# Macrophage Polarization in Response to Infections and Inflammation

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2020 · $1,000,214

## Abstract

Abstract
Macrophages in Greek means “big eaters" are powerful cellular components of innate immunity. They play a
pivotal role in immune defense by ‘eating’ pathogens, dead or cancerous cells. They also contribute to tissue
homeostasis, development and repair. When doing their job, macrophages react to their surroundings and trigger
acute inflammation to resolve the problems. They do so by assuming one of the two states that have been widely
recognized, i.e., immunoreactive (proinflammatory) and immunotolerant (a.k.a, M1 and M2, respectively). While
finite degrees of reactivity and tolerance are desirable in physiology, excess of either state is undesirable and
invariably associated with disease pathogenesis (i.e., the Goldilocks conundrum). For example, hyperreactivity
is recognized as the root cause of tissue injury in a wide array of diseases (colitis, sepsis, NASH) and
hypertolerance is a common determinant that drives most, if not all chronic diseases that are incurable, e.g.,
cancers. Consensus on the definition of these physiologic and pathologic macrophage states has not been
reached, perhaps because of 4 major challenges: heterogeneity, biological robustness, the temporal evolution
of the network, and artifacts (tremendous plasticity of macrophages as they drift rapidly when isolated from
tissues). We have used a novel computational methodology, Boolean Implication Network [Sahoo 2008], to
analyze pooled human macrophage gene expression datasets. This method, which identifies asymmetric gene
expression patterns, blurs noise (heterogeneity/artifacts) but reveals a temporal model of events that is invariably
seen across all datasets. The analysis revealed hitherto unknown continuum transition states between reactive
to tolerant states along five paths; machine-learning identified one of them as the major path which subsequently
stood the rigorous test/validation on multiple publicly available transcriptomic datasets, across species (mouse
and human), macrophage subtypes and disease states. Most importantly, unlike other commonly used gene
cluster signatures, the Boolean path can prognosticate outcomes across diverse diseases. Preliminary validation
studies on a genetic model confirm that the path could be exploited for modulating macrophage polarization by
altering LPS/TLR4 responses. We will now interrogate the impact of these discoveries using an iterative
approach, i.e., model-driven experimentation and experiment-driven model refinement, through three aims:
Unravel the importance of novel molecular drivers in the newly identified gene signatures of macrophage
polarization using semi-HTP chemical/genetic screens on murine and human monocyte-derived macrophages
(Aim 1), in murine disease models of hyperreactivity and hypertolerance (Aim 2) and in “Humanoids”, i.e., human
organoid-based microbe/immune cells co-culture models (“gut-in-a-dish”; Aim 3). Although our focus is
gastrointestinal infection and inflammation, the findings will defi...

## Key facts

- **NIH application ID:** 10100201
- **Project number:** 1R01AI155696-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Soumita Das
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $1,000,214
- **Award type:** 1
- **Project period:** 2020-09-22 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10100201

## Citation

> US National Institutes of Health, RePORTER application 10100201, Macrophage Polarization in Response to Infections and Inflammation (1R01AI155696-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10100201. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*