Current Bioinformatics - Online First

Pre-mRNA alternative splicing (AS) is a prevalent phenomenon in mammals, playing a crucial role in various biological processes such as embryonic development, tissue differentiation, and disease pathogenesis. Despite the advancements in single-cell RNA sequencing (scRNA-seq) technology, the extent of AS heterogeneity at the transcript level during early mouse embryonic development remains largely unexplored.

The BRIE2 and expedition were employed to identify and quantify splicing events. Cell clustering was performed with Scanpy based on Percent Spliced In (PSI) values and gene expression levels. Then, marker AS events and differential AS events were detected by the Wlicocon rank-sum test and BRIE2's Mode-2 quantification mode. GO and KEGG enrichment analysis were conducted by ClusterProfiler.

The results suggested substantial heterogeneity in AS events and elucidated PSI values as a critical index of cell heterogeneity during early mouse embryonic development, shedding light on the regulatory mechanisms underlying these processes. By examining marker and differential AS events, the study provided a comprehensive understanding of the dynamic changes in splicing patterns throughout early mouse embryonic development.

This study revealed the heterogeneity of AS and elucidated its implications during early mouse embryonic development by analyzing AS at the single-cell level. However, the results are theoretical and lack experimental validation.

The findings offer critical insights into studying mouse embryonic development from the perspective of RNA cellular heterogeneity, emphasizing the importance of AS in shaping cellular diversity and developmental processes.

Bacterial and viral infections have been linked to an increased risk of coronary heart disease (CHD), potentially through natural killer (NK) cell-mediated innate immune mechanisms. This study aimed to integrate single-cell RNA sequencing (scRNA-seq) and bulk transcriptomics data to identify NK cell-associated genetic biomarkers that could aid in the diagnosis and assessment of CHD.

Publicly available single-cell and bulk RNA-seq datasets were analyzed to identify differentially expressed genes (DEGs). Functional enrichment analysis, protein-protein interaction (PPI) network construction, and biomarker validation were performed using standard bioinformatics pipelines.

A total of 106 shared DEGs were identified through integrated cross-comparative analysis. Enrichment analysis revealed involvement in immune activation, signal transduction, T-cell receptor signaling, and TYROBP signaling pathways. PPI network analysis identified key hub proteins, including CDK1 and PTPRC, as potential biomarkers. Regulatory analysis revealed transcription factors (TP53, YY1, and RELA) and post-transcriptional miRNAs (hsa-miR-195-5p, hsa-miR-34a-5p, and hsa-miR-16-5p) that may influence CHD-associated gene expression. Several small molecules were also predicted to interact with these targets, suggesting potential therapeutic applications.

The findings underscore the role of NK cell-mediated immune pathways in CHD pathogenesis. Hub genes such as CDK1 (involved in cell cycle regulation) and PTPRC (an immune signaling regulator) show promise as diagnostic biomarkers. The discovery of regulatory factors and druggable targets supports a complex, multi-level mechanism involving transcriptional and immune modulation.

This integrative study identifies novel NK cell-related molecular signatures and therapeutic targets, offering promising avenues for CHD diagnosis and the development of personalized treatment strategies.

Disease is a major threat to life, and extensive efforts have been made over the past centuries to develop effective treatments. Identifying drug-disease and disease-target associations is crucial for therapeutic advancements, whereas drug-target associations facilitate the design of more effective treatment strategies. However, traditional experimental approaches for identifying these associations are costly and time-consuming. Numerous computational models have been developed to predict drug-target, drug-disease, and disease-target associations. However, these models are designed individually and cannot directly predict drug-target-disease associations, which involve interconnections among drugs, targets, and diseases. Such triple associations provide deeper insights into disease mechanisms and therapeutic interventions by capturing high-order associations.

This study proposes a computational model named PDTDAHN to predict drug-target-disease triple associations.

Six association types retrieved from public databases are used to construct a heterogeneous network comprising drugs, targets, and diseases. The network embedding algorithm Mashup is applied to extract features for drugs, targets, and diseases, which are then combined to represent each drug-target-disease association. The classification model is trained using LightGBM.

Cross-validation on eight datasets demonstrates the high performance of PDTDAHN, with AUROC and AUPR exceeding 0.9. This model outperforms previous models based on pairwise association predictions.

The proposed model effectively predicts drug-target-disease triple associations.