Stefano De Pretis

Post Doc

Research Lines

Genomic Science

Center

CGS@SEMM Milano

Contacts

Center for Genomic Science, Via Adamello 16, Milano

About

Biosketch

With a mixed background in both Biotechnology (BS) and Bioinformatics (MD, Ph.D.), Dr. Stefano de Pretis developed skills in bioinformatics, statistics, mathematics, and genomics analyses. Since 2012 he is working as a Postdoc in the Lab of Mattia Pelizzola, contributing to the development of novel techniques for the analysis of Next Generation Sequencing (NGS) data. In particular, he contributed to the development of software for the coupled analysis of nascent and total RNA data that is able to infer the rates of the RNA metabolism (synthesis, processing, and degradation) from either steady-states or time course observations (de Pretis et al, 2015). His work originated in several collaborations. In particular, the study of the RNA metabolism was applied to non-coding genes (Marzi et al, 2016; Mukherjee et al, 2017), to the characterization of the transcription termination defects (Austenaa et al, 2015), to the role of translation for the process of decay (Biasini et al, 2020), and to the biology of the Myc oncogene (de Pretis et al, 2017).

Involved in more than 15 peer-reviewed publications, among which 6 first-authored and 1 last-authored, he developed within the lab of Epigenomics and Transcriptional Regulation two main lines of research:

  • RNA metabolism
  • RNA polymerase recruitment and progression dynamics 

Education

  • Ph.D., Bioinformatics, Università del Sannio, 2012 (Visiting student at Harvard Medical School, 2011-2012)
  • MS, Bioinformatics, Università degli Studi di Milano-Bicocca, 2008
  • BS, Molecular Biotechnology, Università degli Studi di Milano-Bicocca, 2005

Projects

RNA metabolism

The field of transcriptional regulation generally assumes that changes in transcripts levels reflect changes in the transcriptional status of the corresponding gene. While this assumption might hold true for a large population of transcripts, a considerable and still unrecognized fraction of the variation might involve other steps of the RNA lifecycle, i.e. the processing of the premature RNA, and degradation of the mature RNA. Discrimination between these layers requires complementary experimental techniques, such as RNA metabolic labeling or block of transcription experiments. Recently, metabolic labeling experiments became the standard de-facto in the field and allowed the dissection of RNA levels into the components of its metabolism in several cell types and conditions. We contributed to this field by the development of INSPEcT (de Pretis et al, 2015) software that is able to estimate the rates of synthesis, processing, and degradation of the RNAs, at the genome-wide scale, both in steady-state conditions and time-course experimental design.

inspect plotGene

The analysis of labeled RNA is not effortless, in particular when the RNA purification is required. Recently, the chemical conversion of labeled nucleosides has been introduced, which solved some pitfalls of the analysis linked to the purification step. Nonetheless, those new techniques suffer from low sensitivity, require a high amount of starting RNA, and can only be applied to cultured cells. For this reason, we developed new methods that exploit the sole analysis of premature and mature RNA from total or polyA selected RNA-seq experiments. Those techniques are able to infer the complete RNA metabolism and its variations from time-course RNA-seq experiments and can identify genes that were post-transcriptionally regulated between two steady-state conditions (Furlan et al, 2020).

inspectMinus steadyState

RNA polymerase recruitment and progression dynamics

The footprint of the RNA polymerase on the genome has been measured to date hundreds of times by ChIP-seq. These observations led to the important observation that the enzyme occupies the promoter region of most eukaryotic genes in a paused state, waiting for transactivating signals that release it into an actively transcribing state (pause-release). This step is now considered one of the most important for eukaryotic gene regulation and genes are classified more or less "paused" based on the ratio between the occupancy density in the promoter and the gene body regions (pausing-index). These measure neglect that the density of the polymerase on both promoter and gene-bodies are the result of multiple processes that include for example the loading of the polymerase at the promoter and the elongation rate of the polymerase within the gene bodies, other than the pause-release. For this reason, by integrating the RNA polymerase ChIP-seq information with the quantification of nascent RNA at the same genomic units we were able to increase the resolution of the processing to other regulatory steps of the RNA polymerase and to return the temporal regulation profiles of different clusters of genes in response to the activation of the oncogene Myc (de Pretis et al, 2017).

RNAPII cycle

 

 

IIT Publications

  • 2020
  • Furlan M.iit, Tanaka I.iit, Leonardi T.iit, de Pretis S.iit, Pelizzola M.iit
    DOI

    Direct RNA Sequencing for the Study of Synthesis, Processing, and Degradation of Modified Transcripts

    Frontiers in Genetics, vol. 11
  • de Pretis S.iit, Furlan M.iit, Pelizzola M.iit
    DOI

    INSPEcT-GUI Reveals the Impact of the Kinetic Rates of RNA Synthesis, Processing, and Degradation, on Premature and Mature RNA Species

    Frontiers in Genetics, vol. 11
  • 2019
  • Tesi A.iit, de Pretis S.iit, Furlan M.iit, Filipuzzi M., Morelli M.J.iit, Andronache A.iit, Doni M., Verrecchia A., Pelizzola M.iit, Amati B., Sabo A.
    DOI

    An early Myc-dependent transcriptional program orchestrates cell growth during B-cell activation

    EMBO Reports, vol. 20, (no. 9)
  • Furlan M.iit, Galeota E.iit, de Pretis S.iit, Caselle M., Pelizzola M.iit
    DOI

    M6A-dependent RNA dynamics in T cell differentiation

    Genes, vol. 10, (no. 1)
  • 2017
  • De Pretis S.iit, Kress T.R.iit, Morelli M.J.iit, Sabo A.iit, Locarno C.iit, Verrecchia A., Doni M., Campaner S.iit, Amati B.iit, Pelizzola M.iit
    DOI

    Integrative analysis of RNA polymerase II and transcriptional dynamics upon MYC activation

    Genome Research, vol. 27, (no. 10), pp. 1658-1664
  • Mukherjee N., Calviello L., Hirsekorn A., De Pretis S.iit, Pelizzola M.iit, Ohler U.
    DOI

    Integrative classification of human coding and noncoding genes through RNA metabolism profiles

    Nature Structural and Molecular Biology, vol. 24, (no. 1), pp. 86-96
  • 2016
  • Marzi M.J.iit, Ghini F.iit, Cerruti B.iit, De Pretis S.iit, Bonetti P.iit, Giacomelli C.iit, Gorski M.M., Kress T.iit, Pelizzola M.iit, Muller H.iit, Amati B.iit, Nicassio F.iit
    DOI

    Degradation dynamics of micrornas revealed by a novel pulse-chase approach

    Genome Research, vol. 26, (no. 4), pp. 554-565
  • Marzi M. J., Ghini F., Cerruti B., de Pretis S.iit, Nicassio F.iit

    Insights into function and regulation of microRNAs by decoding degradation dynamics

    Keystone Symposia: Small RNA Silencing: Little Guides, Big Biology (A6)
  • Bianchi V.iit, Ceol A.iit, Ogier A.G.E., De Pretis S.iit, Galeota E.iit, Kishore K.iit, Bora P.iit, Croci O.iit, Campaner S.iit, Amati B.iit, Morelli M.J.iit, Pelizzola M.iit
    DOI

    Integrated systems for NGS data management and analysis: Open issues and available solutions

    Frontiers in Genetics, vol. 7, (no. MAY)
  • Melloni G.E.M.iit, de Pretis S.iit, Riva L.iit, Pelizzola M.iit, Ceol A.iit, Costanza J.iit, Muller H.iit, Zammataro L.iit
    DOI

    LowMACA: Exploiting protein family analysis for the identification of rare driver mutations in cancer

    BMC Bioinformatics, vol. 17, (no. 1)
  • 2015
  • De Pretis S.iit, Kress T.iit, Morelli M.J.iit, Melloni G.E.M.iit, Riva L.iit, Amati B.iit, Pelizzola M.iit
    DOI

    INSPEcT: A computational tool to infer mRNA synthesis, processing and degradation dynamics from RNA- and 4sU-seq time course experiments

    Bioinformatics, vol. 31, (no. 17), pp. 2829-2835
  • Kishore K.iit, de Pretis S.iit, Lister R., Morelli M.J.iit, Bianchi V.iit, Amati B.iit, Ecker J.R., Pelizzola M.iit
    DOI

    methylPipe and compEpiTools: A suite of R packages for the integrative analysis of epigenomics data

    BMC Bioinformatics, vol. 16, (no. 1)
  • Pelizzola M.iit, Morelli M.J.iit, Sabo A.iit, Kress T.R.iit, de Pretis S.iit, Amati B.iit
    DOI

    Selective transcriptional regulation by Myc: Experimental design and computational analysis of high-throughput sequencing data

    Data in Brief, vol. 3, pp. 40-46
  • Austenaa L.M.I., Barozzi I., Simonatto M., Masella S., Della Chiara G., Ghisletti S., Curina A., de Wit E., Bouwman B.A.M., de Pretis S.iit, Piccolo V., Termanini A., Prosperini E., Pelizzola M.iit, de Laat W., Natoli G.
    DOI

    Transcription of Mammalian cis-Regulatory Elements Is Restrained by Actively Enforced Early Termination

    Molecular Cell, vol. 60, (no. 3), pp. 460-474
  • 2014
  • de Pretis S.iit, Pelizzola M.iit
    DOI

    Computational and experimental methods to decipher the epigenetic code

    Frontiers in Genetics, vol. 5, (no. SEP)
  • Melloni G.E.M.iit, Ogier A.G.E., de Pretis S.iit, Mazzarella L., Pelizzola M.iit, Pelicci P.G., Riva L.iit
    DOI

    DOTS-Finder: A comprehensive tool for assessing driver genes in cancer genomes

    Genome Medicine, vol. 6, (no. 6)
  • Sabo A.iit, Kress T.R.iit, Pelizzola M.iit, De Pretis S.iit, Gorski M.M., Tesi A.iit, Morelli M.J.iit, Bora P.iit, Doni M., Verrecchia A., Tonelli C., Faga G., Bianchi V.iit, Ronchi A.iit, Low D., Muller H.iit, Guccione E., Campaner S.iit, Amati B.iit
    DOI

    Selective transcriptional regulation by Myc in cellular growth control and lymphomagenesis

    Nature, vol. 511, (no. 7510), pp. 488-492