Bioinformatic modelling of the impact of probiotic supplementation on microbiomes of breeding ponds and of digestive tract of the Common carp (Cyprinus carpio)
Investigators: J. Szyda, M. Mielczarek, J. Jakimowicz, T. Suchocki, D. Słomian
Students involved: P. Hajduk, L. Jarosz, M. Sztuka
Bioinformatic modelling of the impact of probiotic supplementation on microbiomes of breeding ponds and of digestive tract of the Common carp (Cyprinus carpio)
In recent years, there is great interest in the use of effective microorganisms as probiotic supplementation in aquaculture to improve water quality, inhibit pathogens, and promote the growth of farmed fish. The use of probiotics, which control pathogens through a variety of mechanisms is viewed as an alternative to antibiotics and has become a major field in the development of aquaculture. Considering the potential benefits of adding probiotics, many farmers have recently been using commercially available products in their fish farms.
Although the practical efficacy of probiotic products in aquaculture has been extensively studied, there is a shortage of research related to practical, on-farm (i.e. not experimental) application of probiotic products in the Common carp (Cyprinus carpio) breeding. Especially when probiotics are implemented as a mixture of effective microorganism communities and not as a particular bacteria species.
Having the above in mind, the general goal of the project is to assess the dynamics of water, sediment, and fish intestinal microbiota diversity as well as the differences in water metatranscriptome compositions in earthen ponds, which express practical fish breeding conditions. The two major factors potentially influencing the dynamics that are considered in the project are (i) the probiotic supplementation of water and (ii) the probiotic supplementation of feed.
The sequencing process went well. The number of sequences for water samples can be found below.
The plots below express the quality of raw 16S rRNA data.
For the 16s RNA analysis, the QIIME2 software was used. First of all, adapter trimming eliminated non-biological information was done. A quality threshold of 30 was applied to exclude sequences with a quality score below that threshold. Moreover, sequences shorter than 200 nucleotides were removed. Paired ends were merged with specific thresholds, maintaining sequence length consistency within 16S regions. The denoising step was performed, which produced Amplicon Sequence Variants (ASVs), distinguishing single-nucleotide differing sequences—an advancement over Operational Taxonomic Unit (OTU) tables. These tables were then used to determine the microbial composition of the samples. After that taxonomic assignment was performed. The alpha diversity metric was computed based on the taxonomic assignment. Furthermore, ANCOM was employed to conduct a differential abundance test for detecting alterations in the microbial composition between experimental groups.
The next step was to assign the ASV to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. To achieve this, we used pictrus2 software to assign ASV to Kegg orthologs (KO), then KOs were used as an input to ggpicrust2 library (in R) that assign pathways based on KO’s. The last step in this part was to check if there are significant differences in the abundance of KEGG pathways between different design setups; this was done using Deseq2 package in R.