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

机译:肠道菌群与虾病关系的稳健性和普遍性的荟萃分析研究

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Summary 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.
机译:摘要:密集的案例研究已经确定了肠道微生物群与虾疾病关系中的生态失调;然而,实验设计的可变性和疾病的多样性产生了一个问题,即无论病原体如何,某些肠道指标在响应虾的健康状况方面是否可靠和普遍。通过无偏倚的受试者水平荟萃分析框架,我们重新分析了 10 项研究,包括 261 个样本、四个生命阶段和 6 种不同的疾病(病原体是病毒、细菌、真核病原体或未知病原体)。结果表明,虾病可重复改变肠道细菌群落结构,但不能改变多样性。在排除了生命阶段和疾病特异性歧视类群(不同疾病依赖指标)后,我们确定了 18 个常见的疾病歧视类群(指示健康状况,不考虑病原体),无论疾病如何,都能准确诊断(准确率为 90.0%)虾的健康状况。这些优化大大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模型的有效性。与相应的健康队列相比,病虾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,我们的研究结果举例说明了微生物组荟萃分析在确定定量诊断疾病发病率的稳健和可重复特征方面的效用,以及从生态前景来看对虾发病机制的下游后果。

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