We focus on two fundamental pillars: Foundations and Applications using strategies that encompass the integration of synthetic circuits to
1) unravel the optimal design of synthetic networks with minimal impact on cell physiology
2) develop automated tools for integration of high-throughput data and rapid implementation of tissue-specific therapeutic switches
3) develop technologies synergizing with state-of-the-art methods for cell-based therapies.
RNA encoded devices
By combining protein engineering and structural modeling, we engineered new RBPs and proteases that respond to cognate proteases in mammalian cells. We then combined post-transcriptional (siRNA, RBPs) and post-translational (protease) control to expand the operational landscape of RNA-encoded genetic circuits and created a set of regulatory devices including switches, protein sensors and multilayered cascade (Cella et al 2018).
Design of resource-aware mammalian genetic circuits
Competition for intracellular resources which we named ‘burden’, is a core problem for the poor predictability of gene expression circuits (Frei T, Cella F et al Nature Communications 2020). This competition results in coupling of otherwise independent exogenous and endogenous genes, generating a divergence between intended and actual functions. In collaboration with the groups of Khammash (ETH, Switzerland) and Stan (Imperial College London-ICL, UK), we engineered microRNA-based incoherent feedforward (iFFL) circuits to effectively ensure robust circuits performance, independently of the host cell line.
Automated miRNA-based cell classifier
microRNAs are differentially expressed between cell types and have been used to ensure cell-specificity of synthetic circuits. Until recently, the design of miRNA-regulated circuits was performed manually but then, automated tools based on miRNA abundance in selected cell lines became available. However, miRNA expression level does not accurately reflect miRNA activity, thus limiting the accuracy of the classifiers. To overcome this limitation, we are developing a classifier that integrates both expression and activity of miRNAs.
Addressing T cell dysfunctions with synthetic devices
The Synbio lab is actively working on the design of more efficient immunotherapies that address an important limitation of T cell dysfunctions, a condition arising from chronic antigen exposure. The idea developed into a 5-year grant proposal has been awarded with a European Research Council Starting Grant focused on reprogramming exhausted T-cells by synthetic biology circuits that sense the onset of the dysfunction and rewire downstream pathways to neutralize it, thus ensuring long term efficacy of cell-based immunotherapy.
Drug repositioning to improve T cell functionality
We applied the Drug Enrichment Analysis (DSEA) to transcriptional profiles of tumor infiltrating lymphocytes (TILs). Eight FDA-approved small-molecule drugs were predicted to up-regulate these two pathways from DSEA analysis and to counteract T cells exhaustion when administered to T cells.
Identification of mechanical triggers of T cell dysfunctions
The solid tumour microenvironment is biomechanically distinct from physiological conditions, being characterized by higher interstitial pressures, higher stiffness and a distinctive vascular architecture. We focus on understanding the contribution of the mechanical cues of tumor microenvironment to the development of exhaustion in tumor infiltrating lymphocytes.
Synthetic photobiology for light controllable active matter
In collaboration with Prof. Di Leonardo (University La Sapienza) we provide the genetic building blocks for a light controllable active matter having reliable, reconfigurable and interactively tunable dynamical properties. From a Physics and Engineering standpoint, swimming bacteria are a formidable example of self-propelled micro-machines. Together with their synthetic counterpart, self-propelled colloids, represent the “living” atoms of active matter, an exciting branch of contemporary soft matter and statistical mechanics.
Neural connectivity toolbox: from circuit tracing to rewiring
We develop a synthetic biology-based approach for a deeper understanding of signal propagation in the connectome of neuron cells in collaboration with the groups of Morsut and Quadrato (USC, US). By using synthetic receptors, we will engineer ‘transceiver’ neurons that can both receive signal from a ‘donor’ neuron and propagate the signal to the rest of the network. With this strategy we will trace an entire neural network in a single experiment. The system will be tested in C. elegans first and later in brain organoids.