Members

Group Leader:

Dr. Gian Gaetano Tartaglia (ICREA Research Professor)

Postdoctoral Fellows:

Dr. Benedetta Bolognesi, Dr. Teresa Botta-Orfila, Dr. Carmen Maria Livi (until May 2015), Dr. Stefanie Marti (from May 2015)

Technicians:

Dr. Silvia Rodriguez (until September 2015), Dr. Joana Ribeiro, Maria de las Nieves Lorenzo

Doctoral Students:

Petr Klus, Davide Cirillo, Riccardo delli Ponti, Domenica Marchese, Alexandros Armaos (from November 2015), Fernando Cid (from November 2015)

Master Students:

Maria de las Nieves Lorenzo

Selected Publications

Cirillo D, Botta-Orfila T, Tartaglia GG.
“By the company they keep: interaction networks define the binding ability of transcription factors.”
Nucleic Acids Res, 43(19):e125 (2015).

Klus P, Cirillo D, Botta Orfila T, Tartaglia GG.
“Neurodegeneration and Cancer: Where the Disorder Prevails.”
Sci Rep, 5:15390 (2015).

Livi CM, Klus P, Delli Ponti R, Tartaglia GG.
“catRAPID signature: identification of ribonucleoproteins and RNA-binding regions.”
Bioinformatics, pii: btv629 (2015).

Murakami T et al.
“ALS/FTD Mutation-Induced Phase Transition of FUS Liquid Droplets and Reversible Hydrogels into Irreversible Hydrogels Impairs RNP Granule Function.”
Neuron, 88(4):678-90 (2015).

Klus P, Ponti RD, Livi CM, Tartaglia GG.
“Protein aggregation, structural disorder and RNA-binding ability: a new approach for physico-chemical and gene ontology classification of multiple datasets.”
BMC Genomics, 16:1071 (2015).

Summary

Our research is highly interdisciplinary, involves a number of international collaborators and focuses on the uncharted territory of non-coding RNAs involved in transcriptional and translational regulation (X-chromosome inactivation, differentiation processes) and neurodegenerative diseases (Parkinson’s α-synuclein, Alzheimer’s disease amyloid protein APP, TDP-43 and FUS). The group plays a role in projects aiming to uncover regulatory mechanisms controlling protein production. More specifically, we are interested in understanding interactions whose alteration lead to aberrant accumulation of proteins. We previously observed that association between proteins and their cognate mRNAs (autogenous associations) induce feedback loops that are crucial for protein homeostasis. We recently investigated human protein-RNA networks and identified transcripts that bind specifically to aggregation-prone proteins and we are characterising protein-RNA assemblies whose formation is toxic.

Figure 1. Transcription factors networks. — Figure 1

Figure 2. Cancer vs central nervous system diseases. — Figure 2

Research projects

Transcription factor networks. We found that transcription factors recognize DNA patterns together with their cofactors. We developed a method, PAnDA, to predict protein-DNA interactions based on cell-specific protein networks, which opens up the way for efficient and effective manipulation of cellular processes (Figure 1).
Cancer and Neurodegenerative disease. It has been reported that genes up-regulated in cancer are often down regulated in neurodegenerative disorders and vice versa. We performed a large-scale analysis of physico-chemical properties on human genes to understand which specific features could differentiate classes of diseases (Figure 2).
Discovery of RNA-binding regions. A number of studies indicate that a previously unknown number of proteins have RNA-binding ability. We introduced a method to identify RNA-binding regions by considering physico-chemical properties of polypeptide chains.
Ribonucleoprotein assemblies and disease. We are interested in understanding the relationship between formation of ribonucleoprotein assemblies and pathology. We propose a model in which low-complexity domains of Fused in Sarcoma FUS drive its physiologically reversible assembly into membrane-free, liquid droplet and hydrogel-like structures that occur in Amyotrophic Lateral Sclerosis.
Gene sets classification. We developed a new approach to provide accurate classifications of physico-chemical features and annotations of biological processes, molecular functions and cellular components, which is extremely useful for the discovery and characterization of trends in protein datasets.