Welcome

IntroThe research line in Genomic Science applies modern genomic technologies towards a better understanding of complex biological processes and diseases, with particular emphasis on Cancer.

Our general aim is to reduce pathological traits to their molecular components, which might correspond to disease markers or potential targets for pharmacological intervention. In particular, we are focusing on mechanisms of gene expression control at the transcriptional, post-transcriptional and epigenetic level.

  1. Research teams

Our research line includes four research units:

  • Noncoding Genome (Francesco Nicassio), which study the function and regulation of non-coding transcripts (miRNAs and long non-coding RNAs), with a particular emphasis on how regulatory RNAs shape the identity and properties of mammalian cells in cancer stem cells and during cancer evolution (therapy resistance, metastatic spread).
  • Cancer Biology (Stefano Campaner), which combines genetically modified mouse models (GEMMs) that faithfully recapitulate tumor progression with forward and reverse genetic screenings for the identification of transcriptional and epigenomic alterations causally involved in cancer.
  • Epigenomics and Transcriptional Regulation (Mattia Pelizzola), which applies an interdisciplinary approach combining mathematical modeling and genomics to study the dynamics of transcription (including RNA synthesis, processing and degradation), and how these are affected by RNA modifications and chromatin regulation in cancer.
  • Computational RNA biology (Tommaso Leonardi), which develops computational methods and algorithms to study the function and regulation of RNA molecules, with a particular focus on the development of analytical strategies based on Nanopore direct RNA sequencing (dsRNAseq).

    People

  1. Key biological questions and Model systems
  • Breast Cancer and Cancer Stem Cells.The cancer stem cell (CSC) hypothesis posits that, similar to normal tissues, a SC (the CSC) sitsat the apex of the hierarchical organization of tumors and is responsible for therapy resistance and metastasis development. We are currently studying non-coding RNAs (miRNAs and long non-coding RNAs) as critical modulators of stemness, their related mechanisms and potential application as markers or novel targets for treatment.
  • Transcriptional plasticity in advanced (breast) cancer. Cancer cells have the ability of adapting their phenotype in response to challenging environmental conditions by reshaping their transcriptome, a process that has been termed as ‘transcriptional plasticity’. Plasticity has recently emerged as a key adaptive mechanism hijacked upon stress (such as during metastasis) or to resist to anti-cancer treatments by entering into a reversible drug-tolerant state. We are characterizing the transcriptional and epigenetic mechanisms of transcriptional plasticity, with a focus on non-coding RNAs as key components of adaptation pathways.
  • Transcription as a selective target for cancer therapy. Previous pharmaco-genomics studies in our laboratory have highlighted transcriptional plasticity in the regulation of RNA polymerase activity following pharmacological inhibition of elongation. Ensuing adaptive responses leads to selective impairment of mRNA synthesis on selected genes. Singe cells and single molecule analysis will be used to gain further mechanistic insight into these adaptive responses.
  • Oncogene induced genomic instability. Oncogenes like the transcription factor c-Myc, while triggering cell cycle entry and DNA synthesis, also induce a replicative stress which has to be dealt with in order to allow faithful tumour growth. Thus, cancer cell survival relies on several pathways which, by dampening replicative stress, prevent accumulation of cytotoxic DNA damage. By means of a reverse genetics screen, we have recently identified genes required to maintain genome stability in Myc driven tumours. In particular we are focusing on those genes that regulate the interplay between transcription and DNA damage, for their role in coordinating mRNA synthesis and DNA replication, as wells as in preventing potential topological conflicts.
  • Transcriptional control of cell identity and cell growth. YAP and TAZ are transcriptional co-activators regulating cell growth and cell identity. We are exploiting loss of function and gain of function mouse models in order to understand at the genome wide-level, both in population studies and in single-cell analysis, how YAP and TAZ control transcriptional programs relevant to tumour growth and cell regeneration.
  • Endogenous retroviruses and their regulation in health and disease.  During the course of evolution the human germline was infected multiple times by retroviral particles, with the result that ancient pro-viral genomes now account for approximately 5-8% of the genome of present day humans. In response to these insults we have evolved precise defence mechanisms, which in normal conditions control and regulate the expression and activity of these endogenous retroviruses (ERVs). However, when these mechanisms of control fail, the de-regulated expression of ERVs can interfere with the physiological processes of the cell and has the potential to lead to or contribute to the development of diseases such as cancer. Our research aims to identify the molecular mechanisms responsible for the aberrant expression of ERVs in cancer. In particular, we develop and apply innovative computational methods based on modern sequencing technology, such as Nanopore direct RNA sequencing, to accurately profile ERV expression and identify the regulatory molecules responsible for their deregulation in cancer.
  • Epigenomics and epitranscriptional determinants of RNA dynamics. 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, setting their abundance and ability to respond to stresses. The fate of a large fraction of cellular transcripts is influenced by numerous epitranscriptional modifications, among which N6-methyladenosine (m6A) is the most abundant. We are characterizing the crosstalk between the m6A epitranscriptome and the regulation of chromatin, and how this interplay affects RNA dynamics. In particular, we are focusing on the role of these regulatory layers in the context of the transcriptional responses elicited by the MYC transcription factor and oncogene in liver cancer.

  1. Technological platforms

    The recent development of novel technological platforms has increased the range of applications for genomic analyses to new areas of investigation. We are interested in exploiting these novel technologies to gain insight into the mechanisms of gene regulation and into the biology of cancer evolution.

    In order to solve the challenge of intra-sample heterogeneity at the single cell level, we are using single-cell sequencingwith the Chromium 10X Genomicsplatform, a robust and standardized microfluidic technology for single cell analysis that is well established in our institute. Several ongoing projects use this technology to characterize heterogeneous cell populations,measuring transcripts (scRNA-seq) directly or together with surface marker detection (cell hashing + scRNA-seq), mapping chromatin accessibility and active regulatory regions (ATAC-seq), or measuring genomic alterations at DNA level (scCNV-seq).

    In addition, we have set up approaches that combine single-cell transcriptome profiling with either DNA or RNA barcodes at single cell level (CROP-seq, Perturb-seq) that will be used in: i) pooled CRISPR screening, directly linking sgRNA expression to transcriptome responses in thousands of individual cells, thus facilitating high-throughput functional dissection of complex regulatory mechanisms and heterogeneous cell populations; ii) tracing transcriptional patterns over time, useful to resolve adaptive transcriptional mechanisms of cancer cells.

    Finally, we are developing applications that use Nanopore sequencing (Oxford Nanopore Technologies) to natively sequence full-length RNA molecules (direct RNA-Seq). We are applying this technique for the identification of RNA modifications and the profiling of nascent RNA (through RNA metabolic labeling).


    Labs

  2. Facilities

    A number of facilities are available and fully operational:

    1. Genomic Unit, responsible for the routine use of Sequencing applications (Illumina, Nanopore, 10X Genomics) to perform a broad range of analysis of DNA and RNA and provide technical support to the research groups for routine use of genomic application and development of tailored protocols. DNA analysis includes: Whole Genome Analysis, Targeted DNA Analysis, Chromatin-immunoprecitipation (ChIP), Chromatin-Accessibility (ATACseq), Methylation analysis, and Single Cell Copy Number Variation (CNV). RNA applications include: whole-transcriptome Analysis (RNAseq), Analysis of Small RNAs (sRNAseq), Nascent RNA analysis (4sU/TT-seq); Epitranscriptomics (Methylated RNA), immunoprecipated RNA (RIP/RAP), Single Cell transcriptomics (sc-RNA) and single molecule direct RNA sequencing (dRNAseq).
    2. Computational Unit, which ensures optimized data-flow from the sequencing platforms to the servers and provides advanced computational analysis to support genomic applications. The computational infrastructure operational at the Center for Genomic Science includes: a Laboratory Information Management System (the SMITH LIMS), that manages the flow and traceability of samples metadata from the samples preparation in the Genomic Unit till the release of the sequencing data; a Workflow Management System for high-throughput sequencing data (HTS-flow), which takes care of routine bioinformatics analyses, ensuring traceability, reproducibility and exchange of the data and their analyses. The system is accessible through a web interface, it is fully automatized, and can be used without specific training. It supports the primary analysis of sequencing data (quality controls, filtering, reads alignment) and offers various pipelines tailored for the downstream analysis of various data types (including ChIP-seq, absolute and differential RNA-seq, RNA-dynamics, DNA methylation, DNA accessibility and digital footprinting).