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

Prof Samir Brahmachari (mentor & vision)

Dr. Bhavana Prasher MD Ayurveda 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

Acknowledgements

Letters DNA

Sentences Genes

Chapter Chromosome

Book Genome

colour

color

coolor

cooler

cool

kool

look

cool-look

Genome: The Book of Life

deletion

deletion

insertion

substitution

substitution

inversion

fusion

Variation of Skin Colour “soaking

up the sun”

Vitamin D production Folate degradation

UV Light-Skin Dark-Skin

Human phenotypes, adaptation & genetic variations

SLC24A5 (skin pigmentation)

Variation in genes correlate

with skin pigmentation

Paradigm shift from genetic to genomic medicine

2001 : First draft

The Human Genome Project : Elucidating the Book of life

Reference Human Genome is “elusive”

Genome Variations

Dilemma in genotype to phenotype prediction : Sickle cell mutation

Sickle cell mutation

Disease modifying genes

Primary mutation

Intermediate patho-phenotype

Patho-phenotypes

Environment

Normal RBC mutant

Current challenge: Identifying patterns from possibilities

GIS of a human

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

C.Su.30/26

P4 medicine vis-à-vis Ayurveda

Aim: Maintenance of health in healthy & alleviation of disorders in diseased

Hetu/Causes

EEF & IEF

Aushadha/

Therapeutics

Linga/laksanas

Features

TRISUTRA (subject matter)

• Each axis- as Independent science

• Interconnections – subject matter for

Translational Medicine

• Stratified Approach – important for Predictive & Personalized medicine

Healthy &

Diseased

C.Su.1/24

Tridoshas: common organizing principle

Three most contrasting types are the most vulnerable

c.su.20/9

The proportion of Vata, Pitta & Kapha invariant in an individual

Perturbation in doshic proportions beyond threshold leads to disease

Doshas are restrained within normal limits in health

Goal of treatment is restoration of basal levels

Vata

Pitta

Kapha

Tissue/organ/system Phenotypic features

Proportion of doshas

Prakriti

Tridosha Variability contribute to inter-individual differences

7 Prakriti types

Somatotype

Phototype

Chronotype

Personality traits

Physiology

Metabolism

Sensory perception

Physical activities

Contrasting Prakriti types as phenotype scaffolds

Prakriti assesment

C. I.1/5

ethnicity familial geography time age

Individual variation

Determinant of human individuality

genetics

epigenetics

Ayurveda describes seven broad constitution types

Journal of Genetics (In Press) , 2015

Tridosha : Temporal variations

Journal of Genetics (In Press) , 2015

Prakriti types and disease susceptibility

Vata

Pitta

Arrhythmia

Speech disorder

Developmental

anomalies

Neurological

Psychiatric

Skin disease Bleeding disorders ulcer

Kapha

Atherosclerotic conditions, obesity

Prakriti types and disease susceptibility---contd

VATA

PITTA

Neurological disorder

Transport & Signalling

KAPHA Obesity/CAD

3 Hydroxy 3 MethylGlutaryl CoA

Cholestrol

LDL LDL receptor receptor

lysosome lysosome

Cholesterol deposition

3 Hydroxy 3 MethylGlutaryl CoA

Cholestrol

Storage

Prakriti: Molecular correlates??

Glucose Glucose

Glucose 6 phosphate Glucose 6 phosphate

Fructose 6 phosphate Fructose 6 phosphate

Fructose 1, 6 bisphosphate Fructose 1, 6 bisphosphate

DHAP DHAP PGAL PGAL

1, 3 bisphosphoglycerate 1, 3 bisphosphoglycerate

3 phosphoglycerate 3 phosphoglycerate

2 phosphoglycerate 2 phosphoglycerate

Phosphoenolpyruvate Phosphoenolpyruvate

Pyruvate Pyruvate

Metabolism

Hemorrhagic disorders

Sub classify normal individuals from Russian & Indian

population on basis of Prakriti

Biochemical profiles – lipid profiles, hematocrit, liver

functions micronutrients etc

Genome wide Expression

Profiles

Genome wide DNA variations

Correlations of Signatures of Prakriti

with biological processes in different pathways & diseases

Exploring the molecular basis of Prakriti

Ayurveda Genomics Synthesis

Inter-individual differences can be captured by Prakriti methods

Genetic Landscape of India

Identification of Predominant Prakritis (~3% in a population)

Normal

K P V

Disease Disease

Predominant Prakriti exhibit molecular differences

VATA KAPHA PITTA

Vata regulates cell division and morphogenesis

% In

div

idu

als

Units

Kapha Pitta

rs480902 (T/C)

0.28 0.77

‘’T’’ allele freq.

Sea level dwellers who develop High Altitude Pulmonary Edema and Kapha have similar genotypes

Natives naturally adapted to high altitude conditions and Pitta have similar genotypes

Ayurgenomics in genetic discoveries

Ayurvedic Prakriti based screening can help identify individuals who would be at risk at high altitudes

Adaptation??

EGLN

1

exp

ress

ion

EGLN1 – oxygen sensor gene

Low High Adaptation to hypoxia

Arid regions

http://pubs.usgs.gov/gip/deserts

High altitudes

http://www.worldmapsonline.com

Ancient descriptions of high altitude !!!!!

Different base-line thresholds in Prakriti can modulate diseases

Pitta

Kapha

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

• Myocardial ischemia

• Recovery from stroke

• Treatment of inflammatory

diseases

EGLN1 as a therapeutic target

Replicated in multiple studies in HA adaptation

vWF high Thrombosis risk

Thrombosis linked allele in vWF significantly low in Pitta compared to Kapha

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

K P V VPK IE

C vWF (rs1063856)

Pitta Bleeding

atherosclerotic conditions Kapha

Selection in Pitta for VWF variation Pitta

Kapha

Genetic cross-talk between hypoxia and hemostasis axis in Prakriti

Hypoxia and Hemostasis axes linked in predominant Prakriti Validation

Vehicle D5 D100.0

0.1

0.2

0.3

0.4

0.5 *

Mice Groups

VW

F c

on

ce

ntr

ati

on

(n

g/u

l o

f p

lasm

a)

Increased VWF Increased platelet activation Reduced bleeding time

Chemical inhibition of EGLN1: clotting risk EGLN1 siRNA

Increased platelet activation

Thrombosis linked allele fixed in high altitude

Pitta Bleeding

atherosclerotic conditions Kapha

Predisposition

Ayurveda : Blood characteristics, hemopoeisis & inter- individual variability

Inter-individual variability in health & implication in disease

3500 year old Knowledge

VATA

PITTA KAPHA

Stratification of Healthy based on prakriti Axes of variation in Prakriti

EGLN1-HIF axis in HAA/HAPE/asthma

Hypoxia responsiveness (EGLN1 –HIF1-vWF)

Hypoxia axis differentially regulated in VADU cohort

Hypoxia and Hemostasis axes linked through Prakriti

VWF levels also differ in VADU cohort

14,000 individuals from diverse ethnic and geo-climatic regions are being studied

Translational Research and Innovative Science Through Ayurgenomics

CSIR’s Ayurgenomics Unit - TRISUTRA

Inter-disciplinary networked centre for Ayurgenomics research established and functional

CSIR-TRISUTRA Team @ IGIB

• Phenome Stratification and objective measures

– PI: Bhavana Prasher

– PS: Arvind Kumar, Bharat Krushna

– PA: Pratibhan, Ankita Srivastava

– Vivek Natrajan, Pramod Gautum, Tav Pritesh, Shilpi Aggarwal

• Exome and Metagenome

- PIs Debasis Dash, Mitali Mukerji

– PS: Rajesh Pandey

– Ankita Narang, Anupam Mondal, Pushkar Dakle, Rutuja Patil,

• Genotyping

– Mitali Mukerji , Binuja Varma (PS)

– Anubhuti Tripati, Roshini Thomas, Pradeep Tiwari , Uma Sunil Anwardekar, Ankita Narang, Pramod Gautam, Samarth

• Gene Expression & Biochemical studies

– Mitali Mukerji, Bhavana Prahsher

– Mahua Maulik (PS), Binuja Varma (PS) Shilipi Aggarwal, Rintu Kutum, Pradeep Tiwari, Amit Mandal, Tav Pritesh Sethi, Anubhuti Tripati

• Model System

– PI: Bhavana Prasher,

– Anurag Agrawal; VP Singh

– PS Mohau Maulik

– Atish Gheware

• Modelling of Prakriti

– PI: Bhavana Prasher, Debasis Dash

– Pradeep Tiwari, Rintu Kutum, Tav Pritesh Sethi

– Sourav Ghosh

• Biorepository

– Binuja Varma (PS), Mahua Maulik (PS) Anubhuti Tripati, Roshini Thomas,

• Data Repository

– Debasis Dash

– Vijetha, Shazia

• Sample processing at sites

– Binuja Varma (PS), Rutuja Patil, Priyanka Bhat, Pratibha Sambrekar, Saheli Banerjee, Ranbala Kumar

– Integrative Analysis mentors and team

Mitali Mukerji, Bhavana Prasher, Debasis Dash, Vivek Natarajan

PI (Genomics): Dr. Mitali Mukerji, PI (Ayurveda): Dr. Bhavana Prasher