Quantifying Your Dynamic Human Body (Including Its Microbiome), Will Move Us From a Sickcare System to a Healthcare System

“Quantifying Your Dynamic Human Body (Including Its Microbiome), Will Move Us

From a Sickcare System to a Healthcare System”

Invited Presentation Microbiology and the Microbiome and the Implications for Human Health

Analytic, Life Science & Diagnostic Association (ALDA) 2016 Senior Management ConferenceHalf Moon Bay, CA

October 3, 2016

Dr. Larry SmarrDirector, California Institute for Telecommunications and Information Technology

Harry E. Gruber Professor, Dept. of Computer Science and Engineering

Jacobs School of Engineering, UCSDhttp://lsmarr.calit2.net

1

Conference Abstract

“For the past several years, Dr. Smarr has been engaged in a computer-aided study of his body. Larry has been charting his bodily input and output, as well as taking periodic blood and stool tests for five years as part of a new generation of medical research that is focusing on early detection of disease states. Studying the microbiome is part of this area of medical research since there are 100 times as many genes on the microbial DNA as your human DNA and yet this is currently outside of medical practice. Larry believes that over the next 10-20 years efforts like his will enable scientists to create computational models of your body, grounded in you and your microbiome's genome, and—using longitudinal time series of data refreshed continually with measurements from your body and collated with similar readings from millions of other similarly monitored bodies. Mining this enormous database, software will produce detailed guidance about diet, supplements, exercise, medication, or treatment—guidance based on a precise reading of your own body’s peculiarities and its status in real time. And, at that time, says Larry, you will have a scientific basis for medicine and the current US "Sickcare" system will be replaced by a true "Healthcare" system.

From One to a Trillion Data Points Defining Me in 15 Years:The Exponential Rise in Body Data

Weight

Blood BiomarkerTime Series

Human Genome SNPs

Microbiome MetagenomicTime Series

Improving Body

Discovering Disease

Human Genome

Genomics Big Data Tsunami

Imagine FollowingA Hundred MillionQuantified People

Calit2 Has Been Had a Vision of “the Digital Transformation of Health” for 15 Years

• Next Step—Putting You On-Line!– Wireless Internet Transmission– Key Metabolic and Physical Variables– Model -- Dozens of Processors and 60 Sensors /

Actuators Inside of our Cars• Post-Genomic Individualized Medicine

– Combine –Genetic Code –Body Data Flow

– Use Powerful AI Data Mining Techniques

www.bodymedia.com

The Content of This Slide from 2001 Larry Smarr Calit2 Talk on Digitally Enabled Genomic Medicine

Over the Last Decade, I Have Used a Variety of Personal SensorsTo Quantify My Body & Drive Behavioral Change

Withings/iPhone-Blood Pressure

Zeo-SleepAzumio-Heart Rate

MyFitnessPal-Calories Ingested

FitBit -Daily Steps &

Calories Burned

Withings WiFi Scale -Daily Weight

Wireless Monitoring Produced Time Series That Helped Me Improve My Health

Since Starting November 3, 2011Total Distance Tracked 6180 miles = Round Trip San Diego to Nome, Alaska

Total Vertical Distance Climbed 190,000 ft. = 6.5x Mt. Everest

My Resting Heartrate Fell from 70 to 40!

Elliptical

Walking

Sunday January 17, 2016137

42

I Increased Walking,

Aerobic, and Resistance Training,

All of WhichHave Health

Benefits

From Measuring Macro-Variables to Measuring Your Internal Variables

www.technologyreview.com/biomedicine/39636

As a Model for the Precision Medicine Initiative, I Have Tracked My Internal Biomarkers To Understand My Body’s Dynamics

My Quarterly Blood DrawCalit2 64 Megapixel VROOM

Only One of My Blood Measurements Was Far Out of Range

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

Doctor: “Come Back When You Have a Symptom”

Normal Range <1 mg/L

First Peak Was an Early Warning Sign of Developing Internal Disease State

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

Longitudinal Time Series RevealedOscillatory Behavior in an Immune Variable That is Antibacterial

Normal Range<7.3 µg/mL

124x Upper Limit for Healthy

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

TypicalLactoferrin Value

for Active

Inflammatory Bowel Disease

(IBD)

Time Series Reveals Oscillations in Immune BiomarkersAssociated with Time Progression of Autoimmune Disease

Immune &Inflammation

Variables

Weekly Symptoms

PharmaTherapies

StoolSamples

2009 20142013201220112010 2015

Monitoring Your BodyWould Have SuggestedIntervention Now!

Descending Colon

Sigmoid ColonThreading Iliac Arteries

Major Kink

Confirming the IBD (Colonic Crohn’s) Hypothesis:Finding the “Smoking Gun” with MRI Imaging

I Obtained the MRI Slices From UCSD Medical Services

and Converted to Interactive 3D Working With Calit2 Staff

Transverse ColonLiver

Small Intestine

Diseased Sigmoid ColonCross SectionMRI Jan 2012

Severe ColonWall Swelling

Why Did I Have an Autoimmune Disease like Crohn’s Disease?

Despite decades of research, the etiology of Crohn's disease

remains unknown. Its pathogenesis may involve a complex interplay between

host genetics, immune dysfunction,

and microbial or environmental factors.--The Role of Microbes in Crohn's Disease

Paul B. Eckburg & David A. RelmanClin Infect Dis. 44:256-262 (2007) 

I Have Been Quantifying All Three

I Found I Had One of the Earliest Known SNPsAssociated with Crohn’s Disease

From www.23andme.com

SNPs Associated with CD

Polymorphism in Interleukin-23 Receptor Gene

— 80% Higher Risk of Pro-inflammatoryImmune Response

NOD2

IRGM

ATG16L1

There May Be a Correlation Between CD SNPsand Where and When the Disease Manifests

Me-MaleCD Onset

At 60-Years Old

Il-23RRs1004819

1.8x Increased Risk

Female CD Onset

At 20-Years Old

NOD2 (1)Rs2066844

2.08x Increased Risk

Subject withIleal Crohn’s

Subject withColonic Crohn’s

Source: Larry Smarr and 23andme

IBD is a “Spectrum” Disorder Stratified by a Personal Combination of the 163 Known SNP Loci Associated with IBD

The width of the bar is proportional to the variance explained by that locus

“Host–microbe interactions have shaped the genetic architecture of inflammatory bowel disease,” Jostins, et al. Nature 491, 119-124 (2012)

23andme Has Collected 10,000 IBD Patient’s SNPs

Using Supercomputers and Deep Metagenomicsto Discover the Shifts in Microbiome Ecology in Health and Disease

An Initial Study of the Variation of the Human Gut MicrobiomeAcross Populations and Within an Individual Over Time

5 Ileal Crohn’s Patients, 3 Points in Time

2 Ulcerative Colitis Patients, 6 Points in Time

“Healthy” Individuals

Larry Smarr, Weizhong Li, Sitao Wu, UCSDGraphic Source: Jerry Sheehan, Calit2

Total of 27 Billion ReadsOr 2.7 Trillion Bases

Inflammatory Bowel Disease (IBD) Patients250 Subjects

1 Point in Time

7 Points in Time

Each Sample Has 100-200 Million Illumina Short Reads (100 bases)

Larry Smarr(Colonic Crohn’s)

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 Subjects

Illumina HiSeq 2000 at JCVI

SDSC Gordon Data Supercomputer

The Supercomputer Converts Tens of Billions of DNA Fragments Into Relative Abundance of Hundreds of Microbial Species

Average Over 250 Healthy PeopleFrom NIH Human Microbiome Project

Note Log Scale

Clostridium difficile

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

Most Common Microbial

Phyla

Average HE

Average Ulcerative ColitisAverage LS

Colonic Crohn’sAverage Ileal Crohn’s

Collapse of BacteroidetesGreat Increase in Actinobacteria

Explosion of Proteobacteria

Hybrid of UC and CDHigh Level of Archaea

Metagenomic Sequencing the Stool of 300 PatientsSorted Out Their Health or Disease Type

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

Dell | Information Management Group, Dell Software

Healthy

Ulcerative Colitis

Colonic Crohn’s

Ileal Crohn’s

Exploring the Dynamicsof the Human Microbiome Ecology

The Human Gut as a Super-Evolutionary Microbial Cauldron

• Enormous Density– 1000x Ocean Water

• Highly Dynamic Microbial Ecology– Hundreds to Thousands of Species

• Horizontal Gene Transfer• Phages• Adaptive Selection Pressures (Immune System)

– Innate Immune System– Adaptive Immune System– Macrophages and Antimicrobial proteins

• Constantly Changing Environmental Pressures– Diet– Antibiotics– Pharmaceuticals

Time Series Reveals Autoimmune Dynamics of Gut Microbiome by Phyla

Therapy

Six Metagenomic Time Samples Over 16 Months

Lessons From Ecological Dynamics I:Invasive Species Dominate After Major Species Destroyed

 ”In many areas following these burns invasive species are able to establish themselves,

crowding out native species.”Source: Ponderosa Pine Fire Ecology

http://cpluhna.nau.edu/Biota/ponderosafire.htm

Almost All Abundant Species (≥1%) in Healthy SubjectsAre Severely Depleted in Larry’s Gut Microbiome

Invasive Species Take Over Gut Microbiome in Disease State

152x

765x

148x

849x483x

220x201x

522x169x

20 Most Abundant Species

Source: Sequencing JCVI; Analysis Weizhong Li, UCSDLS December 28, 2011 Stool Sample

Relative AbundanceIn Gut Microbiome

Lessons from Ecological Dynamics II: Gut Microbiome Has Multiple Relatively Stable Equilibria

“The Application of Ecological Theory Toward an Understanding of the Human Microbiome,” Elizabeth Costello, Keaton Stagaman, Les Dethlefsen, Brendan Bohannan, David RelmanScience 336, 1255-62 (2012)

We are Genomically Analyzing My Stool Time Series in a Collaboration with the UCSD Knight Lab

Larry’s 40 Stool Samples Over 3.5 Years to Rob’s lab on April 30, 2015

LS Weekly Weight During Period of 16S Microbiome AnalysisAbrupt Change in Weight and in Symptoms at January 1, 2014

Lialda

Uceris

Frequent IBD SymptomsWeight Loss

Few IBD SymptomsWeight Gain

Source: Larry Smarr, UCSD

My Microbiome Ecology Time Series Over 3 Years

Source Justine Debelius, Knight Lab, UC San Diego

Coloring Samples Before (Blue) and After (Red) January 2014Reveals Clustering

Source Justine Debelius, Knight Lab, UC San Diego

An Apparent Sudden Phase Change Occurs

Source Justine Debelius, Knight Lab, UC San Diego

My Gut Microbiome Ecology Shifted After Drug Therapy Between Two Time-Stable Equilibriums Correlated to Physical Symptoms

Lialda &

Uceris

12/1/13 to

1/1/14

12/1/13-1/1/14

Frequent IBD SymptomsWeight Loss

7/1/12 to 12/1/14

Blue Balls on Diagram to the Right

Principal Coordinate Analysis of Microbiome Ecology

PCoA by Justine Debelius and Jose Navas, Knight Lab, UCSD

Weight Data from Larry Smarr, Calit2, UCSD

Weekly Weight

Few IBD SymptomsWeight Gain 1/1/14 to 8/1/15

Red Balls on Diagram to the Right

My Fasting Glucose Level Seems to Have Also Shifted in January 2014

Glucose Best Range70 to 100

Prediabetes Range100 to 125

Weight gain started

From N=1 to a Population of People with Disease

Inflammatory Bowel Disease BiobankFor Healthy and Disease Patients

Drs. William J. Sandborn, John Chang, & Brigid BolandUCSD School of Medicine, Division of Gastroenterology

Over 300 Enrolled

Announced November 7, 2014

To Expand IBD Project the Knight/Smarr Labs Were Awarded ~ 1 CPU-Century Supercomputing Time

• Smarr Gut Microbiome Time Series– From 7 Samples Over 1.5 Years – To 50 Samples Over 4 Years

• IBD Patients: From 5 Crohn’s Disease and 2 Ulcerative Colitis Patients to ~100 Patients– 50 Carefully Phenotyped Patients Drawn from Sandborn BioBank– 43 Metagenomes from the RISK Cohort of Newly Diagnosed IBD patients

• New Software Suite from Knight Lab– Re-annotation of Reference Genomes, Functional / Taxonomic Variations– Novel Compute-Intensive Assembly Algorithms from Pavel Pevzner

8x Compute Resources Over Prior Study

N=1 Microbiome Time Series Compared to Populations of Healthy and SickUsing Machine Learning and Data Analytics

Toward Computational Modelsof the Interaction of the Human Host and Its Microbiome

Forty Years of Computing Gravitational Waves From Colliding Black Holes – One Billion Times Increase in Supercomputer Speed!

1977

L. Smarr and K. EppleyGravitational Radiation Computed

from an Axisymmetric Black Hole Collision 40 Years

2016

LIGO ConsortiumSpiral Black Hole Collision

MegaFLOPS PetaFLOPSHolst, et al. Bull. Amer. Math. Soc 53, 513-554 (1916)

Complexity of Computing First Gut Microbiome DynamicsVersus First Dynamics of Colliding Black Holes

• My 1975 PhD Dissertation– Solving Einstein’s Equations of General Relativity for Colliding Black Holes and Grav Waves

– CDC 6600 Megaflop/s– Hundred Hours of Computing

• Rob Knight & Smarr Gut Microbiome Map Using 800,000 Core-Hours on SDSC’s Comet– Mapping From Illumina Sequencing to Taxonomy and Gene Abundance Dynamics

– Comet Petaflop/s – Comet Core is 40,000x CDC6600 Speed

– ~Million Core-Hours– 10,000x Supercomputer Time

• Gut Microbiome Takes ~ ½ Billion Times the Compute Power of Early Solutions of Dynamic General Relativity

NCSA Numerical Astrophysics GroupUsed NCSA Supercomputers to Explain Cosmic Phenomena

Mike Norman, Charles Evans, Roger Ove, John Hawley, Dean Sumi, Rob Wolff, Larry Smarr

Gas Accretion Onto a Black HoleCreates “Exhaust Channels”

Cosmic JetsEmerge from

Galactic CentersCollision of Neutron Stars

“A Whole-Cell Computational ModelPredicts Phenotype from Genotype”

A model of Mycoplasma genitalium, • 525 genes• Using 1,900

experimental observations

• From 900 studies, • They created the

software model, • Which requires 128

computers to run

Early Attempts at Modeling the Systems Biology of the Gut Microbiome and the Human Immune System

How Automobiles Went Froma Sickcare System to a Healthcare System

The Transformation in Automobile HealthcareGives Us Insight into the Human Healthcare Shift to Come

http://onlinelibrary.wiley.com/doi/10.1002/biot.201100495/abstract

Modern Cars Have Massive Sensor Arrays Which Record Time SeriesEnabling Computer Diagnostics For Early Warning

http://blog.asautoparts.com/5-common-symptoms-of-faulty-car-sensors/

Before the computer diagnostics technology,

most car owners did not know

something was wrong with the engine until something

drastic happened, such as overheating or

running out of gas. www.thepeoplehistory.com/carelectronics.html

The Transition from Car “Sickcare” to Car “Healthcare” Was Enabled by Pattern Recognition Using Big Data Analytics

“… using IBM big data and analytics technology, all available data sources can be analyzed

to discover patterns and anomalies to predict and anticipate maintenance needs. 

From Reactive Repairs for “Chronic Disease”to Quantified Cars That “Keep Themselves Healthy”

“In the not-too-distant future, analytics will help organizations prevent incidents from occurring,

rather than just being a tool to rapidly react to incidents.” --Rich Radi, director, Driver Excellence for ARI, the world’s largest

privately held family-owned fleet management company

The Planetary Computer Fed by a Trillion SensorsWill Drive a Global Industrial Internet

www.tsensorssummit.org

www-bsac.eecs.berkeley.edu/frontpagefiles/BSACGrowingMEMS_Markets_%20SEMI.ORG.html

Next Decade

One Trillion

GE’s Industrial Internet is CurrentlyGenerating 10,000 TB/Day

Toward a Future Healthcare System

Big Data Analytics is a Key Componentof The Future of Supercomputing

Next Generation Telescopes Will Keep Track of the Entire Universe

On-Line in Five Years,Tracks ~40B Objects,

Creates 10M Alerts/NightWithin 1 Minute of Observing

2x40Gbps

NCSA Supercomputer

Artificial Intelligence (AI) is Advancing at a Amazing Pace:Deep Learning Algorithms Working on Massive Datasets

Training on 30M Moves, Then Playing Against Itself

Less Than 2 Years!

From Self-Driving Cars to Personalized Medical AssistantsDeep Learning Will Provide Artificial Intelligence to Coach Us to Wellness

Where Medicine Coaching is Now

Where Wellness Coaching is Going

January 10, 2014

Can a Planetary Supercomputer with Artificial IntelligenceTransform Our Sickcare System to a Healthcare System?

Using this data, the planetary computer will be able to build a computational model of your body

and compare your sensor stream with millions of others. Besides providing early detection of internal changes

that could lead to disease, cloud-powered voice-recognition wellness coaches

could provide continual personalized support on lifestyle choices, potentially staving off disease

and making health care affordable for everyone.

ESSAYAn Evolution Toward a Programmable UniverseBy LARRY SMARRPublished: December 5, 2011

Thanks to Our Great Team!

Calit2@UCSD Future Patient TeamJerry SheehanTom DeFanti Joe Keefe John GrahamKevin PatrickMehrdad YazdaniJurgen Schulze Andrew Prudhomme Philip Weber Fred RaabErnesto Ramirez

JCVI TeamKaren Nelson Shibu Yooseph Manolito Torralba

AyasdiDevi RamananPek Lum

UCSD Metagenomics TeamWeizhong Li Sitao Wu

SDSC TeamMichael Norman Mahidhar Tatineni Robert Sinkovits

UCSD Health Sciences TeamDavid BrennerRob Knight Lab Justine Debelius Jose Navas Gail Ackermann Greg HumphreyWilliam J. Sandborn Lab Elisabeth Evans John Chang Brigid Boland

Dell/R SystemsBrian KucicJohn Thompson