Description

Epigenetic alterations are now recognized as key drivers of complex human diseases, providing an additional regulatory layer that complements genetic variation. These mechanisms offer powerful opportunities for advancing precision medicine and enabling more personalized treatment strategies.

Spondyloarthritis (SpA) is a common chronic inflammatory rheumatic disease that can lead to complete spinal ankylosis through pathological bone formation, as well as destruction of peripheral joints. Its development arises from a complex interplay between genetic predisposition and environmental exposures. Positioned at the crossroads of these influences, epigenetic regulation - particularly microRNAs - represents a critical and still underexplored component of SpA pathophysiology.

In this context, we have generated a unique high-resolution dataset of microRNA expression profiles from FACS-sorted immune cell populations (CD4+ and CD8+ T cells, and monocytes) in 100 patients undergoing biologic therapies (anti-TNF or anti-IL17). Samples were collected prior to treatment initiation alongside detailed clinical follow-up. This work has revealed extensive, cell-type-specific microRNA alterations in patients compared to healthy controls. Importantly, we have identified a subset of microRNAs whose baseline expression levels are predictive of clinical response to therapy.

This PhD project aims to take these findings to the next level by:


translating microRNA expression patterns into a robust predictive signature, to be validated in an independent cohort using a novel multiplex digital PCR approach;
uncovering the molecular mechanisms through which selected microRNAs modulate treatment response;
testing, in in vitro systems and potentially in vivo models, whether modulation of these microRNAs can enhance or synergize with existing biologic therapies.


This project offers a unique opportunity to work at the interface of molecular biology, immunology, and data science, with strong translational potential. It is expected to deliver a compelling proof of concept for the use of microRNA-based biomarkers in guiding treatment decisions in complex diseases - beyond the field of oncology.