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

Abstract

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-onedataset-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 metaanalyses in identifying robust and reproducible features for quantitatively diagnosing disease incidence, and the downstream consequences for shrimp pathogenesis from an ecological prospective.

Consistent features of the gut microbiota in response to diverse shrimp Litopenaeus vannamei diseases: A meta‐analysis

“…Selection is a deterministic process that includes homogeneous selection (e.g., host filtering) and variable selection (e.g., heterogeneity of the local environment) (Chase & Myers, 2011). Notably, the relative importance of deterministic processes that govern the gut microbiota in diseased shrimp was consistently lower than that in corresponding healthy controls (Figure 3), thus supporting the findings in previous studies (Sha et al, 2022; Zhu et al, 2016). The occurrence of diseases could weaken host filtering on external taxa, resulting in a compromised homogeneous selection (Xiong et al, 2018).…”

“…Ample evidence has shown that shrimp lifestages, different diseases, hypervariable regions and sequencing platforms, are driving factors shaping the gut microbiota (Cornejo‐Granados et al, 2018; Garcia‐Lopez et al, 2020; Sha et al, 2022). These biotic and technical variables complicate the identification of common gut microbiota features that characterise diverse shrimp diseases, although this knowledge is fundamental to uncover etiology.…”

“…For terrestrial animals, the cause‐and‐effect relationship between the gut microbiota and host disease has been established, of which a healthy gut microbiome affects the colonisation, growth and virulence of invading pathogens (Baumler & Sperandio, 2016; Sassone‐Corsi & Raffatellu, 2015; Xiong et al, 2018), but equivalent studies in aquatic animals are still lacking. Some studies have uncovered that dysbiosis in the shrimp gut microbiota is closely associated with the occurrence of diverse diseases (Holt et al, 2021; Sha et al, 2022; Yu et al, 2018). The magnitude of microbiome departure to healthy gut microbiome is linearly associated with the severity of shrimp disease (Chen et al, 2017; Lu et al, 2020).…”

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

“…A previous meta-analysis demonstrated that supervised learning models for soil microbiomes may be used to predict the potential occurrence of Fusarium wilt disease in plants ( Yuan et al., 2020 ). In addition, supervised learning methods have identified robust and reproducible features relevant for diagnosing shrimp diseases according to meta-analyses of gut microbiota ( Sha et al., 2022 ). The origin and quality of sea cucumber cultured in diverse geographic regions has also been accurately predicted using random forest models for gut microbiota ( Zhao et al., 2022 ).…”

Towards a ‘systems’ approach for viral challenge experiments in shrimp: Reporting guidelines for publication

“…Beyond immunological and morphological development, progressive exposure and interaction with the external environment associated with shrimp development also results in dynamic temporal succession of shrimp microbiota composition. 63,73 The dynamics of the microbiome are associated with shrimp health and immune capacity, 74,75 thus, changes in its composition and diversity during ontogenesis may further influence disease susceptibility. 37,76,77 Characterisation and analysis of the microbiome is yet to become a standard practice in PCE studies and currently remains cost-prohibitive in many instances.…”