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.

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.