Background: The box jellyfish, Chironex fleckeri, is the largest and most dangerous cubozoan jellyfish to humans. It produces potent and rapid-acting venom and its sting causes severe localized and systemic effects that are potentially life-threatening. In this study, a combined transcriptomic and proteomic approach was used to identify C. fleckeri proteins that elicit toxic effects in envenoming.
Results: More than 40,000,000 Illumina reads were used to de novo assemble ∼ 34,000 contiguous cDNA sequences and ∼ 20,000 proteins were predicted based on homology searches, protein motifs, gene ontology and biological pathway mapping. More than 170 potential toxin proteins were identified from the transcriptome on the basis of homology to known toxins in publicly available sequence databases. MS/MS analysis of C. fleckeri venom identified over 250 proteins, including a subset of the toxins predicted from analysis of the transcriptome. Potential toxins identified using MS/MS included metalloproteinases, an alpha-macroglobulin domain containing protein, two CRISP proteins and a turripeptide-like protease inhibitor. Nine novel examples of a taxonomically restricted family of potent cnidarian pore-forming toxins were also identified. Members of this toxin family are potently haemolytic and cause pain, inflammation, dermonecrosis, cardiovascular collapse and death in experimental animals, suggesting that these toxins are responsible for many of the symptoms of C. fleckeri envenomation.
Conclusions: This study provides the first overview of a box jellyfish transcriptome which, coupled with venom proteomics data, enhances our current understanding of box jellyfish venom composition and the molecular structure and function of cnidarian toxins. The generated data represent a useful resource to guide future comparative studies, novel protein/peptide discovery and the development of more effective treatments for jellyfish stings in humans. (Length: 300).
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http://dx.doi.org/10.1186/s12864-015-1568-3 | DOI Listing |
Toxins (Basel)
January 2025
Medical Science Research Equipment Center, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand.
The multiple-tentacle box jellyfish, (Sucharitakul, 2017) and (Horst, 1907), are venomous species found in Thai waters. They are responsible for numerous envenomations through their stinging organelles, nematocysts. These specialized microscopic structures discharge venom, yet detailed knowledge of their types and morphology in these species remains limited.
View Article and Find Full Text PDFMar Drugs
January 2025
A.V. Zhirmunsky National Scientific Center of Marine Biology, Far Eastern Branch, Russian Academy of Sciences, ul. Palchevskogo 17, Vladivostok 690041, Russia.
The phylum Cnidaria comprises five main classes-Hydrozoa, Scyphozoa, Hexacorallia, Octocorallia and Cubozoa-that include such widely distributed and well-known animals as hard and soft corals, sea anemones, sea pens, gorgonians, hydroids, and jellyfish. Cnidarians play a very important role in marine ecosystems. The composition of their fatty acids (FAs) depends on food (plankton and particulate organic matter), symbiotic photosynthetic dinoflagellates and bacteria, and de novo biosynthesis in host tissues.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Oral & Maxillofacial Surgery and Diagnostic Sciences, Faculty of Dentistry, Taif University, 21944, Taif, Saudi Arabia.
This study investigates the use of machine learning models to predict solubility of rivaroxaban in binary solvents based on temperature (T), mass fraction (w), and solvent type. Using a dataset with over 250 data points and including solvents encoded with one-hot encoding, four models were compared: Gradient Boosting (GB), Light Gradient Boosting (LGB), Extra Trees (ET), and Random Forest (RF). The Jellyfish Optimizer (JO) algorithm was applied to tune hyperparameters, enhancing model performance.
View Article and Find Full Text PDFSci Rep
January 2025
Department of EEE, JCT College of Engineering and Technology, Coimbatore, Tamil Nadu, 641105, India.
This manuscript proposes the Jellyfish Search Optimization (JSO) algorithm-based Fractional Order Proportional-Integral-Derivative (FOPID) controller tuning for a paper machine headbox. The novelty of this method lies in integrating the JSO technique for optimizing the parameters of the FOPID controller to monitor and control headbox pressure and stock level efficiently and effectively. The JSO algorithm ensures optimal tuning of controller parameters by minimizing error indices such as Integral of Squared Error (ISE), Integral of Time Absolute Error (ITAE), and Integral of Absolute Error (IAE).
View Article and Find Full Text PDFInt Marit Health
January 2025
Department of Epidemiology and Tropical Medicine; Military Institute of Medicine - National Research Institute, Warsaw, Poland.
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