Integrating molecular and ecological approaches to identify potential polymicrobial pathogens over a shrimp disease progression

Abstract

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.

Response of host–bacterial colonization in shrimp to developmental stage, environment and disease

“…In particular, this interplay was disrupted when shrimp disease occurred (Supporting Information Figure S5B). Disease-induced changes in the gut functional composition have been extensively detected in shrimp (Dai et al, 2018a;Xiong et al, 2015a), fish (Li et al, 2017) and humans (Hollister et al, 2015). A possible explanation is that disturbance, for example disease, skews the association between host genetics and its associated microbiota, with consequent dysbiosis in functions (Moya & Ferrer, 2016;Wegner et al, 2013).…”

“…However, there is increasing evidence of strong host filters on the symbiotic microbiota, where hosts of the same species living in different aquatic habitats share similar gut bacterial communities (Rungrassamee et al., ; Schmidt, Smith, Melvin, & Amaralzettler, ; Tzeng et al., ). Recently, it became clear that host diseases could attribute to dysbiosis (shifts in the microbial community that have a negative impact on the host [Petersen & Round, ]) in the gut microbiota (Dai, Yu, Xuan, Zhen, & Xiong, ; Nie, Zhou, Qiao, & Chen, ; Xiong et al., ; Yu, Cao, Dai, Qiu, & Xiong, ). In this regard, a better understanding of the route of transmission or acquisition of gut symbionts is indispensable, because it aids in designing better disease management strategies.…”

“…diseases could attribute to dysbiosis (shifts in the microbial community that have a negative impact on the host [Petersen & Round, 2014]) in the gut microbiota (Dai, Yu, Xuan, Zhen, & Xiong, 2018a;Nie, Zhou, Qiao, & Chen, 2017;Xiong et al, 2015a;Yu, Cao, Dai, Qiu, & Xiong, 2018). In this regard, a better understanding of the route of transmission or acquisition of gut symbionts is indispensable, because it aids in designing better disease management strategies.…”

Identification of Multigene Biomarker for Shrimp White Feces Syndrome by Full-Length Transcriptome Sequencing

“…Studies on the shrimp IM-disease relationship have identified long lists of implicated microbes that may reflect biomarkers of disease (Xiong et al, 2017;Dai et al, 2018;Hou et al, 2018), without clearly elucidating either the altered microbial functions or the host response against pathogens (Xiong, 2018). In the present study, we identified predictive multigene biomarkers for WFS by combining metagenome and full-length transcriptome data.…”

“…Similarly, our study showed that the a-diversity of IM was significantly less pronounced in WFS ( Figure 1A), providing more clues to establish that the beta-diversity may be associated with disease. In our previous work (Hou et al, 2018) and other related studies (Xiong et al, 2017;Dai et al, 2018), a series of complicated taxa were found to be associated with WFS by comparing the microbial compositional differences between healthy and diseased shrimp. However, the functional differences between Control and WFS shrimp still remain unclear.…”