Erik Dassi

AURA 2: Empowering discovery of post-transcriptional networks.

Post-transcriptional regulation (PTR) of gene expression is now recognized as a major determinant of cell phenotypes. The recent availability of methods to map protein-RNA interactions in entire transcriptomes such as RIP, CLIP and their variants, together with global polysomal and ribosome profiling techniques, are driving the exponential accumulation of vast amounts of data on mRNA contacts in cells, and of corresponding predictions of PTR events. However, this exceptional quantity of information cannot be exploited at its best to reconstruct potential PTR networks, as it still lies scattered throughout several databases and in isolated reports of single interactions. To address this issue, we developed the second and vastly enhanced version of the Atlas of UTR Regulatory Activity (AURA 2), a meta-database centered on mapping interaction of trans-factors with human and mouse UTRs. AURA 2 includes experimentally demonstrated binding sites for RBPs, ncRNAs, thousands of cis-elements, variations, RNA epigenetics data and more. Its user-friendly interface offers various data-mining features including co-regulation search, network generation and regulatory enrichment testing. Gene expression profiles for many tissues and cell lines can be also combined with these analyses to display only the interactions possible in the system under study. AURA 2 aims at becoming a valuable toolbox for PTR studies and at tracing the road for how PTR network-building tools should be designed. AURA 2 is available at http://aura.science.unitn.it.

AURA: Atlas of UTR Regulatory Activity

SUMMARY The Atlas of UTR Regulatory Activity (AURA) is a manually curated and comprehensive catalog of human mRNA untranslated regions (UTRs) and UTR regulatory annotations. Through its intuitive web interface, it provides full access to a wealth of information on UTRs that integrates phylogenetic conservation, RNA sequence and structure data, single nucleotide variation, gene expression and gene functional descriptions from literature and specialized databases. AVAILABILITY http://aura.science.unitn.it CONTACT aura@science.unitn.it; dassi@science.unitn SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Hyper conserved elements in vertebrate mRNA 3′-UTRs reveal a translational network of RNA-binding proteins controlled by HuR

Little is known regarding the post-transcriptional networks that control gene expression in eukaryotes. Additionally, we still need to understand how these networks evolve, and the relative role played in them by their sequence-dependent regulatory factors, non-coding RNAs (ncRNAs) and RNA-binding proteins (RBPs). Here, we used an approach that relied on both phylogenetic sequence sharing and conservation in the whole mapped 3′-untranslated regions (3′-UTRs) of vertebrate species to gain knowledge on core post-transcriptional networks. The identified human hyper conserved elements (HCEs) were predicted to be preferred binding sites for RBPs and not for ncRNAs, namely microRNAs and long ncRNAs. We found that the HCE map identified a well-known network that post-transcriptionally regulates histone mRNAs. We were then able to discover and experimentally confirm a translational network composed of RNA Recognition Motif (RRM)-type RBP mRNAs that are positively controlled by HuR, another RRM-type RBP. HuR shows a preference for these RBP mRNAs bound in stem–loop motifs, confirming its role as a ‘regulator of regulators’. Analysis of the transcriptome-wide HCE distribution revealed a profile of prevalently small clusters separated by unconserved intercluster RNA stretches, which predicts the formation of discrete small ribonucleoprotein complexes in the 3′-UTRs.

Transcriptome profiling in engrailed-2 mutant mice reveals common molecular pathways associated with autism spectrum disorders

BackgroundTranscriptome analysis has been used in autism spectrum disorder (ASD) to unravel common pathogenic pathways based on the assumption that distinct rare genetic variants or epigenetic modifications affect common biological pathways. To unravel recurrent ASD-related neuropathological mechanisms, we took advantage of the En2-/- mouse model and performed transcriptome profiling on cerebellar and hippocampal adult tissues.MethodsCerebellar and hippocampal tissue samples from three En2-/- and wild type (WT) littermate mice were assessed for differential gene expression using microarray hybridization followed by RankProd analysis. To identify functional categories overrepresented in the differentially expressed genes, we used integrated gene-network analysis, gene ontology enrichment and mouse phenotype ontology analysis. Furthermore, we performed direct enrichment analysis of ASD-associated genes from the SFARI repository in our differentially expressed genes.ResultsGiven the limited number of animals used in the study, we used permissive criteria and identified 842 differentially expressed genes in En2-/- cerebellum and 862 in the En2-/- hippocampus. Our functional analysis revealed that the molecular signature of En2-/- cerebellum and hippocampus shares convergent pathological pathways with ASD, including abnormal synaptic transmission, altered developmental processes and increased immune response. Furthermore, when directly compared to the repository of the SFARI database, our differentially expressed genes in the hippocampus showed enrichment of ASD-associated genes significantly higher than previously reported. qPCR was performed for representative genes to confirm relative transcript levels compared to those detected in microarrays.ConclusionsDespite the limited number of animals used in the study, our bioinformatic analysis indicates the En2-/- mouse is a valuable tool for investigating molecular alterations related to ASD.

Enhanced microbial diversity in the saliva microbiome induced by short-term probiotic intake revealed by 16S rRNA sequencing on the IonTorrent PGM platform.

Microbial communities populating several human body habitats are important determinants of human health. Cultivation-free community-wide approaches like bacterial 16S rRNA sequencing recently revolutionized the study of such human-associated microbial diversity, and the continuously decreasing cost/throughput ratio of current sequencing platforms is further enhancing the availability and effectiveness of microbiome research. The IonTorrent PGM platform is among the latest available commercial high-throughput sequencing tools, but it is just starting to be used for 16S rRNA surveys with only episodic assessments of its performance. We present here the first IonTorrent profiling of the human saliva microbiome collected from 12 healthy individuals. In this cohort, a subset of the volunteers was asked to assume a probiotic product, in order to investigate its impact on the composition and the structure of the saliva microbiome. Analysis of the generated dataset suggests the suitability of the IonTorrent platform for 16S rRNA surveys, even though some platform-specific choices are required to optimize the consistency of the obtained bacterial profiles. Interestingly, we found a marked and statistically significant increase of the overall bacterial diversity in the saliva of individuals who received the probiotic product compared to the control group, suggesting a short-term effect of probiotic product administration on oral microbiome composition.

tRanslatome: an R/Bioconductor package to portray translational control

Summary: High-throughput technologies have led to an explosion of genomic data available for automated analysis. The consequent possibility to simultaneously sample multiple layers of variation along the gene expression flow requires computational methods integrating raw information from different ‘-omics’. It has been recently demonstrated that translational control is a widespread phenomenon, with profound and still underestimated regulation capabilities. Although detecting changes in the levels of total messenger RNAs (mRNAs; the transcriptome), of polysomally loaded mRNAs (the translatome) and of proteins (the proteome) is experimentally feasible in a high-throughput way, the integration of these levels is still far from being robustly approached. Here we introduce tRanslatome, a new R/Bioconductor package, which is a complete platform for the simultaneous pairwise analysis of transcriptome, translatome and proteome data. The package includes most of the available statistical methods developed for the analysis of high-throughput data, allowing the parallel comparison of differentially expressed genes and the corresponding differentially enriched biological themes. Notably, it also enables the prediction of translational regulatory elements on mRNA sequences. The utility of this tool is demonstrated with two case studies. Availability and implementation: tRanslatome is available in Bioconductor. Contact: t.tebaldi@unitn.it Supplementary information: Supplementary data are available at Bioinformatics online.

Tuning the engine: An introduction to resources on post-transcriptional regulation of gene expression

In the last years post-transcriptional regulation (PTR) of gene expression has been increasingly recognized to be a powerful and general determinant of the quantitative changes in proteomes, and therefore a driving force for cell phenotypes. By means of networks of trans-factors on one hand, and cis-elements found primarily in untranslated regions (UTRs) of mRNA on the other hand, mRNA availability to translation and translation rates are tightly controlled and can be rapidly tuned according to the changing state of the cell. A number of dedicated resources and tools, including databases and predictive algorithms, have been proposed as bioinformatics aids for the study of this fundamental layer of gene expression regulation. Their use, however, is rendered difficult by heterogeneity and fragmentation. This review aims to locate these resources in their proper space, classifying them according to their goals, limitations and integration capabilities and, in the end, to provide the user with an initial toolbox for the bioinformatic analysis of post-transcriptional regulation of gene expression. The accompanying website, available at www.ptrguide.org, lists all resources, provides summary and features for each one and will be regularly updated in the future.

Handshakes and Fights: The Regulatory Interplay of RNA-Binding Proteins

What drives the flow of signals controlling the outcome of post-transcriptional regulation of gene expression? This regulatory layer, presiding to processes ranging from splicing to mRNA stability and localization, is a key determinant of protein levels and thus cell phenotypes. RNA-binding proteins (RBPs) form a remarkable army of post-transcriptional regulators, strong of more than 1,500 genes implementing this expression fine-tuning plan and implicated in both cell physiology and pathology. RBPs can bind and control a wide array of RNA targets. This sheer amount of interactions form complex regulatory networks (PTRNs) where the action of individual RBPs cannot be easily untangled from each other. While past studies have mostly focused on the action of individual RBPs on their targets, we are now observing an increasing amount of evidence describing the occurrence of interactions between RBPs, defining how common target RNAs are regulated. This suggests that the flow of signals in PTRNs is driven by the intertwined contribution of multiple RBPs, concurrently acting on each of their targets. Understanding how RBPs cooperate and compete is thus of paramount importance to chart the wiring of PTRNs and their impact on cell phenotypes. Here we review the current knowledge about patterns of RBP interaction and attempt at describing their general principles. We also discuss future directions which should be taken to reach a comprehensive understanding of this fundamental aspect of gene expression regulation.

Translational compensation of genomic instability in neuroblastoma

Cancer-associated gene expression imbalances are conventionally studied at the genomic, epigenomic and transcriptomic levels. Given the relevance of translational control in determining cell phenotypes, we evaluated the translatome, i.e., the transcriptome engaged in translation, as a descriptor of the effects of genetic instability in cancer. We performed this evaluation in high-risk neuroblastomas, which are characterized by a low frequency of point mutations or known cancer-driving genes and by the presence of several segmental chromosomal aberrations that produce gene-copy imbalances that guide aggressiveness. We thus integrated genome, transcriptome, translatome and miRome profiles in a representative panel of high-risk neuroblastoma cell lines. We identified a number of genes whose genomic imbalance was corrected by compensatory adaptations in translational efficiency. The transcriptomic level of these genes was predictive of poor prognosis in more than half of cases, and the genomic imbalances found in their loci were shared by 27 other tumor types. This homeostatic process is also not limited to copy number-altered genes, as we showed the translational stoichiometric rebalance of histone genes. We suggest that the translational buffering of fluctuations in these dose-sensitive transcripts is a potential driving process of neuroblastoma evolution.

Regulation of HuR structure and function by dihydrotanshinone-I

Abstract The Human antigen R protein (HuR) is an RNA-binding protein that recognizes U/AU-rich elements in diverse RNAs through two RNA-recognition motifs, RRM1 and RRM2, and post-transcriptionally regulates the fate of target RNAs. The natural product dihydrotanshinone-I (DHTS) prevents the association of HuR and target RNAs in vitro and in cultured cells by interfering with the binding of HuR to RNA. Here, we report the structural determinants of the interaction between DHTS and HuR and the impact of DHTS on HuR binding to target mRNAs transcriptome-wide. NMR titration and Molecular Dynamics simulation identified the residues within RRM1 and RRM2 responsible for the interaction between DHTS and HuR. RNA Electromobility Shifts and Alpha Screen Assays showed that DHTS interacts with HuR through the same binding regions as target RNAs, stabilizing HuR in a locked conformation that hampers RNA binding competitively. HuR ribonucleoprotein immunoprecipitation followed by microarray (RIP-chip) analysis showed that DHTS treatment of HeLa cells paradoxically enriched HuR binding to mRNAs with longer 3′UTR and with higher density of U/AU-rich elements, suggesting that DHTS inhibits the association of HuR to weaker target mRNAs. In vivo, DHTS potently inhibited xenograft tumor growth in a HuR-dependent model without systemic toxicity.