Markov field network model of multi-modal data predicts effects of immune system perturbations on intravenous BCG vaccination in macaques

Shu Wang; Amy J Myers; Edward B Irvine; Chuangqi Wang; Pauline Maiello; Mark A Rodgers; Jaime Tomko; Kara Kracinovsky; H Jacob Borish; Michael C Chao; Douaa Mugahid; Patricia A Darrah; Robert A Seder; Mario Roederer; Charles A Scanga; Philana Ling Lin; Galit Alter; Sarah M Fortune; JoAnne L Flynn; Douglas A Lauffenburger

doi:10.1016/j.cels.2024.10.001

Markov field network model of multi-modal data predicts effects of immune system perturbations on intravenous BCG vaccination in macaques

Cell Syst. 2024 Oct 29:S2405-4712(24)00298-9. doi: 10.1016/j.cels.2024.10.001. Online ahead of print.

Authors

Shu Wang¹, Amy J Myers², Edward B Irvine³, Chuangqi Wang⁴, Pauline Maiello², Mark A Rodgers², Jaime Tomko², Kara Kracinovsky², H Jacob Borish², Michael C Chao⁵, Douaa Mugahid⁵, Patricia A Darrah⁶, Robert A Seder⁶, Mario Roederer⁶, Charles A Scanga², Philana Ling Lin⁷, Galit Alter⁸, Sarah M Fortune³, JoAnne L Flynn², Douglas A Lauffenburger⁹

Affiliations

¹ Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.
² Department of Microbiology and Molecular Genetics, University of Pittsburgh School of Medicine and Center for Vaccine Research, University of Pittsburgh, Pittsburgh, PA 15261, USA.
³ Ragon Institute of Massachusetts General Hospital, MIT and Harvard, Cambridge, MA 02139, USA; Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
⁴ Department of Immunology and Microbiology, University of Colorado, Anschuntz Medical Campus, Aurora, CO 80045, USA.
⁵ Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
⁶ Vaccine Research Center, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD 20814, USA.
⁷ Department of Pediatrics, University of Pittsburgh School of Medicine, UPMC Children's Hospital of Pittsburgh, and Center for Vaccine Research, University of Pittsburgh, Pittsburgh, PA 15620, USA.
⁸ Ragon Institute of Massachusetts General Hospital, MIT and Harvard, Cambridge, MA 02139, USA.
⁹ Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02142, USA. Electronic address: lauffen@mit.edu.

PMID: 39504969
DOI: 10.1016/j.cels.2024.10.001

Abstract

Analysis of multi-modal datasets can identify multi-scale interactions underlying biological systems but can be beset by spurious connections due to indirect impacts propagating through an unmapped biological network. For example, studies in macaques have shown that Bacillus Calmette-Guerin (BCG) vaccination by an intravenous route protects against tuberculosis, correlating with changes across various immune data modes. To eliminate spurious correlations and identify critical immune interactions in a public multi-modal dataset (systems serology, cytokines, and cytometry) of vaccinated macaques, we applied Markov fields (MFs), a data-driven approach that explains vaccine efficacy and immune correlations via multivariate network paths, without requiring large numbers of samples (i.e., macaques) relative to multivariate features. We find that integrating multiple data modes with MFs helps remove spurious connections. Finally, we used the MF to predict outcomes of perturbations at various immune nodes, including an experimentally validated B cell depletion that induced network-wide shifts without reducing vaccine protection.

Keywords: infectious disease; network modeling; systems immunology; tuberculosis; vaccines.

Grants and funding

U19 AI167899/AI/NIAID NIH HHS/United States