Biology subjects

Giorgetti, A.

Publications and source records attributed to Giorgetti, A..

4 recordsLinked to original sources

bioRxiv2024

Trajectory analysis of hepatic stellate cell differentiation reveals metabolic regulation of cell commitment and fibrosis

Defining the trajectory of cells during differentiation and disease offers the possibility to understand the mechanisms driving cell fate and identity. However, trajectories of human cells are largely unexplored. By investigating the proteome trajectory of iPSCs differentiation to hepatic stellate cells (dHSCs), we identified RORA as a key transcription factor governing the metabolic reprogramming of HSCs necessary for HSCs commitment, identity, and activation. Using RORA deficient iPSCs and pharmacologic interventions, we showed that RORA is required for mesoderm differentiation and prevents dHSCs activation by reducing the high energetic state of the cells. While RORA knockout mice had enhanced fibrosis, RORA agonists rescued multi- organ fibrosis in in vivo models. RORA expression was consistently found to be negatively correlated with liver fibrosis and HSCs activation markers in patients with liver disease. This study reveals that RORA regulates cell metabolic plasticity, crucial for mesoderm differentiation, pericyte quiescence, and fibrosis, influencing cell commitment and disease mechanisms. SummaryThis study describes the trajectory of induced pluripotent stem cells (iPSCs) differentiation to hepatic stellate cells (dHSCs). We identify RAR-related orphan receptor alpha (RORA) as a transcription factor essential for mesoderm commitment and dHSCs identity and fibrogenic activation by regulating metabolic plasticity.

cell biology↗

bioRxiv2023

AI-based novel-chemotype GPCRs drugs: introducing ligand type classifiers and systems biology

Identifying the correct chemotype of ligands targeting receptors (i.e., agonist or antagonist) is a challenge for in silico screening campaigns. Here we present an approach that identifies novel chemotype ligands by combining structural data with a random forest agonist/antagonist classifier and a signal-transduction kinetic model. As a test case, we apply this approach to identify novel antagonists of the human adenosine transmembrane receptor type 2A, an attractive target against Parkinsons disease and cancer. The identified antagonists were tested here in a radioligand binding assay. Among those, we found a promising ligand whose chemotype differs significantly from all so-far reported antagonists, with a binding affinity of 310{+/-}23.4 nM. Thus, our protocol emerges as a powerful approach to identify promising ligand candidates with novel chemotypes while preserving antagonistic potential and affinity in the nanomolar range.

bioinformatics↗

bioRxiv2022

SSB toolkit: from molecular structure to subcellular signaling pathways.

We present, here, an open-source systems biology toolkit to simulate mathematical models of the signal-transduction pathways of G-protein coupled receptors (GPCRs). By merging structural macromolecular data with systems biology simulations, we developed a framework to simulate the signal-transduction kinetics induced by ligand-GPCR interactions, as well as the consequent change of concentration of signaling molecular species, as a function of time and ligand concentration. Therefore, this tool brings to the light the possibility to investigate the subcellular effects of ligand binding upon receptor activation, deepening the understanding of the relationship between the molecular level of ligand-target interactions and higher-level cellular and physiologic or pathological response mechanisms.

bioinformatics↗

bioRxiv2022

Modelling eNvironment for Isoforms (MoNvIso): A general platform to predict structural determinants of protein isoforms in genetic diseases

The seamless integration of human disease-related mutation data into protein structures is an essential component of any attempt to correctly assess the impact of the mutation. The key step preliminary to any structural modelling is the identification of the correct isoform onto which mutations should be mapped because there are several functionally different protein isoforms from the same gene. To handle large sets of data coming from omics techniques, this challenging task should be automatized. Here we present our code MoNvIso (Modelling eNvironment for Isoforms), which pinpoints the correct isoform associated with the mutation of interest and builds a structural model of both the wild type isoform and the related variants starting from the name of the gene and the list of mutations of interest.

bioinformatics↗