/cmb/2020.66.7.23

Issue

Vol. 66 No. 7: Issue 7

Issue Published : November 9, 2020

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Analysis of topological properties and drug discovery for bipolar disorder and associated diseases: A bioinformatics approach

https://doi.org/10.14715/cmb/2020.66.7.23

Khairul Alam Shadhin

Department of Software Engineering, Daffodil International University, Sukrabad, Dhanmondi, Dhaka, Bangladesh

Md. Rakibul Hasan

Department of Software Engineering, Daffodil International University, Sukrabad, Dhanmondi, Dhaka, Bangladesh

Bikash Kumar Paul

Department of Software Engineering, Daffodil International University, Sukrabad, Dhanmondi, Dhaka, Bangladesh

Kawsar Ahmed

2Department of Information and Communication Technology (ICT), Mawlana Bhashani Science and Technology University (MBSTU), Santosh, Tangail, 1902, Bangladesh.

Mohammad Ali Moni

WHO Collaborating Centre on eHealth, School of Public Health and Community Medicine, Faculty of Medicine, UNSW Sydney, Australia

Danial Kahrizi

Touhid Bhuyian

Department of Software Engineering, Daffodil International University, Sukrabad, Dhanmondi, Dhaka, Bangladesh

Sobhy M. Ibrahim

Department of Biochemistry, College of Science, King Saud University, P.O. Box: 2455, Riyadh 11451, Saudi Arabia

Corresponding Author(s) : Danial Kahrizi

d.kahrizi@cellmolbiol.org

Cellular and Molecular Biology, Vol. 66 No. 7: Issue 7
Article Published : October 31, 2020

Abstract

With the advancement and development of sophisticated bioinformatics tools, the area of computational bioinformatics and systems biology analysis is expanding day by day. The bipolar or manic-depressive disorder might be characterized as one of the most crippling mental problems that affect the people of early age and grown-ups. The objective of the present study was to investigate the association between genetic mutations in the four above listed diseases and to create a Protein-protein interaction (PPI) network or common pathways. Firstly, we need to find out the genetic relationship between them. Thus it will help us to understand the genetic association between them and help to develop the drug design for all the diseases. Genes responsible for these diseases are gathered, pre-processed, processed and mining using python scripts. This exploration is expected to carry out further measurements in the field of drug structure and also contributes to the biological and biomedical sectors.

Keywords

Bioinformatics Bipolar Disorders Schizophrenia Coronary Heart Diseases Stroke Genetic Association PPI network Clustering Gene Regulatory network PDI network PCI network

Shadhin, K. A., Hasan, M. R., Paul, B. K., Ahmed, K., Moni, M. A., Kahrizi, D., Bhuyian, T., & Ibrahim, S. M. (2020). Analysis of topological properties and drug discovery for bipolar disorder and associated diseases: A bioinformatics approach. Cellular and Molecular Biology, 66(7), 152–160. https://doi.org/10.14715/cmb/2020.66.7.23

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References

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WHO. The World Health Report 2001: Mental health: new understanding, new hope. World Health Org. 2001. Last access: 10 December, 2019.
WHO. The world health report 2002: reducing risks, promoting healthy life. World Health Org. 2002. Last access: 10 December, 2019
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Murray CJ, Lopez AD, WHO. The global burden of disease: a comprehensive assessment of mortality and disability from diseases, injuries, and risk factors in 1990 and projected to 2020: summary. World Health Org. 1996; Last access: 10 December, 2019.
Patel V, Prince M. Global mental health: a new global health field comes of age. JAMA 2010; 303(19): 1976-77.
Torrey EF, Bowler A. Geographical distribution of insanity in America: evidence for an urban factor. Schizophr Bull 1990; 16(4): 591-604.
Cardno AG, Owen MJ. Genetic relationships between schizophrenia, bipolar disorder, and schizoaffective disorder. Schizophr Bull 2014; 40(3): 504-15.
Johnson W, Onuma O, Owolabi M, Sachdev S. Stroke: a global response is needed. Bull World Health Org 2016; 94(9): 634.
Redon J, Olsen MH, Cooper RS, Zurriaga O, Martinez-Beneito MA, Laurent S, et al. Stroke mortality and trends from 1990 to 2006 in 39 countries from Europe and Central Asia: implications for control of high blood pressure. Eur Heart J 2011; 32(11):1424-31.
Feigin VL, Forouzanfar MH, Krishnamurthi R, Mensah GA, Connor M, Bennett DA, et al. Global and regional burden of stroke during 1990–2010: findings from the Global Burden of Disease Study 2010. Lancet 2014; 383(9913): 245-55.
Asplund K, Karvanen J, Giampaoli S, Jousilahti P, NiemelaÌˆ M, Broda G, et al. Relative risks for stroke by age, sex, and population based on follow-up of 18 European populations in the MORGAM Project. Stroke 2009; 40(7): 2319-26.
Struijs JN, Van Genugten ML, Evers SM, Ament AJ, Baan CA, Van Den Bos GA. Future costs of stroke in the Netherlands: the impact of stroke services. Int J Tech. Assessment in Health Care 2006; 22(4): 518-24.
Wolfe CJ, Kohane IS, Butte AJ. Systematic survey reveals general applicability of guilt-by-association within gene coexpression networks. BMC Bioinformatics 2005; 6(1): 227.
Amar D, Safer H, Shamir R. Dissection of regulatory networks that are altered in disease via; differential co-expression. PLoS Comput Biol 2013, 9(3): e1002955.
Likas A, Vlassis N, Verbeek JJ. The global k-means clustering algorithm. Pattern Recognition 2003; 36(2): 451-61.
Shih YK, Parthasarathy S. Identifying functional modules in interaction networks through overlapping Markov clustering. Bioinformatics 2012; 28(18): i473-9.
Canduri F, de Azevedo J, Walter F. Protein crystallography in drug discovery. Curr Drug Targets 2008; 9(12): 1048-53.
Oravcová, J, Lindner W. Protein–Drug Interactions. Encyclopedia of Analytical Chemistry: Applications, Theory and Instrumentation 2006; 1-26. doi:10.1002/9780470027318.a1627.
Kuhn M, Szklarczyk D, Pletscher-Frankild S, Blicher TH, Von Mering C, Jensen LJ, et al. STITCH 4: integration of protein–chemical interactions with user data. Nucleic Acids Res 2013; 42(D1): D401-7.
Zhou G, Soufan O, Ewald J, Hancock RE, Basu N, Xia J. NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res 2019; 47(W1): W234-41.
Frishman D, Valencia A. Modern genome annotation. The BioSapiens Network 2009; 213-38.
Hasan MR, Paul BK, Ahmed K, Bhuyian T. Design protein-protein interaction network and protein-drug interaction network for common cancer diseases: A bioinformatics approach. Inform Med Unlocked 2020; 18: 100311.
Jain AK, Dubes RC. Algorithms for clustering data. Englewood Cliffs: Prentice Hall, 1988. ISBN: 978-0-13-022278-7.
Wu J. Advances in K-means clustering: a data mining thinking. Springer Science & Business Media 2012;1-177.
Van Dongen S. Graph Clustering by Flow Simulation. In PhD Thesis University of Utrecht; 2000.
Vijesh N, Chakrabarti SK, Sreekumar J. Modeling of gene regulatory networks: a review. J Biomed Sci Eng 2013, 6(02): 223.
Szklarczyk D, Santos A, von Mering C, Jensen LJ, Bork P, Kuhn M. STITCH 5: augmenting protein–chemical interaction networks with tissue and affinity data. Nucleic Acids Res 2015; 44(D1): D380-D384.
Hasan MR, Paul BK, Ahmed K, Mahmud S, Dutta M, Hosen MS, et al. Computational analysis of network model based relationship of mental disorder with depression. Biointerface Res Appl Chem 2020; 10(5): 6293-305.
Kuhn M, Szklarczyk D, Franceschini A, Von Mering C, Jensen LJ, Bork P. STITCH 3: zooming in on protein–chemical interactions. Nucleic Acids Res 2011; 40(D1): D876-80.

References

WHO. Promoting mental health: Concepts, emerging evidence, practice: Summary report. World Health Org. 2004. Last access: 10 December, 2019.

WHO. The World Health Report 2001: Mental health: new understanding, new hope. World Health Org. 2001. Last access: 10 December, 2019.

WHO. The world health report 2002: reducing risks, promoting healthy life. World Health Org. 2002. Last access: 10 December, 2019

Vos T, Allen C, Arora M, Barber RM, Bhutta ZA, Brown A, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 2016; 388(10053): 1545-602.

Alonso J, Petukhova M, Vilagut G, Chatterji S, Heeringa S, íœstün TB, et al. Days out of role due to common physical and mental conditions: results from the WHO World Mental Health surveys. Mol Psychiatry 2011; 16(12):1234.

Murray CJ, Lopez AD, WHO. The global burden of disease: a comprehensive assessment of mortality and disability from diseases, injuries, and risk factors in 1990 and projected to 2020: summary. World Health Org. 1996; Last access: 10 December, 2019.

Patel V, Prince M. Global mental health: a new global health field comes of age. JAMA 2010; 303(19): 1976-77.

Torrey EF, Bowler A. Geographical distribution of insanity in America: evidence for an urban factor. Schizophr Bull 1990; 16(4): 591-604.

Cardno AG, Owen MJ. Genetic relationships between schizophrenia, bipolar disorder, and schizoaffective disorder. Schizophr Bull 2014; 40(3): 504-15.

Johnson W, Onuma O, Owolabi M, Sachdev S. Stroke: a global response is needed. Bull World Health Org 2016; 94(9): 634.

Redon J, Olsen MH, Cooper RS, Zurriaga O, Martinez-Beneito MA, Laurent S, et al. Stroke mortality and trends from 1990 to 2006 in 39 countries from Europe and Central Asia: implications for control of high blood pressure. Eur Heart J 2011; 32(11):1424-31.

Feigin VL, Forouzanfar MH, Krishnamurthi R, Mensah GA, Connor M, Bennett DA, et al. Global and regional burden of stroke during 1990–2010: findings from the Global Burden of Disease Study 2010. Lancet 2014; 383(9913): 245-55.

Asplund K, Karvanen J, Giampaoli S, Jousilahti P, NiemelaÌˆ M, Broda G, et al. Relative risks for stroke by age, sex, and population based on follow-up of 18 European populations in the MORGAM Project. Stroke 2009; 40(7): 2319-26.

Struijs JN, Van Genugten ML, Evers SM, Ament AJ, Baan CA, Van Den Bos GA. Future costs of stroke in the Netherlands: the impact of stroke services. Int J Tech. Assessment in Health Care 2006; 22(4): 518-24.

Wolfe CJ, Kohane IS, Butte AJ. Systematic survey reveals general applicability of guilt-by-association within gene coexpression networks. BMC Bioinformatics 2005; 6(1): 227.

Amar D, Safer H, Shamir R. Dissection of regulatory networks that are altered in disease via; differential co-expression. PLoS Comput Biol 2013, 9(3): e1002955.

Likas A, Vlassis N, Verbeek JJ. The global k-means clustering algorithm. Pattern Recognition 2003; 36(2): 451-61.

Shih YK, Parthasarathy S. Identifying functional modules in interaction networks through overlapping Markov clustering. Bioinformatics 2012; 28(18): i473-9.

Canduri F, de Azevedo J, Walter F. Protein crystallography in drug discovery. Curr Drug Targets 2008; 9(12): 1048-53.

Oravcová, J, Lindner W. Protein–Drug Interactions. Encyclopedia of Analytical Chemistry: Applications, Theory and Instrumentation 2006; 1-26. doi:10.1002/9780470027318.a1627.

Kuhn M, Szklarczyk D, Pletscher-Frankild S, Blicher TH, Von Mering C, Jensen LJ, et al. STITCH 4: integration of protein–chemical interactions with user data. Nucleic Acids Res 2013; 42(D1): D401-7.

Zhou G, Soufan O, Ewald J, Hancock RE, Basu N, Xia J. NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res 2019; 47(W1): W234-41.

Frishman D, Valencia A. Modern genome annotation. The BioSapiens Network 2009; 213-38.

Hasan MR, Paul BK, Ahmed K, Bhuyian T. Design protein-protein interaction network and protein-drug interaction network for common cancer diseases: A bioinformatics approach. Inform Med Unlocked 2020; 18: 100311.

Jain AK, Dubes RC. Algorithms for clustering data. Englewood Cliffs: Prentice Hall, 1988. ISBN: 978-0-13-022278-7.

Wu J. Advances in K-means clustering: a data mining thinking. Springer Science & Business Media 2012;1-177.

Van Dongen S. Graph Clustering by Flow Simulation. In PhD Thesis University of Utrecht; 2000.

Vijesh N, Chakrabarti SK, Sreekumar J. Modeling of gene regulatory networks: a review. J Biomed Sci Eng 2013, 6(02): 223.

Szklarczyk D, Santos A, von Mering C, Jensen LJ, Bork P, Kuhn M. STITCH 5: augmenting protein–chemical interaction networks with tissue and affinity data. Nucleic Acids Res 2015; 44(D1): D380-D384.

Hasan MR, Paul BK, Ahmed K, Mahmud S, Dutta M, Hosen MS, et al. Computational analysis of network model based relationship of mental disorder with depression. Biointerface Res Appl Chem 2020; 10(5): 6293-305.

Kuhn M, Szklarczyk D, Franceschini A, Von Mering C, Jensen LJ, Bork P. STITCH 3: zooming in on protein–chemical interactions. Nucleic Acids Res 2011; 40(D1): D876-80.

Author biographies is not available.