A meta-analysis study of the robustness and universality of gut microbiota-shrimp diseases relationship

Rationally designed probiotics prevent shrimp white feces syndrome via the probiotics-gut microbiome-immunity axis

Increasing evidence infers that some complex diseases are attributed to co-infection with multiple pathogens, such as shrimp white feces syndrome (WFS); however, there is a lack of experimental evidence to validate such causal link. This deficiency further impedes rational design of probiotics to elicit desired benefits to shrimp WFS resistance. Herein, we validated the causal roles of Vibrio fluvialis, V. coralliilyticus and V. tubiashii (in a ratio of 7:2:1) in shrimp WFS etiology, which fully satisfied Koch's postulates. Correspondingly, we precisely designed four antagonistic strains: Ruegeria lacuscaerulensis, Nioella nitratireducens, Bacillus subtilis and Streptomyces euryhalinus in a ratio of 4:3:2:1, which efficiently guarded against WFS. Dietary supplementation of the probiotics stimulated beneficial gut populations, streptomycin, short chain fatty acids, taurine metabolism potentials, network stability, tight junction, and host selection, while reducing turnover rate and average variation degree of gut microbiota, thereby facilitating ecological and mechanical barriers against pathogens. Additionally, shrimp immune pathways, such as Fcγ R-mediated phagocytosis, Toll-like receptor and RIG-I-like receptor signaling pathways conferring immune barrier, were activated by probiotics supplementation. Collectively, we establish an updated framework for precisely validating co-infection with multiple pathogens and rationally designing antagonistic probiotics. Furthermore, our findings uncover the underlying beneficial mechanisms of designed probiotics from the probiotics-gut microbiome-host immunity axis.

Stochastic Assembly Increases the Complexity and Stability of Shrimp Gut Microbiota During Aquaculture Progression

The gut microbiota of aquaculture species contributes to their food metabolism and regulates their health, which has been shown to vary during aquaculture progression of their hosts. However, limited research has examined the outcomes and mechanisms of these changes in the gut microbiota of hosts. Here, Kuruma shrimps from the beginning, middle, and late stages of aquaculture progression (about a time duration of 2 months between each stage) were collected and variations in the gut microbiota of Kuruma shrimp during the whole aquaculture process were examined. High-throughput sequencing demonstrated increases in the diversity and richness of the shrimp gut microbiota with aquaculture progression. In addition, the gut microbiota composition differed among cultural stages, with enrichment of Firmicutes, RF39, and Megamonas and a reduction in Proteobacteria in the mid-stage. Notably, only very few taxa were persistent in the shrimp gut microbiota during the whole aquaculture progression, while the number of taxa that specific to the end of aquaculture was high. Network analysis revealed increasing complexity of the shrimp gut microbiota during aquaculture progression. Moreover, the shrimp gut microbiota became significantly more stable towards the end of aquaculture. According to the results of neutral community model, contribution of stochastic processes for shaping the shrimp gut microbiota was elevated along the aquaculture progression. This study showed substantial variations in shrimp gut microbiota during aquaculture progression and explored the underlying mechanisms regulating these changes.

The assembly of gut microbiota implicates shrimp acute hepatopancreas necrosis disease progression

Ample evidence shows dysbiosis in the gut microbiota when comparing healthy shrimp with those affected by severe acute hepatopancreatic necrosis disease (AHPND). However, the static comparison used in available studies leads to the uncertainties regarding how and to what extent the gut microbiota responds to the progressive severity of AHPND. In addition, shrimp AHPND is featured by rapid and massive mortality, thus the initiation of AHPND must be diagnosed for preemptive therapy. For these reasons, we explored the ecological assembly of gut microbiota over shrimp AHPND progression. Increasing AHPND severity was associated with linear increase in the copies of pirAB genes, relative abundance of gut Vibrio and potentially pathogenic, and reduction in the gut bacterial diversity, stability, and relative abundance of Bdellovibrio. Negative and significant association between gut Vibrio and Bdellovibrio were noted, indicating that compromised predation exerts a role in AHPND progression. Notably, the extents of departure to the healthy shrimp gut microbiota were positively coupled with the increasing severity of AHPND. After controlling the temporal variation in the gut microbiota as healthy shrimp age, we constructed a diagnosis model that accurately diagnosed the initial, progressed or moribund stages of AHPND, with an overall accuracy of 86.5%. Shrimp AHPND induced more stochastic gut microbiotas as a consequence of the attenuated ability of diseased shrimp to select their commensals, resulting in convergent bacterial communities between gut and rearing water over AHPND progression. Collectively, our findings provide important step toward the ecological assembly of gut microbiota implicating in AHPND etiology and in diagnosing AHPND stages. KEY POINTS: • The departure of shrimp gut microbiota positively linked with AHPND severity. • The diagnosis model accurately diagnosed the stages of AHPND. • Shrimp AHPND induced more stochastic gut microbiota.

Temporal stability and assembly mechanisms of gut microbiota in sea cucumbers response to nanoplastics treatment

Aquaculture provides essential food for humans, and the health of farmed species is particularly important for the aquaculture industry. Aquaculture environment could be a sink of plastic debris (PDs) due to the enclosed character and heavy use of plastics. Gut microbiota of aquaculture species could respond to the exogenous pollutants and regulate the health of hosts. Here, variations in gut microbiota of Apostichopus japonicus induced by the ingested nanoplastics (NPs) were investigated by a lab experiment. We selected a NPs concentration gradient of 100 mg/kg and 500 mg/kg to simulate microplastic pollution to A. japonicus, and the significant differences in gut microbiota composition after 21 days of NP exposure were evaluated. According to the high-throughput sequencing from time series samples, a decrease of diversity in gut microbiota of A. japonicus with dietary NPs was observed. In addition, the gut microbiota compositions of sea cucumbers with and without NPs exposure were also distinct, expressing as enrichment of Bacteroidota while reducement of Proteobacteria under NPs stresses. Combined the results of network analysis, the less complexity and stability of gut microbiota in sea cucumbers with dietary NPs were proved. Based on the neutral community model, the ingested NPs elevated the contribution of stochastic processes for the gut microbiota assembly in sea cucumbers. Our study showed that substantial variations in gut microbiota of A. japonicus under NPs stresses, and also explored the underlying mechanisms regulating these changes. This research would offer new meaningful insights into the toxicity of NPs on sea cucumbers, contributing a solid fundament to improve the health of sea cucumbers under NPs stresses.

Accurate prediction of huanglongbing occurrence in citrus plants by machine learning-based analysis of symbiotic bacteria

Huanglongbing (HLB), the most prevalent citrus disease worldwide, is responsible for substantial yield and economic losses. Phytobiomes, which have critical effects on plant health, are associated with HLB outcomes. The development of a refined model for predicting HLB outbreaks based on phytobiome markers may facilitate early disease detection, thus enabling growers to minimize damages. Although some investigations have focused on differences in the phytobiomes of HLB-infected citrus plants and healthy ones, individual studies are inappropriate for generating common biomarkers useful for detecting HLB on a global scale. In this study, we therefore obtained bacterial information from several independent datasets representing hundreds of citrus samples from six continents and used these data to construct HLB prediction models based on 10 machine learning algorithms. We detected clear differences in the phyllosphere and rhizosphere microbiomes of HLB-infected and healthy citrus samples. Moreover, phytobiome alpha diversity indices were consistently higher for healthy samples. Furthermore, the contribution of stochastic processes to citrus rhizosphere and phyllosphere microbiome assemblies decreased in response to HLB. Comparison of all constructed models indicated that a random forest model based on 28 bacterial genera in the rhizosphere and a bagging model based on 17 bacterial species in the phyllosphere predicted the health status of citrus plants with almost 100% accuracy. Our results thus demonstrate that machine learning models and phytobiome biomarkers may be applied to evaluate the health status of citrus plants.