Invited Remote Presentation To Weekly Team Meeting Dermot McGovern, Director, Translational Medicine, Inflammatory Bowel and Immunobiology Research Institute, Gastroenterology, Cedars-Sinai, Los Angeles, CA April 28, 2015
Recognizing the Patterns Within: How Biomedical Data Can Reveal Health vs. Di...Larry Smarr
Keynote
Session on Challenges of Pattern Recognition in Biomedical Data
The Pacific Symposium on Biocomputing (PSB) 2018
Big Island, HI
January 6, 2018
Tracking Large Variations in My Immune Biomarkers and My Gut Microbiome: Infl...Larry Smarr
This document provides a 3-sentence summary of a presentation by Dr. Larry Smarr on tracking changes in his immune biomarkers and gut microbiome in relation to inflammation, Crohn's disease, and colon cancer:
Over the past decade, Dr. Smarr has quantified over a billion data points on his body through measures like blood tests, MRI/CT scans, and analysis of his gut microbiome, discovering through this data that he has episodic chronic inflammation and Crohn's disease affecting his colon. By comparing his biomarkers and symptoms over time and visualizing his microbiome ecology, Dr. Smarr has gained insights into the dynamics and invasiveness of species in his gut microbiome as it relates to his autoimmune
Individual, Consumer-Driven Care of the Future: Taking Wellness One Step FurtherLarry Smarr
Calit2 Director Larry Smarr gives the closing keynote address to the 2nd annual Learning Conference on Integrated Delivery Systems in San Diego on May 7, 2014.
Big Data and Superorganism Genomics: Microbial Metagenomics Meets Human GenomicsLarry Smarr
This presentation on February 27, 2014 to NGS and the Future of Medicine at Illumina Headquarters in La Jolla, CA, was made by Calit2 Director Larry Smarr.
Using Dell’s HPC Cloud & Advanced Analytic Software to Discover Radical Chang...Larry Smarr
This document summarizes a talk given by Dr. Larry Smarr on how he used Dell's HPC Cloud and advanced analytics software to analyze over 300 human gut microbiome samples. He was able to discover distinct microbial signatures associated with health and different diseases like ulcerative colitis and Crohn's disease. Dell's analytics software effectively separated and classified the samples by health status and disease type using only a few key microbial species. This research could lead to new microbial diagnostics for inflammatory bowel diseases.
Using Data Analytics to Discover the 100 Trillion Bacteria Living Within Each...Larry Smarr
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.
Large Memory High Performance ComputingEnables Comparison Across Human Gut M...Larry Smarr
This document summarizes a talk about research analyzing gut microbiome data from patients with autoimmune diseases and healthy subjects. The research used large memory high performance computing on the Gordon supercomputer to analyze over 1.2 trillion DNA bases of metagenomic sequencing data from the gut microbiomes. Analysis found major shifts in microbial ecology between healthy subjects and those with Crohn's disease or ulcerative colitis. Therapies for one subject's Crohn's disease reduced certain phyla but others remained at high levels. The research aims to develop noninvasive microbial diagnostics and new therapeutic tools for managing the microbiome.
Recognizing the Patterns Within: How Biomedical Data Can Reveal Health vs. Di...Larry Smarr
Keynote
Session on Challenges of Pattern Recognition in Biomedical Data
The Pacific Symposium on Biocomputing (PSB) 2018
Big Island, HI
January 6, 2018
Tracking Large Variations in My Immune Biomarkers and My Gut Microbiome: Infl...Larry Smarr
This document provides a 3-sentence summary of a presentation by Dr. Larry Smarr on tracking changes in his immune biomarkers and gut microbiome in relation to inflammation, Crohn's disease, and colon cancer:
Over the past decade, Dr. Smarr has quantified over a billion data points on his body through measures like blood tests, MRI/CT scans, and analysis of his gut microbiome, discovering through this data that he has episodic chronic inflammation and Crohn's disease affecting his colon. By comparing his biomarkers and symptoms over time and visualizing his microbiome ecology, Dr. Smarr has gained insights into the dynamics and invasiveness of species in his gut microbiome as it relates to his autoimmune
Individual, Consumer-Driven Care of the Future: Taking Wellness One Step FurtherLarry Smarr
Calit2 Director Larry Smarr gives the closing keynote address to the 2nd annual Learning Conference on Integrated Delivery Systems in San Diego on May 7, 2014.
Big Data and Superorganism Genomics: Microbial Metagenomics Meets Human GenomicsLarry Smarr
This presentation on February 27, 2014 to NGS and the Future of Medicine at Illumina Headquarters in La Jolla, CA, was made by Calit2 Director Larry Smarr.
Using Dell’s HPC Cloud & Advanced Analytic Software to Discover Radical Chang...Larry Smarr
This document summarizes a talk given by Dr. Larry Smarr on how he used Dell's HPC Cloud and advanced analytics software to analyze over 300 human gut microbiome samples. He was able to discover distinct microbial signatures associated with health and different diseases like ulcerative colitis and Crohn's disease. Dell's analytics software effectively separated and classified the samples by health status and disease type using only a few key microbial species. This research could lead to new microbial diagnostics for inflammatory bowel diseases.
Using Data Analytics to Discover the 100 Trillion Bacteria Living Within Each...Larry Smarr
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.
Large Memory High Performance ComputingEnables Comparison Across Human Gut M...Larry Smarr
This document summarizes a talk about research analyzing gut microbiome data from patients with autoimmune diseases and healthy subjects. The research used large memory high performance computing on the Gordon supercomputer to analyze over 1.2 trillion DNA bases of metagenomic sequencing data from the gut microbiomes. Analysis found major shifts in microbial ecology between healthy subjects and those with Crohn's disease or ulcerative colitis. Therapies for one subject's Crohn's disease reduced certain phyla but others remained at high levels. The research aims to develop noninvasive microbial diagnostics and new therapeutic tools for managing the microbiome.
Finding the Patterns in the Big Data From Human Microbiome EcologyLarry Smarr
This document summarizes a talk on analyzing human microbiome data to better understand health and disease. It discusses how sequencing and supercomputing is used to map microbial ecology in hundreds of people. Advanced analytics tools like Ayasdi are helping discover patterns separating healthy from disease states like inflammatory bowel disease. Future goals include applying these techniques to larger datasets and using molecular networks to better understand disease development at the genetic and protein level.
Linking Phenotype Changes to Internal/External Longitudinal Time Series in a ...Larry Smarr
This document summarizes Dr. Larry Smarr's presentation on quantifying physiological data from his own body over the past decade. Some key points:
- Smarr has gathered longitudinal time series data on over 200 biomarkers and microbiome samples to study phenotype changes from his autoimmune disease.
- Sensors have tracked daily metrics like weight, activity levels, and symptoms, revealing oscillations and episodes of inflammation.
- Imaging and biomarker analysis identified the specific location and nature of his Crohn's disease.
- Analysis of his microbiome samples over time uncovered a shift in microbial ecology that correlated with changes in drugs and symptoms.
- Expanding this type of personalized, quantitative approach could transform medicine by deeply characterizing individuals
Discovering the 100 Trillion Bacteria Living Within Each of UsLarry Smarr
This document provides a summary of a lecture on the human microbiome given by Dr. Larry Smarr. Some key points:
- The human microbiome refers to the trillions of bacteria that live within the human body. Each person contains 100 trillion bacteria, outnumbering human cells.
- Research into the microbiome is a rapidly growing field that provides insights into health and disease. The microbiome plays a role in processes like drug metabolism and immunity.
- The microbiome is established early in life and influenced by factors like birth method and antibiotic use in the first years. This early development can impact future health.
- Microbiome imbalances are linked to diseases like inflammatory bowel disease. New treatments are
The document discusses supercomputing analysis of the human microbiome. It describes how the human body hosts 100 trillion microorganisms containing 300 times as many genes as human DNA. Dr. Smarr has been collecting extensive personal health data over 7 years, including microbiome samples, to study the coupled immune-microbial system. Analyzing this data requires elaborate software running on high performance computers. The analysis can compare individuals with diseases to healthy populations and track disease progression over time.
Tracking Immune Biomarkers and the Human Gut Microbiome: Inflammation, Croh...Larry Smarr
Larry Smarr presented on tracking immune biomarkers and the human gut microbiome in relation to inflammation, Crohn's disease, and colon cancer. He turned his own body into a "genomic observatory" by tracking over 100 of his own blood and stool biomarkers and sequencing his gut microbiome multiple times. His research found high levels of inflammation and an abundance of Fusobacteria in his microbiome when inflammation was highest. Following antibiotic and steroid therapy, inflammation and Fusobacteria were greatly reduced. This integrated personal omics approach provides insights into the links between inflammation, gut microbes, and colon cancer risk.
Machine Learning Opportunities in the Explosion of Personalized Precision Med...Larry Smarr
This document summarizes a presentation given by Dr. Larry Smarr on machine learning opportunities in personalized precision medicine using massive datasets from individuals. Some key points:
- Smarr has tracked over 100 of his own blood biomarkers and microbiome over time, revealing health issues like chronic inflammation.
- Analysis of Smarr's microbiome alongside others revealed major shifts between healthy and disease states that can be classified using machine learning.
- Further analysis of microbial proteins identified which were over or under abundant in disease, helping characterize Smarr's own condition.
- Smarr's microbiome appeared to undergo an abrupt shift between two stable states correlated with a change in symptoms and drug therapy.
Stability in Health vs. Abrupt Changes in Disease in the Human Gut Microbiome...Larry Smarr
The document summarizes research on analyzing changes in human gut microbiome composition over time using 16S rRNA gene sequencing and the UniFrac metric. It presents findings that:
1) A healthy person's gut microbiome composition generally remains stable over periods of 60 days based on samples from multiple body sites.
2) In contrast, for people with C. difficile infections, their gut microbiome composition can abruptly shift to a healthy state within days after a fecal microbiota transplant from a healthy donor.
3) Analysis of one individual's gut microbiome samples over 3.5 years found the composition abruptly shifted between two distinct stable states that correlated with changes in symptoms and weight, before and after a
Dynamics of Your Gut Microbiome in Health and DiseaseLarry Smarr
This document summarizes a presentation by Dr. Larry Smarr on the dynamics of the gut microbiome in health and disease. It discusses how the gut microbiome contains hundreds of microbial species that vary significantly between healthy and diseased states. Dr. Smarr has tracked his own gut microbiome and biomarkers over time, discovering an autoimmune disease. He is now collaborating on a project combining deep metagenomic sequencing and supercomputing to map differences in the gut microbiome between healthy and inflammatory bowel disease patients.
Observing the Dynamics of the Human Immune System Coupled to the Microbiome i...Larry Smarr
Calit2 Director Larry Smarr delivered this presentation to the CASIS Workshop on Biomedical Research Aboard the ISS at Columbia University in NY, NY, on May 28, 2014.
The Human Microbiome and the Revolution in Digital HealthLarry Smarr
2014.01.22
Calit2 Director Larry Smarr speaks as part of the Pensacola Evening Lecture Series, organized by the Florida Institute for Human and Machine Cognition, in Pensacola, FL.
Using Supercomputers to Discover the 100 Trillion Bacteria Living Within Each...Larry Smarr
This document summarizes a talk given by Dr. Larry Smarr on using supercomputers to analyze the human microbiome. It discusses how next-generation sequencing and analysis of microbial DNA reveals major differences between healthy and diseased gut microbiomes. Computational analysis of Smarr's own microbiome time series, in addition to data from hundreds of individuals, provides insights into inflammatory bowel disease. Large supercomputers and visualization resources were crucial for processing and comparing petabytes of sequencing data to advance understanding of microbiome dynamics and their links to human health and disease.
This document discusses how advances in genetic sequencing and computing are enabling humans to read and understand the "software of life" encoded in their human and microbiome DNA. It notes that the human microbiome contains millions of microbial genes compared to the 23,000 genes in human cells. The author details how the cost of DNA sequencing has fallen over 100,000-fold, allowing sequencing of both human and microbial genomes. Machine learning will be needed to understand differences between healthy and diseased states by analyzing enormous genomic and microbiome datasets. The author provides an example of analyzing their own gut microbiome over time and comparing to healthy/IBD populations.
The Human Gut Microbiome: A New Diagnostic for Disease?Larry Smarr
The document summarizes research on analyzing human gut microbiomes to better understand health and disease. It describes how the researchers used 25 CPU-years of computing and analyzed over 2.7 trillion DNA bases to map and compare the microbial ecology between healthy individuals and those with inflammatory bowel disease. Their analyses revealed major differences in microbial phyla between healthy and diseased states. Machine learning techniques discovered specific protein families that differentiate disease subtypes and healthy cohorts. The researchers aim to develop a novel microbiome-based diagnostic for disease by continuing to study gut microbiome dynamics using larger datasets and populations over time.
Measuring the Human Brain-Gut Microbiome-Immune System Dynamics: a Big Data C...Larry Smarr
This document summarizes a talk given by Dr. Larry Smarr on measuring the human brain-gut microbiome-immune system dynamics and the challenges of analyzing big data related to these systems. It discusses how understanding the interactions between human genetics, behavior, and the human microbiome is important for understanding human health and disease. As an example, it details Dr. Smarr's own research into his Crohn's disease, analyzing his gut microbiome, immune markers, genetics, and symptoms over time. It shows how computational analysis of metagenomic sequencing data from many healthy and IBD patients can reveal differences in microbial ecology and gene families between health and disease states.
Inflammation, Gut Microbiome, Bacteriophages, and the Initiation of Colorecta...Larry Smarr
This document summarizes a lecture on inflammation, the gut microbiome, bacteriophages, and the initiation of colorectal cancer. The lecturer discusses his personal experience with Crohn's disease and extensive self-monitoring. Analysis of his microbiome data over time revealed shifts correlated with inflammation levels. Certain bacteria like Fusobacterium nucleatum and E. coli were found at highest levels during peak inflammation. The lecturer's genetic analysis revealed SNPs linked to autoimmune disease that may have predisposed him to Crohn's. The lecture explores the role of the microbiome in mediating inflammation and cancer initiation in the gut.
Finding the Patterns in the Big Data From Human Microbiome EcologyLarry Smarr
This document summarizes a talk on analyzing human microbiome data to better understand health and disease. It discusses how sequencing and supercomputing is used to map microbial ecology in hundreds of people. Advanced analytics tools like Ayasdi are helping discover patterns separating healthy from disease states like inflammatory bowel disease. Future goals include applying these techniques to larger datasets and using molecular networks to better understand disease development at the genetic and protein level.
Linking Phenotype Changes to Internal/External Longitudinal Time Series in a ...Larry Smarr
This document summarizes Dr. Larry Smarr's presentation on quantifying physiological data from his own body over the past decade. Some key points:
- Smarr has gathered longitudinal time series data on over 200 biomarkers and microbiome samples to study phenotype changes from his autoimmune disease.
- Sensors have tracked daily metrics like weight, activity levels, and symptoms, revealing oscillations and episodes of inflammation.
- Imaging and biomarker analysis identified the specific location and nature of his Crohn's disease.
- Analysis of his microbiome samples over time uncovered a shift in microbial ecology that correlated with changes in drugs and symptoms.
- Expanding this type of personalized, quantitative approach could transform medicine by deeply characterizing individuals
Discovering the 100 Trillion Bacteria Living Within Each of UsLarry Smarr
This document provides a summary of a lecture on the human microbiome given by Dr. Larry Smarr. Some key points:
- The human microbiome refers to the trillions of bacteria that live within the human body. Each person contains 100 trillion bacteria, outnumbering human cells.
- Research into the microbiome is a rapidly growing field that provides insights into health and disease. The microbiome plays a role in processes like drug metabolism and immunity.
- The microbiome is established early in life and influenced by factors like birth method and antibiotic use in the first years. This early development can impact future health.
- Microbiome imbalances are linked to diseases like inflammatory bowel disease. New treatments are
The document discusses supercomputing analysis of the human microbiome. It describes how the human body hosts 100 trillion microorganisms containing 300 times as many genes as human DNA. Dr. Smarr has been collecting extensive personal health data over 7 years, including microbiome samples, to study the coupled immune-microbial system. Analyzing this data requires elaborate software running on high performance computers. The analysis can compare individuals with diseases to healthy populations and track disease progression over time.
Tracking Immune Biomarkers and the Human Gut Microbiome: Inflammation, Croh...Larry Smarr
Larry Smarr presented on tracking immune biomarkers and the human gut microbiome in relation to inflammation, Crohn's disease, and colon cancer. He turned his own body into a "genomic observatory" by tracking over 100 of his own blood and stool biomarkers and sequencing his gut microbiome multiple times. His research found high levels of inflammation and an abundance of Fusobacteria in his microbiome when inflammation was highest. Following antibiotic and steroid therapy, inflammation and Fusobacteria were greatly reduced. This integrated personal omics approach provides insights into the links between inflammation, gut microbes, and colon cancer risk.
Machine Learning Opportunities in the Explosion of Personalized Precision Med...Larry Smarr
This document summarizes a presentation given by Dr. Larry Smarr on machine learning opportunities in personalized precision medicine using massive datasets from individuals. Some key points:
- Smarr has tracked over 100 of his own blood biomarkers and microbiome over time, revealing health issues like chronic inflammation.
- Analysis of Smarr's microbiome alongside others revealed major shifts between healthy and disease states that can be classified using machine learning.
- Further analysis of microbial proteins identified which were over or under abundant in disease, helping characterize Smarr's own condition.
- Smarr's microbiome appeared to undergo an abrupt shift between two stable states correlated with a change in symptoms and drug therapy.
Stability in Health vs. Abrupt Changes in Disease in the Human Gut Microbiome...Larry Smarr
The document summarizes research on analyzing changes in human gut microbiome composition over time using 16S rRNA gene sequencing and the UniFrac metric. It presents findings that:
1) A healthy person's gut microbiome composition generally remains stable over periods of 60 days based on samples from multiple body sites.
2) In contrast, for people with C. difficile infections, their gut microbiome composition can abruptly shift to a healthy state within days after a fecal microbiota transplant from a healthy donor.
3) Analysis of one individual's gut microbiome samples over 3.5 years found the composition abruptly shifted between two distinct stable states that correlated with changes in symptoms and weight, before and after a
Dynamics of Your Gut Microbiome in Health and DiseaseLarry Smarr
This document summarizes a presentation by Dr. Larry Smarr on the dynamics of the gut microbiome in health and disease. It discusses how the gut microbiome contains hundreds of microbial species that vary significantly between healthy and diseased states. Dr. Smarr has tracked his own gut microbiome and biomarkers over time, discovering an autoimmune disease. He is now collaborating on a project combining deep metagenomic sequencing and supercomputing to map differences in the gut microbiome between healthy and inflammatory bowel disease patients.
Observing the Dynamics of the Human Immune System Coupled to the Microbiome i...Larry Smarr
Calit2 Director Larry Smarr delivered this presentation to the CASIS Workshop on Biomedical Research Aboard the ISS at Columbia University in NY, NY, on May 28, 2014.
The Human Microbiome and the Revolution in Digital HealthLarry Smarr
2014.01.22
Calit2 Director Larry Smarr speaks as part of the Pensacola Evening Lecture Series, organized by the Florida Institute for Human and Machine Cognition, in Pensacola, FL.
Using Supercomputers to Discover the 100 Trillion Bacteria Living Within Each...Larry Smarr
This document summarizes a talk given by Dr. Larry Smarr on using supercomputers to analyze the human microbiome. It discusses how next-generation sequencing and analysis of microbial DNA reveals major differences between healthy and diseased gut microbiomes. Computational analysis of Smarr's own microbiome time series, in addition to data from hundreds of individuals, provides insights into inflammatory bowel disease. Large supercomputers and visualization resources were crucial for processing and comparing petabytes of sequencing data to advance understanding of microbiome dynamics and their links to human health and disease.
This document discusses how advances in genetic sequencing and computing are enabling humans to read and understand the "software of life" encoded in their human and microbiome DNA. It notes that the human microbiome contains millions of microbial genes compared to the 23,000 genes in human cells. The author details how the cost of DNA sequencing has fallen over 100,000-fold, allowing sequencing of both human and microbial genomes. Machine learning will be needed to understand differences between healthy and diseased states by analyzing enormous genomic and microbiome datasets. The author provides an example of analyzing their own gut microbiome over time and comparing to healthy/IBD populations.
The Human Gut Microbiome: A New Diagnostic for Disease?Larry Smarr
The document summarizes research on analyzing human gut microbiomes to better understand health and disease. It describes how the researchers used 25 CPU-years of computing and analyzed over 2.7 trillion DNA bases to map and compare the microbial ecology between healthy individuals and those with inflammatory bowel disease. Their analyses revealed major differences in microbial phyla between healthy and diseased states. Machine learning techniques discovered specific protein families that differentiate disease subtypes and healthy cohorts. The researchers aim to develop a novel microbiome-based diagnostic for disease by continuing to study gut microbiome dynamics using larger datasets and populations over time.
Measuring the Human Brain-Gut Microbiome-Immune System Dynamics: a Big Data C...Larry Smarr
This document summarizes a talk given by Dr. Larry Smarr on measuring the human brain-gut microbiome-immune system dynamics and the challenges of analyzing big data related to these systems. It discusses how understanding the interactions between human genetics, behavior, and the human microbiome is important for understanding human health and disease. As an example, it details Dr. Smarr's own research into his Crohn's disease, analyzing his gut microbiome, immune markers, genetics, and symptoms over time. It shows how computational analysis of metagenomic sequencing data from many healthy and IBD patients can reveal differences in microbial ecology and gene families between health and disease states.
Inflammation, Gut Microbiome, Bacteriophages, and the Initiation of Colorecta...Larry Smarr
This document summarizes a lecture on inflammation, the gut microbiome, bacteriophages, and the initiation of colorectal cancer. The lecturer discusses his personal experience with Crohn's disease and extensive self-monitoring. Analysis of his microbiome data over time revealed shifts correlated with inflammation levels. Certain bacteria like Fusobacterium nucleatum and E. coli were found at highest levels during peak inflammation. The lecturer's genetic analysis revealed SNPs linked to autoimmune disease that may have predisposed him to Crohn's. The lecture explores the role of the microbiome in mediating inflammation and cancer initiation in the gut.
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
Capturing the Interactive Dynamics of the Human Host/Microbiome SystemLarry Smarr
1) Dr. Larry Smarr reported on results from a decade of self-quantification, including longitudinal measurements of his gut microbiome and over 100 biomarkers, to better understand the interactive dynamics of the human-microbiome system in health and disease.
2) Analysis found that Smarr's gut microbiome was unstable with high levels of E. coli, unlike healthy individuals, and computational analysis linked this dysbiosis to chronic inflammation identified in his biomarkers.
3) Smarr underwent robotic colon resection surgery in 2016, and analysis found his gut microbiome changed more dramatically after surgery than from colonoscopy or typical differences between individuals, eventually achieving a healthy post-surgical state.
Using Genetic Sequencing to Unravel the Dynamics of Your Superorganism BodyLarry Smarr
The document summarizes a talk given by Dr. Larry Smarr on his research tracking extensive health data on himself over many years. Some key points:
1) Smarr collected over a billion data points defining his body, including DNA sequencing, medical images, and daily biomarkers, revealing episodic inflammation related to his Crohn's disease.
2) Analysis of his gut microbiome via metagenomic sequencing showed many typically abundant bacterial species were severely depleted compared to healthy individuals.
3) Tracking changes over time demonstrated the coupled dynamics of his immune system and gut microbiome in response to therapies, similar to ecological models of invasive species dominating after natives are disturbed.
The Systems Biology Dynamics of the Human Immune System and Gut MicrobiomeLarry Smarr
This document summarizes Dr. Larry Smarr's talk on modeling the human immune system and gut microbiome dynamics. It discusses how the growing diversity of gut bacteria after birth helps train the immune system. In health, constant feedback between the immune system and microbiome leads to homeostasis, but in diseases like Crohn's, this balance fails. The talk demonstrates this dysbiotic state using data from Dr. Smarr's own gut microbiome and biomarkers over five years. It reviews efforts to computationally model this important biological system.
Exploring the Dynamics of The Microbiome in Health and DiseaseLarry Smarr
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
Quantifying the Time Progression of a Human Autoimmune Disease using Genome S...Larry Smarr
Larry Smarr has been collecting extensive data on his own health for over 5 years to study his diagnosis of Crohn's disease. Analysis of this data using genome sequencing and supercomputers has demonstrated the episodic evolution of his coupled immune-microbial system. High resolution metagenomic sequencing at JCVI and computational analysis with several CPU-decades of supercomputer time at SDSC has revealed the complex time-varying dynamics of Smarr's microbial ecology, shedding light on the autoimmune disease process. Comparisons to data from healthy individuals and those with IBD from the NIH Human Microbiome Project provide insights into how inflammation can alter the gut microbiome.
Know Thyself: Quantifying Your Human Body and Its One Hundred Trillion MicrobesLarry Smarr
Understanding Cultures and Addressing Disparities in Society: Degrees of Health and Well-Being Public Lecture Series
University of California, San Diego
January 20, 2016
In a speech for the Global Health Program at the Council on Foreign Relations in New York City, Calit2 director Larry Smarr addresses the issue of biological diversity and the importance of monitoring the microbiome.
Mapping the Human Gut Microbiome in Health and Disease Using Sequencing, Supe...Larry Smarr
Invited Talk Delivered by Mehrdad Yazdani, Calit2 Ayasdi Sponsored Lunch & Learn American Society of Human Genetics (ASHG) San Diego Convention Center October 19, 2014
Quantifying Your Dynamic Human Body (Including Its Microbiome), Will Move Us ...Larry Smarr
Invited Presentation Microbiology and the Microbiome and the Implications for Human Health Analytic, Life Science & Diagnostic Association (ALDA) 2016 Senior Management Conference
Half Moon Bay, CA
October 3, 2016
Toward Novel Human Microbiome Surveillance Diagnostics to Support Public HealthLarry Smarr
The document discusses ongoing research into understanding the human microbiome and its role in health and disease. It outlines how sequencing costs have dropped dramatically, enabling analysis of both human and microbial genomes. Several studies are highlighted that use microbiome profiling to differentiate between healthy individuals and those with various forms of inflammatory bowel disease.
Assay Lab Within Your Body: Biometrics and BiomesLarry Smarr
This document summarizes a lecture about analyzing the human microbiome and its relationship to human health. It discusses how the human body contains 100 trillion microbial cells that contain 100 times as many genes as human DNA. Analysis of the speaker's own gut microbiome over time revealed changes in bacterial phyla between healthy and inflammatory bowel disease states. Collecting biomarkers from the speaker's body over years showed oscillations linked to gut microbes and immune response. Ongoing research aims to better understand dynamics of the human immune system and gut microbiome.
Assay Lab Within Your Body: Biometrics and BiomesLarry Smarr
This document summarizes a lecture about analyzing the human microbiome and its relationship to human health. It discusses how the human body contains 100 trillion microbial cells that contain 100 times as many genes as human DNA. Recent advances now allow sequencing these microbial genomes and analyzing massive datasets to map the dynamics of the immune-microbial system and its connection to disease states. A key focus is generating high-resolution time series data of the gut microbiome and immune variables from large cohorts to understand how they influence conditions like inflammatory bowel disease. There is potential to design gut microbes as sensors of disease states by programming them to detect specific conditions.
Similar to Using Supercomputing & Advanced Analytic Software to Discover Radical Changes in the Human Microbiome in Health and Disease (18)
My Remembrances of Mike Norman Over The Last 45 YearsLarry Smarr
Mike Norman has been a leader in computational astrophysics for over 45 years. Some of his influential work includes:
- Cosmic jet simulations in the early 1980s which helped explain phenomena from galactic centers.
- Pioneering the use of adaptive mesh refinement in the 1990s to achieve dynamic load balancing on supercomputers.
- Massive cosmology simulations in the late 2000s with over 100 trillion particles using thousands of processors across multiple supercomputing sites, producing petabytes of data.
- Developing end-to-end workflows in the 2000s to couple supercomputers, high-speed networks, and large visualization systems to enable real-time analysis of extremely large astrophysics simulations.
Metagenics How Do I Quantify My Body and Try to Improve its Health? June 18 2019Larry Smarr
Larry Smarr discusses quantifying his body and health over time through extensive self-tracking. He measures various biomarkers through regular blood tests and analyzes his gut microbiome by sequencing stool samples. This revealed issues like chronic inflammation and an unhealthy microbiome. Smarr then took steps like a restricted eating window and increasing plant diversity in his diet, which reversed metabolic syndrome issues and correlated with shifts in his microbiome ecology. His goal is to continue precisely measuring factors like toxins, hormones, gut permeability and food/supplement impacts to further optimize his health.
Panel: Reaching More Minority Serving InstitutionsLarry Smarr
This document discusses engaging more minority serving institutions (MSIs) in cyberinfrastructure development through regional networks. It provides data showing the importance of MSIs like historically black colleges and universities (HBCUs) in educating underrepresented minority students in STEM fields. Regional networks can help equalize opportunities by assisting MSIs in overcoming barriers to resources through training, networking infrastructure support, and helping institutions obtain necessary staffing and funding. Strategies mentioned include collaborating with MSIs on grants and addressing issues identified in surveys like lack of vision for data use beyond compliance. The goal is to broaden participation in STEAM fields by leveraging the success MSIs have shown in supporting underrepresented students.
Global Network Advancement Group - Next Generation Network-Integrated SystemsLarry Smarr
This document summarizes a presentation on global petascale to exascale workflows for data intensive sciences. It discusses a partnership convened by the GNA-G Data Intensive Sciences Working Group with the mission of meeting challenges faced by data-intensive science programs. Cornerstone concepts that will be demonstrated include integrated network and site resource management, model-driven frameworks for resource orchestration, end-to-end monitoring with machine learning-optimized data transfers, and integrating Qualcomm's GradientGraph with network services to optimize applications and science workflows.
Wireless FasterData and Distributed Open Compute Opportunities and (some) Us...Larry Smarr
This document discusses opportunities for ESnet to support wireless edge computing through developing a strategy around self-guided field laboratories (SGFL). It outlines several potential science use cases that could benefit from wireless and distributed computing capabilities, both in the short term through technologies like 5G, LoRa and Starlink, and longer term through the vision of automated SGFL. The document proposes some initial ideas for deploying and testing wireless edge computing technologies through existing projects to help enable the SGFL vision and further scientific opportunities. It emphasizes that exploring these emerging areas could help drive new science possibilities if done at a reasonable scale.
The Asia Pacific and Korea Research Platforms: An Overview Jeonghoon MoonLarry Smarr
This document provides an overview of Asia Pacific and Korea research platforms. It discusses the Asia Pacific Research Platform working group in APAN, including its objectives to promote HPC ecosystems and engage members. It describes the Asi@Connect project which provides high-capacity internet connectivity for research across Asia-Pacific. It also discusses the Korea Research Platform and efforts to expand it to 25 national research institutes in Korea. New related projects on smart hospitals, agriculture, and environment are mentioned. The conclusion discusses enhancing APAN and the Korea Research Platform and expanding into new areas like disaster and AI education.
Panel: Reaching More Minority Serving InstitutionsLarry Smarr
This document discusses engaging more minority serving institutions (MSIs) in the National Research Platform (NRP). It provides data showing that MSIs serve a disproportionate number of underrepresented minority students and are important producers of STEM graduates from these groups. The NRP can help broaden participation in STEAM fields by providing MSIs access to advanced cyberinfrastructure resources, new learning modalities, and opportunities for collaborative research between MSIs and other institutions. Regional networks also have a role to play in helping MSIs overcome barriers and attracting them to collaborative grants. The goal is to tear down walls between research and teaching and reinvent the university experience for more inclusive learning and innovation.
Panel: The Global Research Platform: An OverviewLarry Smarr
The document provides an overview of the Global Research Platform (GRP), an international collaborative partnership creating a distributed environment for data-intensive global science. The GRP facilitates high-performance data gathering, analytics, transport up to terabits per second, computing, and storage to support large-scale global science cyberinfrastructure ecosystems. It aims to orchestrate research across multiple domains using international testbeds for investigating new technologies related to data-intensive science. Examples of instruments generating exabytes of data that would benefit include the Korea Superconducting Tokamak, the High Luminosity LHC, genomics, the SKA radio telescope, and the Vera Rubin Observatory.
Panel: Future Wireless Extensions of Regional Optical NetworksLarry Smarr
CENIC is a non-profit organization that operates an 8,000+ mile fiber optic network connecting over 12,000 sites across California, including K-12 schools, universities, libraries, and research organizations. It has over 750 private sector partners and contributes over $100 million annually to the California economy. CENIC's network enables research and education collaborations, innovation, and economic growth statewide. It also operates a wireless research network called PRP that connects wireless sensors to supercomputers, supporting applications like wildfire modeling.
Global Research Platform Workshops - Maxine BrownLarry Smarr
The document announces a workshop on global research platforms that will be held virtually in 2021 and in Salt Lake City in 2022, with topics including large-scale science, next-generation platforms, data transport, and international testbeds. It also announces the 4th Global Research Platform Workshop to be held in October 2023 in Limassol, Cyprus co-located with the IEEE eScience 2023 conference.
EPOC and NetSage provide engagement and network monitoring services to support research and education. NetSage collects anonymized network flow data to help understand traffic patterns and troubleshoot performance issues. It provides dashboards and analysis to answer common questions from network engineers and end users. Examples of NetSage deployments and use cases were shown for the CENIC network, including top sources and destinations of traffic, debugging slow flows, and analyzing international traffic patterns by country over time.
The document discusses accelerating science discovery with AI inference-as-a-service. It describes showcases using this approach for high energy physics and gravitational wave experiments. It outlines the vision of the A3D3 institute to unite domain scientists, computer scientists, and engineers to achieve real-time AI and transform science. Examples are provided of using AI inference-as-a-service to accelerate workflows for CMS, ProtoDUNE, LIGO, and other experiments.
Democratizing Science through Cyberinfrastructure - Manish ParasharLarry Smarr
This document summarizes a presentation by Manish Parashar on democratizing science through cyberinfrastructure. The key points are:
1) Broad, fair, and equitable access to advanced cyberinfrastructure is essential for democratizing 21st century science, but there are significant barriers related to knowledge, technical issues, social factors, and balancing capabilities.
2) An advanced cyberinfrastructure ecosystem for all requires integrated portals, access to local and national resources through high-speed networks, diverse allocation modes, embedded expertise networks, and broad training.
3) Realizing this vision will require a scalable federated ecosystem with diverse capabilities and incentives for partnerships to meet growing needs for cyberinfrastructure and
Panel: Building the NRP Ecosystem with the Regional Networks on their Campuses;Larry Smarr
This document summarizes a panel discussion on building the National Research Platform ecosystem with regional networks. The panelists discussed how their regional networks are connecting to and using the Nautilus nodes of the NRP. Examples included using NRP for deep learning and computer vision research at the University of Missouri, challenges of adoption in Nevada and potential solutions, and Georgia Tech's new involvement through the Southern Crossroads regional network. The regional networks see opportunities to expand NRP access and training to enable more researchers in their regions to take advantage of the platform.
Open Force Field: Scavenging pre-emptible CPU hours* in the age of COVID - Je...Larry Smarr
The document discusses Open Force Field (OpenFF), an open-source project that enables rapid development of molecular force fields through automated infrastructure, open data and software, and an open science approach. OpenFF provides access to large quantum chemical datasets, runs quantum chemistry calculations on pre-emptible cloud resources with minimal human intervention, and facilitates easy iteration and testing of new force field hypotheses through an open development model.
Panel: Open Infrastructure for an Open Society: OSG, Commercial Clouds, and B...Larry Smarr
The document discusses open infrastructure for an open society and the role of commercial clouds. It describes how the National Research Platform (NRP), Open Science Grid (OSG), and Open Science Data Federation (OSDF) provide open infrastructure through open source components that anyone can contribute to and use. It then discusses how Southwestern Oklahoma State University leveraged NRP resources on their campus and engaged students and local teachers. Finally, it outlines the pros and cons of commercial clouds, when they may be suitable to use, and how tools like CloudBank and Kubernetes can help facilitate science users' access to cloud resources.
Panel: Open Infrastructure for an Open Society: OSG, Commercial Clouds, and B...Larry Smarr
The document discusses open infrastructure for an open society and the role of commercial clouds. It describes how the National Research Platform (NRP), Open Science Grid (OSG), and Open Science Data Federation (OSDF) provide open infrastructure through open source components that anyone can contribute to and use. It then discusses how Southwestern Oklahoma State University leveraged NRP resources on their campus and engaged students and local teachers. Finally, it outlines the pros and cons of commercial clouds, noting they provide huge capacity and variety but are very expensive for regular use. Facilitating science users on clouds requires services like CloudBank and Kubernetes federation.
Panel: Open Infrastructure for an Open Society: OSG, Commercial Clouds, and B...Larry Smarr
The document discusses open infrastructure for an open society and the role of commercial clouds. It describes how the National Research Platform (NRP), Open Science Grid (OSG), and Open Science Data Federation (OSDF) provide open infrastructure through open source components that anyone can contribute to and use. It then discusses how Southwestern Oklahoma State University leveraged NRP resources on their campus and engaged students and local teachers. Finally, it outlines the pros and cons of commercial clouds, noting they provide huge capacity and variety but are very expensive for regular use. Facilitating science users on clouds requires tools for account management, documentation, and integrating cloud resources through HTCondor and Kubernetes.
Frank Würthwein - NRP and the Path forwardLarry Smarr
NRP will replace PRP and aims to democratize access to national research cyberinfrastructure. The long term vision is to create an open national cyberinfrastructure by federating resources across research institutions. Key innovations include an innovative network fabric, application libraries for FPGAs, a "bring your own resource" model, and innovative scheduling and data infrastructure. The NSF has funded the Prototype National Research Platform project to support NRP for the next 5 years. NRP aims to grow resources, introduce new capabilities, and be driven by the research community.
Generative Classifiers: Classifying with Bayesian decision theory, Bayes’ rule, Naïve Bayes classifier.
Discriminative Classifiers: Logistic Regression, Decision Trees: Training and Visualizing a Decision Tree, Making Predictions, Estimating Class Probabilities, The CART Training Algorithm, Attribute selection measures- Gini impurity; Entropy, Regularization Hyperparameters, Regression Trees, Linear Support vector machines.
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of March 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
Did you know that drowning is a leading cause of unintentional death among young children? According to recent data, children aged 1-4 years are at the highest risk. Let's raise awareness and take steps to prevent these tragic incidents. Supervision, barriers around pools, and learning CPR can make a difference. Stay safe this summer!
Using Supercomputing & Advanced Analytic Software to Discover Radical Changes in the Human Microbiome in Health and Disease
1. “Using Supercomputing & Advanced Analytic Software
to Discover Radical Changes in the Human Microbiome
in Health and Disease”
Invited Remote Presentation To Weekly Team Meeting
Dermot McGovern, Director, Translational Medicine,
Inflammatory Bowel and Immunobiology Research Institute,
Gastroenterology, Cedars-Sinai
Los Angeles, CA
April 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 Discovered I Had IBD By Analyzing
150 Blood and Stool Variables, Each Over 5-10 Years
Calit2 64 megapixel VROOM
One Blood Draw
For Me
3. Only One of My Blood Measurements
Was Far Out of Range--Indicating Chronic Inflammation
Normal Range <1 mg/L
Normal
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
A Likelihood of My Having IBD
Normal Range
<7.3 µg/mL
124x Upper Limit
Lactoferrin is a Glycoprotein Shed from Neutrophils -
An Antibacterial that Sequesters Iron
Typical
Lactoferrin
Value for
Active
IBD
Hypothesis: Lactoferrin Oscillations
Coupled to Relative Abundance
of Microbes that Require Iron
5. Dynamical Innate and Adaptive Immune Oscillations
From Stool Samples
Normal <600
Innate Immune System
Normal 50 to 200
Adaptive Immune System
7. 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
rs1004819
NOD2
IRGM
ATG16L1
8. 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
2.08x Increased Risk
Il-23R
Rs1004819
1.8x Increased Risk
Subject with
Ileal Crohn’s
Subject with
Colonic Crohn’s
Source: Larry Smarr and 23andme
9. A Statistical Study is Needed to Determine
If NOD2 and IL23R Are Associated with Different Disease Phenotypes
“Associations Between NOD2/CARD15 Genotype and Phenotype in Crohn’s Disease-Are We there Yet?,”
Radford-Smith and Pandeya, World J. of Gastroentrology, 28, 7097-7103 (2006)
10. 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)
11. So IBD May be Stratified by a Personalized Combination
of the 163 Known SNPs Associated with IBD
• The width of the bar is proportional to the variance explained by that locus
• Bars are connected together if they are identified as being associated with both phenotypes
• Loci are labelled if they explain more than 1% of the total variance explained by all loci
“Host–microbe interactions have shaped the genetic architecture
of inflammatory bowel disease,” Jostins, et al. Nature 491, 119-124 (2012)
The Current Division of IBD Into Crohn’s Disease and Ulcerative Colitis
May Turn Out to be Superseded by a More Accurate Human Genetic Stratification
12. 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)
13. JCVI Sequenced My Gut Microbiome and We Downloaded
~270 More from the NIH Human Microbiome Project For Comparative Analysis
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)
14. We Created a Reference Database
Of Known Gut Genomes
• NCBI April 2013
– 2471 Complete + 5543 Draft Bacteria & Archaea Genomes
– 2399 Complete Virus Genomes
– 26 Complete Fungi Genomes
– 309 HMP Eukaryote Reference Genomes
• Total 10,741 genomes, ~30 GB of sequences
Now to Align Our 27 Billion Reads
Against the Reference Database
Source: Weizhong Li, Sitao Wu, CRBS, UCSD
15. Computational NextGen Sequencing Pipeline:
From Sequence to Taxonomy and Function
PI: (Weizhong Li, CRBS, UCSD):
NIH R01HG005978 (2010-2013, $1.1M)
16. Next Step
Programmability, Scalability and Reproducibility using bioKepler
www.kepler-project.org
www.biokepler.org
National
Resources
(Gordon) (Comet)
(Stampede)(Lonestar)
Cloud
Resources
Optimized
Local Cluster
Resources
Source:
Ilkay
Altintas,
SDSC
18. 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
Collapse of Bacteroidetes
Explosion of Actinobacteria
Explosion of
Proteobacteria
Hybrid of UC and CD
High Level of Archaea
19. 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
20. 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
21. Our Relative Abundance Results Across ~300 People
Show Why Dell Analytics Tree Classifier Works
UC 100x Healthy
LS 100x UC
We Produced Similar Results for ~2500 Microbial Species
Healthy 100x CD
22. Ileal Crohn’s and UC Patients Have Reduced Abundance
of Anti-Inflammatory Faecalibacterium prausnitzii
However, Colonic Crohn’s (LS)
Have Increased Abundance
23. 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
24. Is the Gut Microbial Ecology Different
in Crohn’s Disease Subtypes?
Ben Willing, GASTROENTEROLOGY 2010;139:1844 –1854
Colonic
Crohn’s
Disease
(CCD)
Ileal Crohn’s Disease (ICD)
25. It Appears That Metabolomics Can Differentiate
Ileum vs. Colon Inflammation in Crohn’s Disease
blue N= Ileum (ICD)
red N= Colon (CCD)
green N= Healthy
Jansson, et al. PLOS ONE, July 2009 | Volume 4 | Issue 7 | e6386
26. In a “Healthy” Gut Microbiome:
Large Taxonomy Variation, Low Protein Family Variation
Source: Nature, 486, 207-212 (2012)
Over 200 People
27. Ratio of One of the Healthy Subjects to the Average KEGG for 35 Healthy:
Test to see How Much Inter-Personal 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
Nonzero KEGGs
We Computed
the Relative
Abundance of
10,000 KEGGs
in 35 Healthy
And 25 IBD
Patients
28. However, Our Research Shows Large Changes
in Protein Families Between Health and Disease
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
Note 700 KEGGs
With Ratio >10
Note 1000 KEGGs
With Ratio <0.1
29. Can We 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 (see spreadsheet)
• A New Way to Define What is Wrong with the Microbiome in Disease?
30. Using Ayasdi Interactively to Explore
Protein Families in Healthy and Disease States
Source: Pek Lum,
Formerly Chief Data Scientist, Ayasdi
Dataset from Larry Smarr Team
With 60 Subjects (HE, CD, UC, LS)
Each with 10,000 KEGGs -
600,000 Cells
31. 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_/_e
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
32. Disease Arises from Perturbed Protein Family Networks:
Dynamics of a Prion Perturbed Network in Mice
Source: Lee Hood, ISB 32
Our Next Goal is to Create
Such Perturbed Networks in Humans
33. Next Step: Compute Genes and Function
For All ~300 People’s Gut Microbiome
Full Processing to Function:
Genes & Protein Families
(COGs, KEGGs)
Would Require
~1-2 Million
Core-Hours
34. UC San Diego Will Be Carrying Out
a Major Clinical Study of IBD Using These Techniques
Inflammatory Bowel Disease Biobank
For Healthy and Disease Patients
Drs. William J. Sandborn, John Chang, & Brigid Boland
UCSD School of Medicine, Division of Gastroenterology
Already 185 Enrolled,
Goal is 1500
Announced November 7, 2014!
35. 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