analyzing the human gut microbiome dynamics in health and disease using supercomputers and...
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“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
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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.
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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
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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
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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
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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)
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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
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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
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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
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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
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The Carl Woese Tree of LifeShows The Most Life on Earth is Bacterial
Nature Microbiology Hug, et al.
Source: Carl Woese, et al (1990)
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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
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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
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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)
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Computational NextGen Sequencing Pipeline:From Sequence to Taxonomy and Function
PI: (Weizhong Li, CRBS, UCSD): NIH R01HG005978 (2010-2013, $1.1M)
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Results Include Relative Abundance of Hundreds of Microbial Species
Average Over 250 Healthy PeopleFrom NIH Human Microbiome Project
Note Log Scale
Clostridium difficile
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Using Microbiome Profiles to Survey 155 Subjects for Unhealthy Candidates
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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
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Time Series Reveals Oscillations in Immune BiomarkersAssociated with Time Progression of Autoimmune Disease
Immune &Inflammation
Variables
Weekly Symptoms
PharmaTherapies
StoolSamples
2009 20142013201220112010 2015
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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
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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
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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
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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
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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
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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/
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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
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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
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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
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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