CamoTSS: analysis of alternative transcription start sites for cellular phenotypes and regulatory patterns from 5' scRNA-seq data.

Five-prime single-cell RNA-seq (scRNA-seq) has been widely employed to profile cellular transcriptomes, however, its power of analysing transcription start sites (TSS) has not been fully utilised. Here, we present a computational method suite, CamoTSS, to precisely identify TSS and quantify its expression by leveraging the cDNA on read 1, which enables effective detection of alternative TSS usage. With various experimental data sets, we have demonstrated that CamoTSS can accurately identify TSS and the detected alternative TSS usages showed strong specificity in different biological processes, including cell types across human organs, the development of human thymus, and cancer conditions. As evidenced in nasopharyngeal cancer, alternative TSS usage can also reveal regulatory patterns including systematic TSS dysregulations.

The role of intercellular junction proteins in the penetration resistance of Drosophila larvae to avermectin.

Insecticide resistance has become an increasingly serious challenge for agriculture in the world. To reveal the mechanisms of insecticide resistance, majority of studies have been carried out on the insensitivity of insecticide targets and the metabolism of insecticides. However, the mechanism of the insecticide penetration resistance in insects remains unclear. This study aimed to reveal the mechanism underlying the penetration resistance of Drosophila larvae to insecticide avermectin (AVM). Levels of intercellular junction proteins (IJPs) in the larvae were determined by Western blotting analysis and immunofluorescence assay. The result showed that the expression of IJPs septate junction and adherens junction proteins increased in the AVM-resistant insects compared with those in the AVM-susceptible ones, and the upregulation of the IJPs was mediated by the activation of protein kinase C (PKC) pathway. That AVM induced the activation of PKC was found not only in the Drosophila larvae but also in Drosophila S2 cells. These findings revealed that AVM could activate PKC pathway in Drosophila larvae, which mediated the upregulation of the IJPs and then led to the resistance to AVM, suggesting that the chemicals that can disrupt PKC activation may potentially be used to circumvent the resistance to AVM in insects.

Genomic sequences and RNA-binding proteins predict RNA splicing efficiency in various single-cell contexts.

MOTIVATION: The RNA splicing efficiency is of high interest for both understanding the regulatory machinery of gene expression and estimating the RNA velocity in single cells. However, its genomic regulation and stochasticity across contexts remain poorly understood. RESULTS: Here, by leveraging the recent RNA velocity tool, we estimated the relative splicing efficiency across a variety of single-cell RNA-Seq data sets. We further extracted large sets of genomic features and 120 RNA-binding protein features and found they are highly predictive to relative RNA splicing efficiency across multiple tissues and organs on human and mouse. This predictive power brings promise to reveal the complexity of RNA processing and to enhance the analysis of single-cell transcription activities. AVAILABILITY AND IMPLEMENTATION: In order to ensure reproducibility, all preprocessed datasets and scripts used for the prediction and figure generation are publicly available at https://doi.org/10.5281/zenodo.6513669. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Computational Prediction of Protein Arginine Methylation Based on Composition-Transition-Distribution Features.

Arginine methylation is one of the most essential protein post-translational modifications. Identifying the site of arginine methylation is a critical problem in biology research. Unfortunately, biological experiments such as mass spectrometry are expensive and time-consuming. Hence, predicting arginine methylation by machine learning is an alternative fast and efficient way. In this paper, we focus on the systematic characterization of arginine methylation with composition-transition-distribution (CTD) features. The presented framework consists of three stages. In the first stage, we extract CTD features from 1750 samples and exploit decision tree to generate accurate prediction. The accuracy of prediction can reach 96%. In the second stage, the support vector machine can predict the number of arginine methylation sites with 0.36 R-squared. In the third stage, experiments carried out with the updated arginine methylation site data set show that utilizing CTD features and adopting random forest as the classifier outperform previous methods. The accuracy of identification can reach 82.1 and 82.5% in single methylarginine and double methylarginine data sets, respectively. The discovery presented in this paper can be helpful for future research on arginine methylation.

Predicting ATP-Binding Cassette Transporters Using the Random Forest Method.

ATP-binding cassette (ABC) proteins play important roles in a wide variety of species. These proteins are involved in absorbing nutrients, exporting toxic substances, and regulating potassium channels, and they contribute to drug resistance in cancer cells. Therefore, the identification of ABC transporters is an urgent task. The present study used 188D as the feature extraction method, which is based on sequence information and physicochemical properties. We also visualized the feature extracted by t-Distributed Stochastic Neighbor Embedding (t-SNE). The sample based on the features extracted by 188D may be separated. Further, random forest (RF) is an efficient classifier to identify proteins. Under the 10-fold cross-validation of the model proposed here for a training set, the average accuracy rate of 10 training sets was 89.54%. We obtained values of 0.87 for specificity, 0.92 for sensitivity, and 0.79 for MCC. In the testing set, the accuracy achieved was 89%. These results suggest that the model combining 188D with RF is an optimal tool to identify ABC transporters.

PolyG mitigates silica-induced pulmonary fibrosis by inhibiting nucleolin and regulating DNA damage repair pathway.

Polyguanylic acid potassium salt (PolyG) has an anti-fibrotic G-quadruplex (G4) structure. It could inhibit the expression of nucleolin, a protein involved in cell proliferation and apoptosis. However, its role in regulating nucleolin in silicosis is still unknown. After instillation of 50 µl of crystalline silica suspension (50 mg/ml) into the trachea of C57BL/6 mice, we show that nucleolin expression is upregulated in mouse pulmonary tissue following the treatment with silica and that PolyG, which were injected 2.5 mg/kg body weight into mice by abdomen, could alleviate pulmonary fibrosis through inhibiting the expression of nucleolin. Further, we demonstrated that the expression of the DNA double-strand break (DSB) marker, γ-H2AX, increased in response to silica treatment. PolyG could efficiently reduce the protein expression of γ-H2AX and decreased the level of fibrosis-related genes, such as Col1a1 and Col3a1, as well as the levels of fibrosis-associated proteins α-SMA and vimentin in the lungs of silica-treated mice. These findings show that PolyG could regulate nucleolin and DNA damage repair to control fibrotic response in experimental silicosis and provide a new target for preventive intervention.