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Ayurgenomics: Understanding Human Individuality Through Integration of Ayurveda and Genomics for Stratified Medicine

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Ayurgenomics: Understanding Human Individuality Through Integration of Ayurveda and Genomics for Stratified Medicine Ayurgenomics: Understanding human individuality through integration of Ayurveda and Genomics for stratified medicine Mitali Mukerji Programme Director- CSIR-TRISUTRA (Translational Research and Innovative Science Through Ayurgenomics) & Scientist CSIR-IGIB Public health Modern medicine Ayurveda others Acknowledgements Prof Samir Brahmachari Dr. Bhavana Prasher MD Ayurveda (mentor & vision) Senior Scientist TRISUTRA @IGIB & Co-PI Ayurgenomics Dr. Sapna Negi, Dr. Shilpi Aggarwal, Tav Pritesh Sethi, Amit. K. Mandal, Sangeeta Khanna, Gaurav Garg, Mohd. Farhan, Tsering Stobdan, Pankaj Jha, Prashant K. Singh, Tanvir Ahmad, Atish Gheware, Pramod Gautam, Ankita Narang Collaborators Dr. Anurag Agrawal (IGIB), Dr. Qadar Pasha (IGIB), Dr Saurav Ghosh (ISI Kolkata) Dr. Sudha Purohit, Dr Shailaja Deshmukh, (Pune University), Abhay Sharma, Dr. Shantanu Sengupta, KEMHRC: Sanjay Juvekar, Bhushan Girase, Ankita Shrivastava, Rutuja Patil, Dheeraj Aggarwal, Bharat Choudhury Indian Genome Variation Consortium CSIR-TRISUTRA Team @IGIB DST & CSIR for financial support Genome: The Book of Life cool-look fusion look inversion Letters Sentences Chapter Book DNA Genes Chromosome Genome kool cool substitution colour deletion deletion cooler color substitution insertion coolor Human phenotypes, adaptation & genetic variations Vitamin D production Folate degradation SLC24A5 (skin pigmentation) Variation in UV genesVariation correlate of Skin Light-Skin Dark-Skin Colourwith “soaking skin pigmentationup the sun” The Human Genome Project : Elucidating the Book of life Paradigm shift from genetic to genomic medicine 2001 : First draft Reference Human Genome is “elusive” Genome Variations Dilemma in genotype to phenotype prediction : Sickle cell mutation Sickle cell mutation Normal RBC mutant Disease modifying genes Environment Primary mutation Intermediate patho-phenotype Patho-phenotypes Current challenge: Identifying patterns from possibilities Predictive, Preventive, Personalised & Participatory (P4) medicine • Prevalence of common and complex disorder & also combined monogenic disorders is 1-5% in all populations • Life time prevalence, Increased life expectancy , long term medications, side effects of therapeutic intervention further add to the global health burden • Major aim is to prevent disease or maintain quality of life GIS of a human P4 medicine vis-à-vis Ayurveda Aim: Maintenance of health in healthy & alleviation of disorders in diseased TRISUTRA (subject matter) Hetu/Causes • Each axis- as Independent science EEF & IEF • Interconnections – subject matter for Healthy Translational Medicine & Diseased • Stratified Approach – important for Predictive & Personalized medicine Linga/laksanas Aushadha/ Features Therapeutics C.Su.30/26 C.Su.1/24 Tridoshas: common organizing principle The proportion of Vata, Pitta & Kapha invariant in an individual Doshas are restrained within normal limits in health Perturbation in doshic proportions beyond threshold leads to disease Goal of treatment is restoration of basal levels Three most contrasting types are the most vulnerable c.su.20/9 Tridosha Variability contribute to inter-individual differences Vata Pitta Kapha 7 Prakriti types Proportion of doshas Tissue/organ/system Phenotypic features Prakriti Contrasting Prakriti types as phenotype scaffolds Somatotype Phototype Chronotype Physiology Metabolism Physical activities Sensory perception Personality traits Prakriti assesment Determinant of human individuality Ayurveda describes seven broad constitution types Journal of Genetics (In Press) , 2015 epigenetics genetics ethnicity familial geography time age Individual variation C. I.1/5 Tridosha : Temporal variations Journal of Genetics (In Press) , 2015 Prakriti types and disease susceptibility Vata Arrhythmia Speech disorder Developmental anomalies Neurological Psychiatric Pitta Skin disease Bleeding disorders ulcer Prakriti types and disease susceptibility---contd Kapha Atherosclerotic conditions, obesity Prakriti: Molecular correlates?? Metabolism Storage Cholesterol deposition Glucose Hemorrhagic disorders Glucose 6 phosphate Fructose 6 phosphate PITTA Obesity/CADKAPHA Fructose 1,1, 6 bisphosphate lysosome receptor PGAL DHAP LDL Cholestrol 1,1, 3 bisphosphoglycerate Neurological disorder 3 Hydroxy 3 3 phosphoglycerate VATA MethylGlutaryl CoA 2 phosphoglycerate Transport & Signalling Phosphoenolpyruvate Pyruvate Exploring the molecular basis of Prakriti Biochemical profiles – lipid Correlations of Signatures of profiles, hematocrit, liver Prakriti functions micronutrients etc with biological processes in different pathways & diseases Genome wide Expression Profiles Sub classify normal individuals from Russian & Indian population on basis of Genome wide Prakriti DNA variations Ayurveda Genomics Synthesis Inter-individual differences can be captured by Prakriti methods Identification of Predominant Prakritis (~3% in a population) Genetic Landscape of India Predominant Prakriti exhibit molecular differences Normal Disease Disease % Individuals P K V Units Vata regulates cell division and morphogenesis VATA PITTA KAPHA Ayurgenomics in genetic discoveries EGLN1 – oxygen sensor gene 1 EGLN expression Natives naturally adapted to Low Adaptation to hypoxia High high altitude conditions and Pitta have similar genotypes Pitta Kapha Adaptation?? Sea level dwellers who develop High Altitude Pulmonary Edema and Kapha have similar genotypes rs480902 (T/C) 0.28 0.77 ‘’T’’ allele freq. Ayurvedic Prakriti based screening can help identify individuals who would be at risk at high altitudes Ancient descriptions of high altitude !!!!! High altitudes Arid regions http://www.worldmapsonline.com http://pubs.usgs.gov/gip/deserts Different base-line thresholds in Prakriti can modulate diseases Pitta Replicated in multiple studies in HA adaptation EGLN1 as a therapeutic target Therapeutic target where hypoxia is cause or consequence • Cartilage repair • Wound healing improvement • Arteriogenic phenotype, • Brain tumour • Renal anemia • Cardiovascular disease • Therapeutic revascularization after visceral surgery Kapha • Myocardial ischemia • Recovery from stroke • Treatment of inflammatory diseases Genetic cross-talk between hypoxia and hemostasis axis in Prakriti vWF high Thrombosis risk Pitta Selection in Pitta for VWF variation Thrombosis linked allele in vWF significantly low in Pitta compared to Kapha 0.4 C vWF (rs1063856) 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 Kapha K P V VPK IE Pitta Bleeding atherosclerotic conditions Kapha Hypoxia and Hemostasis axes linked in predominant Prakriti Validation Chemical inhibition of EGLN1: clotting risk EGLN1 siRNA 0.5 * 0.4 0.3 0.2 0.1 VWF concentration (ng/ul of plasma) of (ng/ul concentration VWF 0.0 Vehicle D5 D10 Mice Groups Increased VWF Increased platelet activation Reduced bleeding time Increased platelet activation Thrombosis linked allele fixed in high altitude Ayurveda : Blood characteristics, hemopoeisis & inter- individual variability Predisposition Pitta Bleeding atherosclerotic conditions Kapha Inter-individual variability in health & implication in disease VATA PITTA KAPHA Stratification of Healthy based on prakriti Axes of variation in Prakriti 3500 year old Knowledge Hypoxia responsiveness (EGLN1 –HIF1-vWF) Hypoxia and Hemostasis axes linked through Prakriti EGLN1-HIF axis in HAA/HAPE/asthma VWF levels also differ Hypoxia axis differentially in VADU cohort regulated in VADU cohort CSIR’s Ayurgenomics Unit - TRISUTRA Translational Research and Innovative Science Through Ayurgenomics Inter-disciplinary networked centre for Ayurgenomics research established and functional 14,000 individuals from diverse ethnic and geo-climatic regions are being studied CSIR-TRISUTRA Team @ IGIB PI (Genomics): Dr. Mitali Mukerji, • PI (Ayurveda): Dr. Bhavana Prasher Model System – PI: Bhavana Prasher, • Phenome Stratification and objective measures – Anurag Agrawal; VP Singh – PI: Bhavana Prasher – PS Mohau Maulik – PS: Arvind Kumar, Bharat Krushna – Atish Gheware – PA: Pratibhan, Ankita Srivastava • Modelling of Prakriti – Vivek Natrajan, Pramod Gautum, Tav Pritesh, Shilpi Aggarwal – PI: Bhavana Prasher, Debasis Dash • Exome and Metagenome – Pradeep Tiwari, Rintu Kutum, Tav Pritesh Sethi - PIs Debasis Dash, Mitali Mukerji – Sourav Ghosh – PS: Rajesh Pandey • Biorepository – Ankita Narang, Anupam Mondal, Pushkar Dakle, – Binuja Varma (PS), Mahua Maulik (PS) Rutuja Patil, Anubhuti Tripati, Roshini Thomas, • Genotyping • Data Repository – Mitali Mukerji , Binuja Varma (PS) – Debasis Dash – Anubhuti Tripati, Roshini Thomas, Pradeep Tiwari , – Vijetha, Shazia Uma Sunil Anwardekar, Ankita Narang, Pramod • Sample processing at sites Gautam, Samarth – Binuja Varma (PS), Rutuja Patil, Priyanka • Gene Expression & Biochemical studies Bhat, Pratibha Sambrekar, Saheli Banerjee, – Mitali Mukerji, Bhavana Prahsher Ranbala Kumar – Mahua Maulik (PS), Binuja Varma (PS) Shilipi – Integrative Analysis mentors and team Aggarwal, Rintu Kutum, Pradeep Tiwari, Amit Mitali Mukerji, Bhavana Prasher, Debasis Dash, Mandal, Tav Pritesh Sethi, Anubhuti Tripati Vivek Natarajan .
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