Epigenomics and Transcriptional Regulation

The RNA life cycle is controlled by three fundamental and tightly regulated steps: synthesis, processing and degradation. The integrated action of these processes, governed by the corresponding kinetic rates, defines the dynamics of individual transcripts.

The fate of a large fraction of cellular transcripts is markedly influenced by numerous epitranscriptional modifications, among which N6-methyladenosine (m6A) is the most abundant. m6A patterning is controlled by the action of writers and erasers, and can modulate the affinity of RNA binding proteins, thus impacting key biological processes.

Despite recent advances in the field, our understanding on the molecular determinants and functional consequences of m6A and other epitranscriptional modifications is limited.

OutlineIn particular, we are far from understanding how the combinatorial occurrence of m6A and other RNA modifications impinge on RNA dynamics. Moreover, despite the physical proximity of the m6A machinery to the chromatin, it remains to be determined whether the m6A-dependent RNA dynamics can be affected by chromatin-associated factors, and by the dynamics of RNA polymerase II (RNAPII).

As part of the European Epitranscriptomic Network (EPITRAN), we aim at answering these fundamental questions with a unique interdisciplinary approach, combining experimental and computational methods, including metabolic labeling of nascent RNA, epitranscriptome profiling and their integrative analysis through mathematical modeling. Altogether, the characterization of these mechanisms shall provide new perspectives on the roles of chromatin and the epitranscriptome in RNA dynamics, and shall open new avenues of research by strengthening the connection between RNA biology and chromatin regulation.


Current research activities


DynamicsRNA dynamics (de Pretis S, Furlan). We developed an R/Bioconductor package (INSPEcT) for the study of RNA dynamics through the integrative analysis and mathematical modeling of nascent and total RNA-seq data (de Pretis S et al, Bioinformatics 2015). This tool allows, both at steady-state and in time-course experiments, the genome-wide quantification of the kinetic rates of pre-RNA synthesis, its processing into mature RNA, and the degradation of the mature form.
We are finalizing the development of a new version of INSPEcT that will allow the study of RNA dynamics in total RNA-seq experiments, without requiring the profiling of nascent mRNA (Furlan M, in preparation).


RNAPII dynamics (de Pretis S). We model the RNAPII life cycle through a set of ordinary differential equations, in which the rates governing RNAPII recruitment and progression are fully determined model parameters.
This allowed us to characterize how the activation of the MYC transcription factor and oncogene impacts the dynamics of RNAPII, leading to transcriptional and post-transcriptional regulation of MYC target genes.
We found that MYC elicits a previously unanticipated level of post-transcriptional regulation, in terms of both processing and degradation. Moreover, we revealed that the step of the RNAPII life cycle that is mostly affected by MYC activation is its recruitment to promoters. Finally, these analyses suggested that repression of MYC targets is a passive event, at least partially due to competition for limiting amount of polymerase (de Pretis S et al, Genome Research 2017).



Post-transcriptional regulation in dozens of tissue and disease conditions (de Pretis S, Furlan M, Galeota E). We are characterizing RNA dynamics in thousands of RNA-seq experiments, across various tissue and disease conditions, focusing on:

  • the identification of genes and conditions characterized by atypical patterns of post-transcriptional regulation (Furlan M, in preparation)
  • the study of the ability of pre-RNA processing to delay the modulation of mature RNAs following a transcriptional change (de Pretis S, in preparation)

To this end, we developed the R/Bioconductor package Onassis (Galeota E, submitted) to associate metadata of omics experiments to specific tissue and disease conditions, leveraging on natural language processing methods and the information contained in biomedical ontologies (Galeota E et al. Briefings In Bioinformatics 2016).


Group Members

  • Mattia Pelizzola - computational biologist (PI)
  • Stefano de Pretis - bioinformatician (postdoc): mathematical modeling of m6A and RNA dynamics
  • Eugenia Galeota - computer scientist (postdoc): integrative analysis of large-scale high-throughput data
  • Nunzio del Gaudio - molecular biologist (postdoc): m6A-dependent RNA dynamics and their alteration in cancer
  • Mattia Furlan - theoretical physicist (PhD student): mathematical modeling of RNA dynamics



  • We are co-editing a special issue on Computational Epitranscriptomics [Apr 2019]
  • A collaborative paper on the m6A-epitranscriptome in testicular cancer is out in the Journal of Translational Medicine [Mar 2019]
  • A new collaborative paper on MYC-dependent RNA and RNAPII dynamics is on bioRxiv [Mar 2019]
  • Our manuscript on the deconvolution of RNA dynamics from total RNA-seq data is on bioRxiv [Jan 2019]
  • Our paper on m6A-dependent RNA dynamics in T cells differentiation was published on Genes [Jan 2019]
  • A collaborative paper on inner ear DNA methylation dynamics is out on Scientific Reports [Jan 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]



Selected publications

  1. Furlan M, .., Pelizzola M (2019). Dynamics of transcriptional regulation from total RNA-seq experiments. bioRxiv
  2. Furlan M, .., Pelizzola M (2019). m6A-Dependent RNA Dynamics in T Cell Differentiation. Genes
  3. de Pretis S, .. , Pelizzola M (2017). Integrative analysis of RNA polymerase II and transcriptional dynamics upon MYC activation. Genome Research
  4. Galeota E, Pelizzola M (2017). Ontology-based annotations and semantic relations in large-scale (epi)genomics data. Briefings in Bioinformatics
  5. Marzi MJ, .. , Nicassio F (2016). Degradation dynamics of microRNAs revealed by a novel pulse-chase approach. Genome Research
  6. Mukherjee N, .. , Ohler U (2016). Integrative classification of human coding and noncoding genes through RNA metabolism profiles. Nature Structural & Molecular Biology
  7. Austenaa LMI, .. , Natoli G (2015). Transcription of mammalian cis-regulatory elements is restrained by actively enforced early termination. Molecular Cell
  8. 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
  9. Kishore K, .. , Pelizzola M (2015). methylPipe and compEpiTools: a suite of R packages for the integrative analysis of epigenomics data. BMC Bioinformatics
  10. Sabò A*, Kress TR*, Pelizzola M*, .. , Amati B (2014). Selective transcriptional regulation by Myc in cellular growth control and lymphomagenesis. Nature

* indicates co-authorship.