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Sparks R, Rachmaninoff N, Lau WW, Hirsch DC, Bansal N, Martins AJ, Chen J, Liu CC, Cheung F, Failla LE, Biancotto A, Fantoni G, Sellers BA, Chawla DG, Howe KN, Mostaghimi D, Farmer R, Kotliarov Y, Calvo KR, Palmer C, Daub J, Foruraghi L, Kreuzburg S, Treat JD, Urban AK, Jones A, Romeo T, Deuitch NT, Moura NS, Weinstein B, Moir S, Ferrucci L, Barron KS, Aksentijevich I, Kleinstein SH, Townsley DM, Young NS, Frischmeyer-Guerrerio PA, Uzel G, Pinto-Patarroyo GP, Cudrici CD, Hoffmann P, Stone DL, Ombrello AK, Freeman AF, Zerbe CS, Kastner DL, Holland SM, Tsang JS. A unified metric of human immune health. Nat Med 2024:10.1038/s41591-024-03092-6. [PMID: 38961223 DOI: 10.1038/s41591-024-03092-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 05/28/2024] [Indexed: 07/05/2024]
Abstract
Immunological health has been challenging to characterize but could be defined as the absence of immune pathology. While shared features of some immune diseases and the concept of immunologic resilience based on age-independent adaptation to antigenic stimulation have been developed, general metrics of immune health and its utility for assessing clinically healthy individuals remain ill defined. Here we integrated transcriptomics, serum protein, peripheral immune cell frequency and clinical data from 228 patients with 22 monogenic conditions impacting key immunological pathways together with 42 age- and sex-matched healthy controls. Despite the high penetrance of monogenic lesions, differences between individuals in diverse immune parameters tended to dominate over those attributable to disease conditions or medication use. Unsupervised or supervised machine learning independently identified a score that distinguished healthy participants from patients with monogenic diseases, thus suggesting a quantitative immune health metric (IHM). In ten independent datasets, the IHM discriminated healthy from polygenic autoimmune and inflammatory disease states, marked aging in clinically healthy individuals, tracked disease activities and treatment responses in both immunological and nonimmunological diseases, and predicted age-dependent antibody responses to immunizations with different vaccines. This discriminatory power goes beyond that of the classical inflammatory biomarkers C-reactive protein and interleukin-6. Thus, deviations from health in diverse conditions, including aging, have shared systemic immune consequences, and we provide a web platform for calculating the IHM for other datasets, which could empower precision medicine.
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Grants
- 1ZIAAI001152 Division of Intramural Research, National Institute of Allergy and Infectious Diseases (Division of Intramural Research of the NIAID)
- 1ZIAAI001152 Division of Intramural Research, National Institute of Allergy and Infectious Diseases (Division of Intramural Research of the NIAID)
- 1ZIAAI001152 Division of Intramural Research, National Institute of Allergy and Infectious Diseases (Division of Intramural Research of the NIAID)
- 1ZIAAI001152 Division of Intramural Research, National Institute of Allergy and Infectious Diseases (Division of Intramural Research of the NIAID)
- 1ZIAAI001152 Division of Intramural Research, National Institute of Allergy and Infectious Diseases (Division of Intramural Research of the NIAID)
- 1ZIAAI001152 Division of Intramural Research, National Institute of Allergy and Infectious Diseases (Division of Intramural Research of the NIAID)
- 1ZIAAI001152 Division of Intramural Research, National Institute of Allergy and Infectious Diseases (Division of Intramural Research of the NIAID)
- 1ZIAAI001152 Division of Intramural Research, National Institute of Allergy and Infectious Diseases (Division of Intramural Research of the NIAID)
- 1ZIAAI001152 Division of Intramural Research, National Institute of Allergy and Infectious Diseases (Division of Intramural Research of the NIAID)
- 1ZIAAI001152 Division of Intramural Research, National Institute of Allergy and Infectious Diseases (Division of Intramural Research of the NIAID)
- Z01 AI000825-24 LIR Division of Intramural Research, National Institute of Allergy and Infectious Diseases (Division of Intramural Research of the NIAID)
- 1 ZIA AI001202-07 Division of Intramural Research, National Institute of Allergy and Infectious Diseases (Division of Intramural Research of the NIAID)
- Z-01-A-00646 Division of Intramural Research, National Institute of Allergy and Infectious Diseases (Division of Intramural Research of the NIAID)
- Z-01-A-00647 Division of Intramural Research, National Institute of Allergy and Infectious Diseases (Division of Intramural Research of the NIAID)
- Z-01-A-00646 Division of Intramural Research, National Institute of Allergy and Infectious Diseases (Division of Intramural Research of the NIAID)
- 5R01AI170116 U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID)
- U19AI089992 U.S. Department of Health & Human Services | NIH | Office of Extramural Research, National Institutes of Health (OER)
- U19AI089992 U.S. Department of Health & Human Services | NIH | Office of Extramural Research, National Institutes of Health (OER)
- ZIA-HL006089-12 U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)
- 1ZIAHG200371 U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI)
- 1ZIAHG200373 U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI)
- 1ZIAHG200374 U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI)
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Affiliation(s)
- Rachel Sparks
- Multiscale Systems Biology Section, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA
| | - Nicholas Rachmaninoff
- Multiscale Systems Biology Section, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA
- Graduate Program in Biological Sciences, University of Maryland, College Park, MD, USA
| | - William W Lau
- Multiscale Systems Biology Section, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA
| | - Dylan C Hirsch
- Multiscale Systems Biology Section, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA
| | - Neha Bansal
- Multiscale Systems Biology Section, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA
| | - Andrew J Martins
- Multiscale Systems Biology Section, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA
| | - Jinguo Chen
- NIH Center for Human Immunology, Inflammation, and Autoimmunity, NIAID, NIH, Bethesda, MD, USA
| | - Candace C Liu
- Multiscale Systems Biology Section, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA
| | - Foo Cheung
- NIH Center for Human Immunology, Inflammation, and Autoimmunity, NIAID, NIH, Bethesda, MD, USA
| | - Laura E Failla
- Multiscale Systems Biology Section, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA
| | - Angelique Biancotto
- NIH Center for Human Immunology, Inflammation, and Autoimmunity, NIAID, NIH, Bethesda, MD, USA
| | - Giovanna Fantoni
- NIH Center for Human Immunology, Inflammation, and Autoimmunity, NIAID, NIH, Bethesda, MD, USA
| | - Brian A Sellers
- NIH Center for Human Immunology, Inflammation, and Autoimmunity, NIAID, NIH, Bethesda, MD, USA
| | - Daniel G Chawla
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - Katherine N Howe
- Laboratory of Clinical Immunology and Microbiology, NIAID, NIH, Bethesda, MD, USA
| | - Darius Mostaghimi
- Multiscale Systems Biology Section, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA
| | - Rohit Farmer
- NIH Center for Human Immunology, Inflammation, and Autoimmunity, NIAID, NIH, Bethesda, MD, USA
| | - Yuri Kotliarov
- NIH Center for Human Immunology, Inflammation, and Autoimmunity, NIAID, NIH, Bethesda, MD, USA
| | - Katherine R Calvo
- Hematology Section, Department of Laboratory Medicine, Clinical Center, NIH, Bethesda, MD, USA
| | - Cindy Palmer
- Laboratory of Clinical Immunology and Microbiology, NIAID, NIH, Bethesda, MD, USA
| | - Janine Daub
- Laboratory of Clinical Immunology and Microbiology, NIAID, NIH, Bethesda, MD, USA
| | - Ladan Foruraghi
- Laboratory of Clinical Immunology and Microbiology, NIAID, NIH, Bethesda, MD, USA
| | - Samantha Kreuzburg
- Laboratory of Clinical Immunology and Microbiology, NIAID, NIH, Bethesda, MD, USA
| | - Jennifer D Treat
- Laboratory of Clinical Immunology and Microbiology, NIAID, NIH, Bethesda, MD, USA
| | - Amanda K Urban
- Clinical Research Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Anne Jones
- Inflammatory Disease Section, NHGRI, NIH, Bethesda, MD, USA
| | - Tina Romeo
- Inflammatory Disease Section, NHGRI, NIH, Bethesda, MD, USA
| | | | | | | | - Susan Moir
- Laboratory of Immunoregulation, NIAID, NIH, Bethesda, MD, USA
| | - Luigi Ferrucci
- Translational Gerontology Branch, NIA, Baltimore, MD, USA
| | - Karyl S Barron
- Division of Intramural Research, NIAID, NIH, Bethesda, MD, USA
| | | | - Steven H Kleinstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | | | - Neal S Young
- Hematology Branch, NHLBI, NIH, Bethesda, MD, USA
| | | | - Gulbu Uzel
- Laboratory of Clinical Immunology and Microbiology, NIAID, NIH, Bethesda, MD, USA
| | | | | | | | | | | | - Alexandra F Freeman
- Laboratory of Clinical Immunology and Microbiology, NIAID, NIH, Bethesda, MD, USA
| | - Christa S Zerbe
- Laboratory of Clinical Immunology and Microbiology, NIAID, NIH, Bethesda, MD, USA
| | | | - Steven M Holland
- Laboratory of Clinical Immunology and Microbiology, NIAID, NIH, Bethesda, MD, USA
| | - John S Tsang
- Multiscale Systems Biology Section, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA.
- NIH Center for Human Immunology, Inflammation, and Autoimmunity, NIAID, NIH, Bethesda, MD, USA.
- Center for Systems and Engineering Immunology, Departments of Immunobiology and Biomedical Engineering, Yale University School of Medicine, New Haven, CT, USA.
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Hendrickx JO, van Gastel J, Leysen H, Martin B, Maudsley S. High-dimensionality Data Analysis of Pharmacological Systems Associated with Complex Diseases. Pharmacol Rev 2020; 72:191-217. [PMID: 31843941 DOI: 10.1124/pr.119.017921] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
It is widely accepted that molecular reductionist views of highly complex human physiologic activity, e.g., the aging process, as well as therapeutic drug efficacy are largely oversimplifications. Currently some of the most effective appreciation of biologic disease and drug response complexity is achieved using high-dimensionality (H-D) data streams from transcriptomic, proteomic, metabolomics, or epigenomic pipelines. Multiple H-D data sets are now common and freely accessible for complex diseases such as metabolic syndrome, cardiovascular disease, and neurodegenerative conditions such as Alzheimer's disease. Over the last decade our ability to interrogate these high-dimensionality data streams has been profoundly enhanced through the development and implementation of highly effective bioinformatic platforms. Employing these computational approaches to understand the complexity of age-related diseases provides a facile mechanism to then synergize this pathologic appreciation with a similar level of understanding of therapeutic-mediated signaling. For informative pathology and drug-based analytics that are able to generate meaningful therapeutic insight across diverse data streams, novel informatics processes such as latent semantic indexing and topological data analyses will likely be important. Elucidation of H-D molecular disease signatures from diverse data streams will likely generate and refine new therapeutic strategies that will be designed with a cognizance of a realistic appreciation of the complexity of human age-related disease and drug effects. We contend that informatic platforms should be synergistic with more advanced chemical/drug and phenotypic cellular/tissue-based analytical predictive models to assist in either de novo drug prioritization or effective repurposing for the intervention of aging-related diseases. SIGNIFICANCE STATEMENT: All diseases, as well as pharmacological mechanisms, are far more complex than previously thought a decade ago. With the advent of commonplace access to technologies that produce large volumes of high-dimensionality data (e.g., transcriptomics, proteomics, metabolomics), it is now imperative that effective tools to appreciate this highly nuanced data are developed. Being able to appreciate the subtleties of high-dimensionality data will allow molecular pharmacologists to develop the most effective multidimensional therapeutics with effectively engineered efficacy profiles.
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Affiliation(s)
- Jhana O Hendrickx
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| | - Jaana van Gastel
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| | - Hanne Leysen
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| | - Bronwen Martin
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| | - Stuart Maudsley
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
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4
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Tacchella A, Romano S, Ferraldeschi M, Salvetti M, Zaccaria A, Crisanti A, Grassi F. Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course: a proof-of-principle study. F1000Res 2017; 6:2172. [PMID: 29904574 PMCID: PMC5990125 DOI: 10.12688/f1000research.13114.1] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/30/2018] [Indexed: 09/02/2023] Open
Abstract
Background: Multiple sclerosis has an extremely variable natural course. In most patients, disease starts with a relapsing-remitting (RR) phase, which proceeds to a secondary progressive (SP) form. The duration of the RR phase is hard to predict, and to date predictions on the rate of disease progression remain suboptimal. This limits the opportunity to tailor therapy on an individual patient's prognosis, in spite of the choice of several therapeutic options. Approaches to improve clinical decisions, such as collective intelligence of human groups and machine learning algorithms are widely investigated. Methods: Medical students and a machine learning algorithm predicted the course of disease on the basis of randomly chosen clinical records of patients that attended at the Multiple Sclerosis service of Sant'Andrea hospital in Rome. Results: A significant improvement of predictive ability was obtained when predictions were combined with a weight that depends on the consistence of human (or algorithm) forecasts on a given clinical record. Conclusions: In this work we present proof-of-principle that human-machine hybrid predictions yield better prognoses than machine learning algorithms or groups of humans alone. To strengthen and generalize this preliminary result, we propose a crowdsourcing initiative to collect prognoses by physicians on an expanded set of patients.
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Affiliation(s)
- Andrea Tacchella
- Institute for Complex Systems, National Research Council - UOS Sapienza, Rome, 00185, Italy
| | - Silvia Romano
- Center for Experimental Neurological Therapies (CENTERS), Dept. of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, 00189, Italy
| | - Michela Ferraldeschi
- Center for Experimental Neurological Therapies (CENTERS), Dept. of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, 00189, Italy
| | - Marco Salvetti
- Center for Experimental Neurological Therapies (CENTERS), Dept. of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, 00189, Italy
- IRCCS Neuromed , Istituto Neurologico Mediterraneo, Pozzilli, 86077, Italy
| | - Andrea Zaccaria
- Institute for Complex Systems, National Research Council - UOS Sapienza, Rome, 00185, Italy
| | - Andrea Crisanti
- Department of Physics, Sapienza University of Rome, Rome, 00185, Italy
| | - Francesca Grassi
- Institute Pasteur-Cenci Bolognetti Foundation, Dept. Physiology and Pharmacology, Sapienza University of Rome, Rome, 00185, Italy
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5
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Lau WW, Sparks R, Tsang JS. Meta-analysis of crowdsourced data compendia suggests pan-disease transcriptional signatures of autoimmunity. F1000Res 2016; 5:2884. [PMID: 28491277 PMCID: PMC5399965 DOI: 10.12688/f1000research.10465.1] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/19/2016] [Indexed: 12/20/2022] Open
Abstract
Background: The proliferation of publicly accessible large-scale biological data together with increasing availability of bioinformatics tools have the potential to transform biomedical research. Here we report a crowdsourcing Jamboree that explored whether a team of volunteer biologists without formal bioinformatics training could use OMiCC, a crowdsourcing web platform that facilitates the reuse and (meta-) analysis of public gene expression data, to compile and annotate gene expression data, and design comparisons between disease and control sample groups. Methods: The Jamboree focused on several common human autoimmune diseases, including systemic lupus erythematosus (SLE), multiple sclerosis (MS), type I diabetes (DM1), and rheumatoid arthritis (RA), and the corresponding mouse models. Meta-analyses were performed in OMiCC using comparisons constructed by the participants to identify 1) gene expression signatures for each disease (disease versus healthy controls at the gene expression and biological pathway levels), 2) conserved signatures across all diseases within each species (pan-disease signatures), and 3) conserved signatures between species for each disease and across all diseases (cross-species signatures). Results: A large number of differentially expressed genes were identified for each disease based on meta-analysis, with observed overlap among diseases both within and across species. Gene set/pathway enrichment of upregulated genes suggested conserved signatures (e.g., interferon) across all human and mouse conditions. Conclusions: Our Jamboree exercise provides evidence that when enabled by appropriate tools, a "crowd" of biologists can work together to accelerate the pace by which the increasingly large amounts of public data can be reused and meta-analyzed for generating and testing hypotheses. Our encouraging experience suggests that a similar crowdsourcing approach can be used to explore other biological questions.
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Affiliation(s)
- William W Lau
- Office of Intramural Research, Center for Information Technology, National Institutes of Health, Bethesda, Maryland, USA
| | - Rachel Sparks
- Systems Genomics and Bioinformatics Unit, Laboratory of Systems Biology, National Institutes of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | | | - John S Tsang
- Systems Genomics and Bioinformatics Unit, Laboratory of Systems Biology, National Institutes of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
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