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![I Also Had an Increased Risk for Ulcerative Colitis,
But a SNP that is Also Associated with Colonic CD
I Have a
33% Increased Risk
for Ulcerative Colitis
HLA-DRA (rs2395185)
I Have the Same Level
of HLA-DRA Increased Risk
as Another Male Who Has Had
Ulcerative Colitis for 20 Years
“Our results suggest that at least for the SNPs investigated
[including HLA-DRA],
colonic CD and UC have common genetic basis.”
-Waterman, et al., IBD 17, 1936-42 (2011)](https://image.slidesharecdn.com/bioengineeringucsdfeb2016-160331182041/85/Supercomputing-Your-Inner-Microbiome-25-320.jpg)
Uploaded byLarry Smarr
Supercomputing Your Inner Microbiome
The document summarizes a seminar given by Dr. Larry Smarr on supercomputing the human microbiome. Some key points: - The human microbiome contains 100 trillion microorganisms and their DNA contains 300 times as many genes as human DNA. - Dr. Smarr has been collecting extensive data from his own body over 7 years to study his personal microbiome and immune system interactions using high performance computing. - Analyzing microbiome data requires massive computing resources, such as millions of core hours on supercomputers. This reveals details of microbial ecology and genetics in health and disease. - Computational analysis of microbiome sequencing data from many subjects shows major shifts in microbial populations between healthy and
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Supercomputing Your Inner Microbiome
- 1. “Supercomputing Your InnerMicrobiome” Seminar Department of Bioengineering University of California, San Diego February 12, 2016 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.net 1
- 2. The human bodyis 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. 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 seven years, which reveals detailed examples of the episodic evolution of this coupled immune-microbial system. An elaborate software pipeline, running on high performance computers, reveals the details of the microbial ecology and its genetic components, in health as well as in disease. Not only can we compare a person with a disease to a healthy population, but we can also follow the dynamics of the diseased patient. We can look forward to revolutionary changes in medical practice over the next decade.
- 3. Forty Years ofComputing Gravitational Waves From Colliding Black Holes 1977 L. Smarr and K. Eppley Gravitational Radiation Computed from an Axisymmetric Black Hole Collision 2016 LIGO Consortium Spiral Black Hole Collision 40 Years
- 4. Complexity of ComputingFirst Gut Microbiome Dynamics Versus 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 – Hundreds of Hours • Rob Knight and Smarr Gut Microbiome Map – 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
- 5. Building a UCSan Diego Cyberinfrastructure to Support Integrative Omics FIONA 12 Cores/GPU 128 GB RAM 3.5 TB SSD 48TB Disk 10Gbps NIC Knight Lab 10Gbps Gordon Prism@UCSD Data Oasis 7.5PB, 200GB/s Knight 1024 Cluster In SDSC Co-Lo CHERuB 100Gbps Emperor & Other Vis Tools 64Mpixel Data Analysis Wall 120Gbps 40Gbps 1.3Tbps PRP/
- 6. The Pacific WavePlatform Creates 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 Calit2 Co-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
- 7. Calit2’s Qualcomm InstituteHas Established a Pattern Recognition Lab On the PRP, For Machine Learning on non-von Neumann Processors “On the drawing board are collections of 64, 256, 1024, and 4096 chips. ‘It’s only limited by money, not imagination,’ Modha says.” Source: Dr. Dharmendra Modha Founding Director, IBM Cognitive Computing Group August 8, 2014 UCSD ECE Professor Ken Kreutz-Delgado Brings the IBM TrueNorth Chip to Start Calit2’s Qualcomm Institute Pattern Recognition Laboratory September 16, 2015
- 8. From One toa Trillion Data Points Defining Me in 15 Years: The Exponential Rise in Body Data Weight Blood Biomarker Time Series Human Genome SNPs Microbial Genome Time Series Improving Body Discovering Disease Human Genome
- 9. I Decided toTrack My Internal Biomarkers To Understand My Body’s Dynamics My Blood Draw YesterdayCalit2 64 Megapixel VROOM
- 10. Only One ofMy 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
- 11. Adding Stool TestsRevealed Oscillatory 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 Typical Lactoferrin Value for Active Inflammatory Bowel Disease (IBD)
- 12. Descending Colon Sigmoid Colon ThreadingIliac 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 Colon Liver Small Intestine Diseased Sigmoid Colon Cross Section MRI Jan 2012 Severe Colon Wall Swelling
- 13. Evolving Microbiome EnvironmentalPressures: Dynamical Innate and Adaptive Immune Oscillations in Colon Normal <600 Innate Immune System Normal 50 to 200 Adaptive Immune System These Must Be Coupled to A Dynamic Microbiome Ecology
- 14. The Human Gut asa 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
- 15. To Understand theInteraction of Genetics and the Immune System We Must Consider the Human Microbiome Your Microbiome is Your “Near-Body” Environment and its Cells Contain 300x as Many DNA Genes As Your Human DNA-Bearing Cells Your Body Has 10 Times As Many Microbe Cells As DNA-Bearing Human Cells Inclusion of the “Dark Matter” of the Body Will Radically Alter Medicine
- 16. New Estimates In2016 Estimate a Human Body Contains ~30 Trillion Human Cells and ~40 Trillion Microbes However, Red Blood Cells and Platelets Have No Nuclear DNA. Therefore, Ratio of DNA-Bearing Cells for Human vs. Microbiome is Still >10:1 DNA-Bearing Cells
- 17. We Gathered RawIllumina Reads on 275 Humans and 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, Calit2 Weizhong Li, Sitao Wu, CRBS, UCSD Total of 27 Billion Reads Or 2.7 Trillion Bases Inflammatory Bowel Disease (IBD) Patients 250 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)
- 18. To Map Outthe Dynamics of Autoimmune Microbiome Ecology Couples Next Generation Genome Sequencers to Big Data Supercomputers Source: Weizhong Li, UCSD Our Team Used 25 CPU-years to Compute Comparative Gut Microbiomes Starting From 2.7 Trillion DNA Bases of My Samples and Healthy and IBD Controls Illumina HiSeq 2000 at JCVI SDSC Gordon Data Supercomputer
- 19. Results Include RelativeAbundance of Hundreds of Microbial Species Average Over 250 Healthy People From NIH Human Microbiome Project Note Log Scale Clostridium difficile
- 20. Using Microbiome Profilesto Survey 155 Subjects for Unhealthy Candidates
- 21. Comparing Gut Microbiomeof Healthy People with Ileal Crohn’s, Ulcerative Colitis, and Colonic Crohn’s Patients
- 22. We Found MajorState Shifts in Microbial Ecology Phyla Between Healthy and Three Forms of IBD Most Common Microbial Phyla Average HE Average Ulcerative Colitis Average LS Colonic Crohn’s Disease Average Ileal Crohn’s Disease
- 23. I Found IHad One of the Earliest Known SNPs Associated with Crohn’s Disease From www.23andme.com SNPs Associated with CD Polymorphism in Interleukin-23 Receptor Gene — 80% Higher Risk of Pro-inflammatory Immune Response NOD2 IRGM ATG16L1 23andme is Now Collecting 10,000 IBD Patient’s SNPs
- 24. There Is Likelya Correlation Between CD SNPs and Where and When the Disease Manifests Me-Male CD Onset At 60-Years Old Female CD Onset At 20-Years Old NOD2 (1) rs2066844 Il-23R rs1004819 Subject with Ileal Crohn’s Subject with Colonic Crohn’s Source: Larry Smarr and 23andme
- 25. I Also Hadan Increased Risk for Ulcerative Colitis, But a SNP that is Also Associated with Colonic CD I Have a 33% Increased Risk for Ulcerative Colitis HLA-DRA (rs2395185) I Have the Same Level of HLA-DRA Increased Risk as Another Male Who Has Had Ulcerative Colitis for 20 Years “Our results suggest that at least for the SNPs investigated [including HLA-DRA], colonic CD and UC have common genetic basis.” -Waterman, et al., IBD 17, 1936-42 (2011)
- 26. Ileal Crohn’s andUC Patients Have Reduced Abundance of Anti-Inflammatory Faecalibacterium prausnitzii However, Colonic Crohn’s (LS) Have Increased Abundance
- 27. 0 0,01 0,02 0,03 0,04 0,05 0,06 0,07 H CCD ICD 0 0,01 0,02 0,03 0,04 0,05 0,06 0,07 0,08 0,09 HCCD ICD fecesileum biopsies 0 0,02 0,04 0,06 0,08 0,1 0,12 H CCD ICD c distal colon biopsies Faecalibacterium prausnitzii One of the main producers of butyrate Important for colonic health. Willing et al., 2009.Inflammatory Bowel Diseases A Noninvasive Diagnostic?? - Faecalibacterium is Depleted in Ileal CD and Increased in Colonic CD Slide from Janet Jansson, PNNL
- 28. We Computed theRelative Abundance of Microbial Gene Families - ~10,000 KEGG Orthologous Genes, Across Healthy and IBD Subjects How Large is the Microbiome’s Genetic Change Between Health and Disease States?
- 29. In a “Healthy”Gut Microbiome: Large Taxonomy Variation, Low Protein Family Variation Source: Nature, 486, 207-212 (2012) Over 200 People
- 30. Ratio of HE11529to Ave HE Test to see How Much Variation There is Within Healthy Most KEGGs Are Within 10x Of Healthy for a Random HE Ratio of Random HE11529 to Healthy Average for Each Nonzero KEGG Similar to HMP Healthy Results
- 31. However, Our ResearchShows Large Changes in Protein Families Between Health and Disease – Ileal Crohns Most KEGGs Are Within 10x In Healthy and Ileal Crohn’s Disease KEGGs Greatly Increased In the Disease State KEGGs Greatly Decreased In the Disease State Over 7000 KEGGs Which Are Nonzero in Health and Disease States Ratio of CD Average to Healthy Average for Each Nonzero KEGG Note Hi/Low Symmetry
- 32. Note Ulcerative ColitisHas Few KEGGs that are Much Smaller than HE; Note Asymmetry Between High & Low Most KEGGs Are Within 10x In Healthy and Ulcerative Colitis KEGGs Greatly Increased In the Disease State KEGGs Greatly Decreased In the Disease State Ratio of UC Average to Healthy Average for Each Nonzero KEGG Note Hi/Low Asymmetry
- 33. Note LS001 HasFew KEGGs that are Much Smaller than HE; But Many More (1337) That are >10x HE Than CD (~700) or UC (~900) Ratio of LS001 Average to Healthy Average for Each Nonzero KEGG Most KEGGs Are Within 10x In Healthy and LS001 KEGGs Greatly Increased In the Disease State KEGGs Greatly Decreased In the Disease State
- 34. Disease Arises fromPerturbed Protein Family Networks: Dynamics of a Prion Perturbed Network in Mice Source: Lee Hood, ISB 34 Our Next Goal is to Create Such Perturbed Networks in Humans
- 35. Calit2’s Qualcomm InstituteHas Developed Interactive Scalable Visualization for Biological Networks 20,000 Samples 60,000 OTUs 18 Million Edges Runs Native on 64Million Pixels
- 36. Time Series RevealsOscillations in Immune Biomarkers Associated with Time Progression of Autoimmune Disease Immune & Inflammation Variables Weekly Symptoms Pharma Therapies Stool Samples 2009 20142013201220112010 2015
- 37. Larry Smarr WeeklyWeight Time Period of 16S Source: Larry Smarr, UCSD
- 38. LS Weekly WeightDuring Period of 16S Microbiome Analysis Abrupt Change in Weight and in Symptoms at January 1, 2014 Antibiotics Prednisone Lialda Uceris Frequent IBD Symptoms Weight Loss Few IBD Symptoms Weight Gain Source: Larry Smarr, UCSD
- 39. In 2016 WeAre Extending My Stool Time Series by Collaborating with the UCSD Knight Lab Larry’s 40 Stool Samples Over 3.5 Years to Rob’s lab on April 30, 2015
- 40. Variation in MyGut Microbiome Phyla Over 3.5 Years: No Clear Pattern Break at January 2014 Data from Justine Debelius & Jose Navas, Knight Lab, UCSD; Larry Smarr Analysis, January 2016
- 41. LS Gut Microbiome 16SEvolution by Family-Again No Clear Separation at January 2014 Data from Justine Debelius & Jose Navas, Knight Lab, UCSD; Larry Smarr Analysis, January 2016
- 42. Time Development Over3.5 Years of Larry Smarr’s Gut Microbiome Ecology Blue: 1/2012 to 1/2014 Red: 1/2014 to 8/1/2015 Movie and Data Analysis by Justine Debelius & Jose Navas, Knight Lab, UCSD Unweighted UniFrac Distance of 16S OTUs was Computed and then Transformed into Principle Coordinates Space Using QIIME. Principle Coordinates were Visualized in Emperor.
- 43. Larry Smarr GutMicrobiome 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 Symptoms Weight Loss 5/1/12 to 12/1/14 Blue Balls on Diagram to the Right Few IBD Symptoms Weight Gain 1/1/14 to 1/1/16 Red 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 Antibiotics Prednisone 1/1/12 to 5/1/12 5/1/12 Weekly Weight (Red Dots Stool Sample) Few IBD Symptoms Weight Gain 1/1/14 to 1/1/16 Red Balls on Diagram to the Right
- 44. To Expand IBDProject 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
- 45. Center for Microbiome Innovation Seminars Faculty Hiring Education UCSD MicrobialSciences Initiative Instrument Cores Seed Grants Fellowships Chancellor Khosla Launched the UC San Diego Microbiome and Microbial Sciences Initiative October 29, 2015
- 46. UC San DiegoMicrobiome and Microbial Sciences Initiative: Leadership Team Rob Knight Pediatrics/CSE Pieter Dorrestein Pharmacy Kit Pogliano Biol Sci Bernhard Palsson BioE/Pediatrics Bill Sandborn Gastroenterology Jeff Hasty Biol Sci Karsten Zengler Pediatrics Larry Smarr Calit2 /CSE Paul Jensen SIO Pavel Pevzner CSE Rachel Dutton Biol Sci Rommie Amaro Chem & Biochem Victor Nizet Pediatrics/Pharmacy Vineet Bafna CSE
- 47. Thanks to OurGreat Team! Calit2@UCSD Future Patient Team Jerry Sheehan Tom DeFanti Joe Keefe John Graham Kevin Patrick Mehrdad Yazdani Jurgen Schulze Andrew Prudhomme Philip Weber Fred Raab Ernesto Ramirez JCVI Team Karen Nelson Shibu Yooseph Manolito Torralba Ayasdi Devi Ramanan Pek Lum UCSD Metagenomics Team Weizhong Li Sitao Wu SDSC Team Michael Norman Mahidhar Tatineni Robert Sinkovits UCSD Health Sciences Team David Brenner Rob Knight Lab Justine Debelius Jose Navas Gail Ackermann Greg Humphrey William J. Sandborn Lab Elisabeth Evans John Chang Brigid Boland Dell/R Systems Brian Kucic John Thompson