Xin, R. | bioRxiv Science

bioRxiv2022

SWAP1-SFPS-RRC1 splicing factor complex modulates pre-mRNA splicing to promote photomorphogenesis in Arabidopsis

Light signals perceived by a group of photoreceptors have profound effects on the physiology, growth, and development of plants. The red/far-red light absorbing phytochromes modulate these aspects by intricately regulating gene expression at multiple levels. Previously, we reported that two splicing factors SFPS (SPLICING FACTOR FOR PHYTOCHROME SIGNALING) and RRC1 (REDUCED RED LIGHT RESPONSES IN CRY1CRY2 BACKGROUND 1), interact with photoactivated phyB to regulate light-mediated pre-mRNA alternative splicing (AS). Here, we report the identification and functional characterization of an RNA binding splicing factor, SWAP1 (SUPPRESSOR-OF-WHITE-APRICOT/SURP RNA-BINDING DOMAIN-CONTAINING PROTEIN1). Loss-of-function swap1-1 mutant is hyposensitive to red light and exhibits a day light-independent early flowering phenotype. SWAP1 physically interacts with both SFPS and RRC1 in a light-independent manner and forms a ternary complex. In addition, SWAP1 also physically interacts with photoactivated phyB and colocalizes with nuclear phyB photobodies. Deep RNA-seq analyses show that SWAP1 regulates the gene expression and pre-mRNA alternative splicing of a large number of genes including those involved in plant responses to light signaling. A comparison with SFPS- and RRC1-regulated events shows that all three splicing factors coordinately regulate the alternative splicing of a subset of genes. Collectively, our study uncovered the function of a new splicing factor, which interacts with photoactivated phyB, in modulating light-regulated development in plants. SIGNIFICANCERegulation of transcription and pre-mRNA alternative splicing is essential for the transcript diversity and modulation of light signaling in plants. Although several transcription factors involved in light signaling have been discovered and characterized in-depth, only a few splicing factors have been shown to be involved in the regulation of light signaling pathways. In this study, we describe the identification and characterization of a new splicing factor SWAP1, which interact with two previously characterized splicing factors, SFPS and RRC1, forming a ternary complex. We show that, like SFPS and RRC1, SWAP1 also interacts with photoactivated phyB, and consistently, swap1 seedlings are hyposensitive to red light. SWAP1 modulates alternative splicing of a large number of genes and a subset of these genes are coordinately regulated by SFPS, RRC1 and SWAP1. These results highlight the importance of not only the transcription factors but also the phyB-interacting splicing factors in light-regulated plant development.

plant biology↗

bioRxiv2018

scCapsNet: a deep learning classifier with the capability of interpretable feature extraction, applicable for single cell RNA data analysis

Recently deep learning methods have been applied to process biological data and greatly pushed the development of the biological research forward. However, the interpretability of the deep learning methods still needs to improve. Here for the first time, we present scCapsNet, a totally interpretable deep learning model adapted from CapsNet. The scCapsNet model retains the capsule parts of CapsNet but replaces the part of convolutional neural networks with several parallel fully connected neural networks. We apply scCapsNet to scRNA-seq data. The results show that scCapsNet performs well as a classifier and also that the parallel fully connected neural networks function like feature extractors as we supposed. The scCapsNet model provides contribution of each extracted feature to the cell type recognition. Evidences show that some extracted features are nearly orthogonal to each other. After training, through analysis of the internal weights of each neural network connected inputs and primary capsule, and with the information about the contribution of each extracted feature to the cell type recognition, the scCapsNet model could relate gene sets from inputs to cell types. The specific gene set is responsible for the identification of its corresponding cell types but does not affect the recognition of other cell types by the model. Many well-studied cell type markers are in the gene set with corresponding cell type. The internal weights of neural network for those well-studied cell type markers are different for different primary capsules. The internal weights of neural network connected to a primary capsule could be viewed as an embedding for genes, convert genes to real value low dimensional vectors. Furthermore, we mix the RNA expression data of two cells with different cell types and then use the scCapsNet model trained with non-mixed data to predict the cell types in the mixed data. Our scCapsNet model could predict cell types in a cell mixture with high accuracy.

bioinformatics↗