RESEARCH PROGRAM OVERVIEW
Biological systems are beautifully complex and their nonlinear dynamics mediate the difference between health or disease, treatment success or treatment failure. The Yang Lab seeks fundamental, first-principles understanding into the molecular mechanisms underlying disease progression and cure for chronic and infectious diseases.
We leverage advances in systems biology and biomedical data science to drive our discovery of mechanistic insights. We employ quantitative, live-cell, dynamic, high-throughput, and multi-OMIC experimental approaches. We couple these to network modeling, machine learning and bioinformatic analyses. Our research focuses on the greatest global health challenges of our time, including antimicrobial resistance, tuberculosis, and heart failure.
MACHINE LEARNING-GUIDED DISCOVERY
Yang JH, Cell 2019
Recent advances in high-throughput experimental technologies and data analyses have enabled the unprecedented observation, quantification and association of biological signals with cellular and clinical phenotypes. We are developing integrated network modeling and interpretable machine learning-based approaches to rapidly reveal causal mechanisms underlying therapeutic efficacy and disease pathogenesis.
Relevant Publications:
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Yang JH, Wright SN*, Hamblin M*, McCloskey D, Alcantar MA, Schrübbers L, Lopatkin AJ, Satish S, Nili A, Palsson BO, Walker GC, Collins JJ. A white-box machine learning approach for revealing antibiotic mechanisms of action. Cell. 2019; 177(6):1649-1661.
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Sample V*, DiPilato LM*, Yang JH*, Ni Q, Saucerman JJ, Zhang J. Regulation of nuclear PKA revealed by spatiotemporal manipulation of cAMP. Nat Chem Biol. 2012; 8(4):375-82.
ENGINEERED IMMUNE CELL THERAPIES
Lim WA, Cell 2017
Advances in genetics and other experimental platforms are for the first time providing unprecedented tools for engineering biological organisms that can perform useful functions. Based on our discoveries on how innate immune cells make context-dependent decisions, we are applying approaches from synthetic biology to engineer next generation cell-based immunotherapies for chronic and infectious diseases. We are inspired by the natural complexity of living systems and seek to engineer high-order gene circuits for controlling immune cell behaviors. We combine dynamic live-cell imaging experiments, phenotypic assays with network modeling and machine learning to reverse engineer the biological circuits underlying how immune cells process extracellular cues. We use this information to forward engineer immune cell therapies for complex and chronic diseases.
ANTIMICROBIAL RESISTANCE
Antimicrobial resistance poses an urgent and growing threat to global health. Despite knowledge on the primary targets for conventional antibiotics, it remains unclear why antibiotic treatment can sometimes fail. We are combining high-throughput assays, OMICS-characterization, fluorescence microscopy and animal experiments with network modeling, machine learning and bioinformatic analyses to explore how bacterial metabolism contributes to antimicrobial efficacy.
Relevant Publications:
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Yang JH*, Bhargava P*, McCloskey D, Mao N, Palsson BO, Collins JJ. Antibiotic-induced changes to the host metabolic environment inhibit drug efficacy and alter immune function. Cell Host Microbe. 2017; 22(6):757-765.
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Yang JH, Wright SN*, Hamblin M*, McCloskey D, Alcantar MA, Schrübbers L, Lopatkin AJ, Satish S, Nili A, Palsson BO, Walker GC, Collins JJ. A white-box machine learning approach for revealing antibiotic mechanisms of action. Cell. 2019; 177(6):1649-1661.
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Lopatkin AJ, Bening SC, Manson AL, Stokes JM, Kohanski MA, Badran AH, Earl AM, Cheney NJ, Yang JH, Collins JJ.. Clinically relevant mutations in core metabolic genes confer antibiotic resistance. Science. 2021; 371(6531):eaba0862.
Lopatkin AJ, Science 2021
TUBERCULOSIS
NIAID
Tuberculosis (TB) is the leading cause of death from a single infectious agent from across the world. Standard treatment for TB involves combination therapy with at least 4 antibiotics for a minimum of 6 months. Even so, TB infection relapse rates are ~5% and patients may harbor undetectable latent infections lasting years before relapse. We are applying our network modeling and machine learning methods towards understanding mechanisms underlying the mechanisms underlying TB relapse and latency from human-derived biospecimens. We experimentally validate these mechanisms in a shared BSL-3 facility in the Center for Emerging and Re-Emerging Pathogens.
Relevant Publications:
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Kuchina A, Yang J, Aldridge B, Janes KA, Subramanian N, Krogan NJ, Bouhaddou M, Einav S, Papin J, Germain RN. How can systems approaches help us understand and treat infectious disease?. Cell Syst. 2022; 13(12):945-949.
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Chitale P, Lemenze AD, Fogarty EC, Shah A, Grady C, Odom-Mabey AR, Johnson WE, Yang JH, Eren AM, Brosch R, Kumar P, Alland D. A comprehensive update to the Mycobacterium tuberculosis H37Rv reference genome. Nat Commun. 2022; 13(1):7068.
HEART FAILURE
Cardiovascular diseases are the leading causes of death around the world. First-line therapeutics target the same cell signaling pathways as those used by the body to adapt to changes in oxygen demand in healthy individuals. We previously demonstrated how network topology and subcellular compartmentation both regulate β-adrenergic signaling responses in cardiac myocytes, and how these are able to select between healthy physiologic responses vs. pathophysiologic responses to receptor activation. We are combining dynamic live-cell imaging experiments, phenotypic assays with network modeling and machine learning to understand cell signaling dynamics in cardiac cells in the context of heart failure.
Relevant Publications:
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Yang JH, Polanowska-Grabowska RK, Smith JS, Shields CW, Saucerman JJ. PKA catalytic subunit compartmentation regulates contractile and hypertrophic responses to β-adrenergic signaling. J Mol Cell Cardiol. 2014; 66:83-93.
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Yang JH, Saucerman JJ. Phospholemman is a negative feed-forward regulator of Ca2+ in β-adrenergic signaling, accelerating β-adrenergic inotropy. J Mol Cell Cardiol. 2012; 52(5):1048-55.
COLLABORATORS
David Alland, Rutgers New Jersey Medical School
Caleb Bashor, Rice University
James Collins, Massachusetts Institute of Technology
Veronique Dartois, Hackensack Meridian Health
Cheryl Day, Emory University
Allison Lopatkin, Barnard College
Shuyi Ma, Seattle Children's Hospital
Bernhard Palssön, University of California, San Diego
Dane Parker, Rutgers New Jersey Medical School
Jyothi Rengarajan, Emory University
Jeanne Salje, Rutgers New Jersey Medical School
Jeffrey Saucerman, University of Virginia
Padmini Salgame, Rutgers New Jersey Medical School
David Sherman, University of Washington
Graham Walker, Massachusetts Institute of Technology
Jin Zhang, University of California, San Diego
RESEARCH SUPPORT
Our work is generously supported by the following sponsors:
