Gian Gaetano Tartaglia (Rome, 23/10/1976), is an Italian biochemist and computational biologists. He is currently a full professor in biochemistry at the University of Rome
Sapienza and a Principal Investigator at the
Italian Institute of Technology (IIT). He is an avid runner, a music player and a led zeppelin fan.
Gian Gaetano Tartaglia graduated at the University La Sapienza in the academic year 1999-2000 with a thesis on mathematical modelling of neurons (Physics department). Between 2001 and 2005, Tartaglia carried out his doctoral studies at the University of Zurich CH (Biochemistry department) focusing on the folding and misfolding of proteins associated with neurodegenerative disorders. As an associate researcher, he worked at the University of Cambridge UK (Chemistry and Genetics departments) under the supervision of Chris Dobson and Michele Vendruscolo. In the period 2005-2010, he participated in several computational and experimental studies on amyloidosis. He is currently a life member of the Clare Hall College in Cambridge UK since 2011.
In 2010 Gian Tartaglia became PI at the Centre for Genomic Regulation (CRG) in Barcelona. He was awarded the prestigious ERC grant in 2013 for his studies on the role of coding and non-coding transcripts in the regulation of amyloid genes ( ERC 309545). In 2014 Tartaglia was tenured in Catalonia as a professor of Life and Medical Sciences ( ICREA). In December 2018, Tartaglia became a full professor of Biochemistry in the Department of Biology at University La Sapienza through a procedure of “chiara fama. In 2019 he started to work at the Italian Institute of Technology (IIT) as a PI. In 2020, Tartaglia was awarded an ERC grant https://erc.europa.eu/ for the study on the composition of phase-separated assemblies ( ERC synergy results).
The Tartaglia lab focuses on the understanding of 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 will open up therapeutic avenues for the treatment of a broad range of human disorders. They aim to discover the involvement of RNA molecules in regulatory networks controlling protein production and the Tartaglia lab is interested in understanding mechanisms whose alteration lead to aberrant aggregation, such as the one observed in amyloid diseases. The Tartaglia lab recently observed that interaction between proteins and RNAs induce feedback loops that are crucial in protein homeostasis. They also found that specific, structured RNAs promote phase separation of proteins in the cytosol and nucleus and we are designing synthetic RNA molecules to manipulate the formation of complexes.
Tartaglia's group discovered that RNA plays a central role in protein aggregation, which offers the opportunity to develop methods to combat diseases such as Amyotrophic Lateral Sclerosis. They found that FMR1 mRNA ( https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6315285/) and Xist non-coding RNA ( https://pubmed.ncbi.nlm.nih.gov/31061525/) are able to trigger the formation of large phase-separated assemblies, while other transcripts such as HSP70 mRNA ( https://www.nature.com/articles/s41467-019-10923-5 ) and aptamers ( https://pubmed.ncbi.nlm.nih.gov/30742796/ ) can prevent the aggregation of specific proteins acting as `solubilizers’. These observations strongly indicate that RNA-based molecules may be ideal candidates for the stabilisation of proteins in their native conformation by emulating the natural binding partners.
More specifically, the Tartaglia lab found that messenger RNA is a potent solubilizer blocking the formation of toxic aggregates that are potentially toxic to our organisms ( https://www.nature.com/articles/s41467-019-10923-5). In particular, they observed that the transcript coding for Heat Shock Protein 70 (HSP70) interacts with many proteins and has a strong effect on protein aggregation. They experimentally demonstrated for the first time that, under conditions of stress, HSP70 mRNA has the ability to promote the removal of the protein aggregates that are responsible for serious neurodegenerative diseases such as Alzheimer’s and Amyotrophic Lateral Sclerosis.
These results stem from previous computational analyses revealing that interactions of aggregation-prone proteins with messenger RNA have a solubilizing effect ( https://pubmed.ncbi.nlm.nih.gov/24003031/). The works lead to important discoveries on the regulation of a gene involved in Parkinson’s disease alpha-synuclein ( https://pubmed.ncbi.nlm.nih.gov/29149290/)
• In 2019, the Tartaglia lab released the first multi-organism transcriptome-wide database of protein-RNA interactions https://rnact.crg.eu/, which has been incorporated in Uniprot https://www.uniprot.org/. Earlier n 2014, Gian’s lab reported the first large-scale analysis of ribonucleoprotein networks (Genome Biology; http://www.ncbi.nlm.nih.gov/pubmed/24401680). The work, anticipated by a pilot project published in Nucleic Acids Research ( http://www.ncbi.nlm.nih.gov/pubmed/24003031), sheds light on the relationship between functional and dysfunctional associations of protein and RNA molecules. In 2019 Gian’s lab released the first multi-organism transcriptome-wide database of protein-RNA interactions https://rnact.crg.eu/, which has been incorporated in Uniprot https://www.uniprot.org/
Discovering the interactions of long non-coding RNAs
• In 2011, Tartaglia's group introduced a method to perform large-scale predictions of protein-RNA associations (the first paper published by the laboratory was Nature Methods). The algorithm, ‘fast predictions of RNA and protein interactions and domains at the Center for Genomic Regulation, Barcelona, Catalonia’ (catRAPID http://www.ncbi.nlm.nih.gov/pubmed/21623348), evaluates the interaction propensities of polypeptide and nucleotide chains using their physicochemical properties. The algorithm shows performances comparable to experimental tools, as reported in recent surveys http://www.biocompare.com/Editorial-Articles/150507-Probe-Noncoding-RNA-Biology-with-Protein-lncRNA-Interaction-Tools/.
Solubility as an engine of evolution
• In the period 2007-2010, Gian Tartaglia investigated the relationship between expression and solubility of gene. He found a close link between mRNA expression levels and protein aggregation rates. The original observations were published in Trends in Biological Science and received ( http://www.ncbi.nlm.nih.gov/pubmed/17419062). An experimental follow up was published in Journal of the American Chemical Society http://www.ncbi.nlm.nih.gov/pubmed/21650202. Based on the experimental results, he developed an approach for prediction of heterologous expression in E. coli ( http://www.ncbi.nlm.nih.gov/pubmed/19281824).
Rationalizing the determinants of protein aggregation
• In the period 2009-2012 Tartaglia studied the toxicity of protein aggregates in the cellular context and determined the fraction of proteome that interacts with insoluble aggregates ( http://www.ncbi.nlm.nih.gov/pubmed/21215370). He also studied interactions with molecular chaperones ( http://www.ncbi.nlm.nih.gov/pubmed/19281824) and their role in preventing aggregation ( http://www.ncbi.nlm.nih.gov/pubmed/22832197).
• In 2008 Tartaglia developed a method to predict the kinetics of aggregation under a variety of environmental conditions. The method is of the top cited articles in the Journal of Molecular Biology ( http://www.ncbi.nlm.nih.gov/pubmed/18514226). Importantly, Gian used the algorithm to design protein toxins that were expressed in the central nervous system of D. melanogaster (Biophysical Journal http://www.ncbi.nlm.nih.gov/pubmed/20409489 and PLoS Biology http://www.ncbi.nlm.nih.gov/pubmed/17973577).
• In 2004-2005, Gian Tartaglia developed the first parameter-free set of equations to predict aggregation rates of proteins using physico-chemical properties. The method reproduces to a remarkable extent the changes of aggregation rates observed in vitro for a large set of peptide and proteins, including those associated with neurological disease. The articles are highly cited ( https://pubmed.ncbi.nlm.nih.gov/15169952/ and https://pubmed.ncbi.nlm.nih.gov/16195556/).
Phase separation drives X-chromosome inactivation: a hypothesis.Cerase A, Armaos A, Neumayer C, Avner P, Guttman M, Tartaglia GG. Nat Struct Mol Biol. 2019 May;26(5):331-334
RNA structure drives interaction with proteins. Sanchez de Groot N, Armaos A, Graña -Montes R, Alriquet M, Calloni G, Vabulas RM, Tartaglia GG. Nat Commun. 2019 Jul 19;10(1):3246
An Integrative Study of Protein-RNA Condensates Identifies Scaffolding RNAs [...] in Fragile X- Associated Tremor/Ataxia Syndrome. Cid-Samper F, Gelabert-Baldrich M, Lang B, [...]., Botta-Orfila T, Tartaglia GG. Cell Rep. 2018 Dec 18;25(12):3422- 3434
Quantitative predictions of protein interactions with long noncoding RNAs'.Cirillo D, Blanco M, Armaos A, Buness A, Avner P, Guttman M, Cerase A, Tartaglia GG. Nat Methods. 2017 29;14(1):5-6
Predicting protein associations with long noncoding RNAs. Bellucci M, Agostini F, Masin M, Tartaglia GG. Nat Methods. 2011 Jun;8(6):444-5
All information is up to date on July 10th 2020.
https://twitter.com/tartaglialab?lang=en
https://scholar.google.com/citations?user=pCimmCAAAAAJ&hl=en
https://www.icrea.cat/Web/ScientificStaff/gian-gaetano-tartaglia-579
https://www.crg.eu/ca/programmes-groups/tartaglia-lab
https://www.iit.it/research/lines/rna-systems-biology
-
^ Sanchez De Groot, Natalia. "RNA Structure Drives Interaction With Proteins".
doi:
10.1038/s41467-019-10923-5.
{{ cite journal}}: Cite journal requires|journal=( help) -
^ Vandelli, andrea.
"CROSSalive: A Web Server for Predicting the in Vivo Structure of RNA Molecules".
doi:
10.1093/bioinformatics/btz666.
{{ cite journal}}: Cite journal requires|journal=( help) -
^ Bellucci, Matteo. "Predicting Protein Associations With Long Noncoding RNAs".
doi:
10.1038/nmeth.1611.
{{ cite journal}}: Cite journal requires|journal=( help) -
^ Agostini, Federico. "X-inactivation: Quantitative Predictions of Protein Interactions in the Xist Network".
doi:
10.1093/nar/gks968.
{{ cite journal}}: Cite journal requires|journal=( help) -
^ Cirillo, Davive. "Quantitative Predictions of Protein Interactions With Long Noncoding RNAs".
doi:
10.1038/nmeth.4100.
{{ cite journal}}: Cite journal requires|journal=( help)
Gian Gaetano Tartaglia (Rome, 23/10/1976), is an Italian biochemist and computational biologists. He is currently a full professor in biochemistry at the University of Rome
Sapienza and a Principal Investigator at the
Italian Institute of Technology (IIT). He is an avid runner, a music player and a led zeppelin fan.
Gian Gaetano Tartaglia graduated at the University La Sapienza in the academic year 1999-2000 with a thesis on mathematical modelling of neurons (Physics department). Between 2001 and 2005, Tartaglia carried out his doctoral studies at the University of Zurich CH (Biochemistry department) focusing on the folding and misfolding of proteins associated with neurodegenerative disorders. As an associate researcher, he worked at the University of Cambridge UK (Chemistry and Genetics departments) under the supervision of Chris Dobson and Michele Vendruscolo. In the period 2005-2010, he participated in several computational and experimental studies on amyloidosis. He is currently a life member of the Clare Hall College in Cambridge UK since 2011.
In 2010 Gian Tartaglia became PI at the Centre for Genomic Regulation (CRG) in Barcelona. He was awarded the prestigious ERC grant in 2013 for his studies on the role of coding and non-coding transcripts in the regulation of amyloid genes ( ERC 309545). In 2014 Tartaglia was tenured in Catalonia as a professor of Life and Medical Sciences ( ICREA). In December 2018, Tartaglia became a full professor of Biochemistry in the Department of Biology at University La Sapienza through a procedure of “chiara fama. In 2019 he started to work at the Italian Institute of Technology (IIT) as a PI. In 2020, Tartaglia was awarded an ERC grant https://erc.europa.eu/ for the study on the composition of phase-separated assemblies ( ERC synergy results).
The Tartaglia lab focuses on the understanding of 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 will open up therapeutic avenues for the treatment of a broad range of human disorders. They aim to discover the involvement of RNA molecules in regulatory networks controlling protein production and the Tartaglia lab is interested in understanding mechanisms whose alteration lead to aberrant aggregation, such as the one observed in amyloid diseases. The Tartaglia lab recently observed that interaction between proteins and RNAs induce feedback loops that are crucial in protein homeostasis. They also found that specific, structured RNAs promote phase separation of proteins in the cytosol and nucleus and we are designing synthetic RNA molecules to manipulate the formation of complexes.
Tartaglia's group discovered that RNA plays a central role in protein aggregation, which offers the opportunity to develop methods to combat diseases such as Amyotrophic Lateral Sclerosis. They found that FMR1 mRNA ( https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6315285/) and Xist non-coding RNA ( https://pubmed.ncbi.nlm.nih.gov/31061525/) are able to trigger the formation of large phase-separated assemblies, while other transcripts such as HSP70 mRNA ( https://www.nature.com/articles/s41467-019-10923-5 ) and aptamers ( https://pubmed.ncbi.nlm.nih.gov/30742796/ ) can prevent the aggregation of specific proteins acting as `solubilizers’. These observations strongly indicate that RNA-based molecules may be ideal candidates for the stabilisation of proteins in their native conformation by emulating the natural binding partners.
More specifically, the Tartaglia lab found that messenger RNA is a potent solubilizer blocking the formation of toxic aggregates that are potentially toxic to our organisms ( https://www.nature.com/articles/s41467-019-10923-5). In particular, they observed that the transcript coding for Heat Shock Protein 70 (HSP70) interacts with many proteins and has a strong effect on protein aggregation. They experimentally demonstrated for the first time that, under conditions of stress, HSP70 mRNA has the ability to promote the removal of the protein aggregates that are responsible for serious neurodegenerative diseases such as Alzheimer’s and Amyotrophic Lateral Sclerosis.
These results stem from previous computational analyses revealing that interactions of aggregation-prone proteins with messenger RNA have a solubilizing effect ( https://pubmed.ncbi.nlm.nih.gov/24003031/). The works lead to important discoveries on the regulation of a gene involved in Parkinson’s disease alpha-synuclein ( https://pubmed.ncbi.nlm.nih.gov/29149290/)
• In 2019, the Tartaglia lab released the first multi-organism transcriptome-wide database of protein-RNA interactions https://rnact.crg.eu/, which has been incorporated in Uniprot https://www.uniprot.org/. Earlier n 2014, Gian’s lab reported the first large-scale analysis of ribonucleoprotein networks (Genome Biology; http://www.ncbi.nlm.nih.gov/pubmed/24401680). The work, anticipated by a pilot project published in Nucleic Acids Research ( http://www.ncbi.nlm.nih.gov/pubmed/24003031), sheds light on the relationship between functional and dysfunctional associations of protein and RNA molecules. In 2019 Gian’s lab released the first multi-organism transcriptome-wide database of protein-RNA interactions https://rnact.crg.eu/, which has been incorporated in Uniprot https://www.uniprot.org/
Discovering the interactions of long non-coding RNAs
• In 2011, Tartaglia's group introduced a method to perform large-scale predictions of protein-RNA associations (the first paper published by the laboratory was Nature Methods). The algorithm, ‘fast predictions of RNA and protein interactions and domains at the Center for Genomic Regulation, Barcelona, Catalonia’ (catRAPID http://www.ncbi.nlm.nih.gov/pubmed/21623348), evaluates the interaction propensities of polypeptide and nucleotide chains using their physicochemical properties. The algorithm shows performances comparable to experimental tools, as reported in recent surveys http://www.biocompare.com/Editorial-Articles/150507-Probe-Noncoding-RNA-Biology-with-Protein-lncRNA-Interaction-Tools/.
Solubility as an engine of evolution
• In the period 2007-2010, Gian Tartaglia investigated the relationship between expression and solubility of gene. He found a close link between mRNA expression levels and protein aggregation rates. The original observations were published in Trends in Biological Science and received ( http://www.ncbi.nlm.nih.gov/pubmed/17419062). An experimental follow up was published in Journal of the American Chemical Society http://www.ncbi.nlm.nih.gov/pubmed/21650202. Based on the experimental results, he developed an approach for prediction of heterologous expression in E. coli ( http://www.ncbi.nlm.nih.gov/pubmed/19281824).
Rationalizing the determinants of protein aggregation
• In the period 2009-2012 Tartaglia studied the toxicity of protein aggregates in the cellular context and determined the fraction of proteome that interacts with insoluble aggregates ( http://www.ncbi.nlm.nih.gov/pubmed/21215370). He also studied interactions with molecular chaperones ( http://www.ncbi.nlm.nih.gov/pubmed/19281824) and their role in preventing aggregation ( http://www.ncbi.nlm.nih.gov/pubmed/22832197).
• In 2008 Tartaglia developed a method to predict the kinetics of aggregation under a variety of environmental conditions. The method is of the top cited articles in the Journal of Molecular Biology ( http://www.ncbi.nlm.nih.gov/pubmed/18514226). Importantly, Gian used the algorithm to design protein toxins that were expressed in the central nervous system of D. melanogaster (Biophysical Journal http://www.ncbi.nlm.nih.gov/pubmed/20409489 and PLoS Biology http://www.ncbi.nlm.nih.gov/pubmed/17973577).
• In 2004-2005, Gian Tartaglia developed the first parameter-free set of equations to predict aggregation rates of proteins using physico-chemical properties. The method reproduces to a remarkable extent the changes of aggregation rates observed in vitro for a large set of peptide and proteins, including those associated with neurological disease. The articles are highly cited ( https://pubmed.ncbi.nlm.nih.gov/15169952/ and https://pubmed.ncbi.nlm.nih.gov/16195556/).
Phase separation drives X-chromosome inactivation: a hypothesis.Cerase A, Armaos A, Neumayer C, Avner P, Guttman M, Tartaglia GG. Nat Struct Mol Biol. 2019 May;26(5):331-334
RNA structure drives interaction with proteins. Sanchez de Groot N, Armaos A, Graña -Montes R, Alriquet M, Calloni G, Vabulas RM, Tartaglia GG. Nat Commun. 2019 Jul 19;10(1):3246
An Integrative Study of Protein-RNA Condensates Identifies Scaffolding RNAs [...] in Fragile X- Associated Tremor/Ataxia Syndrome. Cid-Samper F, Gelabert-Baldrich M, Lang B, [...]., Botta-Orfila T, Tartaglia GG. Cell Rep. 2018 Dec 18;25(12):3422- 3434
Quantitative predictions of protein interactions with long noncoding RNAs'.Cirillo D, Blanco M, Armaos A, Buness A, Avner P, Guttman M, Cerase A, Tartaglia GG. Nat Methods. 2017 29;14(1):5-6
Predicting protein associations with long noncoding RNAs. Bellucci M, Agostini F, Masin M, Tartaglia GG. Nat Methods. 2011 Jun;8(6):444-5
All information is up to date on July 10th 2020.
https://twitter.com/tartaglialab?lang=en
https://scholar.google.com/citations?user=pCimmCAAAAAJ&hl=en
https://www.icrea.cat/Web/ScientificStaff/gian-gaetano-tartaglia-579
https://www.crg.eu/ca/programmes-groups/tartaglia-lab
https://www.iit.it/research/lines/rna-systems-biology
-
^ Sanchez De Groot, Natalia. "RNA Structure Drives Interaction With Proteins".
doi:
10.1038/s41467-019-10923-5.
{{ cite journal}}: Cite journal requires|journal=( help) -
^ Vandelli, andrea.
"CROSSalive: A Web Server for Predicting the in Vivo Structure of RNA Molecules".
doi:
10.1093/bioinformatics/btz666.
{{ cite journal}}: Cite journal requires|journal=( help) -
^ Bellucci, Matteo. "Predicting Protein Associations With Long Noncoding RNAs".
doi:
10.1038/nmeth.1611.
{{ cite journal}}: Cite journal requires|journal=( help) -
^ Agostini, Federico. "X-inactivation: Quantitative Predictions of Protein Interactions in the Xist Network".
doi:
10.1093/nar/gks968.
{{ cite journal}}: Cite journal requires|journal=( help) -
^ Cirillo, Davive. "Quantitative Predictions of Protein Interactions With Long Noncoding RNAs".
doi:
10.1038/nmeth.4100.
{{ cite journal}}: Cite journal requires|journal=( help)