Using Data Analytics to Discover the 100 Trillion Bacteria Living Within Each of Us
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The document summarizes Dr. Larry Smarr's talk on using data analytics to analyze the human microbiome. Some key points: - Next-generation sequencing and supercomputing are used to map the microbiomes of hundreds of people to analyze bacterial species abundance in health and diseases like IBD. - Analysis with Dell Analytics and Ayasdi reveals major differences in bacterial phyla and protein families between healthy and disease states that can be used to noninvasively diagnose disease. Certain species are found at much higher or lower levels in disease states. - Continued microbiome profiling and topological data analysis may help discover new diagnostic biomarkers for disease states and track disease progression.
Using Data Analytics to Discover the 100 Trillion Bacteria Living Within Each of Us
- 1. “Using Data Analytics to Discover the 100 Trillion Bacteria Living Within Each of Us” Invited Talk Ayasdi Menlo Park, CA December 5, 2014 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. From One to a Billion Data Points Defining Me: The Exponential Rise in Body Data in Just One Decade Billion: My Full DNA, MRI/CT Images Million: My DNA SNPs, Zeo, FitBit One: Hundred: My Blood Variables WeigMhyt Weight Blood Variables SNPs Microbial Genome Improving Body Discovering Disease
- 3. How Will Detailed Knowledge of Microbiome Ecology Radically Change Medicine and Wellness? Your Body Has 10 Times As Many Microbe Cells As Human Cells 99% of Your DNA Genes Are in Microbe Cells Not Human Cells Challenge: Map Out Microbial Ecology and Function in Health and Disease States
- 4. Intense Scientific Research is Underway on Understanding the Human Microbiome June 8, 2012 June 14, 2012 August 18, 2012
- 5. To Map Out the Dynamics of Autoimmune Microbiome Ecology Couples Next Generation Genome Sequencers to Big Data Supercomputers • Metagenomic Sequencing – JCVI Produced – ~150 Billion DNA Bases From Seven of LS Stool Samples Over 1.5 Years – We Downloaded ~3 Trillion DNA Bases From NIH Human Microbiome Program Data Base – 255 Healthy People, 21 with IBD • Supercomputing (Weizhong Li, JCVI/HLI/UCSD): – ~20 CPU-Years on SDSC’s Gordon – ~4 CPU-Years on Dell’s HPC Cloud • Produced Relative Abundance of – ~10,000 Bacteria, Archaea, Viruses in ~300 People – ~3Million Filled Spreadsheet Cells Illumina HiSeq 2000 at JCVI SDSC Gordon Data Supercomputer Example: Inflammatory Bowel Disease (IBD)
- 6. Computational NextGen Sequencing Pipeline: From Sequence to Taxonomy and Function PI: (Weizhong Li, CRBS, UCSD): NIH R01HG005978 (2010-2013, $1.1M)
- 7. Next Step Programmability, Scalability and Reproducibility using bioKepler www.kepler-project.org www.biokepler.org National Resources (Gordon) (Comet) (Lonestar) (Stampede) Optimized Cloud Resources Local Cluster Resources Source: Ilkay Altintas, SDSC
- 8. How Best to Analyze The Microbiome Datasets to Discover Patterns in Health and Disease? Can We Find New Noninvasive Diagnostics In Microbiome Ecologies?
- 9. We Found Major State Shifts in Microbial Ecology Phyla Between Healthy and Two Forms of IBD Most Common Microbial Phyla Average HE Average Ulcerative Colitis Average LS Average Crohn’s Disease Collapse of Bacteroidetes Explosion of Actinobacteria Explosion of Proteobacteria Hybrid of UC and CD High Level of Archaea
- 10. Using Scalable Visualization Allows Comparison of the Relative Abundance of 200 Microbe Species Comparing 3 LS Time Snapshots (Left) with Healthy, Crohn’s, Ulcerative Colitis (Right Top to Bottom) Calit2 VROOM-FuturePatient Expedition
- 11. Using Dell HPC Cloud and Dell Analytics to Discover Microbial Diagnostics for Disease Dynamics • Can We Distinguish Noninvasively Between Health and Disease States? • Are There Subsets of Health or Disease States? • Can We Track Time Development of the Disease State? • Can Novel Microbial Diagnostics Differentiate Health and Disease States?
- 12. Using Microbiome Profiles to Survey 155 Subjects for Unhealthy Candidates
- 13. Dell Analytics Separates The 4 Patient Types in Our Data Using Our Microbiome Species Data Ulcerative Colitis Source: Thomas Hill, Ph.D. Executive Director Analytics Dell | Information Management Group, Dell Software Healthy Colonic Crohn’s Ileal Crohn’s
- 14. 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
- 15. 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
- 16. Dell Analytics Tree Graphs Classifies the 4 Health/Disease States With Just 3 Microbe Species Source: Thomas Hill, Ph.D. Executive Director Analytics Dell | Information Management Group, Dell Software
- 17. Our Relative Abundance Results Across ~300 People Show Why Dell Analytics Tree Classifier Works UC 100x Healthy Healthy 100x CD LS 100x UC We Produced Similar Results for ~2500 Microbial Species
- 18. Using Ayasdi’s Advanced Topological Data Analysis to Separate Healthy from Disease States All Healthy All Healthy All Ileal Crohn’s Using Ayasdi Categorical Data Lens Healthy, Ulcerative Colitis, and LS All Healthy Analysis by Mehrdad Yazdani, Calit2 Talk to Ayasdi in the Intel Booth at SC14
- 19. Ayasdi Enables Discovery of Differences Between Healthy and Disease States Using Microbiome Species Healthy LS Ileal Crohn’s Ulcerative Colitis High in Healthy and LS High in Healthy and Ulcerative Colitis High in Both LS and Ileal Crohn’s Disease Using Multidimensional Scaling Lens with Correlation Metric Analysis by Mehrdad Yazdani, Calit2
- 20. From Taxonomy to Function: Analysis of LS Clusters of Orthologous Groups (COGs) Analysis: Weizhong Li & Sitao Wu, UCSD
- 21. In a “Healthy” Gut Microbiome: Large Taxonomy Variation, Low Protein Family Variation Over 200 People Source: Nature, 486, 207-212 (2012)
- 22. Ratio of HE11529 to Ave HE Test to see How Much Variation There is Within Healthy Ratio of Random HE11529 to Healthy Average for Each Nonzero KEGG Most KEGGs Are Within 10x Of Healthy for a Random HE
- 23. However, Our Research Shows Large Changes in Protein Families Between Health and Disease Ratio of CD Average to Healthy Average for Each Nonzero KEGG KEGGs Greatly Increased In the Disease State Most KEGGs Are Within 10x In Healthy and Ileal Crohn’s Disease KEGGs Greatly Decreased In the Disease State Over 7000 KEGGs Which Are Nonzero in Health and Disease States Note Hi/Low Symmetry
- 24. Note UC Has Many Few KEGGs that are Much Smaller than HE; Also Fewer KEGGs That are Nonzero; Note Asymmetry Between High & Low Ratio of UC Average to Healthy Average for Each Nonzero KEGG Most KEGGs Are Within 10x In Healthy and Ulcerative Colitis KEGGs Greatly Increased In the Disease State KEGGs Greatly Decreased In the Disease State
- 25. Note LS001 Has Many Few KEGGs that are Much Smaller than HE; ~Same # KEGGs That are Nonzero; Note Asymmetry Between High & Low 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
- 26. We Can Define a Subgroup of the 10,000 KEGGs Which Are Extreme in the Disease State • Look for KEGGs That Have the Properties: – Are 100x in All Four Disease States – LS001/Ave HE – Ave CD/ Ave HE – Ave UC/Ave HE – Sick HE Person/Ave HE • There are 48 of These Extreme KEGGs • A New Way to Define What is Wrong with the Microbiome in Disease? • Can We Devise an Ayasdi Lens That Can Separates These Extreme KEGGs?
- 27. Using Ayasdi Interactively to Explore Protein Families in Healthy and Disease States Dataset from Larry Smarr Team With 60 Subjects (HE, CD, UC, LS) Each with 10,000 KEGGs - 600,000 Cells Source: Pek Lum, Formerly Chief Data Scientist, Ayasdi
- 28. CD is Missing a Population of Bacteria That Exists in High Quantities in HE ( Circled with Arrow) • Problem is That These KEGGs Have Moderate Values of Ave CD/ Ave HE • How Can We Change the Ayasdi Lenses So That We Pick Out The Very High Values of Ratios to Ave HE? Low in CD and LS Source: Pek Lum, Formerly Chief Data Scientist, Ayasdi
- 29. This Ayasdi Lens Does Identify KEGGs In Which Ave CD and LS001 Are Less Than Ave HE • Problem is That These KEGGs Have Moderate Low Values of Ave CD/ Ave HE • How Can We Change the Ayasdi Lenses So That We Pick Out The Very High Values of Ratios to Ave HE?
- 30. We Found a Set of Lenes That Clearer Find the 43 Extreme KEGGs K00108(choline_dehydrogenase) K00673(arginine_N-succinyltransferase) K00867(type_I_pantothenate_kinase) K01169(ribonuclease_I_(enterobacter_ribonuclease)) K01484(succinylarginine_dihydrolase) K01682(aconitate_hydratase_2) K01690(phosphogluconate_dehydratase) K01825(3-hydroxyacyl-CoA_dehydrogenase_/_enoyl-CoA_hydratase_/3-hydroxybutyryl-CoA_epimerase_/_enoyl K02173(hypothetical_protein) K02317(DNA_replication_protein_DnaT) K02466(glucitol_operon_activator_protein) K02846(N-methyl-L-tryptophan_oxidase) K03081(3-dehydro-L-gulonate-6-phosphate_decarboxylase) K03119(taurine_dioxygenase) K03181(chorismate--pyruvate_lyase) K03807(AmpE_protein) K05522(endonuclease_VIII) K05775(maltose_operon_periplasmic_protein) K05812(conserved_hypothetical_protein) K05997(Fe-S_cluster_assembly_protein_SufA) K06073(vitamin_B12_transport_system_permease_protein) K06205(MioC_protein) K06445(acyl-CoA_dehydrogenase) K06447(succinylglutamic_semialdehyde_dehydrogenase) K07229(TrkA_domain_protein) K07232(cation_transport_protein_ChaC) K07312(putative_dimethyl_sulfoxide_reductase_subunit_YnfH_(DMSO_reductaseanchor_subunit)) K07336(PKHD-type_hydroxylase) K08989(putative_membrane_protein) K09018(putative_monooxygenase_RutA) K09456(putative_acyl-CoA_dehydrogenase) K09998(arginine_transport_system_permease_protein) K10748(DNA_replication_terminus_site-binding_protein) K11209(GST-like_protein) K11391(ribosomal_RNA_large_subunit_methyltransferase_G) K11734(aromatic_amino_acid_transport_protein_AroP) K11735(GABA_permease) K11925(SgrR_family_transcriptional_regulator) K12288(pilus_assembly_protein_HofM) K13255(ferric_iron_reductase_protein_FhuF) K14588() K15733() K15834() L-Infinity Centrality Lens Using Norm Correlation as Metric (Resolution: 242, Gain: 5.7) Entropy & Variance Lens Using Angle as Metric (Resolution: 30, Gain 3.00) Analysis by Mehrdad Yazdani, Calit2
- 31. Disease Arises from Perturbed Protein Family Networks: Dynamics of a Prion Perturbed Network in Mice Source: Lee Hood, ISB 31 Our Next Goal is to Create Such Perturbed Networks in Humans
- 32. Visualizing Time Series of 150 LS Blood and Stool Variables, Each Over 5-10 Years Calit2 64 megapixel VROOM One Blood Draw For Me
- 33. Only One of My Blood Measurements Was Far Out of Range--Indicating Chronic Inflammation Episodic Peaks in Inflammation Followed by Spontaneous Drops Normal Range <1 mg/L 27x Upper Limit Normal Complex Reactive Protein (CRP) is a Blood Biomarker for Detecting Presence of Inflammation
- 34. Adding Stool Tests Revealed Oscillatory Behavior in an Immune Variable Typical Lactoferrin Value for Active IBD Normal Range <7.3 μg/mL 124x Upper Limit Hypothesis: Lactoferrin Oscillations Coupled to Relative Abundance of Microbes that Require Iron Antibiotics Antibiotics Lactoferrin is a Protein Shed from Neutrophils - An Antibacterial that Sequesters Iron
- 35. Fine Time-Resolution Sampling Enables Analysis of Dynamical Innate and Adaptive Immune Dysfunction Normal Innate Immune System Normal Adaptive Immune System
- 36. By Overlaying a Number of Immune/Inflammation Variables, CRP SED Lact Lyzo SigA Calp It Appears There May be Phase Correlations Data Analytics by Benjamin Smarr, UC Berkeley
- 37. One Can Use Sine Fitting with Least Squares To Try and Approximate the Time Series Dynamics 5 Sines Data Analytics by Benjamin Smarr, UC Berkeley
- 38. With Low Resolution Sine Fitting, There Is Indication of Phase Correlation Data Analytics by Benjamin Smarr, UC Berkeley 2 Sines
- 39. Are There Ayasdi Tools to More Deeply Analyze Such Time Series?
- 40. UC San Diego Will Be Carrying Out a Major Clinical Study of IBD Using These Techniques Announced Last Friday! Inflammatory Bowel Disease Biobank For Healthy and Disease Patients Already 120 Enrolled, Goal is 1500 Drs. William J. Sandborn, John Chang, & Brigid Boland UCSD School of Medicine, Division of Gastroenterology
- 41. Inexpensive Consumer Time Series of Microbiome Now Possible Through Ubiome Data source: LS (Stool Samples); Sequencing and Analysis Ubiome
- 42. By Crowdsourcing, Ubiome Can Show I Have a Major Disruption of My Gut Microbiome (-) (+) LS Sample on September 24, 2014 Visit Ubiome in the Exponential Medicine Healthcare Innovation Lab
- 43. Where I Believe We are Headed: Predictive, Personalized, Preventive, & Participatory Medicine Will Grow to 1000, Then 10,000, Then 100,000 www.newsweek.com/2009/06/26/a-doctor-s-vision-of-the-future-of-medicine.html
- 44. Genetic Sequencing of Humans and Their Microbes Is a Huge Growth Area and the Future Foundation of Medicine Source: @EricTopol Twitter 9/27/2014
- 45. Thanks to Our Great Team! UCSD Metagenomics Team Weizhong Li Sitao Wu Calit2@UCSD Future Patient Team Jerry Sheehan Tom DeFanti Kevin Patrick Jurgen Schulze Andrew Prudhomme Philip Weber Fred Raab Joe Keefe Ernesto Ramirez Ayasdi Devi Ramanan Pek Lum JCVI Team Karen Nelson Shibu Yooseph Manolito Torralba SDSC Team Michael Norman Mahidhar Tatineni Robert Sinkovits Dell/R Systems Brian Kucic John Thompson UCSD Health Sciences Team William J. Sandborn Elisabeth Evans John Chang Brigid Boland David Brenner