Small-Molecule Inhibition of HIV pre-mRNA Splicing as a Novel Antiretroviral Therapy to Overcome Drug Resistance

The development of multidrug-resistant viruses compromises antiretroviral therapy efficacy and limits therapeutic options. Therefore, it is an ongoing task to identify new targets for antiretroviral therapy and to develop new drugs. Here, we show that an indole derivative (IDC16) that interferes with exonic splicing enhancer activity of the SR protein splicing factor SF2/ASF suppresses the production of key viral proteins, thereby compromising subsequent synthesis of full-length HIV-1 pre-mRNA and assembly of infectious particles. IDC16 inhibits replication of macrophage- and T cell–tropic laboratory strains, clinical isolates, and strains with high-level resistance to inhibitors of viral protease and reverse transcriptase. Importantly, drug treatment of primary blood cells did not alter splicing profiles of endogenous genes involved in cell cycle transition and apoptosis. Thus, human splicing factors represent novel and promising drug targets for the development of antiretroviral therapies, particularly for the inhibition of multidrug-resistant viruses

Ribosomal RNA 2′O-methylation as a novel layer of inter-tumour heterogeneity in breast cancer

International audienceRecent epitranscriptomics studies unravelled that ribosomal RNA (rRNA) 2′O-methylation is an additional layer of gene expression regulation highlighting the ribosome as a novel actor of translation control. However, this major finding lies on evidences coming mainly, if not exclusively, from cellular models. Using the innovative next-generation RiboMeth-seq technology, we established the first rRNA 2′O-methylation landscape in 195 primary human breast tumours. We uncovered the existence of compulsory/stable sites, which show limited inter-patient variability in their 2′O-methylation level, which map on functionally important sites of the human ribosome structure and which are surrounded by variable sites found from the second nucleotide layers. Our data demonstrate that some positions within the rRNA molecules can tolerate absence of 2′O-methylation in tumoral and healthy tissues. We also reveal that rRNA 2′O-methylation exhibits intra- and inter-patient variability in breast tumours. Its level is indeed differentially associated with breast cancer subtype and tumour grade. Altogether, our rRNA 2′O-methylation profiling of a large-scale human sample collection provides the first compelling evidence that ribosome variability occurs in humans and suggests that rRNA 2′O-methylation might represent a relevant element of tumour biology useful in clinic. This novel variability at molecular level offers an additional layer to capture the cancer heterogeneity and associates with specific features of tumour biology thus offering a novel targetable molecular signature in cancer

RNA ribose methylation (2′-O-methylation): Occurrence, biosynthesis and biological functions

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Diversity and heterogeneity of extracellular RNA in human plasma

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Holistic Optimization of Bioinformatic Analysis Pipeline for Detection and Quantification of 2′-O-Methylations in RNA by RiboMethSeq

International audienceA major trend in the epitranscriptomics field over the last 5 years has been the high-throughput analysis of RNA modifications by a combination of specific chemical treatment(s), followed by library preparation and deep sequencing. Multiple protocols have been described for several important RNA modifications, such as 5-methylcytosine (m5C), pseudouridine (ψ), 1-methyladenosine (m1A), and 2'-O-methylation (Nm). One commonly used method is the alkaline cleavage-based RiboMethSeq protocol, where positions of reads' 5'-ends are used to distinguish nucleotides protected by ribose methylation. This method was successfully applied to detect and quantify Nm residues in various RNA species such as rRNA, tRNA, and snRNA. Such applications require adaptation of the initially published protocol(s), both at the wet bench and in the bioinformatics analysis. In this manuscript, we describe the optimization of RiboMethSeq bioinformatics at the level of initial read treatment, alignment to the reference sequence, counting the 5'- and 3'- ends, and calculation of the RiboMethSeq scores, allowing precise detection and quantification of the Nm-related signal. These improvements introduced in the original pipeline permit a more accurate detection of Nm candidates and a more precise quantification of Nm level variations. Applications of the improved RiboMethSeq treatment pipeline for different cellular RNA types are discussed