Biology subjects

Ma, X.-K.

Publications and source records attributed to Ma, X.-K..

5 recordsLinked to original sources

bioRxiv2023

A deep learning model embedded framework to distinguish DNA and RNA mutations directly from RNA-seq

We develop a stepwise computational framework, called DEMINING, to directly detect expressed DNA and RNA mutations in RNA deep sequencing data. DEMINING incorporates a deep learning model named DeepDDR, which facilitates the separation of expressed DNA mutations from RNA mutations after RNA-seq read mapping and pileup. When applied in RNA-seq of acute myeloid leukemia patients, DEMINING uncovered previously-underappreciated DNA and RNA mutations, some associated with the upregulated expression of host genes or the production of neoantigens. Finally, we demonstrate that DEMINING could precisely classify DNA and RNA mutations in RNA-seq data from non-primate species through the utilization of transfer learning.

bioinformatics↗

bioRxiv2023

IntS6 and the Integrator phosphatase module tune the efficiency of select premature transcription termination events

The metazoan-specific Integrator complex catalyzes 3 end processing of small nuclear RNAs (snRNAs) as well as premature transcription termination events that attenuate expression of many protein-coding genes. Integrator has RNA endonuclease and protein phosphatase activities, but it remains unclear if both are always required for complex function. Here, we show that IntS6 (Integrator subunit 6) over-expression is sufficient to block Integrator function at a subset of Drosophila protein-coding genes, while having no effect on snRNA processing or attenuation of other loci. Over-expressed IntS6 titrates protein phosphatase 2A (PP2A) subunits from the rest of the Integrator complex and thus the only loci affected are those where the phosphatase module is necessary for Integrator function. IntS6 functions analogous to a PP2A regulatory B subunit as over-expression of canonical B subunits, which do not associate with Integrator, are also sufficient to inhibit Integrator function at select loci. Altogether, these results show that the phosphatase module is critical and limiting at only a subset of Integrator regulated genes and point to recruitment of PP2A via IntS6 as a tunable step that can be used to modulate transcription termination efficiency.

molecular biology↗

bioRxiv2021

Knockout of circRNAs by base editing back-splice sites of circularized exons

A large number of circular RNAs (circRNAs) are produced from back-splicing of exon(s) of precursor mRNAs and generally co-expressed with their cognate linear RNAs from the same gene loci. Methods for circRNA-specific knockout are lacking, largely due to complete sequence-overlaps between circular and cognate linear RNAs. Here, we report to use base editors (BEs) for circRNA depletion. By targeting splice sites involved in both back-splicing and canonical splicing, BEs can repress both circular and linear RNAs expression, which confirms the requirement of canonical splice site signals for back-splice. Importantly, by targeting back-splice sites predominantly for circRNA biogenesis, BEs could efficiently repress the production of circular, but not linear cognate RNAs. As hundreds of exons were found to be predominantly back-spliced to produce circRNAs, this study provides an efficient method to deplete circRNAs for function study.

bioinformatics↗

bioRxiv2019

A CLEAR pipeline for direct comparison of circular and linear RNA expression

Sequences of circular RNAs (circRNAs) produced from back-splicing of exon(s) completely overlap with sequences from cognate linear RNAs transcribed from the same gene loci with the exception of their back-splicing junction (BSJ) sites. Examination of global circRNA expression from RNA-seq datasets generally relies on the detection of RNA-seq fragments spanning BSJ sites, but a direct comparison of circular and linear RNA expression from the same gene loci in a genome-wide manner has remained challenging. This is because quantification of BSJ fragments differs from that of linear RNA expression that uses normalized RNA-seq fragments mapped to the whole gene bodies. Here, we have developed a computational pipeline for circular and linear RNA expression analysis from ribosomal-RNA depleted RNA-seq (CLEAR, https://github.com/YangLab/CLEAR). A new quantitation parameter, FPB (fragments per billion mapped bases), is applied to evaluate circular and linear RNA expression individually by fragments mapped to circRNA-specific BSJ sites or to linear RNA-specific splicing junction (SJ) sites. Then, circular and linear RNA expression are directly compared by dividing FPBcirc by FPBlinear to generate a CIRCscore, which indicates the relative circRNA expression using linear RNA expression as the background. Highly-expressed circRNAs with low cognate linear RNA expression background can be identified for further investigation.

bioinformatics↗

bioRxiv2018

Circular RNAs regulate cancer stem cells by FMRP against CCAR1 complex in hepatocellular carcinoma

Circular RNA (circRNA) possesses great pre-clinical diagnostic and therapeutic potentials in multiple cancers. However, the underlying correlation between circRNAs and cancer stem cells (CSCs) has not been reported. The absence of circZKSCAN1 endowed several malignant properties including cancer stemness and tightly correlated with worse overall and recurrence-free survival rate in HCC cells in vitro and in vivo. Bioinformatics analysis and RNA immunoprecipitation-sequencing (RIP-seq) results revealed that circZKSCAN1 exerted its inhibitive role by competitively binding FMRP, therefore, block the binding between FMRP and {beta}-catenin-binding protein-cell cycle and apoptosis regulator 1 (CCAR1) mRNA, and subsequently restraining the transcriptional activity of Wnt signaling. In addition, RNA-splicing protein Quaking 5 was found downregulated in HCC tissues and responsible for the reduction of circZKSCAN1. Collectively, this study revealed the mechanisms underlying the regulatory role of circZKSCAN1 in HCC CSCs and identified the newly discovered Qki5- circZKSCAN1-FMRP-CCAR1-Wnt signaling axis as a potentially important therapeutic target for HCC treatment.\n\nStatement of significanceO_LICircZKSCAN1, a novel negative regulator for cancer stem cells, was firstly identified with reverse correlation with HCC prognosis.\nC_LIO_LICircZKSCAN1 directly targets FMRP, and competitive binding with {beta}-catenin-binding protein cell cycle and apoptosis regulator 1 (CCAR1) for its activity.\nC_LIO_LIThe decreased expression of Quaking 5 is responsible for the absence of circZKSCAN1 in HCC.\nC_LI

cancer biology↗