Machine learning identifies signatures of macrophage reactivity and tolerance that predict disease outcomes

Pradipta Ghosh; Saptarshi Sinha; Gajanan D Katkar; Daniella Vo; Sahar Taheri; Dharanidhar Dang; Soumita Das; Debashis Sahoo

doi:10.1016/j.ebiom.2023.104719

Machine learning identifies signatures of macrophage reactivity and tolerance that predict disease outcomes

EBioMedicine. 2023 Aug:94:104719. doi: 10.1016/j.ebiom.2023.104719. Epub 2023 Jul 27.

Authors

Pradipta Ghosh¹, Saptarshi Sinha², Gajanan D Katkar³, Daniella Vo⁴, Sahar Taheri⁵, Dharanidhar Dang⁴, Soumita Das⁶, Debashis Sahoo⁷

Affiliations

¹ Department of Cellular and Molecular Medicine, University of California San Diego, USA; Department of Medicine, University of California San Diego, USA; Moores Cancer Center, University of California San Diego, USA. Electronic address: prghosh@ucsd.edu.
² Department of Cellular and Molecular Medicine, University of California San Diego, USA; Department of Pediatrics, University of California San Diego, USA.
³ Department of Cellular and Molecular Medicine, University of California San Diego, USA.
⁴ Department of Pediatrics, University of California San Diego, USA.
⁵ Department of Computer Science and Engineering, Jacob's School of Engineering, University of California San Diego, USA.
⁶ Moores Cancer Center, University of California San Diego, USA; Department of Pathology, University of California San Diego, USA.
⁷ Moores Cancer Center, University of California San Diego, USA; Department of Pediatrics, University of California San Diego, USA; Department of Computer Science and Engineering, Jacob's School of Engineering, University of California San Diego, USA. Electronic address: dsahoo@ucsd.edu.

Abstract

Background: Single-cell transcriptomic studies have greatly improved organ-specific insights into macrophage polarization states are essential for the initiation and resolution of inflammation in all tissues; however, such insights are yet to translate into therapies that can predictably alter macrophage fate.

Method: Using machine learning algorithms on human macrophages, here we reveal the continuum of polarization states that is shared across diverse contexts. A path, comprised of 338 genes accurately identified both physiologic and pathologic spectra of "reactivity" and "tolerance", and remained relevant across tissues, organs, species, and immune cells (>12,500 diverse datasets).

Findings: This 338-gene signature identified macrophage polarization states at single-cell resolution, in physiology and across diverse human diseases, and in murine pre-clinical disease models. The signature consistently outperformed conventional signatures in the degree of transcriptome-proteome overlap, and in detecting disease states; it also prognosticated outcomes across diverse acute and chronic diseases, e.g., sepsis, liver fibrosis, aging, and cancers. Crowd-sourced genetic and pharmacologic studies confirmed that model-rationalized interventions trigger predictable macrophage fates.

Interpretation: These findings provide a formal and universally relevant definition of macrophage states and a predictive framework (http://hegemon.ucsd.edu/SMaRT) for the scientific community to develop macrophage-targeted precision diagnostics and therapeutics.

Funding: This work was supported by the National Institutes for Health (NIH) grant R01-AI155696 (to P.G, D.S and S.D). Other sources of support include: R01-GM138385 (to D.S), R01-AI141630 (to P.G), R01-DK107585 (to S.D), and UG3TR003355 (to D.S, S.D, and P.G). D.S was also supported by two Padres Pedal the Cause awards (Padres Pedal the Cause/RADY #PTC2017 and San Diego NCI Cancer Centers Council (C3) #PTC2017). S.S, G.D.K, and D.D were supported through The American Association of Immunologists (AAI) Intersect Fellowship Program for Computational Scientists and Immunologists. We also acknowledge support from the Padres Pedal the Cause #PTC2021 and the Torey Coast Foundation, La Jolla (P.G and D.S). D.S, P.G, and S.D were also supported by the Leona M. and Harry B. Helmsley Charitable Trust.

Keywords: Artificial intelligence; Boolean equivalent clusters; Innate immune response; Macrophage; Outcome prediction; Reactive; Tolerant.

MeSH terms

Animals
Humans
Inflammation
Macrophages*
Mice
Physicians*
United States

Abstract

MeSH terms

Grants and funding