Seminar Department of Bioengineering University of California, San Diego February 12, 2016

1. “Supercomputing Your Inner Microbiome”
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 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. 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 of Computing 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 Computing First 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 UC San 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 Wave Platform
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 Institute Has 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 to a 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 to Track My Internal Biomarkers
To Understand My Body’s Dynamics
My Blood Draw
YesterdayCalit2 64 Megapixel VROOM

10. 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

11. Adding Stool Tests Revealed
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
Threading 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 Colon
Liver
Small Intestine
Diseased Sigmoid Colon
Cross Section
MRI Jan 2012
Severe Colon
Wall Swelling

13. Evolving Microbiome Environmental Pressures:
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
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

15. To Understand the Interaction 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 In 2016 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 Raw Illumina 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 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

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

20. Using Microbiome Profiles to Survey 155 Subjects
for Unhealthy Candidates

21. Comparing Gut Microbiome of Healthy People
with Ileal Crohn’s, Ulcerative Colitis, and Colonic Crohn’s Patients

22. We Found Major State 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 I Had 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 Likely a 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 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)

26. Ileal Crohn’s and UC 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
H CCD 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 the Relative 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 HE11529 to 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 Research Shows 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 Colitis Has 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 Has Few 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 from Perturbed 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 Institute Has Developed
Interactive Scalable Visualization for Biological Networks
20,000 Samples
60,000 OTUs
18 Million Edges
Runs Native on 64Million Pixels

36. Time Series Reveals Oscillations 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 Weekly Weight
Time Period of 16S
Source: Larry Smarr, UCSD

38. LS Weekly Weight During 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 We Are 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 My Gut 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
16S Evolution 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 Over 3.5 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 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
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 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

45. Center for
Microbiome
Innovation
Seminars
Faculty
Hiring
Education
UCSD Microbial Sciences Initiative
Instrument
Cores
Seed Grants
Fellowships
Chancellor Khosla Launched the UC San Diego
Microbiome and Microbial Sciences Initiative October 29, 2015

46. UC San Diego Microbiome 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 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
Jose Navas
Gail Ackermann
Greg Humphrey
William J. Sandborn Lab
Elisabeth Evans
John Chang
Brigid Boland
Dell/R Systems
Brian Kucic
John Thompson