Paolo Provero
Paolo Provero
affiliation: Università di Torino
research area(s): Computational Biology, Genetics And Genomics
Course: Biomedical Sciences and Human Oncology
University/Istitution: Università di Torino
University of Genoa, Italy, PhD, Theoretical Physics 1992
University of Turin, Italy, MS, Theoretical Physics 1988

Current Position
University of Turin, Italy,
Department of Genetics, Biology and Biochemistry
Assistant Professor (2006-present)
Computational approaches to functional genomics and genetics of diseases based on gene expression data

Transcriptional regulatory networks

Gene expression prognostic signatures in cancer
1: Piro RM, Ala U, Molineris I, Grassi E, Bracco C, Perego GP, Provero P, Di
Cunto F. An atlas of tissue-specific conserved coexpression for functional
annotation and disease gene prediction. Eur J Hum Genet. 2011 Jun 8. doi:
10.1038/ejhg.2011.96. [Epub ahead of print] PubMed PMID: 21654723.

2: Mauti LA, Le Bitoux MA, Baumer K, Stehle JC, Golshayan D, Provero P,
Stamenkovic I. Myeloid-derived suppressor cells are implicated in regulating
permissiveness for tumor metastasis during mouse gestation. J Clin Invest. 2011
Jun 6. pii: 41936. doi: 10.1172/JCI41936. [Epub ahead of print] PubMed PMID:

3: Planche A, Bacac M, Provero P, Fusco C, Delorenzi M, Stehle JC, Stamenkovic I.
Identification of prognostic molecular features in the reactive stroma of human
breast and prostate cancer. PLoS One. 2011;6(5):e18640. Epub 2011 May 18. PubMed
PMID: 21611158; PubMed Central PMCID: PMC3097176.

4: Vanneschi L, Farinaccio A, Mauri G, Antoniotti M, Provero P, Giacobini M. A
comparison of machine learning techniques for survival prediction in breast
cancer. BioData Min. 2011 May 11;4:12. PubMed PMID: 21569330; PubMed Central
PMCID: PMC3108919.

5: Penna E, Orso F, Cimino D, Tenaglia E, Lembo A, Quaglino E, Poliseno L,
Haimovic A, Osella-Abate S, De Pittà C, Pinatel E, Stadler MB, Provero P,
Bernengo MG, Osman I, Taverna D. microRNA-214 contributes to melanoma tumour
progression through suppression of TFAP2C. EMBO J. 2011 May 18;30(10):1990-2007.
Epub 2011 Apr 5. PubMed PMID: 21468029.

6: Damasco C, Lembo A, Somma MP, Gatti M, Di Cunto F, Provero P. A signature
inferred from Drosophila mitotic genes predicts survival of breast cancer
patients. PLoS One. 2011 Feb 28;6(2):e14737. PubMed PMID: 21386884; PubMed
Central PMCID: PMC3046113.

7: Molineris I, Grassi E, Ala U, Di Cunto F, Provero P. Evolution of promoter
affinity for transcription factors in the human lineage. Mol Biol Evol. 2011 Feb
18. [Epub ahead of print] PubMed PMID: 21335606.

8: Bacac M, Fusco C, Planche A, Santodomingo J, Demaurex N, Leemann-Zakaryan R,
Provero P, Stamenkovic I. Securin and separase modulate membrane traffic by
affecting endosomal acidification. Traffic. 2011 May;12(5):615-26. doi:
10.1111/j.1600-0854.2011.01169.x. Epub 2011 Feb 25. PubMed PMID: 21272169.

9: Bisanzio D, Bertolotti L, Tomassone L, Amore G, Ragagli C, Mannelli A,
Giacobini M, Provero P. Modeling the spread of vector-borne diseases on bipartite
networks. PLoS One. 2010 Nov 12;5(11):e13796. PubMed PMID: 21103064; PubMed
Central PMCID: PMC2980486.

10: Demaria M, Giorgi C, Lebiedzinska M, Esposito G, D'Angeli L, Bartoli A, Gough
DJ, Turkson J, Levy DE, Watson CJ, Wieckowski MR, Provero P, Pinton P, Poli V. A
STAT3-mediated metabolic switch is involved in tumour transformation and STAT3
addiction. Aging (Albany NY). 2010 Nov;2(11):823-42. PubMed PMID: 21084727;
PubMed Central PMCID: PMC3006025.
Project Title:
Predicting gene expression from transcription factor affinity profiles
The aim of the project is to develop models for the prediction of gene expression based on total binding affinity (TBA) profiles of regulatory regions for transcription factors (TF) and other trans-acting regulators. We have recently established (PMID: 21335606) that TBA for TF is a good predictor of TF binding in mammals, and can be used to study the evolution of cis-regulatory regions. Now we would like to determine whether TBA can be used to predict gene expression, either alone or in combination with experimental data on chromatin state. We also plan to extend the use of TBA to mRNA-binding regulators, such as microRNAs. If a predictive model of gene expression can be built based on TBA, it can then be used to infer the activity of TFs and other regulators in different physiological and pathological contexts and thus help unraveling key regulatory mechanisms.