In silico tools to boost shrimp farming
In the past few months, we have been working a lot with the immunological responses of shrimps, and that is why we always have at hand the review of Fajardo & colleagues (2021).
We like it so much that we’d like to share some aspects of the review on our blog today, for customers who haven’t taken full advantage of their trials and challenges, or for omics enthusiasts like us (the passionomics ☺).
Thanks to the progress made in the field of transcriptomic analysis through massive mRNA sequencing technology, currently exists a significant amount of data about the crustacean immune system.
The review showed shrimps’ genes and metabolic pathways associated with the immune system through the development of a meta-analysis. The researchers analyzed 1,195 transcriptomic data sets from 14 species from the Penaeus genus from the Sequence Read Archive (SRA) database of the National Center for Biotechnology Information (NCBI).
The literature about these data sets explored how the shrimp’s immune response changes owing to challenges with pathogens, abiotic stressors (ammonia, temperature, salinity, oxygen deprivation, and pH), dietary factors, and beneficial microbes (pre-and probiotics).
Certainly, the in silico information from sequences’ database can be converted into commercial applications to boost shrimp farming, develop more sustainable feeds, and select more disease-resistant strains… But such a vast amount of information needs to be analyzed using multidisciplinary approaches and reliable data analysis tools.
This is particularly important in the so-called training immunology, a concept that has been studied in P. vannamei, P. monodon, P. japonicus, and Procambarus clarkia and that considers the effects of ‘vaccines’ on the memory of the innate immune system and in nutrigenomics, a new discipline that studies metabolic pathways to get better combinations of nutrients and optimize feeds.
Furthermore, NGS technology, which generated massive amounts of sequencing data, requires more computing capacity and more efficient software to produce accurate results, or the incorporation of algorithms based on artificial intelligence (AI) and machine learning, which can aid in the use of such large data sets by allowing the understanding of immune responses, for example.
At the same time, cloud-based services can also be useful since they can incorporate AI-based approaches to provide vast graphical processing units to support AI applications. This, in conjunction with next-generation AI pipelines, is introducing new algorithms to obtain predictive models, allowing researchers to design better tests and obtain better data to improve future iterations.
Finally, the review highlights the importance of unifying shrimp databases to establish ‘multi-omic’ pipelines to be used by industry and academia to develop tools that can be used to improve shrimp farming.
Every day researchers advance more and more with complete genome assemblies, enabling the industry to have a better comprehension of genes’ function, but, can anyone manage all of this information alone?
Do your department need help?
If you are interested in exploring training immunology, nutrigenomics or have a try with artificial intelligence and machine learning,
Fajardo C, Martinez-Rodriguez G, Costas B, et al. Shrimp immune response: A transcriptomic perspective. Rev Aquac. 2022.
Roy S, Bossier P, Norouzitallab P, Vanrompay D. Trained immunity and perspectives for shrimp aquaculture. Rev Aquac. 2019;12(4):2351-2370. https://doi.org/10.1111/raq.12438
Santos CA, Blanck DV, de Freitas PD. RNA-seq as a powerful tool for penaeid shrimp genetic progress. Front Genet. 2014;5:298.