Exploring oak processionary caterpillar induced lepidopterism (Part 1): unveiling molecular insights through transcriptomics and proteomics.
Andrea SeldeslachtsMarius F MaurstadJan Philip ØyenEivind Andreas Baste UndheimSteve PeigneurJan TytgatPublished in: Cellular and molecular life sciences : CMLS (2024)
Lepidopterism, a skin inflammation condition caused by direct or airborne exposure to irritating hairs (setae) from processionary caterpillars, is becoming a significant public health concern. Recent outbreaks of the oak processionary caterpillar (Thaumetopoea processionea) have caused noteworthy health and economic consequences, with a rising frequency expected in the future, exacerbated by global warming promoting the survival of the caterpillar. Current medical treatments focus on symptom relief due to the lack of an effective therapy. While the source is known, understanding the precise causes of symptoms remain incomplete understood. In this study, we employed an advanced method to extract venom from the setae and identify the venom components through high-quality de novo transcriptomics, venom proteomics, and bioinformatic analysis. A total of 171 venom components were identified, including allergens, odorant binding proteins, small peptides, enzymes, enzyme inhibitors, and chitin biosynthesis products, potentially responsible for inflammatory and allergic reactions. This work presents the first comprehensive proteotranscriptomic database of T. processionea, contributing to understanding the complexity of lepidopterism. Furthermore, these findings hold promise for advancing therapeutic approaches to mitigate the global health impact of T. processionea and related caterpillars.
Keyphrases
- public health
- global health
- oxidative stress
- single cell
- healthcare
- mass spectrometry
- high glucose
- particulate matter
- emergency department
- current status
- big data
- machine learning
- endothelial cells
- soft tissue
- cell therapy
- adverse drug
- allergic rhinitis
- replacement therapy
- atopic dermatitis
- infectious diseases
- air pollution
- deep learning