Stefano de Pretis

Selective transcriptional regulation by Myc in cellular growth control and lymphomagenesis

The c-myc proto-oncogene product, Myc, is a transcription factor that binds thousands of genomic loci. Recent work suggested that rather than up- and downregulating selected groups of genes, Myc targets all active promoters and enhancers in the genome (a phenomenon termed ‘invasion’) and acts as a general amplifier of transcription. However, the available data did not readily discriminate between direct and indirect effects of Myc on RNA biogenesis. We addressed this issue with genome-wide chromatin immunoprecipitation and RNA expression profiles during B-cell lymphomagenesis in mice, in cultured B cells and fibroblasts. Consistent with long-standing observations, we detected general increases in total RNA or messenger RNA copies per cell (hereby termed ‘amplification’) when comparing actively proliferating cells with control quiescent cells: this was true whether cells were stimulated by mitogens (requiring endogenous Myc for a proliferative response) or by deregulated, oncogenic Myc activity. RNA amplification and promoter/enhancer invasion by Myc were separable phenomena that could occur without one another. Moreover, whether or not associated with RNA amplification, Myc drove the differential expression of distinct subsets of target genes. Hence, although having the potential to interact with all active or poised regulatory elements in the genome, Myc does not directly act as a global transcriptional amplifier. Instead, our results indicate that Myc activates and represses transcription of discrete gene sets, leading to changes in cellular state that can in turn feed back on global RNA production and turnover.

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

The regulation of miRNAs is critical to the definition of cell identity and behavior in normal physiology and disease. To date, the dynamics of miRNA degradation and the mechanisms involved in remain largely obscure, in particular, in higher organisms. Here, we developed a pulse-chase approach based on metabolic RNA labeling to calculate miRNA decay rates at genome-wide scale in mammalian cells. Our analysis revealed heterogeneous miRNA half-lives, with many species behaving as stable molecules (T1/2> 24 h), while others, including passenger miRNAs and a number (25/129) of guide miRNAs, are quickly turned over (T1/2= 4-14 h). Decay rates were coupled with other features, including genomic organization, transcription rates, structural heterogeneity (isomiRs), and target abundance, measured through quantitative experimental approaches. This comprehensive analysis highlighted functional mechanisms that mediate miRNA degradation, as well as the importance of decay dynamics in the regulation of the miRNA pool under both steady-state conditions and during cell transitions.

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

Pervasive transcription of the human genome results in a heterogeneous mix of coding RNAs and long noncoding RNAs (lncRNAs). Only a small fraction of lncRNAs have demonstrated regulatory functions, thus making functional lncRNAs difficult to distinguish from nonfunctional transcriptional byproducts. This difficulty has resulted in numerous competing human lncRNA classifications that are complicated by a steady increase in the number of annotated lncRNAs. To address these challenges, we quantitatively examined transcription, splicing, degradation, localization and translation for coding and noncoding human genes. We observed that annotated lncRNAs had lower synthesis and higher degradation rates than mRNAs and discovered mechanistic differences explaining slower lncRNA splicing. We grouped genes into classes with similar RNA metabolism profiles, containing both mRNAs and lncRNAs to varying extents. These classes exhibited distinct RNA metabolism, different evolutionary patterns and differential sensitivity to cellular RNA-regulatory pathways. Our classification provides an alternative to genomic context-driven annotations of lncRNAs.

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

Upon recruitment to active enhancers and promoters, RNA polymerase II (Pol II) generates short non-coding transcripts of unclear function. The mechanisms that control the length and the amount of ncRNAs generated by cis-regulatory elements are largely unknown. Here, we show that the adaptor protein WDR82 and its associated complexes actively limit such non-coding transcription. WDR82 targets the SET1 H3K4 methyltransferases and the nuclear protein phosphatase 1 (PP1) complexes to the initiating Pol II. WDR82 and PP1 also interact with components of the transcriptional termination and RNA processing machineries. Depletion of WDR82, SET1, or the PP1 subunit required for its nuclear import caused distinct but overlapping transcription termination defects at highly expressed genes and active enhancers and promoters, thus enabling the increased synthesis of unusually long ncRNAs. These data indicate that transcription initiated from cis-regulatory elements is tightly coordinated with termination mechanisms that impose the synthesis of short RNAs.

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

BackgroundNumerous methods are available to profile several epigenetic marks, providing data with different genome coverage and resolution. Large epigenomic datasets are then generated, and often combined with other high-throughput data, including RNA-seq, ChIP-seq for transcription factors (TFs) binding and DNase-seq experiments. Despite the numerous computational tools covering specific steps in the analysis of large-scale epigenomics data, comprehensive software solutions for their integrative analysis are still missing. Multiple tools must be identified and combined to jointly analyze histone marks, TFs binding and other -omics data together with DNA methylation data, complicating the analysis of these data and their integration with publicly available datasets.ResultsTo overcome the burden of integrating various data types with multiple tools, we developed two companion R/Bioconductor packages. The former, methylPipe, is tailored to the analysis of high- or low-resolution DNA methylomes in several species, accommodating (hydroxy-)methyl-cytosines in both CpG and non-CpG sequence context. The analysis of multiple whole-genome bisulfite sequencing experiments is supported, while maintaining the ability of integrating targeted genomic data. The latter, compEpiTools, seamlessly incorporates the results obtained with methylPipe and supports their integration with other epigenomics data. It provides a number of methods to score these data in regions of interest, leading to the identification of enhancers, lncRNAs, and RNAPII stalling/elongation dynamics. Moreover, it allows a fast and comprehensive annotation of the resulting genomic regions, and the association of the corresponding genes with non-redundant GeneOntology terms. Finally, the package includes a flexible method based on heatmaps for the integration of various data types, combining annotation tracks with continuous or categorical data tracks.ConclusionsmethylPipe and compEpiTools provide a comprehensive Bioconductor-compliant solution for the integrative analysis of heterogeneous epigenomics data. These packages are instrumental in providing biologists with minimal R skills a complete toolkit facilitating the analysis of their own data, or in accelerating the analyses performed by more experienced bioinformaticians.

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

MOTIVATION Cellular mRNA levels originate from the combined action of multiple regulatory processes, which can be recapitulated by the rates of pre-mRNA synthesis, pre-mRNA processing and mRNA degradation. Recent experimental and computational advances set the basis to study these intertwined levels of regulation. Nevertheless, software for the comprehensive quantification of RNA dynamics is still lacking. RESULTS INSPEcT is an R package for the integrative analysis of RNA- and 4sU-seq data to study the dynamics of transcriptional regulation. INSPEcT provides gene-level quantification of these rates, and a modeling framework to identify which of these regulatory processes are most likely to explain the observed mRNA and pre-mRNA concentrations. Software performance is tested on a synthetic dataset, instrumental to guide the choice of the modeling parameters and the experimental design. AVAILABILITY AND IMPLEMENTATION INSPEcT is submitted to Bioconductor and is currently available as Supplementary Additional File S1. CONTACT mattia.pelizzola@iit.it SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

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

A key challenge in the analysis of cancer genomes is the identification of driver genes from the vast number of mutations present in a cohort of patients. DOTS-Finder is a new tool that allows the detection of driver genes through the sequential application of functional and frequentist approaches, and is specifically tailored to the analysis of few tumor samples. We have identified driver genes in the genomic data of 34 tumor types derived from existing exploratory projects such as The Cancer Genome Atlas and from studies investigating the usefulness of genomic information in the clinical settings. DOTS-Finder is available athttps://cgsb.genomics.iit.it/wiki/projects/DOTS-Finder/.

Integrated Systems for NGS Data Management and Analysis: Open Issues and Available Solutions.

Next-generation sequencing (NGS) technologies have deeply changed our understanding of cellular processes by delivering an astonishing amount of data at affordable prices; nowadays, many biology laboratories have already accumulated a large number of sequenced samples. However, managing and analyzing these data poses new challenges, which may easily be underestimated by research groups devoid of IT and quantitative skills. In this perspective, we identify five issues that should be carefully addressed by research groups approaching NGS technologies. In particular, the five key issues to be considered concern: (1) adopting a laboratory management system (LIMS) and safeguard the resulting raw data structure in downstream analyses; (2) monitoring the flow of the data and standardizing input and output directories and file names, even when multiple analysis protocols are used on the same data; (3) ensuring complete traceability of the analysis performed; (4) enabling non-experienced users to run analyses through a graphical user interface (GUI) acting as a front-end for the pipelines; (5) relying on standard metadata to annotate the datasets, and when possible using controlled vocabularies, ideally derived from biomedical ontologies. Finally, we discuss the currently available tools in the light of these issues, and we introduce HTS-flow, a new workflow management system conceived to address the concerns we raised. HTS-flow is able to retrieve information from a LIMS database, manages data analyses through a simple GUI, outputs data in standard locations and allows the complete traceability of datasets, accompanying metadata and analysis scripts.

Computational and experimental methods to decipher the epigenetic code

A multi-layered set of epigenetic marks, including post-translational modifications of histones and methylation of DNA, is finely tuned to define the epigenetic state of chromatin in any given cell type under specific conditions. Recently, the knowledge about the combinations of epigenetic marks occurring in the genome of different cell types under various conditions is rapidly increasing. Computational methods were developed for the identification of these states, unraveling the combinatorial nature of epigenetic marks and their association to genomic functional elements and transcriptional states. Nevertheless, the precise rules defining the interplay between all these marks remain poorly characterized. In this perspective we review the current state of this research field, illustrating the power and the limitations of current approaches. Finally, we sketch future avenues of research illustrating how the adoption of specific experimental designs coupled with available experimental approaches could be critical for a significant progress in this area.

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

BackgroundThe increasing availability of resequencing data has led to a better understanding of the most important genes in cancer development. Nevertheless, the mutational landscape of many tumor types is heterogeneous and encompasses a long tail of potential driver genes that are systematically excluded by currently available methods due to the low frequency of their mutations. We developed LowMACA (Low frequency Mutations Analysis via Consensus Alignment), a method that combines the mutations of various proteins sharing the same functional domains to identify conserved residues that harbor clustered mutations in multiple sequence alignments. LowMACA is designed to visualize and statistically assess potential driver genes through the identification of their mutational hotspots.ResultsWe analyzed the Ras superfamily exploiting the known driver mutations of the trio K-N-HRAS, identifying new putative driver mutations and genes belonging to less known members of the Rho, Rab and Rheb subfamilies. Furthermore, we applied the same concept to a list of known and candidate driver genes, and observed that low confidence genes show similar patterns of mutation compared to high confidence genes of the same protein family.ConclusionsLowMACA is a software for the identification of gain-of-function mutations in putative oncogenic families, increasing the amount of information on functional domains and their possible role in cancer. In this context LowMACA emphasizes the role of genes mutated at low frequency otherwise undetectable by classical single gene analysis.LowMACA is an R package available at http://www.bioconductor.org/packages/release/bioc/html/LowMACA.html. It is also available as a GUI standalone downloadable at: https://cgsb.genomics.iit.it/wiki/projects/LowMACA