Aquafeed companies aim to provide solutions to the various challenges related to nutrition and health in aquaculture. Solutions to promote feed efficiency and growth, as well as improving the fish health or protect the fish gut from inflammation may include dietary additives such as prebiotics and probiotics. The general assumption is that feed additives can alter the fish microbiota which, in turn, interacts with the host immune system. However, the exact mechanisms by which feed influences host-microbe-immune interactions in fish still remain largely unexplored. Zebrafish rapidly have become a well-recognized animal model to study host-microbe-immune interactions because of the diverse set of research tools available for these small cyprinids. Genome editing technologies can create specific gene-deficient zebrafish that may contribute to our understanding of immune functions. Zebrafish larvae are optically transparent, which allows for in vivo imaging of specific (immune) cell populations in whole transgenic organisms. Germ-free individuals can be reared to study host-microbe interactions. Altogether, these unique zebrafish features may help shed light on the mechanisms by which feed influences host-microbe-immune interactions and ultimately fish health. In this review, we first describe the anatomy and function of the zebrafish gut: the main surface where feed influences host-microbe-immune interactions. Then, we further describe what is currently known about the molecular pathways that underlie this interaction in the zebrafish gut. Finally, we summarize and critically review most of the recent research on prebiotics and probiotics in relation to alterations of zebrafish microbiota and immune responses. We discuss the advantages and disadvantages of the zebrafish as an animal model for other fish species to study feed effects on host-microbe-immune interactions.
Figure 1. Immuno-modulatory molecular pathways regarding the microbe-host interaction in the epithelium of the zebrafish intestine. We depicted the molecules involved in the proliferation of epithelial cells and in the neutrophil influx as a host-responses to microbiota in the zebrafish gut. In black arrows activation processes, in red inhibition processes. Genes are in italics and host-associated responses are underlined. Numbers correspond to articles proving such molecular interactions: 1: Bates et al. (37); 2: Koch et al. (38); 3: Troll et al. (39) 4: Kanther et al. (40), 5: Murdoch et al. (41), 6: Cheesman et al. (42), and 7: Rolig et al. (43).
Figure 2. Overview of the interaction of pre- and probiotics, immune system and microbiota in the zebrafish intestine. We summarized the interactions of microbiota and feed components, immune system and feed components and microbiota and immune system. We highlighted the questions that still remain unsolved in the field.
These experiments demonstrate that P. tomentosa infection disrupts zebrafish gut microbiome composition and identifies potential interactions between the gut microbiota and parasite success. The microbiome may also provide a diagnostic that would enable non-destructive passive sampling for P. tomentosa and other intestinal pathogens in zebrafish facilities.
Pathological changes were scored by a pathologist (M.K.) based on examination of tissues from each intestine. Following our previous study , two broad categories, inflammation and hyperplastic changes, were scored in zebrafish intestines. A total histopathology score, which is the sum of the inflammation and hyperplasia scores, was also calculated. A description of our scoring criteria follows.
The sequence variant table generated using DADA2 was imported into R (version 3.3.2) and rarefied to a read depth 5000 counts (using R, vegan v2.4.6) to produce a sequence variant table that contained 4129 sequence variants (Additional file 3). Two zebrafish fecal samples were filtered during rarefaction as they had fewer than 5000 total counts. All downstream analyses were conducted with this rarefied sequence variant table. Alpha-diversity was assessed using Shannon entropy (R, vegan). Stepwise regression was calculated to determine the set of variables that best explained the variance in Shannon entropy. The base formula used for this analysis was as follows:
To elucidate the potential interactions between intestinal parasites and the zebrafish gut microbiome, we quantified how the relative abundance of specific gut taxa covaried with intestinal parasite burden over time, wherein statistical associations between parasite burden and microbiota relative abundance implicate their interaction in the gut. Briefly, a baseline negative binomial generalized linear model with only exposure and days post exposure variables was constructed. A second model that included genus abundance, exposure, and days post exposure was then constructed for each genus individually. The Akaike information criterion was used to select the best-fit model for each genus. We found numerous associations between microbial abundance and parasite burden (Fig. 5, Additional file 13). The significant negative slopes for the abundance of members of the genera Plesiomonas, Shewanella, and Cetobacterium indicate that the abundance of these taxa negatively associates with parasite burden. Conversely, significant positive slopes were observed for the majority (186 of 198) of genera examined, including the abundant genera Pseudomonas and Pelomonas, which reveal that their abundance increases as parasite burden increases.
A growing body of evidence suggests that the gastrointestinal microbiome mediates parasitic infections and their impact on host physiology [14, 34,35,36]. Microbiota that interacts with intestinal helminths represents putative resources for the treatment, prevention, and diagnosis of parasitic infections. Such resources are especially important to identify given the global and rapid rise of anthelmintic drug resistance [6, 37]. Yet, relatively little investigation of the gut microbiome-parasite dynamic has been conducted [38,39,40,41]. This study represents, to our knowledge, the most extensive longitudinal assessment of host-parasite-microbiome dynamics to date. By applying robust regression approaches, we identified associations between parasite burden, infection associated pathologies, and host gut microbiome composition, which suggests that microbiome taxa might inhibit or enhance parasite success in the gut. Finally, we used machine learning to develop a diagnostic algorithm, based on our novel non-destructive sampling methodology that is able to predict P. tomentosa exposure status in zebrafish.
One of the goals of clinical microbiome research is to develop microbiome-based diagnostics of disease. Current diagnostic procedures for P. tomentosa involve euthanizing several individuals from a zebrafish colony and visually examining the intestine for evidence of parasite infection. Although effective, non-destructive diagnostics are considered preferable as they reduce animal use. Machine learning has previously been used to classify disease risk based on the microbiome . Following this work, we built a random forest classifier that identified fish exposed to P. tomentosa with high accuracy. Importantly, this classifier performed equally well on a small-unpublished dataset indicating that it is robust to study effects. However, given the variability of zebrafish gut microbiomes across facility and strain , data from additional zebrafish facilities and strains may be needed to optimize the accuracy of this classifier. In addition, it is unclear if the parameters that influence the model are uniquely diagnostic of P. tomentosa or rather diagnostic of a disturbed gut microenvironment. For example, the genera Plesiomonas and Pseudomonas were both highly important variables in our classifier. In this study, the abundance of Plesiomonas was decreased and the abundance of Pseudomonas was increased in exposed animals relative to unexposed controls. We have previously observed increased Pseudomonas and decreased Plesiomonas in antimicrobial exposed fish  suggesting that this pattern may be an indicator of disturbed guts rather than parasite infection specifically. A more general classifier of disturbed guts may, in fact, be more useful as this could help identify specific tanks of zebrafish that are unlikely to be suitable for experimentation. Future work should determine the specificity of this classifier in the context of multiple diverse exposures.
It is also critical that future work strives to determine if the perturbation of the gut microbiome by P. tomentosa affects zebrafish physiology and other health parameters. P. tomentosa is a frequent parasite found in zebrafish research facilities, and thus challenges the maintenance of functional experimental animal colonies. In addition to directly endangering animal health, P. tomentosa infections may introduce potential confounding experimental results, especially in the case where the infection is cryptic . Given that disruption of the zebrafish gut microbiome is linked to altered host physiology [59,60,61,62], behavior , and disease [64, 65], it is possible that the effect of P. tomentosa infection on host physiology is driven in part due to changes in the microbiome. We found that infection resulted in restructuring of the microbiome that persisted over the duration of the experiment, even as burden decreased during later time points. However, because infection was never cleared in these fish, the resilience of the microbiome upon resolution of parasite infection either naturally or through treatment  remains unclear. If the microbiome remains permanently altered following infection, these fish may be unsuitable for subsequent investigations. 153554b96e