Invited Talk, New Applications of Computer Analysis to Biomedical Data Sets, QB3 Seminar, San Francisco, CA - May 28, 2015

1. “Using Data Analytics to Discover
the 100 Trillion Bacteria Living Within Each of Us”
Invited Talk
New Applications of Computer Analysis to Biomedical Data Sets
QB3 Seminar
San Francisco, CA
May 28, 2015
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. I Have Turned My Body into
a Genomic and Biomarker Observatory
One Blood Draw
For MeCalit2 64 Megapixel VROOM
Over 100 Blood and Stool
Biomarker Time Series

3. 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

4. Adding Stool Tests Revealed
Oscillatory Behavior in an Immune Variable Which is Antibacterial
Normal Range
<7.3 µg/mL
124x Upper Limit
Lactoferrin is a Protein Shed from Neutrophils -
An Antibacterial that Sequesters Iron
Typical
Lactoferrin Value for
Active Inflammatory
Bowel Disease
(IBD)

5. Dynamical Innate and Adaptive Immune Oscillations
From Stool Samples
Normal <600
Innate Immune System
Normal 50 to 200
Adaptive Immune System

6. Methods Needed for Biomarker Time Series Correlations -
Adding 40 Microbial Ecology Time Series
Immune &
Inflammation
Variables
Weekly
Symptoms
Pharma
Therapies
Stool
Samples

7. How Will Detailed Knowledge of Microbiome Ecology
Radically Change Medicine and Wellness?
99% of Your
DNA Genes
Are in Microbe Cells
Not Human Cells
Your Body Has 10 Times
As Many Microbe Cells As Human Cells
Challenge:
Map Out Microbial Ecology and Function
in Health and Disease States

8. For Deep Analysis of Changes in the Gut Microbiome Ecology
Our Team Compared a Healthy Population to Patients with Disease
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 2.7 Trillion DNA Bases
Inflammatory Bowel Disease Patients
250 Subjects
1 Point in Time
7 Points in Time
Larry Smarr
(Colonic Crohn’s)
Example: Inflammatory Bowel Disease (IBD)

9. 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-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

10. We Found Major State Shifts in Microbial Ecology Phyla
Between Healthy and Two Forms of IBD
Most
Common
Microbial
Phyla
Average Healthy
Average
Ulcerative Colitis
Average
Colonic Crohn’s Disease
Average
Ileal Crohn’s Disease
Collapse of Bacteroidetes
Explosion of Actinobacteria
Explosion of
Proteobacteria
Hybrid of UC and CD
High Level of Archaea
Based on ~10,000 Bacteria, Archaea, Viruses
Whose Genomes are Known

11. Dell Analytics Separates The 4 Patient Types in Our Data
Using Our Microbiome Species Data
Source: Thomas Hill, Ph.D.
Executive Director Analytics
Dell | Information Management Group, Dell Software
Healthy
Ulcerative Colitis
Colonic Crohn’s
Ileal Crohn’s

12. 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

13. 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
Now Extending 7 to 40 Time Samples

14. Large Changes in Genus Relative Abundances Observed
Over the Seven Smarr Samples (1.5 Years)-Clearly Not Healthy!

15. Can We Extend These Approaches from Hundreds to Thousands
by Using Crowdsourcing Gut Microbiome Sequencing?
Source: Ubiome provided kits and taxonomic analysis
Graph by Larry Smarr
Three Years of
Larry Smarr’s
Gut Microbiome
(38 Samples)
Phyla Distribution
Analyzed
Using Ubiome

16. Applying Ayasdi to Ubiome Gut Microbiome
Genus Relative Abundance Time Series
Analysis by Mehrdad Yazdani, Calit2; Devi Ramanan, Ubiome
Metric: Variance Normalized Euclidean with PCA Lens
of Ecology Change Over All Time Pairs

17. Sorted by KS stat
Apply Ayasdi Statistical Tests
Using Ayasdi to Discover Abrupt Changes in Time
in Gut Microbiome Ecology
Large Ecology Change
In Short Time Interval
Analysis by Mehrdad Yazdani, Calit2; Devi Ramanan, Ubiome

18. Abrupt Changes Discovered in
Genus Faecalibacterium Relative Abundance Time Series
Source: Ubiome provided kits and taxonomic analysis
Graph by Larry Smarr
35x

19. Genetic Sequencing of Humans and Their Microbes
Is a Huge Growth Area and the Future Foundation of Medicine
Source: @EricTopol
Twitter 9/27/2014

20. 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
JCVI Team
Karen Nelson
Shibu Yooseph
Manolito Torralba
SDSC Team
Michael Norman
Ilkay Altintas
Shweta Purawat
Mahidhar Tatineni
Robert Sinkovits
UCSD Health Sciences Team
William J. Sandborn
Elisabeth Evans
John Chang
Brigid Boland
David Brenner
Dell/R Systems and Dell Analytics
Brian Kucic
John Thompson
Tom Hill