Rice field purification regulates carp intestinal flora to improve muscle mass

doi:10.21203/rs.3.rs-2791587/v1

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Rice field purification regulates carp intestinal flora to improve muscle mass

https://doi.org/10.21203/rs.3.rs-2791587/v1

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Background: To explore the mechanism underlying suitable rice purification time and the resulting regulation of muscle mass in fish raised in rice paddies, 16S rDNA and metabolomics methods were used to perform analyses of intestinal microbial diversity and muscle metabolome in common carp.

Results: There were significant differences in Chao1 and observed OTUs indices between groups C and D and between groups A and E (p< 0.05), and the Shannon indices of Group A were significantly lower than those of groups B, D, and E (p < 0.05). The dominant genera were Cetobacterium, ZOR0006, Romboutsia, Clostridium_sensu_stricto_1, and Pirellulaceae_unclassified. Metabolites that differed between groups were mainly enriched in biosynthesis of unsaturated fatty acids, purine metabolism, glutathione metabolism, and glycerophospholipid metabolism.

Conclusions: In summary, rice purification significantly improved the composition of the intestinal flora of the carp and increased the abundance and diversity of intestinal microorganisms. Rice purification can regulate carp purine metabolism and promote the production of more umami substances such as Guanosine monophosphate (GMP) and can increase muscle mass by inhibiting the production of Hypoxanthine (Hx) and inosine from Inosine-5‘-monophosphate (IMP). Rice field purification improved muscle mass in the carp by increasing the abundance of probiotics in the intestines.

rice-fish symbiosis

common carp

intestinal microbiota

muscle metabolomics

Rice-fish coculture is an ecological fishery breeding scheme that comprises two main modes: feeding fish in paddy fields during the rice growth period and purifying fish in paddy fields via culture without feeding. Research in this area has been focused on rice–fish symbiotic ecosystems, rice cultivation, and fish health and nutrition [1, 2, 3]. Studies have shown that rice–fish paddy fields have greater soil diversity than rice monocropping paddies [4]. Yang et al. showed that the content of essential amino acids in loach muscle from fish raised in paddy field culture was significantly higher than in fish from the pond culture mode [5]. Fang et al. found that carp feed primarily on algae and rice pests in rice fields when left unfed [6]. However, studies concerning purifying fish in rice fields to improve muscle mass have not been performed to date.

In a state of hunger, the fish’s carbohydrate, fat, and protein metabolism causes organic compounds in the body to be converted to flavor and taste substances, and the duration of starvation required to improve the quality of fish meat is related to the fish variety and the aquatic environment. Micro-flowing water depuration improved the muscle quality of Salmo salar and reduced the fishy smell [7]. Lv et al. found that depuration treatment for 20 days could increase the contents of flavorful amino acids and nucleotides in grass carp muscle [8]. The nutritional value of carp muscle was improved after 14 days of starvation treatment [9]. Depuration treatment for eight days significantly improved the edible quality of the muscle of Megalobrama amblycephala [10]. Therefore, how to reasonably control the duration of starvation is an urgent problem to be solved in the rice field purification fish model.

Recent studies have shown that different breeding environments and feeding regimes can affect the composition of fish intestinal flora [11]. Metabolomics technologies enable quantitative and qualitative analyses of metabolites in the organism [12], and these methods have been widely used in studies related to muscle quality of aquatic organisms. In the present study, market-size common carp were depurated in a rice–fish coculture field. The influence of purification time on muscle metabolites and intestinal flora was explored using 16S rDNA and metabolomics methods, and the underlying regulatory mechanism was elaborated. The results will provide a theoretical reference for the purification of carp in paddy fields.

2.1 Experimental design and sampling

The experiment was carried out at the rice–fish coculture test site of Chongqing Three Gorges Vocational College in Chongqing, China. The paddy had a one-sided ditch design (8 m × 1.2 m × 1.5 m, length × width × depth) that provided appropriate space for rice cultivation and a refuge for common carp reared in the paddy (Fig. 1). Eighty-one healthy and similarly sized common carp (1156.60 ± 43.07g) were selected and randomly placed into three paddy fields (farming density: 150 tails / 667 m²) and were kept without feeding during the test period. Five common carp were randomly collected every 10 days (A: 0d; B: 10d; C: 20d; D 30d; E: 40d), and the body lengths and weights were measured. The muscle was sampled and placed in liquid nitrogen for testing, and the intestinal contents were collected and stored at − 20°C for analysis.

2.2 16s rRNA gene sequencing for the intestinal microbiota analysis

The genomic DNA of different samples was extracted according to the instructions of an E.Z.N.A.® Stool DNA Kit (D4015, Omega, Inc., USA). The total DNA was eluted in 50 µl elution buffer and stored at -80°C until use. PCR was performed by LC-Biotechnology (Hangzhou, China). PCR amplification was performed in a 25 µl reaction mixture to obtain the V3-V4 region of 16s rRNA gene using the primers 341F (5'-CCTACGGGNGGCWGCAG-3') and 806R (5'-GGACTACHVGGGTATCTAATCC-3'). The 5′ ends of the primers were tagged with specific barcodes for each sample and were sequenced with universal primers. The PCR conditions consisted of an initial denaturation at 98°C for 30 s, 32 cycles of denaturation at 98°C for 10 s, annealing at 54°C for 30 s, and extension at 72°C for 45 s followed by a final extension at 72°C for 10 min. The PCR product was confirmed by 2% agarose gel electrophoresis, purified using AMPure XT beads (Beckman Coulter Genomics, Danvers, USA), and quantified by Qubit (Invitrogen, USA). The amplicon pools were prepared for sequencing, and the size and quantity of the amplicon library were assessed using an Agilent 2100 Bioanalyzer (Agilent, USA) and a Library Quantification Kit for Illumina (Kapa Biosciences, Woburn, USA), respectively. The libraries were sequenced on the NovaSeq PE250 platform for generating 250-bp paired-end reads.

2.3 LC-MS conditions for metabolomics analysis

The metabolomics analysis of the liver samples was conducted using an ultra-performance liquid chromatography (UPLC) system (SCIEX, UK). An Acuity UPLC T3 column (100 mm × 2.1 mm, 1.8 µm, Waters, UK) maintained at 35°C was used for the reversed-phase separation. The mobile phase consisted of solvent A (water with 0.1% formic acid) and solvent B (acetonitrile with 0.1% formic acid) for the metabolite separation. The gradient elution conditions using a flow rate of 0.4 mL/min were as follows: 5% solvent B for 0-0.5 min, 5-100% solvent B for 0.5-7.0 min, 100% solvent B for 7.0–8.0 min, 100-5% solvent B for 8.0-8.1 min, and 5% solvent B for 8.1–10.0 min.

A TripleTOF 5600 Plus high-resolution tandem mass spectrometer (SCIEX, Warrington, UK) was used to detect the eluted metabolites. The Q-TOF was operated in both positive and negative ion modes. The ion spray floating voltage was set at 5 kV under positive-ion mode and − 4.5 kV under negative-ion mode. The mass spectrometry (MS) data were acquired in the IDA mode. The TOF mass range was from 60 to 1200 Da. The survey scans were acquired in 150 MS, and as many as 12 product ion scans were collected if they exceeded a threshold of 100 counts per second (counts/s) with a 1 + charge state. The total cycle time was fixed to 0.56 s. Four-time bins were summed for each scan at a pulse frequency of 11 kHz by monitoring the 40 GHz multichannel TDC detector with four-anode/channel detection. Dynamic exclusion was set as 4 s. During the entire acquisition period, the mass accuracy was calibrated after every 20 samples. Furthermore, a quality control (QC) sample was analyzed after every 10 samples to evaluate the stability of the LC-MS during the entire period of acquisition.

2.4 Data analysis

2.4.1 16S rRNA data analysis

Paired-end reads were assigned to samples based on their unique barcode and were truncated by cutting off the barcode and primer sequences, then merged using FLASH. Quality filtering of the raw reads was performed under specific filtering conditions to obtain high-quality clean tags via fqtrim (v0.94). Chimeric sequences were filtered using Vsearch software (v2.3.4). The feature table and sequence were obtained after dereplication with DADA2. According to the SILVA (release 132) classifier, feature abundance was normalized using the relative abundance of each sample. The α-diversity was analyzed to evaluate the species complexity through five indices, Observed species, Chao1, Shannon, Simpson, and Goods coverage using QIIME2. The β-diversity was analyzed to investigate the differences within and between groups via principal component analysis (PCA), principal coordinates analysis (PCoA), and UPGMA clustering analysis. BLAST was used for sequence alignment, and the feature sequences were annotated with the SILVA database for each representative sequence. In addition, the microbial species and potential phenotypes were identified and analyzed with QIIME2. All data processing and analyses were performed using the Omi Studio tools at https://www.omicstudio.cn/tool.

2.4.2 Metabolomics data processing

The acquired LC-MS data pretreatment including peak picking, peak grouping, retention time correction, second peak grouping, and annotation of isotopes and adducts were performed using XCMS software. The raw data files were converted into mzXML format and then processed using the XCMS, CAMERA, and metaX toolbox included in R software (AT&T, USA). Each ion was identified by combining retention time (RT) and m/z data. The intensity of each peak was recorded, and a three-dimensional matrix containing arbitrarily assigned peak indices (retention time–m/z pairs), sample names (observations), and ion intensity information (variables) was generated. The metabolites were annotated by matching the exact molecular mass data (m/z) of samples with those from online databases, including the Kyoto Encyclopedia of Genes and Genomes (KEGG) and human metabolome databases (HMDB). If the mass difference between the observed value and the database value was < 10 ppm, the metabolite would be annotated, and the molecular formula of the metabolite would be identified and validated by the isotopic distribution measurements. We also used an in-house fragment spectrum library of metabolites to validate the metabolite identification.

The intensity values of the peak data were further pre-processed by metaX. Features that were detected in < 50% of the QC samples or 80% of the biological samples were removed, and the remaining peaks with missing values were imputed with the k-nearest neighbor algorithm to further improve the data quality. PCA was performed to detect outliers and evaluate batch effects using the pre-processed dataset. QC-based robust LOESS signal correction was fitted to the QC data with respect to the order of injection to minimize signal intensity drift over time. In addition, the relative standard deviations (SDs) of the metabolic features were calculated across all QC samples, and those > 30% were removed. The analysis methods included PCA and PLS-DA. MetaX software was used to quantify differential metabolites and differential metabolite screenings. Supervised PLS-DA was conducted using metaX to discriminate different variables between groups. The variable importance in projection (VIP) was calculated, and a VIP cutoff of 1.0 was used to select important features (VIP ≥ 1.0; ratio ≥ 2.0 or ratio ≤ 1/2; q ≤ 0.05). The differential metabolites and metabolic pathways were also explored through pairwise comparisons. The corresponding graphs and heatmaps were drawn using the Omi Studio tool at https://www.omicstudio.cn/tool.

2.5 Statistical analysis

One-way analysis of variance (ANOVA) was used for evaluating the between-group differences using SPSS 25.0 (Chicago, IL, United States). A value of P < 0.05 was set as the significance threshold. Spearman’s correlation analysis was conducted to analyze the relationships between the microbiota and metabolites using the data of significantly differential genus-level microbiota and differential secondary metabolites. A scaled heatmap was constructed for the correlation matrix using the default clustering method.

3.1 Common carp growth performance

Figure 2 shows that the body weights of common carp began to decrease significantly in group B (p < 0.05). There were no significant differences in group B, C, or D (p > 0.05), while the body weight of group E was significantly decreased (p < 0.05). Fat filling decreased sequentially from group B (p < 0.05), but there was no significant difference between groups C and D (p > 0.05).

3.2 Intestinal microbiota characteristics

3.2.1 Operational taxonomic unit (OTU) analysis

Figure 3 shows the sequences that were clustered with 97% similarity, resulting in 12147 OTUs. The maximum number of OTUs was in group D with 3493; the minimum number of OTUs was in group A with 1343. Fifty-seven OTUs were shared by groups B and C; 110 OTUs were shared by groups C and D, and 209 OTUs were shared by the five groups.

3.2.2 Intestinal microbiota diversity analysis

Figure 4A and Fig. 4B show that there was a significant difference in Chao1 and observed OTU indices between groups C and D and between groups A and E (p < 0.05), while there was no difference between groups B and C (p > 0 .05). Figure 4C shows that the Shannon index of group A was significantly lower than those of groups B, D, and E (p < 0.05), but the difference with group C was not significant (p > 0.05), while the difference between groups C and D was significant (p < 0.05). NMDS analysis (shown in Fig. 4D) indicated that all samples in group A clustered together, and the remaining four samples were not significantly separated (p < 0.05). The samples of group B were clustered together relative to the samples of group E.

3.2.3 Microbial species analysis

After the sequences were identified, there were 22 genera of bacteria with relative abundance greater than 1% at the genus level (Fig. 5A). The dominant taxa were Cetobacterium, ZOR0006, Romboutsia, Clostridium_sensu_stricto_1, and Pirellulaceae_unclassified. The dominant bacterial genus in group A was Cetobacterium; the dominant bacteria in group C was Clostridium_sensu_stricto_1, and the dominant bacteria in group D was ZOR0006. Figure 5B shows that the dominant bacteria between groups at the genus level included Cetobacterium, ZOR0006, Romboutsia, Clostridium_sensu_stricto_1, and Pirellulaceae_unclassified. The anti-stress ability of the intestinal microbiota phenotypic function in group D was significantly higher than in other groups (Fig. 5C), and the prediction of bacterial phenotypic functions showed that Proteobacteria was related to anti-stress function (Fig. 5D).

3.3 Metabolomics analysis

Figure 6A shows that group A overlapped with group B; group E intersected with the samples of group D; group B was related to group C, and group C was clustered in one group relative to group D. Figures 6B and 6C show that group B was adjusted upward by 60 and 118 differential metabolites relative to group A, primarily being enriched in glutathione metabolism, glycerophospholipid metabolism, ABC transporters, and purine metabolism, while group C was adjusted downward by 52 and 132 differential metabolites relative to group B. The main enrichment was in biosynthesis of unsaturated fatty acids, and the D group was adjusted downward by 119 and 123 differential metabolites relative to group C, being enriched in biosynthesis of unsaturated fatty acids, amino sugar, and nucleotide sugar metabolism. The E group was upregulated by 71 metabolites relative to group D, with 117 major enrichments in biosynthesis of unsaturated fatty acids being adjusted downward.

There were no significant differences in muscle amino acids or saturated fatty acids between the groups (Figs. 7A and 7C; p > 0.05). Figure 7B shows that the glutamic acid content in group A was significantly lower than in other groups (p < 0.05). The unsaturated fatty acids in group C were significantly lower than those in group A (Fig. 7D; p < 0.05), but there were no significant differences with group B, D, or E (p > 0.05).

The Hx content of group B relative to group D decreased sequentially in order (Fig. 8A); the inosine content in group B was decreased, and in group D was significantly lower than that in group A (p < 0.05) (Fig. 8B). The contents of IMP, GMP, and taurine in groups B, C, D, and E were significantly higher than those in group A (p < 0.05), and the content in group C was the highest (Figs. 8C, 8D and 8E). The contents of histamine in groups A, B, and C were unchanged, while in group D this was significantly lower than in group C (p < 0.05) (Fig. 8F). The results of purine metabolic enrichment analysis showed that AMP, IMP, GMP, and IDP were upregulated, and inosine, xanthosine, guanine, guanosine, Hx, urate, and allantoate were downregulated (Fig. 9).

3.4 Correlation analysis of 16S sequencing and metabolomics

A Spearman correlation analysis of 16S sequencing and metabolomics was performed to explore the relationship between the intestinal microbiota and host metabolism using the data of differential genus-level microbiota and differential secondary metabolites (Figs. 10A and 10B). Clostridium, Streptococcus, Pirellula, Cyanobium_PCC-6307, Methylocystis, Rhodopirellula, Deltaproteobacteria_unclassified, and Synechocystis_ BDHKU-20401 were positively correlated with IMP and GMP and negatively correlated with Hx and inosine.

Rice-fish symbiosis is a green aquaculture model that can improve the quality of both rice and fish as well as the ecological environment of paddy fields [13]. However, the duration of starvation conditions will affect the muscle metabolism and intestinal flora of carp raised in paddy fields.

4.1 Rice field purification can increase the richness and diversity of common carp gut microorganisms

The results of the 16S rDNA analysis showed that the intestinal microorganisms in common carp had varied numbers of OTUs in different periods under rice field purification. The purification improved both the richness and diversity of carp intestinal microorganisms. The 30-day purification significantly increased the richness of the intestinal microbiota, and the 10-day purification significantly improved microbial diversity. After rice field purification, the dominant bacteria in the common carp intestines were Cetobacterium, ZOR0006, Romboutsia, Clostridium_sensu_stricto_1, and Pirellulaceae_unclassified. Nie et al. studied the intestinal flora of common carp under unfed conditions, and the results were similar to those in this study [14]. Cetobacterium was the most abundant genus in common carp, consistent with previous reports [15]. This result was closely related to host protein digestion [16], as Cetobacterium can effectively promote organic matter and zooplankton digestion and decomposition [17]. ZOR0006 belongs to the phylum Firmicutes, a group that can degrade polysaccharides [18] and affect lipid metabolism [19]. Romboutsia is an intestinal probiotic [20, 21], and its changes in abundance are related to intestinal mucosa and disease [22, 23]. The relative abundance of Clostridium_sensu_stricto_1 is positively correlated with short-chain fatty acid content [24]. Yang et al. found that the abundances of Enterococcus and Firmicutes were higher in carp after feeding duckweed and earthworms [25] similar to the present study, a result that may be related to the ingestion of organic matter and plankton by common carp in rice fields during purification.

4.2 Rice field purification can improve carp muscle quality

The 10-day purification treatment altered carp muscle metabolism primarily by affecting the biosynthesis of unsaturated fatty acids, glycerophospholipid metabolism, purine metabolism, and other metabolic pathways. There were no significant differences in amino acids or fatty acids in muscle metabolites among the various groups, indicating that there was no effect on the normal metabolism and decomposition of amino acids or fatty acids in carp during paddy field purification. This was somewhat different from the effect of micro-flowing water purification on the decomposition of amino acids and fatty acids in fish [8], a result that may be related to the fact that carp can also feed on other organisms in the rice field during purification. However, the amount of muscle glutamate was significantly increased during purification; this amino acid gives fish an umami taste [26]. It is worth noting that the content of muscle sulfuric acid in this study increased significantly after purification. Taurine has anti-oxidative and anti-inflammatory properties [27], and long-term dietary taurine supplementation can effectively prevent hypercholesterolemia [28]. Histamine is a biogenic amine that can affect the quality of aquatic products [29], and high concentrations of histamine can cause allergic reactions and poisoning symptoms [30, 31]. Overall, the results indicate that rice field purification can improve common carp muscle quality.

The results for metabolites related to purine metabolism showed that during paddy field purification, AMP, IMP, GMP, and IDP were upregulated, as were inosine, xanthosine, guanine, guanosine, Hx, urate, and allantoate. IMP is an intracellular precursor of adenosine monophosphate and guanosine monophosphate, and the production of inosine-5’-monophosphate dehydrogenase, a rate-limiting enzyme produced by GMP, plays a central role in IMP purine metabolism [32]. The results of this experiment showed that the decomposition of AMP into Hx, inosine, and urate in carp muscle during the purification process was inhibited, and more IMP and GMP were generated, similar to the results obtained via purification using microfluidic water. Hx and inosine can give muscle a bitter taste [33], and IMP and GMP can increase the umami taste of muscle [34, 35]. The increases in these metabolites suggest that rice field purification could improve the taste of carp muscle.

4.3 Rice field purification regulates common carp gut probiotics to improve muscle quality

To further elucidate the relationship between gut microbes and carp muscle taste, we conducted Spearman correlation analysis on differential microbial genera and purine metabolites. The results showed that Clostridium, Streptococcus, and Cyanobium_PCC-6307 were positively correlated with IMP and GMP. Clostridium [36] and Streptococcus [37] produce short-chain fatty acids. Clostridium feeding improves muscle mass and unsaturated fatty acid content [38, 39]. Cyanobium_PCC-6307 is a type of freshwater blue-green algae [40] that is rich in unsaturated fatty acids [41], and fatty acids are important flavor precursors [42, 43]. The positive correlation in the regulation of IMP and GMP may be by the two bacteria. The mechanism of Clostridium and Streptococcus acting as probiotics and increasing carp muscle mass needs to be further studied.

5.1 Purification of carp in paddy fields for 30 days significantly improved the composition of the intestinal flora.

5.2 Rice field purification of carp for 30 days significantly increased muscle mass.

5.3 Rice field purification increased muscle mass by improving the composition of carp intestinal flora and increasing the abundance of probiotics.

Ethics approval and Consent to participate

All animal experimental procedures used in this study were approved by the the Tab of Animal Experimental Ethical Inspection of Laboratory Animal Centre, Huazhong Agriculture University.

Consent for publication

Not applicable.

Availability of data and materials

The datasets supporting the results and conclusions of this article were deposited in the NCBI Sequence Read Archive database under the accession numbers PRJNA957971 (microbiota raw sequencing data). All other data are contained within the main manuscript.

Competing interests

The authors declare that they have no competing interests.

Funding

This work was supported by project of science and technology research program of Chongqing Education Commission of China (KJQN202203502) and Chongqing Wanzhou District Science and Technology Plan Project.

Authors' contributions

YZ, YX, CZ, SY and JM conceived and designed the research. YZ, YX, XH and XX performed the experiment and investigation. YZ and YX analyzed the data and draw the figures. YZ, YX, and CZ wrote the original manuscript. XH, XX, GT, LLZ, and QL analyzed the data. YZ, YX, SY, and JM revised the manuscript. YZ, CZ, SY and JM supervised the work. All authors contributed to the manuscript. The authors read and approved the final manuscript.

Acknowledgements

We acknowledge the members of Hangzhou Lianchuan Biotechnology (LCBio)

for their assistance with data analysis.

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Rice field purification regulates carp intestinal flora to improve muscle mass

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Abstract

Figures

1. Introduction

2. Materials And Methods

2.1 Experimental design and sampling

2.2 16s rRNA gene sequencing for the intestinal microbiota analysis

2.3 LC-MS conditions for metabolomics analysis

2.4 Data analysis

2.4.1 16S rRNA data analysis

2.4.2 Metabolomics data processing

2.5 Statistical analysis

3. Results

3.1 Common carp growth performance

3.2 Intestinal microbiota characteristics

3.2.1 Operational taxonomic unit (OTU) analysis

3.2.2 Intestinal microbiota diversity analysis

3.2.3 Microbial species analysis

3.3 Metabolomics analysis

3.4 Correlation analysis of 16S sequencing and metabolomics

4. Discussion

4.1 Rice field purification can increase the richness and diversity of common carp gut microorganisms

4.2 Rice field purification can improve carp muscle quality

4.3 Rice field purification regulates common carp gut probiotics to improve muscle quality

5. Conclusion

Declarations

References

Additional Declarations

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