This research was conducted at a private fish farm in Ismailia Province, Egypt. Two hundred forty Sea bream (initial average body weight 6.10 ± 0.06 g) were randomly assigned to four experimental groups (three replicates per treatment). The fish were housed in 12 fiberglass tanks (1 m³) with 20 fish per tank. The control group was without supplementation. The three treatment groups received supplemention with Bacillus species probiotics, which are commercially sold as SANOLIFERPRO-W (a mixture of Bacillus subtilis and B. licheniformis (BSL) at 5 × 10⁵ CFU/g; INVE Aquaculture, Belgium) at levels of 0.01, 0.02, and 0.03 g/m³ water, respectively. Fish were fed on a basal diet consisted of a commercial fish feed sourced from Aller Aqua (ALLER MARINE 42/15 EX) (https://www.aller-aqua.com). The given feed was divided equally into three portions and offered to fish three times a day (8.00, 12.00, and 16.00 h). The chemical composition of this feed was: Crude protein (%); 42, Crude fat (%); 15, NFE (%); 24.4, Ash (%); 7.4, Fibre (%); 3.2, P (%); 1.1, Gross energy (MJ); 20.6 and Digestible energy (MJ); 14.8. Tanks were equipped with compressed air through air stones using air pumps. A daily water change rate of 25% for the control group and 5% for the treatment groups was used throughout the ten-week experiment.

Water quality was monitored throughout the 10-week experiment. A SensoDirect150 MultiMeter was utilized to evaluate salinity, dissolved oxygen, temperature, and pH. Nitrogenous compounds (NH₃ and TAN) were analyzed with a DREL 2000 spectrophotometer (HACH) following (APHA. American Public Health Association, 1998) guidelines.

Fish growth performance and feed utilization were evaluated using standard equations established by (Cho and Kaushik, 1990), and further referenced by (Eissa et al., 2024a). Key parameters assessed included average weight gain (AWG), average daily gain (ADG), specific growth rate (SGR), feed conversion ratio (FCR), and survival rate (%). These metrics are essential for accurately assessing growth efficiency, nutrient utilization, and the overall health and performance of the fish.

The proximate composition of the experimental fish crude protein, dry matter, ash, and crude lipid was determined following (AOAC, 2000) protocols. At trial end, three fish per replicate (n = 9 per treatment) were frozen at -18°C for analysis. Dry matter was assessed by drying at 105°C, ash by incineration at 550°C, crude lipid via Soxhlet extraction, and crude protein using the Kjeldahl method (N × 6.25). Dry weight was determined after dehydration at 55°C.

For blood analysis, samples (n = 6 per treatment) were collected after anesthetizing fish with clove oil (5 mL/L). Blood drawn from caudal vessels (2 mL) was divided into two portions: one with anticoagulant (0.1 mL sodium citrate) for hematological assessments (Red blood cells (RBCs), hematocrit, hemoglobin, and phagocytic activity), and another without anticoagulant for serum analysis. The serum was centrifuged at 2000 × g for 10 min. and saved at -20°C for biochemical assessments, including lysozyme activity and immune responses.

Biochemical parameters were analyzed using standard protocols: Red blood cells (RBCs) and Packed Cell Volume (PCV) were determined with a Neubauer hemocytometer, hemoglobin concentration via the cyanomethemoglobin method, and serum aspartate aminotransferase (AST) and alanine aminotransferase (ALT) activities using commercial kits. Total protein, albumin, and globulin levels were measured following established methods.

Hematological assessments included Total immunoglobulin (Ig) and Immunoglobulin M (IgM) levels using assay kits. Serum lysozyme activity was evaluated as per (Ellis, 1990), while phagocytic activity and index were calculated following (Kawahara et al., 1991). Antioxidant activity was measured using diagnostic kits: Superoxide dismutase (SOD) activity via (Nishikimi et al., 1972), catalase (CAT) by (Koroliuk et al., 1988), Malondialdehyde (MDA) following (Buege and Aust, 1978), and Total Antioxidant Capacity (TAC) using (Galaktionova et al., 1998).

Amylase and lipase were assayed in serum using Mindray BS-230 kits and a Tecan Infinite 200 PRO spectrophotometer. Amylase activity (405 nm) was determined kinetically based on 2-chloro-4-nitrophenol formation, with results expressed as U/mg protein (Eissa et al., 2025b). Lipase activity (580 nm) was measured using a kinetic assay (García-Meilán et al., 2023), with a buffer containing Tris, taurodeoxycholate, deoxycholate, tartrate, DGGR, CaCl₂, mannitol, and colipase (pH 8.3). Lipase activity was also expressed as U/mg protein.

Anterior intestinal samples from S. aurata (n = 3 per treatment) were carefully collected, tissues were trimmed into small pieces (~1 cm³) before fixation to ensure proper penetration and immediately fixed in 10% buffered neutral formalin for 24 hours. Following fixation, the tissues were dehydrated via a series of ethanol (70%, 80%, 95%, and 100%) (half hour for each conc.), cleared in xylene I, and xylene II (1hour and half for each solu.), and embedded in paraffin wax I, and II (1hour and half for each wax.). Sections of 5 µm thickness were cut using a Leica RM 2155 microtome (Leica, England). These sections were stained routinely with hematoxylin and eosin, then examined under a light microscope according to standard histological procedures (Suvarna et al., 2018).

For histomorphometric analysis, key measurements included villus width (VW), villus height (VL), and absorptive surface area (ASA). Fifty well-oriented villi were selected from each intestinal section, and the values were averaged per fish. Villus height (VL) was measured from the tip to the base, while VW was measured at the midpoint of the villus. The ASA was calculated using the formula: ASA (mm²) = VL × VW, as described by (Mohammady et al., 2021). All measurements were done using a high-resolution light microscope with an HD camera (Leica Microsystems, Germany) and image J analysis software version 1.x.

Total RNA was extracted from fish liver tissues (n = 3 per treatment) using a commercial kit (Thermo Fisher Scientific, USA) following the manufacturer’s instructions. DNase treatment was applied during RNA extraction to eliminate any residual genomic DNA contamination. RNA purity and concentration were measured with a NanoDrop Lite spectrophotometer and all samples showed acceptable A260/280 ratios within the 1.8-2.0 range. For cDNA synthesis, 1 µg of RNA was reverse-transcribed using the SuperScript™ III system (Invitrogen, USA) with Oligo-dT primers and stored at -20°C. Gene expression of IGF-1, IGF-2, TNF-α, GHR, IL-10, and IL-1β was quantified by qPCR (SensiFast SYBR Lo-Rox kit, Bioline, UK) under thermal cycling conditions: 95°C for 10 min, 40 cycles of 95°C for 15 s, 60°C for 30 s, and 85°C for 5 min. qPCR was performed using (Applied Biosystems QuantStudio series, QuantStudio 7 Pro) instrument and melting curve was included to confirm the specificity of the amplified products. Expression levels were normalized to EF-1 α using the 2^−ΔΔCT method (Table 1).

Twenty fish from each treatment group, totaling 80 fish, were placed in 50-liter tanks. Both treated and control groups were exposed to a virulent strain of V. parahaemolyticus (obtained from the Sakha Animal Production Research Station, Egypt) at a concentration of 10⁷ CFU/mL. The bacterial challenge was conducted via immersion in water containing the prepared suspension for 24 hours at 28°C, following the method described by (Balcázar et al., 2007).

The Kolmogorov-Smirnov and Levene’s tests were used to check the normal distribution of the data and the homogeneity of variances. All data were analyzed with IBM SPSS Statistics (version 25.0) and are reported as the mean ± SE. To compare means across groups, a one-way analysis of variance (ANOVA) was employed. Before ANOVA, the assumption of equal variances was verified using Levene’s test. If this assumption was met, post hoc analysis was completed using the LSD test to determine specific group differences. The threshold for statistical significance was set at p < 0.05. The heatmap was visualized using GraphPad Prism 8.

The impacts of various BSL (0, 0.01, 0.02, and 0.03 g/m³, designated Control, BSL1, BSL2, and BSL3, respectively) on water quality are explained in Table 2. The water additives of B. subtilis and B. licheniformis significantly increased the dissolved oxygen (mg/L) in a dose-dependent way. In contrast, the values of TAN were significantly reduced by increasing the levels of BSL in the water. The NH₃ levels were the lowest in BSL2 and BSL3 compared to other groups; however, BSL1 was lower than the control group. For pH, BSL2 and BSL3 significantly reduced the water pH values compared to the remaining groups. The salinity and temperature of the water were not affected by the administration of B. subtilis and B. licheniformis (Table 2).

Supplementation of water treatments with B. subtilis and B. licheniformis showed significant effects on feed efficiency and growth performance in Sparus aurata (Table 3). The BSL3 group exhibited higher final body weight, weight gain, specific growth rate (SGR), and average daily gain (ADG) compared to the other groups (P < 0.05). The feed conversion ratio (FCR) was higher in the control group, with the lowest values observed in the BSL3 group (P < 0.05). Feed intake decreased in the BSL3 group but increased in the other experimental groups (P > 0.05). The survival rate was improved by BSL administration. Overall, supplementing S. aurata water with BSL significantly enhanced growth indices and feed efficiency, particularly at a dose of 0.03 g/m³ water.

The dry matter was not affected by the water administration of B. licheniformis and B. subtilis probiotics (Table 4). Adding B. licheniformis and B. subtilis probiotics to water significantly improved the crude protein content in S. aurata, with maximum values observed in the BSL3 group. Ash content in all BSL groups was greater compared to the control group (P < 0.05). Lipid content significantly decreased with increasing levels of probiotics in the water (P < 0.05). BSL3 had the lowest lipid content and the highest ash content compared to other groups (P < 0.05).

The water addition of combined probiotic B. licheniformis and B. subtilis significantly improved the PCV, RBCs, and Hb in a dose-dependent way (P<0.05) compared to the control group (Table 5). BSL3 exhibited the highest values of PCV, RBCs, and Hb compared to other groups. The values of MCV, MCH, and MCHC were higher in BSL2 and BSL3 groups compared to other groups (P<0.05). BSL1 had greater MCV and MCH than those of the control group (P<0.05). Total protein, albumin, globulin, and glucose were significantly increased in a dose-dependent manner by increasing the levels of probiotic in the water (P<0.05). BSL3 had the highest values of total protein, albumin, and globulin compared to other BSL and control groups (P<0.05). BSL2 and BSL3 showed similar levels of glucose, urea, and creatinine (P>0.05). The addition of probiotics significantly reduced the levels of AST and ALT compared to the control group, with the lowest values shown in BSL3 followed by BSL2 (P<0.05). Urea levels were decreased by the addition of B. licheniformis and B. subtilis (P < 0.05).

The results in Table 6 indicate that the water additives Bacillus subtilis and B. licheniformis significantly increase the phagocytic activity, phagocytic index, lysozyme (LYZ) activity, IgM, total Ig, and white blood cells (WBCs) compared to the control group. This increase in the investigated immunological parameters of S. aurata was independent (P<0.05). The highest levels for the highest values of immunological parameters of S. aurata were shown with the addition of 0.3 g/m³.

Significant reductions in MDA levels were shown in all water additive B. subtilis and B. licheniformis groups (Table 7), with the lowest values shown in BSL2 and BSL3 groups (P < 0.05). In contrast, adding B. subtilis and B. licheniformis at various levels significantly improved the antioxidant status, such as SOD, CAT, and TAC, of S. aurata. The BSL3 group had the greatest values of SOD, CAT, and TAC, followed by BSL2 and BSL1 with statistical differences among all groups (P < 0.05).

Supplementation with combined probiotics at different levels of B. subtilis and B. licheniformis (0.1, 0.2, and 0.3 g/m3) significantly improved the amylase (Figure 1A) and lipase (Figure 1B) activity in Sparus aurata. The high levels of probiotics exhibited the greatest values of lipase and amylase, followed by the BSL2 groups with significant differences (P<0.05).

All groups of fish intestines in the “control to BSL3” category exhibited normal histological structures (Figure 2). The intestinal villi were lined with simple columnar epithelium, and both the submucosal and muscular layers appeared intact and well-organized, indicating no signs of pathological alterations across the examined groups. Moreover, a gradual improvement in villous length and width was demonstrated from the control group to the BSL3 group, respectively (Figure 2A–D). Figure 3 illustrates the histo-morphometric analysis of villus length (Figure 3A), width (Figure 3B), and absorption surface area (Figure 3C) among different groups. Despite improvements in villus length, width, and absorption surface area in all BSL groups with increasing levels, there were no statistically significant differences among all groups (P > 0.05).

Effect of various water additives B. subtilis and B. licheniformis probiotics on gene expression of Sparus aurata for 10 weeks are shon in Figures 4A–F. These genes include mRNA expressions of growth factors IGF-1 (Figure 4A), IGF-2 (Figure 4B), and GHR (Figure 4C), as well as immune-related genes such as TNF-α (Figure 4D), IL-1β (Figure 4E), and IL-10 (Figure 4F). The addition of B. subtilis and B. licheniformis probiotics significantly improved the growth factors IGF-1 (Figure 4A), IGF-2 (Figure 4B), and GHR compared to the control group (P<0.05) in a dose-dependent manner. It was shown that increasing the levels of B. subtilis and B. licheniformis significantly increased the levels of growth genes (P<0.05) with statistical differences among all groups. The same trend was shown for immune-related genes such as TNF-α (Figure 4D), IL-1β (Figure 4E), and IL-10 (Figure 4F), indicating the growth beneficial effects of probiotics and immune modulatory effects.

In the post-challenge period, fish reared in water with BSL showed improved immune status, resulting in reduced mortality rates in the treated groups (Figure 5). BSL3 exhibited significantly lower mortality (25%), followed by BSL2 (30%), BSL1 (40%), and Control (60%).

This heatmap illustrates the normalized values of key biochemical parameters (e.g., total protein, albumin, AST, ALT, urea, creatinine) and immunological parameters (e.g., phagocytic activity, lysozyme, immunoglobulins, WBCs) across different probiotic treatments (Control, BSL1, BSL2, BSL3) (Figure 6). The color gradients indicate relative increases or decreases compared to other groups. The BSL3 group exhibits the most favorable biochemical and immunological profile, suggesting improved health status with probiotic supplementation.