Epigenomics and Transcriptional Regulation

RNA abundance and its variations are determined by the kinetic rates of the three fundamental steps, which collectively define the dynamics of RNA metabolism: pre-RNA synthesis, pre-RNA processing into its mature form, and mature RNA degradation.

The precise control over these dynamics depends on the coordinated action of co- and post-transcriptional events, which ultimately direct the action of RNA polymerase II (RNAPII), and multiple RNA binding proteins. Increasing evidence indicates the involvement of epitranscriptional RNA modifications, the most abundant of them being the N6-methyladenosine (m6A).

By bringing together two poorly connected areas of research, chromatin regulation and epitranscriptional dynamics, we aim to:

  1. comprehensively characterize the influence of the m6A-epitranscriptome on the fate of RNA species
  2. decipher how this interplay is mediated by chromatin regulation
  3. unravel how this interplay is altered in cancer.

These goals 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. 


Outline

Contacts

Mattia Pelizzola
This email address is being protected from spambots. You need JavaScript enabled to view it.
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., bioRxiv 2019).
  • 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.

 

Dynamics

 

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)
  • Lucia Coscujuela - molecular biologist (postdoc): m6A-dependent RNA dynamics in cancer
  • Mattia Furlan - theoretical physicist (postdoc): mathematical modeling of RNA dynamics
  • Eugenia Galeota - computer scientist (postdoc): integrative analysis of large-scale high-throughput data
  • Nunzio del Gaudio - visiting molecular biologist (postdoc): m6A-dependent RNA dynamics in cancer
  • Stefano de Pretis - bioinformatician (postdoc): mathematical modeling of m6A and RNA dynamics
  • Iris Tanaka - molecular biologist (postdoc): m6A-dependent RNA dynamics in cancer

News

  • A collaborative paper on the LSD1 histone demethylase is out on Nucleic Acids Research [Dec 2019]
  • Welcome Lucia in the group! [Nov 2019]
  • A collaborative paper on muscular dystrophy is out on PLoS Genetics [Oct 2019]
  • Welcome Iris in the group! [Aug 2019]
  • A collaborative paper on Myc-dependent transcriptional responses in B-cells is out on EMBO reports [Jul 2019]
  • We are co-editing a special issue on Computational Epitranscriptomics [Apr 2019]
  • A collaborative paper on m6A in testicular cancer is out in the Journal of Translational Medicine [Mar 2019]
  • A 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]

 

  

 Cover

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.