Comprehensive isoform-level analysis reveals the contribution of alternative isoforms to venom evolution and repertoire diversity.

Animal venom systems have emerged as valuable models for investigating how novel polygenic phenotypes may arise from gene evolution by varying molecular mechanisms. However, a significant portion of venom genes produce alternative mRNA isoforms that have not been extensively characterized, hindering a comprehensive understanding of venom biology. In this study, we present a full-length isoform-level profiling workflow integrating multiple RNA sequencing technologies, allowing us to reconstruct a high-resolution transcriptome landscape of venom genes in the parasitoid wasp Pteromalus puparum Our findings demonstrate that more than half of the venom genes generate multiple isoforms within the venom gland. Through mass spectrometry analysis, we confirm that alternative splicing contributes to the diversity of venom proteins, acting as a mechanism for expanding the venom repertoire. Notably, we identified seven venom genes that exhibit distinct isoform usages between the venom gland and other tissues. Furthermore, evolutionary analyses of venom serpin3 and orcokinin further reveal that the co-option of an ancient isoform and a newly evolved isoform, respectively, contributes to venom recruitment, providing valuable insights into the genetic mechanisms driving venom evolution in parasitoid wasps. Overall, our study presents a comprehensive investigation of venom genes at the isoform level, significantly advancing our understanding of alternative isoforms in venom diversity and evolution and setting the stage for further in-depth research on venoms.

Global-local aware Heterogeneous Graph Contrastive Learning for multifaceted association prediction in miRNA-gene-disease networks.

Unraveling the intricate network of associations among microRNAs (miRNAs), genes, and diseases is pivotal for deciphering molecular mechanisms, refining disease diagnosis, and crafting targeted therapies. Computational strategies, leveraging link prediction within biological graphs, present a cost-efficient alternative to high-cost empirical assays. However, while plenty of methods excel at predicting specific associations, such as miRNA-disease associations (MDAs), miRNA-target interactions (MTIs), and disease-gene associations (DGAs), a holistic approach harnessing diverse data sources for multifaceted association prediction remains largely unexplored. The limited availability of high-quality data, as vitro experiments to comprehensively confirm associations are often expensive and time-consuming, results in a sparse and noisy heterogeneous graph, hindering an accurate prediction of these complex associations. To address this challenge, we propose a novel framework called Global-local aware Heterogeneous Graph Contrastive Learning (GlaHGCL). GlaHGCL combines global and local contrastive learning to improve node embeddings in the heterogeneous graph. In particular, global contrastive learning enhances the robustness of node embeddings against noise by aligning global representations of the original graph and its augmented counterpart. Local contrastive learning enforces representation consistency between functionally similar or connected nodes across diverse data sources, effectively leveraging data heterogeneity and mitigating the issue of data scarcity. The refined node representations are applied to downstream tasks, such as MDA, MTI, and DGA prediction. Experiments show GlaHGCL outperforming state-of-the-art methods, and case studies further demonstrate its ability to accurately uncover new associations among miRNAs, genes, and diseases. We have made the datasets and source code publicly available at https://github.com/Sue-syx/GlaHGCL.

DeepAlgPro: an interpretable deep neural network model for predicting allergenic proteins.

Allergies have become an emerging public health problem worldwide. The most effective way to prevent allergies is to find the causative allergen at the source and avoid re-exposure. However, most of the current computational methods used to identify allergens were based on homology or conventional machine learning methods, which were inefficient and still had room to be improved for the detection of allergens with low homology. In addition, few methods based on deep learning were reported, although deep learning has been successfully applied to several tasks in protein sequence analysis. In the present work, a deep neural network-based model, called DeepAlgPro, was proposed to identify allergens. We showed its great accuracy and applicability to large-scale forecasts by comparing it to other available tools. Additionally, we used ablation experiments to demonstrate the critical importance of the convolutional module in our model. Moreover, further analyses showed that epitope features contributed to model decision-making, thus improving the model's interpretability. Finally, we found that DeepAlgPro was capable of detecting potential new allergens. Overall, DeepAlgPro can serve as powerful software for identifying allergens.