PARP inhibition leads to synthetic lethality with key splicing-factor mutations in myelodysplastic syndromes.

BACKGROUND: Splicing factors are frequently mutated in patients with myelodysplastic syndromes and acute myeloid leukaemia. Recent studies have revealed convergent molecular defects caused by splicing factor mutations, among which R-loop dysregulation and resultant genome instability are suggested as contributing factors to disease progression. On the other hand, understanding how mutant cells survive upon aberrant R-loop formation and genome instability is essential for developing novel therapeutics. METHODS: The immunoprecipitation was performed to identify R-loops in association with PARP1/poly-ADP-ribosylation. The western blot, immunofluorescence, and flow cytometry assays were used to test the cell viability, cell cycle arrest, apoptosis, and ATM activation in mutant cells following the treatment of the PARP inhibitor. The Srsf2(P95H) knock-in murine hematopoietic cells and MLL-AF9 transformed leukaemia model were generated to investigate the potential of the PARP inhibitor as a therapy for haematological malignancies. RESULTS: The disease-causing mutations in SRSF2 activate PARP and elevate the overall poly-ADP-ribosylation levels of proteins in response to R-loop dysregulation. In accordance, mutant cells are more vulnerable to the PARP inhibitors in comparison to the wild-type counterpart. Notably, the synthetic lethality was further validated in the Srsf2(P95H) knock-in murine hematopoietic cell and MLL-AF9 leukaemia model. CONCLUSIONS: Our findings suggest that mutant cells antagonise the genome threat caused by R-loop disruption by PARP activation, thus making PARP targeting a promising therapeutic strategy for myeloid cancers with mutations in SRSF2.

nASAP: A Nascent RNA Profiling Data Analysis Platform.

Although nascent RNA profiling data are widely used in transcriptional regulation studies, the development and standardization of data processing pipeline lags far behind RNA-seq. We are filling this gap by establishing the nASAP web server (https://grobase.top/nasap/) to provide practical quality evaluation and comprehensive analysis of nascent RNA datasets. In nASAP, four customized analysis modules are provided, including i) quality assessment, which summarizes the sequencing statistics, mapping ratio, and evaluates RNA integrity and mRNA contamination; ii) quantification analysis for mRNAs, lncRNAs and eRNAs; iii) pausing analysis across the whole genome based on sequencing reads distribution; and iv) network analysis to better understand the gene regulatory mechanism by obtaining annotated enhancer-promoter interactomes. The nASAP is user-friendly and outperforms the existing pipeline for quality control of nascent RNA profiling data. We anticipate that nASAP, which eases both basic and advanced analysis of nascent RNA data, will be extremely useful in various fields.

R-loopBase: a knowledgebase for genome-wide R-loop formation and regulation.

R-loops play versatile roles in many physiological and pathological processes, and are of great interest to scientists in multiple fields. However, controversy about their genomic localization and incomplete understanding of their regulatory network raise great challenges for R-loop research. Here, we present R-loopBase (https://rloopbase.nju.edu.cn) to tackle these pressing issues by systematic integration of genomics and literature data. First, based on 107 high-quality genome-wide R-loop mapping datasets generated by 11 different technologies, we present a reference set of human R-loop zones for high-confidence R-loop localization, and spot conservative genomic features associated with R-loop formation. Second, through literature mining and multi-omics analyses, we curate the most comprehensive list of R-loop regulatory proteins and their targeted R-loops in multiple species to date. These efforts help reveal a global regulatory network of R-loop dynamics and its potential links to the development of cancers and neurological diseases. Finally, we integrate billions of functional genomic annotations, and develop interactive interfaces to search, visualize, download and analyze R-loops and R-loop regulators in a well-annotated genomic context. R-loopBase allows all users, including those with little bioinformatics background to utilize these data for their own research. We anticipate R-loopBase will become a one-stop resource for the R-loop community.