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MicroRNAs (miRNAs) are predicted to regulate the expression of >60% of mammalian genes and play fundamental roles in most biological processes. Deregulation of miRNA expression is a hallmark of most cancers and further investigation of mechanisms controlling miRNA biogenesis is needed. The double stranded RNA-binding protein, NF90 has been shown to act as a competitor of Microprocessor for a limited number of primary miRNAs (pri-miRNAs). Here, we show that NF90 has a more widespread effect on pri-miRNA biogenesis than previously thought. Genome-wide approaches revealed that NF90 is associated with the stem region of 38 pri-miRNAs, in a manner that is largely exclusive of Microprocessor. Following loss of NF90, 22 NF90-bound pri-miRNAs showed increased abundance of mature miRNA products. NF90-targeted pri-miRNAs are highly stable, having a lower free energy and fewer mismatches compared to all pri-miRNAs. Mutations leading to less stable structures reduced NF90 binding while increasing pri-miRNA stability led to acquisition of NF90 association, as determined by RNA electrophoretic mobility shift assay (EMSA). NF90-bound and downregulated pri-miRNAs are embedded in introns of host genes and expression of several host genes is concomitantly reduced. These data suggest that NF90 controls the processing of a subset of highly stable, intronic miRNAs.

Accurate quantification and detection of intron retention levels require specialized software. Building on our previous software, we create a suite of tools called IRFinder-S, to analyze and explore intron retention events in multiple samples. Specifically, IRFinder-S allows a better identification of true intron retention events using a convolutional neural network, allows the sharing of intron retention results between labs, integrates a dynamic database to explore and contrast available samples, and provides a tested method to detect differential levels of intron retention. Supplementary Information The online version contains supplementary material available at 10.1186/s13059-021-02515-8.

BACKGROUND Background: Harnessing the power of big data has unexplored potential in the field of dementia and brain health research. However, as interest in big data increases it is important to learn what the public understands about the use of their routinely collected healthcare data for research purposes, and their attitudes to such use. Participants’ data is increasingly collected in studies with open-data access processes in place, and through informed consent processes, participants show their willingness to share their data in this way. There remains an inherent flaw in research studies whereby the participants may not reflect the population at large representing a sampling bias. Access to medical records allows research studies to include a wholly representative sample. OBJECTIVE This study aimed to explore attitudes held by members of the public on the use of their healthcare data for dementia research purposes. METHODS Methods: Data was collected in a series of focus groups with semi-structured discussions. Transcripts from the focus groups were analysed using thematic analysis. RESULTS Results: Participants reported a willingness for their anonymised healthcare data to be accessed and used for research purposes, with some caveats for identifiable or highly sensitive data. Participants were happier for trusted organisations, such as the UK’s National Health Service and universities, to access their data compared to pharmaceutical companies. Clear and transparent communication about both the use of healthcare data in research studies and about study results was highlighted as important to participants. There was general misunderstanding about what healthcare data included and how researchers use healthcare data. CONCLUSIONS Conclusions: Overall, our findings underline the importance of clear communication to build trust and understand in the public about how their healthcare data can be used to support high quality dementia and brain health focussed data research. CLINICALTRIAL NA

Diffuse grade II IDH−mutant gliomas are slow−growing brain tumors that progress into high−grade gliomas. They present intratumoral cell heterogeneity, and no reliable markers are available to distinguish the different cell subtypes. The molecular mechanisms underlying the formation of this cell diversity is also ill−defined. Here, we report that SOX9 and OLIG1 transcription factors, which specifically label astrocytes and oligodendrocytes in the normal brain, revealed the presence of two largely nonoverlapping tumoral populations in IDH1−mutant oligodendrogliomas and astrocytomas. Astrocyte−like SOX9+ cells additionally stained for APOE, CRYAB, ID4, KCNN3, while oligodendrocyte−like OLIG1+ cells stained for ASCL1, EGFR, IDH1, PDGFRA, PTPRZ1, SOX4, and SOX8. GPR17, an oligodendrocytic marker, was expressed by both cells. These two subpopulations appear to have distinct BMP, NOTCH1, and MAPK active pathways as stainings for BMP4, HEY1, HEY2, p−SMAD1/5 and p−ERK were higher in SOX9+ cells. We used primary cultures and a new cell line to explore the influence of NOTCH1 activation and BMP treatment on the IDH1−mutant glioma cell phenotype. This revealed that NOTCH1 globally reduced oligodendrocytic markers and IDH1 expression while upregulating APOE, CRYAB, HEY1/2, and an electrophysiologically−active Ca2+−activated apamin−sensitive K+ channel (KCNN3/SK3). This was accompanied by a reduction in proliferation. Similar effects of NOTCH1 activation were observed in nontumoral human oligodendrocytic cells, which additionally induced strong SOX9 expression. BMP treatment reduced OLIG1/2 expression and strongly upregulated CRYAB and NOGGIN, a negative regulator of BMP. The presence of astrocyte−like SOX9+ and oligodendrocyte−like OLIG1+ cells in grade II IDH1−mutant gliomas raises new questions about their role in the pathology.

Background Breast cancer is amongst the 10 first causes of death in women worldwide. Around 20% of patients are misdiagnosed leading to early metastasis, resistance to treatment and relapse. Many clinical and gene expression profiles have been successfully used to classify breast tumours into 5 major types with different prognosis and sensitivity to specific treatments. Unfortunately, these profiles have failed to subclassify breast tumours into more subtypes to improve diagnostics and survival rate. Alternative splicing is emerging as a new source of highly specific biomarkers to classify tumours in different grades. Taking advantage of extensive public transcriptomics datasets in breast cancer cell lines (CCLE) and breast cancer tumours (TCGA), we have addressed the capacity of alternative splice variants to subclassify highly aggressive breast cancers. Results Transcriptomics analysis of alternative splicing events between luminal, basal A and basal B breast cancer cell lines identified a unique splicing signature for a subtype of tumours, the basal B, whose classification is not in use in the clinic yet. Basal B cell lines, in contrast with luminal and basal A, are highly metastatic and express epithelial-to-mesenchymal (EMT) markers, which are hallmarks of cell invasion and resistance to drugs. By developing a semi-supervised machine learning approach, we transferred the molecular knowledge gained from these cell lines into patients to subclassify basal-like triple negative tumours into basal A- and basal B-like categories. Changes in splicing of 25 alternative exons, intimately related to EMT and cell invasion such as ENAH, CD44 and CTNND1, were sufficient to identify the basal-like patients with the worst prognosis. Moreover, patients expressing this basal B-specific splicing signature also expressed newly identified biomarkers of metastasis-initiating cells, like CD36, supporting a more invasive phenotype for this basal B-like breast cancer subtype. Conclusions Using a novel machine learning approach, we have identified an EMT-related splicing signature capable of subclassifying the most aggressive type of breast cancer, which are basal-like triple negative tumours. This proof-of-concept demonstrates that the biological knowledge acquired from cell lines can be transferred to patients data for further clinical investigation. More studies, particularly in 3D culture and organoids, will increase the accuracy of this transfer of knowledge, which will open new perspectives into the development of novel therapeutic strategies and the further identification of specific biomarkers for drug resistance and cancer relapse.

IDH1-mutated gliomas are slow growing brain tumours, which progress into high-grade gliomas. They present intra-tumoural cell heterogeneity, but no good markers are available to distinguish the different cell subtypes. The molecular mechanisms underlying the formation of this cell diversity is also ill defined. Here we report that the SOX9 and OLIG1 transcription factors, which specifically label astrocytes and oligodendrocytes in the normal brain, reveal the presence of two largely non-overlapping tumoural populations in IDH1-mutated oligodendrogliomas and astrocytomas. Astro-like SOX9+ cells additionally stain for APOE, CRYAB, ID4, KCNN3, while oligo-like OLIG1+ cells stain for ASCL1, EGFR, IDH1, PDGFRA, PTPRZ1, SOX4, and SOX8. GPR17, an oligodendrocytic marker, was expressed by both cells. These two sub-populations appear to have distinct BMP, NOTCH1, and MAPK active pathways as stainings for BMP4, HEY1, HEY2, p-SMAD1/5 and p-ERK were higher in SOX9+ cells. We used primary cultures and a new cell line to explore the influence of NOTCH1 activation and BMP treatment on low-grade glioma cell phenotype. This revealed that NOTCH1 globally reduced oligodendrocytic markers and IDH1 expression while upregulating APOE, CRYAB, HEY1/2 and an electrophysiologically Ca2+-activated apamin-sensitive K+ channel (KCNN3/SK3). This was accompanied by reduction in proliferation. Similar effects of NOTCH1 activation were observed in non-tumoural human oligodendrocytic cells, which additionally induced strong SOX9 expression. BMP treatment reduced OLIG1/2 expression and strongly upregulated CRYAB and NOGGIN, a negative regulator of BMP. The presence of astro-like SOX9+ and oligo-like OLIG1+ cells in diffuse low-grade gliomas raise new questions about their role in the pathology.

Simple Summary Diffuse grade II IDH-mutant gliomas are rare brain tumors mainly affecting young patients. These tumors are composed of different populations of tumoral cells. Little is known of these cells and how they are generated. These different cells may show different sensitivity to treatments, so our aim was to study them in detail by directly using patient resections. We identified two clearly distinct tumoral populations and defined reliable markers for them. We also uncovered part of the molecular mechanisms that generate them. Finally, we found that the two cell types have different electrical activity. This article provides unique data and new issues on these rare tumors, which need to be further investigated to develop innovative treatments. Abstract Diffuse grade II IDH-mutant gliomas are slow-growing brain tumors that progress into high-grade gliomas. They present intratumoral cell heterogeneity, and no reliable markers are available to distinguish the different cell subtypes. The molecular mechanisms underlying the formation of this cell diversity is also ill-defined. Here, we report that SOX9 and OLIG1 transcription factors, which specifically label astrocytes and oligodendrocytes in the normal brain, revealed the presence of two largely nonoverlapping tumoral populations in IDH1-mutant oligodendrogliomas and astrocytomas. Astrocyte-like SOX9+ cells additionally stained for APOE, CRYAB, ID4, KCNN3, while oligodendrocyte-like OLIG1+ cells stained for ASCL1, EGFR, IDH1, PDGFRA, PTPRZ1, SOX4, and SOX8. GPR17, an oligodendrocytic marker, was expressed by both cells. These two subpopulations appear to have distinct BMP, NOTCH1, and MAPK active pathways as stainings for BMP4, HEY1, HEY2, p-SMAD1/5 and p-ERK were higher in SOX9+ cells. We used primary cultures and a new cell line to explore the influence of NOTCH1 activation and BMP treatment on the IDH1-mutant glioma cell phenotype. This revealed that NOTCH1 globally reduced oligodendrocytic markers and IDH1 expression while upregulating APOE, CRYAB, HEY1/2, and an electrophysiologically-active Ca2+-activated apamin-sensitive K+ channel (KCNN3/SK3). This was accompanied by a reduction in proliferation. Similar effects of NOTCH1 activation were observed in nontumoral human oligodendrocytic cells, which additionally induced strong SOX9 expression. BMP treatment reduced OLIG1/2 expression and strongly upregulated CRYAB and NOGGIN, a negative regulator of BMP. The presence of astrocyte-like SOX9+ and oligodendrocyte-like OLIG1+ cells in grade II IDH1-mutant gliomas raises new questions about their role in the pathology.

Intron retention (IR) occurs when a complete and unspliced intron remains in mature mRNA. An increasing body of literature has demonstrated a major role for IR in numerous biological functions, including several that impact human health and disease. Although experimental technologies used to study other forms of mRNA splicing can also be used to investigate IR, a specialized downstream computational analysis is optimal for IR discovery and analysis. Here we provide a review of IR and its biological implications, as well as a practical guide for how to detect and analyze it. Several methods, including long read third generation direct RNA sequencing, are described. We have developed an R package, FakIR, to facilitate the execution of the bioinformatic tasks recommended in this review and a tutorial on how to fit them to users aims. Additionally, we provide guidelines and experimental protocols to validate IR discovery and to evaluate the potential impact of IR on gene expression and protein output. This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA Processing > Splicing Regulation/Alternative Splicing RNA Methods > RNA Analyses in vitro and In Silico.

iMOKA (interactive multi-objective k-mer analysis) is a software that enables comprehensive analysis of sequencing data from large cohorts to generate robust classification models or explore specific genetic elements associated with disease etiology. iMOKA uses a fast and accurate feature reduction step that combines a Naïve Bayes classifier augmented by an adaptive entropy filter and a graph-based filter to rapidly reduce the search space. By using a flexible file format and distributed indexing, iMOKA can easily integrate data from multiple experiments and also reduces disk space requirements and identifies changes in transcript levels and single nucleotide variants. iMOKA is available at https://github.com/RitchieLabIGH/iMOKA and Zenodo 10.5281/zenodo.4008947.

Motivation Long-read sequencing technologies are invaluable for determining complex RNA transcript architectures but are error-prone. Numerous ‘hybrid correction’ algorithms have been developed for genomic data that correct long reads by exploiting the accuracy and depth of short reads sequenced from the same sample. These algorithms are not suited for correcting more complex transcriptome sequencing data. Results We have created a novel reference-free algorithm called Transcript-level Aware Long-Read Correction (TALC) which models changes in RNA expression and isoform representation in a weighted De Bruijn graph to correct long reads from transcriptome studies. We show that transcript-level aware correction by TALC improves the accuracy of the whole spectrum of downstream RNA-seq applications and is thus necessary for transcriptome analyses that use long read technology. Availability and implementation TALC is implemented in C++ and available at https://github.com/lbroseus/TALC. Supplementary information Supplementary data are available at Bioinformatics online.

Accurate quantification of intron retention levels is currently the crux for detecting and interpreting the function of retained introns. Using both simulated and real RNA-seq datasets, we show that current methods suffer from several biases and artefacts, which impair the analysis of intron retention. We designed a new approach to measure intron retention levels called the Stable Intron Retention ratio that we have implemented in a novel algorithm to detect and measure intron retention called S-IRFindeR. We demonstrate that it provides a significant improvement in accuracy, higher consistency between replicates and agreement with IR-levels computed from long-read sequencing data.S-IRFindeR is freely available at:https://github.com/lbroseus/SIRFindeR/.

MicroRNAs are predicted to regulate the expression of more than 60% of mammalian genes and play fundamental roles in most biological processes. Deregulation of miRNA expression is a hallmark of most cancers and further investigation of mechanisms controlling miRNA biogenesis is needed. The dsRNA-binding protein, NF90 has been shown to act as a competitor of Microprocessor for a limited number of pri-miRNAs. Here, we show that NF90 has a more widespread effect on pri-miRNA biogenesis than previously thought. Genome-wide approaches revealed that NF90 is associated with the stem region of 38 pri-miRNAs, in a manner that is largely exclusive of Microprocessor. Following loss of NF90, 22 NF90-bound pri-miRNAs showed increased abundance of mature miRNA products. NF90-targeted pri-miRNAs are highly stable, having a lower free energy and fewer mismatches compared to all pri-miRNAs. Mutations leading to less stable structures reduced NF90 binding while increasing pri-miRNA stability led to acquisition of NF90 association, as determined by RNA EMSA. NF90-bound and modulated pri-miRNAs are embedded in introns of host genes and expression of several host genes is concomitantly modulated. These data suggest that NF90 controls the processing of a subset of highly stable, intronic miRNAs.

MotivationLong-read sequencing technologies are invaluable for determining complex RNA transcript architectures but are error-prone. Numerous “hybrid correction” algorithms have been developed for genomic data that correct long reads by exploiting the accuracy and depth of short reads sequenced from the same sample. These algorithms are not suited for correcting more complex transcriptome sequencing data.ResultsWe have created a novel reference-free algorithm called TALC (Transcription Aware Long Read Correction) which models changes in RNA expression and isoform representation in a weighted De-Bruijn graph to correct long reads from transcriptome studies. We show that transcription aware correction by TALC improves the accuracy of the whole spectrum of downstream RNA-seq applications and is thus necessary for transcriptome analyses that use long read technology.Availability and ImplementationTALC is implemented in C++ and available athttps://gitlab.igh.cnrs.fr/lbroseus/TALC.Contactwilliam.ritchie@igh.cnrs.fr

Myelopoiesis involves differentiation of hematopoietic stem cells to cellular populations that are restricted in their self-renewal capacity, beginning with the common myeloid progenitor (CMP) and leading to mature cells including monocytes and granulocytes. This complex process is regulated by various extracellular and intracellular signals including microRNAs (miRNAs). We characterised the miRNA profile of human CD34+CD38+ myeloid progenitor cells, and mature monocytes and granulocytes isolated from cord blood using TaqMan Low Density Arrays. We identified 19 miRNAs that increased in both cell types relative to the CMP and 27 that decreased. miR-125b and miR-10a were decreased by 10-fold and 100-fold respectively in the mature cells. Using in vitro granulopoietic differentiation of human CD34+ cells we show that decreases in both miR-125b and miR-10a correlate with a loss of CD34 expression and gain of CD11b and CD15 expression. Candidate target mRNAs were identified by co-incident predictions between the miRanda algorithm and genes with increased expression during differentiation. Using luciferase assays we confirmed MCL1 and FUT4 as targets of miR-125b and the transcription factor KLF4 as a target of miR-10a. Together, our data identify miRNAs with differential expression during myeloid development and reveal some relevant miRNA-target pairs that may contribute to physiological differentiation.

Comparative analysis of high throughput sequencing data between multiple conditions often involves mapping of sequencing reads to a reference and downstream bioinformatics analyses. Both of these steps may introduce heavy bias and potential data loss. This is especially true in studies where patient transcriptomes or genomes may vary from their references, such as in cancer. Here we describe a novel approach and associated software that makes use of advances in genetic algorithms and feature selection to comprehensively explore massive volumes of sequencing data to classify and discover new sequences of interest without a mapping step and without intensive use of specialized bioinformatics pipelines. We demonstrate that our approach called GECKO for GEnetic Classification using k-mer Optimization is effective at classifying and extracting meaningful sequences from multiple types of sequencing approaches including mRNA, microRNA, and DNA methylome data., Aubin Thomas, Sylvain Barriere et al. present a computational method for classifying and extracting meaningful sequences from high-throughput sequencing data. The method, called GECKO, uses k-mer counts that are able to classify the input data with high accuracy.

International audience; Multiple myeloma (MM) is still an incurable hematological malignancy. Despite recent progress due to new anti-myeloma agents, the pathology is characterized by a high frequency of de novo or acquired resistance. Delineating the mechanisms of MM resistance is essential for therapeutic advances. We previously showed that long-term genotoxic stress induces the establishment of a senescence-associated secretory phenotype, a pro-inflammatory response that favors the emergence of cells with cancer stem-like properties. Here, we studied the short-term response of MM cells following treatment with various DNA damaging agents such as the energetic C-ion irradiation. MM cells are highly resistant to all treatments and do not enter apoptosis after they arrest cycling at the G2 phase. Although the DNA damage response pathway was activated, DNA breaks remained chronically in damaged MM cells. We found, using a transcriptomic approach that RAD50, a major DNA repair gene was downregulated early after genotoxic stress. In two gerosuppression situations: induction of hypoxia and inhibition of the mammalian target of rapamycin (mTOR) pathway, we observed, after the treatment with a DNA damaging agent, a normalization of RAD50 expression concomitant with the absence of cell cycle arrest. We propose that combining inhibitors of mTOR with genotoxic agents could avoid MM cells to senesce and secrete pro-inflammatory factors responsible for cancer stem-like cell emergence and, in turn, relapse of MM patients.

Intron retention (IR) is a form of alternative splicing that can impact mRNA levels through nonsense-mediated decay or by nuclear mRNA detention. A complex, dynamic IR pattern has been described in maturing mammalian granulocytes, but it is unknown whether IR occurs broadly in other hematopoietic lineages. We globally assessed IR in primary maturing mammalian erythroid and megakaryocyte (MK) lineages as well as their common progenitor cells (MEPs). Both lineages exhibit an extensive differential IR program involving hundreds of introns and genes with an overwhelming loss of IR in erythroid cells and MKs compared to MEPs. Moreover, complex IR patterns were seen throughout murine erythroid maturation. Similarly complex patterns were observed in human erythroid differentiation, but not involving the murine orthologous introns or genes. Despite the common origin of erythroid cells and MKs, and overlapping gene expression patterns, the MK IR program is entirely distinct from that of the erythroid lineage with regards to introns, genes, and affected gene ontologies. Importantly, our results suggest that IR serves to broadly regulate mRNA levels. These findings highlight the importance of this understudied form of alternative splicing in gene regulation and provide a useful resource for studies on gene expression in the MK and erythroid lineages.

Angiogenesis, the process of forming new blood vessels, is crucial in the physiological response to ischemia, though it can be detrimental as part of inflammation and tumorigenesis. We have previously shown that high-density lipoproteins (HDL) modulate angiogenesis in a context-specific manner via distinct classical signalling pathways, enhancing hypoxia-induced angiogenesis while suppressing inflammatory-driven angiogenesis. Whether additional novel targets exist to account for these effects are unknown. A microarray approach identified two novel genes, cyclic-adenosine-monophosphate-response-element-binding protein 3 regulatory factor (CREBRF) and tripartite motif-containing protein 2 (TRIM2) that were upregulated by reconstituted HDL (rHDL). We measured CREBRF and TRIM2 expression in human coronary artery endothelial cells following incubation with rHDL and exposure to either hypoxia or an inflammatory stimulus. We found that CREBRF and TRIM2 mRNA were significantly upregulated by rHDL, particularly in response to its phospholipid component 1-palmitoyl-2-linoleoyl-phosphatidylcholine, however, protein expression was not significantly altered. Knockdown of TRIM2 impaired endothelial cell tubulogenesis in vitro in both hypoxia and inflammation, implying a necessary role in angiogenesis. Furthermore, TRIM2 knockdown attenuated rHDL-induced tubule formation in hypoxia, suggesting that it is important in mediating the pro-angiogenic action of rHDL. Our study has implications for understanding the regulation of angiogenesis in both of these pathophysiological contexts by HDL.