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Genetic, transcriptional, and post-transcriptional variations shape the transcriptome of individual cells, rendering establishing an exhaustive set of reference RNAs a complicated matter. Current reference transcriptomes, which are based on carefully curated transcripts, are lagging behind the extensive RNA variation revealed by massively parallel sequencing. Much may be missed by ignoring this unreferenced RNA diversity. There is plentiful evidence for non-reference transcripts with important phenotypic effects. Although reference transcriptomes are inestimable for gene expression analysis, they may turn limiting in important medical applications. We discuss computational strategies for retrieving hidden transcript diversity.

Background The detection of genome variants, including point mutations, indels and structural variants, is a fundamental and challenging computational problem. We address here the problem of variant detection between two deep-sequencing (DNA-seq) samples, such as two human samples from an individual patient, or two samples from distinct bacterial strains. The preferred strategy in such a case is to align each sample to a common reference genome, collect all variants and compare these variants between samples. Such mapping-based protocols have several limitations. DNA sequences with large indels, aggregated mutations and structural variants are hard to map to the reference. Furthermore, DNA sequences cannot be mapped reliably to genomic low complexity regions and repeats. Results We introduce 2-kupl, a k-mer based, mapping-free protocol to detect variants between two DNA-seq samples. On simulated and actual data, 2-kupl achieves higher accuracy than other mapping-free protocols. Applying 2-kupl to prostate cancer whole exome sequencing data, we identify a number of candidate variants in hard-to-map regions and propose potential novel recurrent variants in this disease. Conclusions We developed a mapping-free protocol for variant calling between matched DNA-seq samples. Our protocol is suitable for variant detection in unmappable genome regions or in the absence of a reference genome.

Mobile group II introns are site-specific retrotransposons composed of a large self-splicing ribozyme and an intron-encoded reverse transcriptase that are widespread in bacterial and organellar genomes. Sequence and structural variations of the ribozyme and the associated reverse transcriptase define several lineages of bacterial group II introns. Interestingly, some of these intron families evolved different mobility strategies while others colonize particular genetic contexts. Here, we have investigated the mobility activity of an Escherichia coli group II intron that is inserted into the stop codon of the stress-response gene groEL . Using mobility assays based on over-expression from a donor plasmid, we demonstrate that this intron is a highly efficient and site-specific retrotransposon, capable of colonizing the groEL gene of an E. coli host strain according to the insertion pattern observed in natural genomes. Furthermore, we provide evidence that a chromosomal copy of the full-length retrotransposon can be expressed from its native genetic locus to yield mobile retroelement particles. This intron constitutes a novel model system that could help reveal original mobility strategies used by some group II intron retrotransposons to colonize bacterial genomes.

Indexing techniques relying on k-mers have proven effective in searching for RNA sequences across thousands of RNA-seq libraries, but without enabling direct RNA quantification. We show here that arbitrary RNA sequences can be quantified in seconds through their decomposition into k-mers, with a precision akin to that of conventional RNA quantification methods. Using an index of the Cancer Cell Line Encyclopedia (CCLE) collection consisting of 1019 RNA-seq samples, we show that k-mer indexing offers a powerful means to reveal non-reference sequences, and variant RNAs induced by specific gene alterations, for instance in splicing factors.

Clostridioides difficile is the major cause of nosocomial infections associated with antibiotic therapy. The severity of C. difficile infections increased worldwide with the emergence of hypervirulent strains, including 027 ribotype epidemic strains. Many aspects of C. difficile adaptation strategies during pathogenesis remain poorly understood. This pathogen thrives in gut communities that are rich in microbes and phages. To regulate horizontal transfer of genetic material during its infection cycle, C. difficile relies on diverse mechanisms. More specifically, CRISPR (clustered regularly interspaced short palindromic repeats)-Cas and Toxin-Antitoxin (TA) systems contribute to prophage maintenance, prevention of phage infection, and stress response. Abortive infection (Abi) systems can provide additional lines of anti-phage defense. RNAs have emerged as key components of these systems including CRISPR RNAs and antitoxin RNAs within type I and type III TA. We report here the identification of a new AbiF-like system within a prophage of the hypervirulent C. difficile strain R20291. It is associated with an Abi_2/AbiD/F protein family largely distributed in Bacillota and Pseudomonadota with structural links to ancestral Cas13 proteins at the origin of the RNA-targeting CRISPR-Cas13 systems. We demonstrated toxic activity of the AbiF Cd protein in C. difficile and in Escherichia coli and negative regulation of the abiF Cd expression by an associated non-coding RNA RCd22. RCd22 contains two conserved abiF motifs and is active both in cis and in trans to neutralize the toxin by direct RNA-protein interaction, similar to RNA antitoxin in type III TA. A mass spectrometry interactomics analysis of protein fractions from MS2-Affinity Purification coupled with RNA sequencing (MAPS) revealed the AbiF Cd protein among the most enriched RCd22 partners in C. difficile . Structural modeling of the RNA-protein complex and mutagenesis analysis revealed key positions on both protein and RNA partners for this interaction and toxic activity. In summary, these findings provide valuable insights into the mechanisms of interaction between bacteria and phages, which are pertinent to the advancement of phage therapy, genome editing, epidemiological surveillance, and the formulation of novel therapeutic approaches.

We introduce a k -mer-based computational protocol, DE-kupl, for capturing local RNA variation in a set of RNA-seq libraries, independently of a reference genome or transcriptome. DE-kupl extracts all k -mers with differential abundance directly from the raw data files. This enables the retrieval of virtually all variation present in an RNA-seq data set. This variation is subsequently assigned to biological events or entities such as differential long non-coding RNAs, splice and polyadenylation variants, introns, repeats, editing or mutation events, and exogenous RNA. Applying DE-kupl to human RNA-seq data sets identified multiple types of novel events, reproducibly across independent RNA-seq experiments.

Background RNA-seq data are increasingly used to derive prognostic signatures for cancer outcome prediction. A limitation of current predictors is their reliance on reference gene annotations, which amounts to ignoring large numbers of non-canonical RNAs produced in disease tissues. A recently introduced kind of transcriptome classifier operates entirely in a reference-free manner, relying on k-mers extracted from patient RNA-seq data. Methods In this paper, we set out to compare conventional and reference-free signatures in risk and relapse prediction of prostate cancer. To compare the two approaches as fairly as possible, we set up a common procedure that takes as input either a k-mer count matrix or a gene expression matrix, extracts a signature and evaluates this signature in an independent dataset. Results We find that both gene-based and k-mer based classifiers had similarly high performances for risk prediction and a markedly lower performance for relapse prediction. Interestingly, the reference-free signatures included a set of sequences mapping to novel lncRNAs or variable regions of cancer driver genes that were not part of gene-based signatures. Conclusions Reference-free classifiers are thus a promising strategy for the identification of novel prognostic RNA biomarkers.

Tamoxifen has been the mainstay therapy to treat early, locally advanced, and metastatic estrogen receptor-positive (ER + ) breast cancer, constituting around 75% of all cases. However, the emergence of resistance is common, necessitating the identification of novel therapeutic targets. Here, we demonstrated that long-noncoding RNA LINC00152 confers tamoxifen resistance by blocking tamoxifen-induced ferroptosis, an iron-mediated cell death. Mechanistically, inhibiting LINC00152 reduces the mRNA stability of phosphodiesterase 4D ( PDE4D ), leading to activation of the cAMP/PKA/CREB axis and increased expression of the TRPC1 Ca 2+ channel. This causes cytosolic Ca 2+ overload and generation of reactive oxygen species (ROS) that is, on the one hand, accompanied by downregulation of FTH1, a member of the iron sequestration unit, thus increasing intracellular Fe 2+ levels; and on the other hand, inhibition of the peroxidase activity upon reduced GPX4 and xCT levels, in part by cAMP/CREB. These ultimately restore tamoxifen-dependent lipid peroxidation and ferroptotic cell death which are reversed upon chelating Ca 2+ or overexpressing GPX4 or xCT. Overexpressing PDE4D reverses LINC00152 inhibition-mediated tamoxifen sensitization by de-activating the cAMP/Ca 2+ /ferroptosis axis. Importantly, high LINC00152 expression is significantly correlated with high PDE4D/low ferroptosis and worse survival in multiple cohorts of tamoxifen- or tamoxifen-containing endocrine therapy-treated ER+ breast cancer patients. Overall, we identified LINC00152 inhibition as a novel mechanism of tamoxifen sensitization via restoring tamoxifen-dependent ferroptosis upon destabilizing PDE4D, increasing cAMP and Ca 2+ levels, thus leading to ROS generation and lipid peroxidation. Our findings reveal LINC00152 and its effectors as actionable therapeutic targets to improve clinical outcome in refractory ER+ breast cancer.

We address here the issue of prioritizing non-coding mutations in the tumoral genome. To this aim, we created two independent computational models. The first (germline) model estimates purifying selection based on population SNP data. The second (somatic) model estimates tumor mutation density based on whole genome tumor sequencing. We show that each model reflects a different set of constraints acting either on the normal or tumor genome, and we identify the specific genome features that most contribute to these constraints. Importantly, we show that the somatic mutation model carries independent functional information that can be used to narrow down the non-coding regions that may be relevant to cancer progression. On this basis, we identify positions in non-coding RNAs and the non-coding parts of mRNAs that are both under purifying selection in the germline and protected from mutation in tumors, thus introducing a new strategy for future detection of cancer driver elements in the expressed non-coding genome.

Clostridioides difficile is a major cause of nosocomial infections associated with antibiotic therapy classified as an urgent antibiotic resistance threat. This pathogen interacts with host and gut microbial communities during infection, but the mechanisms of these interactions remain largely to be uncovered. Noncoding RNAs contribute to bacterial virulence and host responses, but their expression has not been explored during C. difficile infection. We took advantage of the conventional mouse model of C. difficile infection to look simultaneously to the dynamics of gene expression in pathogen, its host, and gut microbiota composition, providing valuable resources for future studies. We identified a number of ncRNAs that could mediate the adaptation of C. difficile inside the host and the crosstalk with the host immune response. Promising inflammation markers and potential therapeutic targets emerged from this work open new directions for RNA-based and microbiota-modulatory strategies to improve the efficiency of C. difficile infection treatments.