Remote Invited Provocateur Lecture 2017 Innovation Lab on Quantitative Approaches to Biomedical Data Science: Challenges in our Understanding of the Microbiome San Diego, CA June 19, 2017

1. “Exploring the Dynamics of
The Microbiome in Health and Disease”
Remote Invited Provocateur Lecture
2017 Innovation Lab on Quantitative Approaches to Biomedical Data Science:
Challenges in our Understanding of the Microbiome
San Diego, CA
June 19, 2017
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 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
How Can
Data Science
Elucidate This
Dynamical System?

3. An N=1 Case Study of an Autoimmune Disease

4. As a Model for the Precision Medicine Initiative,
I Have Tracked My Internal Biomarkers To Understand My Body’s Dynamics
My Quarterly
Blood Draw
Calit2 64 Megapixel VROOM

5. Longitudinal Time Series Revealed
Oscillatory Behavior in an Immune Variable That 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)
Hypothesis:
Immune System
Is Coupling to
Time Changing
Gut Microbiome

6. We Downloaded Illumina Raw Reads from the Gut Microbiome
of Healthy and IBD Patients and Compared with My Time Series
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
Over 1.5 Years
Each Sample Has 100-200 Million Illumina Short Reads (100 bases)
Larry Smarr
(Colonic Crohn’s)

7. 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 Time Series,
IBD Patients, & Healthy Controls
Illumina HiSeq 2000 at JCVI
SDSC Gordon Data Supercomputer

8. Computational NextGen Sequencing Pipeline:
From Sequence to Taxonomy to Function
PI: (Weizhong Li, CRBS, UCSD):
NIH R01HG005978 (2010-2013, $1.1M)
11,000
Genomes

9. Results Include Relative Abundance of Hundreds of Microbial Species
Average Over 250 Healthy People
From NIH Human Microbiome Project
Note Log Scale
Clostridium difficile

10. Is The Microbiome Ecology Different in Health and Disease?

11. In a “Healthy” Gut Microbiome:
Large Taxonomy Variation, Low Protein Family Variation
Source: Nature, 486, 207-212 (2012)
Over 200 People

12. We Supercomputed ~10,000 Microbiome Protein Families (KEGGs)
Which Clearly Separate Disease Subtypes Using PCA
Source: Computing Weizhong Li, PCA Mehrdad Yazdani, Calit2
Implies That Disease
Subtypes Have
Distinct Protein
Distributions
Computing KEGGs
Required
10 CPU-Years
On SDSC’s Gordon
Supercomputer

13. Using Machine Learning to Determine Major Differences
Between Gut Microbiome in Health and Disease
IEEE International Conference on Big Data (December 5-8, 2016)

14. Using Machine Learning to Discover the Protein Families
That Differentiate Between the Disease and Healthy Cohorts
Selected
from
Top 100
KS
Scores
Selected
by
Random
Forest
Classifier
From
Holdout
Set
Next Step:
Investigate
Biochemical
Pathways of
Key KEGGs
Source: Computing by Weizhong Li, JCVI; ML by Mehrdad Yazdani, Calit2

15. From N=1
to a Population of People with Disease
Inflammatory Bowel Disease Biobank
For Healthy and Disease Patients
Drs. William J. Sandborn, John Chang, & Brigid Boland
UCSD School of Medicine, Division of Gastroenterology
Knight Lab is Sequencing
~200 Biobank Stool Samples Now
Announced November 7, 2014

16. Lessons From Ecological Dynamics I:
Invasive Species Dominate After Major Species Destroyed
”In many areas following these burns
invasive species are able to establish themselves,
crowding out native species.”
Source: Ponderosa Pine Fire Ecology
http://cpluhna.nau.edu/Biota/ponderosafire.htm

17. Almost All Abundant Species (≥1%) in Healthy Subjects
Are Severely Depleted in Larry’s Gut Microbiome At Maximum Inflammation
• Red Bars Are Relative
Abundance of Top 20
Species in Healthy
People
• Blue Bars Are Relative
Abundance of Same
Species in Larry Smarr
When CRP=27
(12/28/2011)

18. Invasive Species Take Over Gut Microbiome
in Disease State
152x
765x
148x
849x
483x
220x
201x
522x
169x
Source: Sequencing JCVI; Analysis Weizhong Li, UCSD
LS December 28, 2011 Stool Sample
• Blue Bars Are
Relative Abundance
of Top 20 Species
in Larry Smarr
When CRP=27
(12/28/2011)
• Red Bars Are
Relative Abundance
of Same Species
in Healthy People

19. What Can the Dynamics of the Microbiome Ecology Tell Us?

20. I Have Been Collaborating with the UCSD Knight Lab
On Analyzing My Stool Time Series
Larry’s 40 Stool Samples Over 3.5 Years
to Rob’s lab on April 30, 2015

21. Microbiome Ecology Highly Disrupted
by Medical Intervention, But Sometimes Returns to Prior State
Data from Dr. Embriette Hyde,
Rob Knight Group, UCSD
July 2015 Colonoscopy
Shift Induced
By Bowel Cleanse

22. Lessons from Ecological Dynamics II:
Gut Microbiome Has Multiple Relatively Stable Equilibria
“The Application of Ecological Theory Toward an Understanding of the Human Microbiome,”
Elizabeth Costello, Keaton Stagaman, Les Dethlefsen, Brendan Bohannan, David Relman
Science 336, 1255-62 (2012)

23. PCoA by Justine Debelius and Jose Navas,
Knight Lab, UCSD
My 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 Symptoms
Weight Loss
7/1/12 to 12/1/14
Blue Balls on
Diagram to the Right
Principal Coordinate Analysis of
Microbiome Ecology
Weight Data from Larry Smarr, Calit2, UCSD
Weekly Weight
Few IBD Symptoms
Weight Gain 1/1/14 to 8/1/15
Red Balls on
Diagram to the Right

24. Normal <600
Grand Challenge:
What Is the Couplings Between the Immune System and Microbiome?
Innate Immune System
Normal 50 to 200
Adaptive Immune System

25. Thanks to Our Great 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
Embriette Hyde
Jose Navas
Gail Ackermann
Greg Humphrey
William J. Sandborn Lab
Elisabeth Evans
John Chang
Brigid Boland
Dell/R Systems
Brian Kucic
John Thompson