Analyzing the Human Gut Microbiome Dynamics in Health and Disease Using Supercomputers and Supernetworks

“Analyzing the Human Gut Microbiome Dynamics in Health and Disease Using Supercomputers and Supernetworks”

Invited PresentationESnet CrossConnects Bioinformatics Conference

Lawrence Berkeley National LaboratoryApril 12, 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

Abstract

To truly understand the state of the human body in health or disease, we now realize that we must consider a much more complex system than medical science considered heretofore. This is because we now know that the human body is host to 100 trillion microorganisms, ten times the number of DNA bearing cells in the human body and these microbes contain 300 times the number of DNA genes that our human DNA does. The microbial component of our “superorganism” is comprised of hundreds of species with immense biodiversity. Exponential decrease in the cost of genetic sequencing and supercomputing has enabled scientists to finally "read out" the nature of the changes in the microbial ecology in people in health and with disease. We use the fiber optic network of the Pacific Research Platform to rapidly move these large datasets. To put a more personal face on the “patient of the future,” I have been collecting massive amounts of data from my own body over the last five years, which reveals detailed examples of the episodic excursions of my coupled immune microbial system. As similar techniques become more widely applied, we can look forward to revolutionary changes in medical practice over the next decade.

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

Microbial GenomeTime Series

Improving Body

Discovering Disease

Human Genome

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--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 for Healthy

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

TypicalLactoferrin Value for Active Inflammatory

Bowel Disease (IBD)

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

23andme is Now Collecting 10,000 IBD Patient’s SNPs

I Reasoned That The Driver of My Gut Autoimmune DiseaseWas a Disturbance in My Gut Microbiome Ecology

Inclusion of the “Dark Matter” of the BodyWill Radically Alter Medicine

99% of Your DNA Genes

Are in Microbe CellsNot Human Cells

Your Body Has 10 Times As Many Microbe Cells As DNA-Bearing

Human Cells

The Carl Woese Tree of LifeShows The Most Life on Earth is Bacterial

Nature Microbiology Hug, et al.

Source: Carl Woese, et al (1990)

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

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 Gathered Raw Illumina Reads on 275 Humansand Generated a Time Series of My Gut Microbiome

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 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)

Computational NextGen Sequencing Pipeline:From Sequence to Taxonomy and Function

PI: (Weizhong Li, CRBS, UCSD): NIH R01HG005978 (2010-2013, $1.1M)

Results Include Relative Abundance of Hundreds of Microbial Species

Average Over 250 Healthy PeopleFrom NIH Human Microbiome Project

Note Log Scale

Clostridium difficile

Using Microbiome Profiles to Survey 155 Subjects for Unhealthy Candidates

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

Most Common Microbial

Phyla

Average HE

Average Ulcerative Colitis

Average LSColonic Crohn’s Disease

Average Ileal Crohn’s Disease

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

Immune &Inflammation

Variables

Weekly Symptoms

PharmaTherapies

StoolSamples

2009 20142013201220112010 2015

In 2016 We Are Extending My Stool Time Series byCollaborating with the UCSD Knight Lab

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

Precision Medicine: Coupling Longitudinal Phenotypic Changes to Longitudinal Microbiome Evolution

Time Period of 16S Microbial Sequences

Source: Larry Smarr, UCSD

Larry Smarr’s Weight Over 15 Years

Larry Smarr 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

5/1/12 to 12/1/14Blue Balls on Diagram

to the Right

Few IBD SymptomsWeight Gain

1/1/14 to 1/1/16Red 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

Ant

ibio

ticsPrednisone

1/1/12 to 5/1/12

5/1/12

Weekly Weight (Red Dots Stool Sample)

Few IBD SymptomsWeight Gain

1/1/14 to 1/1/16Red Balls on Diagram

to the Right

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

Cancer Genomics Hub (UCSC) Demonstrates Need for SuperNetworks:Large Data Flows to End Users at UCSC, UCB, UCSF, …

1G

8G

Data Source: David Haussler, Brad Smith, UCSC

15GJan 2016

30,000 TBPer Year

Building a UC San Diego High Performance Cyberinfrastructureto Support Distributed Integrative Omics

FIONA12 Cores/GPU128 GB RAM3.5 TB SSD48TB Disk

10Gbps NIC

Knight Lab

10Gbps

Gordon

Prism@UCSD

Data Oasis7.5PB,

200GB/s

Knight 1024 ClusterIn SDSC Co-Lo

CHERuB100Gbps

Emperor & Other Vis Tools

64Mpixel Data Analysis Wall

120Gbps

40Gbps

1.3TbpsPRP/

Based on Community Input and on ESnet’s Science DMZ Concept,NSF Has Funded Over 100 Campuses to Build Local Big Data Freeways

2012-2015 CC-NIE / CC*IIE / CC*DNI PROGRAMS

Red 2012 CC-NIE AwardeesYellow 2013 CC-NIE AwardeesGreen 2014 CC*IIE AwardeesBlue 2015 CC*DNI AwardeesPurple Multiple Time Awardees

Source: NSF

The Pacific Wave PlatformCreates a Regional Science-Driven “Big Data Freeway System”

Source: John Hess, CENIC

Funded by NSF $5M Oct 2015-2020

Flash Disk to Flash Disk File Transfer Rate

PI: Larry Smarr, UC San Diego Calit2Co-PIs:• Camille Crittenden, UC Berkeley CITRIS, • Tom DeFanti, UC San Diego Calit2, • Philip Papadopoulos, UC San Diego SDSC, • Frank Wuerthwein, UC San Diego Physics

and SDSC

The Emergence of Precision or P4 Medicine --Predictive, Preventive, Personalized, Participatory

Systems Biology & Systems Medicine

Consumer-Driven Social Networks

P4 MEDICINE

Digital RevolutionBig Data

How Will the Quantified ConsumerBe Integrated into Healthcare Systems?

Lee Hood, Director ISB

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