Biology subjects

Wei, H.-H.

Publications and source records attributed to Wei, H.-H..

3 recordsLinked to original sources

bioRxiv2022

The splicing switch of SLK controls tumor progression partially via different associations with occludin

RNA splicing control is a pivotal aspect of gene regulation and is closely associated with cancer development. From a pan-cancer transcriptome investigation in the splicing layer, we discovered a critical cancer-associated alternative splicing (AS) event at exon 13 of SLK which produces two isoforms, SLK-L and SLK-S. The splicing is dramatically shifted towards SLK-L across multiple prevalent cancer types. We demonstrated that SLK-L plays an essential role in cancer development, especially in metastasis both in cells and in mice, whereas splicing toward SLK-S inhibits cancer development. RNA-seq revealed the two SLK isoforms play different roles in pathways related to cell migration. Furthermore, different SLK isoforms demonstrate varying binding affinities to certain cell junction markers, in part indicating the AS of SLK contributes to cancer cell migration. In addition, the splicing factor Rbfox2 was identified to specifically inhibit the inclusion of exon 13 by binding intron 12 of SLK. Collectively, our study innovatively uncovers the biological consequences and underlying mechanisms for one of the most mis-spliced genes in cancer, highlighting its potential significance in cancer diagnosis and treatment.

cancer biology↗

bioRxiv2021

Inferring time-lagged causality using the derivative of single-cell expression

Many computational methods have been developed for inferring causality among genes using cross-sectional gene expression data, such as single-cell RNA sequencing (scRNA-seq) data. However, due to the limitations of scRNA-seq technologies, time-lagged causal relationships may be missed by existing methods. In this work, we propose a method, called causal inference with time-lagged information (CITL), to infer time-lagged causal relationships from scRNA-seq data by assessing conditional independence between the changing and current expression levels of genes. CITL estimates the changing expression levels of genes by "RNA velocity". We demonstrate the accuracy and stability of CITL for inferring time-lagged causality on simulation data against other leading approaches. We have applied CITL to real scRNA data and inferred 878 pairs of time-lagged causal relationships, with many of these inferred results supported by the literature. Author summaryComputational causal inference is a promising way to survey causal relationships between genes efficiently. Though many causal inference methods have been applied to gene expression data, none considers the time-lagged causal relationship, which means that some genes may take some time to affect their target genes with several reactions. If relationships between genes are time-lagged, the existing methods assumptions will be violated. The relationships will be challenging to recognize. We demonstrate that this is indeed the case through simulation. Therefore, we develop a method for inferring time-lagged causal relationships of single-cell gene expression data. We assume that a time-lagged causal relationship should present a strong association between the cause and the effects changing. To calculate such correlation, we first estimate the derivative of gene expression using the information from unspliced transcripts. Then, we use conditional independent tests to search gene pairs satisfying our assumption. Our results suggest that we could accurately infer time-lagged causal gene pairs validated by published literature. This method may complement gene regulatory analysis and provide candidate gene pairs for further controlled experiments.

bioinformatics↗

bioRxiv2019

A systematic survey of PRMT interactomes reveals the key roles of arginine methylation in the global control of RNA splicing and translation

Thousands of proteins undergo arginine methylation, a widespread post-translational modification catalyzed by various protein arginine methyltransferases (PRMTs). However, a full picture of the catalytic network for each PRMT is lacking and the global understanding of their biological roles remains limited. Here we systematically identified interacting proteins for all human PRMTs and demonstrated that they are functionally important for mRNA splicing and translation. We showed that the interactomes of human PRMTs are significantly overlapped with the known methylarginine containing proteins, and different PRMTs are functionally complementary with a high degree of overlap in their substrates and high similarities between their putative methylation motifs. Importantly, arginine methylation is significantly enriched in RNA binding proteins involved in regulating RNA splicing and translation, and inhibition of PRMTs leads to global alteration of alternative splicing and suppression of translation. In particular, ribosomal proteins are pervasively modified with methylarginine, and mutations on their methylation sites suppress ribosome assembly, translation, and eventually cell growth. Collectively, our study provides a novel global view of different PRMT networks and uncovers critical functions of arginine methylation in the regulation of mRNA splicing and translation.

molecular biology↗