Bioinformatics and Genomics

gian-g-tartaglia
Gene Function and Evolution

Group Structure

GROUP LEADER:
Dr. Gian Gaetano Tartaglia
POSTDOCTORAL FELLOWS:
Dr. Marianela Masin (co-supervised with Dr. Salvatella, IRB), Dr. Andreas Zanzoni, Dr. Benedetta Bolognesi, Dr. Teresa Botta-Orfila, Dr. Carmen Maria Livi
TECHNICIANS:
Dr. Silvia Rodriguez, Dr. Joana Ribeiro, Maria de las Nieves Lorenzo
DOCTORAL STUDENTS:
Federico Agostini, Davide Cirillo, Domenica Marchese, Petr Klus
PRE-DOCTORAL STUDENTS:
Marta Baldrighi

Summary

Our main focus is to understand the role played by RNA molecules in protein networks. 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.  Our research focuses on associations of coding/non-coding RNAs with proteins involved in i) transcriptional and translational regulation (e.g., X-chromosome inactivation)1 and ii) neurodegenerative diseases (examples include Parkinson’s α-synuclein, Alzheimer’s disease amyloid protein APP, TDP-43 and FUS)2,3.  In particular, we aim to discover the involvement of RNA molecules in regulatory networks controlling protein production. More specifically, we are interested in discovering and understanding mechanisms whose alteration lead to aberrant accumulation of proteins4.  We have recently observed that interaction between proteins and their cognate mRNAs (autogenous associations) induce feedback loops that are crucial in protein homeostasis²
Figure 1 Figure 1


Research Projects

  • X-chromosome inactivation. We use catRAPID5 to investigate interactions of long non-coding RNAs such as Xist with a number of epigenetic modifiers as well as transcription and splicing factors including SUZ12, EZH2, YY1, SAF-A, SFRS1 and SATB¹. Our calculations suggest that localization and confinement of Xist are finely regulated by multiple factors acting at the interface between chromosome X and the nuclear matrix.
  • Autogenous interactions. Recent evidence indicates that a number of proteins are able to interact with cognate mRNAs. These autogenous associations represent important regulatory mechanisms controlling gene expression at the translational level. Our large-scale analysis of biological pathways revealed that aggregation-prone and structurally disordered proteins have the highest propensity to interact with cognate RNAs2.
  • Neurodegenerative diseases. We are interested in ribonucleoprotein interactions linked to inherited intellectual disability, amyotrophic lateral sclerosis, Creutzfeuld-Jakob, Alzheimer’s, and Parkinson’s diseases3. We recently focused on RNA interactions with fragile X mental retardation protein FMRP; protein sequestration caused by CGG repeats; noncoding transcripts regulated by TAR DNA-binding protein 43 TDP-43.
  • Supersaturated proteins and proteome vulnerability. A crucial question in cell biology is why only certain proteins appear to aggregate in vivo, whereas others do not. Using the Zyggregator method, we identified the proteins most vulnerable to aggregation as those whose cellular concentrations are high relative to their solubilities4.
  • catRAPID omics. We developed a new method to allow fast calculation of ribonucleoprotein associations in Caenorhabditis elegans, Danio rerio, Drosophila melanogaster, Homo sapiens, Mus musculus, Rattus norvegicus, Saccharomyces cerevisiae and Xenopus tropicalis5. The algorithm computes the interaction between a molecule (protein/transcript) and the reference library (transcriptome/proteome) in each model organism (p-values<0.05; Figure 2).

Figure 2 Figure 2


Selected Publications

  1. Agostini F, Cirillo D, Bolognesi B & Tartaglia GG.
    “X-inactivation: quantitative predictions of protein interactions in the Xist network.”
    Nucleic Acids Res, 41:e31 (2013).
  2. Zanzoni A et al.
    “Principles of self-organization in biological pathways: a hypothesis on the autogenous association of alpha-synuclein.”
    Nucl. Acids Res, 41:9987–9998 (2013).
  3. Cirillo D et al.
    “Neurodegenerative diseases: Quantitative predictions of protein-RNA interactions.”
    RNA, 19:129–140 (2013).
  4. Ciryam P, Tartaglia GG, Morimoto RI, Dobson CM & Vendruscolo M.
    “Widespread aggregation and neurodegenerative diseases are associated with supersaturated proteins.”
    Cell Rep, 5:781–790 (2013).
  5. Agostini F et al.
    “catRAPID omics: a web server for large-scale prediction of protein-RNA interactions.”
    Bioinformatics, 29:2928–2930 (2013).