Introduction

Probiotics are widely used in aquaculture dietary additives because of their advantages of safety, non-pollution, promoting digestion, enhancing antioxidant levels and immune function, and regulating intestinal micro-ecological homeostasis (Assefa and Abunna 2018; Mai et al. 2023; Ringø 2020). Probiotics operate on the fish intestine by self-secreting digestive enzymes or promoting the production of digestive enzymes to enhance digestion and absorption (Luo et al. 2022; Soto 2017). In addition, one way that probiotics work by competitive exclusion to interfere with the colonization of pathogens is producing inhibitory molecules, and another way is competing for space or oxygen in the intestine (Lu et al. 2022). Studies have shown that probiotics can adhere to intestinal mucosa, thus blocking the adhesion and colonization of pathogenic bacteria in fish (Chabrillón et al. 2005; Nikoskelainen et al. 2001). However, the high temperature and high pressure conditions in the production of pelleted feeds tend to affect the activity of the probiotics added to the feed, and this processing method seriously affects the actual growth-promoting effect of the probiotics, causing an increase in the cost of aquaculture (Kechagia et al. 2013; Kumar and Libchaber 2013). The Lactobacillus is widely used as feed additives, which could be colonized in the intestine of aquatic animals and regulate the microecological homeostasis. Prasoodanan et al. (2021) reported that the Prevotella is associated with diets rich in plant-derived fibers in the human intestine and plays a key role in carbohydrate metabolism. Furthermore, the beneficial effects of compound probiotics on aquatic animals were generally better than those of single strains, such as improving growth performance and enhancing disease resistance (Gabr et al. 2023; Li et al. 2023). Recent studies have investigated the benefits of compound probiotics on aquatic animals (Park et al. 2016; Ren et al. 2022; Xie et al. 2019). Recirculating aquaculture system (RAS), a useful and efficient practice in aquaculture, is designed for intensive farming (Saquib and Sreenivasan 2023). At present, RAS is widely used in the world due to its safety, efficiency, and low water pollution and requirements. RAS has been widely used for a number of aquatic animals, including Clarias gariepinus (Júlia et al. 2023), Sander lucioperca (Géza et al. 2023), Siniperca chuatsi (Zhu et al. 2023), Salmo salar (Fang et al. 2021), and Litopenaeus vannamei (Du et al. 2021).

Whitespotted conger (Conger myriaster), belonging to the class Osteichthyes, order Anguilliformes, family Congridae, genus Conger, is one of the economically important marine fish species in the eastern coast of China, the coast of the Korean Peninsula, and the coast of the Japanese islands (Mu et al. 2021). In recent decades, the fishery resources of C. myriaster have been subjected to predatory fishing, and the seedling resources have become increasingly scarce (Hori et al. 2019). Furthermore, there is limited knowledge of the complex life histories of C. myriaster (Zou et al. 2020), and its fry breeding is a huge challenge. Therefore, the development of breeding technology for C. myriaster is of great importance in order to promote the protection of wild populations and the sustainable development of its industry. At present, there are some studies on the population status and distribution (Li et al. 2020), biological characteristics (Mu et al. 2018), and fishing methods (Kim et al. 2014) of C. myriaster, but few studies in the RAS of C. myriaster.

In this study, we investigated the effects of probiotics on the growth, non-specific immunity, intestinal digestive enzyme, and microbiota of C. myriaster by adding a compound probiotics preparation to the feed. The compound probiotics were added to the feed and processed into dough, and the feed was not pelleted under high temperature and pressure to maintain the effectiveness of the compound probiotics to the maximum extent. The results might provide a data support for the research and application of intestinal micro-ecological regulation and healthy farming of C. myriaster in RAS.

Materials and methods

Diet preparation

The compound probiotics (109 CFU/mL) were obtained from Yantai Dale Biotechnology Co., Ltd. (Yantai, China). The composition of the compound probiotics was 96.19% Lactobacillus, 3.04% Prevotella, 0.35% Clostridium_sensu_stricto_1, 0.02% Escherichia-Shigella, 0.01% unclassified_f__Enterobacteriaceae, and 0.01% Bifidobacterium. The above data for the compound probiotics was sequenced by 16S rRNA sequencing technology and uploaded to the NCBI Sequence Read Archive (SRA) database. The saline was purchased from Beijing Solepolis Technology Co., Ltd. (#IN9000, Beijing, China). Commercial feed was produced by Qingdao Saigelin Biotechnology Co., Ltd. (Qingdao, China), with the following nutritional components: crude protein ≥ 46%, crude ash ≤ 17%, moisture ≤ 12%, crude fiber ≤ 8%, crude lipid ≥ 5%, lysine ≥ 2%, and total phosphorus ≥ 0.8%. Chilled anchovies (Engraulis encrasicholus) were caught from the sea near Haiyang (Yantai, China) and frozen for preservation.

The experimental design was used with 2 groups and 12 replicates per group. The compound probiotics group (PI group) diet was prepared by supplementing the basal diet with the compound probiotics at a final dose of 4.2 × 107 CFU/g. Prior to use, bacterial precipitates were collected via centrifugation at 5000×g for 10 min. The pellets were washed twice with sterile saline and suspended in sterile saline at a final concentration of 109 CFU/mL. The number of bacterial cells in the suspensions was determined by turbidimetry and administered to compound probiotics in the experimental diets at a concentration of 4.2 × 107 CFU/g with slight modifications. Every 40 mL of compound probiotics is coated with 380 g of commercial feed, mixed with 580 g chilled anchovies, and made into dough. The preparation process is as follows: Firstly, the chilled anchovies were fully stirred with a meat grinder (Fengruigi Food Machinery Co., Ltd., Yongkang, China), the experimental diet was then mixed using a 30-L multifunction stirring mixer (Henan Wanjie Intelligent Technology Co., Ltd, Henan, China); Finally, pellets were prepared by passing the dough through a pelleting machine (Baokyong Commercial Co., Busan, South Korea), and the pellets were air-dried for 48–72 h. The control group (CI group) diet was prepared by supplementing the basal diet with the same volume of sterile saline and preparing the feed as described above for the experimental diet. All feeds were stored at 4 °C. The approximate nutritional composition of the test feeds is shown in Table 1.

Table 1 Approximate composition of diets for two groups (%)
Full size table

Experimental fish and feeding trial

Experimental C. myriaster were provided by Haiyang Yellow Sea Fisheries Co., Ltd. (Yantai, China). Healthy and active C. myriaster of similar individual sizes were selected for this study after 2 weeks of domestication, with an average body weight of 169.78 ± 3.25 g and a stocking density of 8.00 ± 0.15 kg/m3. All C. myriaster were fed once at 15:00 every day for 10 weeks, and the feed amount was approximately 1–2% of total bodyweight. Thirty test C. myriaster were randomly selected from each tank every 2 weeks, the average body weight was calculated, and the feeding amount was adjusted according to the changes in the average body weight of the test fish during the experiment. We collected dead fish at 8:00 every day and weighed them for record. Residual baits and feces were pushed to the central drainage pipe for discharge. The relevant parameters during the experiment: temperature 21.5–23.6 °C, ammonia nitrogen < 0.45 mg/L, nitrite nitrogen < 0.06 mg/L, pH 7.8–8.2, salinity 28–32, and DO ≥ 6.0 mg/L.

Experimental aquaculture system

The RAS, including twenty-four 25 m3 water volume of rearing tanks (length 5.00 m, width 5.00 m, and height 1.50 m), was used in the current study located in Haiyang Yellow Sea Fisheries Co., Ltd. This system had a single water-treatment loop. The rearing seawater flowed from the return pipe of every tank, flowed into a drum-filter, and pumped into a biofilter and then ran through a UV light disinfection tank and an oxygen enrichment tank, eventually returned to the rearing tank. The biofilter material is an elastic brush with a diameter of around 0.5 mm. The elastic brush has specific surface area of 360 m2/m3. The total experimental seawater volume is approximately 760 m3. During the experiment, the number of cycles in RAS is about 10 times per day; the seawater renewal rates of this system were approximately 5–10% per day.

Sample collection

After the feeding trial, all fish were fasted for 24 h priorly. One C. myriaster was randomly taken from each sampling tank and anesthetized by using tricaine methane sulfonate (MS-222, Sigma, USA, 200 mg/L). The individual body weight, length, and height were measured. Serum and tissue samples related to experimental C. myriaster were collected on the 35th and 70th days. Blood was collected from the tail vein using 1-mL syringes and centrifuged at 1370 g for 10 min (4 °C). Serum was collected and used for the determination of serum biochemical indices. The body of fish was disinfected using 75% ethanol. The liver and viscera of C. myriaster were dissected and weighed individually; liver and foregut were separated for biochemical index analysis. The samples of the total intestine of other nine C. myriaster were collected on ice and cleaned using saline for microbial diversity analysis.

Nine C. myriaster intestines from each group were placed in 2-mL freezing tubes and quickly snap-frozen in liquid nitrogen. Then, three C. myriaster intestinal samples of each group were placed in a sterile manipulator and crushed with a sterile mortar and grinder, then divided into three 2-mL lyophilized tubes. Compound probiotics samples were collected with 0.22-μm filter membrane. All samples were kept at − 80 °C for further analysis. CI35 represents the fish intestine samples of the CI group at 35 days. CI70 represents the fish intestine samples of the CI group at 70 days, PI35 represents the fish intestine samples of the PI group at 35 days, and PI70 represents the fish intestine samples of the PI group at 70 days. PG represents a compound bacterial preparation.

Growth performance

Weight gain rate (WGR), specific growth rate (SGR), hepatosomatic index (HSI), viscerosomatic index (VSI), condition factor (CF), survival rate (SR), and feed conversion ratio (FCR) were calculated using the following formula:

$$\textrm{WGR}\ \left(\%\right)=\left(\textrm{FBW}-\textrm{IBW}\right)/\textrm{IBW}\times 100\ \left(\textrm{FBW}\ \textrm{is}\ \textrm{final}\ \textrm{body}\ \textrm{weight},\textrm{IBW}\ \textrm{is}\ \textrm{initial}\ \textrm{body}\ \textrm{weight}\right)$$
$$\textrm{SGR}\ \left(\%/\textrm{days}\right)=\left(\ln\ \textrm{FBW}-\ln\ \textrm{IBW}\right)/T\times 100\ \left(T\ \textrm{is}\ \textrm{days}\right)$$
$$\textrm{HSI}\ \left(\%\right)={W}_{\textrm{L}}/\textrm{FBW}\times 100\ \left({W}_{\textrm{L}}\ \textrm{is}\ \textrm{liver}\ \textrm{weight}\right)$$
$$\textrm{VSI}\ \left(\%\right)={W}_{\textrm{V}}/ FBW\times 100\ \left({W}_{\textrm{V}}\ \textrm{is}\ \textrm{viscera}\ \textrm{weight}\right)$$
$$\textrm{CF}\ \left(\%\right)=\left(\textrm{FBW}/{\textrm{FBL}}^3\right)\times 100\ \left(\textrm{FBL}\ \textrm{is}\ \textrm{final}\ \textrm{body}\ \textrm{length}\right)$$
$$\textrm{SR}\ \left(\%\right)=\left({N}_{\textrm{f}}/{N}_{\textrm{i}}\right)\times 100\ \left({N}_{\textrm{f}}\ \textrm{is}\ \textrm{final}\ \textrm{fish}\ \textrm{number},{N}_{\textrm{i}}\ \textrm{is}\ \textrm{initial}\ \textrm{fish}\ \textrm{number}\right)$$
$$\textrm{FCR}={W}_1/\left({W}_2+{W}_3-{W}_4\right)\ \left({W}_1\ \textrm{is}\ \textrm{dry}\ \textrm{feed}\ \textrm{consumed},{W}_2\ \textrm{is}\ \textrm{weight}\ \textrm{of}\ \textrm{death}\ \textrm{fish},{W}_3\ \textrm{is}\ \textrm{initial}\ \textrm{total}\ \textrm{weight},{W}_4\ \textrm{is}\ \textrm{final}\ \textrm{total}\ \textrm{weight}\right)$$

Analysis of non-specific immunity and digestive enzyme

The liver and foregut samples of C. myriaster in triplicates were minced and homogenized in 0.9% saline at a ratio of 1:9 (m/v), and then centrifuged at 3500 r/min for 15 min at 4 °C to collect the supernatant. The serum and supernatant were respectively used to determine non-specific immunity and digestive enzyme parameters with Jiancheng kits (Nanjing, China) according to our preliminary experiment and instructions. The serum was used to determine immunological parameters, including glutamic-oxaloacetic transaminase (AST) activity (Jiancheng kit, C010-1-1) and glutamic-pyruvic transaminase (ALT) activity (Jiancheng kit, C009-1-1). The supernatant of liver was used to determine antioxidant enzymes, including total protein (TP) content (Jiancheng kit, A045-2-1), superoxide dismutase (SOD) activity (Jiancheng kit, A001-1-1), catalase (CAT) activity (Jiancheng kit, A007-1-1), and malondialdehyde (MDA) content (Jiancheng kit, A003-1). And the supernatant of foregut was used to determine digestive enzyme activity including total protein (TP) content (Jiancheng kit, A045-2-1), amylase (AMS) activity (Jiancheng kit, C016-1-1), lipase (LPS) activity (Jiancheng kit, A054-2-1), and trypsin (TPS) activity (Jiancheng kit, A080-2-2).

DNA extraction, PCR amplification, and high-throughput sequencing analysis of the intestinal microbiota

According to manufacturer’s instructions, genomic DNA of all samples (n = 9 individuals/group) was extracted using the E.Z.N.A.® soil DNA Kit (Omega Bio-tek, GA, U.S.). The DNA extract was checked on 1% agarose gel, and DNA concentration and purity were determined with NanoDrop 2000 UV-vis spectrophotometer (Thermo Scientific, Wilmington, USA). The V3–V4 region of the bacterial 16S rRNA gene was amplified with primer pairs 338F and 806R by an ABI GeneAmp® 9700 PCR thermocycler (ABI, CA, USA). The PCR amplification of 16S rRNA gene using Chang’s method (Chang et al. 2019). According to the standard protocols, purified amplicons were pooled and paired-end sequenced on an Illumina NovaSeq PE250 platform (Illumina, San Diego, USA) by Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). The raw reads were deposited into the NCBI SRA database (Accession Number: SRP449727).

The raw fastq files of sequencing reads were demultiplexed, quality-filtered by fastp version 0.20.0, and merged by FLASH version 1.2.7 (Meng et al. 2022). Operational taxonomic units (OTUs) were clustered using a 97% similarity cutoff, and chimeric sequences were subsequently identified and removed. The OTU taxonomic level with 97% similarity was selected, and rarefaction curve and Venn diagram were produced using R software (Version 3.3.1). Alpha diversity index was calculated to reflect the richness and diversity of microbial communities using the Mothur (http://www.mothur.org/). Principal co-ordinates (PCoA) diagram and non-metric multidimensional scaling (NMDS) diagram were drawn to analyze the beta diversity index between the different groups using R software (Version 3.3.1).

The bar diagram at the phylum and genus levels was made using R software (Version 3.3.1) based on the data table in the tax_summary_a folder. Multi-group comparisons at the phylum and genus levels by one-way ANOVA allowed comparison of whether there were significant differences in the distribution of species in each group, and then, post hoc tests were performed on species that differed to identify sample groups that differed in multiple groups. Effect size (LEfSe) and linear discriminant analysis (LDA) (http://huttenhower.sph.harvard.edu/galaxy/root?tool_id=lefse_upload) performed on samples according to different grouping conditions based on taxonomic composition to identify biomarkers that have a significant differential impact on sample delineation. PICRUSt2 functional prediction of 16S amplicon sequencing results based on COG and KEGG databases, respectively.

Statistical analysis

All data were processed by SPSS 26.0 and Microsoft Excel v16, and the analysis results were presented as “means ± standard error of the means (Means ± SEM).” Growth data were analyzed by two-tailed Student’s t-tests for between-group variability (A value of P < 0.05 was statistically significant). Non-specific immunity enzyme, digestive enzyme data, and microbial sequencing results were statistically processed and analyzed by one-way ANOVA; significant differences were tested by Duncan’s multiple comparisons (A value of P < 0.05 was statistically significant).

Results

Growth performance and feed utilization

Growth performance and feed utilization of C. myriaster fed different levels of dietary compound probiotics are shown in Table 2. The inclusion of compound probiotics to the feed generated significant effects on the FBW, FBL, WGR, SGR, and SR of C. myriaster in the PI group (P < 0.05). However, no significant variation was observed in FBH, HSI, VSI, CF, and FCR among 2 groups (P > 0.05).

Table 2 Effects of compound probiotics on growth performance of C. myriaster
Full size table

Non-specific immunity and intestinal digestive enzymes

The results of the non-specific immunity and intestinal digestive enzymes of C. myriaster are presented in Table 3 and Table 4, respectively. At the 35th day, liver TP, liver SOD, liver CAT, intestine TP, intestine LPS, and intestine TPS activities of C. myriaster in the PI group were significantly higher than those in the CI group (P < 0.05), while the compound probiotics had no significant effect on serum AST, serum ALT, liver MDA, and intestine AMS activities (P > 0.05). At the 70th day, liver TP, liver SOD, liver CAT, intestine TP, intestine AMS, intestine LPS, and intestine TPS activities of C. myriaster in the PI group were significantly higher than those in the CI group (P < 0.05), and liver MDA activity was significantly lower than that in the CI group (P < 0.05), while the compound probiotics had no significant effect on serum AST and ALT activities (P > 0.05). In the CI group, liver MDA activity was significantly higher at 70 days than at 35 days (P < 0.05). In the PI group, liver SOD, liver CAT, intestine TP, intestine AMS, and intestine LPS activities were significantly higher at 70 days than at 35 days (P < 0.05).

Table 3 Effects of compound probiotics on non-specific immunity indices of C. myriaster
Full size table
Table 4 Effects of compound probiotics on intestinal digestive enzyme activity of C. myriaster
Full size table

Microbiota

Rarefaction curve and Venn analysis

The rarefaction curve (Fig. 1A) showed that the curves of each sample tended to be flat, indicating an adequate amount of sequencing data. Plotting Venn diagram (Fig. 1B) based on OTUs showed that a total of 1752 OTUs were identified for all samples; 17 OTUs were shared among all samples. The number of unique OTUs in PG, CI35, CI70, PI35, and PI70 samples were 15 OTUs, 122 OTUs, 45 OTUs, 95 OTUs, and 692 OTUs, respectively. This observation suggested that dietary supplementation of compound probiotics led to the addition of unique intestinal microbial populations of C. myriaster.

Fig. 1
figure 1

Sequencing quality and quantity of OTUs of all samples. A Rarefaction curves. B Venn analysis of shared and unique OTUs between samples. PG represents a compound bacterial preparation, CI35 represents the fish intestine samples of the control group at 35 days, CI70 represents the fish intestine samples of the control group at 70 days, PI35 represents the fish intestine samples of the test group at 35 days, and PI70 represents the fish intestine samples of the test group at 70 days, the same as below

Full size image

Alpha diversity analysis

Alpha diversity indices of the intestinal microbiota of C. myriaster (Table 5) can be used to respond to the community richness and diversity. The results show that Ace, Chao1, Sobs, and Shannon indices of Alpha diversity indices in the PI group (PI35 and PI70) were significantly higher than those in the CI group (CI35 and CI70), and Simpson index was significantly lower than that in the CI group (P < 0.05). The coverage index has no significant differences among samples (P > 0.05). In addition, the community richness of PI70 was significantly higher than that of PI35 (P < 0.05), while the community diversity has no significant differences. The community richness of all samples was ranked as PI70 > PI35 > CI35 > CI70, and the community diversity of all samples was ranked as PI70 > PI35 > CI70 > CI35. The community richness and diversity of intestinal microbiota in the PI group were significantly higher than those in the CI group (P < 0.05).

Table 5 Alpha diversity indices of the intestinal microbiota of C. myriaster
Full size table

Beta diversity analysis

The differences of microbiota communities among samples were analyzed by the beta diversity indices. PCoA and NMDS analyses based on Bray–Curtis distance for bacterial profiles were used in this study to indicate the community change in all samples. PCoA analysis (Fig. 2A) showed that the bacterial communities of the three types of samples were obviously separated, CI35 samples were similar to CI70 samples, and PI35 samples were similar to PI70 samples. The results of NMDS analysis (Fig. 2B) are similar to those of PCoA analysis, except that the partitioning of PI group and CI group is not obvious.

Fig. 2
figure 2

Beta diversity analysis of the bacteria communities of samples. A PCoA analysis; B NMDS analysis

Full size image

The composition of the microbial communities of C. myriaster and changes with compound probiotics supplemented

The composition and structure of the microbial community at the phylum level is shown in Fig. 3A, B. The dominant phyla (relative abundance > 1%, as the same below) shared by all samples are Firmicutes and Proteobacteria (Table 6). Multi-group comparisons at the phylum level based on one-way ANOVA (Fig. 4A) showed that the differential bacteria phylum between samples were Firmicutes, Spirochaetota, Bacteroidota, Desulfobacterota, and Actinobacteriota. At the phylum level, Firmicutes, Bacteroidota, and Actinobacteriota of intestinal microbiota in the PI group were significantly higher than those in the CI group (P < 0.05). Compared with the CI35 samples, Firmicutes and Desulfobacterota of CI70 samples were significantly increased (P < 0.05), by 23.11% and 25.64%, respectively; while Proteobacteria and Spirochaetota of CI70 samples were decreased but not significantly different (P > 0.05), by 25.54% and 21.54%, respectively (Table 6). There was no significant difference between PI35 and PI70 samples among the majority of the dominant phylum (P > 0.05). The addition of compound probiotics to the feed significantly increased the relative abundance of Firmicutes, Bacteroidota, and Actinobacteriota in the intestine of C. myriaster (P < 0.05), while significantly decreasing the relative abundance of Spirochaetota in the intestine of C. myriaster (P < 0.05).

Fig. 3
figure 3

The composition of the bacterial communities of the samples. A Column chart of relative abundance of species at phylum level; B Circos diagram at the phylum level; C Column chart of relative abundance of species at genus level; D Heatmap at the genus level (top 50)

Full size image
Table 6 Dominant bacterial phylum of the samples (%)
Full size table
Fig. 4
figure 4

Multi-group comparison. A Phylum level. B Genus level. Values in the same group with different superscript lowercase letters mean significant difference (P < 0.05); no letters in the same group indicate insignificant difference (P > 0.05)

Full size image

The composition and structure of the microbial community at the genus level are illustrated in Fig. 3C, D. A comparison of the different samples revealed a significant difference in the composition of the microbiota community at the genus level. The heatmap cluster analysis in Fig. 3D showed that PI35 and PI70 formed a clade, which was then clustered with PG; CI35 and CI70 formed another clade. This suggests that the bacterial communities in the compound probiotics samples were more similar to the intestine samples of the PI group than the CI group.

Dominant bacterial genus of the samples (Table 7) and multi-group comparisons (Fig. 4B) at the genus level based on one-way ANOVA showed that 24 dominant bacterial genus of PI35, such as Lactobacillus, norank_f_Muribaculaceae, Acinetobacter, Bacteroides, Lachnospiraceae_NK4A136_group, Bifidobacterium, and Corynebacterium, were significantly higher than CI35 (P < 0.05), while Brevinema and Photobacterium were significantly lower than CI35 (P < 0.05). The 21 dominant bacterial genus of PI70, such as Lactobacillus, norank_f_Muribaculaceae, Acinetobacter, Bacteroides, Faecalibacterium, Lachnospiraceae_NK4A136_group, Bifidobacterium, and Blautia, were significantly higher than CI70 (P < 0.05), while Mycoplasma, unclassified_f_Desulfovibrionaceae, and Vibrio were significantly lower than CI70 (P < 0.05). As the test time progressed, Mycoplasma, unclassified_f_Desulfovibrionaceae, and Vibrio of CI70 were significantly higher than CI35 in the CI group (P < 0.05), while most of the genus were not significantly different between PI70 and PI35 in the PI group (P > 0.05).

Table 7 Dominant bacterial genus of the samples (%)
Full size table

LEfSe analysis and functional prediction

To investigate the key biomarkers associated of intestinal microbiota with the addition of compound probiotics, linear discriminant analysis effect size (LEfSe) analysis with LDA for the size effect between the CI35, CI70, PI35, and PI70 samples was carried out. In the cladogram from the LEfSe analysis (Fig. 5A), the red, blue, green, and purple nodes represent the microbial taxa that play important roles in different groups at the genus level. With an LDA score threshold of 4 (Fig. 5B), Brevinema and Photobacterium were enriched in the CI35 samples; Lachnospiraceae_NK4A136_group, Bifidobacterium, Corynebacterium, unclassified_f_Enterobacteriaceae, Romboutsia, Blautia, and Brevundimonas were enriched in the PI35 samples; Mycoplasma was enriched in the CI70 samples; norank_f_Muribaculaceae, Acinetobacter, Bacteroides, Lactobacillus, and Faecalibacterium were enriched in the PI70 samples.

Fig. 5
figure 5

LEfSe analysis. A The taxonomic cladogram; B LDA score

Full size image

PICRUSt2 was performed to predict the fish gut microbiome functions of C. myriaster, which showed that fish in different groups exhibited similar functions at Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways (level 2) and clusters of orthologous group (COG) pathways functional prediction. According to the functional annotation and abundance information of the samples in the KEGG database, the top 46 functions of the abundance and their abundance information in each sample were selected to draw a heat map (Fig. 6A). Based on the KEGG database for functional prediction analysis, the relative abundance of global and overview maps, carbohydrate metabolism, and metabolism of other amino acids in the PI group was significantly higher than the CI group (P < 0.05) (Fig. 6B). According to the COG database annotation results, we selected the functional information of the top 23 in the maximum abundance of each sample and generate a histogram of the relative abundance of functions, to visually view the functions and their proportions with the high relative abundance of each sample (Fig. 6C). Based on the prediction results of the COG database, the relative abundance of carbohydrate transport and metabolism, transcription, and extracellular structures in the PI group was significantly higher than the CI group (P < 0.05) (Fig. 6D).

Fig. 6
figure 6

Function annotation and one-way ANOVA of KEGG and COG. A KEGG function level 2 horizontal thermal map; B One-way ANOVA at the level 2 of KEGG function; C Column chart of COG functional; D One-way ANOVA of COG functional. “*” indicates a significant difference (P < 0.05); “**” and “***” indicate extremely significant difference (P < 0.01 and P < 0.001)

Full size image

Discussion

Probiotics are considered as alternatives to antibiotics and widely used in feed additives of aquaculture because of its safe and non-polluting (Luo et al. 2022; Wang et al. 2022). Earlier study showed that compound probiotics induced the better growth and digestion of hosts compared with individual probiotics (Wang and Xu 2005). A number of encouraging studies that validated compound probiotics had a significantly beneficial effect on the aquatic animals by improving growth performance, immune response, digestive enzyme activity, as well as positive effects on intestinal microbiota (Park et al. 2020; Ren et al. 2022; Tao et al. 2022). The Lactobacillus, Bacillus, and Prevotella can be colonized in the intestinal tract of aquatic animals, which can promote the growth performance of hosts, improve non-specific immune enzyme activity, and regulate the intestinal microecological homeostasis (Doan et al. 2018; Kuebutornye et al. 2020; Wang et al. 2021a). Therefore, this study was conducted to explore the effects of compound probiotics consisting of Lactobacillus and Prevotella on the growth performance, non-specific immunity, and intestinal digestive enzyme activity and microbiota of C. myriaster in RAS.

In this study, the addition of compound probiotics to the feed significantly improved the growth performance of C. myriaster, such as FBW, FBL, WGR, SGR, and SR. Similar results were also found in crucian carps (Carassius auratus gibelio) (Zhang et al. 2021), starry flounder (Platichthys stellatus) (Park et al. 2016), and pacific white shrimp (Litopenaeus vannamei) (Xie et al. 2019). The results in our study could be related to the fact that certain bacteria in compound probiotics could provide essential nutrients for growth of C. myriaster. The growth-stimulant effect of genus Lactobacillus on growth may be partially attributed to the important role in improving digestion and absorption of the feed and producing growth-promoting metabolites in the host intestine (Salih et al. 2023). Moreover, the enhancing effects of compound probiotics may partially return to the interaction mechanism between probiotics. Therefore, we speculate that compound probiotics may improve growth performance of C. myriaster.

Non-specific immune enzyme activity has been widely used as indices of the health level of fish in numerous studies. SOD and CAT are the important antioxidant enzymes which degradate reactive oxygen species (ROS) in fish tissues. SOD can catalyze the dismutation of the O2− to the H2O2 (Fridovich 1995), while CAT can turn H2O2 into O2 and H2O (Dong et al. 2017). MDA usually can evaluate the oxidative damage degree of aquatic animals (Jia et al. 2019). The present results about SOD, CAT, and MDA activities in the CI and PI group, confirming that the benefit role of compound probiotics elevated defense against cell oxidative damage of C. myriaster. Similar results were also found in pacific white shrimp (Xie et al. 2019) and largemouth bass (Micropterus salmoides) (Tao et al. 2022). It has been reported that antioxidant function of probiotics in fish may be attributed to their prompting impacts on modulation of antioxidant genes (Agh et al. 2022; Salih et al. 2023). After compound probiotics enter and colonize the surface of the intestine of aquatic animals, their surface antigens or certain metabolites constantly stimulate the immune defense system as immunogens, thus improving the non-specific immunity of aquatic animals (Du et al. 2022).

Growing evidences have indicated that the activities of AMS, LPS, and TPS in the intestine are intimately associated with the growth of aquatic animals. At the end of the present experiment, TP, AMS, LPS, and TPS activities of C. myriaster in the PI group were significantly higher than those in the CI group (P < 0.05). It has been shown that probiotics (such as Lactobacillus) can produce digestive enzymes, short-chain fatty acids, B-Group vitamins, and other metabolic substrates, which contribute to digestion and metabolism of nutrients in aquatic animals (Bairagi et al. 2002; LeBlanc et al. 2011). Luo et al. (2022) reported that the TP and the activities of AMS, LPS, and TPS of grass carp (Ctenopharyngodon idella) in the duo-strain probiotics (B. subtilis and L. plantarum) groups were significantly improved compared with the control group (Luo et al. 2022), which was similar to the findings of this experimental study. The addition of compound probiotics may stimulate the host to produce endogenous enzymes in some special way; the elevation of the activities of AMS, LPS, and TPS may increase the digestion and absorption of nutrients, which in turn benefited to the improved growth performance (Ziaei-Nejad et al. 2005). The above physiological and biochemical indices in the present study suggested that compound probiotics significantly enhanced the liver’s antioxidant status and digestive enzyme activity of intestine in C. myriaster. Furthermore, the higher growth performance in the PI group might be attributed to the improvement of immune responses and digestive capacity.

Changes in the intestinal microbial composition of aquatic animals may affect the feed utilization, and various physiological indices of aquatic animals, and eventually lead to growth differences in aquatic animals (Dehler et al. 2016). Generally, the higher the richness and diversity of intestinal microbiota, the stronger the resistance to pathogenic bacteria, thus reducing the susceptibility of aquatic animals to disease (Du et al. 2022). In this study, α-diversity indices were calculated to analysis community richness and diversity in the intestinal microbiota within each group at the 35th and 70th day of the experiment. The community richness and diversity of the intestinal microbiota in the PI group were significantly higher than those in the CI group, suggesting that compound probiotics (4.2 × 107 CFU/g) significantly enhance richness and diversity in the C. myriaster intestine microbiota, which means that there is more growth of symbiotic bacteria (Carnevali et al. 2016) and colonization resistance to certain pathogens (Hodgson et al. 2002). Similar results were found in other studies related to compound probiotics in aquatic animals, such as pacific white shrimp (Ren et al. 2022) and Nile tilapia (Oreochromis niloticus) (Tachibana et al. 2020).

The diet additive compound probiotics fed to C. myriaster provided different degrees of changes in the relative abundance of intestinal microbiota. In our study, we found that Firmicutes and Proteobacteria were the most abundant in the C. myriaster intestine microbiota, which was consistent with previous studies in turbot (Scophthalmus maximus) (Guo et al. 2020) and juvenile olive flounder (Paralichthys olivaceus) (Niu et al. 2019). At the phylum level, the relative abundance of Firmicutes, Bacteroidota, and Actinobacteriota of intestinal microbiota in the PI group were significantly higher than those in the CI group. The enhanced activity of liver non-specific immune enzymes and intestinal digestive enzymes in this study may be related to the enhancement of these three phyla in the intestine of C. myriaster by the complex probiotics. Previous studies showed that Firmicutes containing a wide variety of probiotics, such as Lactobacillus spp. and Bacillus spp., were a positive indicator of intestinal health (Huang et al. 2021). Bacteroidota played an important role in host growth and development, digestion, and metabolism of nutrients, as well as immune modulation (Gibiino et al. 2018; Li et al. 2021). Furthermore, Bacteroidota bacteria could produce butyrate, which was a microbiota-derived intestinal mucosal immunity regulator (Million et al. 2018). Actinobacteria was a beneficial and relevant minority for the maintenance of intestinal microbiota (Binda et al. 2018). Some studies found that probiotics can improve the relative abundance of Firmicutes in the intestine, such as Chinese soft-shelled turtle (Trionyx sinensis) (Zhang et al. 2014), Totoaba macdonaldi (González-Félix et al. 2018), Atlantic Salmon (Salmo salar L.) (Jaramillo-Torres et al. 2019), which were consistent with the results of this study.

At the genus level, the relative abundance of Lactobacillus, Acinetobacter, Bacteroides, Faecalibacterium, Lachnospiraceae_NK4A136_group, Bifidobacterium, Blautia, and norank_f_Muribaculaceae in the PI group was significantly higher than those in the CI group at the end of the experiment, while the relative abundance of unclassified_f_Desulfovibrionaceae, Vibrio, and Mycoplasma was significantly lower than CI group. As for the view of the results at the genus level, only the Lactobacillus of the compound probiotics seems to have successfully colonized in the intestine of C. myriaster. Previous studies have presented that the inclusion dosage and duration of the compound probiotics, as well as the developmental period of the aquatic animals affect the colonization of probiotics in the intestine (Niu et al. 2019). Lactobacillus is widely used as probiotics in aquaculture. According to some studies, many Lactobacilli species showed significant antimicrobial performance against potential pathogenic species, such as Vibrio and Aeromonas species (Dhanasekaran et al. 2010). Studies in aquaculture have shown that Acinetobacter can secrete lipase (Breuil and Kushner 1975), while lipase can hydrolyze lipids (Kim et al. 2020). This may also be related to the fact that LPS activity in the intestine of the PI group was significantly higher than that of the CI group. Zafar and Saier (2021) reported that several species of Bacteroides can metabolize polysaccharides and oligosaccharides and provide nutrition and vitamins to the host. Faecalibacterium is a health-promoting bacteria (Gangadoo et al. 2018). Faecalibacterium prausnitzii, a member of the Faecalibacterium genus, promotes epithelial health and produces butyrate (Miquel et al. 2013). Wang et al. (2021b) showed that Lachnospiraceae_NK4A136_group of intestinal microbiota could play a crucial role in improving airway inflammation caused by respiratory syncytial virus (RSV)–infected asthmatic mice. Bifidobacterium is an anaerobic and gram-positive bacterium that is considered a safe probiotic for modulating the intestinal microbiota in humans (Khavari-Daneshvar et al. 2017; Luca et al. 2014). Vibrio, which belongs to Proteobacteria, is one of the most common potentially pathogenic species in aquaculture (Chauhan and Singh 2019). In addition, some studies have reported that Mycoplasma species are often pathogenic in vertebrates and fish species (Gaulke et al. 2019; Gupta et al. 2018; Legrand et al. 2020a; Legrand et al. 2020b). Guo et al. (2022) found that the supplementation of probiotics increased the relative abundances of Bifidobacteium and Faecalibacterium in the intestine of turbot (Scophthalmus maximus), which was consistent with the results of this study. Based on the above analysis of the phylum and genus levels, we reasonably speculate that the improvement in growth performance, non-specific immunity, and intestinal digestive enzymes of C. myriaster in the PI group may be attributed to the fact that the compound probiotics increased the relative abundance of beneficial bacteria and decreased the relative abundance of pathogenic bacteria in the intestine of C. myriaster, thus improving food digestion, nutrient metabolism, and immune modulation.

Meanwhile, we used PICRUSt2 (KEGG and COG functional prediction) to predict the potential functions of different microbes in the CI and PI group. Our results reveal that the intestinal microbiota may participate in carbohydrate metabolism and feed utilization. Carbohydrates are commonly used as inexpensive and plentiful energy sources in aquatic feed (Kumar et al. 2005), while a high carbohydrate content in the feed may affect the health of fish (Boonanuntanasarn et al. 2018; Felip et al. 2012). The enhancement of carbohydrate metabolism could reduce the negative effects caused by plant protein sources containing indigestible carbohydrates and antinutritional factors (Chi and Cho 2016). Based on the fishery surveys of C. myriaster, Mu et al. (2019) showed that C. myriaster is a carnivorous fish that preys mainly on fish and crustaceans. Therefore, the feed was supplemented with compound probiotics to improve the absorption of carbohydrates, which is beneficial for C. myriaster. Carbohydrates are fermented in the intestine by microbiota to produce the energy needed for microbial growth and metabolism; certain microbial metabolites can promote fish intestinal health, thus benefiting the growth of C. myriaster. In our study, the enhanced carbohydrate metabolism in the PI group might have resulted from the increased abundance of Firmicutes, Bacteroidota, and Actinobacteria phyla.

Conclusions

In summary, the current study indicated that the inclusion of compound probiotics (4.2 × 107 CFU/g) in the feed provided better growth performance, non-specific immunity, and intestinal digestive enzyme activity and regulated the homeostasis of intestinal microbiota of C. myriaster. At the same time, 4.2 × 107 CFU/g of compound probiotics improved the community richness and diversity of intestinal microbiota and increased the relative abundance of beneficial bacteria. Based on the KEGG functional prediction analysis, the relative abundance of carbohydrate metabolism function in the PI group was significantly higher than in the CI group. Based on the COG functional prediction analysis, the relative abundance of carbohydrate transport and metabolism function in the PI group was significantly higher than in the CI group. The establishment of a beneficial gut microbiota can promote the healthy growth of C. myriaster. We provide a strong basis for further research into the formulation of compound probiotics additive in the feed of C. myriaster. Also, our findings provide a robust basis for further investigation to study the efficient and healthy rearing of C. myriaster in RAS. There is an increasing trend that compound probiotics were used in aquaculture, and the symbiotic relationships between probiotics are extremely complex and require further analysis through macrogenomics and metabolomics in future studies.