IsoSplitter: identification and characterization of alternative splicing sites without a reference genome

Use of Transcriptomics to Reveal the Joint Immunotoxicity Mechanism Initiated by Difenoconazole and Chlorothalonil in the Human Jurkat T-Cell Line

It is very important to evaluate the immunotoxicity and molecular mechanisms of pesticides. In this study, difenoconazole and chlorothalonil were evaluated for immunotoxicity by using the human Jurkat T-cell line, and the EC50 were 24.66 and 1.17 mg/L, respectively. The joint exposure of difenoconazole and chlorothalonil showed a synergistic effect at low concentrations (lower than 10.58 mg/L) but an antagonistic effect at high concentrations (higher than 10.58 mg/L). With joint exposure at a concentration of EC10, the proportion of late apoptotic cells was 2.26- and 2.91-fold higher than that with exposure to difenoconazole or chlorothalonil alone, respectively. A transcriptomics analysis indicated that the DEGs for single exposure are associated with immunodeficiency disease. Single exposure to chlorothalonil was mainly involved in cation transportation, extracellular matrix organization, and leukocyte cell adhesion. Single exposure to difenoconazole was mainly involved in nervous system development, muscle contraction, and immune system processes. However, when the joint exposure dose was EC10, the DEGs were mainly involved in the formation of cell structures, but the DEGs were mainly involved in cellular processes and metabolism when the joint exposure dose was EC25. The results indicated that the immunotoxicological mechanisms underlying joint exposure to difenoconazole and chlorothalonil are different under low and high doses.

MkcDBGAS: a reference-free approach to identify comprehensive alternative splicing events in a transcriptome

Alternative splicing (AS) is an essential post-transcriptional mechanism that regulates many biological processes. However, identifying comprehensive types of AS events without guidance from a reference genome is still a challenge. Here, we proposed a novel method, MkcDBGAS, to identify all seven types of AS events using transcriptome alone, without a reference genome. MkcDBGAS, modeled by full-length transcripts of human and Arabidopsis thaliana, consists of three modules. In the first module, MkcDBGAS, for the first time, uses a colored de Bruijn graph with dynamic- and mixed- kmers to identify bubbles generated by AS with precision higher than 98.17% and detect AS types overlooked by other tools. In the second module, to further classify types of AS, MkcDBGAS added the motifs of exons to construct the feature matrix followed by the XGBoost-based classifier with the accuracy of classification greater than 93.40%, which outperformed other widely used machine learning models and the state-of-the-art methods. Highly scalable, MkcDBGAS performed well when applied to Iso-Seq data of Amborella and transcriptome of mouse. In the third module, MkcDBGAS provides the analysis of differential splicing across multiple biological conditions when RNA-sequencing data is available. MkcDBGAS is the first accurate and scalable method for detecting all seven types of AS events using the transcriptome alone, which will greatly empower the studies of AS in a wider field.

Comparative Transcriptome Analysis to Investigate the Immunotoxicity Mechanism Triggered by Dimethomorph on Human Jurkat T Cell Lines

Dimethomorph (DMM) is a broad-spectrum fungicide used globally in agricultural production, but little is known regarding the immunotoxicity of DMM in humans. In this study, the immunotoxicity of DMM on human Jurkat T cells was evaluated in vitro. The results indicated that the half-effective concentration (EC₅₀) of DMM for Jurkat cells was 126.01 mg/L (0.32 mM). To further elucidate the underlying mechanism, transcriptomics based on RNA sequencing for exposure doses of EC₂₅ (M21) and EC₁₀ (L4) was performed. The results indicated that compared to untreated samples (Ctr), 121 genes (81 upregulated, 40 downregulated) and 30 genes (17 upregulated, 13 downregulated) were significantly differentially regulated in the L4 and M21 samples, respectively. A gene ontology analysis indicated that the significantly differentially expressed genes (DEGs) were mostly enriched in the negative regulation of cell activities, and a KEGG pathway analysis indicated that the DEGs were mainly enriched in the immune regulation and signal transduction pathways. A quantitative real-time PCR for the selected genes showed that compared to the high-dose exposure (M21), the effect of the low-dose DMM exposure (L4) on gene expression was more significant. The results indicated that DMM has potential immunotoxicity for humans, and this toxicity cannot be ignored even at low concentrations.

DeepASmRNA: Reference-free prediction of alternative splicing events with a scalable and interpretable deep learning model

Alternative splicing is crucial for a wide range of biological processes. However, limited by the availability of reference genomes, genome-wide patterns of alternative splicing remain unknown in most nonmodel organisms. We present an attention-based convolutional neural network model, DeepASmRNA, for predicting alternative splicing events using only transcriptomic data. DeepASmRNA consists of two parts: identification of alternatively spliced transcripts and classification of alternative splicing events, which outperformed the state-of-the-art method, AStrap, and other deep learning models. Then, we utilize transfer learning to increase the performance in species with limited training data and use an interpretation method to decipher splicing codes. Finally, applying Amborella, DeepASmRNA can identify more AS events than AStrap while maintaining the same level of precision, suggesting that DeepASmRNA has superior sensitivity to identify alternative splicing events. In summary, DeepASmRNA is scalable and interpretable for detecting genome-wide patterns of alternative splicing in species without a reference genome.

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DeepASmRNA uses only the transcriptome to predict alternative splicing events

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DeepASmRNA identifies adjacent HSPs to greatly improve the recall

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DeepASmRNA uses attention-based convolutional neural network to classify AS events

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Transfer learning is used to increase the predictive power of a target species