Salmon_Yeasts_Microbiota

This repository contains the codes used to generate the figures in the manuscript

Effect of yeasts and processing on intestinal microbiota of Atlantic salmon (Salmo salar) fed soybean meal-based diets in seawater

Abstract

Background: Yeasts are gaining attention as alternative ingredients in aquafeeds. However, the impact of yeast inclusion on modulation of intestinal microbiota of fish fed plant-based ingredients is limited. Thus, the present study investigates the effects of yeast and processing on composition, diversity and predicted metabolic capacity of gut microbiota of Atlantic salmon smolt fed soybean meal (SBM)-based diet. Two yeasts, Cyberlindnera jadinii (CJ) and Wickerhamomyces anomalus (WA) yeasts were produced in-house and processed by direct heat-inactivation with spray-drying (ICJ and IWA) or autolyzed at 50 ºC for 16 h (ACJ and AWA), followed by spray-drying. In a 45-day feeding experiment, fish were fed one of six diets: a fishmeal (FM)-based diet, a challenging diet with 30% soybean meal (SBM) and four other diets containing 30% SBM and 10% of each of the four yeast fractions (i.e., ICJ, ACJ, IWA and AWA). Microbial profiling of digesta samples was conducted using 16s rRNA gene sequencing, and the predicted metabolic capacities of gut microbiota were determined using genome-scale metabolic models.
Results: The microbial composition and predicted metabolic capacity of gut microbiota was different between fish fed FM diet compared with those fed SBM diet. The digesta of fish fed SBM diet was dominated by members of lactic acid bacteria, which was similar to microbial composition in the digesta of fish fed the inactivated yeasts (ICJ and IWA diets). Inclusion of autolyzed yeasts (ACJ and AWA diets) lowered the richness and diversity of gut microbiota in fish. The gut microbiota of fish fed ACJ diet was dominated by the genus Pediococcus and showed a predicted increase in mucin O-glycan degradation compared with the other diets. The gut microbiota of fish fed AWA diet was highly dominated by the family Bacillaceae.
Conclusions: The present study showed that dietary inclusion of FM and SBM differentially modulate the composition and predicted metabolic capacity of gut microbiota of fish. Inclusion of inactivated yeasts did not modulate the intestinal microbiota of fish fed SBM-based diet. Fish fed ACJ diet increased relative abundance of Pediococcus, and mucin O-glycan degradation pathway compared with the other diets.

Content of files in this resposiotry

• Functions -- These are the functions used for graphs

  • 01_plot_frequency.R -- function used in code '02_Pre-processing.Rmd'. This was used to identify the contaminants in the sample
  • 02_plot_prevalence.R -- function used in code '02_Pre-processing.Rmd'. This was used to identify the contaminants in the sample
  • 03_make_taxa_barplot.R -- function used in code '02_Taxonomic_analysis.Rmd'. This was used to create the taxonomic figures

• Codes -- These are codes used for the analysis

  • 01_Sequence_denoising_dada2.Rmd -- processing of raw data using DADA2 to generate ASV table
  • 02_Pre_processing.Rmd -- creating phyloseq object and pre-processing to remove features
  • 03_Taxonomic_analysis.Rmd -- analysis and visualization of taxonomic composition, core ASVs and microbial overlap
  • 04_Alpha_diversity.Rmd -- visualization and statistical analysis of alpha-diversity
  • 05_Beta_diversity.Rmd -- visualization and statistical analysis of beta-diversity
  • 06_individual_taxa_comparisons -- visualization and statistical analysis of the top 15 abundant taxa
  • 07_Metabolic_reaction_analysis.ipynb -- analysis of predictive metabolic reactions

• Data -- all data used and generated during the analysis

  • Data_microbiota_analysis. These are the rds files saved during the analysis
    • 01_metadata.csv
    • 02_ps.nocontam.rds
    • 03_ps_nocontam_mock.rds
    • 04_contam_neg.rds
    • 05_ps_LULU.rds
  • Data_Metabolic_reaction_analysis -- all the data used in the metabolic reaction analysis
    • 01_agora_reactions.csv
    • 02_agora_subsys.tsv
    • 03_agora_taxa.tsv
    • 04_count_table_jeleel.csv
    • 05_metadata_table_jeleel.csv
    • 06_tax_table_jeleel.csv
  • Data_pre_processing
    • 01_seqtab1
    • 02_seqtab2
    • 03_seqtab.nochim
    • 04_seqtab.taxa
    • 05_setab.mitochon
  • Figure_contaminants -- figures used for identifying the contaminants
    • 01_prevalence_contam.pdf
    • 02_DNA_concentration.pdf
    • 03_frequency_contam.pdf
    • 04_prevalence_contam_mock.pdf

• Results

  • Fig. 1
  • Fig. 2
  • Fig. 3
  • Fig. 4
  • Fig. 5
  • Fig. 6
  • Fig. 7
  • Fig. S1
  • Fig. S2
  • Fig. S3
  • Fig. S4
  • Fig. S5
  • Fig. S6
  • Fig. S7
  • Fig. S8
  • Fig. S9
  • Additional file 2_supplementary tables. Table S2. contaminants removed from the sample. Table S3. core ASVs based on 80% prevalence in the sample

LICENSE

README.md