Non-coding Genome - Francesco Nicassio

Non-coding Genome - Francesco Nicassio

MicroRNAs and non-coding RNAs in Development and Disease

The Non-coding Genome Group studies the function and regulation of non-coding transcripts, focusing on the molecular mechanisms in control of gene expression regulation and how they shape the identity and properties of human stem cells during cancer evolution

WHAT? understanding the role played by ncRNAs in transcriptional/epigenetic plasticity

WHERE? physiology (development) and human (cancer) disease, focusing on cancer evolution (drug resistance, metastasis)

HOW? integration of Experimental models with Computational and –omics methodologies

Current Research

Current Research

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High-resolution characterization of the transcriptional/epigenetic landscape of breast cancer organoids

High-resolution characterization of the transcriptional/epigenetic landscape of breast cancer organoids

 

Cancer organoids recapitulate cancer cell growth in vitro within a 3D environment. Able to copycat the 3D spatial tissue organization and catch the genetic and phenotypic heterogeneity of their tissue of origin, organoids represent a more reliable platform than 2D cultures for studying cancer progression and drug resistance mechanisms. We aim at investigating the role non-coding RNAs and DNA elements play in the adaptive response of breast cancer cells following anticancer therapies and in the context of  tumor organoids  We will use cutting-edge genomic approaches to achieve a high-resolution characterization of the transcriptional/epigenetic landscape of breast cancer organoids. We will focus on the evolutionary trajectories of the different tumour subpopulations that will be characterized both in basal conditions (homeostasis) and in response to anticancer treatments, stimulating adaptive response mechanisms. The goal will be to gain insight into the genetic and epigenetic factors that hinder the success of established anticancer therapies.

Identification of the host proteins interacting with SARS-CoV-2 RNA

Identification of the host proteins interacting with SARS-CoV-2 RNA

SARS-CoV-2 is a positive single-stranded RNA virus which interacts at different stages with the host proteins of infected cells. These interactions are necessary for the host to recognize and block the replication of the virus. But, at the same time, the virus requires host proteins to translate, transcribe and replicate its genetic material. In order to identify the host proteins that interact with SARS-CoV-2 RNA, we adopted the RNA-protein interaction detection coupled to mass spectrometry (RaPID-MS) technology, which allows the purification and identification by MS-based proteomics of the proteins associated to a specific RNA of interest expressed in mammalian cells. In particular, we conducted the analysis on the more structured regions of SARS-CoV-2 RNA and retrieved several proteins specifically associated with each region. Overall, our data revealed a list of proteins associated to SARS-CoV-2 RNA that will be further characterized to understand their role in SARS-CoV-2 infection and viral replication.

CRISPRi screening of LncRNAs in Transcriptional Reprogramming of Breast Cancer Cells

CRISPRi screening of LncRNAs in Transcriptional Reprogramming of Breast Cancer Cells

 

Long non-coding RNAs (lncRNAs) are emerging as promising novel diagnostic and therapeutic molecules. These transcripts provide a flexible and dynamic mechanism of gene expression regulation. Emerging evidence suggests a role for lncRNAs as key components in adaptation pathways. In breast cancer, transcriptional adaptation has been described to occur, in particular during the acquisition of chemoresistance. Unfortunately, the mechanisms behind cancer adaptation and the role lncRNAs play are still to be clarified. We are currently characterizing the role of a group of lncRNAs during adaptive responses in triple-negative breast cancer cells by means of CRISPRinterference. We are functionalizing lncRNAs in a classic-drop out screening during proliferation, drug response and in vivo tumorigenesis. In parallel, we are characterizing the transcriptomic response of lncRNAs modulation by single cell-RNAseq in a CROP-seq like setting.  With this work, we will bring novel knowledge into the regulatory framework played by lncRNAs in cancer cell plasticity.

Meet the team

Meet the team

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IIT Publications Search

2022
Farina S., Labanca I., Acconcia G., Ghezzi A., Farina A., D'Andrea C., Rech I.
Above pile-up fluorescence microscopy with a 32 Mc/s single-channel time-resolved SPAD system
Optics Letters, vol. 47, (no. 1), pp. 82-85
Article Journal
2022
Pizzorni M., Lertora E., Parmiggiani A.
Adhesive bonding of 3D-printed short- and continuous-carbon-fiber composites: An experimental analysis of design methods to improve joint strength
Composites Part B: Engineering, vol. 230
Article Journal
2022
Lettieri S., Battaglino B., Sacco A., Saracco G., Pagliano C.
A green and easy-to-assemble electrochemical biosensor based on thylakoid membranes for photosynthetic herbicides detection
Biosensors and Bioelectronics, vol. 198
Article Journal
2022
Pippo I., Zenzeri J., Berselli G., Torazza D.
An Innovative Mechanical Solution to Better Understand Human-Robot Interaction Forces
Lecture Notes in Mechanical Engineering, pp. 683-690
Conference Paper Book Series
2022
Salina Borello E., Peter C., Panini F., Viberti D.
Application of A∗ algorithm for microstructure and transport properties characterization from 3D rock images
Energy, vol. 239
Article Journal
2022
Demuro S., Sauvey C., Tripathi S.K., Di Martino R.M.C., Shi D., Ortega J.A., Russo D., Balboni B., Giabbai B., Storici P., Girotto S., Abagyan R., Cavalli A.
ARN25068, a versatile starting point towards triple GSK-3β/FYN/DYRK1A inhibitors to tackle tau-related neurological disorders
European Journal of Medicinal Chemistry, vol. 229
Article Journal
2022
De Fazio R., Dinoi L.M., De Vittorio M., Visconti P.
A sensor‐based drone for pollutants detection in eco‐friendly cities: Hardware design and data analysis application
Electronics (Switzerland), vol. 11, (no. 1)
Article Journal
2022
Venezia V., Pota G., Silvestri B., Vitiello G., Di Donato P., Landi G., Mollo V., Verrillo M., Cangemi S., Piccolo A., Luciani G.
A study on structural evolution of hybrid humic Acids-SiO2 nanostructures in pure water: Effects on physico-chemical and functional properties
Chemosphere, vol. 287
Article Journal
2022
Romeo L., Frontoni E.
A Unified Hierarchical XGBoost model for classifying priorities for COVID-19 vaccination campaign
Pattern Recognition, vol. 121
Article Journal
2022
Barontini F., Bettelani G.C., Leporini B., Averta G., Bianchi M.
A User-Centered Approach to Artificial Sensory Substitution for Blind People Assistance
Biosystems and Biorobotics, vol. 28, pp. 599-603
Book Chapter Book Series
2022
Manigrasso J., Chillon I., Genna V., Vidossich P., Somarowthu S., Pyle A.M., De Vivo M., Marcia M.
Author Correction: Visualizing group II intron dynamics between the first and second steps of splicing (Nature Communications, (2020), 11, 1, (2837), 10.1038/s41467-020-16741-4)
Nature Communications, vol. 13, (no. 1)
Erratum Journal
2022
Lazzaroni M., Tabasi A., Toxiri S., Caldwell D.G., Kingma I., De Momi E., Ortiz J.
Back-Support Exoskeleton Control Using User’s Torso Acceleration and Velocity to Assist Manual Material Handling
Biosystems and Biorobotics, vol. 27, pp. 189-193
Book Chapter Book Series
2022
Di Rienzo L., De Flaviis L., Ruocco G., Folli V., Milanetti E.
Binding site identification of G protein-coupled receptors through a 3D Zernike polynomials-based method: application to C. elegans olfactory receptors
Journal of Computer-Aided Molecular Design
Article Journal
2022
Bartoli M., Giorcelli M., Vigliaturo R., Jagdale P., Rovere M., Tagliaferro A.
Bio-derived and Waste Fats Use for the Production of Drop-In Fuels
Energy, Environment, and Sustainability, pp. 125-139
Book Chapter Book Series
2022
Tabasi A., Lazzaroni M., Brouwer N.P., Kingma I., van Dijk W., de Looze M.P., Toxiri S., Ortiz J., van Dieen J.H.
Calibrating an EMG-Driven Muscle Model and a Regression Model to Estimate Moments Generated Actively by Back Muscles for Controlling an Actuated Exoskeleton with Limited Data
Biosystems and Biorobotics, vol. 27, pp. 401-405
Book Chapter Book Series

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