GROUP LEADER: Dr. Gian Gaetano Tartaglia (ICREA Research Professor)

POSTDOCTORAL FELLOWS: Dr. Stefanie Marti , Dr. Natalia Sanchez, Dr. Teresa Botta-Orfila, Dr. Ricardo Grana Montes

TECHNICIANS: Magda Arnal, Dr. Elias Bechara , Dr. Iona Gelabert

DOCTORAL STUDENTS: Fernando Cid , Andrea Vandelli , Alessandro Dasti , Alexandra Soriano, Riccardo delli Ponti , Alexandros Armaos , Maria Carla Antonelli

MASTER STUDENTS / ERASMUS: Andrea Vandelli

SUMMARY

Characterizing protein-RNA associations is key to unravel the complexity and functionality of mammalian genomes and could open up therapeutic avenues for the treatment of a broad range of neurodegenerative disorders.  My CRG/ICREA laboratory works on associations of non-coding RNAs, such as Xist, with proteins involved in transcriptional and translational regulation as well as neurodegenerative diseases (examples include Parkinson’s SNCA, FXTAS’ FMRP, ALS-related TDP-43 and FUS).  We aim to discover the involvement of RNA molecules in regulatory networks controlling protein production. More specifically, we are interested in understanding mechanisms whose alteration lead to aberrant accumulation of proteins.   Recently, we started to work on ribonucleoprotein condensates and their implication for cell toxicity. We discovered that specific RNA act as scaffolds to attract protein in large, granule-like assemblies that are often implicated in neurodevelopmental disorders.

Scaffolding RNAs make protein aggregate. Using theoretical approaches, we investigated how an RNA called FMR1 sequesters proteins in a disease called FXTAS. We worked out the proteins that bind to FMR1 using novel experimental approaches and identified one of them as a protein called TRA2A. Using cells, mouse models of FXTAS and post-mortem samples from patients, we confirmed that TRA2A aggregates with FMR1 in this disease and we studied the consequences of its aggregation. Now we know the components of some of these aggregates, we can begin to understand what is causing this disease and it may reveal new ways to treat it.

Fig. 1

Scaffolding RNAs make protein aggregate. Using theoretical approaches, we investigated how an RNA called FMR1 sequesters proteins in a disease called FXTAS. We worked out the proteins that bind to FMR1 using novel experimental approaches and identified one of them as a protein called TRA2A. Using cells, mouse models of FXTAS and post-mortem samples from patients, we confirmed that TRA2A aggregates with FMR1 in this disease and we studied the consequences of its aggregation. Now we know the components of some of these aggregates, we can begin to understand what is causing this disease and it may reveal new ways to treat it.

The largest ever protein-RNA network. (A) Interaction propensity scores for the background (2 billion human protein–RNA pairs; light red) and positive set (212256 high-confidence interactions; cyan) (B) The area under the ROC curve of 0.78 (0.71 upon length normalization) indicates the predictive performance of the method on high-confidence data from the ENCODE project.

Fig. 2

The largest ever protein-RNA network. (A) Interaction propensity scores for the background (2 billion human protein–RNA pairs; light red) and positive set (212256 high-confidence interactions; cyan) (B) The area under the ROC curve of 0.78 (0.71 upon length normalization) indicates the predictive performance of the method on high-confidence data from the ENCODE project.

RESEARCH PROJECTS

Post-transcriptional networks. We generated the largest-ever post-transcriptional network. Our analysis covers the H. sapiens, M. musculus and S. cerevisiae genomes and contains a total of 5.87 billion pairwise interactions. This reflects nearly 120 years of computation time on the Centre for Genomic Regulation’s high-performance computing cluster.

A novel type of alignments using predictions of structural properties of RNAs. To compare the secondary structure profiles of RNA molecules we developed the CROSSalign method. CROSSalign is based on the combination of the Computational Recognition Of Secondary Structure (CROSS) algorithm to predict the RNA secondary structure profile at single-nucleotide resolution and the Dynamic Time Warping (DTW) method to align profiles of different lengths. We find that non-coding RNAs contain structured regions that are conserved across organisms.

Predictions of thermal stability using structural properties of proteins. We introduced a novel graph-theoretical framework to assess thermal stability of proteins based on the structure without any a priori information. The method has broad applications for evolutionary analysis and protein engineering.

Scaffolding RNAs and Disease. RNAs act as a scaffold to hold several proteins that stick to RNAs together, and that certain RNA molecules with distinct properties attract more proteins and encourage proteins to aggregate. We investigated how an RNA called FMR1 is implicated in a neurodegenerative disease called Fragile X Tremor Syndrome, or FXTAS.

SELECTED PUBLICATIONS

Cid-Samper F, Gelabert-Baldrich M, Lang B, Lorenzo-Gotor N, Ponti RD, Severijnen LWFM, Bolognesi B, Gelpi E, Hukema RK, Botta-Orfila T, Tartaglia GG.
“An Integrative Study of Protein-RNA Condensates Identifies Scaffolding RNAs and Reveals Players in Fragile X-Associated Tremor/Ataxia Syndrome.”
Cell Rep, 25(12):3422-3434.e7, doi: 10.1016/j.celrep.2018.11.076, 2018.

Delli Ponti R, Armaos A, Marti S, Tartaglia GG.
“A Method for RNA Structure Prediction Shows Evidence for Structure in lncRNAs.”
Front Mol Biosci, 5:111, doi: 10.3389/fmolb.2018.00111, 2018.

Miotto M, Olimpieri PP, Di Rienzo L, Ambrosetti F, Corsi P, Lepore R, Tartaglia GG (corresponding author), Milanetti E.
“Insights on protein thermal stability: a graph representation of molecular interactions.”
Bioinformatics, doi: 10.1093/bioinformatics/bty1011, 2018.

Lang B, Armaos A, Tartaglia GG.
“RNAct: Protein-RNA interaction predictions for model organisms with supporting experimental data.”
Nucleic Acids Res, doi: 10.1093/nar/gky967, 2018.

Qamar S, Wang G, Randle SJ, […], Tartaglia GG,[…], St George-Hyslop P.
“FUS Phase Separation Is Modulated by a Molecular Chaperone and Methylation of Arginine Cation-π Interactions.”
Cell, 173(3):720-734, 2018.