State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products, Ningbo University, Ningbo, 315211, China.
School of Marine Sciences, Ningbo University, Ningbo, 315211, China.
Environ Microbiol. 2022 Sep;24(9):3924-3938. doi: 10.1111/1462-2920.16024. Epub 2022 May 15.
Intensive case study has established dysbiosis in the gut microbiota-shrimp disease relationship; however, variability in experimental design and the diversity of diseases arise the question of whether some gut indicators are robust and universal in response to shrimp health status, irrespective of causal agents. Through an unbiased subject-level meta-analysis framework, we re-analysed 10 studies, including 261 samples, four lifestages and six different diseases (the causal agents are virus, bacterial, eukaryotic pathogens, or unknown). Results showed that shrimp diseases reproducibly altered the structure of gut bacterial community, but not diversity. After ruling out the lifestage- and disease specific- discriminatory taxa (different diseases dependent indicators), we identify 18 common disease-discriminatory taxa (indicative of health status, irrespective of causal agents) that accurately diagnosed (90.0% accuracy) shrimp health status, regardless of different diseases. These optimizations substantially improved the performance (62.6% vs. 90.0%) diagnosing model. The robustness and universality of model were validated for effectiveness via leave-one-dataset-out validation and independent cohorts. Interspecies interaction and stability of the gut microbiotas were consistently compromised in diseased shrimp compared with corresponding healthy cohorts, while stochasticity and beta-dispersion exhibited the opposite trend. Collectively, our findings exemplify the utility of microbiome meta-analyses in identifying robust and reproducible features for quantitatively diagnosing disease incidence, and the downstream consequences for shrimp pathogenesis from an ecological prospective.
通过无偏的个体水平荟萃分析框架,我们重新分析了 10 项研究,共包含 261 个样本、4 个生活阶段和 6 种不同疾病(病原体为病毒、细菌、真核病原体或未知)。结果表明,虾病可重复性地改变肠道细菌群落的结构,但不会改变多样性。在排除生活阶段和特定疾病的有区别的分类群(不同疾病依赖的指标)后,我们确定了 18 种常见的疾病区分分类群(指示健康状况,与病原体无关),可准确诊断(准确率为 90.0%)虾的健康状况,无论疾病是否不同。这些优化显著提高了诊断模型的性能(62.6%对 90.0%)。通过留一数据集验证和独立队列验证,验证了模型的稳健性和普遍性。与相应的健康虾群相比,患病虾群的种间相互作用和肠道微生物群的稳定性始终受到损害,而随机性和β分散性则表现出相反的趋势。总的来说,我们的研究结果表明,微生物组荟萃分析可用于识别稳健且可重复的特征,用于定量诊断疾病发病率,以及从生态角度研究虾病发病机制的下游后果。