Analysis of the fecal microbiota of fast-and slow-growing rainbow trout (Oncorhynchus mykiss)

Abstract

Background: Diverse microbial communities colonizing the intestine of fish contribute to their growth, digestion, nutrition, and immune function. We hypothesized that fecal samples representing the gut microbiota of rainbow trout could be associated with differential growth rates observed in fish breeding programs. If true, harnessing the functionality of this microbiota can improve the profitability of aquaculture. The first objective of this study was to test this hypothesis if gut microbiota is associated with fish growth rate (body weight). Four full-sibling families were stocked in the same tank and fed an identical diet. Two fast-growing and two slow-growing fish were selected from each family for 16S rRNA microbiota profiling. Microbiota diversity varies with different DNA extraction methods. The second objective of this study was to compare the effects of five commonly used DNA extraction methods on the microbiota profiling and to determine the most appropriate extraction method for this study. These methods were Promega-Maxwell, Phenol-chloroform, MO-BIO, Qiagen-Blood/Tissue, and Qiagen-Stool. Methods were compared according to DNA integrity, cost, feasibility and inter-sample variation based on non-metric multidimensional scaling ordination (nMDS) clusters. Results: Differences in DNA extraction methods resulted in significant variation in the identification of bacteria that compose the gut microbiota. Promega-Maxwell had the lowest inter-sample variation and was therefore used for the subsequent analyses. Beta diversity of the bacterial communities showed significant variation between breeding families but not between the fast-and slow-growing fish. However, an indicator analysis determined that cellulose, amylose degrading and amino acid fermenting bacteria (Clostridium, Leptotrichia, and Peptostreptococcus) are indicator taxa of the fast-growing fish. In contrary, pathogenic bacteria (Corynebacterium and Paeniclostridium) were identified as indicator taxa for the slow-growing fish. Conclusion: DNA extraction methodology should be carefully considered for accurate profiling of the gut microbiota. Although the microbiota was not significantly different between the fast-and slow-growing fish groups, some bacterial taxa with functional implications were indicative of fish growth rate. Further studies are warranted to explore how bacteria are transmitted and potential usage of the indicator bacteria of fast-growing fish for development of probiotics that may improve fish health and growth.