Shankar Subramaniam

shsubramaniam@ucsd.edu genome.ucsd.edu

Shankar Subramaniam is a Distinguished Professor of Bioengineering, Computer Science and Engineering, Cellular and Molecular Medicine, Chemistry and Biochemistry and Nano Engineering. He was recently the Chair of the Bioengineering Department at the University of California at San Diego (2008-13). He holds the inaugural Joan and Irwin Jacobs Endowed Chair in Bioengineering and Systems Biology. He was the Founding Director of the Bioinformatics Graduate Program at the University of California at San Diego. Prior to moving to UC San Diego, Dr. Subramaniam was a Professor of Biophysics, Biochemistry, Molecular and Integrative Physiology, Chemical Engineering and Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign (UIUC). In 2013 he was elected as a Fellow of AAAS. In 2002 he received the Genome Technology All Star Award. He is a fellow of the AIMBE and is a recipient of Smithsonian Foundation and Association of Laboratory Automation Awards and his research work is described below. In 2008 he was awarded the Faculty Excellence in Research Award at the University of California at San Diego. In 2011 he was appointed as a Distinguished Scientist at the San Diego Supercomputer Center. In 2014 he was appointed as a Distinguished Professor. He serves on the External Advisory Boards for several Bio/Biomedical Engineering Departments including Johns Hopkins U., Case Western Reserve U., U. Penn, Rice U. and UT Austin. He is currently an overseas advisor for the Department of Biotechnology of the Government of India. In 2012, he was elected as the Chair of the College of Fellows of AIMBE. He also serves on the Scientific Advisory Board of Janssen Pharmaceuticals (the research arm of Johnson and Johnson). He has served on the Scientific Councils of NIGMS and NHGRI (NIH Institutes) and as a Chair of 3 distinct study sections at the National Institutes of Health. Subramaniam's innovative work has major impact on research and development in academia and industry by allowing the synthesis of complex biological and medical information from genes and molecules into integrated knowledge at cellular and system levels, thus providing important basis for drug discovery and innovation. He was a pioneer in bioinformatics with his development of the Biology Workbench, the first of its kind in web based infrastructures. He has fostered training and research in systems biology and bioinformatics at the national level, serving on the NIH Director’s Advisory Committee on Bioinformatics and played a key role in the formulation of the NIH Director’s Roadmap which places a major emphasis on the use of quantitative approaches of engineering to biomedical research in health and disease. He has been instrumental in raising national awareness of the roles of these engineering approaches to biomedical research. He founded the UCSD Bioinformatics program and was Chair of the nationally top-ranked bioengineering program from 2008-2013. Subramaniam has collaborated with colleagues in clinical medicine to elucidate the molecular and genomic basis of the pathogenesis of diabetes, inflammation, atherosclerosis and myopathies by using modern approaches of systems biology and bioinformatics to analyze physiological and pathophysiological data, leading to the development of novel therapeutic measures and drug discovery.

Systems Engineering Approaches for Nextgen

Healthcare

Shankar Subramaniam

Jacobs School of Engineering

13 May 2016

The Digital Human

- Identifying the genotype

- Genotype-Phenotype

Mapping

- The Human Microbiome

- Markers of Pathology

- Response to Pathology &

Treatment

Design of Devices/Sensors for Invasive and Non-

Invasive Measurements

- Wearable Sensors

- Remote Sensors

- Molecular Measurements (Omics)

- Medical Devices

Individualized Health

- Individualized Systems

Pharmacology

- Regenerative Engineering –

Treating self with self

- Lifestyle Recommendations based

on the Digital Human

Systems Engineering&

Medicine

Envisioning the Future of Healthcare

Cell TissueClinical

medicineOrganRNA

3

The Living System and Multi-Scale ModelsGenes Cell Tissue

ClinicalmedicineOrganProteinsRNA

Adapted from Hunter and Borg. Nat Rev Mol Cell Biol 2003

Ritchie et al Nat Rev Genet 2015

Exemplars of recent Systems Approaches to Human Healthcare

How can we use a systems perspective for identifying a drug target?

Glioblastoma as a case study

Friedmann-Morvinski D, Bhargava V, Gupta S, Verma IM, Subramaniam S. Identification of therapeutic targets for glioblastoma by network analysis. Oncogene. 2016 Feb 4;35(5):608-20.

7

Experimental Design

RNA sequencing:–Low amount of mRNA because the cells were derived from mice (primaryculture)–Transcriptome profiling of enriched populations of ESCs, NSCs, neurons andastrocytes along with transformed neurons and astrocytes using DP-seqBhargava et al., Sci Rep. 2013; 3:1740

Functional Network

• Databases used:Transfac, STRING,HPRD

• Genes Up‐regulatedin DedifferentiatedNeurons wereprojected on the PPInetwork.

• Gene interactionnetwork reconfirmsinvolvement offollowing pathways:

– Cell cycle– Focal adhesion– Wnt pathway

Focal AdhesionWnt Pathway

Cell Cycle

Functional Network

• Databases used:Transfac, STRING,HPRD

• Genes Up‐regulatedin DedifferentiatedNeurons wereprojected on the PPInetwork.

• Gene interactionnetwork reconfirmsinvolvement offollowing pathways:

– Cell cycle– Focal adhesion– Wnt pathway

Focal AdhesionWnt Pathway

Cell Cycle

Blocking OPN compromises dedifferentiation of transformed neurons. (a) Schematic representation of the lentivectors. In the presence of Cre recombinase, the loxP-RFP-loxP cassette in the HRas-shp53 lentivector is cut out, and only GFP is expressed in the transformed cells (NSynR53). (c) Bright and immunofluorescent images of NSynR53-shIRR and NSynR53-shOPN transduced cells. Green=GFP, fluorescent reporter in HRas-shp53lentivector

Friedmann-Morvinski et al., 2016

x10

Anti‐OPN (1:100)Control Anti‐OPN (1:50)

Astrocytes transduced with LV‐HRAS‐shp53, switched to stem cell media and incubated for 5 days in the presence of neutralizing anti‐OPN antibody

Why does a drug fail? A systems perspective

Caldwell A, Yuan S, Wagner SL, Subramaniam S. -secretase modulators revert phenotype in Alzheimer’s patients with presenilin mutations using a complex network of interactions and phenotypes. 2016 (in preparation).

Alzheimer’s Disease

Krystic and Knuesel. 2013 Nat. Rev. Neurol. 9, 25-34

• AD is a form of dementia (60-70% ofdementia cases) affecting ~50mpeople worldwide (~3m/year)

• Aberrant APP processing to form Aβpeptides and subsequent misfoldinglead to senile plaques

• Tau hyper-phosphorylation leads toneurofibrillary tangles

Gamma Secretase Inhibitor: Semagacestat

Semagacestat

• Developed by Eli Lilly in the late 2000s to lower Aβ by blocking γ-Secretase cleavageof APP

• Showed significant decrease in levels of Aβ peptides and tau hyperphosphorylation• Phase III Trial was halted in 2013, after drug was shown to induce cognitive

worsening compared to placebo• Post-trial, it was presumed that the drug’s main complication was from blocking Notch

cleavage and subsequent Notch Signaling

• Blocking γ-Secretase activity has adverse side effects• Could γ-Secretase be modulated to allow for proper cleavage at the APP ε-site, as

well as alternative γ-Secretase substrates like Notch, but prevent cleavage at APP γ-sites that forms Aβ40 and Aβ42?

Can Gamma Secretase activity be modulated?

Gamma Secretase Modulation?

Wagner Lab, UCSD

• The GSM BPN-15606, developed bySteve Wagner, allows for ε-sitecleavage of γ-Secretase whilepreventing cleavage at the γ42 andγ40 sites that produce Aβ40 and Aβ42

iPSC-derived Neurons as a model system to test AD therapeutics

Yuan Lab, UCSD

Fibroblasts fromNDC and PS1 donor patients

HiPSC Neuron (>90%) andGlial cells

CD24+/CD184-/CD44-

selectionOct3/4, Klf4, Sox2, c-myc

Experimental Design

• Treat Non-Demented Control (NDC) and PS1 (A246E mutation) HiPSC-derivedneurons with either vehicle (DMSO), GSM (BPN-15606), or GSI (Semagacestat)

• PE75 RNA-Seq on Illumina HiSeq4000, technical triplicates• Reads aligned with Top Hat 2.0• Quantification and Differential Expression with Cufflinks and Cuffdiff in illumina’s

Basespace Cloud-based Analytics Platform

Gary Schroth, illumina

A potential model for PS1-mediated AD progression

PSEN1*

Akt

Aberrant Cell Cycle Reentry

Senescence

Notch REST

NeuronalDe-differentiation

*A246E mutation

p53

p21p16

Hes1PTEN

Notch:PI3K

PS1 mutation-mediated Neurodegeneration

HDAC1RCOR2

Hes1

miR-9miR-124

LET-7

GSK3βCyclinD1

CDK6E2F1

A potential model for PS1-mediated AD progression

PSEN1*

Akt

Aberrant Cell Cycle Reentry

Senescence

Notch REST

NeuronalDe-differentiation

*A246E mutation

p53

p21p16

Hes1PTEN

Notch:PI3K

PS1 mutation-mediated Neurodegeneration

HDAC1RCOR2

Hes1

miR-9miR-124

LET-7

GSK3βCyclinD1

CDK6E2F1

GSM

GSM

GSM

GSM

GSM

What are systems-level markers of pathology that can be detected by relatively non-invasive mechanisms?

A study of fatty liver disease

Gorden DL, Myers DS, Ivanova PT, Fahy E, Maurya MR, Gupta S, Min J, Spann NJ, McDonald JG, Kelly SL, Duan J, Sullards MC, Leiker TJ, Barkley RM, Quehenberger O, Armando AM, Milne SB, Mathews TP, Armstrong MD, Li C, Melvin WV, Clements RH, Washington MK, Mendonsa AM, Witztum JL, Guan Z, Glass CK, Murphy RC, Dennis EA, Merrill AH Jr, Russell DW, Subramaniam S, Brown HA. Biomarkers of NAFLD progression: a lipidomics approach to an epidemic. J Lipid Res. 2015 Mar;56(3):722-36. Zarrinpar A, Gupta S, Maurya MR, Subramaniam S, Loomba R. Serum microRNAs explain discordance of non-alcoholic fatty liver disease in monozygotic and dizygotic twins: a prospective study.Gut. 2015 May 22. pii: gutjnl-2015-309456.

Introduction: Liver• Largest internalorgan

• Complex anatomyand physiology

• Diverse functions• Digestion,

metabolism,detoxification,protein production,immune response,coagulation, etc

writepass.co.uk

Cohen et al. 2011, Science

Reversible stages Non-reversible stages

Liver Study

Common metabolites between liver and plasma. Major lipid classes, DAGs, TAGs, CEs, and GPLs are almost all polyunsaturated while sphingo‐lipids are primarily longer chain species

LDA on plasma (A) liver species (B) with top 80 ANOVA scores classified samples by disease status. Blue: steatosis, black: normal; orange: steatohepatitis; red: cirrhosis

What are systems level mechanisms of a pathology associated with development?

Biliary Atresia – the bile duct defect

Example 1: Biliary Atresia, a liver disease

• Any disturbance or blockagecan cause BA

• Leading cause of livertransplantation in infants andchildren• 1:15,000 incidence

• Unknownetiology/pathogenesis

• Different forms• Acquired (~80%)• Congenital• Cystic

• Kasai operation (Fig)• Still requires liver transplantation

• Investigate the complexmechanism of biliary atresia

http://krames.sjmctx.com/HealthSheets/3,S,88701

Transcriptomic Changes

Exonic Changes

Known SNPs

Target Sequencing

Novel SNPs

Developmental Genes Ciliary Genes

Novel SNPs

Variants

Experimentally validated

GenomicChanges

AF>0.4AN>10

dbSNP 138

AF>0.4AN>10

dbSNP 138

(B) (B)

enrichment SYSCILIA Gold Standard

Multi-ome of Children with Biliary Atresia

SNPDevelopmentImmunologicInflammationFibrosis

dGenes

Functional mapping of aBiliary Atresia network

Size of a node ∝ number ofinteracting genes

SNPNeighborCiliarywExomeGWAS/RNAseq/tSeq

dGenes

Highly condensed and interpretable network

Biliary atresia network

Size of a node ∝ number ofinteracting genes

ARF6

Can a systems view be brought to bear on vaccinology? How can we improve efficacy of vaccine in elderly population?

A case study with influenza vaccination

Nakaya HI, Hagan T, Duraisingham SS, Lee EK, Kwissa M, Rouphael N, Frasca D, Gersten M, Mehta AK, Gaujoux R, Li GM, Gupta S, Ahmed R, Mulligan MJ, Shen-Orr S, Blomberg BB, Subramaniam S, Pulendran B. Systems Analysis of Immunity to Influenza Vaccination across Multiple Years and in Diverse Populations Reveals Shared Molecular Signatures. Immunity. 2015 Dec 15;43(6):1186-98.

Vaccine Response Prediction

Signatures associated with the antibody response are consistent across seasons

Monocytes are increased and exhibit proinflammatory status in elderly subjects

THE DYNAMIC HUMANSCOPEPERSONALIZING DIAGNOSIS AND TREATMENTSYSTEMS PHARMACOLOGY

ENGINEERING & HEALTH CARE – THE FUTURE

Human Organs on a Chip

Personalized Regenerative Engineering

Pathways for ESC Cardiogenesis

Nodal, Wnt,

(BMP)

BMP, Wnt (canonical and

non-canonical) FGF

Nodal, TGF

8-14Day 0 2 4 5

EndodermSignals = Cerberus; VEGF

Human ESCs or iPSCs

Mature Ventricular

Cardiomyocytes

Uncommitted mesoderm (Bra/T+)

Cardiac progenitors (Nkx2.5+,Tbx5+, Isl1+

Mef2c+)

Fetal myocyteMHC+)

Cardiogenic mesoderm (Gsc+)

Cardiac precursors (MesP1,KDR+)

Cardiac SpecificationMesoderm Patterning

Mesoderm Induction

Cardiac Differentiation

Myocyte Maturation

Wnt AntagonistDickkopf-1

14 days-6 years

Engineering mental health

Human pluripotent stem cells from normal and Parkinson patients

The personalized model!iPSC Differentiation to Neuronscollaboration with Buckley Lab 

(Oxford University)

Neuronal Differentiation from iPSc

http://www.stemcell.com/~/media/Technical%20Resources/8/6/3/2/6/CSC%20Differentiation%20Wallchart.pdf

A model that illustrates neuronal cell fate

The Digital Human

- Identifying the genotype

- Genotype-Phenotype

Mapping

- Markers of Pathology

- Response to Pathology &

Treatment

Design of Devices/Sensors for Invasive and Non-

Invasive Measurements

- Wearable Sensors

- Remote Sensors

- Molecular Measurements (Omics)

- Medical Devices

Individualized Health

- Individualized Systems

Pharmacology

- Regenerative Engineering –

Treating self with self

- Lifestyle Recommendations based

on the Digital Human

Systems Engineering&

Medicine

Envisioning the Future of Healthcare