Cell-cycle-dependent mRNA localization in P-bodies.

Understanding the dynamics of RNA targeting to membraneless organelles is essential to disentangle their functions. Here, we investigate how P-bodies (PBs) evolve during cell-cycle progression in HEK293 cells. PB purification across the cell cycle uncovers widespread changes in their RNA content, partly uncoupled from cell-cycle-dependent changes in RNA expression. Single-molecule fluorescence in situ hybridization (FISH) shows various mRNA localization patterns in PBs peaking in G1, S, or G2, with examples illustrating the timely capture of mRNAs in PBs when their encoded protein becomes dispensable. Rather than directly reflecting absence of translation, cyclic mRNA localization in PBs can be controlled by RBPs, such as HuR in G2, and by RNA features. Indeed, while PB mRNAs are AU rich at all cell-cycle phases, they are specifically longer in G1, possibly related to post-mitotic PB reassembly. Altogether, our study supports a model where PBs are more than a default location for excess untranslated mRNAs.

KaMRaT: a C++ toolkit for k-mer count matrix dimension reduction.

MOTIVATION: KaMRaT is designed for processing large k-mer count tables derived from multi-sample, RNA-seq data. Its primary objective is to identify condition-specific or differentially expressed sequences, regardless of gene or transcript annotation. RESULTS: KaMRaT is implemented in C++. Major functions include scoring k-mers based on count statistics, merging overlapping k-mers into contigs and selecting k-mers based on their occurrence across specific samples. AVAILABILITY AND IMPLEMENTATION: Source code and documentation are available via https://github.com/Transipedia/KaMRaT.

Transinteractome analysis reveals distinct niche requirements for isotype-based plasma cell subsets in the bone marrow.

Bone marrow (BM) long-lived plasma cells (PCs) are essential for long-term protection against infection, and their persistence within this organ relies on interactions with Cxcl12-expressing stromal cells that are still not clearly identified. Here, using single cell RNAseq and in silico transinteractome analyses, we identified Leptin receptor positive (LepR+ ) mesenchymal cells as the stromal cell subset most likely to interact with PCs within the BM. Moreover, we demonstrated that depending on the isotype they express, PCs may use different sets of integrins and adhesion molecules to interact with these stromal cells. Altogether, our results constitute an unprecedented characterization of PC subset stromal niches and open new avenues for the specific targeting of BM PCs based on their isotype.

Nonsense-Mediated Decay Restricts LncRNA Levels in Yeast Unless Blocked by Double-Stranded RNA Structure.

Antisense long non-coding (aslnc)RNAs represent a substantial part of eukaryotic transcriptomes that are, in yeast, controlled by the Xrn1 exonuclease. Nonsense-Mediated Decay (NMD) destabilizes the Xrn1-sensitive aslncRNAs (XUT), but what determines their sensitivity remains unclear. We report that 3' single-stranded (3'-ss) extension mediates XUTs degradation by NMD, assisted by the Mtr4 and Dbp2 helicases. Single-gene investigation, genome-wide RNA analyses, and double-stranded (ds)RNA mapping revealed that 3'-ss extensions discriminate the NMD-targeted XUTs from stable lncRNAs. Ribosome profiling showed that XUT are translated, locking them for NMD activity. Interestingly, mutants of the Mtr4 and Dbp2 helicases accumulated XUTs, suggesting that dsRNA unwinding is a critical step for degradation. Indeed, expression of anticomplementary transcripts protects cryptic intergenic lncRNAs from NMD. Our results indicate that aslncRNAs form dsRNA that are only translated and targeted to NMD if dissociated by Mtr4 and Dbp2. We propose that NMD buffers genome expression by discarding pervasive regulatory transcripts.

Rfam 14: expanded coverage of metagenomic, viral and microRNA families.

Rfam is a database of RNA families where each of the 3444 families is represented by a multiple sequence alignment of known RNA sequences and a covariance model that can be used to search for additional members of the family. Recent developments have involved expert collaborations to improve the quality and coverage of Rfam data, focusing on microRNAs, viral and bacterial RNAs. We have completed the first phase of synchronising microRNA families in Rfam and miRBase, creating 356 new Rfam families and updating 40. We established a procedure for comprehensive annotation of viral RNA families starting with Flavivirus and Coronaviridae RNAs. We have also increased the coverage of bacterial and metagenome-based RNA families from the ZWD database. These developments have enabled a significant growth of the database, with the addition of 759 new families in Rfam 14. To facilitate further community contribution to Rfam, expert users are now able to build and submit new families using the newly developed Rfam Cloud family curation system. New Rfam website features include a new sequence similarity search powered by RNAcentral, as well as search and visualisation of families with pseudoknots. Rfam is freely available at https://rfam.org.

2-kupl: mapping-free variant detection from DNA-seq data of matched samples.

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.

REINDEER: efficient indexing of k-mer presence and abundance in sequencing datasets.

MOTIVATION: In this work we present REINDEER, a novel computational method that performs indexing of sequences and records their abundances across a collection of datasets. To the best of our knowledge, other indexing methods have so far been unable to record abundances efficiently across large datasets. RESULTS: We used REINDEER to index the abundances of sequences within 2585 human RNA-seq experiments in 45 h using only 56 GB of RAM. This makes REINDEER the first method able to record abundances at the scale of ∼4 billion distinct k-mers across 2585 datasets. REINDEER also supports exact presence/absence queries of k-mers. Briefly, REINDEER constructs the compacted de Bruijn graph of each dataset, then conceptually merges those de Bruijn graphs into a single global one. Then, REINDEER constructs and indexes monotigs, which in a nutshell are groups of k-mers of similar abundances. AVAILABILITY AND IMPLEMENTATION: https://github.com/kamimrcht/REINDEER. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Landscape of the Noncoding Transcriptome Response of Two Arabidopsis Ecotypes to Phosphate Starvation.

Root architecture varies widely between species; it even varies between ecotypes of the same species, despite strong conservation of the coding portion of their genomes. By contrast, noncoding RNAs evolve rapidly between ecotypes and may control their differential responses to the environment, since several long noncoding RNAs (lncRNAs) are known to quantitatively regulate gene expression. Roots from ecotypes Columbia and Landsberg erecta of Arabidopsis (Arabidopsis thaliana) respond differently to phosphate starvation. Here, we compared transcriptomes (mRNAs, lncRNAs, and small RNAs) of root tips from these two ecotypes during early phosphate starvation. We identified thousands of lncRNAs that were largely conserved at the DNA level in these ecotypes. In contrast to coding genes, many lncRNAs were specifically transcribed in one ecotype and/or differentially expressed between ecotypes independent of phosphate availability. We further characterized these ecotype-related lncRNAs and studied their link with small interfering RNAs. Our analysis identified 675 lncRNAs differentially expressed between the two ecotypes, including antisense RNAs targeting key regulators of root-growth responses. Misregulation of several lincRNAs showed that at least two ecotype-related lncRNAs regulate primary root growth in ecotype Columbia. RNA-sequencing analysis following deregulation of lncRNA NPC48 revealed a potential link with root growth and transport functions. This exploration of the noncoding transcriptome identified ecotype-specific lncRNA-mediated regulation in root apexes. The noncoding genome may harbor further mechanisms involved in ecotype adaptation of roots to different soil environments.

Reference-free transcriptome signatures for prostate cancer prognosis.

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.

Accurate characterization of Escherichia coli tRNA modifications with a simple method of deep-sequencing library preparation.

In conventional RNA high-throughput sequencing, modified bases prevent a large fraction of tRNA transcripts to be converted into cDNA libraries. Recent proposals aiming at resolving this issue take advantage of the interference of base modifications with RT enzymes to detect and identify them by establishing signals from aborted cDNA transcripts. Because some modifications, such as methyl groups, do almost not allow RT bypassing, demethylation and highly processive RT enzymes have been used to overcome these obstacles. Working with Escherichia coli as a model system, we show that with a conventional (albeit still engineered) RT enzyme and key optimizations in library preparation, all RT-impairing modifications can be highlighted along the entire tRNA length without demethylation procedure. This is achieved by combining deep-sequencing samples, which allows to establish aborted transcription signal of higher accuracy and reproducibility, with the potential for differentiating tiny differences in the state of modification of all cellular tRNAs. In addition, our protocol provides estimates of the relative tRNA abundance.

Identification of RNAs bound by Hfq reveals widespread RNA partners and a sporulation regulator in the human pathogen Clostridioides difficile.

Noncoding RNAs (ncRNA) have emerged as important components of regulatory networks governing bacterial physiology and virulence. Previous deep-sequencing analysis identified a large diversity of ncRNAs in the human enteropathogen Clostridioides (Clostridium) difficile. Some of them are trans-encoded RNAs that could require the RNA chaperone protein Hfq for their action. Recent analysis suggested a pleiotropic role of Hfq in C. difficile with the most pronounced effect on sporulation, a key process during the infectious cycle of this pathogen. However, a global view of RNAs interacting with C. difficile Hfq is missing. In the present study, we performed RNA immunoprecipitation high-throughput sequencing (RIP-Seq) to identify Hfq-associated RNAs in C. difficile. Our work revealed a large set of Hfq-interacting mRNAs and ncRNAs, including mRNA leaders and coding regions, known and potential new ncRNAs. In addition to trans-encoded RNAs, new categories of Hfq ligands were found including cis-antisense RNAs, riboswitches and CRISPR RNAs. ncRNA-mRNA and ncRNA-ncRNA pairings were postulated through computational predictions. Investigation of one of the Hfq-associated ncRNAs, RCd1, suggests that this RNA contributes to the control of late stages of sporulation in C. difficile. Altogether, these data provide essential molecular basis for further studies of post-transcriptional regulatory network in this enteropathogen.

Native elongating transcript sequencing reveals global anti-correlation between sense and antisense nascent transcription in fission yeast.

Antisense transcription can regulate sense gene expression. However, previous annotations of antisense transcription units have been based on detection of mature antisense long noncoding (aslnc)RNAs by RNA-seq and/or microarrays, only giving a partial view of the antisense transcription landscape and incomplete molecular bases for antisense-mediated regulation. Here, we used native elongating transcript sequencing to map genome-wide nascent antisense transcription in fission yeast. Strikingly, antisense transcription was detected for most protein-coding genes, correlating with low sense transcription, especially when overlapping the mRNA start site. RNA profiling revealed that the resulting aslncRNAs mainly correspond to cryptic Xrn1/Exo2-sensitive transcripts (XUTs). ChIP-seq analyses showed that antisense (as)XUT's expression is associated with specific histone modification patterns. Finally, we showed that asXUTs are controlled by the histone chaperone Spt6 and respond to meiosis induction, in both cases anti-correlating with levels of the paired-sense mRNAs, supporting physiological significance to antisense-mediated gene attenuation. Our work highlights that antisense transcription is much more extended than anticipated and might constitute an additional nonpromoter determinant of gene regulation complexity.

CRISPRCasFinder, an update of CRISRFinder, includes a portable version, enhanced performance and integrates search for Cas proteins.

CRISPR (clustered regularly interspaced short palindromic repeats) arrays and their associated (Cas) proteins confer bacteria and archaea adaptive immunity against exogenous mobile genetic elements, such as phages or plasmids. CRISPRCasFinder allows the identification of both CRISPR arrays and Cas proteins. The program includes: (i) an improved CRISPR array detection tool facilitating expert validation based on a rating system, (ii) prediction of CRISPR orientation and (iii) a Cas protein detection and typing tool updated to match the latest classification scheme of these systems. CRISPRCasFinder can either be used online or as a standalone tool compatible with Linux operating system. All third-party software packages employed by the program are freely available. CRISPRCasFinder is available at https://crisprcas.i2bc.paris-saclay.fr.

The conserved regulatory RNA RsaE down-regulates the arginine degradation pathway in Staphylococcus aureus.

RsaE is a regulatory RNA highly conserved amongst Firmicutes that lowers the amount of mRNAs associated with the TCA cycle and folate metabolism. A search for new RsaE targets in Staphylococcus aureus revealed that in addition to previously described substrates, RsaE down-regulates several genes associated with arginine catabolism. In particular, RsaE targets the arginase rocF mRNA via direct interactions involving G-rich motifs. Two duplicated C-rich motifs of RsaE can independently downregulate rocF expression. The faster growth rate of ΔrsaE compared to its parental strain in media containing amino acids as sole carbon source points to an underlying role for RsaE in amino acid catabolism. Collectively, the data support a model in which RsaE acts as a global regulator of functions associated with metabolic adaptation.

A benchmark study of scoring methods for non-coding mutations.

Motivation: Detailed knowledge of coding sequences has led to different candidate models for pathogenic variant prioritization. Several deleteriousness scores have been proposed for the non-coding part of the genome, but no large-scale comparison has been realized to date to assess their performance. Results: We compared the leading scoring tools (CADD, FATHMM-MKL, Funseq2 and GWAVA) and some recent competitors (DANN, SNP and SOM scores) for their ability to discriminate assumed pathogenic variants from assumed benign variants (using the ClinVar, COSMIC and 1000 genomes project databases). Using the ClinVar benchmark, CADD was the best tool for detecting the pathogenic variants that are mainly located in protein coding gene regions. Using the COSMIC benchmark, FATHMM-MKL, GWAVA and SOMliver outperformed the other tools for pathogenic variants that are typically located in lincRNAs, pseudogenes and other parts of the non-coding genome. However, all tools had low precision, which could potentially be improved by future non-coding genome feature discoveries. These results may have been influenced by the presence of potential benign variants in the COSMIC database. The development of a gold standard as consistent as ClinVar for these regions will be necessary to confirm our tool ranking. Availability and implementation: The Snakemake, C++ and R codes are freely available from https://github.com/Oncostat/BenchmarkNCVTools and supported on Linux. Contact: damien.drubay@gustaveroussy.fr or stefan.michiels@gustaveroussy.fr. Supplementary information: Supplementary data are available at Bioinformatics online.

Revisiting the structure/function relationships of H/ACA(-like) RNAs: a unified model for Euryarchaea and Crenarchaea.

A structural and functional classification of H/ACA and H/ACA-like motifs is obtained from the analysis of the H/ACA guide RNAs which have been identified previously in the genomes of Euryarchaea (Pyrococcus) and Crenarchaea (Pyrobaculum). A unified structure/function model is proposed based on the common structural determinants shared by H/ACA and H/ACA-like motifs in both Euryarchaea and Crenarchaea. Using a computational approach, structural and energetic rules for the guide:target RNA-RNA interactions are derived from structural and functional data on the H/ACA RNP particles. H/ACA(-like) motifs found in Pyrococcus are evaluated through the classification and their biological relevance is discussed. Extra-ribosomal targets found in both Pyrococcus and Pyrobaculum might support the hypothesis of a gene regulation mediated by H/ACA(-like) guide RNAs in archaea.

In plants, decapping prevents RDR6-dependent production of small interfering RNAs from endogenous mRNAs.

Cytoplasmic degradation of endogenous RNAs is an integral part of RNA quality control (RQC) and often relies on the removal of the 5' cap structure and their subsequent 5' to 3' degradation in cytoplasmic processing (P-)bodies. In parallel, many eukaryotes degrade exogenous and selected endogenous RNAs through post-transcriptional gene silencing (PTGS). In plants, PTGS depends on small interfering (si)RNAs produced after the conversion of single-stranded RNAs to double-stranded RNAs by the cellular RNA-dependent RNA polymerase 6 (RDR6) in cytoplasmic siRNA-bodies. PTGS and RQC compete for transgene-derived RNAs, but it is unknown whether this competition also occurs for endogenous transcripts. We show that the lethality of decapping mutants is suppressed by impairing RDR6 activity. We establish that upon decapping impairment hundreds of endogenous mRNAs give rise to a new class of rqc-siRNAs, that over-accumulate when RQC processes are impaired, a subset of which depending on RDR6 for their production. We observe that P- and siRNA-bodies often are dynamically juxtaposed, potentially allowing for cross-talk of the two machineries. Our results suggest that the decapping of endogenous RNA limits their entry into the PTGS pathway. We anticipate that the rqc-siRNAs identified in decapping mutants represent a subset of a larger ensemble of endogenous siRNAs.

A Dual Model for Prioritizing Cancer Mutations in the Non-coding Genome Based on Germline and Somatic Events.

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.

Detection of generic differential RNA processing events from RNA-seq data.

RNA-seq data analysis has revealed abundant alternative splicing in eukaryotic mRNAs. However, splicing is only one of many processing events that transcripts may undergo during their lifetime. We present here RNAprof (RNA profile analysis), a program for the detection of differential processing events from the comparison of RNA-seq experiments. RNAprof implements a specific gene-level normalization procedure and compares RNA-seq coverage profiles at nucleotide resolution to detect regions of significant coverage differences, independently of splice sites or other gene features. We used RNAprof to analyze the effect of alternative-splicing regulators NSRa and NSRb on the Arabidopsis thaliana transcriptome. A number of intron retention events and alternative transcript structures were specifically detected by RNAprof and confirmed by qRT-PCR. Further tests using a public Mus musculus RNA-seq dataset and comparisons with other RNA isoform predictors showed that RNAprof uniquely identified sets of highly significant processing events as well as other relevant library-specific differences in RNA-seq profiles. This highlights an important layer of variation that remains undetected by current protocols for RNA-seq analysis.

An assessment of bacterial small RNA target prediction programs.

Most bacterial regulatory RNAs exert their function through base-pairing with target RNAs. Computational prediction of targets is a busy research field that offers biologists a variety of web sites and software. However, it is difficult for a non-expert to evaluate how reliable those programs are. Here, we provide a simple benchmark for bacterial sRNA target prediction based on trusted E. coli sRNA/target pairs. We use this benchmark to assess the most recent RNA target predictors as well as earlier programs for RNA-RNA hybrid prediction. Moreover, we consider how the definition of mRNA boundaries can impact overall predictions. Recent algorithms that exploit both conservation of targets and accessibility information offer improved accuracy over previous software. However, even with the best predictors, the number of true biological targets with low scores and non-targets with high scores remains puzzling.