Multiomics integration of 22 immune-mediated monogenic diseases reveals an emergent axis of human immune health

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Monogenic diseases are often studied in isolation due to their rarity. Here we utilize multiomics to assess 22 monogenic immune-mediated conditions with age- and sex-matched healthy controls. Despite clearly detectable disease-specific and “pan-disease” signatures, individuals possess stable personal immune states over time. Temporally stable differences among subjects tend to dominate over differences attributable to disease conditions or medication use. Unsupervised principal variation analysis of personal immune states and machine learning classification distinguishing between healthy controls and patients converge to a metric of immune health (IHM). The IHM discriminates healthy from multiple polygenic autoimmune and inflammatory disease states in independent cohorts, marks healthy aging, and is a pre-vaccination predictor of antibody responses to influenza vaccination in the elderly. We identified easy-to-measure circulating protein biomarker surrogates of the IHM that capture immune health variations beyond age. Our work provides a conceptual framework and biomarkers for defining and measuring human immune health.

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Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

Yoav Benjamini, Yosef Hochberg (1995)

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Gene Ontology: tool for the unification of biology

Michael Ashburner, Catherine A. Ball, Judith Blake … (2002)

Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.

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WGCNA: an R package for weighted correlation network analysis

Peter Langfelder, Steve Horvath (2008)

Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .

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Journal

Journal ID (nlm-ta): Res Sq

Journal ID (publisher-id): ResearchSquare

Title: Research Square

Publisher: American Journal Experts

Publication date (Electronic): 20 March 2023

Electronic Location Identifier: rs.3.rs-2070975

Affiliations

[1 ]Multiscale Systems Biology Section, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD 20892, USA

[2 ]Graduate Program in Biological Sciences, University of Maryland, College Park, MD 20742, USA

[3 ]Office of Intramural Research, CIT, NIH, Bethesda, MD 20892, USA

[4 ]NIH Center for Human Immunology, NIAID, NIH, Bethesda, MD 20892, USA

[5 ]Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA

[6 ]Laboratory of Clinical Immunology and Microbiology, NIAID, NIH, Bethesda, MD 20892, USA

[7 ]Hematology Section, Department of Laboratory Medicine, NIH Clinical Center, Bethesda, MD 20892, USA

[8 ]Clinical Research Directorate, Frederick National Laboratory for Cancer Research, National Cancer Institute, NIH, Frederick, MD 21701, USA.

[9 ]Inflammatory Diseases Section, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA

[10 ]Hematology Branch, National Heart, Lung, and Blood Institute, NIH, Bethesda, MD 20892, USA

[11 ]Translational Gerontology Branch, National Institute on Aging, Baltimore, MD 21224, USA

[12 ]Divison of Intramural Research, NIAID, NIH, Bethesda, MD 20892, USA

[13 ]Department of Immunobiology, Yale University School of Medicine, New Haven, CT 06510, USA

[14 ]Department of Pathology, Yale University School of Medicine, New Haven, CT 06510, USA

[15 ]Laboratory of Immunoregulation, NIAID, NIH, Bethesda, MD 20892, USA

[16 ]Laboratory of Allergic Diseases, NIAID, NIH, Bethesda, MD 20892, USA

[17 ]National Institute of Arthritis and Musculoskeletal and Skin Diseases, NIH, Bethesda MD 20892, USA

Author notes

[# ]Correspondence: john.tsang@ 123456nih.gov

[18]

These authors contributed equally

[19]

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Rachel Sparks http://orcid.org/0000-0001-8216-5084

John S. Tsang http://orcid.org/0000-0003-3186-3047

Article

Other ID: 10.21203/rs.3.rs-2070975

DOI: 10.21203/rs.3.rs-2070975/v1

PMC ID: 10055521

PubMed ID: 36993430

SO-VID: c719f1ad-d45b-4c10-bb26-e4ef5c0b46f6

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This work is licensed under a Creative Commons Attribution 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.

Multiomics integration of 22 immune-mediated monogenic diseases reveals an emergent axis of human immune health

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Most cited references 90

Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

Gene Ontology: tool for the unification of biology

WGCNA: an R package for weighted correlation network analysis

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