Effects of Red Kojic Rice Supplementation on Growth, Immunity, Antioxidant Capacity, and Intestinal Health of Litopenaeus vannamei Fed a Diet with Fish Meal Replacement by Soybean Meal

Abstract

This study aimed to investigate the effects of adding Red kojic rice (RKR) on the growth performance, digestive enzyme activity, non-specific immunity, antioxidant capacity, and intestinal health of Litopenaeus vannamei fed a diet with fishmeal replacement by soybean meal. Shrimps (initial mean weight = 1.88 ± 0.03 g) were fed six experimental diets for 8 weeks, including a normal fishmeal control group (FM), a soybean meal-replaced fishmeal group (H0), and four soybean meal-replaced fishmeal groups supplemented with 0.5%, 1%, 2%, and 4% RKR, respectively, which are designated as H1, H2, H3, and H4, respectively. Each group had three replicates, with 30 shrimp per replicate. The results showed that the final average weight (FWG), weight gain rate (WG), and specific growth rate (SGR) of H2 group were significantly higher than those of H0, H3, and H4 groups (p < 0.05). The feed conversion ratio (FCR) of H2 group was significantly lower than that of H0 and H4 groups (p < 0.05). In contrast to the H0 group, the blood ACP activity in the H2 group was significantly increased (p < 0.05). The blood lysozyme (LYZ) activity in H3 group was significantly higher than that in H1 group (p < 0.05), while the opposite was true for phenoloxidase (PO). The activities of trypsin and amylase in hepatopancreas of H3 group were significantly higher than those of H0 and H1 groups (p < 0.05). Compared with the FM group, the hepatopancreatic malondialdehyde (MDA) levels in H0, H3, and H4 groups were significantly increased (p < 0.05). Compared with the H0 group, the hepatopancreatic MDA levels in H1 and H2 groups were significantly decreased (p < 0.05). Analysis of gene expression levels in hepatopancreas revealed that antioxidant (gpx, sod, cat, gst, nrf2, trx, and ho-1), non-specific immune (tnf-α, il-1β, and ifn-γ), and digestive (trypsin and α-amylase) genes were suppressed in the H0 group but enhanced by RKR supplementation. Similar expression patterns of those genes were observed in the intestine. Microbial community analysis showed reduced diversity and altered composition in the H0 group, which were partially restored by RKR. Network analysis showed “small-world” property in microbial co-occurrence network. Metabolomic analysis revealed that among the differential metabolites, Bismurrayaquinone A and Harmol exhibit highly significant differences. Correlation analysis revealed that beneficial bacteria Rhodococcus_C and Oceanobacillus in H2 group exhibited higher richness and showed significant correlation. In conclusion, supplementation of 0.5–2% RKR promoted the growth performance, digestive enzyme activity, non-specific immunity, antioxidant capacity, and intestinal health of shrimp fed a diet with fishmeal replacement by soybean meal. The optimal RKR supplementing content is 1%.

1. Introduction

Litopenaeus vannamei is characterized by rapid growth, high stress resistance, and wide salinity tolerance, and is widely cultivated worldwide [1]. According to FAO data, the total global aquaculture production in 2022 was 94.4 billion tons, of which mariculture accounted for 35.5 billion tons, and the output of L. vannamei was 6.8 million tons [2].

Fishmeal is the main source of protein in aquaculture. It possesses the advantages of high protein content, excellent amino acid profile, good palatability, and no anti-nutritional factors, which play an especially important role in aquatic feed raw materials [3]. Due to the unstable fishmeal supply and fluctuating fishmeal prices, developing high-quality fishmeal protein substitutes become more crucial for the high-quality development of aquaculture. There are usually four sources of fishmeal protein substitutes, including animal protein sources [4], plant protein sources [5], insect protein sources [6], and single-cell protein sources [7]. Soybean meal is a high-quality plant protein source in aquafeed, and using it to replace fishmeal has become a relatively common alternative [8]. There have been many reports on the effects of using soybean meal to replace fishmeal. Some studies suggested that this substitution did not significantly reduce the growth performance of the organism [9,10], whereas others demonstrated that soybean meal substitution could inhibit animal growth, immunity, and antioxidant capacity [11,12]. Plant protein substitution for fishmeal may lead to adverse effects, and the main reasons may include the presence of fiber, antinutritional factors, and amino acid imbalance [13]. Single protein source substitution has limitations. Therefore, it is necessary to explore appropriate strategies to mitigate the adverse effects caused by low-fishmeal diets. The main improvement strategies include supplementing nutrients to balance amino acids [14], adding probiotics [15], incorporating functional additives [16], and processing raw materials [17].

Red kojic rice (RKR) is produced by fermenting rice with Monascus spp. It has a history of over a thousand years in China, and its fermentation technology is quite sophisticated [18]. Notably, it boasts a well-established production chain and stable supply. RKR is widely used in food and medicine due to its health-beneficial biological activities, and related studies have shown that RKR plays a beneficial role as a feed additive [19]. RKR was produced by the fermentation of Monascus purpureus, which is a type of probiotic that produces beneficial metabolites including monascus pigments, monacolin K, and Ergosterol [20]. Relevant studies have confirmed that Monascus is rich in antioxidant and immunomodulatory functional substances. Monascus pigments possess antioxidant activity, which can scavenge free radicals such as hydroxyl radical and superoxide anion radical, and increase antioxidant enzyme activity to reduce reactive oxygen species (ROS) levels, thereby inhibiting the degree of lipid peroxidation and decreasing malondialdehyde (MDA) levels [21]. Monacolin K can induce apoptosis of human glioma U251 cells by triggering ROS-mediated oxidative damage and regulating the mitogen-activated protein kinase (mapk) and nuclear factor kappa B (nf-κb) signaling pathways [22]. γ-Aminobutyric acid (GABA) exerts beneficial effects on the antioxidant status and resistance to nitrogen stress in salmon [23]. Ergosterol alleviates cigarette smoke extract-induced chronic obstructive pulmonary disease (COPD) by regulating inflammation, oxidative stress, and apoptosis both in vitro and in vivo [24]. This experiment was designed to investigate whether RKR can alleviate the adverse effects caused by replacing fishmeal with soybean meal, thereby exploring the appropriate dietary supplementation level of RKR within the range of 0–4%.

Hemolymph plays crucial regulatory role in the disease resistance and stress tolerance of shrimp, which has certain immune components. Among these components, acid phosphatase (ACP), alkaline phosphatase (AKP), phenoloxidase (PO), and nitric oxide synthase (NOS) all exert pivotal functions in resisting pathogen infection [25,26,27]. Additionally, lysozyme (LYZ) plays a significant role in the antibacterial responses of innate immunity [28]. The hepatopancreas plays a crucial regulatory role in the non-specific immunity of crustaceans, while the intestine maintains immune homeostasis through its immune barrier [29]. Both are important immune organs. The nf-κb signaling pathway plays a crucial role in regulating host immunity and anti-inflammatory responses. The nf-κb family is a key player in regulating innate immunity and adaptive immunity, and nf-κb activity is crucial for the survival and activation of lymphocytes as well as normal immune responses [30]. In the cell nucleus, nf-κb binds to target genes, regulating and promoting the expression of tumor necrosis factor-α (tnf-α) and interleukin-1β (il-1β, respectively, thereby amplifying the body’s inflammatory response) [31]. Interferon-γ (ifn-γ) is a typical pro-inflammatory cytokine that disrupts cellular homeostasis. Transforming growth factor-β (tgf-β) is a potent anti-inflammatory cytokine that negatively regulates the development of inflammation [32].

Antioxidant capacity is also an integral part of innate immunity. The nrf2-are signaling pathway can promote the expression of antioxidant enzyme genes, including superoxide dismutase (sod), catalase (cat), glutathione S-transferase (gst), and glutathione peroxidase (gpx) [33,34]. Nuclear factor erythroid 2-related factor 2 (nrf2) is a transcription factor that regulates cellular defense against toxic and oxidative damage by inducing the expression of genes involved in oxidative stress responses and drug detoxification [35,36]. Thioredoxin (trx) can exhibit reciprocal regulation with nrf2, exerts anti-inflammatory and antioxidant effects on inflammatory damage, and can inhibit the expression of nf-κb promoted by MAPK phosphorylation [37]. Heme oxygenase-1 (ho-1) is also one of the downstream regulatory genes of nrf2, which is upregulated in cells upon various stimuli and can mediate the production of corresponding antioxidants [38]. Glutathione Reductase (GR) is crucial for the regeneration of reduced glutathione and is an important indicator reflecting antioxidant capacity [39]. Total antioxidant capacity (T-AOC) is a comprehensive indicator for evaluating the antioxidant capacity of both enzymatic and non-enzymatic systems [40]. MDA is a recognized marker of oxidative stress, which can reflect the degree of oxidative damage to the organism [41].

The animal intestine is an important organ, and most of its functions, such as immunity, health regulation, and nutrient absorption, are realized through bacterial metabolism in the intestine [42,43]. Investigating the role of the shrimp intestinal microbiota plays a crucial role in shrimp research. Studies have shown that a diet containing RKR can alleviate oxidative stress-related inflammation and improve the intestinal flora in mouse models [44]. Other studies have indicated that polysaccharides derived from RKR can protect the intestine by enhancing intestinal barrier integrity, regulating the composition of mouse intestinal microbiota, and adjusting the levels of intestinal metabolites [45]. RKR fermented by Monascus purpureus SHM1105 and monascus pigments had a regulatory effect on the intestinal flora of rats fed a high-fat diet [46]. RKR treatment can cause significant structural changes in the intestinal microbiota of mice fed a high-fat diet [47]. All the above studies suggested that dietary addition of RKR tended to improve the diversity of intestinal microbiota and metabolic levels in animals. Microbial co-occurrence networks can be used to investigate the interrelationships within microbial communities in specific environments [48]. By constructing random networks, which can compare with the real network, we could identify the impact of each type of microorganism on the microbial community network. Modularity can reflect the niche differentiation of microbial communities, be used to evaluate microbial synergy and stability, and to a certain extent, indicate the host’s adaptability to the environment and health status [49]. The “small-world” refers to a phenomenon in which, in the observed network, node connectivity is higher than that of random networks of the same size [50]. It exhibits high clustering similar to regular networks (with a clustering coefficient much higher than that of random patterns), while possessing a short characteristic path length similar to random graphs (with an average path length close to that of random patterns) [51].

Numerous studies have been reported on RKR in livestock and poultry, most of which have demonstrated its favorable effects as a feed additive. Studies have shown that compared with the control group, the red yeast rice treatment group has no adverse effects on the total feed intake, daily feed intake, egg production, egg weight, and feed conversion rate of chickens [52]. Some studies have indicated that a RKR diet can reduce inflammation related to oxidative stress and improve the intestinal flora in mouse models [44]. Additionally, studies have shown that RKR-derived polysaccharides can protect the intestine by improving and enhancing intestinal barrier integrity, regulating the composition of mouse intestinal microbiota, and adjusting intestinal metabolite levels [45]. The above reports showed the application of RKR in livestock, poultry, and mammals, but there are few reports on RKR in the aquatic field. Currently, studies have shown that Monascus purpureus M-32 can promote the growth, immunity, intestinal health, and disease resistance of L. vannamei [53]. Meanwhile, replacing soybean meal with soybean meal fermented by Monascus purpureus M-32 can enhance the growth of L. vannamei, enhance body immunity, improve morphological indicators, and ameliorate gut microbial diversity as well as metabolic levels [54]. Studies are very limited on using RKR as an additive to mitigate the adverse effects caused by fishmeal substitution in L. vannamei diets.

Therefore, it is highly necessary to conduct relevant research. In view of this, our research aims to investigate and evaluate the effects of RKR supplementation on growth performance, digestive enzyme activity, non-specific immunity, antioxidant capacity, and intestinal health of L. vannamei fed a diet with fishmeal replacement by soybean meal.

2. Materials and Methods

2.1. Diet Preparation and Experimental Design

In this experiment, six diets were formulated. The high fishmeal diet contained 32% fishmeal (FM), and the diet with fishmeal replaced by soybean meal (H0) was formulated in which 3% fishmeal was replaced with soybean meal. The remaining four diets were supplemented with 0%, 0.5%, 1%, and 2% RKR (H1, H2, H3, and H4) on the basis of H0. RKR was provided by Shandong Zhonghui Biotechnology Co., Ltd. Diet formulations and nutritional composition are detailed in

Table 1. Formulation and nutrient levels of the experiment diets (dry matter).

Ingredients	FM	H0	H1 (0.5%)	H2 (1%)	H3 (2%)	H4 (4%)
Fishmeals	320	290	290	290	290	290
Corn protein powder	30	30	30	30	30	30
Soybean meal	226	267	267	267	267	267
Peanut meal	70	70	70	70	70	70
Shrimp shell powder	20	20	20	20	20	20
Squid paste	30	30	30	30	30	30
Flour	146.8	146.8	146.8	146.8	146.8	146.8
Rice meal	91	80	75	70	60	40
Soybean phospholipid oil	20	20	20	20	20	20
Choline chloride	0.2	0.2	0.2	0.2	0.2	0.2
Propionate antifungal agent	0.5	0.5	0.5	0.5	0.5	0.5
Choline chloride	2	2	2	2	2	2
Dimethyl-beta-propiothetin	0.5	0.5	0.5	0.5	0.5	0.5
Monocalcium phosphate	15	15	15	15	15	15
Minerals premixⅠ	3	3	3	3	3	3
Minerals premixⅡ	2	2	2	2	2	2
Zeolite powder	20	20	20	20	20	20
Binder	3	3	3	3	3	3
Red kojic rice	0	0	5	10	20	40
Total	1000	1000	1000	1000	1000	1000
Nutrient levels
Crude protein (CP)	41.63	41.36	41.36	41.36	41.36	41.36
Dry Matter (DM)	89.72	89.61	89.61	89.61	89.61	89.61
Ether Extract (EE)	6.49	6.29	6.29	6.29	6.29	6.29
Nitrogen-Free Extract (NFE)	26.17	26.53	26.53	26.53	26.53	26.53
Lysine (Lys)	2.50	2.47	2.47	2.47	2.47	2.47
Methionine (Met)	0.87	0.84	0.84	0.84	0.84	0.84
Arginine (Arg)	2.51	2.54	2.54	2.54	2.54	2.54

Vitamin and mineral premix (per kg of diet) includes the following contents: vitamin A, 350,000 IU; vitamin D3, 80,000 IU; vitamin E, 3000 mg; vitamin K3, 350 mg; thiamine, 240 mg; riboflavin, 250 mg; pyridoxine, 250 mg; cyanocobalamin, 2.00 mg; vitamin C, 3500 mg; calcium pantothenate, 750 mg; niacinamide, 1500 mg; folic acid, 150 mg; biotin, 3.6 mg; inositol, 2000 mg; MgSO₄·H₂O (as Mg, 400 mg), ZnSO₄·H₂O (as Zn, 1100 mg), MnSO₄·H₂O (as Mn, 650 mg), CuSO₄·5H₂O (as Cu, 630 mg), FeSO₄·H₂O (as Fe, 6600 mg), Co compound (as Co, 72 mg), I compound (as I, 54 mg), Na₂SeO₃ (as Se, 25 mg), Carrier (limestone and rice hulls), antioxidant (ethoxyquin), provided by Guangzhou Nutriera Biotechnology Co., Ltd., Guangzhou.

. All ingredients were pulverized into fine powder using a grinder (Model: 4500A, Yongkang City Hongtaiyang Electromechanical Co., Ltd., Yongkang, China) and weighed according to the formulation table. Then all ingredients were mixed uniformly and sieved through a 60-mesh screen. Each addition was adequately homogenized before the next ingredient was added. Oil and water were emulsified separately and uniformly incorporated into the mixture. All the mixtures were sequentially blended using a twin-shaft mixer (Model: CH-50, Daxiang Mechanical Equipment Co., Ltd., Guangzhou, China). The homogenized mixture was extruded through a twin-screw extruder (Model: F-26, South China University of Technology, Guangzhou, China) with die diameters of 1.0 mm and 1.5 mm. Pellets were cut to 1–2 mm length using a pelletizer (Model: G-500, South China University of Technology, Guangzhou, China). Pellets were thermally conditioned at 55 °C for 2 h to enhance stability, air-dried in a ventilated environment until all the diets were dried, and then stored at 4 °C in airtight containers until use.

2.2. Shrimp Rearing and Experimental Conditions

The aquacultural trial was carried out at the Shenzhen Experimental Base of South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences. The experiment used 500 L culture buckets equipped with 200–250 L of seawater, as well as corresponding oxygen supply, temperature control, and water inlet or outlet devices. The aquaculture system adopted a running water farming mode. All shrimps used in the experiment were provided by Shenzhen Haiyuan Biotechnology Co., Ltd. (Shenzhen, China). All shrimp larvae were fed with commercial feed for 2 weeks to adapt to the aquaculture system. A total of 540 healthy shrimps with basically consistent specifications and weights were randomly selected, with an initial individual weight of 1.88 ± 0.03g. There were 3 replicates per group, a total of 18 buckets, and 30 shrimps per replicate. Before the start of the aquaculture experiment, the shrimps were starved for 24 h. During the experiment, shrimps were fed three times a day at 8 a.m., 5 p.m., and 10 p.m., with the feed amount being approximately 1–2% of the shrimps’ body weight. The culture period was 8 weeks. The culture buckets were subjected to daily water changes, with 20% of the total culture water volume replaced per water change. A temperature-regulatable heating rod system was operated for 24 h continuous heating to sustain the stable temperature conditions, and the water temperature was maintained between 25 °C and 28 °C. Nitrite and ammonia nitrogen levels were measured on a weekly basis (Catalog No. 52414, Henan Nanhua Qianmu Biotechnology Co., Ltd., Zhengzhou, China). The ammonia nitrogen content was less than 0.005 mg/L; the nitrite content was less than 0.05 mg/L. Throughout the experiment, the light cycle followed the natural day–night photoperiod.

2.3. Sample Collection

After 8 weeks of the breeding experiment, all shrimps ceased feeding for 24 h before sampling to empty their digestive tracts. Before sampling, we measured the body weight and relevant indicators of the shrimps to calculate the growth performance. Ten shrimps were selected from each group and anesthetized with MS—222 (100 mg/L). Hepatopancreas, muscle, and intestinal tissues of shrimp were collected using sterilized scalpels, scissors, and forceps, respectively. Blood samples were collected using a sterile syringe preloaded with acid-citrate dextrose (ACD)-containing anticoagulant (Catalog No. R10202, Beijing Leagene Biotechnology Co., Ltd., Beijing, China), which was inserted into the pericardial cavity from the posteroventral side of the cephalothorax. The blood samples were placed in centrifuge tubes and centrifuged at 5000 r/min for 10 min to obtain the plasma, and then transferred to a −80 °C refrigerator. The enzyme-activity samples were placed in cryogenic storage tubes, quickly put into liquid nitrogen, and then rapidly transferred to a −80 °C refrigerator for storage until the samples were tested. The molecular samples were placed in cryogenic storage tubes containing RNA later, quickly put into liquid nitrogen, and then rapidly transferred to a −80 °C refrigerator for storage until the samples were tested. The intestinal flora samples were placed in cryogenic storage tubes, quickly put into liquid nitrogen, and then rapidly transferred to a −80 °C refrigerator for storage until the samples were tested.

2.4. Determination of Indicators

2.4.1. Growth Performance Parameters

The main measurements include survival rate (SR), specific growth rate (SGR), weight gain rate (WGR), feed coefficient rate (FCR), condition factor (CF), and hepato-somatic index (HIS). The measurement indicators are as follows:

Initial body weight (IBW, g) = initial biomass/initial amount of shrimp

Final body weight (FBW, g) = final biomass/final amount of shrimp

Survival rate (SR,%) = (Number of shrimp at the end of the test/Number of shrimp at the beginning of the test) × 100

Weight gain rate (WGR,%) = [(Average weight of shrimp at the end of the test − Average weight of shrimp at the beginning of the test)/Average weight of shrimp at the beginning of the test] × 100

Specific growth ratio (SGR,%/d) = [ln(Average weight of shrimp at the end of the test) − ln(Average weight of shrimp at the beginning of the test)/Test period] × 100

Feed conversion rate (FCR) = Total feed consumption/Total weight gain of shrimp

Hepato-somatic index (HSI,%) = (Hepatopancreas weight of shrimp at the end of the test/Body weight of shrimp at the end of the test) × 100

Condition factor (CF,%) = (Body weight of shrimp at the end of the test/(Body length of shrimp at the end of the test)³) × 100

2.4.2. Tissue Homogenate Preparation

Muscle samples (0.3–0.5 g) were weighed, blotted dry with filter paper to remove tissue fluid and blood, and then mixed with 1 mL of physiological saline (containing 20 μL of 0.05 mol/L acetic acid). Ultrasonic homogenization was performed for 20 min to achieve complete tissue disruption. Centrifugation was conducted at 3000 r/min for 10 min, and the supernatant was collected. For the precipitate, 0.5 mL of physiological saline was added, and the above homogenization and centrifugation steps were repeated. The supernatants from both centrifugations were combined. The pH of the mixed supernatant was adjusted to 7.4 using 25 μL of 0.05 mol/L NaOH, yielding the tissue homogenate sample. A total of 10 μL of the supernatant was pipetted into a defined volume of protein reagent for protein content determination, and the protein content was calculated by measuring the absorbance of the mixture.

2.4.3. Determination of Calcium and Phosphorus Contents in Muscle

A blank group and a standard group were set up (samples were treated with distilled water and standard solution, respectively.). Tissue samples were mixed thoroughly with reagents [Ca (Cat. No.: HY-N0021, Beijing Sinouk Institute of Biological Technology, Beijing, China) and P (Cat. No.: HY-N0022, Beijing Sinouk Institute of Biological Technology, Beijing, China)], then incubated for 5 min. The mixture was placed into a preheated spectrophotometer (Model: AF-230E, Haiguang Instrument Co., Ltd., Beijing, China). The reaction temperature was maintained at 37 °C, and the detection wavelengths for Ca and P were set at 600 nm and 340 nm, respectively. Corresponding absorbances (Acontrol and Asample) were recorded. Detailed procedures refer to the kit instruction manual. The element concentration was calculated using the following formula:

Element concentration = (Asample/Acontrol) × Standard concentration (The standard concentration is 2.5 mmol/L for Ca and 1.29 mmol/L for P.)

2.4.4. Plasma Non-Specific Immune Parameters Assays

Control groups and standard groups were set up (Samples were treated with distilled water and standard solution, respectively.). The plasma samples were mixed thoroughly with reagents from Beijing Sinouk Institute of Biological Technology [Phenoloxidase(PO, Cat. No. HY-M0070, Beijing Sinouk Institute of Biological Technology, Beijing, China), lysozyme (LYZ, Cat. No. HY-M0043, Beijing Sinouk Institute of Biological Technology, Beijing, China) and acid phosphatase (ACP, Cat. No. HY-N0006, Beijing Sinouk Institute of Biological Technology, Beijing, China)], and the mixture was then placed in a microplate reader (Model: DR-200BS, Wuxi Huaweidelang Instrument Co., Ltd., Wuxi, China). Wavelengths were set to measure the absorbance of PO (at 410 nm), LYZ (at 530 nm), and ACP (at 410 nm). The unit concentrations of the standards after serial dilution with buffer were used as the x-axis, and the absorbance (OD values) of the corresponding tubes were used as the y-axis to plot a standard curve. The corresponding ACP concentrations of the test tubes were determined by finding their OD values on the curve. Detailed procedures refer to the kit instruction manual.

The determination of alkaline phosphatase (AKP) required the setup of blank groups (Samples were treated with distilled water and reagents consistent with the experiment samples, respectively.). The samples were mixed thoroughly with the reagent (Cat. No. HY-N0005, Beijing Sinouk Institute of Biological Technology, Beijing, China), and the mixture was then placed in microplate reader (Model: DR-200BS, Wuxi Hiwell-Diatek Instruments Co., Ltd., Wuxi, China). The change in absorbance within one min was measured. Detailed procedures refer to the kit instruction manual. The calculation formula is as follows:

F = Vt/(Vs × Molar Extinction Coefficient) × 1000

Vt = Total reaction volume, vs. = Sample volume, Molar extinction coefficient = 3433 (at 405 nm)

2.4.5. Digestive Enzyme Activities Analysis

The control group and standard group were set up (samples were treated with distilled water and standard solution, respectively.). Hepatopancreas and intestine homogenate samples were mixed with reagents [Trypsin (Cat. No. HY-M0035, Beijing Sinouk Institute of Biological Technology, Beijing, China) lipase (Cat. No. HY-M0021, Beijing Sinouk Institute of Biological Technology, Beijing, China) and α-amylase (Cat. No. HY-M0045, Beijing Sinouk Institute of Biological Technology, Beijing, China)] thoroughly, then placed in a Microplate reader (Model: DR-200BS, Wuxi Hiwell-Diatek Instruments Co., Ltd., Wuxi, China). The detection wavelengths were set (555 nm for trypsin, 405 nm for α-amylase, and 410 nm for lipase) and the absorbance values were measured. We used the unit concentration of the standard after serial dilution with buffer solution as the x-axis, and the corresponding absorbance (OD value) of each tube as the y-axis to plot a standard curve. The corresponding concentration of the test tube was determined by finding its OD value on the standard curve. Detailed procedures refer to the kit instruction manual.

2.4.6. Antioxidant Enzyme Activities Analysis

Blank groups and standard groups were set up. The hepatopancreas homogenate samples were mixed with reagents from Beijing Sinouk Institute of Biological Technology [Glutathione peroxidase (GSH-PX, Cat. No. HY-M0004), malondialdehyde (MDA, Cat. No. HY-M0003), total antioxidant capacity (T-AOC, Cat. No. HY-M0011), total superoxide dismutase (SOD, Cat. No. HY-M0001), catalase (CAT, Cat. No. HY-M0018), glutathione s-transferase (GST, Cat. No. HY-M0007) and glutathione reductase (GR, Cat. No. HY-M0008)], and the mixture was then placed in a microplate reader (Model: DR-200BS, Wuxi Huaweidelang Instrument Co., Ltd., Wuxi, China). Corresponding wavelengths were set, and a distilled water tube with corresponding reagents was used as the blank tube for zero adjustment. The unit concentrations of the standards after serial dilution with buffer (R1) were used as the x-axis, and the absorbance (OD values) of the corresponding tubes were used as the y-axis to plot a standard curve. The corresponding concentrations of the test tubes were determined by finding their OD values on the curve. Detailed procedures refer to the kit instruction manual.

2.5. Quantitative Real-Time PCR (qRT-PCR)

For the collected hepatopancreas and intestinal samples, we used the Trizol reagent (Cat.R480101, Magen Biotechnology Co., Ltd., Guangzhou, China) for RNA extraction, strictly following the operating protocol established by the manufacturer. After RNA extraction, it was first analyzed by agarose gel electrophoresis in order to confirm the integrity of RNA. The concentration and purity of RNA samples were quantified and assessed using a NanoDrop™ One/OneC Microvolume UV-Vis Spectrophotometer (Cat.840-317500, Thermo Fisher Scientific Inc., Waltham, MA, USA). The PrimeScript™ RT Kit from Dalian Takara Company was used to reverse transcribe total RNA into cDNA in strict accordance with the manufacturer’s operating protocol, in preparation for qRT-PCR analysis. The qRT-PCR experiment was performed using a reverse transcription kit (Nanjing Vazyme Biotech Co., Ltd., Nanjing, China). The specific primer sequences used for qRT-PCR are shown in Table S1. We adopted the 2^−ΔΔCt method to ascertain the relative expression levels of each gene across diverse groups, and all results were normalized against the expression of the housekeeping gene β-actin to ensure the consistency and accuracy of the results [55].

2.6. Intestinal Microbiota Analysis

Microbial genomic DNA was isolated by means of the OMEGA Soil DNA Kit (M5635-02, Omega Bio-Tek, Norcross, GA, USA), following the manufacturer’s standard operating procedure. The V3-V4 hypervariable regions of the bacterial 16S rRNA gene were amplified by PCR using the forward primer 338F (5′-ACTCCTACGGGAGGCAGCA-3′) and the reverse primer 806R (5′-GGACTACHVGGGTWTCTAAT-3′). The PCR reaction system included 5 μL 5× buffer, 0.25 μL Fast pfu DNA Polymerase (5 U/μL), 2 μL dNTPs (2.5 mM), 1 μL (10 μM) for both forward and reverse primers, 1 μL DNA template, and 14.75 μL ddH₂O. Triplicate reactions were performed for each sample. PCR amplicons underwent purification with Vazyme VAHTSTM DNA Clean Beads (Nanjing Vazyme Biotech Co., Ltd., Nanjing, China) and were quantitated via the Quant-iT PicoGreen dsDNA Assay Kit (Invitrogen, Carlsbad, CA, USA). Following individual quantification, the amplicons were combined in equivalent quantities, and pair-end 2250 bp sequencing was carried out on the Illumina NovaSeq platform NovaSeq6000 (Illumina, Inc., San Diego, CA, USA) using the NovaSeq 6000 SP Reagent Kit (Suzhou PANOMIX Biomedical Tech Co. Ltd., Suzhou, China). Raw sequence data were demultiplexed using the demux plugin in QIIME2, followed by primer trimming with the Cutadapt plugin. The resulting sequences were subsequently quality-filtered, denoised, merged, and chimera-removed using the DADA2 plugin to generate high-quality amplicon sequence variants (ASVs). Non-singleton ASVs (i.e., ASVs detected in ≥2 samples) were aligned using MAFFT, and the aligned sequences were used to construct a phylogenetic tree with FastTree2.Alpha-diversity metrics (Chao1, Observed species, Shannon, Simpson, Faith’s PD, Pielou’s evenness, and Good’s coverage), and beta diversity metrics (Bray–Curtis dissimilarity) were estimated using the diversity plugin with samples that were rarefied. Sequence data analyses were mainly performed using QIIME2 and R packages (v3.2.0). ASV-level alpha diversity indices were calculated using the ASV table in QIIME2 and visualized as box plots. ASV-level ranked abundance curves were generated to compare the richness and evenness of ASVs among samples. Beta diversity analysis used Bray–Curtis metrics and was visualized via principal coordinate analysis (PCoA), nonmetric multidimensional scaling (NMDS), and unweighted pair-group method with arithmetic means (UPGMA) hierarchical clustering.

2.7. Intestinal Microbial Metabolome Analysis

Based on the results of growth performance and enzyme activity analysis, we selected the intestinal content microflora from the FM, H0, and H2 groups for metabolomics analysis to investigate the effect of adding RKR to low-fishmeal diets on the metabolic level of L. vannamei. For each experimental group, three biological replicates were prepared: each replicate was a pooled sample containing homogenized gut microbiota from three shrimps, ensuring consistent sample representation across replicates. An appropriate amount of sample was accurately weighed and placed in a 2 mL centrifuge tube. Then, 600 µL of methanol (containing 4 ppm 2-amino-3-(2-chlorophenyl)-propionic acid) was added, followed by vortexing for 30 s. A steel bead was added to the tube, which was then placed in a tissue grinder (Shanghai Jingxin Industrial Development Co., Ltd., Shanghai, China) and ground at 50 Hz for 120 s. The sample was sonicated at room temperature for 10 min and then centrifuged at 12,000 rpm and 4 °C for 10 min. The supernatant was filtered through a 0.22 μm filter membrane and transferred to a detection vial for liquid chromatography–mass spectrometry (LC-MS) analysis. The supernatant was sent to Suzhou Panomix Biomedical Technology Co., Ltd. (Suzhou, China) for detection. Data preprocessing was performed using Proteowizard (v3.0.8789) and R XCMS package. Substance identification was conducted using public databases such as HMDB and MassBank. Data analysis was carried out with the R package Ropls, and the enrichment analysis was performed using the hypergeometric distribution method.

2.8. Network Analysis

The genus-level OTU data (top 80 genera in abundance) of FM, H0, and H2 groups from the 16S rRNA microbiome diversity data were selected to construct the microbial co-occurrence network and analyze the network data. A Spearman’s rank correlation matrix among genera was calculated based on the abundance of various bacterial genera. The relative correlation coefficient threshold was set at 0.04, and the p-value threshold for the relative coefficient was set at 0.05. Microbial co-occurrence network analysis was completed using the Wekemo Bioincloud (https://www.bioincloud.tech (accessed on 5 November 2025)). The experimental network was constructed by nodes (genera) and edges (Spearman’s correlation coefficient between two genera), and network visualization was performed using Gephi software (version 0.10.1). Modularity is an essential characteristic of ecological networks, measuring the degree to which a network is compartmentalized into different modules. Network diagrams were plotted according to phylum classification and modularity, respectively. The core genera were identified by their connectivity scale and the number of connected edges, with the most connected genera considered the core genera. Using the “igraph” package in R, Erdös-Réyni random models were run 10,000 times maintaining the same number of nodes and edges as the experimental network, generating corresponding Gephi files for subsequent visualization in Gephi. (R commands are provided in Method S1).

2.9. Statistical Analysis

Data statistical analysis was performed using SPSS 20.0 software for one-way analysis of variance (ANOVA) and Duncan’s multiple comparison test. Prior to ANOVA, the Shapiro–Wilk test was used to verify data normality, and Levene’s test was applied to confirm the homogeneity of variance, ensuring the validity of parametric test application. Data were expressed as mean ± standard error (mean ± SE), and a value of p < 0.05 was considered to indicate a significant difference.

3. Results

3.1. Growth Performance and Survival Rate

The data on the growth performance and feed utilization of L. vannamei are detailed in

Table 2. Effect of RKR supplementation on growth performance of L. vannamei fed a diet with fishmeal replacement by soybean meal.

Parameters	FM	H0	H1	H2	H3	H4
IBW (g)	1.87 ± 0.02	1.90 ± 0.02	1.85 ± 0.01	1.88 ± 0.07	1.87 ± 0.02	1.89 ± 0.02
FBW (g)	6.67 ± 0.39 ab	6.12 ± 0.28 bc	6.54 ± 0.16 ab	7.16 ± 0.10 a	6.25 ± 0.16 bc	5.58 ± 0.07 c
WGR (%)	256.51 ± 20.54 ab	222.68 ± 16.38 bc	252.60 ± 8.23 ab	267.99 ± 7.00 a	234.82 ± 10.15 abc	194.78 ± 6.02 c
SGR (%)	2.26 ± 0.11 ab	2.09 ± 0.09 bc	2.25 ± 0.04 ab	2.33 ± 0.03 a	2.16 ± 0.05 ab	1.93 ± 0.04 c
FCR	1.53 ± 0.13 bc	1.73 ± 0.13 ab	1.54 ± 0.05 bc	1.40 ± 0.05 c	1.65 ± 0.06 bc	1.96 ± 0.04 a
HSI (%)	0.054 ± 0.002 ab	0.05 ± 0.001 ab	0.049 ± 0.001 b	0.049 ± 0.001 b	0.053 ± 0.001 ab	0.055 ± 0.001 a
CF	0.8 ± 0.08	0.75 ± 0.11	0.81 ± 0.13	0.75 ± 0.06	0.77 ± 0.11	0.76 ± 0.07
SR (%)	92.22 ± 1.11	93.33 ± 5.09	97.78 ± 4.01	94.44 ± 2.94	90.00 ± 1.92	88.89 ± 2.94

Data are presented as mean ± SE. The data of the same line were significantly different in different small letters (p < 0.05).

. Compared with the FM group, the growth performance of L. vannamei in the H0 group tended to decrease, whereas it exhibited a trend of first increasing and then decreasing with the addition of RKR. The FBW in H2 group was significantly higher than that in H0, H3, and H4 groups, while the FBW in H4 group was significantly lower than that in FM and H1 groups (p < 0.05). The WGR in H2 group was significantly higher than that in H0 and H4 groups, and the WGR in H4 group was significantly lower than that in FM and H1 groups (p < 0.05). The SGR in H2 group was significantly higher than that in H0 and H4 groups, and the SGR in H4 group was significantly lower than that in FM, H1, and H3 groups (p < 0.05). Conversely, the FCR in H2 group was significantly lower than that in H0 and H4 groups (p < 0.05), and the FCR in H4 group was significantly higher than that in FM, H1, and H3 groups. The HSI in H4 group was significantly higher than that in H1 and H2 groups (p < 0.05). There were no significant differences in IBW, CF, and SR among all groups (p > 0.05).

3.2. Contents of Calcium and Phosphorus in Muscle

The data on the calcium and phosphorus contents in the muscles of L. vannamei are detailed in

Table 3. Effect of RKR supplementation on muscular calcium and phosphorus contents of L. vannamei fed a diet with fishmeal replacement by soybean meal.

Parameters	FM	H0	H1	H2	H3	H4
Ca (mg/100 g)	143.12 ± 20.89 a	114.87 ± 8.49 ab	125.36 ± 15.24 ab	117.36 ± 2.47 ab	120.96 ± 5.95 ab	98.48 ± 3.81 b
P (mg/100 g)	199.04 ± 14.09 b	235.43 ± 16.71 ab	217.45 ± 14.66 ab	263.47 ± 22.03 a	242.06 ± 18.11 ab	256.84 ± 24.55 ab

Data are presented as mean ± SE. The data of the same line were significantly different in different small letters (p < 0.05).

. The Ca content in FM group was significantly higher than that in H4 group (p < 0.05). The P content in H2 group was significantly higher than that in FM group (p < 0.05). Both the Ca and P content in H2 and H3 groups were higher than H0 group, but there was no significant difference (p > 0.05).

3.3. Non-Specific Immune Indicators

The data of non-specific immune enzyme activities in the plasma and hepatopancreas of L. vannamei are detailed in

Table 4. Effect of RKR supplementation on plasma non-specific immune parameters of L. vannamei fed a diet with fishmeal replacement by soybean meal.

Parameters	FM	H0	H1	H2	H3	H4
ACP (U/L)	6.74 ± 0.47 ab	4.33 ± 0.78 bc	3.77 ± 0.62 c	7.12 ± 1.23 a	6.34 ± 0.38 ab	5.56 ± 0.57 abc
AKP (U/L)	1371.8 ± 10.49 a	713.71 ± 33.24 b	1180.64 ± 226.52 ab	919.56 ± 97.6 ab	888.39 ± 297.58 ab	1189.15 ± 64.67 ab
PO (U/mL)	58.8 ± 8.4 ab	49.42 ± 9.15 ab	43.39 ± 3.24 b	56.63 ± 7.54 ab	69.71 ± 4.83 a	55.55 ± 4.65 ab
LYZ (U/mL)	572.61 ± 70.1 ab	560.63 ± 92.82 ab	693.18 ± 82.82 a	581.25 ± 83.83 ab	412.6 ± 25.73 b	536.83 ± 74.27 ab

Data are presented as mean ± SE. The data of the same line were significantly different in different small letters (p < 0.05).

and

Table 5. Effect of RKR supplementation on hepatopancreatic non-specific immune parameters of L. vannamei fed a diet with fishmeal replacement by soybean meal.

Parameters	FM	H0	H1	H2	H3	H4
ACP (U/L)	4.25 ± 1.24	3.53 ± 0.99	2.64 ± 0.27	3.78 ± 1.46	5.3 ± 0.6	3.69 ± 0.73
AKP (U/L)	464.39 ± 65.91 b	452.48 ± 99.12 b	357.52 ± 105.98 b	473.26 ± 89.33 b	647.22 ± 78.69 ab	890.38 ± 97.07 a
NOS (U/mL)	18.63 ± 1.98 ab	18.53 ± 3.08 ab	25.18 ± 2.24 a	19.92 ± 2.71 ab	12.63 ± 0.63 b	16.92 ± 3.16 ab

The data of the same line were significantly different in different small letters (p < 0.05).

, respectively. In plasma, compared with FM group, the ACP activity in H1 group was significantly decreased (p < 0.05), and the AKP activity in H0 group was significantly decreased (p < 0.05). Compared with H0 group, the ACP activity in H2 group was significantly increased (p < 0.05). Both the ACP activity and LYZ activity in H3 group were significantly higher than those in H1 group (p < 0.05), but the PO activity showed the opposite trend (p < 0.05). In hepatopancreas, the AKP activity in H4 group was significantly higher than that in FM, H0, H1, and H2 groups (p < 0.05). The NOS activity in H1 group was significantly higher than that of H3 group (p < 0.05). There was no significant difference in ACP activity among all groups (p > 0.05).

3.4. Digestive Enzyme Activities

The data on the digestive enzyme activities of L. vannamei are shown in

Table 6. Effect of RKR supplementation on hepatopancreatic digestive enzyme activities of L. vannamei fed a diet with fishmeal replacement by soybean meal.

Parameters	FM	H0	H1	H2	H3	H4
Trypsin (U/mL)	78.39 ± 19.07 ab	42.29 ± 7.05 b	34.78 ± 3.89 b	80.43 ± 16.41 ab	113.18 ± 17.09 a	61.49 ± 15.5 ab
Lipase (U/L)	2377.86 ± 167.89	2406 ± 275.87	2337.28 ± 129.19	2364.53 ± 34.37	2402.46 ± 81.92	2498.98 ± 147.52
Amylase (U/L)	10.56 ± 1.32 ab	7.79 ± 0.24 b	7.63 ± 0.63 b	10.5 ± 1.61 ab	12.59 ± 1.12 a	9.07 ± 0.99 ab

Data are presented as mean ± SE. The data of the same line were significantly different in different small letters (p < 0.05).

. The activities of trypsin and amylase in H3 group were significantly higher than those in H0 and H1 groups (p < 0.05). There was no significant difference in lipase activity among all groups (p > 0.05).

3.5. Antioxidative Parameters

The data on antioxidant enzyme activities of L. vannamei are shown in

Table 7. Effect of RKR supplementation on hepatopancreatic antioxidant enzyme activities of L. vannamei fed a diet with fishmeal replacement by soybean meal.

Parameters	FM	H0	H1	H2	H3	H4
GSH-PX (U/mL)	155.59 ± 8.17	153.08 ± 10.06	164.03 ± 7.84	158.23 ± 15.05	135.3 ± 4.81	151.41 ± 8.04
MDA (nmoL/mL)	4.03 ± 0.57 b	6.42 ± 0.32 a	4.16 ± 0.34 b	4.11 ± 0.51 b	6.24 ± 0.51 a	5.72 ± 0.62 a
T-AOC (U/mL)	13.96 ± 0.47 a	7.86 ± 1.01 b	8.06 ± 1.44 b	8.6 ± 2.04 b	5.08 ± 0.66 b	5.3 ± 1.42 b
SOD (U/mL)	201.53 ± 11.12 ab	198.96 ± 7.93 ab	207.98 ± 10.56 ab	217.5 ± 16.8 a	175.17 ± 6.96 b	191.55 ± 7.84 ab
CAT (U/mL)	41.13 ± 3.38	43.89 ± 4.28	47.44 ± 1.37	44.77 ± 7.25	36.83 ± 1.59	43.12 ± 3.51
GST (U/L)	38.95 ± 4.52	38.22 ± 3.97	41.08 ± 2.48	40.17 ± 4.48	30.32 ± 1.09	36.13 ± 3.34
GR (U/L)	7.57 ± 0.83 bc	6.04 ± 0.7 c	6.32 ± 1.08 c	10.91 ± 1.14 a	10 ± 1.12 ab	10.27 ± 0.7 ab

Data are presented as mean ± SE. The data of the same line were significantly different in different small letters (p < 0.05).

. Compared with FM group, the T-AOC activities in H0-H4 groups were significantly decreased (p < 0.05), and the GR activity in group H2 was significantly increased (p < 0.05), while the MDA activities in groups H0, H3, and H4 were significantly increased (p < 0.05). Compared with group H0, the GR activities in groups H2-H4 were significantly increased (p < 0.05). The SOD activity in group H3 was significantly lower than that in group H2 (p < 0.05). The GR activities in groups H3 and H4 were significantly higher than those in groups H0 and H1 (p < 0.05). The MDA activities in groups H1 and H2 were significantly lower than those in groups H0, H3, and H4 (p < 0.05). There were no significant differences in GSH-Px, CAT, and GST activities among all groups (p > 0.05).

3.6. Expression Levels of nf-κb Inflammatory Signaling Pathway Related Genes

In hepatopancreas (Figure 1A–E), compared with FM group, the mRNA expression of nf-κb, tnf-α, and il-1β in H0 group were significantly increased (p < 0.05), while the mRNA expression of tgf-β showed the opposite trend. Compared with H0 group, the mRNA expression of nf-κb, tnf-α, il-1β, and ifn-γ in the H1-H4 groups, as well as Tgf-β in H1 group, were significantly decreased (p < 0.05), while the tgf-β mRNA expression in H2 and H3 groups showed the opposite trend. Compared with FM group, the mRNA expression of nf-κb in H1, H3, and H4 groups, and ifn-γ and tgf-β in H1-H4 groups, were significantly decreased (p < 0.05), while the expression levels of tnf-α in H1 and H4 groups, and il-1β in H1, H3, and H4 groups showed the opposite trend. The mRNA expression levels of nf-κb and Ifn-γ in H2 group were significantly higher than those in H1, H3, and H4 groups (p < 0.05). The mRNA expression of tnf-α in H4 group was significantly higher than that in H1-H3 groups (p < 0.05). The mRNA expression of il-1β in H2 group was significantly lower than that in H1, H3, and H4 groups (p < 0.05). The mRNA expression of tgf-β in H2 and H4 groups were significantly higher than those in H1 and H3 groups (p < 0.05). The mRNA expression of tgf-β in H2 group was significantly higher than that in H4 group (p < 0.05). There were no significant differences among the remaining groups (p > 0.05).

In the intestine, a similar trend was observed (Figure 1F–J). Compared with FM group, the mRNA expression of nf-κb, tnf-α, and il-1β in H0 group were all significantly increased (p < 0.05), while the mRNA expression of tgf-β showed the opposite trend. Compared with H0 group, the mRNA expression of nf-κb, tnf-α, il-1β, and ifn-γ in H1 to H4 groups, as well as tgf-β in H1 group, were significantly decreased (p < 0.05), while the tgf-β mRNA expression in H2 and H3 groups showed the opposite trend. Compared with FM group, the mRNA expression of nf-κb, tnf-α, ifn-γ, and tgf-β in H1 to H4 groups, and il-1β in the H4 group, were significantly decreased (p < 0.05), while the il-1β mRNA expression in H1 group showed the opposite trend. The mRNA expression of nf-κb in H2 group was significantly higher than that in H1, H3, and H4 groups (p < 0.05). The mRNA expression of nf-κb in H4 group was significantly lower than that in H1 and H3 groups (p < 0.05). The mRNA expression of tnf-α in H3 group was significantly lower than that in H1 and H2 groups (p < 0.05). The mRNA expression of il-1β in H1 group was significantly higher than that in H2 to H4 groups (p < 0.05). The mRNA expression of Il-1β in H4 group was significantly lower than that in H2 and H3 groups (p < 0.05). The mRNA expression of ifn-γ in H1 group was significantly lower than that in H2 to H4 groups (p < 0.05). The mRNA expression level of tgf-β in H2 group was significantly higher than that in H1, H3, and H4 groups (p < 0.05). There were no significant differences among the remaining groups (p > 0.05).

3.7. Expression Levels of Antioxidant-Related Genes

In hepatopancreas (Figure 2A–G), compared with FM group, the mRNA expression of gpx, cat, sod, gst, nrf2, and trx in H0 group were all significantly decreased (p < 0.05). Compared with H0 group, the mRNA expression of gpx and gst in H1 to H4 groups, cat in H2 to H4 groups, sod in H3 to H4 groups, nrf2 in H2 group, trx in H2 and H3 groups, and ho-1 in H2 group were significantly decreased (p < 0.05), while the mRNA expression of nrf2 in H4 group and trx in H1 and H4 groups showed the opposite trend. Compared with FM group, the mRNA expression of gpx in H1 and H3 groups, cat in H4 group, sod in H4 group, gst in H3 and H4 groups, and nrf2 in H2 group were all significantly increased (p < 0.05), while the mRNA expression of cat in the H1 group, sod in H1 to H3 groups, nrf2 in H1, H3, and H4 groups, trx in H1 and H4 groups, and ho-1 showed the opposite trend. The mRNA expression of gpx in H3 group was significantly higher than that in H1, H2, and H4 groups (p < 0.05). The mRNA expression level of cat in H4 group was significantly higher than that in H1 to H3 groups (p < 0.05). The mRNA expression level of cat in H1 group was significantly lower than that in H2 and H3 groups (p < 0.05). The mRNA expression levels of sod in H1 to H4 groups increased significantly in sequence (p < 0.05). The mRNA expression levels of gst in H3 and H4 groups were significantly higher than those in H1 and H2 groups (p < 0.05). The mRNA expression levels of nrf2 and ho-1 in H2 group were significantly higher than those in H1, H3, and H4 groups (p < 0.05). The mRNA expression of trx in H3 group was significantly higher than that in H1, H2, and H4 groups (p < 0.05). The mRNA expression of trx in H2 group was significantly lower than that in H1 and H4 groups (p < 0.05). There were no significant differences among the remaining groups (p > 0.05).

In the intestine, a similar trend was observed (Figure 2H–N). Compared with FM group, the expression levels of gpx, sod, cat, gst, nrf2, trx, and ho-1 in the H0 group were significantly downregulated (p < 0.05). Compared with the H0 group, gpx expression was significantly upregulated in the H1–H4 groups, sod expression was significantly upregulated in the H2–H4 groups (p < 0.05), nrf2 expression was significantly upregulated in the H2 and H4 groups (p < 0.05), and cat, gst, trx, and nrf2 expressions were significantly upregulated in the H2 group (p < 0.05). Compared with the FM group, the expression levels of gpx, sod, cat, gst, nrf2, trx, and ho-1 in H0–H4 groups were significantly downregulated (p < 0.05). The expression levels of gpx, sod, cat, gst, nrf2, and ho-1 in H2 group were significantly higher than those in the H1, H3, and H4 groups (p < 0.05). The expression of gpx in the H3 group was significantly lower than that in the H1 and H4 groups (p < 0.05). The sod expression in the H1 group was significantly higher than that in the H3 and H4 groups (p < 0.05). The expression of gst in the H1 group was significantly higher than that in the H4 group (p < 0.05), and expression of gst in H4 group was significantly higher than that in the H3 group (p < 0.05).

3.8. Expression Levels of Digestive Enzyme Related Genes

In hepatopancreas (Figure 3A,B), compared with FM group, the mRNA expression of trypsin and α-amylase in H0 group were both significantly decreased (p < 0.05). Compared with H0 group, the mRNA expression of trypsin in H1, H2, and H4 groups, and α-amylase in H2 group were significantly increased (p < 0.05). Compared with FM group, the mRNA expression level of trypsin in H1 group was significantly increased (p < 0.05), while the mRNA expression of trypsin in H2 and H3 groups, and α-amylase in H1 to H4 groups showed the opposite trend. The mRNA expression of trypsin in H3, H2, H4, and H1 groups increased significantly in order of mentioned groups (p < 0.05). The mRNA expression of α-amylase in H2 group was significantly higher than that in H1, H3, and H4 groups (p < 0.05). There were no significant differences among the remaining groups (p > 0.05).

In the intestine, a similar trend was observed (Figure 3C,D). In the intestine (Figure 3C,D), compared with FM group, the mRNA expression of trypsin and α-amylase in H0 group were both significantly decreased (p < 0.05). Compared with H0 group, the mRNA expression of trypsin and α-amylase in H1 and H2 groups were all significantly increased (p < 0.05). Compared with FM group, the mRNA expression of trypsin in H1 to H4 groups, and α-amylase in H1, H3, and H4 groups were significantly decreased (p < 0.05). The mRNA expression of trypsin and α-amylase in H2 group were significantly higher than those in H1, H3, and H4 groups (p < 0.05). The mRNA expression of trypsin and α-amylase in H1 group were significantly higher than those in H3 and H4 groups (p < 0.05). There were no significant differences among the remaining groups (p > 0.05).

3.9. Intestinal Microbial Diversity Analysis

At the phylum level (Figure 4), Proteobacteria, Actinobacteriota, Firmicutes_D, Bacteroidota, and Verrucomicrobiota were the dominant phyla in the intestinal tracts of shrimp across all groups, though there are certain differences in the proportion of each phylum among the FM, H0, and H2 groups. Proteobacteria is the most abundant bacterial phylum in all experimental groups. Compared with the FM group, the H0 group shows an increasing trend in the relative abundance of Proteobacteria, Bacteroidota, and Verrucomicrobiota, while a decreasing trend in the relative abundance of Actinobacteriota. Compared with H0 group, H2 group exhibits an increasing trend in the relative abundance of Firmicutes and Dependentiae, and a decreasing trend in the relative abundance of Proteobacteria, Bacteroidota, and Verrucomicrobiota. Compared with FM group, the H2 group shows an increasing trend in the relative abundance of Firmicutes and Dependentiae, and a decreasing trend in the relative abundance of Actinobacteriota, Bacteroidota, and Verrucomicrobiota.

At the genus level (Figure 5), a total of 20 dominant bacterial genera were identified; Celeribacter_A, Ruegeria_B, Formosa, Demequina, Haloferula, and Brevibacterium were the most prominent genus. Compared with the FM group, the H0 group showed an increasing trend in the relative abundance of Ruegeria, Formosa, Demequina, Haloferula, Brevibacterium, and Sediminicola, while a decreasing trend was observed in the relative abundance of Celeribacter, Brevibacterium, Staphylococcus, Mammaliicoccus, and Brevibacillus. Compared with the H0 group, the H2 group exhibited an increasing trend in the relative abundance of Harenicola, Shimia_A, and Donghicola, whereas the relative abundance of Celeribacter, Ruegeria, Formosa, Demequina, and Haloferula decreased. Compared with the FM group, the H2 group displayed an increasing trend in the relative abundance of Ruegeria, Harenicola, Shimia_A, and Donghicola, while the relative abundance of Celeribacter, Brevibacterium, Staphylococcus, Mammaliicoccus, and Brevibacillus decreased.

As shown in

Table 8. Effect of RKR supplementation on intestinal microbiota alpha diversity of L. vannamei fed a diet with fishmeal replacement by soybean meal.

I0.	Groups
	FM	H0	H2
OTU	286.67 ± 12.14	183.33 ± 10.81	319.33 ± 63.67
Chao1	1265.06 ± 36.25 b	700.87 ± 72.26 a	1064.09 ± 192.1 ab
Observed_species	1186.73 ± 30.98 b	689.80 ± 62.58 a	1028.10 ± 187.93 ab
Goods_coverage	0.9952 ± 0.0003 a	0.9990 ± 0.0006 c	0.9974 ± 0.0004 b
Pielou_evenness	0.62 ± 0.002	0.62 ± 0.004	0.62 ± 0.031
Simpson	0.901 ± 0.001	0.936 ± 0.004	0.895 ± 0.031
Shannon	6.29 ± 0.04	5.82 ± 0.12	6.14 ± 0.48
Faith_pd	54.12 ± 1.74	36.55 ± 0.91	56.07 ± 9.45

Data are presented as mean ± SE. The data of the same line were significantly different in different small letters (p < 0.05). The number of OTUs represents the number of different microbial species in the samples, and the above table shows the number of OTUs classified at the species level. Chao1: the richness of the community. Observed_species index: the community richness. Good’s coverage index: low proportion degree of undetected species in the samples. Pielou’s evenness index: the distribution evenness of the community. Simpson index: the diversity of community; Shannon index: the diversity of community. Faith_pd index: A higher Faith_pd index implies that the evolutionary history of the samples is more abundant, which may contain more unique evolutionary lineages.

the addition of RKR affected the diversity of intestinal flora composition in shrimp. Compared with FM group, the indexes of Chao1 and Observed_species in H0 group were significantly decreased (p < 0.05), while the indexes of OTUs, Shannon, and Faith_pd decreased without significant differences (p > 0.05). Compared with H0 group, H2 group exhibited an increasing trend in the indexes of OTUs, Chao1, Observed_species, Shannon, and Faith_pd, and a decreasing trend in the Simpson index, with no significant differences (p > 0.05); the indexes of Goods_coverage and Pielou_evenness were similar between the two groups. Compared with FM group, H2 group showed a decreasing trend in the indexes of OTUs, Chao1, Observed_species, Shannon, and Faith_pd, and an increasing trend in the Simpson index, with no significant differences (p > 0.05); the Goods_coverage index and Pielou_evenness index were comparable between the two groups.

The PCoA plot reveals significant differences in the intestinal microbial communities among the three experimental groups (Figure S1). Each group formed distinct clusters within the group (except for a certain degree of dispersion in the H2 group), with a certain distance between groups. The coordinate axes collectively explained 72.4% of the variation (PCoA1 = 43.5%, PCoA2 = 28.9%), indicating a good goodness of fit.

As shown in Figure 6 a total of 3 phyla, 4 classes, 11 orders, 18 families, and 24 genera met the screening criteria for differential flora. At the phylum level, the high-abundance representatives of the FM, H0, and H2 groups were Actinobacteriota, Verrucomicrobiota, and Firmicutes_D, respectively. At the class level, the high-abundance representatives of the FM group were Actinomycetia and Gammaproteobacteria; the high-abundance representative of the H0 group was Verrucomicrobiae; and the high-abundance representative of the H2 group was Bacilli. At the genus level, the high-abundance representatives of the FM group were Brevundimonas, Glutamicibacter, Rhodococcus, and Peptostreptococcales. The high-abundance representatives of the H0 group were Haloferula and Longivirga. The high-abundance representatives of the H2 group were Mammaliicoccus, Staphylococcus, Oceanobacillus, and Rothia.

3.10. Microbial Co-Occurrence Network Analysis

In this study, 80 nodes and 778 edges were generated in the network analysis, of which 552 edges showed a positive correlation between the two nodes and 226 edges showed a negative correlation (Figure 7A). Positive connections in the correlation network usually indicate shared functions and associations, while negative connections reflect regulatory and inhibitory interactions. Proteobacteria had the highest correlation, with a microbial community proportion of 67.5%, followed by Firmicutes (15%) and Actinobacteria (11.25%), which accounted for the majority of network associations. To verify the characteristics of the network, the Erdös–Réyni random network (ER network) was visualized (Figure S2), and this network was constructed and compared with the existing experimental network. In the real network, the average path length (APL) was 2.098, the average clustering coefficient (CC) was 0.653, and the modularity (MD) value was 0.409 (greater than 0.4). In the ER network, the APL (random) was 1.759, the CC (random) was 0.245, and the MD (random) was 0.16. The ratio of CC to CC (random) was 2.67. The ratio of APL to APL (random) was 1.19 (

Table 9. Topological parameters of the experimental networks of floc.

Network Type	Modularity (MD)	Clustering Coefficient (CC)	Average Path Length (APL)	Network Diameter (ND)	Average Degree (AD)	Graph Density (GD)
Real network	4.08	0.653	2.098	4	19.45	0.246
ER random network	0.16	0.245	1.759	3	19.45	0.246

).

3.11. Metabolomics Analysis

Under positive and negative ion modes, the samples of the H2 vs. H0 metabolome clustered separately, showing a trend of separation from each other (Figure S3). The permutation test of the PLS-DA model revealed that the blue dots in the upper right corner (i.e., Q2 values) of the first three groups of treatments were all higher than those on the left, indicating that the model had no overfitting phenomenon and was reliable. A total of 241 differential metabolites were screened out in the H2 vs. H0 metabolome, among which 121 were up-regulated and 120 were down-regulated. The screened differential metabolites are mainly classified into lipids and lipid-like molecules, organic heterocyclic compounds, benzenoid derivatives, organic acids and their derivatives, phenylpropanoids, and polyketides.

Table 10. Significant differential metabolites between H2 and H0 group (n = 3).

Differential Metabolites	FC	p-Value	VIP	Trend
N-Desmethyl sildenafil (UK-103,320)	252.02	0.002	1.66	Up
Piperonyl butoxide	122.01	0.009	1.58	Up
Bismurrayaquinone A	120.77	0.003	1.64	Up
Imidafenacin	76.44	0.002	1.65	Up
Harmol	74.41	0.001	1.57	Up
(2-trans-6-trans)-farnesoate	53.54	0.002	1.56	Up
Chanoclavine-I aldehyde	52.10	0.001	1.58	Up
N-debutylhalofantrine	44.45	0.005	1.63	Up
N-Methylcoclaurine	43.79	0.000	1.70	Up
Methyl cellulose	35.93	0.008	1.60	Up
C16 Sphinganine	0.03	0.006	1.53	down
Tetradecanedioic acid	0.02	0.006	1.61	down
trans-2-Methyl-5-isopropylhexa-2,5-dienal	0.02	0.008	1.69	down
239AB 3,5-Indolizidine (5E,9E)	0.02	0.007	1.51	down
Indole-3-acetylglutamic acid	0.02	0.003	1.55	down
Kanzonol K	0.02	0.001	1.57	down
Myristoleoylcarnitine	0.01	0.005	1.53	down
(3beta,5alpha,6alpha,7beta,14alpha,22E,24R)-5,6-Epoxyergosta-8,22-diene-3,7,14-triol	0.01	0.006	1.52	down
Piperine	0.01	0.001	1.58	down
Lauramine oxide	0.01	0.004	1.53	down

presents the top 10 and bottom 10 differential metabolites based on the fold change (FC) values. N-desmethyl sildenafil, piperonyl butoxide, and Bismurrayaquinone A were the metabolites with the most significant differences in this group. Bismurrayaquinone A and Hormols showed higher total content in H2 group than that in H0 (Figure S4A,B).

As shown in Figure 8, a total of 20 signaling pathways related to differential metabolites were identified in the H2 vs. H0 metabolome based on the p-value. The top six signaling pathways most significantly affected by differential metabolites were as follows: Arachidonic acid metabolism was enriched with six upregulated and four downregulated differential metabolites. Mast Cell FcεRI-Mediated Signaling Pathway was enriched with three upregulated differential metabolites. Asthma was enriched with two upregulated differential metabolites. Apoptosis-fly was enriched with one downregulated differential metabolite. Isoquinoline alkaloid biosynthesis was enriched with seven upregulated and five downregulated differential metabolites. Indole alkaloid biosynthesis was enriched with five upregulated and three downregulated differential metabolites.

3.12. Correlation Analysis of Intestinal Microbiota and the Metabolome

The results of correlation analysis between flora and metabolites in the H2 vs. H0 group were shown in Figure 9. Pseudobizionia and Pseudoruegeria were significantly positively correlated with 26 differential metabolites (p < 0.05), while being significantly negatively correlated with 24 differential metabolites (p < 0.05). JAAUTG01 is significantly positively correlated with 23 differential metabolites like Pyropheophorbide-A, Retaspimycin, and Tetracosahexaenoic acid (p < 0.05), and significantly negatively correlated with 26 differential metabolites such as Trimethylthiazole, Picolinic acid, and Metaxalone (p < 0.05). Rhodococcus_C shows a significant positive correlation with 24 differential metabolites including Pyropheophorbide-A, Retaspimycin, and Tetracosahexaenoic acid (p < 0.05), and a significant negative correlation with 26 differential metabolites such as Trimethylthiazole, Picolinic acid, and Metaxalone (p < 0.05).

4. Discussion

To reduce the use of fishmeal in aquatic feeds, we have explored different types of fishmeal alternatives. Soybean meal is a commonly used substitute. However, due to its inherent drawbacks such as nutritional imbalance and the presence of anti-nutritional factors, its application may exert adverse effects on feeds. Therefore, it is crucial to develop strategies to mitigate these negative impacts. Our study found that replacing fishmeal content in the diet significantly decreased the FBW, WGR, and SGR of L. vannamei, indicating that growth performance was inhibited. Notably, under the condition of diet with fishmeal replaced by soybean meal, dietary supplementation with RKR reversed this trend, suggesting that RKR can alleviate growth inhibition to a certain extent. Calcium and phosphorus are essential factors for the growth and exoskeleton development of crustaceans, which can reflect growth status to a certain extent. We further determined the calcium and phosphorus contents in the shrimp muscle. We found that these contents exhibited an increasing trend following RKR supplementation, which validated the improvement of growth performance. These findings indicate that RKR addition could alleviate the adverse effects of replacing fishmeal with soybean meal to a certain extent. Studies have shown that either dietary supplementation with Monascus purpureus or replacement of soybean meal with Monascus purpureus-fermented soybean meal can improve the growth performance of L. vannamei, which is consistent with our findings. FCR can reflect feed utilization and feeding cost, with a lower FCR value indicating higher feed conversion efficiency [56]. Supplementation of RKR decreased the FCR of shrimps, which further confirms the improvement in growth performance from the perspective of feed utilization.

Given that RKR possesses antioxidant and immunomodulatory properties, we hypothesize that the growth-promoting effects on L. vannamei are mediated by regulating the shrimp’s immune and antioxidant capacities. Immune components in plasma play crucial regulatory roles in the disease resistance and stress tolerance of shrimp, such as ACP, AKP, PO, LYZ, and NOS [27,57]. Firstly, we determined the related non-specific immune indices in the plasma of L. vannamei and found that the activity of ACP was significantly increased following dietary supplementation with RKR. Similarly to our study, it has been reported that RKR can enhance the immune status of yellow-feathered chickens [58]. We further detected the expression levels of immunity-related genes in the hepatopancreas and intestine. We found that the pro-inflammatory cytokines nf-κb, tnf-α, il-1β, and ifn-γ were significantly upregulated in the H0 group, while the anti-inflammatory cytokine tgf-β was significantly downregulated. These trends were similarly observed in both the hepatopancreas and the intestine. Notably, dietary RKR supplementation reversed this trend. The downregulation of pro-inflammatory factors and the upregulation of anti-inflammatory factors can reflect an enhancement in the body’s anti-inflammatory capacity. We inferred that the improvement of shrimp immune function induced by RKR is mediated through the regulation of the nf-κb signaling pathway, which further validates the previous results.

Antioxidant-related enzymes and indices play significant roles in responding to organismal stress and resisting oxidative damage, such as GPX, CAT, T-AOC, and GR [39,40,59]. MDA is a well-recognized marker of oxidative stress [41]. We determined the antioxidant indices in the shrimp hepatopancreas. It was found that the MDA level was significantly decreased, while the GR activity was significantly increased, indicating that RKR improved the antioxidant capacity of the shrimp hepatopancreas. Based on the enzyme activity assays, we further detected the expression levels of antioxidant-related genes in the hepatopancreas and intestine. We found that factors related to the nrf2 signaling pathway in shrimp were regulated. In the hepatopancreas of the H0 group, the expression levels of nrf2 and trx were significantly downregulated, while its downstream target gene ho-1 and antioxidant genes (gpx, cat, sod, and gst) were also significantly decreased. This downward trend of the aforementioned genes was reversed in the RKR-supplemented groups, with the most pronounced effect observed in the H2 group. A similar expression pattern was noted in the intestine. Thus, it can be inferred that RKR enhances the antioxidant capacity of shrimp by regulating the nrf2 signaling pathway and the expression levels of its downstream antioxidant genes, which is consistent with the previous results. Studies have shown that dietary supplementation with red yeast rice polyphenol extract (RYRE) can enhance the antioxidant activity of goats, indicating that RKR supplementation improves the antioxidant capacity of animals. This is consistent with our findings.

In addition to investigating the immune and antioxidant capacities of L. vannamei, we also explored the effects of RKR on the digestive capacity of the shrimp. Intestinal digestive enzymes are capable of facilitating the digestion, absorption, and utilization of dietary nutrients in shrimp, such as trypsin, lipase, and α-amylase. In our experiment, the addition of RKR effectively improved the digestive enzyme indices of shrimp. Detection of the digestive enzyme related genes revealed that both trypsin and α-amylase genes exhibited a significant upward trend in the hepatopancreas and intestine of the RKR-supplemented groups, which is consistent with the trend of enzyme indices. It has been reported that red yeast rice distiller’s grains can effectively improve nutrient digestibility in goats, with the 10% treatment group achieving the best results [60]. The research results indicated that adding RKR can enhance the digestive capacity of animals, which was consistent with our findings.

To investigate the effect of dietary RKR addition on the intestinal microbiota of shrimp, we selected the H2 group, which showed better growth performance and enzyme activity, along with the FM and H0 groups for 16S rRNA sequencing. In this experiment, the addition of RKR led to an increase in the four diversity indices of the intestinal microbiota, namely Chao1, Observed_species, Shannon, and Faith_pd. These results indicated that the addition of RKR can improve the community diversity of the intestinal microbiota in shrimp to a certain extent. We also found that Proteobacteria accounted for the largest proportion in the shrimp intestine, followed by Actinobacteriota and Firmicutes. Firmicutes, as one of the dominant bacterial groups in the shrimp intestine, possesses homeostatic functions such as metabolism, genetic information processing, and environmental information processing. Bacillus, widely recognized as a type of probiotic, can participate in regulating the balance of intestinal flora, promote the metabolism and utilization of nutrients, and enhance immunity. The results showed that the abundance of Firmicutes in the H2 group tended to increase, and there was an increasing trend in the abundance of Bacillus at both the class and family levels. At the genus level, the abundances of Oceanobacillus and Cytobacillus in the H2 group both showed an increasing trend. Some studies have found that Oceanobacillus has good biofilm-destroying activity against potential food pathogens [61]. Other studies have indicated that Cytobacillus has the potential of antibacterial or anti-biofilm activity and plays a potential ecological role in protecting its host from predation [62]. The above results suggest that the addition of RKR to the feed can improve the stability of the intestinal microbial ecosystem, thereby exerting a positive impact on the growth and digestion of shrimp. We also found that the abundances of Harenicola and Shimia_A in the H2 group showed an increasing trend. These two bacterial species were isolated and identified from seawater in recent years, but their functions in the animal intestine remain unclear. Relevant studies have demonstrated that RKR can modulate the structure of the intestinal microbiota in mice and exert a beneficial regulatory effect, which is consistent with our findings.

To further investigate the effect of dietary supplementation with RKR on shrimp microbial communities, we constructed the experimental real network, namely the microbial co-occurrence network. We also constructed an ER random network matched to the real network in order to verify the characteristics of the experimental network. In this experiment, the value of CC/CC (random network) was 2.67, and APL/APL (random network) was 1.19, indicating that the experimental network has “small-world” characteristics. In the microbial co-occurrence network of this experiment, the three most relevant phyla are Proteobacteria, Firmicutes, and Actinobacteriota. The most relevant genera are Mammaliicoccus, Rhodobacteraceae-related genera, Sporosarcina, Ruegeria_B, etc. Notably, 5 out of the top 10 relevant genera belong to the Rhodobacteraceae family. Rhodobacter are ecologically important probiotics, exhibiting functions such as complex carbohydrate degradation, aerobic denitrification, assimilatory nitrate reduction, ammonium assimilation, and sulfur oxidation [63]. Modularization of the microbial co-occurrence network can achieve the division of network community structure [64]. A total of four modules were identified in this experiment, each with distinct microbial community structures (Figure 7B). Proteobacteria was mainly distributed in Module 1, Firmicutes in Module 2, and Actinobacteria in Module 3 (Table S2). It can be concluded that the network in this experiment has an obvious community structure. Comparing the co-occurrence patterns between the experimental network (Observed) and random networks (RER, RTheo), we can reflect the interactions among microbial groups of the same or different phyla in the network as well as their non-random assembly patterns [50]. In this study, we found Firmicutes and Actinobacteria exhibited a relatively high intra-phylum co-occurrence incidence in the actual network, with both O/RER and O/RTheo values greater than 1. Firmicutes and Proteobacteria showed a high inter-phylum co-occurrence incidence of 15.04% (Table S3). The high co-occurrence rate of Firmicutes and Proteobacteria indicates that these two phyla play a significant role in the microbial community structure. Thus, dietary supplementation with RKR exerts a regulatory effect on the intestinal microbial community network of L. vannamei.

In this study, the results of secondary differential metabolites showed that the metabolites with relatively high positive differential levels in H2 vs. H0 group were N-desmethyl sildenafil, piperonyl butoxide, indole, and its derivatives Bismurrayaquinone A, Imidafenacin, and Harmol. The results could reflect that RKR has a regulatory effect on the intestinal metabolic level of L. vannamei. Bismurrayaquinone A is a type of carbazole phytochemical. Carbazole alkaloids exhibit various biological activities such as antibacterial, anti-inflammatory, antioxidant, anticancer, and anti-Alzheimer’s effects [65]. Studies have shown that Bismurrayaquinone A has the function of targeting dihydrofolate reductase and possesses potential in anticancer and antibacterial aspects, though its specific mechanism remains to be further confirmed [66]. β-carboline alkaloids are naturally occurring plant substances with extensive neuropharmacological, psychopharmacological, and antitumor effects [67]. Harmol, a β-carboline alkaloid, has been extensively studied in various diseases. Some studies have indicated that it can promote the degradation of α-synuclein by regulating the autophagy–lysosome pathway and alleviate Parkinson’s symptoms [68]. Other studies have shown that it can reduce dimethylhydrazine-induced colon cancer by downregulating the expression of Bcl2/IL-6/Tnf-α related to p53-mediated apoptosis [69]. Among the signaling pathways enriched with differentially expressed metabolites, the top three with the highest degree of difference play important roles in organismal immunity, inflammation, antioxidant capacity, and other related processes. Specifically, the arachidonic acid signaling pathway exerts a function in the organism’s antioxidant defense [70]. Fc epsilon RI signaling pathway can alleviate inflammation and regulate the intestinal barrier [71]. The asthma signaling pathway is closely associated with the organism’s immune and inflammatory pathways [72]. Thus, the ameliorative effects of RKR may be achieved by regulating pathways related to immunity, inflammation, and antioxidant capacity. Based on the above, it can be inferred that the flora in the RKR-added group may improve the growth and metabolic levels of L. vannamei by influencing functions such as antibacterial, anticancer, and antioxidant activities.

The correlation analysis between 16S rRNA and metabolomics can reveal the interactions and mechanisms between microbial community structure and host metabolic functions. The results showed that Pseudobizionia, Pseudoruegeria, and Rhodococcus_C were significantly correlated with 50 types of metabolites. Studies have indicated that Pseudoruegeria is a probiotic in the intestine of shrimp and may play a role in inhibiting the growth of pathogenic bacteria such as Vibrio harveyi [73]. Rhodococcus is widely used in bioremediation of environmental pollution, such as degradation of xenobiotics, adsorption and reduction in heavy metals, and biological desulfurization [74]. Oceanobacillus has good biofilm-destroying activity against potential food pathogens [61]. Results showed that the abundance of Pseudoruegeria in the H0 group was higher than that in the H2 group (Figure S5A), while the abundance of Rhodococcus_C in the H2 group was higher than that in the H0 group (Figure S5B). In addition, Oceanobacillus was significantly positively correlated with 21 differential metabolites and significantly negatively correlated with 20 differential metabolites, indicating a high correlation, and its abundance in the H2 group was higher than that in the H0 group (Figure S5C). The above results suggested that RKR may regulate the intestinal metabolism of shrimp by regulating these types of bacteria to a certain extent.

5. Conclusions

Replacing the fishmeal with soybean meal significantly decreased the plasma AKP activity and hepatopancreatic T-AOC level of L. vannamei while significantly increasing the hepatopancreatic MDA level, reducing the expression levels of digestive enzyme genes, activating the NF-κB inflammatory signaling pathway, and inhibiting the antioxidant signaling pathway. These changes exerted adverse effects on the body’s non-specific immunity and antioxidant capacity.

Supplementation of RKR in diets could significantly improve the growth performance of L. vannamei fed a diet with fishmeal replacement by soybean meal, as well as increase the plasma ACP activity, hepatopancreatic trypsin activity, and hepatopancreatic lipase activity. Meanwhile, it significantly decreased the MDA level, increased the expression levels of digestive enzyme genes, and enhanced the body’s non-specific immunity and antioxidant capacity by inhibiting the NF-κB inflammatory signaling pathway and activating the antioxidant signaling pathway. Additionally, through microbiomics and metabolomics analyses, the intestinal microbiota structure, metabolite profiles of L. vannamei, and their correlations were elucidated. Through network analysis, the microbial co-occurrence network exhibited a unique community structure and “small-world” property. Those revealed that supplementation of RKR in diet with fishmeal replaced by soybean meal could improve the intestinal health of L. vannamei.

Under the condition of this experiment, supplementation of 0.5–2% RKR could enhance the growth performance, digestive enzyme activity, non-specific immunity, and antioxidant capacity of L. vannamei fed a diet with fishmeal replacement by soybean meal, among which the supplementation of 1% RKR yielded the best results.