Epigenomics and Transcriptional Regulation

Transcript’s abundance is typically considered a proxy of the corresponding gene’s transcriptional activity. Yet, a poorly transcribed gene could see many of its RNA molecules accumulate just because they are highly stable. Conversely, if a gene is very actively transcribed but its transcripts are highly unstable, only a few copies of its RNAs are found in the cell. Indeed, RNA abundance and its variation arise from the combined action of cellular machineries responsible for the synthesis of novel transcripts, their processing into mature species, and the degradation of the latter.

Our lab develops methods to study the dynamics of RNA metabolism (henceforth RNA dynamics)
, and aims at: (i) deciphering how they are shaped by epitranscriptional RNA modifications; (ii) characterizing the role of RNA dynamics in gene expression programs orchestrated by transcription factors (TFs); (iii) revealing the impact of altered RNA dynamics in disease conditions.

These objectives are pursued through a unique interdisciplinary approach that combines experimental and computational methods, including RNA metabolic labeling and epitranscriptome profiling together with their integrated analysis through mathematical modeling. This reflects into the current composition of the group, which includes an established computational part tightly collaborating with a more recent experimental part.



Mattia Pelizzola
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Twitter: @MattiaPelizzola
(+39) 02 94 375 019

Current research

Development of novel approaches for the study of RNA dynamics

The characterization of RNA dynamics is typically based on the joint profiling of total and nascent RNAs, the latter profiled through RNA metabolic labeling. The integrative analysis of these data, which ultimately allows disentangling pre-existing from newly synthetized transcripts, allows us to decipher how gene expression programs are established by the balance of synthesis, processing and degradation. Achievements and ongoing activity:
  • The development of INSPEcT (de Pretis S et al., Bioinformatics 2015), which allowed for the first time the comprehensive analysis of RNA dynamics.
  • We developed a novel version of INSPEcT, which allows the quantification of RNA dynamics without requiring the quantification of nascent RNA (Furlan M et al., Genome Res 2020).
  • INSPEcT was used to characterize how the activation of MYC impacts RNA dynamics (de Pretis et al., Genome Res 2017; Tesi A et al., Biorxiv 2019). These studies provided unprecedented details on how this fundamental transcription factor and oncogene controls the transcription of thousands of target genes.




Development of novel approaches for the study of RNA Polymerase II (RNAPII) dynamics

RNAPII is a key actor in genes’ transcription and a complex regulatory hub. The life-cycle of the RNAPII complex includes its recruitment and assembly to promoters, followed by pause-release, elongation, and detachment from the 3’ end of genes. Most of the studies dealing with the RNAPII life-cycle rely on ChIP-seq data, while it has been shown that these are unable to univocally quantify the dynamics of each step. For example, a variation in the density of RNAPII bound to promoters can be due to a change in either its recruitment or in its pause-release rate (or both). We recently developed a method to properly address this issue (de Pretis et al., Genome Res 2017), which was used to provide critical insights on the mechanisms through which MYC modulates its targets. Achievements and ongoing activity:
  • The development of a computational method that allows the quantification of the kinetic rates of RNAPII recruitment, pause-release, elongation and detachment (de Pretis et al., Genome Res 2017)
  • This method, applied in the context of MYC activation, revealed that the most prominent effect of the binding of this factor is not the promotion of pause-release, as previously suggested, while RNAPII recruitment (de Pretis et al., Genome Res 2017; Tesi A. et al., biorxiv 2019). Moreover, this suggested that MYC primarily acts as an activator, as recently independently confirmed. These data indeed suggest that the repression of a subset of MYC target genes is mostly a passive consequence acute MYC activation.


The functional role and the determinants of the m6A-epitranscriptome

Despite the rapid progress in the field, various aspects of the functional role of this mark remain poorly characterized. First, we are far from a complete understanding of how m6A influences the dynamics of marked transcripts. Second, despite key components of the m6A machinery are enriched in the nuclear speckles, indicating their physical proximity to chromatin, the interplay between m6A epitranscriptome and chromatin regulation has been largely neglected. We are currently deciphering the impact of m6A patterning on RNA and RNAPII dynamics, and we are characterizing the interplay between the m6A-epitranscriptome and various regulatory factors. Achievements and ongoing activity:
  • We are finely characterizing how the presence of m6A in various portions of transcripts impacts the RNA kinetic rates, shedding light on the context-dependent effect of m6A.
  • We are studying the impact of m6A on RNAPII dynamics.
  • We are characterizing the crosstalk between various transcription factors and the m6A epitranscriptome, and how this eventually impacts the RNA dynamics.


Future directions

The group is actively moving towards the study of:

  • Interplay between m6A effectors.
  • Novel epigenetic factors influencing the m6A-epitranscriptome.
  • Epitranscriptional dynamics in various tumors.


Group Members

  • Mattia Pelizzola - computational biologist (PI)
  • Roberto Albanese - bioinformatician (Master student @UniMi): (epi)transcriptional dynamics through long reads sequencing data
  • Lucia Coscujuela - molecular biologist (postdoc): (epi)transcriptional dynamics in cancer
  • Mattia Furlan - theoretical physicist (postdoc): mathematical modeling of RNA dynamics
  • Nunzio del Gaudio - visiting molecular biologist (postdoc): (epi)transcriptional dynamics in cancer
  • Stefano de Pretis - bioinformatician (postdoc): mathematical modeling of RNA and RNAPII dynamics
  • Francesca Ratti - engineer (PhD student @PoliMi): mathematical modeling of RNAPII dynamics
  • Iris Tanaka - molecular biologist (postdoc): (epi)transcriptional RNA dynamics in cancer


  • Welcome Francesca and Roberto in the group! [Sep 2020]
  • Congratulations to Eugenia Galeota for her novel appointment at INGM [Apr 2020]
  • We are co-editing a special issue on the role of RNA modifications - Epigenomes MDPI [Feb 2020]
  • Welcome Lucia in the group! [Nov 2019]
  • Welcome Iris in the group! [Aug 2019]
  • We are co-editing a special issue on Computational Epitranscriptomics - Frontiers In Genetics [Apr 2019]
  • A postdoctoral position for a molecular biologist is open! [Jan 2019]
  • Eugenia's 2017 paper nominated in the 2018 Best Paper Selection by the International Medical Informatics Association [Sep 2018]
  • European Epitranscriptomic Network (EPITRAN): the position paper was published [May 2018]
  • One PhD position is open for a computational or experimental PhD student [May 2018]
  • Welcome Nunzio in the group! [Mar 2018]
  • Congratulations to Stefano for the Cover in the October Genome Research issue! [Oct 2017]




Recent Publications

For the complete list please refer to our Google Scholar page.

  1. Furlan M .. Pelizzola M (2020). Genome-wide dynamics of RNA synthesis, processing, and degradation without RNA metabolic labeling. Genome Research
  2. de Pretis S .. Pelizzola M (2020). INSPEcT-GUI Reveals the Impact of the Kinetic Rates of RNA Synthesis, Processing, and Degradation, on Premature and Mature RNA Species. Frontiers in Genetics
  3. Furlan M .. Pelizzola M (2020). Direct RNA Sequencing for the Study of Synthesis, Processing, and Degradation of Modified Transcripts. Frontiers in Genetics
  4. Galeota E, Pelizzola M (2020). Ontology-driven integrative analysis of omics data through Onassis. Scientific Reports
  5. Furlan M .. Pelizzola M (2019). m6A-Dependent RNA Dynamics in T Cell Differentiation. Genes
  6. de Pretis S .. Pelizzola M (2017). Integrative analysis of RNA polymerase II and transcriptional dynamics upon MYC activation. Genome Research
  7. Galeota E, Pelizzola M (2017). Ontology-based annotations and semantic relations in large-scale (epi)genomics data. Briefings in Bioinformatics
  8. Marzi MJ .. Nicassio F (2016). Degradation dynamics of microRNAs revealed by a novel pulse-chase approach. Genome Research
  9. Mukherjee N .. Ohler U (2016). Integrative classification of human coding and noncoding genes through RNA metabolism profiles. Nature Structural & Molecular Biology
  10. Austenaa LMI .. Natoli G (2015). Transcription of mammalian cis-regulatory elements is restrained by actively enforced early termination. Molecular Cell
  11. de Pretis S .. Pelizzola M (2015). INSPEcT: a computational tool to infer mRNA synthesis, processing and degradation dynamics from RNA- and 4sU-seq time course experiments. Bioinformatics
  12. Kishore K .. Pelizzola M (2015). methylPipe and compEpiTools: a suite of R packages for the integrative analysis of epigenomics data. BMC Bioinformatics
  13. Sabò A*, Kress TR*, Pelizzola M*, .. , Amati B (2014). Selective transcriptional regulation by Myc in cellular growth control and lymphomagenesis. Nature

* indicates co-authorship.