The Connection Between Mycobacterium Tuberculosis and Macrophage Cells in Lung Cancer Progression

An exploratory study for investigating how M. tuberculosis infection alters macrophage metabolism and identifies potential drug targets using genome-scale metabolic modeling.

Overview

Chronic Mycobacterium tuberculosis (Mtb) infection promotes inflammation and metabolic reprogramming in macrophages, potentially accelerating lung cancer development. This computational study uses genome-scale metabolic modeling to identify infection-specific metabolic vulnerabilities as potential drug targets.

Research Question

How does Mtb infection alter macrophage metabolism, and which metabolic reactions are unique to infected cells?

Key Findings

Metabolic Burden of Infection

  • Normal macrophage: Biomass growth rate 0.02699 h⁻¹
  • Mtb-infected macrophage: Biomass growth rate 0.00210 h⁻¹
  • 92% reduction in metabolic efficiency due to infection

Drug Target Identification

  • 117 essential reactions in healthy macrophages
  • 375 essential reactions in infected macrophages
  • 223 unique reactions identified as candidate drug targets

Critical Nutrients

Medium minimization revealed glucose and glutamine as essential substrates—blocking either flux reduced biomass growth to near zero.

Methodology

Genome-Scale Metabolic Models

  • iAB-AMØ-1410: Normal macrophage metabolism
  • iAB-AMØ-1410-Mt-661: Mtb-infected macrophage metabolism

Computational Analysis (COBRApy)

  1. Flux Balance Analysis (FBA): Predicted biomass growth and steady-state flux
  2. Flux Variability Analysis (FVA): Evaluated reaction redundancy at 90% optimality
  3. Reaction Classification: Identified essential, reversible, dead, and fixed reactions
  4. Systematic Knockout: Compared infected vs. healthy models to isolate infection-specific dependencies

Key Insight

By comparing essential reactions between healthy and infected models, we identified 223 infection-specific metabolic vulnerabilities that could be targeted therapeutically without affecting normal cell function.

Clinical Implications

This computational framework enables:

  • Targeted drug discovery for Mtb-associated lung cancer
  • Metabolic intervention strategies to disrupt infection-driven tumorigenesis
  • Host-pathogen interaction insights for understanding chronic infection consequences

Supervision

Conducted under Dr. Bridget Bannerman (Department of Medicine and Biochemistry, University of Cambridge) and Prof. Jorge Júlvez (Information and Communication Technologies, University of Zaragoza) at Pembroke College, University of Cambridge.