Cellular Heterogeneity

Cellular heterogeneity significantly influences the outcome of infections by viruses and bacteria, as individual differences among cells can affect their susceptibility to infection and the efficiency of pathogen replication. Variations in cell surface receptors, intracellular signaling pathways, and immune response activation can lead to a diverse range of cellular responses to the same pathogen, impacting the overall progression of the infection. This heterogeneity can also contribute to the development of resistant bacterial cell populations, complicating treatment efforts. We use a combination of experiments and mathematical modeling to investigate how such variability arises in RNA virus infections and anti-microbial resistance.

Stochasticity in virus infection 

Heterogeneous outcomes of cell infection (where only a fraction of cells are infected in spite of the entry of pathogen) is a common observation. Infection by a small number of pathogens, heterogeneity in cellular gene expression, complex interplay of host proteins with pathogen components and counter-mechanisms by pathogen can contribute to non-intuitive and highly diverse outcomes of virus or bacteria growth during infection. We have employed mathematical modeling and imaging tools to understand contributing factors that leads to emergence of infection heterogeneity. For example, a simple mathematical framework to describe the temporal evolution of various viral molecules by modeling all the intracellular processes relevant to the viral RNA (for the Flaviviridae family) was developed (Chhajer, et. al. RSIF 2021). We used a stochastic framework to address resource (viral RNA) sharing during the start of infection when viral RNA is present in low copy. This model accurately captures the experimentally measured viral dynamics of Hepatitis C virus. Based on our findings, we propose a new definition of Multiplicity of Infection (MOI) that would be useful in analysis of candidate fitness functions like infectivity, viral loads and mutability. The stochastic model predicts the importance of additional cellular processes like maturation of the VMS that have not been investigated before and its role in heterogeneous outcomes of infection. 


Harsh Chhajer

Emergence of Anti-Microbial Resistance 

Bacterial resistance to antibiotics is a major threat to public health. Significant efforts in research and health policy have been directed towards understanding and combatting this issue. While genetic mechanisms of resistance development are well understood, there are several phenotypic responses such as persistence, tolerance and heteroresistance resulting in transient variations within sub populations of cells that allow them to survive antimicrobial stress. Clinically, these phenomena have been linked to recurrence of various infections such as tuberculosis, cystic fibrosis and urinary tract infections. To overcome these issues, several alternatives to antibiotics are being tested, including antimicrobial peptides (AMPs). These molecules are part of the innate immune system, known for their broad-spectrum activity and immunomodulatory effects. While they have shown promising results in combatting resistant bacteria, the efficacy of these peptides on persistent and heteroresistant cells haven’t been thoroughly investigated.

We tested conventional antibiotics and AMPs on actively growing and growth arrested E. coli. While both are equally efficient on exponentially growing cells, diverse responses were observed with stationary phase cells. Variability existed even amongst the peptides. Particularly, colistin was found to induce heteroresistant behaviour, linked to hypermutations in the bacterial genome. In addition, cross resistance to other antimicrobials was also observed. Single cell imaging of this heteroresistant population confirmed the variability in response of individual cells to colistin treatment. Overall, deeper understanding of the interaction between bacterial cells and these antimicrobials was obtained, which can form a basis for designing efficient treatment strategies in the future.