Summary Aquatic animals encounter suites of novel planktonic microbes during their development. Although hosts have been shown to exert strong selection on their gut microbiota from surrounding environment, to what extent and the generality that the gut microbiota and the underlying ecological processes are affected by biotic and abiotic variations are largely unclear. Here, these concerns were explored by coupling spatiotemporal data on gut and rearing water bacterial communities with environmental variables over shrimp life stages at spatially distant locations. Shrimp gut microbiotas significantly changed mirroring their development, as evidenced by gut bacterial signatures of shrimp life stage contributing 95.5% stratification accuracy. Shrimp sourced little (2.6%–15.8%) of their gut microbiota from their rearing water. This microbial resistance was reflected by weak compositional differences between shrimp farming spatially distinct locations where species pools were distinct. Consistently, the assembly of shrimp gut microbiota was not adequately explained by the rearing water variables and bacterial community, but rather by host‐age‐associated biotic features. The successions of shrimp gut microbiota were droved by replacement (βsim), rather than by nestedness (βnes), while those of bacterioplankton communities were equally governed by replacement and nestedness. Our study highlights how shrimp gut bacterial community assembly is coupled to their development, rearing species pool, and that the successional pattern of host‐associated communities is differed from that of free‐living bacteria.

Increasing evidence has revealed a close interplay between the gut bacterial communities and host growth performance. However, until recently, studies generally ignored the contribution of eukaryotes, endobiotic organisms. To fill this gap, we used Illumina sequencing technology on eukaryotic 18S rRNA gene to compare the structures of gut eukaryotic communities among cohabitating retarded, overgrown, and normal shrimp obtained from identically managed ponds. Results showed that a significant difference between gut eukaryotic communities differed significantly between water and intestine and among three shrimp categories. Structural equation modeling revealed that changes in the gut eukaryotic community were positively related to digestive enzyme activities, which in turn influenced shrimp growth performance (λ = 0.97, P < 0.001). Overgrown shrimp exhibited a more complex and cooperative gut eukaryotic interspecies interaction than retarded and normal shrimp, which may facilitate their nutrient acquisition efficiency. Notably, the distribution of dominant eukaryotic genera and shifts in keystone species were closely concordant with shrimp growth performance. In summary, this study provides an integrated overview on direct roles of gut eukaryotic communities in shrimp growth performance instead of well-studied bacterial assembly.

It is now recognized that some gut diseases attribute to polymicrobial pathogens infections. Thus, traditional isolation of single pathogen from disease subjects could bias the identification of causal agents. To fill this gap, using Illumina sequencing of the bacterial 16S rRNA gene, we explored the dynamics of gut bacterial communities over a shrimp disease progression. The results showed significant differences in the gut bacterial communities between healthy and diseased shrimp. Potential pathogens were inferred by a local pathogens database, of which two OTUs (affiliated with Vibrio tubiashii and Vibrio harveyi) exhibited significantly higher abundances in diseased shrimp as compared to healthy subjects. The two OTUs cumulatively contributed 64.5% dissimilarity in the gut microbiotas between shrimp health status. Notably, the random Forest model depicted that profiles of the two OTUs contributed 78.5% predicted accuracy of shrimp health status. Removal of the two OTUs from co-occurrence networks led to network fragmentation, suggesting their gatekeeper features. For these evidences, the two OTUs were inferred as candidate pathogens. Three virulence genes (bca, tlpA, and fdeC) that were coded by the two candidate pathogens were inferred by a virulence factor database, which were enriched significantly (P < 0.05 in the three cases, as validated by qPCR) in diseased shrimp as compared to healthy ones. The two candidate pathogens were repressed by Flavobacteriaceae, Garvieae, and Photobacrerium species in healthy shrimp, while these interactions shifted into synergy in disease cohorts. Collectively, our findings offer a frame to identify potential polymicrobial pathogen infections from an ecological perspective.

One common notion is emerging that gut eukaryotes are commensal or beneficial, rather than detrimental. To date, however, surprisingly few studies have been taken to discern the factors that govern the assembly of gut eukaryotes, despite growing interest in the dysbiosis of gut microbiota-disease relationship. Herein, we firstly explored how the gut eukaryotic microbiotas were assembled over shrimp postlarval to adult stages and a disease progression. The gut eukaryotic communities changed markedly as healthy shrimp aged, and converged toward an adult-microbiota configuration. However, the adult-like stability was distorted by disease exacerbation. A null model untangled that the deterministic processes that governed the gut eukaryotic assembly tended to be more important over healthy shrimp development, whereas this trend was inverted as the disease progressed. After ruling out the baseline of gut eukaryotes over shrimp ages, we identified disease-discriminatory taxa (species level afforded the highest accuracy of prediction) that characteristic of shrimp health status. The profiles of these taxa contributed an overall 92.4% accuracy in predicting shrimp health status. Notably, this model can accurately diagnose the onset of shrimp disease. Interspecies interaction analysis depicted how the disease-discriminatory taxa interacted with one another in sustaining shrimp health. Taken together, our findings offer novel insights into the underlying ecological processes that govern the assembly of gut eukaryotes over shrimp postlarval to adult stages and a disease progression. Intriguingly, the established model can quantitatively and accurately predict the incidences of shrimp disease.

Increasing evidence indicates that dysbiosis in the gut microbiota contributes to disease pathogenesis. However, whether certain taxa are universally indicative of diverse shrimp diseases is unclear thus far. We conducted a meta-analysis to explore the divergences in gut microbiota between healthy and diseased shrimp. The gut bacterial communities of healthy shrimp varied significantly (P < 0.05 in each comparison) over ontogenetic stages, and were distinct from the corresponding diseased cohorts at each life stage. Both phylogenetic-based mean nearest taxon distance analysis and multivariate dispersion testing revealed that shrimp disease weakened the relative importance of deterministic processes in governing the gut microbiota. Partitioning beta diversity analysis indicated that temporal turnover governed the gut microbiota as healthy shrimp aged, whereas this trend was retarded in disease cohorts, concurrent with an increased nestedness. After ruling out the age-discriminatory and disease-specific orders, a high diagnosed accuracy (85.9%) of shrimp health status was achieved by using the profiles of the 11 universal disease-discriminatory orders as independent variables. These findings improve current understanding of how disease alters the ecological processes that govern the shrimp gut microbiota assembly, and exemplifies the potential application of universal bacterial signatures to diagnose the incidence of diverse shrimp diseases, irrespective of causal pathogens.

There is evidence that gut microbial signatures are indicative of host health status. However, few efforts have been devoted to establishing an applicable technique for determining disease incidence by using gut microbial signatures. Herein, we established a quantitative PCR (qPCR)-based approach to detect the relative abundances of gut disease-discriminatory phyla, which in turn afforded independent variables for quantitatively determining the incidence of shrimp disease. Given the temporal dynamics of gut bacterial communities as healthy shrimp aged, we identified disease-discriminatory phyla after ruling out age-discriminatory phyla. The top 10 disease-discriminatory phyla contributed to an overall 93.2% accuracy in diagnosis ( = 103 samples from shrimp that were determined with high confidence to be healthy or that exhibited apparent disease symptoms and subsequent death), with 70% diagnosis accuracy at the disease onset stage, when symptoms or signs of disease were not apparent. 16S rRNA gene-targeted group-specific primers of five disease-discriminatory phyla were then designed according to their compositions within shrimp gut microbiota, and other primers were borrowed from previous studies. The relative abundances of the 10 disease-discriminatory phyla assayed by qPCR exhibited a high consistency ( = 0.946, < 0.001) with those detected by Illumina sequencing. Notably, using the profiles of disease-discriminatory phyla assayed by qPCR and the corresponding weight coefficients as independent variables, we were able to accurately estimate the incidences of future disease outcome. This work establishes an applicable technique to quantitatively determine the incidence and onset of shrimp disease, which is a valuable attempt to translate scientific research into a practical application. Current studies have identified gut microbial signatures of host health using high-throughput sequencing (HTS) techniques. However, HTS is still expensive and time-consuming and requires a high technical ability, thereby impeding its application in routine monitoring in aquaculture. Hence, it is necessary to seek an alternative strategy to overcome these shortcomings. Herein, we establish a qPCR-based approach to detect the relative abundances of gut disease-discriminatory phyla, which in turn afford independent variables to quantitatively determine the incidence and onset of shrimp disease. Notably, there is a high consistency between the accuracies of disease diagnosis achieved by qPCR and HTS. This applicable technique makes important progress toward defining a diseased state in shrimp and toward solving an important animal health management-driven economic problem.