Vera Pancaldi

Whole-genome fingerprint of the DNA methylome during human B cell differentiation.

We analyzed the DNA methylome of ten subpopulations spanning the entire B cell differentiation program by whole-genome bisulfite sequencing and high-density microarrays. We observed that non-CpG methylation disappeared upon B cell commitment, whereas CpG methylation changed extensively during B cell maturation, showing an accumulative pattern and affecting around 30% of all measured CpG sites. Early differentiation stages mainly displayed enhancer demethylation, which was associated with upregulation of key B cell transcription factors and affected multiple genes involved in B cell biology. Late differentiation stages, in contrast, showed extensive demethylation of heterochromatin and methylation gain at Polycomb-repressed areas, and genes with apparent functional impact in B cells were not affected. This signature, which has previously been linked to aging and cancer, was particularly widespread in mature cells with an extended lifespan. Comparing B cell neoplasms with their normal counterparts, we determined that they frequently acquire methylation changes in regions already undergoing dynamic methylation during normal B cell differentiation.

Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells

Summary Characterizing the multifaceted contribution of genetic and epigenetic factors to disease phenotypes is a major challenge in human genetics and medicine. We carried out high-resolution genetic, epigenetic, and transcriptomic profiling in three major human immune cell types (CD14+ monocytes, CD16+ neutrophils, and naive CD4+ T cells) from up to 197 individuals. We assess, quantitatively, the relative contribution of cis-genetic and epigenetic factors to transcription and evaluate their impact as potential sources of confounding in epigenome-wide association studies. Further, we characterize highly coordinated genetic effects on gene expression, methylation, and histone variation through quantitative trait locus (QTL) mapping and allele-specific (AS) analyses. Finally, we demonstrate colocalization of molecular trait QTLs at 345 unique immune disease loci. This expansive, high-resolution atlas of multi-omics changes yields insights into cell-type-specific correlation between diverse genomic inputs, more generalizable correlations between these inputs, and defines molecular events that may underpin complex disease risk.

In silico characterization and prediction of global protein–mRNA interactions in yeast

Post-transcriptional gene regulation is mediated through complex networks of protein–RNA interactions. The targets of only a few RNA binding proteins (RBPs) are known, even in the well-characterized budding yeast. In silico prediction of protein–RNA interactions is therefore useful to guide experiments and to provide insight into regulatory networks. Computational approaches have identified RBP targets based on sequence binding preferences. We investigate here to what extent RBP–RNA interactions can be predicted based on RBP and mRNA features other than sequence motifs. We analyze global relationships between gene and protein properties in general and between selected RBPs and known mRNA targets in particular. Highly translated RBPs tend to bind to shorter transcripts, and transcripts bound by the same RBP show high expression correlation across different biological conditions. Surprisingly, a given RBP preferentially binds to mRNAs that encode interaction partners for this RBP, suggesting coordinated post-transcriptional auto-regulation of protein complexes. We apply a machine-learning approach to predict specific RBP targets in yeast. Although this approach performs well for RBPs with known targets, predictions for uncharacterized RBPs remain challenging due to limiting experimental data. We also predict targets of fission yeast RBPs, indicating that the suggested framework could be applied to other species once more experimental data are available.

Systematic screen for mutants resistant to TORC1 inhibition in fission yeast reveals genes involved in cellular ageing and growth

Summary Target of rapamycin complex 1 (TORC1), which controls growth in response to nutrients, promotes ageing in multiple organisms. The fission yeast Schizosaccharomyces pombe emerges as a valuable genetic model system to study TORC1 function and cellular ageing. Here we exploited the combinatorial action of rapamycin and caffeine, which inhibit fission yeast growth in a TORC1-dependent manner. We screened a deletion library, comprising ∼84% of all non-essential fission yeast genes, for drug-resistant mutants. This screen identified 33 genes encoding functions such as transcription, kinases, mitochondrial respiration, biosynthesis, intra-cellular trafficking, and stress response. Among the corresponding mutants, 5 showed shortened and 21 showed increased maximal chronological lifespans; 15 of the latter mutants showed no further lifespan increase with rapamycin and might thus represent key targets downstream of TORC1. We pursued the long-lived sck2 mutant with additional functional analyses, revealing that the Sck2p kinase functions within the TORC1 network and is required for normal cell growth, global protein translation, and ribosomal S6 protein phosphorylation in a nutrient-dependent manner. Notably, slow cell growth was associated with all long-lived mutants while oxidative-stress resistance was not.

Genome-wide analysis of differential transcriptional and epigenetic variability across human immune cell types

BackgroundA healthy immune system requires immune cells that adapt rapidly to environmental challenges. This phenotypic plasticity can be mediated by transcriptional and epigenetic variability.ResultsWe apply a novel analytical approach to measure and compare transcriptional and epigenetic variability genome-wide across CD14+CD16− monocytes, CD66b+CD16+ neutrophils, and CD4+CD45RA+ naïve T cells from the same 125 healthy individuals. We discover substantially increased variability in neutrophils compared to monocytes and T cells. In neutrophils, genes with hypervariable expression are found to be implicated in key immune pathways and are associated with cellular properties and environmental exposure. We also observe increased sex-specific gene expression differences in neutrophils. Neutrophil-specific DNA methylation hypervariable sites are enriched at dynamic chromatin regions and active enhancers.ConclusionsOur data highlight the importance of transcriptional and epigenetic variability for the key role of neutrophils as the first responders to inflammatory stimuli. We provide a resource to enable further functional studies into the plasticity of immune cells, which can be accessed from: http://blueprint-dev.bioinfo.cnio.es/WP10/hypervariability.

Higher gene expression variability in the more aggressive subtype of chronic lymphocytic leukemia

BackgroundChronic lymphocytic leukemia (CLL) presents two subtypes which have drastically different clinical outcomes, IgVH mutated (M-CLL) and IgVH unmutated (U-CLL). So far, these two subtypes are not associated to clear differences in gene expression profiles. Interestingly, recent results have highlighted important roles for heterogeneity, both at the genetic and at the epigenetic level in CLL progression.MethodsWe analyzed gene expression data of two large cohorts of CLL patients and quantified expression variability across individuals to investigate differences between the two subtypes using different measures and statistical tests. Functional significance was explored by pathway enrichment and network analyses. Furthermore, we implemented a random forest approach based on expression variability to classify patients into disease subtypes.ResultsWe found that U-CLL, the more aggressive type of the disease, shows significantly increased variability of gene expression across patients and that, overall, genes that show higher variability in the aggressive subtype are related to cell cycle, development and inter-cellular communication. These functions indicate a potential relation between gene expression variability and the faster progression of this CLL subtype. Finally, a classifier based on gene expression variability was able to correctly predict the disease subtype of CLL patients.ConclusionsThere are strong relations between gene expression variability and disease subtype linking significantly increased expression variability to phenotypes such as aggressiveness and resistance to therapy in CLL.

AnGeLi: A Tool for the Analysis of Gene Lists from Fission Yeast.

Genome-wide assays and screens typically result in large lists of genes or proteins. Enrichments of functional or other biological properties within such lists can provide valuable insights and testable hypotheses. To systematically detect these enrichments can be challenging and time-consuming, because relevant data to compare against query gene lists are spread over many different sources. We have developed AnGeLi (Analysis of Gene Lists), an intuitive, integrated web-tool for comprehensive and customized interrogation of gene lists from the fission yeast, Schizosaccharomyces pombe. AnGeLi searches for significant enrichments among multiple qualitative and quantitative information sources, including gene and phenotype ontologies, genetic and protein interactions, numerous features of genes, transcripts, translation, and proteins such as copy numbers, chromosomal positions, genetic diversity, RNA polymerase II and ribosome occupancy, localization, conservation, half-lives, domains, and molecular weight among others, as well as diverse sets of genes that are co-regulated or lead to the same phenotypes when mutated. AnGeLi uses robust statistics which can be tailored to specific needs. It also provides the option to upload user-defined gene sets to compare against the query list. Through an integrated data submission form, AnGeLi encourages the community to contribute additional curated gene lists to further increase the usefulness of this resource and to get the most from the ever increasing large-scale experiments. AnGeLi offers a rigorous yet flexible statistical analysis platform for rich insights into functional enrichments and biological context for query gene lists, thus providing a powerful exploratory tool through which S. pombe researchers can uncover fresh perspectives and unexpected connections from genomic data. AnGeLi is freely available at: www.bahlerlab.info/AnGeLi

Predicting the Fission Yeast Protein Interaction Network

A systems-level understanding of biological processes and information flow requires the mapping of cellular component interactions, among which protein–protein interactions are particularly important. Fission yeast (Schizosaccharomyces pombe) is a valuable model organism for which no systematic protein-interaction data are available. We exploited gene and protein properties, global genome regulation datasets, and conservation of interactions between budding and fission yeast to predict fission yeast protein interactions in silico. We have extensively tested our method in three ways: first, by predicting with 70–80% accuracy a selected high-confidence test set; second, by recapitulating interactions between members of the well-characterized SAGA co-activator complex; and third, by verifying predicted interactions of the Cbf11 transcription factor using mass spectrometry of TAP-purified protein complexes. Given the importance of the pathway in cell physiology and human disease, we explore the predicted sub-networks centered on the Tor1/2 kinases. Moreover, we predict the histidine kinases Mak1/2/3 to be vital hubs in the fission yeast stress response network, and we suggest interactors of argonaute 1, the principal component of the siRNA-mediated gene silencing pathway, lost in budding yeast but preserved in S. pombe. Of the new high-quality interactions that were discovered after we started this work, 73% were found in our predictions. Even though any predicted interactome is imperfect, the protein network presented here can provide a valuable basis to explore biological processes and to guide wet-lab experiments in fission yeast and beyond. Our predicted protein interactions are freely available through PInt, an online resource on our website (www.bahlerlab.info/PInt).

Integrating epigenomic data and 3D genomic structure with a new measure of chromatin assortativity

BackgroundNetwork analysis is a powerful way of modeling chromatin interactions. Assortativity is a network property used in social sciences to identify factors affecting how people establish social ties. We propose a new approach, using chromatin assortativity, to integrate the epigenomic landscape of a specific cell type with its chromatin interaction network and thus investigate which proteins or chromatin marks mediate genomic contacts.ResultsWe use high-resolution promoter capture Hi-C and Hi-Cap data as well as ChIA-PET data from mouse embryonic stem cells to investigate promoter-centered chromatin interaction networks and calculate the presence of specific epigenomic features in the chromatin fragments constituting the nodes of the network. We estimate the association of these features with the topology of four chromatin interaction networks and identify features localized in connected areas of the network. Polycomb group proteins and associated histone marks are the features with the highest chromatin assortativity in promoter-centered networks. We then ask which features distinguish contacts amongst promoters from contacts between promoters and other genomic elements. We observe higher chromatin assortativity of the actively elongating form of RNA polymerase 2 (RNAPII) compared with inactive forms only in interactions between promoters and other elements.ConclusionsContacts among promoters and between promoters and other elements have different characteristic epigenomic features. We identify a possible role for the elongating form of RNAPII in mediating interactions among promoters, enhancers, and transcribed gene bodies. Our approach facilitates the study of multiple genome-wide epigenomic profiles, considering network topology and allowing the comparison of chromatin interaction networks.

Chromatin regulators as a guide for cancer treatment choice

The limited capacity to predict a patients response to distinct chemotherapeutic agents is a major hurdle in cancer management. The efficiency of a large fraction of current cancer therapeutics (radio- and chemotherapies) is influenced by chromatin structure. Reciprocally, alterations in chromatin organization may affect resistance mechanisms. Here, we explore how the misexpression of chromatin regulators—factors involved in the establishment and maintenance of functional chromatin domains—can inform about the extent of docetaxel response. We exploit Affymetrix and NanoString gene expression data for a set of chromatin regulators generated from breast cancer patient-derived xenograft models and patient samples treated with docetaxel. Random Forest classification reveals specific panels of chromatin regulators, including key components of the SWI/SNF chromatin remodeler, which readily distinguish docetaxel high-responders and poor-responders. Further exploration of SWI/SNF components in the comprehensive NCI-60 dataset reveals that the expression inversely correlates with docetaxel sensitivity. Finally, we show that loss of the SWI/SNF subunit BRG1 (SMARCA4) in a model cell line leads to enhanced docetaxel sensitivity. Altogether, our findings point toward chromatin regulators as biomarkers for drug response as well as therapeutic targets to sensitize patients toward docetaxel and combat drug resistance. Mol Cancer Ther; 15(7); 1768–77. ©2016 AACR.