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.

Genomic Profiling of Metastatic Castration-Resistant Prostate Cancer Samples Resistant to Androgen Receptor Pathway Inhibitors.

PURPOSE: The androgen receptor axis inhibitors (ARPI; e.g., enzalutamide, abiraterone acetate) are administered in daily practice for men with metastatic castration-resistant prostate cancer (mCRPC). However, not all patients respond, and mechanisms of both primary and acquired resistance remain largely unknown. EXPERIMENTAL DESIGN: In the prospective trial MATCH-R (NCT02517892), 59 patients with mCRPC underwent whole-exome sequencing (WES) and/or RNA sequencing (RNA-seq) of samples collected before starting ARPI. Also, 18 patients with mCRPC underwent biopsy at time of resistance. The objectives were to identify genomic alterations associated with resistance to ARPIs as well as to describe clonal evolution. Associations of genomic and transcriptomic alterations with primary resistance were determined using Wilcoxon and Fisher exact tests. RESULTS: WES analysis indicated that no single-gene genomic alterations were strongly associated with primary resistance. RNA-seq analysis showed that androgen receptor (AR) gene alterations and expression levels were similar between responders and nonresponders. RNA-based pathway analysis found that patients with primary resistance had a higher Hedgehog pathway score, a lower AR pathway score and a lower NOTCH pathway score than patients with a response. Subclonal evolution and acquisition of new alterations in AR-related genes or neuroendocrine differentiation are associated with acquired resistance. ARPIs do not induce significant changes in the tumor transcriptome of most patients; however, programs associated with cell proliferation are enriched in resistant samples. CONCLUSIONS: Low AR activity, activation of stemness programs, and Hedgehog pathway were associated with primary ARPIs' resistance, whereas most acquired resistance was associated with subclonal evolution, AR-related events, and neuroendocrine differentiation. See related commentary by Slovin, p. 4323.

Integrative Pan-Cancer Genomic and Transcriptomic Analyses of Refractory Metastatic Cancer.

Metastatic relapse after treatment is the leading cause of cancer mortality, and known resistance mechanisms are missing for most treatments administered to patients. To bridge this gap, we analyze a pan-cancer cohort (META-PRISM) of 1,031 refractory metastatic tumors profiled via whole-exome and transcriptome sequencing. META-PRISM tumors, particularly prostate, bladder, and pancreatic types, displayed the most transformed genomes compared with primary untreated tumors. Standard-of-care resistance biomarkers were identified only in lung and colon cancers-9.6% of META-PRISM tumors, indicating that too few resistance mechanisms have received clinical validation. In contrast, we verified the enrichment of multiple investigational and hypothetical resistance mechanisms in treated compared with nontreated patients, thereby confirming their putative role in treatment resistance. Additionally, we demonstrated that molecular markers improve 6-month survival prediction, particularly in patients with advanced breast cancer. Our analysis establishes the utility of the META-PRISM cohort for investigating resistance mechanisms and performing predictive analyses in cancer. SIGNIFICANCE: This study highlights the paucity of standard-of-care markers that explain treatment resistance and the promise of investigational and hypothetical markers awaiting further validation. It also demonstrates the utility of molecular profiling in advanced-stage cancers, particularly breast cancer, to improve the survival prediction and assess eligibility to phase I clinical trials. This article is highlighted in the In This Issue feature, p. 1027.

Targeting LINC00152 activates cAMP/Ca2+/ferroptosis axis and overcomes tamoxifen resistance in ER+ breast cancer.

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, emergence of resistance is common, necessitating the identification of novel therapeutic targets. Here, we demonstrated that long-noncoding RNA LINC00152 confers tamoxifen resistance via blocking tamoxifen-induced ferroptosis, an iron-mediated cell death. Mechanistically, inhibiting LINC00152 reduces the mRNA stability of phosphodiesterase 4D (PDE4D), leading to activation of cAMP/PKA/CREB axis and increased expression of TRPC1 Ca2+ channel. This causes cytosolic Ca2+ overload and generation of reactive oxygen species (ROS) that is, on one hand, accompanied by downregulation of FTH1, a member of the iron sequestration unit, thus increasing intracellular Fe2+ levels; and on the other hand, inhibition of the peroxidase activity upon reduced GPX4 and xCT levels. These ultimately induce lipid peroxidation and ferroptotic cell death in combination with tamoxifen. Overexpressing PDE4D rescues LINC00152 inhibition-mediated tamoxifen sensitization by de-activating the cAMP/Ca2+/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 ferroptosis induction and tamoxifen sensitization, thereby revealing LINC00152 and its effectors as actionable therapeutic targets to improve clinical outcome in refractory ER+ breast cancer.

Macrophage migration inhibitory factor is overproduced through EGR1 in TET2low resting monocytes.

Somatic mutation in TET2 gene is one of the most common clonal genetic events detected in age-related clonal hematopoiesis as well as in chronic myelomonocytic leukemia (CMML). In addition to being a pre-malignant state, TET2 mutated clones are associated with an increased risk of death from cardiovascular disease, which could involve cytokine/chemokine overproduction by monocytic cells. Here, we show in mice and in human cells that, in the absence of any inflammatory challenge, TET2 downregulation promotes the production of MIF (macrophage migration inhibitory factor), a pivotal mediator of atherosclerotic lesion formation. In healthy monocytes, TET2 is recruited to MIF promoter and interacts with the transcription factor EGR1 and histone deacetylases. Disruption of these interactions as a consequence of TET2-decreased expression favors EGR1-driven transcription of MIF gene and its secretion. MIF favors monocytic differentiation of myeloid progenitors. These results designate MIF as a chronically overproduced chemokine and a potential therapeutic target in patients with clonal TET2 downregulation in myeloid cells.

The contribution of uncharted RNA sequences to tumor identity in lung adenocarcinoma.

The identity of cancer cells is defined by the interplay between genetic, epigenetic transcriptional and post-transcriptional variation. A lot of this variation is present in RNA-seq data and can be captured at once using reference-free, k-mer analysis. An important issue with k-mer analysis, however, is the difficulty of distinguishing signal from noise. Here, we use two independent lung adenocarcinoma datasets to identify all reproducible events at the k-mer level, in a tumor versus normal setting. We find reproducible events in many different locations (introns, intergenic, repeats) and forms (spliced, polyadenylated, chimeric etc.). We systematically analyze events that are ignored in conventional transcriptomics and assess their value as biomarkers and for tumor classification, survival prediction, neoantigen prediction and correlation with the immune microenvironment. We find that unannotated lincRNAs, novel splice variants, endogenous HERV, Line1 and Alu repeats and bacterial RNAs each contribute to different, important aspects of tumor identity. We argue that differential RNA-seq analysis of tumor/normal sample collections would benefit from this type k-mer analysis to cast a wider net on important cancer-related events. The code is available at https://github.com/Transipedia/dekupl-lung-cancer-inter-cohort.

Multi-omics prediction in melanoma immunotherapy: A new brick in the wall.

In this issue of Cancer Cell, Newell et al. introduce whole-genome and methylome data to melanoma immunotherapy response analysis. Genome breaks are more frequent in resistant tumors, but the best response classifiers remain mutation burden and interferon-É£ signature. Clinical translation will need aggregation of many such datasets.

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.

Kmerator Suite: design of specific k-mer signatures and automatic metadata discovery in large RNA-seq datasets.

The huge body of publicly available RNA-sequencing (RNA-seq) libraries is a treasure of functional information allowing to quantify the expression of known or novel transcripts in tissues. However, transcript quantification commonly relies on alignment methods requiring a lot of computational resources and processing time, which does not scale easily to large datasets. K-mer decomposition constitutes a new way to process RNA-seq data for the identification of transcriptional signatures, as k-mers can be used to quantify accurately gene expression in a less resource-consuming way. We present the Kmerator Suite, a set of three tools designed to extract specific k-mer signatures, quantify these k-mers into RNA-seq datasets and quickly visualize large dataset characteristics. The core tool, Kmerator, produces specific k-mers for 97% of human genes, enabling the measure of gene expression with high accuracy in simulated datasets. KmerExploR, a direct application of Kmerator, uses a set of predictor gene-specific k-mers to infer metadata including library protocol, sample features or contaminations from RNA-seq datasets. KmerExploR results are visualized through a user-friendly interface. Moreover, we demonstrate that the Kmerator Suite can be used for advanced queries targeting known or new biomarkers such as mutations, gene fusions or long non-coding RNAs for human health applications.

Applications of single-cell and bulk RNA sequencing in onco-immunology.

The rising interest for precise characterization of the tumour immune contexture has recently brought forward the high potential of RNA sequencing (RNA-seq) in identifying molecular mechanisms engaged in the response to immunotherapy. In this review, we provide an overview of the major principles of single-cell and conventional (bulk) RNA-seq applied to onco-immunology. We describe standard preprocessing and statistical analyses of data obtained from such techniques and highlight some computational challenges relative to the sequencing of individual cells. We notably provide examples of gene expression analyses such as differential expression analysis, dimensionality reduction, clustering and enrichment analysis. Additionally, we used public data sets to exemplify how deconvolution algorithms can identify and quantify multiple immune subpopulations from either bulk or single-cell RNA-seq. We give examples of machine and deep learning models used to predict patient outcomes and treatment effect from high-dimensional data. Finally, we balance the strengths and weaknesses of single-cell and bulk RNA-seq regarding their applications in the clinic.

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.

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.

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.

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.

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 exploration reveals novel RNAs for prostate cancer diagnosis.

The use of RNA-sequencing technologies held a promise of improved diagnostic tools based on comprehensive transcript sets. However, mining human transcriptome data for disease biomarkers in clinical specimens are restricted by the limited power of conventional reference-based protocols relying on unique and annotated transcripts. Here, we implemented a blind reference-free computational protocol, DE-kupl, to infer yet unreferenced RNA variations from total stranded RNA-sequencing datasets of tissue origin. As a bench test, this protocol was powered for detection of RNA subsequences embedded into putative long noncoding (lnc)RNAs expressed in prostate cancer. Through filtering of 1,179 candidates, we defined 21 lncRNAs that were further validated by NanoString for robust tumor-specific expression in 144 tissue specimens. Predictive modeling yielded a restricted probe panel enabling more than 90% of true-positive detections of cancer in an independent The Cancer Genome Atlas cohort. Remarkably, this clinical signature made of only nine unannotated lncRNAs largely outperformed PCA3, the only used prostate cancer lncRNA biomarker, in detection of high-risk tumors. This modular workflow is highly sensitive and can be applied to any pathology or clinical application.

Bridging the gap between reference and real transcriptomes.

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.

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.