“Using Data Analytics to Discover the 100 Trillion Bacteria Living Within Each of Us”

Invited Talk

New Applications of Computer Analysis to Biomedical Data Sets

QB3 Seminar

San Francisco, CA

May 28, 2015

Dr. Larry Smarr

Director, California Institute for Telecommunications and Information Technology

Harry E. Gruber Professor,

Dept. of Computer Science and Engineering

Jacobs School of Engineering, UCSD

http://lsmarr.calit2.net1

I Have Turned My Body into a Genomic and Biomarker Observatory

One Blood DrawFor MeCalit2 64 Megapixel VROOM

Over 100 Blood and Stool Biomarker Time Series

Only One of My Blood Measurements Was Far Out of Range--Indicating Chronic Inflammation

Normal Range <1 mg/L

27x Upper Limit

Complex Reactive Protein (CRP) is a Blood Biomarker for Detecting Presence of Inflammation

Episodic Peaks in Inflammation Followed by Spontaneous Drops

Adding Stool Tests RevealedOscillatory Behavior in an Immune Variable Which is Antibacterial

Normal Range<7.3 µg/mL

124x Upper Limit

Lactoferrin is a Protein Shed from Neutrophils -An Antibacterial that Sequesters Iron

TypicalLactoferrin Value for Active Inflammatory

Bowel Disease (IBD)

Dynamical Innate and Adaptive Immune OscillationsFrom Stool Samples

Normal <600

Innate Immune System

Normal 50 to 200

Adaptive Immune System

Methods Needed for Biomarker Time Series Correlations -Adding 40 Microbial Ecology Time Series

Immune &Inflammation

Variables

Weekly Symptoms

PharmaTherapies

StoolSamples

How Will Detailed Knowledge of Microbiome Ecology Radically Change Medicine and Wellness?

99% of Your DNA Genes

Are in Microbe CellsNot Human Cells

Your Body Has 10 Times As Many Microbe Cells As Human Cells

Challenge: Map Out Microbial Ecology and Function

in Health and Disease States

For Deep Analysis of Changes in the Gut Microbiome EcologyOur Team Compared a Healthy Population to Patients with Disease

5 Ileal Crohn’s Patients, 3 Points in Time

2 Ulcerative Colitis Patients, 6 Points in Time

“Healthy” Individuals

Source: Jerry Sheehan, Calit2Weizhong Li, Sitao Wu, CRBS, UCSD

Total of 2.7 Trillion DNA Bases

Inflammatory Bowel Disease Patients

250 Subjects1 Point in Time

7 Points in Time

Larry Smarr(Colonic Crohn’s)

Example: Inflammatory Bowel Disease (IBD)

To Map Out the Dynamics of Autoimmune Microbiome Ecology Couples Next Generation Genome Sequencers to Big Data Supercomputers

Source: Weizhong Li, UCSD

Our team used 25 CPU-yearsto compute

comparative gut microbiomesstarting from

2.7 trillion DNA bases of my samples

and healthy and IBD controls

Illumina HiSeq 2000 at JCVI

SDSC Gordon Data Supercomputer

We Found Major State Shifts in Microbial Ecology PhylaBetween Healthy and Two Forms of IBD

Most Common Microbial

Phyla

Average Healthy

Average Ulcerative Colitis

Average Colonic Crohn’s Disease

Average Ileal Crohn’s Disease

Collapse of BacteroidetesExplosion of Actinobacteria

Explosion of Proteobacteria

Hybrid of UC and CDHigh Level of Archaea

Based on ~10,000 Bacteria, Archaea, VirusesWhose Genomes are Known

Dell Analytics Separates The 4 Patient Types in Our DataUsing Our Microbiome Species Data

Source: Thomas Hill, Ph.D.Executive Director Analytics

Dell | Information Management Group, Dell Software

Healthy

Ulcerative Colitis

Colonic Crohn’s

Ileal Crohn’s

I Built on Dell Analytics to Show Dynamic Evolution of My Microbiome Toward and Away from Healthy State – Colonic Crohn’s

Source: Thomas Hill, Ph.D.Executive Director Analytics

Dell | Information Management Group, Dell Software

I Built on Dell Analytics to Show Dynamic Evolution of My Microbiome Toward and Away from Healthy State – Colonic Crohn’s

Healthy

Ileal Crohn’s

Seven Time Samples Over 1.5 Years

Colonic Crohn’s

Now Extending 7 to 40 Time Samples

Large Changes in Genus Relative Abundances ObservedOver the Seven Smarr Samples (1.5 Years)-Clearly Not Healthy!

Can We Extend These Approaches from Hundreds to Thousands by Using Crowdsourcing Gut Microbiome Sequencing?

Source: Ubiome provided kits and taxonomic analysisGraph by Larry Smarr

Three Years of Larry Smarr’s

Gut Microbiome(38 Samples)

Phyla Distribution Analyzed

Using Ubiome

Applying Ayasdi to Ubiome Gut Microbiome Genus Relative Abundance Time Series

Analysis by Mehrdad Yazdani, Calit2; Devi Ramanan, Ubiome

Metric: Variance Normalized Euclidean with PCA Lensof Ecology Change Over All Time Pairs

Sorted by KS stat

Apply Ayasdi Statistical Tests

Using Ayasdi to Discover Abrupt Changes in Timein Gut Microbiome Ecology

Large Ecology ChangeIn Short Time Interval

Analysis by Mehrdad Yazdani, Calit2; Devi Ramanan, Ubiome

Abrupt Changes Discovered inGenus Faecalibacterium Relative Abundance Time Series

Source: Ubiome provided kits and taxonomic analysisGraph by Larry Smarr

35x

Genetic Sequencing of Humans and Their MicrobesIs a Huge Growth Area and the Future Foundation of Medicine

Source: @EricTopolTwitter 9/27/2014

Thanks to Our Great Team!

UCSD Metagenomics TeamWeizhong LiSitao Wu

Calit2@UCSD Future Patient TeamJerry SheehanTom DeFantiKevin PatrickJurgen SchulzeAndrew PrudhommePhilip WeberFred RaabJoe KeefeErnesto Ramirez

JCVI TeamKaren NelsonShibu YoosephManolito Torralba

SDSC TeamMichael NormanIlkay AltintasShweta PurawatMahidhar Tatineni Robert Sinkovits

UCSD Health Sciences TeamWilliam J. SandbornElisabeth EvansJohn ChangBrigid BolandDavid Brenner

Dell/R Systems and Dell AnalyticsBrian KucicJohn ThompsonTom Hill