Association of Aquaculture Environment Microbiota with Metabolism in American Shad (Alosa Sapidissima)

The environment microbiome affects the growth and development of sh species. Information of the environment microbiome is benecial to increase the production of sh in different aquaculture systems. In this study we analyzed differences in environmental microbial composition, intestinal metabolites and differentially expressed genes (DEGs) in American shad living in tank aquaculture and pond aquaculture environment. The results demonstrated that the dominant phyla were Bacteroidetes, Actinobacteria, Fusobacteria, Gemmatimonadetes, Firmicutes, Acidobacteria, Nitrospirae and Epsilonbacteraeota in two different environments. There were signicantly changed of metabolites in different aquaculture environment, including DP-ethanolamine, L-proline, sulfuric acid, L-valine, L-tryptophan, creatinine, uric acid and L-isoleucine. The transcriptome data revealed eight genes (As23G026314, As04G005148, As21G024434, As04G005193, As23G026314, As13G016035, As02G001872 and As07G009244) related to metabolisms signicantly changed in pond aquaculture group compared to tank aquaculture group. In addition, the body weight, amino acid metabolism, and glycerophospholipid metabolism of American shad also signicantly changed in the pond aquaculture environment. Therefore, identifying the predominant microbiome in the aquaculture environment may be prevent the disease from occurring and maintain healthy sh reared in the aquaculture environment. values the (2012) functions were used to identify the DEGs based on SizeFactors and nbinom Test. The signicantly differential expression was showed with the thresholds of P < 0.05 and fold change >2 or fold < 0.5. The expression patterns were executed by hierarchical cluster analysis of DEGs. DEGs were analyzed by GO term enrichment and KEGG pathway enrichment following the hypergeometric distribution (Kanehisa 2008).


Introduction
Microorganisms play the critical role in the sediment and water of various aquaculture environments due to participating in biogeochemical cycles and food network interactions (Wörmer et al., 2019). Bacteria and some kinds of eukaryotic microorganisms involve in maintain a balance of elements (such as nitrogen, phosphorus, sulfur) and participate in a range of physiological functions in the aquatic environment and sediment (Grujcic et al., 2018). The nutrients of aquatic organisms usually sink into the sediment of aquaculture water and eventually become a source of nutrients for bacterial and eukaryotic (Shaughnessy et al., 2019). In addition, microorganisms affect the quality of water, such as involved in aerobic chemoheterotrophy and phototrophy, organic matter decomposition, and sulfur compound transformation, which indirectly affected the growth of aquatic organisms ( American shad (Alosa sapidissima) is an anadromous species and mainly originates in the western Atlantic Ocean and the east coast of Canada and the United States (Nack et al., 2019). To alternative the Reeves shad (Tenualosa reevesii), arti cial farming techniques of American shad are becoming mature in China . However, American shad is vulnerable and overreacting to the environment, and any improper handling will result in the death of this species. Some researchers have focused on enhancing the reproductive capacity and improving the breeding conditions of American shad, including arti cially induced spawning, environmental condition regulation, and breeding improved variety (St. Pierre, 1992; Liu et al., 2006). The breeding conditions, such as water quality, temperature, illumination intensity, and oxygen content, can induce oxidative stress reaction, enhance mortality, and affect the growth and development of American shad. It has suggested that the environmental requirements of its ontogenetic development are vital for the growth performance of American shad broodstocks .
Pond aquaculture plays an important role in the freshwater aquaculture of China, and the application area nearly accounts for 50% of the country's aquaculture (National Bureau of Fisheries and China Society of Fisheries, 2019). However, commercial aquaculture models have also emerged in China, especially in American shad aquaculture . The microbial species in the water and sediment are closely relevant to the sh culture environment, because of the activities of the aquaculture species and the characteristics of environmental requirements in aquaculture, including dissolved oxygen, lighting, and the frequency of mechanical stirring. Microorganisms in commercial aquaculture and pond aquaculture are also closely related to the culture environment of sh (Zhao et al., 2015). The growth and development of American shad were different in tank aquaculture and pond aquaculture. The correlation between the development of American shad and microorganisms has seldom been analyzed in the aquaculture environment. In addition, the characteristics and differences of microorganisms' distributions in tank and pond aquaculture are still unclear. Thus, in the survey, we compared microorganisms in two different aquaculture tankage in Hongze of China and analyzed their in uence on the growth and development, intestinal metabolites, and differentially expressed genes (DEGs) in the gut of American shad. The purpose is to reveal the differences in the distribution patterns of microorganism species between tank aquaculture and pond aquaculture and show the in uence on growth, metabolites, and transcription of crucial genes in different aquaculture environments. This study provides the information to evaluate growth parameters in the different aquatic pond, which is helpful to improve the e ciency of pond aquaculture.

Chemicals and reagents
Water, methanol, acetonitrile, and formic acid were purchased from CNW Technologies GmbH (Düsseldorf, Germany). L-2chlorophenylalanine was purchased from Shanghai Hengchuang Biotechnology Co., Ltd. ( Fish culture conditions, treatments and sample collection Two-month-old sh were raised in a greenhouse tank recirculating aquaculture system (RAS) and underwent sixth-generation selection in indoor concrete tanks at Hongze Fishseeds Biotech. Inc., Jiangsu, China. RAS can ensure the excellence of the water quality due to the physical ltration, oxygenation and bio-ltration . The system was a specially designed dualdrain RAS equipped with an online infrared dome camera to monitor the growth, maturation and natural spawning behavior of shad broodstocks. Other two-month-old sh were also raised in a greenhouse pond aquaculture system at Hongze Fishseeds Biotech. Inc., Jiangsu, China. Temperature and dissolved oxygen were measured by a YSI 550A (Y.S.I. Environmental Inc.) every morning in both locations. Water quality was monitored by testing ammonia and nitrite levels every four days in both locations. Both recirculating aquaculture system and the pond were maintained under a natural photoperiod in the greenhouse, with the sunlight intensity attenuated by a roof cover with 40% light penetration. The daily average light intensity ranged from 800 to 1600 lux. Fish were fed commercial pellets three times a day for 20 min each time during the entire culture period.
Sixty sh (two months postfertilization) were randomly selected and divided into three tanks or three ponds (20 sh per tank or pond). After thirteen month cultured, the ten-individual sh from each tank and pond (n = 3) sampled to measure the body weight. Then these sh were washed with puri ed water and euthanized. The intestinal parts were dissected and gathered for metabolites and RNA. Four biological replicates per group were used for LC-MS analysis and three biological replicates per group were used for RNA-Seq analysis. The sediment at the bottom of the tank aquaculture and pond aquaculture was also collected. Four tubes (>2mg/tube) of sediment were gathered for each group to ensure a su cient number of DNA.

Metabolite extraction
The process of metabolite extraction referenced Lu et al., (2016). The ACQUITY UHPLC system (Waters Corporation, Milford, USA) coupled to an AB SCIEX Triple TOF 5600 System (AB SCIEX, Framingham, MA) was used to reveal the metabolic pro les with ESI. The modes of positive and negative ion executed by using the C18 column. Water/formic acid and acetonitrile/formic acid were used to elute, and the separation parameter followed the instructions.
RNA extraction, library construction and sequencing RNA was extracted from 3 mg of intestinal tissue using a mirVana miRNA Isolation Kit (Ambion). The RNA-Seq process was performed as described in Zhang et al. (2021). RNA integrity was evaluated using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). Samples with an RNA integrity number (RIN) ≥ 7 were used for subsequent analysis. Libraries were constructed using a TruSeq Stranded mRNA LT Sample Prep Kit (Illumina, San Diego, CA, USA) according to the manufacturer's instructions. Then, these libraries were sequenced on an Illumina sequencing platform (Illumina HiSeq X Ten), and 125 bp/150 bp paired-end reads were generated. DNA extraction and library construction DNA was extracted from gut samples using TruSeq Nano DNA LT Sample Prepararion Kit (Illumina). The V4 region of bacterial 16S rRNA was ampli ed using DNA samples. Agencourt AMPure XP (BECKMAN COULTER, USA) was used for purifying PCR products. Sequencing libraries were constructed using KAPA Library Quanti cation Kits (Illumina, USA). The Illumina Miseq platform was used for paired-end sequencing (Kapa Biosystems, USA).
Data preprocessing and statistical analysis LC-MS metabolomics data analysis LC-MS metabolomic analysis was done by OE Biotech Co. Ltd. (Shanghai, China). Progenesis QI software was used for acquired LC-MS raw data analysis (Waters Corporation, Milford, USA). Progenesis QI Data Processing Software was used to analyze the metabolites (Waters Corporation, Milford, USA). The public databases were referenced to explore, including http://www.hmdb.ca/; http://www.lipidmaps.org/ and in-house-developed databases. The changes of metabolite were analyzed by Principal component analysis (PCA) and orthogonal partial least-squares-discriminant analysis (OPLS-DA). The Hotelling's T2 region shows the 95% con dence interval in the model. Variable importance in the projection (VIP) shows the contribution rate of each variable in the OPLS-DA model. The differential metabolites were obtained based on the combination of a statistically signi cant threshold of variable in uence on projection (VIP) values selected from the OPLS-DA model and p values from a two-tailed Student's t-test on the normalized peak areas. The differential metabolites were with VIP>1 and p values<0.05.

RNA sequencing data analysis
Transcriptome sequencing and analysis were also done by OE Biotech Co., Ltd. Trimmomatic was used to process raw data (raw reads) (Bolger et al., 2014). Clean reads were obtained by removing poly-N sequences and low-quality reads. Hisat2 was used to map the clean reads and genome (Kim et al., 2015). For transcript-level quanti cation, bowtie2 was used to analyze the values of the transcript. DESeq (2012) functions were used to identify the DEGs based on SizeFactors and nbinom Test. The signi cantly differential expression was showed with the thresholds of P < 0.05 and fold change >2 or fold change < 0.5. The transcript expression patterns were executed by hierarchical cluster analysis of DEGs. DEGs were analyzed by GO term enrichment and KEGG pathway enrichment following the hypergeometric distribution (Kanehisa et al., 2008).
16S rRNA metagenetics and bioinformatics analysis FASTQ format was used for the raw sequence data. Trimmomatic software (Bolger et al., 2014) was used to preprocess the pairedend reads and eliminated ambiguous bases (N). FLASH software was used to assemble the left paired-end reads (Reyon et al., 2012) as follows: overlapping was from 10bp to 200bp, and the mismatch rate was from 0% to 20%. QIIME software was used to denoise and clean the reads (Caporaso et al., 2010) (version 1.8.0). The primer sequences were removed from the clean reads, and Vsearch software was used to execute the OTUs (Rognes et al., 2016). QIIME package was used to select the representative read in every OTU. RDP classi er was used to annotate and blast the usual reads (Wang et al., 2007). All representative reads were annotated and blasted against Unite database (ITSs rDNA) using blast (Lobo et al., 2008).
Functional analysis of integrated metabolomic and transcriptomic data KEGG database (https://www.kegg.jp/) was used to de ne the metabolic pathways and integrated metabolomic and transcriptomic datasets. The markers list was introduced into the KEGG database to nd out metabolic pathways induced by ENR treatment. The speci c metabolite is related to a particular gene if they shared one common KEGG pathway.

Bodyweight
As shown in Fig. 1, the body weight (577.11 ± 21.56 g) was signi cantly increased in the pond aquaculture groups compared to that in the tank aquaculture group (559.63 ± 20.19 g). Metabolomic Alterations Induced By Different Aquaculture Environments Metabolomic analysis was done to analyze the alterations in metabolic pro les of sh gut cultured in tank aquaculture and pond aquaculture. Ninety-four metabolites signi cantly changed (p < 0.05) after culture in the pond aquaculture (72 metabolites upregulated and 22 metabolites downregulated) ( Supplementary Fig. 1). Several of the metabolites were associated with the tank aquaculture and the pond aquaculture ( Supplementary Fig. 1). The heatmap shows the relative changes in the metabolite concentrations of each group with colors ( Fig. 2A). Red and blue represent the upregulation and downregulation of metabolites, respectively. The OPLS-DA score plot shows a clear separation of metabolic pro les between the tank aquaculture group and the pond aquaculture group (Fig. 2B). In the OPLS-DA model, R 2 X = 0.787, R 2 Y = 0.93, Q2(cum) = 0.441 and R2 = 0.943, indicating the usable of the model. The VIP values and coe cients of the OPLS-DA model were analyzed to nd out the effect of metabolites on metabolic alterations in different aquaculture environments (Supplementary material 1). The most metabolites affected in pond aquaculture were related to aminoacyl-tRNA biosynthesis, phenylalanine, tyrosine and tryptophan biosynthesis, ABC transporters and valine, leucine and isoleucine biosynthesis metabolic pathways. Metabolic pathways of top-20 were shown in Fig. 2C. Four metabolic pathways were signi cantly different at P > 0.01, while seven metabolic pathways were signi cantly different at P > 0.05. The coe cients and p-value of the metabolic pathways were analyzed to show the correlation of metabolites on metabolomic alterations in the different aquaculture environments (Supplementary material 2). The signi cantly enriched pathway was selected to construct a bubble diagram. Figure 2D showed that among the altered metabolic pathways, the rich factor was the largest for the aminoacyl − tRNA biosynthesis and ABC transporters pathways. And these two pathways were related to three common metabolites such as L-Histidine, L-Lysine and L-Valine.

Transcriptomics Of Different Cultured Environments
276 DEGs were found in the tank aquaculture and the pong aquaculture treatment groups (P < 0.05), precisely, 169 upregulated and 107 downregulated. Two parts of hierarchical clusters were shown in different cultured environments according to DEGs expression levels (Fig. 3A, cluster A and cluster B). Cluster A represented upregulated genes, while cluster B represented downregulated genes. As shown in Table 1, the top-twenty of DEGs involved in different molecular functions, such as protein binding, succinate dehydrogenase activity, iron ion binding, inorganic anion exchanger activity and motor activity. The volcano plot revealed the overall distribution of the DEGs (Supplementary Fig. 2). The distribution comparison diagram of DEGs and all genes by GO enrichment analysis at level 2 was shown in Fig. 3B. The three major categories, such as biological processes, cellular components and molecular functions, were revealed in Fig. 3B. The distribution comparison diagram of upregulated and downregulated DEGs were shown in GO level 2 analysis (Supplementary Fig. 3). Among the identi ed pathways, the thirty most important pathways involved in lipid metabolism and energy metabolism were showed in Fig. 3C, and some DEGs involved in growth and development. KEGG database showed the diverse ranges of functionally characterize DEGs, with the 276 DEGs between the tank aquaculture and the pond aquaculture groups assigned to 168 pathways. The top-twenty pathways identi ed by KEGG enrichment analysis were the focus. A bubble diagram was constructed using the -log10 (p-value) to visualize the expression patterns of DEGs. The pathways of DEGs were mainly included viral myocarditis, immunode ciency, allograft rejection, and autoimmune thyroid disease pathways (Fig. 3D).

Association between the altered pathways with metabolites and transcriptomic pathway
The KEGG database were used to analyze the effects of two environment on transcriptomics pathway and metabolomics pathway. The pond aquaculture groups induced signi cant alterations in the metabolites and genes in the gut of American shad compared to the tank aquaculture groups. To investigate the potential associations, Venn diagrams (Fig. 4A) were generated by identifying the common KEGG pathways. Five of the common metabolite pathways (glycerophospholipid metabolism, arginine and proline metabolism, purine metabolism, valine, leucine and isoleucine degradation, and glycine, serine and threonine metabolism) shared nine genes (As13G016035, As21G024434, As04G005193, As04G005148, As02G001872, As07G009244, As15G018635, As13G016748, and As23G026314) and eight metabolites (CDP-ethanolamine, L-proline, sulfuric acid, L-valine, L-tryptophan, creatinine, uric acid and L-isoleucine). The total metabolites levels increased upon pond aquaculture, and the nine genes expression changed ( Table 2 in detail). The association network map was constructed based on the results of the association analysis of differential genes and differential metabolites (Fig. 4B). The total differential metabolites levels increased upon pond aquaculture, whereas up to 5 genes (As13G016035, As02G001872, As07G009244, As15G018635 and As23G026314) related to the glycine, serine and threonine metabolism, purine metabolism, arginine and proline metabolism, glycerophospholipid metabolism appeared to downregulate ( Table 2 in detail, in Fig. 5A-D). In addition, the total differential metabolites levels increased upon pond aquaculture, whereas up to 4 genes (As13G016748, As21G024434, As04G005193 and As04G005148) related to valine, leucine and isoleucine degradation and glycerophospholipid metabolism appeared to be upregulated (Table 2 and, in detail, in Fig. 5E). The effect of metabolites in different aquaculture environmental remains to be investigated.

Microbial Community Analysis
Microbial community analysis according to high-throughput sequencing was done to reveal the changes of microbiome community in the tank and pond aquaculture environment. The two environment samples were all dominated by the phyla of Proteobacteria and Bacteroidetes (Fig. 6A). The compositions of the microbial communities in all the samples were similar at the rank of the phylum. Proteobacteria was the most abundant bacterial phylum in all eight samples, accounting for approximately 60% of the total species. The number of proteobacteria was more signi cant in the pond environment compared to the tank environment. The relative changes in the microbial abundance showed with colors in the heatmap (Fig. 6B). We use alpha diversity indices of the Chao index (Fig. 6C), and the Shannon index (Fig. 6D) to con rm the results. This is a signi cantly higher diversity of the microbiome in the pond environment than the tank environment group. We performed beta diversity index, PCA analysis, PCoA analysis, and UPGMA sample hierarchical cluster analysis to determine the classi ed bacterial taxa between different groups (Fig. 7). In addition, a biomarker analysis using the linear discriminant analysis (LDA) effect size (LEfSe) method was executed to determine the classi ed bacterial taxa with signi cant differences in abundance. As shown in Fig. 8A and B, twenty-eight bacterial clades showed statistically signi cant changes with an LDA threshold of 4.1. Nine bacteria were signi cantly enriched in pond aquaculture environment group, while nineteen clades showed an abundance advantage in the tank aquaculture environment group. The relative abundance of phylum was shown in Fig. 8C, bacteroidia, alphaproteobacteria, fusobacteria, rmicutes, gemmatimonadetes, acidobacteria, nitrospirae and epsilonbacteraeota were enriched in pond aquaculture environment samples. The species correlation at various taxonomic levels re ected in the species correlation network map (Fig. 8D).

Discussion
Our research reveals the differences in the sediment microbial communities in tank aquaculture and pond aquaculture of American shad (Alosa sapidissima). We analyzed the microbiome in the system and provided information on the effect of the environment factor on the American shad growth. The result showed that the microbiome was dominated by the phyla of Proteobacteria, Fusobacteria, Bacteroidetes and Actinobacteria in the pond aquaculture environment. This result is consistent with other ndings in the pond environments. For example, Dai et al., (2021) found bacteroidetes, proteobacteria and actinobacteria were more abundant in the pond environment. The pond aquaculture induced signi cant alterations in the metabolomes and transcriptomic pathways of the gut compared to tank aquaculture. For example, the total amino acid metabolites level increased upon pond aquaculture, whereas up to 5 genes related to the pathway appeared to be downregulated. DP-ethanolamine, L-proline, sulfuric acid, L-valine, Ltryptophan, creatinine, uric acid, and L-isoleucine increased in the pond aquaculture group and positively correlated with the family of B8 (Fusobacteria;f_Fusobacteriaceae) and B9 (Planctomycetes; f_Gemmataceae). Similarly, lysine increased in the ponk aquaculture group and positively correlated with the families of B5 (Chlamydiae;f_unassigned), B14 (Proteobacteria;f_unassigned) and B16 (Proteobacteria; f_Neisseriaceae). Fusobacteriaceae, aeromonadaceae and moraxellaceae were enriched in the pond environment compared to the tank environment. Microorganisms in sediment were mainly related to the enzymes of organic matter decomposition (Dai et al., 2021), which served as a natural nutritional supplement to enhance aquatic organisms' growth (Addo et al., 2021). The nutritional supplement is closely related to the growth development of aquatic animals. The results indicated that greater abundance and diversity of microbial communities in pond aquaculture group induced better growth condition in American shad. The results of the microbial community are consistent with growth indicators.
The metabolomic and transcriptomic analysis showed the metabolic disruption of the gut of American shad. CDP-ethanolamine, Lproline, sulfuric acid, L-valine, L-tryptophan, creatinine, uric acid, and L-isoleucine increased in the pond aquaculture group compared to the tank aquaculture group. The integration data at the pathway level showed the affected metabolic routes in the gut of American shad after the pond aquaculture, including glycerophospholipid metabolism, arginine and proline metabolism, purine metabolism, valine, leucine and isoleucine degradation, and glycine, serine and threonine metabolism. The total energy of sh meets the basic metabolic needs, including growth, development and reproduction (Bunnell et al. 2003;Ye et al., 2011). Amino acids and glycerophospholipid have a crucial role in the development of sh, including providing essential fatty acids, essential protein and enough energy for muscle, tissue and gonad development (Masrizal et al., 2015). The results of both transcriptomic and metabolomic are in agreement with growth indicators.
Fish is the source of the essential protein and fatty acids for humans. At present, aquaculture is the main source for sh production and consumption (FAO, 2020). Bjrnevik et al., (2017) revealed that the improper aquaculture pattern affected the quality and the taste of sh esh, including reducing protein content. Ostbye et al., (2018) reported that the quality of sh, including nutritional composition, was primarily affected by environmental factors. This conclusion is in agreement with our study, indicating the changes of metabolites in a different aquaculture environment, involving DP-ethanolamine, L-proline, sulfuric acid, L-valine, Ltryptophan, creatinine, uric acid, and L-isoleucine. The transcriptome data revealed four genes (As23G026314, As04G005148, As21G024434, and As04G005193) related to these metabolisms (valine, leucine and isoleucine degradation, and glycerophospholipid) signi cantly up-regulated. In comparison, the four genes (As23G026314, As13G016035, As02G001872 and As07G009244) related to metabolisms (glycine, serine and threonine metabolism, purine metabolism, and arginine) signi cantly down-regulated in pond aquaculture group. Based on our analysis, numerous genes and metabolites differentially expressed between the tank and pond aquaculture groups, in agreement with earlier investigations.
also reported that glycine, serine, arginine, proline metabolism and threonine metabolism of oriental river prawn Macrobrachium Nipponese were signi cant differences in response to acute and chronic environment factor stress. This survey provides valuable information on the diversity of microbiomes in different aquaculture. In addition, the potential effects of microbiomes on the growth of these shes are also mentioned.

Conclusion
Our research analyzed the differences in the distributions of microbial communities between the tank aquaculture and the pond aquaculture environment. We found that the dominant phyla were Bacteroidetes, Actinobacteria, Fusobacteria, Gemmatimonadetes, Firmicutes, Acidobacteria, Nitrospirae and Epsilonbacteraeota in both aquaculture environment, by comparing microorganisms in different aquaculture ponds from the same location. The bacterial abundance signi cantly increased in the pond aquaculture than that in tank aquaculture. Some identi ed differential genes and metabolites were associated with the response of American shad to different aquaculture environments. In conclusion, the body weight, amino acid metabolism, and glycerophospholipid metabolism of American shad signi cantly changed due to the pond aquaculture environment. Taken together, during popular aquaculture, the interaction effect of the aquaculture environment on cost bene ts should be carefully evaluated. Besides, meat quality during aquaculture should be seriously considered. This result could potentially serve as microbiome indicators in different aquaculture environments. Our study highlights the necessity to incorporate microbiome toxicology research in the aquaculture industry.

Declarations
Availability of data and materials All data generated or analyzed during this study are included in this published article (and its supplementary information les).  Tables   Table 1 Biological processes and molecular functions of the most differentially expressed genes (top-twenty) after culture different cultured environments.

Supplementary Files
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