Type I IFNs promote cancer cell stemness by triggering the epigenetic regulator KDM1B.

Cancer stem cells (CSCs) are a subpopulation of cancer cells endowed with high tumorigenic, chemoresistant and metastatic potential. Nongenetic mechanisms of acquired resistance are increasingly being discovered, but molecular insights into the evolutionary process of CSCs are limited. Here, we show that type I interferons (IFNs-I) function as molecular hubs of resistance during immunogenic chemotherapy, triggering the epigenetic regulator demethylase 1B (KDM1B) to promote an adaptive, yet reversible, transcriptional rewiring of cancer cells towards stemness and immune escape. Accordingly, KDM1B inhibition prevents the appearance of IFN-I-induced CSCs, both in vitro and in vivo. Notably, IFN-I-induced CSCs are heterogeneous in terms of multidrug resistance, plasticity, invasiveness and immunogenicity. Moreover, in breast cancer (BC) patients receiving anthracycline-based chemotherapy, KDM1B positively correlated with CSC signatures. Our study identifies an IFN-I → KDM1B axis as a potent engine of cancer cell reprogramming, supporting KDM1B targeting as an attractive adjunctive to immunogenic drugs to prevent CSC expansion and increase the long-term benefit of therapy.

Early life adversity affecting the attachment bond alters ventral tegmental area transcriptomic patterning and behavior almost exclusively in female mice.

Early life experiences that affect the attachment bond formation can alter developmental trajectories and result in pathological outcomes in a sex-related manner. However, the molecular basis of sex differences is quite unknown. The dopaminergic system originating from the ventral tegmental area has been proposed to be a key mediator of this process. Here we exploited a murine model of early adversity (Repeated Cross Fostering, RCF) to test how interfering with the attachment bond formation affects the VTA-related functions in a sex-specific manner. Through a comprehensive behavioral screening, within the NiH RDoC framework, and by next-generation RNA-Seq experiments, we analyzed the long-lasting effect of RCF on behavioral and transcriptional profiles related to the VTA, across two different inbred strains of mouse in both sexes. We found that RCF impacted to an extremely greater extent VTA-related behaviors in females than in males and this result mirrored the transcriptional alterations in the VTA that were almost exclusively observed in females. The sexual dimorphism was conserved across two different inbred strains in spite of their divergent long lasting consequences of RCF exposure. Our data suggest that to be female primes a sub-set of genes to respond to early environmental perturbations. This is, to the best of our knowledge, the first evidence of an almost exclusive effect of early life experiences on females, thus mirroring the extremely stronger impact of precocious aversive events reported in clinical studies in women.

COTAN: scRNA-seq data analysis based on gene co-expression.

Estimating the co-expression of cell identity factors in single-cell is crucial. Due to the low efficiency of scRNA-seq methodologies, sensitive computational approaches are critical to accurately infer transcription profiles in a cell population. We introduce COTAN, a statistical and computational method, to analyze the co-expression of gene pairs at single cell level, providing the foundation for single-cell gene interactome analysis. The basic idea is studying the zero UMI counts' distribution instead of focusing on positive counts; this is done with a generalized contingency tables framework. COTAN can assess the correlated or anti-correlated expression of gene pairs, providing a new correlation index with an approximate p-value for the associated test of independence. COTAN can evaluate whether single genes are differentially expressed, scoring them with a newly defined global differentiation index. Similarly to correlation network analysis, it provides ways to plot and cluster genes according to their co-expression pattern with other genes, effectively helping the study of gene interactions, becoming a new tool to identify cell-identity markers. We assayed COTAN on two neural development datasets with very promising results. COTAN is an R package that complements the traditional single cell RNA-seq analysis and it is available at https://github.com/seriph78/COTAN.

BRIO: a web server for RNA sequence and structure motif scan.

The interaction between RNA and RNA-binding proteins (RBPs) has a key role in the regulation of gene expression, in RNA stability, and in many other biological processes. RBPs accomplish these functions by binding target RNA molecules through specific sequence and structure motifs. The identification of these binding motifs is therefore fundamental to improve our knowledge of the cellular processes and how they are regulated. Here, we present BRIO (BEAM RNA Interaction mOtifs), a new web server designed for the identification of sequence and structure RNA-binding motifs in one or more RNA molecules of interest. BRIO enables the user to scan over 2508 sequence motifs and 2296 secondary structure motifs identified in Homo sapiens and Mus musculus, in three different types of experiments (PAR-CLIP, eCLIP, HITS). The motifs are associated with the binding of 186 RBPs and 69 protein domains. The web server is freely available at http://brio.bio.uniroma2.it.

A eutherian-specific microRNA controls the translation of Satb2 in a model of cortical differentiation.

Cerebral cortical development is controlled by key transcription factors that specify the neuronal identities in the different layers. The mechanisms controlling their expression in distinct cells are only partially known. We investigated the expression and stability of Tbr1, Bcl11b, Fezf2, Satb2, and Cux1 mRNAs in single developing mouse cortical cells. We observe that Satb2 mRNA appears much earlier than its protein and in a set of cells broader than expected, suggesting an initial inhibition of its translation, subsequently released during development. Mechanistically, Satb2 3'UTR modulates protein translation of GFP reporters during mouse corticogenesis. We select miR-541, a eutherian-specific miRNA, and miR-92a/b as the best candidates responsible for SATB2 inhibition, being strongly expressed in early and reduced in late progenitor cells. Their inactivation triggers robust and premature SATB2 translation in both mouse and human cortical cells. Our findings indicate RNA interference as a major mechanism in timing cortical cell identities.

Motif Discovery from CLIP Experiments.

RNA primary and secondary motif discovery is an important step in the annotation and characterization of unknown interaction dynamics between RNAs and RNA-Binding Proteins, and several methods have been developed to meet the need of fast and efficient discovery of interaction motifs. Recent advances have increased the amount of data produced by experimental assays and there is no available method suitable for the analysis of all type of results. Here we present a simple workflow to help choosing the more appropriate method, depending on the starting situation, among the three algorithms that best cover the landscape of approaches. A detailed analysis is presented to highlight the need for different algorithms in different working settings. In conclusion, the proposed workflow depends on the nature of the starting data and on the availability of RNA annotations.

Relative Information Gain: Shannon entropy-based measure of the relative structural conservation in RNA alignments.

Structural characterization of RNAs is a dynamic field, offering many modelling possibilities. RNA secondary structure models are usually characterized by an encoding that depicts structural information of the molecule through string representations or graphs. In this work, we provide a generalization of the BEAR encoding (a context-aware structural encoding we previously developed) by expanding the set of alignments used for the construction of substitution matrices and then applying it to secondary structure encodings ranging from fine-grained to more coarse-grained representations. We also introduce a re-interpretation of the Shannon Information applied on RNA alignments, proposing a new scoring metric, the Relative Information Gain (RIG). The RIG score is available for any position in an alignment, showing how different levels of detail encoded in the RNA representation can contribute differently to convey structural information. The approaches presented in this study can be used alongside state-of-the-art tools to synergistically gain insights into the structural elements that RNAs and RNA families are composed of. This additional information could potentially contribute to their improvement or increase the degree of confidence in the secondary structure of families and any set of aligned RNAs.

RISC RNA sequencing in the Dorsal Raphè reveals microRNAs regulatory activities associated with behavioral and functional adaptations to chronic stress.

The Dorsal Raphe (DR) is the primary source of serotonergic input in the brain and a center for the homeostatic maintenance of the serotonergic tone. Under repeated stimulation, it can undergo adaptive modifications that alter serotonergic neurotransmission, which can lead to behavioral dysfunction. Post-transcriptional regulation by microRNAs is implicated in these adaptations. However, a global microRNA/target network effect on the DR neuroplasticity has yet to be elucidated. Here we investigate the microRNAs/mRNAs regulatory activity in the mouse DR after a chronic stress experience. First, we assessed the behavioral consequences of repeated restraint stress exposure and the functional adaptations of the DR by measuring the change in acute stress-induced serotonin release. Then, through next generation RNA-Seq of Argonaute2-bound RNA (RISC-Seq) we identified microRNAs and their targets that are associated to the RISC complex of the DR in unstressed and stressed mice. We mapped the potential microRNA/mRNA network within the stress-altered transcripts, uncovering new interactions that contribute to the chronic stress-induced DR modifications.

Revisiting the "satisfaction of spatial restraints" approach of MODELLER for protein homology modeling.

The most frequently used approach for protein structure prediction is currently homology modeling. The 3D model building phase of this methodology is critical for obtaining an accurate and biologically useful prediction. The most widely employed tool to perform this task is MODELLER. This program implements the "modeling by satisfaction of spatial restraints" strategy and its core algorithm has not been altered significantly since the early 1990s. In this work, we have explored the idea of modifying MODELLER with two effective, yet computationally light strategies to improve its 3D modeling performance. Firstly, we have investigated how the level of accuracy in the estimation of structural variability between a target protein and its templates in the form of σ values profoundly influences 3D modeling. We show that the σ values produced by MODELLER are on average weakly correlated to the true level of structural divergence between target-template pairs and that increasing this correlation greatly improves the program's predictions, especially in multiple-template modeling. Secondly, we have inquired into how the incorporation of statistical potential terms (such as the DOPE potential) in the MODELLER's objective function impacts positively 3D modeling quality by providing a small but consistent improvement in metrics such as GDT-HA and lDDT and a large increase in stereochemical quality. Python modules to harness this second strategy are freely available at https://github.com/pymodproject/altmod. In summary, we show that there is a large room for improving MODELLER in terms of 3D modeling quality and we propose strategies that could be pursued in order to further increase its performance.

Over-Expression of UV-Damage DNA Repair Genes and Ribonucleic Acid Persistence Contribute to the Resilience of Dried Biofilms of the Desert Cyanobacetrium Chroococcidiopsis Exposed to Mars-Like UV Flux and Long-Term Desiccation.

The survival limits of the desert cyanobacterium Chroococcidiopsis were challenged by rewetting dried biofilms and dried biofilms exposed to 1.5 × 103 kJ/m2 of a Mars-like UV, after 7 years of air-dried storage. PCR-stop assays revealed the presence of DNA lesions in dried biofilms and an increased accumulation in dried-UV-irradiated biofilms. Different types and/or amounts of DNA lesions were highlighted by a different expression of uvrA, uvrB, uvrC, phrA, and uvsE genes in dried-rewetted biofilms and dried-UV-irradiated-rewetted biofilms, after rehydration for 30 and 60 min. The up-regulation in dried-rewetted biofilms of uvsE gene encoding an UV damage endonuclease, suggested that UV-damage DNA repair contributed to the repair of desiccation-induced damage. While the phrA gene encoding a photolyase was up-regulated only in dried-UV-irradiated-rewetted biofilms. Nucleotide excision repair genes were over-expressed in dried-rewetted biofilms and dried-UV-irradiated-rewetted biofilms, with uvrC gene showing the highest increase in dried-UV-irradiated-rewetted biofilms. Dried biofilms preserved intact mRNAs (at least of the investigated genes) and 16S ribosomal RNA that the persistence of the ribosome machinery and mRNAs might have played a key role in the early phase recovery. Results have implications for the search of extra-terrestrial life by contributing to the definition of habitability of astrobiologically relevant targets such as Mars or planets orbiting around other stars.

Modeling cancer drug response through drug-specific informative genes.

Recent advances in pharmacogenomics have generated a wealth of data of different types whose analysis have helped in the identification of signatures of different cellular sensitivity/resistance responses to hundreds of chemical compounds. Among the different data types, gene expression has proven to be the more successful for the inference of drug response in cancer cell lines. Although effective, the whole transcriptome can introduce noise in the predictive models, since specific mechanisms are required for different drugs and these realistically involve only part of the proteins encoded in the genome. We analyzed the pharmacogenomics data of 961 cell lines tested with 265 anti-cancer drugs and developed different machine learning approaches for dissecting the genome systematically and predict drug responses using both drug-unspecific and drug-specific genes. These methodologies reach better response predictions for the vast majority of the screened drugs using tens to few hundreds genes specific to each drug instead of the whole genome, thus allowing a better understanding and interpretation of drug-specific response mechanisms which are not necessarily restricted to the drug known targets.

Discovering sequence and structure landscapes in RNA interaction motifs.

RNA molecules are able to bind proteins, DNA and other small or long RNAs using information at primary, secondary or tertiary structure level. Recent techniques that use cross-linking and immunoprecipitation of RNAs can detect these interactions and, if followed by high-throughput sequencing, molecules can be analysed to find recurrent elements shared by interactors, such as sequence and/or structure motifs. Many tools are able to find sequence motifs from lists of target RNAs, while others focus on structure using different approaches to find specific interaction elements. In this work, we make a systematic analysis of RBP-RNA and RNA-RNA datasets to better characterize the interaction landscape with information about multi-motifs on the same RNAs. To achieve this goal, we updated our BEAM algorithm to combine both sequence and structure information to create pairs of patterns that model motifs of interaction. This algorithm was applied to several RNA binding proteins and ncRNAs interactors, confirming already known motifs and discovering new ones. This landscape analysis on interaction variability reflects the diversity of target recognition and underlines that often both primary and secondary structure are involved in molecular recognition.

BEAM web server: a tool for structural RNA motif discovery.

Motivation: RNA structural motif finding is a relevant problem that becomes computationally hard when working on high-throughput data (e.g. eCLIP, PAR-CLIP), often represented by thousands of RNA molecules. Currently, the BEAM server is the only web tool capable to handle tens of thousands of RNA in input with a motif discovery procedure that is only limited by the current secondary structure prediction accuracies. Results: The recently developed method BEAM (BEAr Motifs finder) can analyze tens of thousands of RNA molecules and identify RNA secondary structure motifs associated to a measure of their statistical significance. BEAM is extremely fast thanks to the BEAR encoding that transforms each RNA secondary structure in a string of characters. BEAM also exploits the evolutionary knowledge contained in a substitution matrix of secondary structure elements, extracted from the RFAM database of families of homologous RNAs. The BEAM web server has been designed to streamline data pre-processing by automatically handling folding and encoding of RNA sequences, giving users a choice for the preferred folding program. The server provides an intuitive and informative results page with the list of secondary structure motifs identified, the logo of each motif, its significance, graphic representation and information about its position in the RNA molecules sharing it. Availability and implementation: The web server is freely available at http://beam.uniroma2.it/ and it is implemented in NodeJS and Python with all major browsers supported. Contact: marco.pietrosanto@uniroma2.it. Supplementary information: Supplementary data are available at Bioinformatics online.

A novel method for the identification of conserved structural patterns in RNA: From small scale to high-throughput applications.

Functional RNA regions are often related to recurrent secondary structure patterns (or motifs), which can exert their role in several different ways, particularly in dictating the interaction with RNA-binding proteins, and acting in the regulation of a large number of cellular processes. Among the available motif-finding tools, the majority focuses on sequence patterns, sometimes including secondary structure as additional constraints to improve their performance. Nonetheless, secondary structures motifs may be concurrent to their sequence counterparts or even encode a stronger functional signal. Current methods for searching structural motifs generally require long pipelines and/or high computational efforts or previously aligned sequences. Here, we present BEAM (BEAr Motif finder), a novel method for structural motif discovery from a set of unaligned RNAs, taking advantage of a recently developed encoding for RNA secondary structure named BEAR (Brand nEw Alphabet for RNAs) and of evolutionary substitution rates of secondary structure elements. Tested in a varied set of scenarios, from small- to large-scale, BEAM is successful in retrieving structural motifs even in highly noisy data sets, such as those that can arise in CLIP-Seq or other high-throughput experiments.

Web-Beagle: a web server for the alignment of RNA secondary structures.

Web-Beagle (http://beagle.bio.uniroma2.it) is a web server for the pairwise global or local alignment of RNA secondary structures. The server exploits a new encoding for RNA secondary structure and a substitution matrix of RNA structural elements to perform RNA structural alignments. The web server allows the user to compute up to 10 000 alignments in a single run, taking as input sets of RNA sequences and structures or primary sequences alone. In the latter case, the server computes the secondary structure prediction for the RNAs on-the-fly using RNAfold (free energy minimization). The user can also compare a set of input RNAs to one of five pre-compiled RNA datasets including lncRNAs and 3' UTRs. All types of comparison produce in output the pairwise alignments along with structural similarity and statistical significance measures for each resulting alignment. A graphical color-coded representation of the alignments allows the user to easily identify structural similarities between RNAs. Web-Beagle can be used for finding structurally related regions in two or more RNAs, for the identification of homologous regions or for functional annotation. Benchmark tests show that Web-Beagle has lower computational complexity, running time and better performances than other available methods.