Chan Zuckerberg Biohub Awards $13.7 Million to Fund New Intercampus Collaborative Research Programs to Advance Human Health

FOR IMMEDIATE RELEASE

SAN FRANCISCO — Sept. 26, 2018

  • New funding from CZ Biohub fosters further scientific research collaboration between Stanford, University of California, Berkeley, and University of California, San Francisco
  • CZ Biohub Microbiome Initiative and Intercampus Research Awards provide grant funding to teams that include an interdisciplinary group of 42 project leaders and collaborators together with 10 existing Biohub investigators and advisors.

The Chan Zuckerberg Biohub (CZ Biohub), a nonprofit medical research organization, today announced that it is awarding $13.7 million over three years to support cutting-edge biomedical research from seven teams of scientists, physicians, and engineers, with each team including faculty members from the University of California, San Francisco (UCSF), the University of California, Berkeley (UC Berkeley), and Stanford University.

The awards will fund two new programs: the CZ Biohub Microbiome Initiative and the CZ Biohub Intercampus Research Awards.

“For the first time, these new awards bring together highly talented investigators from all three campuses to collaborate on promising new approaches to major biomedical problems,” said Joe DeRisi, co-president of CZ Biohub. “By drawing on the strengths of all three institutions, we believe these teams will accomplish what is now beyond the reach of individual investigators.”

CZ Biohub Microbiome Initiative

Launched as a pilot program earlier this year, the CZ Biohub Microbiome Initiative provides $4 million over three years to carry out research on the community of microbes within the human body that influence many aspects of health, from nutrition and immune function to drug metabolism. The Microbiome Initiative brings together eight leading microbiome experts from all three campuses based on their highly complementary and synergistic research interests.

CZ Biohub Intercampus Research Awards

Assembling the Microbiome Initiative team inspired CZ Biohub to create the Intercampus Research Awards. The new competitive awards program promotes collaborative research by bringing together clinicians, biologists, chemists, data scientists, mathematicians, engineers, and bioethicists in teams that each include faculty members from all three campuses. The competition drew applications from 83 teams. CZ Biohub initially planned to support three teams but was inspired to increase the number of awards to six, providing $9.7 million over three years.

“This new collaborative team-based funding allows investigators across the three campuses to tackle demanding problems to enhance health,” said Steve Quake, co-president of CZ Biohub. “These research teams will shed new light on a diverse and challenging set of questions that will advance our understanding while developing technologies that open fresh avenues of research.”

Statement from Priscilla Chan & Mark Zuckerberg, Co-Founders, The Chan Zuckerberg Initiative

“We launched the Biohub to bring together some of the brightest scientific minds in the Bay Area with world-class engineering teams, in order to help accelerate the pace of discovery and make faster progress in the fight against disease. Just two years after its launch, it is incredible to see how the Biohub has helped spark promising new collaborations, tools, and research to enable and empower the entire scientific community. Breakthroughs occur when researchers work together, within and across disciplines. By creating even more opportunities to do so through these two new programs, we believe we’ll get closer to our goal of curing, preventing, or managing all disease by the end of the century,” said Priscilla Chan and Mark Zuckerberg, Co-Founders of The Chan Zuckerberg Initiative.

Statements from Stanford, UC Berkeley and UCSF

UC Berkeley Chancellor Carol Christ said, “We are thrilled by the extent to which these awards honor and support the notion that our most pressing challenges can only be surmounted by transcending the lines that have too long divided academic disciplines, departments and even institutions. The awards will also help support our efforts to extend the reach of world-class, fundamental research through initiatives that can speed the translation of discoveries into inventions and services for the benefit of all.”

UCSF Chancellor Sam Hawgood said, “With its unique model of multi-institutional collaboration, the Chan Zuckerberg Biohub has become a keystone of the Bay Area ecosystem of innovation. The new research awards program and the Microbiome Initiative further strengthen the scientific and technological bonds Biohub has already forged among UCSF, Stanford, and UC Berkeley. We look forward to working with our partner institutions on these exciting new projects to advance knowledge and human health.”

Stanford University President Marc Tessier-Lavigne said, “I am excited that the Chan Zuckerberg Biohub has chosen to fund innovative, high-risk research with great potential for enhancing our understanding of human health. By supporting teams that are collaborating across disciplines and across our three institutions, the Biohub will enable scientific breakthroughs that increasingly require insights from multiple disciplines and perspectives.”

 


 

APPENDIX 1

 

Investigator

Institution

Program

Team

Role

Christopher Lowe

Stanford

Intercampus Research Awards

Beyond model systems: insights into genome evolution and cellular innovations

Project Leader

Wallace Marshall

UCSF

Intercampus Research Awards

Beyond model systems: insights into genome evolution and cellular innovations

Project Leader

Stephen Palumbi

Stanford

Intercampus Research Awards

Beyond model systems: insights into genome evolution and cellular innovations

Project Leader

Daniel Rokhsar

Berkeley

Intercampus Research Awards

Beyond model systems: insights into genome evolution and cellular innovations

Project Leader

Irving Weissman

Stanford

Intercampus Research Awards

Beyond model systems: insights into genome evolution and cellular innovations

Project Leader

Ayelet Voskoboynik

Stanford

Intercampus Research Awards

Beyond model systems: insights into genome evolution and cellular innovations

Collaborator

Laurent Coscoy

Berkeley

Intercampus Research Awards

Defining host responses of virus- infected and uninfected neighbor cells

Project Leader

Karla Kirkegaard

Stanford

Intercampus Research Awards

Defining host responses of virus- infected and uninfected neighbor cells

Project Leader

Melanie Ott

UCSF

Gladstone Institutes

Intercampus Research Awards

Defining host responses of virus- infected and uninfected neighbor cells

Project Leader

Peter Sarnow

Stanford

Intercampus Research Awards

Defining host responses of virus- infected and uninfected neighbor cells

Project Leader

Nir Yosef

Berkeley

Intercampus Research Awards

Defining host responses of virus- infected and uninfected neighbor cells

Collaborator

John C. Boothroyd

Stanford

Intercampus Research Awards

Imaging complex biological machines in action

Project Leader

Wah Chiu

Stanford

Intercampus Research Awards

Imaging complex biological machines in action

Project Leader

Carolyn Larabell

UCSF

Intercampus Research Awards

Imaging complex biological machines in action

Project Leader

James A. Sethian

Berkeley

Intercampus Research Awards

Imaging complex biological machines in action

Project Leader

Mark Le Gros

UCSF

Intercampus Research Awards

Imaging complex biological machines in action

Collaborator

W.E. Moerner

Stanford

Intercampus Research Awards

Imaging complex biological machines in action

Collaborator

Russ B. Altman

Stanford

Intercampus Research Awards

Machine learning for interpreting rare genetic variation in comprehensive newborn screening and pharmacogenetics

Project Leader

Steven E. Brenner

Berkeley

Intercampus Research Awards

Machine learning for interpreting rare genetic variation in comprehensive newborn screening and pharmacogenetics

Project Leader

Carlos Bustamante

Stanford

Intercampus Research Awards

Machine learning for interpreting rare genetic variation in comprehensive newborn screening and pharmacogenetics

Project Leader

Renata C. Gallagher

UCSF

Intercampus Research Awards

Machine learning for interpreting rare genetic variation in comprehensive newborn screening and pharmacogenetics

Project Leader

Kathleen M. Giacomini

UCSF

Intercampus Research Awards

Machine learning for interpreting rare genetic variation in comprehensive newborn screening and pharmacogenetics

Project Leader

Michael I. Jordan

Berkeley

Intercampus Research Awards

Machine learning for interpreting rare genetic variation in comprehensive newborn screening and pharmacogenetics

Project Leader

Jodi Halpern

Berkeley

Intercampus Research Awards

Machine learning for interpreting rare genetic variation in comprehensive newborn screening and pharmacogenetics

Collaborator

Barbara Koenig

UCSF

Intercampus Research Awards

Machine learning for interpreting rare genetic variation in comprehensive newborn screening and pharmacogenetics

Collaborator

David Magnus

Stanford

Intercampus Research Awards

Machine learning for interpreting rare genetic variation in comprehensive newborn screening and pharmacogenetics

Collaborator

Jennifer Puck

UCSF

Intercampus Research Awards

Machine learning for interpreting rare genetic variation in comprehensive newborn screening and pharmacogenetics

Collaborator

Jillian Banfield

Berkeley

Microbiome Initiative

Microbiome Initiative

CZBMI

Investigator

Michael Fischbach

Stanford

Microbiome Initiative

Microbiome Initiative

CZBMI

Investigator

Kerwyn (KC) Huang

Stanford

Microbiome Initiative

Microbiome Initiative

CZBMI

Investigator

Alexander (Sandy) Johnson

UCSF

Microbiome Initiative

Microbiome Initiative

CZBMI

Investigator

Katherine Pollard

UCSF

Gladstone Institutes

Microbiome Initiative

Microbiome Initiative

CZBMI

Investigator

David Relman

Stanford

Microbiome Initiative

Microbiome Initiative

CZBMI

Investigator

Justin Sonnenburg

Stanford

Microbiome Initiative

Microbiome Initiative

CZBMI

Investigator

Peter Turnbaugh

UCSF

Microbiome Initiative

Microbiome Initiative

CZBMI

Investigator

Rima Arnaout

UCSF

Intercampus Research Awards

Multi-scale deep learning and single- cell models of cardiovascular health

Project Leader

Euan Ashley

Stanford

Intercampus Research Awards

Multi-scale deep learning and single- cell models of cardiovascular health

Project Leader

J. Ben Brown

Berkeley

Intercampus Research Awards

Multi-scale deep learning and single- cell models of cardiovascular health

Project Leader

Atul Butte

UCSF

Intercampus Research Awards

Multi-scale deep learning and single- cell models of cardiovascular health

Project Leader

James Priest

Stanford

Intercampus Research Awards

Multi-scale deep learning and single- cell models of cardiovascular health

Project Leader

Bin Yu

Berkeley

Intercampus Research Awards

Multi-scale deep learning and single- cell models of cardiovascular health

Project Leader

Chris Ré

Stanford

Intercampus Research Awards

Multi-scale deep learning and single- cell models of cardiovascular health

Collaborator

Deepak Srivastava

UCSF

Gladstone Institutes

Intercampus Research Awards

Multi-scale deep learning and single- cell models of cardiovascular health

Collaborator

Tomasz Nowakowski

UCSF

Intercampus Research Awards

Social Network Analysis of neuroimmune interactions in the developing human brain

Project Leader

Alex Pollen

UCSF

Intercampus Research Awards

Social Network Analysis of neuroimmune interactions in the developing human brain

Project Leader

David Schaffer

Berkeley

Intercampus Research Awards

Social network analysis of neuroimmune interactions in the developing human brain

Project Leader

Alice Ting

Stanford

Intercampus Research Awards

Social Network Analysis of neuroimmune interactions in the developing human brain

Project Leader

Chun Jimmie Ye

UCSF

Intercampus Research Awards

Social Network Analysis of neuroimmune interactions in the developing human brain

Project Leader

James Zou

Stanford

Intercampus Research Awards

Social Network Analysis of neuroimmune interactions in the developing human brain

Project Leader

Kole Roybal

UCSF

Intercampus Research Awards

Social Network Analysis of neuroimmune interactions in the developing human brain

Collaborator

James Wells  UCSF  Intercampus Research Awards  Social Network Analysis of neuroimmune interactions in the developing human brain  Collaborator

 


 

APPENDIX 2

 

INTERCAMPUS RESEARCH AWARDS AND MICROBIOME INITIATIVE TEAM DESCRIPTIONS

Beyond model systems: insights into genome evolution and cellular innovations

Biomedical research usually focuses on a few well-studied model organisms like mice or fruit flies, but some questions in biology are hard to answer using these organisms. For example, it is hard to study limb regeneration in mice, since mice don’t regenerate limbs. The ocean is full of organisms that are built in very different ways and that offer an opportunity to develop new approaches to human health in areas such as regeneration, immunity, stem cell biology, and aging. The team seeks to harness diverse marine organisms in combination with state of the art genomic technology to answer a set of fundamental questions in biology that have gone unanswered for over a century, with a focus on animal development, regeneration, immune function, and genome maintenance and stability.

Defining host responses of virus-infected and uninfected neighbor cells

How viral infections spread within tissues is poorly understood. Infected cells can induce changes in neighboring cells to enhance viral spread (pro-viral responses), or to block infection (antiviral responses). The team brings together expertise in virology, immunology and RNA biology to define the cellular pathways induced in infected and uninfected neighboring cells and their influence on viral replication. The team will focus on how RNA transmission, alterations in viral and cellular RNA stability and nutritional status can elicit pro- or anti- viral responses in neighbors, drawing on their expertise in culturing 3D human organoids to provide realistic models for the complex interactions that occur between cells in human tissue. Understanding the mechanisms affecting viral spread within tissues will lead towards new treatments for viral infections.

Imaging complex biological machines in action

The team will develop an integrated pipeline for determining the structure and function of exceedingly complex molecular machines in intact cells. They will develop new approaches for specimen preparation, data collection and image analysis so that a single sample can be observed using several complementary imaging technologies that reveal different features and span a range of magnifications. This will allow the team to determine with nearly atomic-resolution the structures of molecular machines composed of a large number of distinct proteins while also determining the orientation and position of the machines within cells, providing previously inaccessible functional insights. They will use their methods to characterize the machinery that Toxoplasma and related parasites, like the one responsible for malaria, use to invade human cells.

Machine learning for interpreting rare genetic variation in comprehensive newborn screening and pharmacogenetics

In California, 500,000 babies are born each year, some of whom have genetic mutations that cause disease or altered responses to medications. Recognizing which genetic variants cause problems is surprisingly difficult, impeding the use of genetic information to inform early intervention or the customization of patient care. The team has drawn together experts in biology, computer science, medicine, and ethics to develop new methods for identifying genetic variants that cause disease, focusing on serious newborn diseases and on gene variants that affect patient responses to medications. The team will collect experimental data and develop innovative machine learning techniques to predict the functional consequences of genetic variants.

Multi-scale deep learning and single-cell models of cardiovascular health

The team will develop methods to accelerate the pace of discovery of genetic determinants for cardiovascular disease. They will develop new statistical machine learning tools to analyze morphological and functional parameters of the heart from clinical images, an approach that can be scaled to analyze millions of images. They will also develop machine learning tools based on enhanced iterative random forests (iRF) to identify genetic variants likely to account for some of the variation in cardiovascular morphology and function observed in their analysis of clinical images, utilizing publicly available large-scale clinical data sets and local patient cohorts. Finally, they will identify genetic variants responsible for functional phenotypes using cell-based in vitro model systems.

Social network analysis of neuroimmune interactions in the developing human brain

The team seeks to develop scalable technologies for studying intercellular interactions in the human brain. They will establish a panel of synthetic antibodies that recognize distinct cell types in the human brain and develop tools (“molecular flight recorders”) to monitor and record the history of single cells as they respond to signals and interact with neighboring cells. The team will apply these tools to understand how the specialized innate immune cells in the brain (microglia) interact with other types of brain cells, interactions that have been proposed to play a key role during normal brain development and in psychiatric and neurodegenerative diseases. The tools and technologies developed by the team will be broadly applicable to study interactions between many cell types in diverse organs and tissues.

Microbiome Initiative

Humans are colonized by communities of microbes that influence many aspects of human health, from nutrition and immune function to drug metabolism. Although technological advances have made It easier to enumerate which microbes are present in different individuals and how the composition of microbial communities changes over time, the mechanisms by which these microbial communities contribute to human biology are not well understood. A major question is how changes in an individual’s microbiome and differences in the composition of the microbiome between individuals influence human health. The team will take advantage of new technologies to interrogate and manipulate the microbiome, approaching the microbiome from the perspectives of engineering, ecology, and genetics and focusing on methods of precise measurement that will enable mechanistic insight.