The co-transcriptional assembly of mRNAs into messenger ribonucleoparticles (mRNPs) is pivotal for both genome expression and integrity: indeed, the combinatorial association of RNA-binding proteins (RBPs) to mRNAs does not only define their cellular fate, but also counteracts the accumulation of genotoxic DNA:RNA hybrids, or R-loops. To further understand how these processes are coordinated, especially for functionally related mRNAs, we systematically analyzed genome-wide maps of RBP association and R-loop formation in the budding yeast S. cerevisiae. On the one hand, we have investigated whether specific mRNP biogenesis pathways could take place for mRNAs encoding the subunits of multiprotein complexes. Focusing on the Nuclear Pore, we highlighted a trans-acting mechanism that collectively target the mRNAs encoding the components of this complex, and prevent the accumulation of unassembled subunits. On the other hand, we have searched for cis-acting genomic features regulating R-loop formation. Surprisingly, we found that the presence of introns mitigates the accumulation of…

Considered as the dark matter of genomes, the non-protein coding DNA portion of eukaryotic genomes has, in the last decade, been shown to be much bigger than what was thought. Furthermore, with the advent of deep sequencing it became clear that genomes are pervasively transcribed and that the noncoding fraction generates huge amounts of transcripts that cannot be accounted for as simple “junk” or transcriptional noise. This challenges the central dogma of molecular biology where, for over 50 years, proteins have been considered to be the sole drivers of biological processes. Recent evidence strongly supports that noncoding (nc)RNAs are indeed functional and responsible for a multitude of regulatory processes such as gene regulation, nucleus organization and post-transcriptional regulation. In Phaeodactylum tricornutum over ∼1,500 long intergenic (li)ncRNAs and ∼3,300 natural antisense lncRNAs (NATs) have been identified. These are polyadenylated bona fide discrete transcripts that are expressed under specific physiological states. A ‘guilt by…

The co-evolutive landmarks left by the evolutionary process in protein multiple sequence alignments can be used, among other things, to detect pairs of proteins physically interacting or functionally related. Over time, we have developed different approaches that exploit this idea with the aim of extracting protein interaction networks from the deluge of sequence data that characterizes modern Biology. Current “high-throughput sequencing” technologies have the potential to revolutionize these methods as they generate protein sequences in a fast and cheap way. Nevertheless, more than blindly using all available sequences, it looks like the future will be characterized by a careful selection of those to use, and even the “a-la-carte” re-sequencing of the more suitable species/strains for a particular family/system of interest.

A classic problem in microbiology is that bacteria display two types of growth behavior when cultured on a mixture of two carbon sources: in certain mixtures the bacteria consume the two carbon sources sequentially (diauxie) and in other mixtures the bacteria consume both sources simultaneously (co-utilization). The search for the molecular mechanism of diauxie led to the discovery of the lac operon and gene regulation in general. However, questions remain as why microbes would bother to have different strategies of taking up nutrients and in the case of co-utilization what determines the partition and distribution of carbon sources in the cell. Here we show that diauxie versus co-utilization can be understood from the topological features of the metabolic network. A model of optimal allocation of protein resources quantitatively explains why and how the cell makes the choice when facing multiple carbon sources. When two carbon sources are being co-utilized, the model predicts…

Modeling large multi-molecular assemblies at atomic resolution is a key task in elucidating cell function.   Since for most assemblies there is no single experimental method, that can deliver atomic resolution structures of such large molecules, hybrid methods, which integrate data from various experimental modalities, are being developed for this task. We have developed an integrative method, which combines atomic resolution models of individual assembly components with an intermediate resolution electron microscopy map of the full assembly.  It can also naturally accommodate available chemical cross link (Xlink) data. From a purely geometric viewpoint, this task can be viewed as an assembly of a three dimensional large multiple piece puzzle, where we have relatively accurate models of the individual subcomponents, and a rough, low resolution scan of the full puzzle volume. This is joint work with my M.Sc. students Dan Cohen and Naama Amir.

The three-dimensional (3D) organization of chromatin within the cell nucleus is crucial for regulating genome functionality. The knowledge of its role has greatly advanced thanks to the development of novel imaging and molecular biology techniques. In particular, chromosome conformation capture (3C) and its high-throughput derivatives, based on next generation sequencing, allow investigating chromatin architecture on a genome-wide scale. My research group is focused on the use of 3D chromatin organization data to gain mechanistic insights into transcription and epigenetic regulation at different levels. On a large scale, we investigate mechanisms for the coordinated regulation of large chromatin domains in physiological and disease conditions. These involve, for example, the organization of the genome in distinct structural domains, such as Topologically Associating Domains (TADs), or Lamina Associated Domains (LADs). On a finer scale, instead, we study distal regulatory elements (enhancers) and their epigenetic or genetic alterations in genetics diseases and cancer. In this context,…

The binding of transcription factors (TFs) to their specific motifs in genomic regulatory regions is commonly studied in isolation. However, in order to elucidate the mechanisms of transcriptional regulation, it is essential to determine which TFs bind DNA cooperatively as dimers and to infer the precise nature of these interactions. We will discuss an algorithm for predicting cell-type–specific TF–TF dimerization on DNA on a large scale, using DNase I hypersensitivity cell-type-specific data