Effects of temperature, diet and genotype-induced variations on the gut microbiota of abalone

Wang, Xingze; Tang, Bin; Luo, Xuan; Ke, Caihuan; Huang, Meng; Wang, You; Wang, Ying

doi:10.1016/j.aquaculture.2020.735269

Cited by 20 publications

(14 citation statements)

References 69 publications

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“…The two groups of abalone in this experiment were subjected to the same management, environmental, and nutritional conditions to eliminate additional effects. The intestinal microbiota of all of the abalones were predominated by the phyla Proteobacteria and Tenericutes , which is consistent with previous findings ( Wang et al, 2020 ; Choi et al, 2021 ). Proteobacteria are carbohydrate-fermenting bacteria and a high percentage in the intestines of abalone enhances the ability to utilize carbohydrates and improve production performance ( Elolimy et al, 2019 ).…”

Section: Discussionsupporting

confidence: 92%

“…In this study, the hypothesis that microbiota structure, digestive enzyme activity, and metabolome in the intestines are associated with FE of animals was tested. For the assessment of growth and resistance traits in abalone, the feeding experiment period was generally 60–90 days ( Huang et al, 2018 ; Wang et al, 2020 ). In this experiment, a 72-day feeding experiment period was sufficient to obtain growth and feeding efficiency traits for abalone.…”

Section: Discussionmentioning

confidence: 99%

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Exploring the Intestinal Microbiota and Metabolome Profiles Associated With Feed Efficiency in Pacific Abalone (Haliotis discus hannai)

Shen

et al. 2022

Front. Microbiol.

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Feed efficiency (FE) is critical to the economic and environmental benefits of aquaculture. Both the intestines and intestinal microbiota play a key role in energy acquisition and influence FE. In the current research, intestinal microbiota, metabolome, and key digestive enzyme activities were compared between abalones with high [Residual feed intake (RFI) = −0.029] and low FE (RFI = 0.022). The FE of group A were significantly higher than these of group B. There were significant differences in intestinal microbiota structures between high- and low-FE groups, while higher microbiota diversity was observed in the high-FE group. Differences in FE were also strongly correlated to variations in intestinal digestive enzyme activity that may be caused by Pseudoalteromonas and Cobetia. In addition, Saprospira, Rhodanobacteraceae, Llumatobacteraceae, and Gaiellales may potentially be utilized as biomarkers to distinguish high- from low-FE abalones. Significantly different microorganisms (uncultured beta proteobacterium, BD1_7_clade, and Lautropia) were found to be highly correlated to significantly different metabolites [DL-methionine sulfoxide Arg-Gln, L-pyroglutamic acid, dopamine, tyramine, phosphatidyl cholines (PC) (16:0/16:0), and indoleacetic acid] in the high- and low-FE groups, and intestinal trypsin activity also significantly differed between the two groups. We propose that interactions occur among intestinal microbiota, intestinal metabolites, and enzyme activity, which improve abalone FE by enhancing amino acid metabolism, immune response, and signal transduction pathways. The present study not only elucidates mechanisms of variations in abalone FE, but it also provides important basic knowledge for improving abalone FE by modulating intestinal microbiota.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Exploring the Intestinal Microbiota and Metabolome Profiles Associated With Feed Efficiency in Pacific Abalone (Haliotis discus hannai)

Shen

et al. 2022

Front. Microbiol.

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“…In the present study, although there was no significant difference in alpha diversity between C1 and T1, a reduction was observed in the T2 group with increased exposure time. Similarly, previous studies reported decreased alpha diversity in some mollusks [ 28 , 30 , 45 ] exposed to high-temperature stress. In contrast, some studies detected an increase in the alpha diversity metrics in mollusks [ 29 , 43 ] and crustaceans [ 46 ].…”

Section: Discussionsupporting

confidence: 74%

Characterization of Gut Microbiome in the Mud Snail Cipangopaludina cathayensis in Response to High-Temperature Stress

Cheng

Liu

et al. 2022

Animals

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The mud snail Cipangopaludina cathayensis is a widely distributed species in China. Particularly in Guangxi province, mud snail farming contributes significantly to the economic development. However, global warming in recent decades poses a serious threat to global aquaculture production. The rising water temperature is harmful to aquatic animals. The present study explored the effects of high temperature on the intestinal microbiota of C. cathayensis. Snail intestinal samples were collected from the control and high-temperature groups on days 3 and 7 to determine the gut microbiota composition and diversity. Gut bacterial community composition was investigated using high-throughput sequencing of the V3–V4 region of bacterial 16S rRNA genes. Our results suggested that thermal stress altered the gut microbiome structure of C. cathayensis. At the phylum level, Proteobacteria, Bacteroidetes, and Firmicutes were dominant in C. cathayensis gut microbiota. The T2 treatment (32 ± 1 °C, day 7) significantly decreased the relative abundance of Firmicutes, Actinobacteria, and Deinococcus-Thermus. In T2, the abundance of several genera of putatively beneficial bacteria (Pseudomonas, Aeromonas, Rhodobacter, and Bacteroides) decreased, whereas the abundance of Halomonas—a pathogenic bacterial genus—increased. The functional prediction results indicated that T2 treatment inhibited some carbohydrate metabolism pathways and induced certain disease-related pathways (e.g., those related to systemic lupus erythematosus, Vibrio cholerae infection, hypertrophic cardiomyopathy, and shigellosis). Thus, high temperature profoundly affected the community structure and function of C. cathayensis gut microbiota. The results provide insights into the mechanisms associated with response of C. cathayensis intestinal microbiota to global warming.

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“…They rely on the reference databases on Greengenes (Desantis et al, 2006) and SILVA (Quast et al, 2012). However, owing to the incompleteness of the 16S rRNA marker gene database, large amounts of OTUs cannot be functionally profiled, especially for microbial communities from non-human samples from defective culture media (Pachiadaki et al, 2019;Wang X. et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Microbial Functions Prediction by Graph Aggregated Embedding

et al. 2021

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Matching 16S rRNA gene sequencing data to a metabolic reference database is a meaningful way to predict the metabolic function of bacteria and archaea, bringing greater insight to the working of the microbial community. However, some operational taxonomy units (OTUs) cannot be functionally profiled, especially for microbial communities from non-human samples cultured in defective media. Therefore, we herein report the development of Hierarchical micrObial functions Prediction by graph aggregated Embedding (HOPE), which utilizes co-occurring patterns and nucleotide sequences to predict microbial functions. HOPE integrates topological structures of microbial co-occurrence networks with k-mer compositions of OTU sequences and embeds them into a lower-dimensional continuous latent space, while maximally preserving topological relationships among OTUs. The high imbalance among KEGG Orthology (KO) functions of microbes is recognized in our framework that usually yields poor performance. A hierarchical multitask learning module is used in HOPE to alleviate the challenge brought by the long-tailed distribution among classes. To test the performance of HOPE, we compare it with HOPE-one, HOPE-seq, and GraphSAGE, respectively, in three microbial metagenomic 16s rRNA sequencing datasets, including abalone gut, human gut, and gut of Penaeus monodon. Experiments demonstrate that HOPE outperforms baselines on almost all indexes in all experiments. Furthermore, HOPE reveals significant generalization ability. HOPE's basic idea is suitable for other related scenarios, such as the prediction of gene function based on gene co-expression networks. The source code of HOPE is freely available at https://github.com/adrift00/HOPE.

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Effects of temperature, diet and genotype-induced variations on the gut microbiota of abalone

Cited by 20 publications

References 69 publications

Exploring the Intestinal Microbiota and Metabolome Profiles Associated With Feed Efficiency in Pacific Abalone (Haliotis discus hannai)

Exploring the Intestinal Microbiota and Metabolome Profiles Associated With Feed Efficiency in Pacific Abalone (Haliotis discus hannai)

Characterization of Gut Microbiome in the Mud Snail Cipangopaludina cathayensis in Response to High-Temperature Stress

Hierarchical Microbial Functions Prediction by Graph Aggregated Embedding

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