Identification of Daboia siamensis venome using integrated multi-omics data
peer-reviewed · Scientific Reports · 2022
| Date | 2022-07-30 |
| Type | peer-reviewed |
| Venue | Scientific Reports |
| Publisher | Springer Nature |
| Contribution | downstream-application |
| DOI | 10.1038/s41598-022-17300-1 |
| Citations (OpenAlex) | 16 |
| Venue 2-year citedness | 4.50 |
Abstract
Snakebite, classified by World Health Organization as a neglected tropical disease, causes more than 100,000 deaths and 2 million injuries per year. Currently, available antivenoms do not bind with strong specificity to target toxins, which means that severe complications can still occur despite treatment. Moreover, the cost of antivenom is expensive. Knowledge of venom compositions is fundamental for producing a specific antivenom that has high effectiveness, low side effects, and ease of manufacture. With advances in mass spectrometry techniques, venom proteomes can now be analyzed in great depth at high efficiency. However, these techniques require genomic and transcriptomic data for interpreting mass spectrometry data. This study aims to establish and incorporate genomics, transcriptomics, and proteomics data to study venomics of a venomous snake, Daboia siamensis. Multiple proteins that have not been reported as venom components of this snake such as hyaluronidase-1, phospholipase B, and waprin were discovered. Thus, multi-omics data are advantageous for venomics studies. These findings will be valuable not only for antivenom production but also for the development of novel therapeutics.
Methods and tools
- Daboia siamensis multi-omics venomics: Multi-omics venomics of Russell’s viper (Daboia siamensis, Thai specimens): 10x-linked-read + short-read genome assembly and venom-gland transcriptomics feed a MAKER-annotated CDS database, which is searched with PEAKS Studio (database) plus SMSNet (deep-learning de novo) to identify 1,662 venom proteins including previously unreported hyaluronidase-1, phospholipase B and waprin.
- PEAKS: Commercial DP-based de novo
- SMSNet: Sequence-Mask-Search seq2seq
Cites (1)
Cited by (1)
- Deep Learning Methods for De Novo Peptide Sequencing (2024) crossref