Data-independent acquisition-mass spectrometry proteomic insights into nutritional metabolism of Litopenaeus vannamei exposed to Lactococcus lactis D1813, varied salinity, and dissolved oxygen

Abstract

The study aims to investigate the data-independent acquisition (DIA)-proteomics-based nutritional metabolism of Litopenaeus vannamei subjected to Lactococcus lactis D1813, salinity (8% and 25%), and dissolved oxygen (DO) (8.5 and 3.5 mg/L). The results identified sixty differentially expressed proteins in Huang, Wei, and T3BS groups. Among shrimp groups, the Huang group exhibited the upregulation in aspartate aminotransferase, cystathionine beta-synthase, serine hydroxymethyl transferase, phosphoglycerate mutase, and fructose-bisphosphate aldolase, at salinity and DO of 8% and 8.5 mg/L. Also, the Huang group showed the highest protein expression levels associated with inorganic ion transport and metabolism. Additionally, the Kyoto Encyclopaedia of Genes and Genomes analysis revealed significantly enriched pathways for amino acids, nucleotides, and sphingolipids metabolism pathways in the Huang group. The findings suggest that L. lactis D1813 supplementation at 8% salinity and 8.5 mg/L DO enhances its proteomic profile, improving its nutritional characterisation and supporting sustainable aquaculture practices and seafood production.

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Introduction

Litopenaeus vannamei is the most widely consumed species globally due to its wide adaptability, nutrient-rich profile, and rapid growth (Mohammed et al., 2024). L. vannamei is cultivated in 47 countries around the globe, with an annual production of 6.82 million tons (Adil et al., 2025). Thailand, India, Mexico, Ecuador, Vietnam, Brazil, Indonesia, and China are among the leading shrimp-producing nations, each contributing over 100,000 tons annually. China ranks first, with a production volume of 2.09 million tons (Lichna et al., 2023). L. vannamei is highly regarded as a rich source of high-quality proteins and essential amino acids while being naturally low in saturated fats, making it an excellent choice for a balanced diet. It is also abundant in key vitamins, including cobalamin (vitamin B12), niacin (vitamin B3), tocopherol (vitamin E), and riboflavin (vitamin B2), which supports metabolic processes and antioxidant protection (Adil et al., 2025; Ho et al., 2025). Additionally, L. vannamei provides essential minerals like selenium, phosphorus, and zinc, which play crucial roles in cellular function, bone health, and immune system regulation (Pinho & Emerenciano, 2021). Furthermore, it serves as a source of bioactive compounds such as betaine, chitin, glutathione, astaxanthin, and omega-3 fatty acids, including eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) (Chiu et al., 2021). These nutrients and compounds improve muscle function, nerve transmission, red blood cell production, thyroid regulation, oxidative stress protection, blood pressure control, circulation, and overall immune health (Manzoor et al., 2024a,b; Shao et al., 2023). Lactococcus lactis D1813 is a widely recognised probiotic strain (Pan, 2019) recognised for its positive impact on gut health, growth performance, and survival of shrimps by influencing positive effects on gut microbiota, thereby boosting the digestive enzymatic activities in shrimps (Ringø et al., 2020a,b).

Previous research has demonstrated that probiotics influence the proteomic nutrient profile of shrimp (Xie et al., 2019). Specifically, probiotics have been shown to regulate protein metabolism in shrimp by enhancing muscle protein synthesis and degradation processes, which are mediated through increased amino acid absorption and modulation of proteolytic enzyme activity (Asiedu et al., 2025; Osei Tutu et al., 2024; Xie et al., 2019). Additionally, dietary supplementation with a combination of Bacillus subtilis and Bacillus licheniformis (1.5 × 10⁶ CFU/g feed) has been reported to boost lysozyme activity and elevate total protein and albumin concentrations in the hemolymph of juvenile white shrimp (Abdollahi-Arpanahi et al., 2018). Low salinity (<5 ppt) downregulates proteins involved in ion transport, osmoregulation, and stress response, indicating an adaptive shift in the proteome to maintain homeostasis (Flegel, 2019). High salinity impairs cellular homeostasis by reducing protein folding and stability through protease enzymes, breaking essential muscle and metabolic proteins (Patkaew et al., 2024). Hypoxia (>8 mg/L) in L. vannamei showed a marked decline in metabolic activities in shrimp due to a shift towards anaerobic metabolism caused by oxidative stress and impaired cellular functions (Duan et al., 2022).

Based on mass spectrometry (MS), bottom-up proteomics has become a cornerstone for comprehensive analysis of proteome composition and dynamic regulation across diverse biological and clinical contexts (Darie-Ion et al., 2022). Unlike other methods, data-independent acquisition (DIA) employs a distinct approach by fragmenting precursor ions without real-time selection. Instead, the mass spectrometer sequentially cycles through predefined isolation windows, enabling the simultaneous fragmentation of all precursors within those windows (Lou & Shui, 2024). While this approach eliminates the need for real-time precursor targeting, DIA typically produces more complex MS2 spectra and multiplexed chromatograms with reduced precursor specificity (Zhang et al., 2020).

While significant progress has been made in proteomic studies related to nutritional physiology (Krasny & Huang, 2021) toxicological assessments (Hu et al., 2016) and the impacts of environmental stress and disease diagnostics (Reilly et al., 2023; Osei Tutu et al., 2023; Haider et al., 2025) in aquatic species, the application of proteomics to investigate the combined effects of probiotics, salinity, and dissolved oxygen (DO) on the nutrient profile of L. vannamei remains unexplored. Specifically, DIA proteomic analysis has yet to be utilised to examine nutrient efficiency and the mechanisms of osmoregulation in shrimp under varying salinity and oxygen conditions. To address this gap, the present study used DIA-MS proteomics to characterise the nutritional profile of L. vannamei exposed to L. lactis D1813, different salinity levels, and DO conditions.

Materials and methods

Materials, chemicals, and reagents

Fresh and healthy shrimp samples (250 specimens of each group, 23–24 g in weight each) were obtained from domestic farms in Hunan Province, China, and stored in ice bags at 4 ± 1 °C to maintain their freshness and quality. Among the chemicals and reagents, i.e., methanol, acetonitrile, ethanol, chloroform, isopropanol, hexane, hydrochloric acid, sodium hydroxide, ammonium acetate, formic acid, and acetic acid, were all analytical grade and were purchased from Fisher Chemicals (Saint Louis, MO, USA). Deionised water was obtained from the Milli-Q, Pure Water System manufactured by Millipore Corporation, Bedford, MA, USA. The probiotic strains of L. lactis (spp. D1813) were procured from Symbiotic Biotechnology Co., Ltd., Beijing, China.

Inoculum and sample preparation

Inoculation of L. lactis D1813 was carried out according to the standard protocol outlined by Liu et al. (2022). A known quantity of L. lactis D1813 was added to the rearing water at a concentration of 10⁶ CFU/ml twice daily. The probiotic was also incorporated into the live feeds, consisting of rotifers (Brachionus plicatilis) enriched with microalgae species, namely Nannochloropsis and Isochrysis, 15 min before feeding the shrimp. To ensure uniform distribution, the mixture was gently aerated by a fine bubble diffuser (Aqua Air 8000, Saint Louis, MO, USA, 8-inch diameter, producing bubbles of 20–100 μm) followed by a resting period for a few minutes. To ensure the activity of L. lactis D1813 during rearing, its concentrations were measured using the spread plate method or pour plate method according to the protocol mentioned by Weitzel et al. (2021).

Experimental design

Lactococcus lactis D1813 (10⁶ CFU/ml) inoculated L. vannamei samples were reared for one week, after which they were divided into three groups: Huang (DO = 7.5 ± 0.5 mg/L, salinity = 8 ± 0.1%), T3BS (DO = 3.5 ± 0.5 mg/L, salinity = 25 ± 0.1%) and the Wei control group (freshwater with no probiotics). All shrimp were acclimatised for 1 week to adjust to the designated salinity and DO levels. The experiment then proceeded in 90 L white plastic tanks, each equipped with an aeration system to maintain oxygen levels and ensure uniform water circulation. The tanks were pre-conditioned with water adjusted to the required salinity, temperature (26 ± 1 °C), and pH (7.5 ± 0.5). The pH and DO were maintained during the experiment at 7.5 ± 0.5 and 3.5 ± 0.5 mg/L for the Huang and T3BS groups, respectively. Shrimps had free access to feed provided at 9:00 a.m. and 05:00 p.m., and water ad libitum, with rearing water changed every 5 hr. The brine was aerated continuously for over 24 hr. The survival rate of shrimp was determined using the equation according to the protocol mentioned by Zhou et al. (2009):

Survival Rate (%) = (Final Shrimp Count ÷ Initial Shrimp Count) × 100.

The survival rate of shrimp supplemented with L. lactis D1813 was 63.89%. Feeding was halted 24 hr before the experiment, after which 30 shrimp were randomly chosen from each group. Muscle tissue samples were collected from these shrimps for subsequent proteomic analysis. The experimental procedures adhered to established ethical guidelines (Approval No. L20090101). Shrimp muscles from each treatment group were stored in an ultra-freezer (Thermo Scientific ULT1786-3-V40) at −80 °C for further analysis.

Total protein determination

Protein Extraction and Preparation: The protein extraction process in this study follows mechanical disruption, chemical solubilisation, and centrifugation to isolate soluble proteins. Urea (8 M) and sodium dodecyl sulphate (SDS) (1%) were utilised to denature proteins by breaking hydrogen bonds and hydrophobic interactions, ensuring thorough solubilisation. Protease inhibitors were included to prevent enzymatic degradation during extraction. Frozen samples were retrieved under controlled conditions and immediately placed on ice to ensure stability and minimise degradation. Each sample was homogenised in a protein lysis buffer containing 8 M urea and 1% SDS, supplemented with a protease inhibitor cocktail to block protease activity. The homogenisation process was performed using a high-speed tissue grinder, with three cycles of 40 s each. The resulting mixture was incubated on ice for 30 min, with gentle agitation every 5 min for 5–10 s to maintain uniform mixing. Samples were centrifuged at 16,000 × g for 30 min at 4 °C to remove debris. Protein concentrations in the supernatants were quantified using a bicinchoninic acid assay kit, following the manufacturer’s protocol. Extracted proteins were subsequently separated using SDS-PAGE for downstream analysis. Protein Digestion Protocol: A 100 μg aliquot of protein was dissolved in 100 mM triethylammonium bicarbonate (TEAB) buffer. Reduction of disulphide bonds was achieved by adding 10 mM Tris (2-carboxyethyl) phosphine (TCEP) and incubating at 37 °C for 1 hr. To alkylate-free sulphhydryl groups, 40 mM iodoacetamide (IAM) was added, and the reaction was carried out at room temperature in the dark for 40 min. After alkylation, the sample was centrifuged at 10,000 × g for 20 min at 4 °C, and the pellet was discarded. The supernatant was suspended in 100 μL of 100 mM TEAB buffer, and trypsin was added at a 1:50 enzyme-to-protein mass ratio. Digestion proceeded overnight at 37 °C. Peptide Desalting and Quantification: Following digestion, peptides were extracted using vacuum filtration and reconstituted in 0.1% trifluoroacetic acid (TFA). They were desalted with a hydrophilic–lipophilic balance (HLB) solid-phase extraction cartridge and concentrated using a vacuum concentrator. According to the manufacturer’s guidelines, peptide quantities were measured using the Thermo Fisher Peptide Quantification Kit.

DIA mass detection

Peptides were analysed using an Evosep One liquid chromatography system (Evosep, Odense, Denmark) connected to a timsTOF Pro2 mass spectrometer (Bruker, Germany) at Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). The separation was performed on a C₁₈ column (150 μm × 15 cm, Evosep) with solvent A (water containing 0.1% formic acid) and solvent B (acetonitrile with 0.1% formic acid). Peptide elution was carried out using the 30 samples per day (SPD) gradient at a flow rate of 500 nl/min. Data collection employed Data-Independent Acquisition with parallel accumulation serial fragmentation (DIA-PASEF) mode on the timsTOF Pro2 mass spectrometer. Mass spectrometry data were acquired within an m/z range of 400–1200 and an ion mobility range of 0.57–1.47 Vscm⁻², with both the accumulation time and ramp time set to 100 ms. Each acquisition cycle included 1 MS scan and 10 PASEF MS/MS scans, with dynamic exclusion applied for 0.4 min and 64 DIA-PASEF windows were configured, with 25 isolation windows used per scan.

Protein identification

Protein identification was carried out using Spectronaut software (Version 14) to process the DIA-PASEF raw data. Internal Retention Time (iRT) standards were used to correct retention times, and for quantitative analysis, six peptides per protein and three fragment ions per peptide were selected. To ensure reliable identification, the following criteria were applied: protein false discovery rate (FDR) ≤ 0.01, peptide FDR ≤ 0.01, Peptide Confidence ≥99%, and extracted ion chromatogram (XIC) width ≤ 75 ppm. Shared and modified peptides were excluded from the analysis, and the peak areas of the remaining peptides were calculated and aggregated for quantification. Only proteins with at least one unique peptide were considered for identification.

Proteomic data analysis

The raw data were processed using QI software (version 2.0, Waters) for peak picking, alignment, and normalisation. Subsequently, principal component analysis (PCA) and partial least-squares discrimination analysis (PLS-DA) were performed using EZinfo software (version 3.0, Waters). Metabolites with variable importance in projection (VIP) score > 1 and p < .05 [one-way analysis of variance (ANOVA)] were considered significantly different among the analysed groups of shrimps (Olkowicz et al., 2024). For metabolite identification, databases such as the Human Metabolome Database (https://www.hmdb.ca/), METLIN (https://metlin.scripps.edu/) and MONA (https://mona.fiehnlab.ucdavis.edu/) were utilised for matching. A mass error of 10 ppm was permitted for precursor ion matching, and a 20 ppm mass error was allowed for fragment ion matching. All putatively identified metabolites were manually verified. MetaboAnalyst 5.0 (https://www.metaboanalyst.ca/) was used to generate the heatmap and perform metabolic pathway analysis. Each sample’s abundance levels of endogenous metabolites were determined by normalising the integrated peak areas to the total ion chromatogram (TIC) of the respective data files (Lai et al., 2021).

Statistical analysis

All experimental data were collected in triplicate (n = 3) and expressed as mean ± standard SD. Statistical analyses, including ANOVA, PCA, and orthogonal partial least squares discriminant analysis (OPLS-DA), were conducted using R software (Version 1.6.2). Model stability was assessed through a seven-cycle iterative validation process. The significance of metabolites was evaluated using the VIP scores from OPLS-DA and Student’s t-test, with metabolites meeting the criteria of VIP > 1 and p < .05 considered statistically significant. These significant metabolites were mapped to biochemical pathways through metabolic enrichment and pathway analysis using the KEGG database (http://www.genome.jp/kegg/). The Stats Python package (https://docs.scipy.org/doc/scipy/) was utilised to identify statistically significant enriched pathways.

Results and discussion

Protein profiling

Using DIA-based MS proteomics analysis, a total of 381,264 spectra were initially obtained. After applying data filtering criteria, 121,329 spectra were successfully identified and matched to 6,979 peptides. Protein assembly and identification resulted in 1,145 proteins based on the L. vannamei genome reference from the National Centre for Biotechnology Information. The identified proteins were distributed across molecular weight ranges, with the majority falling into the following categories: 1–21 kDa (177 proteins), 21–41 kDa (355 proteins), 41–61 kDa (258 proteins), 61–81 kDa (158 proteins), 81–101 kDa (68 proteins), 101–121 kDa (37 proteins), 121–141 kDa (17 proteins), 141–161 kDa (14 proteins), 161–181 kDa (9 proteins), 181–201 kDa (9 proteins), 201–221 kDa (6 proteins), 221–241 kDa (3 proteins), 241–261 kDa (5 proteins), 261–281 kDa (6 proteins), 281–301 kDa (3 proteins), and > 301 kDa (20 proteins) (Figure 1A).

Most identified proteins contained only one or two unique peptides (Figure 1B), demonstrating good sequence coverage for protein identification. The distribution of peptide lengths revealed that the majority ranged between 8 and 15 amino acids, with a peak at 9 amino acids (759 peptides) (Figure 1C). This suggests that peptides of moderate length are more frequently detected, likely due to their enhanced stability and compatibility with MS analysis.

Gene ontology mapping

Functional annotations were obtained through the Trinotate suite using Blastx results in UniProt, including Evolutionary Genealogy of Genes: Non-supervised Orthologous Groups (EggNOG), Kyoto Encyclopaedia of Genes and Genomes (KEGG), and Gene Ontology (GO). GO analysis revealed that 18,291 unigenes were successfully mapped, with 2,851 GO assignments (level 2) categorised into three main domains: cellular components (686 or 24.06%), molecular functions (1,227 or 43.03%), and biological processes (1,004 or 36.21%) (Figure 2). Within molecular functions, the predominant annotations were related to “binding” (496) and “catalytic activity” (438), followed by “structural molecular activity” (88). The least represented categories included “molecular function regulator activity” (28) and “translational regulator activity” (24). For biological processes, “cellular processes” (410) and “metabolic processes” (357) were most prominent, while “biological regulation” and “response to stimulus” were the least represented. In the cellular component domain, the majority of unigenes were associated with “cellular anatomical entity” (458), followed by “protein-containing complex” (228).

EggNOG classification identified 1,210 unigenes, with 1,131 functional annotations divided into 20 categories (Figure 3). The most common category was “Function unknown” (340 or 30.06%), followed by “Post-translational modification, protein turnover, chaperones” (158 or 13.96%), “Translation, ribosomal structure, and biogenesis” (111 or 9.81%), and “Signal transduction mechanisms and cytoskeleton” (100 or 8.84%). The least represented categories were “Carbohydrate transport and metabolism” (67 or 5.12%) and “Energy production and conversion” (63 or 5.57%).

Kyoto Encyclopaedia of Genes and Genomes analysis identified 2,394 unigenes mapped to the KEGG database, of which 1,917 had orthologs in the KEGG orthology (KO) database. A total of 1,893 KOs were classified into six primary categories: “Metabolism” (166 or 8.76%), “Genetic information processing” (158 or 8.34%), “Environmental information processing” (159 or 8.39%), “Cellular processes” (224 or 11.83%), “Organismal systems” (459 or 24.24%), and “Human diseases” (727 or 38.40%) (Figure 4). Within these, the most represented orthology was “signal transduction” from the “Environmental information processing” category (159 unigenes). This was followed by the “endocrine system” in the “Organismal systems” category (128 unigenes) and “translation” in the “Genetic information processing” category (89 unigenes).

Multi groups analysis of the differentially expressed proteins

A multigroup analysis of differentially expressed proteins (DEPs) was conducted to examine the relationships among the HMS, WMS, and T3MS groups. This was visualised using PCA and univariate statistical methods (Figure 5A–D). The results anticipate that two principal components, PC1 and PC2, collectively accounted for 44.5% of the total variance, with PC1 and PC2 contributing 23.7% and 20.8%, respectively. The WMS group demonstrated compact clustering near the origin, indicating a stable proteomic profile. In contrast, the HMS group exhibited more excellent dispersion along both PC1 and PC2, suggesting increased variability in protein expression. The T3MS group is distinctly separated along PC1, forming an isolated cluster that differentiates it from WMS and HMS. The distinct proteomic profile observed in T3MS suggests potential adaptive responses to alterations in metabolic pathways and protein regulation. The univariate statistical analysis revealed significant proteomic alterations across the experimental groups. In the HMS versus WMS comparison, 110 proteins were found to be upregulated. At the same time, 91 were downregulated (Figure 5B). For the T3MS versus WMS comparison, 107 proteins were upregulated, and 69 were downregulated (Figure 5C). In the T3MS versus HMS comparison, 73 proteins showed increased expression, whereas 110 proteins were downregulated (Figure 5D), indicating distinct proteomic adaptations in L. vannamei. Using the Mfuzz data package, the DEP ratios across groups were clustered. The clustering of mean expression values with Mfuzz resulted in five distinct clusters. Notably, protein expression levels reached their lowest point in Cluster 2 (15 DEPs) and peaked in Cluster 1 (24 DEPs) (Figure 5E). Additionally, the clustered proteins were visualised in a heat map (Figure 5F).

Identification of DEPs related to amino acid and carbohydrate metabolism

The proteomic analysis of L. vannamei muscle identified 60 DEPs between groups. Huang muscle group showed significant upregulation of protein expression related to amino acid metabolism and carbohydrate metabolism, i.e., aspartate aminotransferase, cystathionine beta-synthase, serine hydroxymethyltransferase, phosphoglycerate mutase, and fructose-bisphosphate aldolase (Table 1). The higher abundance of expressed proteins in the Huang muscle group could be attributed to the inoculation of L. lactis D1813 and favourable levels of salinity and DO. The L. lactis D1813 resulted in synthesising amino acid metabolites, i.e., arginine, leucyl-leucine, methionine, and glutamic acid (Glencross, 2021). L. lactis D1813 enhanced the amino acid bio-availabilities, improved gastrointestinal health and enzyme functionalities, and improved efficiencies of protein metabolism, digestion, and absorption (Akonor et al., 2023a; Acheampong et al., 2024; Osei Tutu et al., 2019; Sun et al., 2015). Similar to our study, Ghaednia et al. (2024) observed a significant increase in the metabolic pathways of amino acids in rainbow trout supplemented with L. lactis at 1 × 10¹⁰ CFU g⁻¹ for 45 days, salinity levels and DO at 6% and 8 mg/L, respectively. Hancz (2022) reported that the use of multi-strain Pediococcus acidilactici at 3 × 10⁹ CFUg⁻¹ under 24% salinity and 2.5 mg/L DO significantly reduced the abundance of metabolic pathways of amino acids. Earlier investigations have linked the improved digestion and absorption of amino acids to L. lactis activities, as these probiotics cause the breakdown of higher proteins into more digestible amino acids (Akonor et al., 2023b; Naiel et al., 2021). Qin et al. (2024) elucidated that moderate salinity levels enhanced the osmoregulatory capacities of shrimps and supported maintaining cellular homeostasis, cellular equilibrium, osmoregulation, and adequate nutrient uptake. Likewise, like the present study, several earlier studies have also reported the significant contribution of probiotics in improving the proteomic profile of nutrients, especially amino acids profiling in L. vannamei (Amenyogbe et al., 2020; de Mesquita & Andrade, 2021; Ringø et al., 2020).

A study conducted by Xie et al. (2020) demonstrated that the addition of Lactobacillus acidophilus in marine fish at 12% salinity and 6 mg/L DO levels significantly upregulate the metabolomic pathways of glucose, glycogen, and galactose, respectively. Earlier, Wang et al. (2022) reported that salinity levels of 28% and 1.5 mg/L DO significantly showed down-regulation of protein expressions. Another study by Chakrapani et al. (2021) elucidated B. subtilis supplementation in Oreochromis niloticus under 7 mg/L DO, and 15% showed up-regulation of protein expression levels associated with glucose and raffinose metabolism. Likewise, Yao et al. (2020) reported that supplementation of Penaeus monodon with B. subtilis at salinity and DO levels of 27% and 2 mg/L showed down-regulation of protein expression levels associated with glycogen and galactose, respectively. L. lactis plays a key role in improving carbohydrate metabolism by regulating glucose levels, which break down complex polysaccharides into simple sugars, enhancing nutrient absorption and reducing metabolic stress (Wang et al., 2016). Panigrahi et al. (2019) delineated that L. lactis showed downregulation of protein expression levels associated with glycogen in Crassostrea gigas during a 45-day culture period. In another study by Fernandes et al. (2019), L. acidophilus, L. rhamnosus, L. lactis, and L. plantarum showed up-regulation of protein expression level-associated carbohydrate metabolism in longfin yellow-tail juveniles. Higher salinity affects ion gradients in cell membranes. Also, Chen et al. (2019) indicated that the osmotic stress induced by high salinity impairs enzyme activities, thus hindering carbohydrate digestion and reducing the absorption and metabolism of monosaccharides (Osei Tutu et al., 2023; Akonor et al., 2022).

Identification of DEPs related to inorganic ion transport and metabolism

Among the identified metabolites Huang muscle group showed significant upregulation of protein expression related to inorganic ion transport and metabolism, i.e., C7M84_022822 (A0A423U5J8), sodium/potassium-transporting ATPase subunit alpha (A0A139Z424), ns145_P3_rec domain-containing protein (A0A423U9W7) (Table 1). The notable Na⁺/K⁺-ATPase increase is responsible for sustaining ionic gradients across cellular membranes for nutrient transport and cellular equilibrium (Fernandes et al., 2021). Sha et al. (2023) demonstrated that probiotics enhance shrimp’s ion transport mechanisms and osmoregulatory resilience. Also, Goh et al. (2023) showed that probiotics improve nutrient bioavailability and upregulation essential ion transport proteins that support ionic homeostasis, energy metabolism, and efficient nutrient uptake. Similarly, Li et al. (2021) observed that moderate salinity optimises ion transporter activity, improves nutrient absorption, and promotes osmotic stability in aquatic organisms. The significant upregulation of C7M84_022822 is a key protein involved in ion metabolism, while the Ins145_P3_rec domain-containing protein linked to ion signal transduction suggests activation of osmoregulatory pathways (Huang et al., 2019). Earlier, Li et al. (2022) demonstrated that salinity (20%–25%) reduces the metabolic energy required for osmoregulation, improving growth and metabolic processes. Conversely, Qin et al. (2021) found that salinity (25%) combined with DO (7.5 mg/L) lowers the abundance of ion transport proteins and improves osmoregulatory function and metabolic efficiency. Additionally, Tian et al. (2024) reported that osmotic stress caused by high salinity has adverse effects on the Na⁺/K⁺-ATPase enzyme, reducing nutrient transport capacity and disrupting ionic homeostasis. Farhadi et al. (2023) found that hypoxic decreased shrimp ion-regulating protein function, producing ionic imbalances and increasing anaerobic metabolism. Also, Guo et al. (2021) reported that low DO exposure prevents the shrimp from producing ATP through oxidative phosphorylation, inhibiting energy-intensive ion transport and food absorption.

Enrichment analysis and enrichment factor analysis of KEGG pathway of DEPs

Huang muscle group showed significant enhancement in amino acids, nucleotides, and sphingolipids metabolism pathways among the top 25 KEGG enrichment pathways (Figure 6A–F). The enrichment could be linked to L. lactis inculcation, moderate salt concentrations, and DO levels, improving growth performance, immunity, and nutrient absorption (Shao et al., 2020; Waseem et al., 2024). Also, L. lactis is involved in sphingolipids’ production, which improves cell protection and enhances immunological responses (Wishart et al., 2007). This group’s optimal salinity and higher DO promote osmotic equilibrium and aerobic respiration, enhancing nucleotides’ metabolic functions (Zheng et al., 2021). Depleted oxygen levels impede the transformation and utilisation of pyridoxine-folates for protein synthesis and cellular functions (Hu et al., 2024; Suleman et al., 2025). Previous research has demonstrated the beneficial effects of probiotics in enhancing amino acid and nucleotide metabolism in L. vannamei (Li et al., 2023; Shao et al., 2019; Song et al., 2024; Wang et al., 2022). In zebrafish larvae, supplementation with L. lactis (IMC 501) was found to suppress the transcription of genes related to cholesterol (hnf4α and npc1l1) and triglyceride (fit2 and mgll) metabolism. This resulted in minimal enrichment of cholesterol and triglyceride pathways while significantly enhancing amino acid and nucleotide pathways (Ruan et al., 2021). Similarly, van Vliet et al. (2021) observed that administering L. lactis to zebrafish for 8 weeks led to notable upregulation in pathways linked to glycine, threonine, pyruvate, folate, and glucose-6-phosphatase metabolism. Du et al. (2019) reported an increased enrichment of amino acid and nucleotide pathways, driven by efficient ATP production from aerobic metabolism, which further supported biosynthetic processes.

In addition, Li et al. (2023) revealed that gradual salinity increases over 21 days caused by osmotic stress, leading to the downregulation of pathways involved in sphingolipid and amino acid biosynthesis, likely due to impaired cellular functions. Conversely, Wang et al. (2022) found that maintaining salinity levels at 10–15 ppt for 14 days significantly boosted amino acid and nucleotide production, which facilitated probiotic proliferation and alleviated osmotic stress. Furthermore, Simchovitz et al. (2017) identified increased carbohydrate production and activation of hypoxia-inducible factor 1 (HIF-1) genes associated with glycolysis in fish gills, indicating a metabolic shift towards anaerobic processes under low oxygen conditions.

Conclusion

The present study demonstrated that L. lactis D1813 supplementation under optimal salinity (8%) and higher DO (8.5 mg/L) improves the nutritional characterisation of L. vannamei through the DIA technique. Among the study groups, the Huang group exhibited the highest expression levels of aspartate aminotransferase, cystathionine beta-synthase, serine hydroxymethyl transferase, phosphoglycerate mutase, and fructose-bisphosphate aldolases. Likewise, the Huang group upregulated the expression of proteins involved in inorganic ion transport, such as Na⁺/K⁺-ATPase, C7M84_022822, and Ins145_P3_rec domain-containing proteins. Additionally, KEGG analysis revealed significantly enriched pathways for amino acids, nucleotides, and sphingolipid metabolism pathways in the Huang group. The findings suggest that L. lactis D1813 at 10⁶ CFU/mL, combined with optimal salinity (8%) and DO (8.5 mg/L), improves the nutrient profile through DIA-based proteomic analysis of L. vannamei. Further research should evaluate the effects of L. lactis D1813-treated shrimp on human health to better understand its potential applications in aquaculture and human nutrition.

Data availability

All proteomic data is available in the Metabolome cloud analysis Agronomy database under accession MJ20230811048. The data analysis pipeline of DIA-proteomic analysis is available on GitHub at https://analysis.majorbio.com/dia4d/report.

Author contributions

Muhammad Adil (Data Curation, Investigation, Methodology, Writing—original draft), Guo Xinbo (Supervision, Conceptualization, Methodology), Junpeng Cai (Supervise, Write, Review, and edit), Muhammad Waseem (R&D, Proofreading, Software, M&M, R&D), Muhammad F. Manzoor (Writing—original draft, Software, Proofreading, R&D, Statistics), Ashiq Hussain (Proofreading, Resource), and Crossby O. Tutu (Writing—review & editing, Software, Validation, Visualisation, Formal analysis).

Funding

None declared.

Conflicts of interest

The authors declare that there is no commercial or financial conflict.

Acknowledgements

The authors would like to thank Shanghai Majorbio Technology Co., Ltd for their assistance with proteomic sequencing.

Ethics statement

This study was carried out per the recommendations of the Animal Ethics Committee of Guangdong Province, China.

References