The impact of culture systems on the gut microbiota and gut metabolome of bighead carp (Hypophthalmichthys nobilis)

Ye, Chen; Geng, Shiyu; Zhang, Yingyu; Qiu, Huimin; Zhou, Jie; Zeng, Qi; Zhao, Yafei; Wu, Di; Yu, Guilan; Gong, Haibo; Hu, Beijuan; Hong, Yijiang

doi:10.1186/s42523-023-00239-7

The impact of culture systems on the gut microbiota and gut metabolome of bighead carp (Hypophthalmichthys nobilis)

Research
Open access
Published: 01 April 2023

Volume 5, article number 20, (2023)
Cite this article

Download PDF

You have full access to this open access article

Animal Microbiome Aims and scope Submit manuscript

The impact of culture systems on the gut microbiota and gut metabolome of bighead carp (Hypophthalmichthys nobilis)

Download PDF

Chen Ye^1,2,
Shiyu Geng^1,2,
Yingyu Zhang^1,2,
Huimin Qiu^1,2,
Jie Zhou^1,2,
Qi Zeng^1,2,
Yafei Zhao¹,
Di Wu^1,2,
Guilan Yu^1,2,
Haibo Gong³,
Beijuan Hu^1,2,4 &
…
Yijiang Hong^1,2,4

2747 Accesses
1 Citation
15 Altmetric
1 Mention
Explore all metrics

Abstract

Background

The gut microbiota of fish confers various effects on the host, including health, nutrition, metabolism, feeding behaviour, and immune response. Environment significantly impacts the community structure of fish gut microbiota. However, there is a lack of comprehensive research on the gut microbiota of bighead carp in culture systems. To demonstrate the impact of culture systems on the gut microbiome and metabolome in bighead carp and investigate a potential relationship between fish muscle quality and gut microbiota, we conducted a study using 16S ribosomal ribonucleic acid sequencing, gas chromatography-mass spectrometry, and liquid chromatography-mass spectrometry techniques on bighead carp in three culture systems.

Results

Our study revealed significant differences in gut microbial communities and metabolic profiles among the three culture systems. We also observed conspicuous changes in muscle structure. The reservoir had higher gut microbiota diversity indices than the pond and lake. We detected significant differences in phyla and genera, such as Fusobacteria, Firmicutes, and Cyanobacteria at the phylum level, Clostridium sensu stricto 1, Macellibacteroides, Blvii28 wastewater sludge group at the genus level. Multivariate statistical models, including principal component analysis and orthogonal projections to latent structures-discriminant analysis, indicated significant differences in the metabolic profiles. Key metabolites were significantly enriched in metabolic pathways involved in "arginine biosynthesis" and "glycine, serine, and threonine metabolism". Variation partitioning analysis revealed that environmental factors, such as pH, ammonium nitrogen, and dissolved oxygen, were the primary drivers of differences in microbial communities.

Conclusions

Our findings demonstrate that the culture system significantly impacted the gut microbiota of bighead carp, resulting in differences in community structure, abundance, and potential metabolic functions, and altered the host's gut metabolism, especially in pathways related to amino acid metabolism. These differences were influenced substantially by environmental factors. Based on our study, we discussed the potential mechanisms by which gut microbes affect muscle quality. Overall, our study contributes to our understanding of the gut microbiota of bighead carp under different culture systems.

Microbial Metagenomics and the Shellfish Microbiome

Comparative study on the gut microbiotas of four economically important Asian carp species

Article 07 May 2018

Community composition, diversity, and metabolism of intestinal microbiota in cultivated European eel (Anguilla anguilla)

Article 08 March 2018

Background

The gut microbiota in fish has been proven to have significant effects on the health [Full size image

OPLS-DA was used to analyse the degree of variability in intergroup samples. High predictability (Q²) and strong goodness of fit (R²X, R²Y) were reflected between every two groups (Additional file 9: Table S5), which demonstrated that the models were stable and could be used to identify significantly differential metabolites further. In the OPLS-DA score plots (Fig. 3B–D), every two groups were clearly separated, indicating distinct differences in metabolic profiling between groups. Permutation tests indicated that the models were not overfitted (Additional file 9: Table S5).

Significantly differential metabolites were screened, revealing 710 metabolites in NC compared with PY, 372 metabolites in PY compared with XHK, and 766 metabolites in XHK compared with NC. A total of 93 common significantly differential metabolites were screened in all intergroup comparisons, which included 32 lipids and lipid-like molecules, 19 organic acids and derivatives, and 7 organic oxygen compounds (Fig. 3E).

MetPA was conducted on all these metabolites, which enriched 27 pathways, of which 11 were significant (hypergeometric test, p < 0.05) (Additional file 10: Table S6). These pathways included 4 related to amino acid metabolism, 3 to carbohydrate metabolism, 1 pathway to translation, 1 to nucleotide metabolism, 1 to lipid metabolism, and 1 to metabolism of cofactors and vitamins. The significantly altered pathways included “aminoacyl-tRNA biosynthesis", "arginine biosynthesis", "glyoxylate and dicarboxylate metabolism", "glycine, serine, and threonine metabolism", "pyrimidine metabolism" (Fig. 3F).

The relationship between gut microbiota and metabolites

To explore potential associations between significant gut microbes and metabolites under environmental influence, we utilised Spearman rank correlation. At the phylum level, 26 associations between 17 metabolites and 5 microbes were found (Spearman rank correlation, r > 0.8, p < 0.05) (Fig. 4A). At the genus level, 30 associations were obtained for 6 microbes and 20 metabolites (Spearman rank correlation, r > 0.8, p < 0.05) (Fig. 4B). The details of the correlation can be seen in Additional file 11: Tables S7 and Additional file 12: Table S8.

The correlation between environmental factors and gut microbiota

Table 1 shows the environmental factors, including water temperature (WT), dissolved oxygen (DO), nitrate nitrogen (NO₃-N), ammonium nitrogen (NH₄-N), pH, and transparency, for the three groups in four seasons. PY had the highest water temperature on average, significantly higher than the other two groups. NC had the highest levels of DO, NO₃-N, NH₄-N, pH, and transparency, with NH₄-N content significantly higher than that in XHK, while other factors were significantly higher than the other groups (Kruskal-Wallis test, FDR < 0.05).

Table 1 Environmental factor parameters for the three groups in four seasons

Full size table

Table 2 presents the contribution of environmental factors to the variation in gut microbiota obtained by VPA. All the variation partitioning fractions were significant in the permutation test (p < 0.05). Among the environmental factors, pH explained the most variation in gut microbiota (43.41%), followed by NH₄-N (43.14%), DO (38.34%), transparency (34.41%), NO₃-N (28.28%), and WT (19.40%).

Table 2 The relative contributions of environmental factors to variation in gut microbiota

Full size table

To further explore the correlation between environmental factors and significant microbes at the phylum and genus levels, redundancy analysis (RDA) was conducted, and the results are shown in Fig. 5. All six canonical axes explain 23.31% of the total variability. The first two axes, which contributed 71.20% and 11.45% of the explained variance (p < 0.01), respectively, were used for further analysis.

The results showed that Fusobacteria and Macellibacteroides were positively correlated with all environmental factors except transparency. Conversely, Spirochaetae, Acidobacteria, Gemmatimonadetes, Brevinema, Gemmatimonas, and Nocardioides exhibited an opposite trend. Firmicutes, Cyanobacteria, Clostridium sensu stricto 1, Blvii28 wastewater sludge group, and Mycobacterium positively correlated with NH₄-N, pH, DO, and NO₃-N, while exhibiting negative correlations with WT and transparency. On the contrary, Proteobacteria and Aeromonas showed the opposite trend.

Discussion

This study demonstrates the impact of culture systems on the gut microbiota and metabolic profiles of bighead carp.

Culture systems greatly influence the gut microbiota of bighead carp.

In the current study, the dominant phyla in bighead carp's gut microbiota were Proteobacteria, Fusobacteria, Actinobacteria, Bacteroidetes, and Firmicutes, consistent with previous findings in marine and freshwater fishes [11, 57,58,59] and supporting Luo's research on bighead carp [21]. Among all phyla, especially Cyanobacteria, it is considered to be directly consumed by filter-feeding fish [60]. As a filter-feeding planktivorous carp, bighead carp easily filter some colony-forming cyanobacteria [27, 61, 62]. The abundance of Cyanobacteria in the hindgut was significantly higher in NC than in the other two groups (Additional file 1: Fig S1C), possibly due to differences in food sources in the three culture systems.

Alpha and beta diversity are important parameters used to evaluate the structural characteristics of microbiota. In our study, gut microbiota diversity and richness were higher in alpine cold-water reservoirs and inner lakes than in ponds, consistent with other research [9, 11, 63, 64]. This could be due to larger habitats providing a wider range of diets. Therefore, fish may be constantly exposed to more bacteria [65].

Beta diversity analysis revealed significant differences in the gut microbial communities of bighead carp in all three culture systems, in agreement with other studies [9, 11]. This may be due to culture environments, diet, and genetics [66]. The VPA results showed that environmental factors explained up to 43.41% of the variation in the gut microbiota, which indicates a great influence of environmental factors. Diet is considered a critical factor in regulating the gut microbiota composition [67], and different bait provided by different culture systems could be an important reason for the differences in gut microbiota diversity. The bighead carp used in our study were from the same batch reared in the same hatchery and shared a similar genetic background, so the effect from the genetic background was relatively small. Therefore, we believe that differences in environmental factors and diet were the main causes of the significant differences observed.

LefSe analysis revealed 15 biomarkers that may cause significant differences in communities. At the phylum level, these included Fusobacteria, Firmicutes, Cyanobacteria, Proteobacteria, Spirochaetae, Acidobacteria, and Gemmatimonadetes; at the genus level, these included Clostridium sensu stricto 1, Macellibacteroides, Blvii28 wastewater sludge group, Mycobacterium, Aeromonas, Brevinema, Gemmatimonas, and Nocardioides. Clostridium is typically considered the most efficient lignocellulose degrader due to the presence of multienzyme complexes [68, 69] consisting of multiple cellulases and hemicellulases in combination with enzyme-free scaffoldin [70,71,72] that synergistically and efficiently degrade lignocellulose. They also contribute to the host's nutrition by supplying fatty acids and vitamins [73]. Macellibacteroides are capable of decomposing cellulose- and hemicellulose-derived sugars [74]. Several species of Aeromonas can produce cellulase [75] and have intensive cellulolytic activity [76, 77]. Although bighead carp are important plankton feeders [78], they lack the cellulase enzyme that breaks down the cell walls of algae in their gut [79]. We hypothesise that the cellulolytic action of these bacteria compensates for this deficiency in bighead carp and that bighead carp derive energy and nutrients from this process [27]. The abundance of both Clostridium sensu stricto 1 and Macellibacteroides was positively correlated with the long and short diameter of muscle fibres (Spearman Rank Correlation). These may suggest that the culture systems improve nutrition levels and muscle quality in bighead carp by affecting gut microbes.

PICRUSt2 is a tool used for metagenome prediction to predict the approximate functional potential of a community [48]. In our study, 278 significantly different KEGG pathways were predicted, most of which were involved in metabolism, and organic systems, revealing the possible functional mechanisms of culture systems affecting gut microbes.

In conclusion, the culture systems significantly alter the community structure of gut microbiota and potentially impact their metabolic profiling.

Culture systems have a significant impact on gut metabolic profiling.

Gut metabolites are the outcome of the joint metabolism of the host and microbial community and can reflect the outcomes of nutrient uptake, digestion, and absorption by the gut microbiota [80].

In this study, multivariate statistical analyses, including PCA and OPLS-DA, revealed significant changes in the intestinal metabolite profile of bighead carp under different culture environments, reflecting the significant influence of the environment on the joint metabolism of host and gut microbes.

Further exploration of the differential metabolites showed that 93 key metabolites were significantly enriched in 11 pathways, of which 4 were related to amino acid metabolism and 1 to translation, including "aminoacyl-tRNA biosynthesis", "arginine biosynthesis", "glycine, serine, and threonine metabolism", "alanine, aspartate, and glutamate metabolism", and "cysteine and methionine metabolism". The enriched pathways suggest that the culture systems exerted an important impact on host-microbe joint metabolism in the gut, particularly on amino acid metabolism and protein translation-related metabolic pathways.

Overall, the culture systems significantly altered intestinal metabolism.

Potential association of fish muscle quality with microbes and metabolites in the gut

Several studies have demonstrated the beneficial role of gut microbes in improving muscle quality. For example, mice supplemented with Lactobacillus plantarum were accompanied by a change in muscle fibre type, that is, a significant increase in the proportion of type I fibres in the gastrocnemius muscle [13]. And when gut microbiota from obese Rongchang pigs and lean Yorkshire pigs were transferred to germ-free mice by faecal transplantation, mice fed Rongchang pig faeces tended to have increased body fat weight, increased percentage of slow muscle fibres, decreased diameter and cross-sectional area of the gastrocnemius muscle, and increased fat in gastrocnemius muscle compared to the mice fed Yorkshire pig faeces [14]. However, the mechanisms underlying these effects are not well understood.

Amino acids are important components of fish muscle quality [81]. Intestinal microorganisms regulate amino acids mainly through two mechanisms. On the one hand, microbes can utilise amino acids, producing acetic acid [82], propionic acid, butyric acid [83], hydrogen sulfide (H₂S) [83, 84], polyamine [85], phenolic and indole compounds [86]. These metabolites play a crucial role in regulating host physiology [87]. However, this process exists in the large intestine and is largely not absorbed by the colonic mucosa [88], making it difficult to influence the host's amino acid metabolism. On the other hand, intestinal microbes can synthesise amino acids de novo [89]. Numerous reports of microbes synthesising amino acids affect the host's amino acid metabolism; for example, microbial lysine can be incorporated into host proteins, as observed in uremic patients and subjects consuming a low-protein diet [90, 91]. The significant contribution of microbial-derived lysine and threonine to free plasma lysine and threonine has also been observed in studies of nitrogen (protein)-sufficient diets in adults [89, 92]. In pigs fed diets incorporating 15N-NH₄Cl and 14C-polyglucose, microbially produced amino acids such as valine, isoleucine leucine, phenylalanine, and lysine were found to be incorporated into human proteins [93] and absorbed mainly from the small intestine [93, 94]. In the present study, we found that L-cysteine, L-lysine, and L-threonine differed significantly among the three groups (Kruskal-Wallis test, FDR < 0.05) (Additional file 3: Fig S3A, S3B, S3C), and their levels were positively correlated with muscle quality. We suggest that these amino acids may be synthesised de novo by gut microbes and absorbed by the host, affecting the changes in amino acids in fish muscle and thus improving the quality of fish muscle.

Based on this hypothesis, we established the association between gut microbes and relevant metabolites using Spearman Rank Correlation with a cutoff of r > 0.65 and consequently inferred which microbes perform the corresponding functions using the R corrr package. According to the analysis, several bacteria, including uncultured Chloroflexi bacterium, Pedobacter, Azohydromonas, Sinomonas, Patulibacter, Sorangium, Altererythrobacter, and Bryobacter, may be potential amino acid-synthesising bacteria, as shown in Additional file 4: Fig S4. However, it's important to note that these findings are based on inference and correlation, and further experimental validation would be necessary to confirm these associations.

Additionally, certain metabolites may contribute to improving muscle quality in fish, such as glutamine which can increase the activity of the mammalian target of rapamycin (mTOR), a protein kinase that regulates protein synthesis in animal tissues and cells [95]. Although not yet available for fish [96], our study found that glutamine was the most abundant metabolite in the XHK group, followed by PY and NC (Additional file 3: Fig S3D), which is consistent with the muscle quality revealed by muscle microstructure. This suggests that glutamine could be a potential metabolite to enhance muscle quality in fish.

The flavour is one of the most important factors in influencing the edible quality of fish [97]. Nucleotides are a taste-active substance [98], among which the nucleotides inosine-5'-monophosphate (5'-IMP) and adenosine-5'-monophosphate (5'-AMP) contribute significantly to umami taste. They work in synergy with glutamate to intensify the taste sensation by binding to the same receptors, taste receptor type 1 members 1 and 3 [99, 100]. In our previous study, bighead carp in XHK exhibited a stronger umami intensity compared to other groups [12]. IMP is also considered an umami substance for fish products [101]. In the current study, 5'-IMP and glutamate followed the same trend of differences among the groups (Kruskal-Wallis test, FDR < 0.05) (Additional file 3: Fig S3E, S3F). This indicates that 5'-IMP and glutamate are probably the metabolites responsible for making the muscle of bighead carp tastier.

These findings suggest that gut microbiota and metabolites may play important roles in determining muscle quality in fish. This provides a practical direction for future research to improve the muscle quality of bighead carp. To verify the role of these gut microbes and metabolites, we may isolate and extract intestinal flora and conduct transplantation in aseptic mice, as well as supplement metabolites.

Conclusions

It is evident that culture systems significantly impact the intestinal microbiota and metabolites of bighead carp. Our findings demonstrate that the culture systems not only alter the gut microbial community of bighead carp, with notable variations in community structure, abundance, and potential metabolic functions, but also affect host gut metabolism, particularly with significant enrichment in pathways related to amino acid metabolism. Moreover, we discuss potential mechanisms that may impact muscle quality. We hypothesize that significantly different amino acids in the gut are the primary cause of the effect on muscle quality. Based on this hypothesis, we suggest that gut microbes may play a role in altering muscle quality in fish through the biosynthesis of amino acids.

Further investigation into the potential association between fish muscle quality and gut microbes and metabolites can provide a foundation for enhancing bighead carp's muscle quality and nutritional value.

Availability of data and materials

The 16S sequencing data were deposited into the National Center of Biotechnology Information (NCBI) Sequence Read Archive (SRA) database under Accession PRJNA933520. Data generated or analysed during this study are included in this published article and its supplementary information files.

Abbreviations

NC:: Nancheng
PY:: Poyang
XHK:: **ahuikeng
DNA:: Deoxyribonucleic acid
WT:: Water temperature
DO:: Dissolved oxygen
NO₃-N:: Nitrate nitrogen
NH₄-N:: Ammonium nitrogen
16S rRNA:: 16S ribosomal ribonucleic acid rRNA
PCR:: Polymerase chain reaction
QIIME:: Quantitative insights into microbial ecology
OTU:: Operational taxonomic units
NMDS:: Non-metric multidimensional scaling
ANOSIM:: A one-way analysis of similarity
LEfSe:: Linear discriminant analysis Effect Size
LDA:: Linear discriminant analysis
FDR:: False discovery rate
PICRUSt2:: Phylogenetic investigation of communities by reconstruction of unobserved states
LC–MS:: Liquid chromatograph–mass spectrometer
GC–MS:: Gas chromatograph–mass spectrometer
QC:: Quality control
MS:: Mass spectrum
RSD:: Relative standard deviation
PCA:: Principal component analysis
OPLS-DA:: Orthogonal projections to latent structures-discriminant analysis
VIP:: Variable importance in projection
MetPA:: Metabolic pathway analysis
VPA:: Variation partitioning analysis
RDA:: Redundancy analysis

References

**ong JB, Nie L, Chen J. Current understanding on the roles of gut microbiota in fish disease and immunity. Zool Res. 2019;40:70–6.
PubMed Google Scholar
Rawls JF, Mahowald MA, Goodman AL, Trent CM, Gordon JI. In vivo imaging and genetic analysis link bacterial motility and symbiosis in the zebrafish gut. Proc Natl Acad Sci U S A. 2007;104:7622–7.
Article CAS PubMed PubMed Central Google Scholar
Koch BEV, Yang S, Lamers G, Stougaard J, Spaink HP. Intestinal microbiome adjusts the innate immune setpoint during colonization through negative regulation of MyD88. Nat Commun. 2018;9:4099.
Article PubMed PubMed Central Google Scholar
Smith TB, Wahl DH, Mackie RI. Volatile fatty acids and anaerobic fermentation in temperate piscivorous and omnivorous freshwater fish. J Fish Biol. 1996;48:829–41.
Article CAS Google Scholar
Ni J, Yan Q, Yu Y, Zhang T. Factors influencing the grass carp gut microbiome and its effect on metabolism. FEMS Microbiol Ecol. 2014;87:704–14.
Article CAS PubMed Google Scholar
Semova I, Carten JD, Stombaugh J, Mackey LC, Knight R, Farber SA, Rawls JF. Microbiota regulate intestinal absorption and metabolism of fatty acids in the zebrafish. Cell Host Microbe. 2012;12:277–88.
Article CAS PubMed Google Scholar
Bereded NK, Abebe GB, Fanta SW, Curto M, Waidbacher H, Meimberg H, Domig KJ. The gut bacterial microbiome of Nile tilapia (Oreochromis niloticus) from lakes across an altitudinal gradient. BMC Microbiol. 2022;22:87.
Article CAS PubMed PubMed Central Google Scholar
Bereded NK, Abebe GB, Fanta SW, Curto M, Waidbacher H, Meimberg H, Domig KJ. the impact of sampling season and catching site (wild and aquaculture) on gut microbiota composition and diversity of Nile tilapia (Oreochromis niloticus). Biology (Basel). 2021;10:180.
PubMed Google Scholar
Sun P, Zhang H, Jiang Y, Gao Q, Tang B, Ling J, Yuan X. Relationships between the gut microbiota of juvenile black sea bream (Acanthopagrus schlegelii) and Associated environment compartments in different habitats. Microorganisms. 2021;9:2557.
Article CAS PubMed PubMed Central Google Scholar
Duan J, Xu D, Liu K, Zhou Y, Xu P. Diversity of intestinal microbiota in Coilia ectenes from Lake Taihu, China. Open Life Sci. 2017;12:315–25.
Article CAS Google Scholar
Dulski T, Kozłowski K, Ciesielski S. Habitat and seasonality shape the structure of tench (Tinca tinca L.) gut microbiome. Sci Rep. 2020;10:4460.
Article CAS PubMed PubMed Central Google Scholar
Hu B, Zhou J, Qiu H, Lai X, Li J, Wu D, Sheng J, Hong Y. Comparison of nutritional quality and volatile flavor compounds among bighead carp from three aquaculture systems. Saudi J Biol Sci. 2021;28:4291–9.
Article CAS PubMed PubMed Central Google Scholar
Chen YM, Wei L, Chiu YS, Hsu YJ, Tsai TY, Wang MF, Huang CC. Lactobacillus plantarum TWK10 supplementation improves exercise performance and increases muscle mass in mice. Nutrients. 2016;8:205.
Article PubMed PubMed Central Google Scholar
Yan H, Diao H, **ao Y, Li W, Yu B, He J, Yu J, Zheng P, Mao X, Luo Y, et al. Gut microbiota can transfer fiber characteristics and lipid metabolic profiles of skeletal muscle from pigs to germ-free mice. Sci Rep. 2016;6:31786.
Article CAS PubMed PubMed Central Google Scholar
Wang Y, Yao J, Luo Y, Tan H, Huang X, Wang S, Qin Q, Zhang C, Tao M, Dabrowski K, Liu S. Two new types of homodiploid fish and polyploid hybrids derived from the distant hybridization of female koi carp and male bighead carp. Mar Biotechnol. 2021;23:628–40.
Article CAS Google Scholar
Li X, Yu Y, Li C, Yan Q. Comparative study on the gut microbiotas of four economically important Asian carp species. Sci China Life Sci. 2018;61:696–705.
Article PubMed Google Scholar
Chen J, **e P, Zhang D, Lei H. In situ studies on the distribution patterns and dynamics of microcystins in a biomanipulation fish—bighead carp (Aristichthys nobilis). Environ Pollut. 2007;147:150–7.
Article CAS PubMed Google Scholar
Li D, Zhao H, Muhammad AI, Song L, Guo M, Liu D. The comparison of ultrasound-assisted thawing, air thawing and water immersion thawing on the quality of slow/fast freezing bighead carp (Aristichthys nobilis) fillets. Food Chem. 2020;320: 126614.
Article CAS PubMed Google Scholar
Tong J, Sun X. Genetic and genomic analyses for economically important traits and their applications in molecular breeding of cultured fish. Sci China Life Sci. 2015;58:178–86.
Article PubMed Google Scholar
FAO: The State of World Fisheries and Aquaculture 2022. Towards Blue Transformation. 2022.
Luo M, An R, Fu J, Wan S, Zhu W, Wang L, Dong Z. Comparative analysis of the gut microbiota in bighead carp under different culture patterns. J Appl Microbiol. 2022;132:1357–69.
Article CAS PubMed Google Scholar
Li T, Long M, Gatesoupe F-J, Zhang Q, Li A, Gong X. Comparative analysis of the intestinal bacterial communities in different species of carp by pyrosequencing. Microb Ecol. 2015;69:25–36.
Article CAS PubMed Google Scholar
Zhu L, Zhang Z, Chen H, Lamer JT, Wang J, Wei W, Fu L, Tang M, Wang C, Lu G. Gut microbiomes of bigheaded carps and hybrids provide insights into invasion: a hologenome perspective. Evol Appl. 2021;14:735–45.
Article PubMed Google Scholar
Meng L-J, Zhang Y, Li X-X, Liu J-H, Wen B, Gao J-Z, Chen Z-Z. Comparative analysis of bacterial communities of water and intestines of silver carp (Hypophthalmichthys molitrix) and bighead carp (H. nobilis) reared in aquaculture pond systems. Aquaculture. 2021;534:736334.
Article CAS Google Scholar
Li XM, Zhu YJ, Yan QY, Ringø E, Yang DG. Do the intestinal microbiotas differ between paddlefish (Polyodon spathala) and bighead carp (Aristichthys nobilis) reared in the same pond? J Appl Microbiol. 2014;117:1245–52.
Article CAS PubMed Google Scholar
Yuan J, Wang Z, Wang B, Mei H, Zhai X, Zhuang Z, Chen M, Zhang Y. Non-specific immunity associated gut microbiome in Aristichthys nobilis under different rearing strategies. Genes (Basel). 2021;12:916.
Article CAS PubMed Google Scholar
Borsodi AK, Szabó A, Krett G, Felföldi T, Specziár A, Boros G. Gut content microbiota of introduced bigheaded carps (Hypophthalmichthys spp.) inhabiting the largest shallow lake in Central Europe. Microbiol Res. 2017;195:40–50.
Article PubMed Google Scholar
Lu Y, Zhang P, Li W, Liu J, Shang X, Cheng Y, Li Y. Comparison of gut microbial communities, free amino acids or fatty acids contents in the muscle of wild Aristichthys nobilis from **nlicheng reservoir and Chagan lake. BMC Microbiol. 2022;22:32.
Article CAS PubMed PubMed Central Google Scholar
Yuan L, Wang L, Li Z-H, Zhang M-Q, Shao W, Sheng G-P. Antibiotic resistance and microbiota in the gut of Chinese four major freshwater carp from retail markets. Environ Pollut. 2019;255: 113327.
Article CAS PubMed Google Scholar
Su C, Hu W, Hu Z, Zhang Z, Wedchaparn O, Liangjie Z, Liu Q. Comparison of high-throughput sequencing analysis of gut contents between silver carp Hypophthalmichthys molitrix and bighead carp Hypophthalmichthys nobilis in mesotrophic and eutrophic lakes. Mar Freshw Res. 2019;71:761–70.
Article Google Scholar
Guo Y, Bai J, Chang O, Lao H, Ye X, Luo J. Molecular structure of the largemouth bass (Micropterus salmoides) Myf5 gene and its effect on skeletal muscle growth. Mol Biol Rep. 2009;36:1497–504.
Article CAS PubMed Google Scholar
Stringer C, Wang T, Michaelos M, Pachitariu M. Cellpose: a generalist algorithm for cellular segmentation. Nat Methods. 2021;18:100–6.
Article CAS PubMed Google Scholar
Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, Preibisch S, Rueden C, Saalfeld S, Schmid B, et al. Fiji: an open-source platform for biological-image analysis. Nat Methods. 2012;9:676–82.
Article CAS PubMed Google Scholar
Kumar PS, Brooker MR, Dowd SE, Camerlengo T. Target region selection is a critical determinant of community fingerprints generated by 16S pyrosequencing. PLoS ONE. 2011;6: e20956.
Article CAS PubMed PubMed Central Google Scholar
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.
Article CAS PubMed PubMed Central Google Scholar
Magoč T, Salzberg SL. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics (Oxford, England). 2011;27:2957–63.
PubMed Google Scholar
Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Peña AG, Goodrich JK, Gordon JI, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6.
Article CAS PubMed PubMed Central Google Scholar
Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4: e2584.
Article PubMed PubMed Central Google Scholar
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glöckner F. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.
Article CAS PubMed Google Scholar
Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007;73:5261–7.
Article CAS PubMed PubMed Central Google Scholar
Chong J, Liu P, Zhou G, **a J. Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data. Nat Protoc. 2020;15:799–821.
Article CAS PubMed Google Scholar
Kruskal JB. Non-metric multidimensional scaling: a numerical method. Psychometrika. 1964;29:115–29.
Article Google Scholar
Clarke KR. Non-parametric multivariate analyses of changes in community structure. Aust J Ecol. 1993;18:117–43.
Article Google Scholar
Warton DI, Wright ST, Wang Y. Distance-based multivariate analyses confound location and dispersion effects. Methods Ecol Evol. 2012;3:89–101.
Article Google Scholar
Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin P, O'Hara RB, Simpson G, Solymos P, Stevens MHH, Wagner H. Vegan: community ecology package. R package version 20-3 2011, 2.
Goslee S, Urban D. The ECODIST package for dissimilarity-based analysis of ecological data. J Stat Softw. 2007;22:1–19.
Article Google Scholar
Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, Huttenhower C. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12:R60.
Article PubMed PubMed Central Google Scholar
Douglas G, Maffei V, Zaneveld J, Yurgel S, Brown J, Taylor C, Huttenhower C, Langille M. PICRUSt2: an improved and extensible approach for metagenome inference. 2019.
Wishart DS, Guo A, Oler E, Wang F, Anjum A, Peters H, Dizon R, Sayeeda Z, Tian S, Lee BL, et al. HMDB 5.0: the Human Metabolome Database for 2022. Nucleic Acids Res. 2022;50:D622–31.
Article CAS PubMed Google Scholar
Fahy E, Subramaniam S, Murphy RC, Nishijima M, Raetz CRH, Shimizu T, Spener F, van Meer G, Wakelam MJO, Dennis EA. Update of the LIPID MAPS comprehensive classification system for lipids1. J Lipid Res. 2009;50:S9–14.
Article PubMed PubMed Central Google Scholar
Smith CA, O’Maille G, Want EJ, Qin C, Trauger SA, Brandon TR, Custodio DE, Abagyan R, Siuzdak G. METLIN: a metabolite mass spectral database. Ther Drug Monit. 2005;27:747–51.
Article CAS PubMed Google Scholar
Pang Z, Chong J, Zhou G, de Lima Morais DA, Chang L, Barrette M, Gauthier C, Jacques P, Li S, **a J. MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights. Nucleic Acids Res. 2021;49:388–96.
Article Google Scholar
Trygg J, Wold S. Orthogonal projections to latent structures (O-PLS). J Chemom. 2002;16:119–28.
Article CAS Google Scholar
Pinto RC, Trygg J, Gottfries J. Advantages of orthogonal inspection in chemometrics. J Chemom. 2012;26:231–5.
Article CAS Google Scholar
Revelle W: psych: procedures for psychological, psychometric, and personality research. R Package Version 1.0–95. Evanston, Illinois 2013.
Team RC. R: a language and environment for statistical computing. MSOR connections 2014, 1.
Kashinskaya EN, Simonov EP, Kabilov MR, Izvekova GI, Andree KB, Solovyev MM. Diet and other environmental factors shape the bacterial communities of fish gut in an eutrophic lake. J Appl Microbiol. 2018;125:1626–41.
Article CAS Google Scholar
Maji UJ, Mohanty S, Mahapatra AS, Mondal HK, Samanta M, Maiti NK. Exploring the gut microbiota composition of Indian major carp, rohu (Labeo rohita), under diverse culture conditions. Genomics. 2022;114: 110354.
Article CAS PubMed Google Scholar
Yukgehnaish K, Kumar P, Sivachandran P, Marimuthu K, Arshad A, Paray BA, Arockiaraj J. Gut microbiota metagenomics in aquaculture: factors influencing gut microbiome and its physiological role in fish. Rev Aquac. 2020;12:1903–27.
Google Scholar
**e P, Liu J. Practical success of biomanipulation using filter-feeding fish to control cyanobacteria blooms: a synthesis of decades of research and application in a subtropical hypereutrophic lake. TheScientificWorldJOURNAL. 2001;1: 276487.
Article Google Scholar
Zhang Z, Shi Y, Zhang J, Liu Q. Experimental observation on the effects of bighead carp (Hypophthalmichthys nobilis) on the plankton and water quality in ponds. Environ Sci Pollut Res. 2022;29:56658–75.
Article Google Scholar
**e P. Gut contents of bighead carp (Aristichthys nobilis) and the processing and digestion of algal cells in the alimentary canal. Aquaculture. 2001;195:149–61.
Article Google Scholar
Ruzauskas M, Armalytė J, Lastauskienė E, Šiugždinienė R, Klimienė I, Mockeliūnas R, Bartkienė E. Microbial and antimicrobial resistance profiles of microbiota in common carps (Cyprinus carpio) from aquacultured and wild fish populations. Animals (Basel). 2021;11:929.
Article PubMed Google Scholar
Tan CK, Natrah I, Suyub IB, Edward MJ, Kaman N, Samsudin AA. Comparative study of gut microbiota in wild and captive Malaysian Mahseer (Tor tambroides). Microbiologyopen. 2019;8: e00734.
Article PubMed Google Scholar
Dehler CE, Secombes CJ, Martin SAM. Environmental and physiological factors shape the gut microbiota of Atlantic salmon parr (Salmo salar L.). Aquaculture. 2017;467:149–57.
Article CAS PubMed PubMed Central Google Scholar
Liu Y, Li Y, Li J, Zhou Q, Li X. Gut microbiome analyses of wild migratory freshwater fish (Megalobrama terminalis) through geographic isolation. Front Microbiol. 2022;13: 858454.
Article PubMed PubMed Central Google Scholar
Li Z, Zhang X, Aweya JJ, Wang S, Hu Z, Li S, Wen X. Formulated diet alters gut microbiota compositions in marine fish Nibea coibor and Nibea diacanthus. Aquac Res. 2019;50:126–38.
Article CAS Google Scholar
Demain AL, Newcomb M, Wu JH. Cellulase, clostridia, and ethanol. Microbiol Mol Biol Rev. 2005;69:124–54.
Article CAS PubMed PubMed Central Google Scholar
Liu L, Jiao J-Y, Fang B-Z, Lv A-P, Ming Y-Z, Li M-M, Salam N, Li W-J. Isolation of Clostridium from Yunnan-Tibet hot springs and description of Clostridium thermarum sp. Nov. with lignocellulosic ethanol production. Syst Appl Microbiol. 2020;43:126104.
Article CAS PubMed Google Scholar
Bayer EA, Belaich JP, Shoham Y, Lamed R. The cellulosomes: multienzyme machines for degradation of plant cell wall polysaccharides. Annu Rev Microbiol. 2004;58:521–54.
Article CAS PubMed Google Scholar
Bayer EA, Lamed R, White BA, Flint HJ. From cellulosomes to cellulosomics. Chem Rec. 2008;8:364–77.
Article CAS PubMed Google Scholar
Xu C, Huang R, Teng L, **g X, Hu J, Cui G, Wang Y, Cui Q, Xu J. Cellulosome stoichiometry in Clostridium cellulolyticum is regulated by selective RNA processing and stabilization. Nat Commun. 2015;6:6900.
Article PubMed Google Scholar
Balcázar JL, Blas ID, Ruiz-Zarzuela I, Cunningham D, Vendrell D, Múzquiz JL. The role of probiotics in aquaculture. Vet Microbiol. 2006;114:173–86.
Article PubMed Google Scholar
Jabari L, Gannoun H, Cayol J-L, Hedi A, Sakamoto M, Falsen E, Ohkuma M, Hamdi M, Fauque G, Ollivier B, Fardeau M-L. Macellibacteroides fermentans gen. nov., sp. Nov., a member of the family Porphyromonadaceae isolated from an upflow anaerobic filter treating abattoir wastewaters. Int J Syst Evol Microbiol. 2012;62:2522–7.
Article CAS PubMed Google Scholar
Jiang Y, Caixia X, Yang G, Gong X, Chen X, Xu L, Bao B. Cellulase-producing bacteria of Aeromonas are dominant and indigenous in the gut of Ctenopharyngodon idellus (Valenciennes). Aquac Res. 2011;42:499–505.
Article CAS Google Scholar
Li H, Wu S, Wirth S, Hao Y, Wang W, Zou H, Li W, Wang G. Diversity and activity of cellulolytic bacteria, isolated from the gut contents of grass carp (Ctenopharyngodon idellus) (Valenciennes) fed on Sudan grass (Sorghum sudanense) or artificial feedstuffs. Aquac Res. 2016;47:153–64.
Article CAS Google Scholar
Li H, Zheng Z, Cong-xin X, Bo H, Chao-yuan W, Gang H. Isolation of cellulose—producing microbes from the intestine of grass carp (Ctenopharyngodon idellus). Environ Biol Fishes. 2009;86:131–5.
Article Google Scholar
Bitterlich G. Digestive enzyme pattern of two stomachless filter feeders, silver carp, Hypophthalmichthys molitrix Val., and bighead carp, Aristichthys nobilis Rich. J Fish Biol. 1985;27:103–12.
Article CAS Google Scholar
Kolar CS, Chapman DC, Courtenay WR, Housel CM, Williams JD, Jennings DP. Bigheaded carps: a biological synopsis and environmental risk assessment. 2007.
Callejón-Leblic B, Selma-Royo M, Collado MC, Gómez-Ariza JL, Abril N, García-Barrera T. Untargeted gut metabolomics to delve the interplay between selenium supplementation and gut microbiota. J Proteome Res. 2022;21:758–67.
Article PubMed Google Scholar
Wang C-L, Wang Z-Y, Song C-W, Luo S, Yuan X-Y, Huang Y-Y, Desouky HE. A comparative study on growth, muscle cellularity and flesh quality of farmed and imitative ecological farming loach, Misgurnus anguillicaudatus. Aquaculture. 2021;543:736933.
Article Google Scholar
Barker HA. Amino acid degradation by anaerobic bacteria. Annu Rev Biochem. 1981;50:23–40.
Article CAS PubMed Google Scholar
Zhao J, Liu H, Michael A, Qiao S. Dietary protein and gut microbiome composition and function. Curr Protein Pept Sci. 2018;19:145–54.
Article Google Scholar
Fan P, Song P, Li L, Huang C, Chen J, Yang W, Qiao S, Wu G, Zhang G, Ma X. Roles of biogenic amines in intestinal signaling. Curr Protein Pept Sci. 2017;18:532–40.
Article CAS PubMed Google Scholar
He L, Han M, Qiao S, He P, Li D, Li N, Ma X. Soybean antigen proteins and their intestinal sensitization activities. Curr Protein Pept Sci. 2015;16(7):613–21.
Article CAS PubMed Google Scholar
Hughes R, Magee EAM, Bingham S. Protein degradation in the large intestine: relevance to colorectal cancer. Curr Issues Intest Microbiol. 2000;1:51–8.
CAS PubMed Google Scholar
Sridharan GV, Choi K, Klemashevich C, Wu C, Prabakaran D, Pan LB, Steinmeyer S, Mueller C, Yousofshahi M, Alaniz RC, et al. Prediction and quantification of bioactive microbiota metabolites in the mouse gut. Nat Commun. 2014;5:5492.
Article CAS PubMed Google Scholar
Neis EP, Dejong CH, Rensen SS. The role of microbial amino acid metabolism in host metabolism. Nutrients. 2015;7:2930–46.
Article CAS PubMed PubMed Central Google Scholar
Metges CC, El-Khoury AE, Henneman L, Petzke KJ, Grant I, Bedri S, Pereira PP, Ajami AM, Fuller MF, Young VR. Availability of intestinal microbial lysine for whole body lysine homeostasis in human subjects. Am J Physiol. 1999;277:E597-607.
CAS PubMed Google Scholar
Giordano C, De Pascale C, Balestrieri C, Cittadini D, Crescenzi A. Incorporation of urea 15N in amino acids of patients with chronic renal failure on low nitrogen diet. Am J Clin Nutr. 1968;21:394–404.
Article CAS PubMed Google Scholar
Tanaka N, Kubo K, Shiraki K, Koishi H, Yoshimura H. A pilot study on protein metabolism in the Papua New Guinea highlanders. J Nutr Sci Vitaminol. 1980;26:247–59.
Article CAS PubMed Google Scholar
Metges CC, Petzke KJ, El-Khoury AE, Henneman L, Grant I, Bedri S, Regan MM, Fuller MF, Young VR. Incorporation of urea and ammonia nitrogen into ileal and fecal microbial proteins and plasma free amino acids in normal men and ileostomates. Am J Clin Nutr. 1999;70:1046–58.
Article CAS PubMed Google Scholar
Torrallardona D, Harris CI, Fuller MF. Pigs’ gastrointestinal microflora provide them with essential amino acids. J Nutr. 2003;133:1127–31.
Article CAS PubMed Google Scholar
Torrallardona D, Harris CI, Fuller MF. Lysine synthesized by the gastrointestinal microflora of pigs is absorbed, mostly in the small intestine. Am J Physiol-Endocrinol Metab. 2003;284:E1177–80.
Article CAS PubMed Google Scholar
Wu G, Bazer FW, Davis TA, Jaeger LA, Johnson GA, Kim SW, Knabe DA, Meininger CJ, Spencer TE, Yin Y-L. Important roles for the arginine family of amino acids in swine nutrition and production. Livest Sci. 2007;112:8–22.
Article Google Scholar
Li P, Mai K, Trushenski J, Wu G. New developments in fish amino acid nutrition: towards functional and environmentally oriented aquafeeds. Amino Acids. 2009;37:43–53.
Article PubMed Google Scholar
Huang X-H, Zheng X, Chen Z-H, Zhang Y-Y, Du M, Dong X-P, Qin L, Zhu B-W. Fresh and grilled eel volatile fingerprinting by e-Nose, GC-O, GC–MS and GC × GC-QTOF combined with purge and trap and solvent-assisted flavor evaporation. Food Res Int. 2019;115:32–43.
Article CAS PubMed Google Scholar
Zhang Y, Venkitasamy C, Pan Z, Wang W. Recent developments on umami ingredients of edible mushrooms—a review. Trends Food Sci Technol. 2013;33:78–92.
Article Google Scholar
Zhu W, Luan H, Bu Y, Li J, Li X, Zhang Y. Changes in taste substances during fermentation of fish sauce and the correlation with protease activity. Food Res Int. 2021;144: 110349.
Article CAS PubMed Google Scholar
Mouritsen OG, Khandelia H. Molecular mechanism of the allosteric enhancement of the umami taste sensation. FEBS J. 2012;279:3112–20.
Article CAS PubMed Google Scholar
Zhang L, Li Q, Lyu J, Kong C, Song S, Luo Y. The impact of stunning methods on stress conditions and quality of silver carp (Hypophthalmichthys molitrix) fillets stored at 4°C during 72h postmortem. Food Chem. 2017;216:130–7.
Article CAS PubMed Google Scholar

Download references

Acknowledgements

We appreciate the assistance of Liuzheng Wu, ** Li, Rui **ao, Yaying Huang, Feng **, Bin Sheng, and Runming Guo for the experiments and manuscript.

Funding

This work was supported by China Agriculture Research System No. 49 (CARS-49) and the Key Research Development Program of the Jiangxi Province of China (20212BBF61010).

Author information

Authors and Affiliations

School of Life Science, Nanchang University, Nanchang, 330031, China
Chen Ye, Shiyu Geng, Yingyu Zhang, Huimin Qiu, Jie Zhou, Qi Zeng, Yafei Zhao, Di Wu, Guilan Yu, Beijuan Hu & Yijiang Hong
Jiangxi Province Key Laboratory of Aquatic Animal Resources and Utilization, Nanchang University, Nanchang, 330031, China
Chen Ye, Shiyu Geng, Yingyu Zhang, Huimin Qiu, Jie Zhou, Qi Zeng, Di Wu, Guilan Yu, Beijuan Hu & Yijiang Hong
Jiangxi Provincial Aquatic Biology Protection and Rescue Center, Nanchang, 330000, China
Haibo Gong
Modern Agricultural Research Institute, Nanchang University, Nanchang, 330031, China
Beijuan Hu & Yijiang Hong

Authors

Chen Ye
View author publications
You can also search for this author in PubMed Google Scholar
Shiyu Geng
View author publications
You can also search for this author in PubMed Google Scholar
Yingyu Zhang
View author publications
You can also search for this author in PubMed Google Scholar
Huimin Qiu
View author publications
You can also search for this author in PubMed Google Scholar
Jie Zhou
View author publications
You can also search for this author in PubMed Google Scholar
Qi Zeng
View author publications
You can also search for this author in PubMed Google Scholar
Yafei Zhao
View author publications
You can also search for this author in PubMed Google Scholar
Di Wu
View author publications
You can also search for this author in PubMed Google Scholar
Guilan Yu
View author publications
You can also search for this author in PubMed Google Scholar
Haibo Gong
View author publications
You can also search for this author in PubMed Google Scholar
Beijuan Hu
View author publications
You can also search for this author in PubMed Google Scholar
Yijiang Hong
View author publications
You can also search for this author in PubMed Google Scholar

Contributions

BH and YH conceived and designed the experiments. CY, HG and BH conducted the experiments. CY analysed the experimental data and wrote the manuscript. SG, YZ, HQ, and JZ made the paraffin sections. QZ, YZ, DW, and GY provided the study ideas. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Beijuan Hu or Yijiang Hong.

Ethics declarations

Ethics approval and consent to participate

The authors declare that all experiments involving bighead carp were ethical. All our experiments were conducted in compliance with the standard ethical guidelines and under the control of Nanchang University.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1

. Fig S1. The relative abundance of indicator taxa at the phylum level. (A) Fusobacteria. (B) Firmicutes. (C) Cyanobacteria. (D) Proteobacteria. (E) Spirochaetae. (F) Acidobacteria. (G) Gemmatimonadetes. Significance levels with *, **, and *** represent FDR < 0.05, 0.01, and 0.001 between groups, respectively (Kruskal-Wallis test).

Additional file 2

. Fig S2. The relative abundance of indicator taxa at the genus level. (A) Clostridium sensu stricto 1. (B) Macellibacteroides. (C) Blvii28 wastewater sludge group. (D) Mycobacterium. (E) Aeromonas. (F) Brevinema. (G) Gemmatimonas. (H) Nocardioides. Significance levels with *, **, and *** represent FDR < 0.05, 0.01, and 0.001 between groups, respectively (Kruskal-Wallis test).

Additional file 3

. Fig S3. Normalised peak intensity of potential metabolites which influence fish muscle quality (A) L-cysteine. (B) L-lysine. (C) L-threonine. (D) glutamine. (E) 5’-IMP. (F) L-glutamate. Significance levels with *, **, and *** represent FDR < 0.05, 0.01, and 0.001 between groups, respectively (Kruskal-Wallis test).

Additional file 4

. Fig S4. Potential microbes synthesising amino acids. The curves represent the correlation between metabolites and microbes, with greener colours representing stronger positive correlations and yellower colours representing stronger negative correlations.

Additional file 5

. Table S1. The abundance and taxonomy of OTUs.

Additional file 6

. Table S2. The abundance and significance of top 10 microbes at the phylum level.

Additional file 7

. Table S3. The abundance and significance of top 10 microbes at the genus level.

Additional file 8

. Table S4. The abundance and classification of metabolites.

Additional file 9

. Table S5. The parameters and evaluation of PCA and OPLS-DA models.

Additional file 10

. Table S6. The significance and impact of enriched pathways.

Additional file 11

. Table S7. The potential metabolites and microbes at the phylum level.

Additional file 12

. Table S8. The potential metabolites and microbes at the genus level.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Cite this article

Ye, C., Geng, S., Zhang, Y. et al. The impact of culture systems on the gut microbiota and gut metabolome of bighead carp (Hypophthalmichthys nobilis). anim microbiome 5, 20 (2023). https://doi.org/10.1186/s42523-023-00239-7

Download citation

Received: 21 October 2022
Accepted: 08 March 2023
Published: 01 April 2023
DOI: https://doi.org/10.1186/s42523-023-00239-7

The impact of culture systems on the gut microbiota and gut metabolome of bighead carp (Hypophthalmichthys nobilis)

Abstract

Background

Results

Conclusions

Similar content being viewed by others

Background

The relationship between gut microbiota and metabolites

The correlation between environmental factors and gut microbiota

Discussion

Culture systems greatly influence the gut microbiota of bighead carp.

Culture systems have a significant impact on gut metabolic profiling.

Potential association of fish muscle quality with microbes and metabolites in the gut

Conclusions

Availability of data and materials

Abbreviations

References

Acknowledgements

Funding

Author information

Authors and Affiliations

Contributions

Corresponding authors

Ethics declarations

Ethics approval and consent to participate

Consent for publication

Competing interests

Additional information

Publisher's Note

Supplementary Information

Rights and permissions

About this article

Cite this article

Share this article

Keywords

Search

Navigation