Ayurgenomics: Understanding human individuality through integration of 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 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

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 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 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