Host miRNAs as prognostic biomarkers of toxoplasmosis

Faculty: Prof. Meetali Singh (DBG) and Prof. Debnath Pal (CDS)

Toxoplasma gondii is one of the leading parasites in terms of absolute number of infections, with a prevalence of 20-30% worldwide. Often time healthy individuals remain asymptomatic but any event of immune compromise can activate latent cysts leading to severe neurological or ocular symptoms. In the current study, we aim to take an unbiased approach to identify all the misregulated miRNAs in human toxoplasmosis patients and correlate levels of misregulation with infection severity. Identification of these circulating miRNAs will assist in identifying potential biomarkers of infection. We will integrate miRNA sequencing from infected cell models and patient samples to identify bona fide candidates that can reliably predict prognosis from asymptomatic to symptomatic. We will compare miRNA levels across patients showing clinical symptoms, asymptomatic with non-infected individuals. We will use machine learning to predict the transition from asymptomatic to symptomatic to narrow down on candidate(s) for prognosis/diagnosis. Further we will also investigate potential of these identified miRNAs as host determinant of infection. A very important aspect of miRNA function is that it is guided more by its structure than its sequence. So, the candidate miRNAs identified in the study will also be subjected to structure-based function, dynamics and interaction studies to elucidate its mechanistic principles of action. This will boost the Generative AI approaches for assessing disease prognosis and diagnosis.