Omics-guided aquafeed innovation: raw ingredients to functional nutrition

1 Introduction

The expansion of global aquaculture has mandated a paradigm shift in formulation of fish nutrition. In past years culture of carnivorous and omnivorous species relied much on marine-derived ingredients, like fish meal and fish oil. However, over exploitation of pelagic sources and increasing global temperature, has imposed lots of difficulties in this industry. Consequently, aquafeed innovation in restricted by the 3Rs framework, which is replacement, reduction and refinement of marine ingredients (1). But this framework needs substitution of marine ingredients with insect derived meals, plant-based protein sources, and microbial single cell proteins. While this transition looks easy, the integration of these novel ingredients introduces a complex physiological and metabolic challenge across fish species (2). The primary obstacle in this nutritional transition is the presence of antinutritional factors (ANFs) from the plant-based ingredients. As they contain compounds such as saponins, phytates, protease inhibitors and non-starch polysaccharides that precipitate poor feed efficiency (EFF) an initiate severe gastrointestinal inflammation (GI) (3). A change is necessary from broad navigation to a narrow aquafeed innovation, acknowledging the vast innovation of evolutionary teleost diversity, and prioritizing genetics related interpretation to understand diet-host compatibility (4). The advent of high-throughput multi-omics technologies encompassing genomics, transcriptomics, proteomics, metabolomics, and epigenomics is providing unprecedented mechanistic resolution of into how different species interact with novel dietary formulations. Rather than relying on traditional key performance indicators (KPIs) such as weight gain, specific growth rate, and morphological indices.

These high-resolution analytical approaches enable a comprehensive understanding of feed ingredients at the molecular level, surpassing traditional compositional analyses that often provide only bulk nutrient profiles (5). Metabolomics, in particular, allows for the identification and quantification of bioactive compounds and anti-nutritional factors (ANFs) within plant-based feed ingredients, offering insights into their structure dependent physiological effects such as antioxidant activity, immunomodulation, and antimicrobial properties (6, 7). By precisely profiling specific metabolites including phenolic acids, flavonoids, saponins, protease inhibitors, and glucosinolates nutritionists can design feeds that maximize functional benefits while mitigating adverse effects through targeted processing strategies (8). Proteomics complements metabolomics by enabling detailed assessment of protein quality, digestibility, and post-translational modifications. Unlike crude protein measurements, proteomic analyses reveal enzymatic cleavage sites, resistant peptide sequences, and the presence of allergenic proteins, thereby informing processing conditions that preserve nutritional integrity and bioavailability (9). In combination with transcriptomics and microbiomics, these platforms provide a systems-level view of how feed components interact with host metabolism, immune response, and gut microbial communities, facilitating mechanistic insights into functional feed efficacy (10). The integration of multi-omics data underpins a data-driven framework for feed formulation and high-fidelity in vitro screening. Advanced in silico models and dynamic gastrointestinal simulation systems such as TIM-1 enable prediction of nutrient bio accessibility, protein digestibility, and prebiotic potential before in vivo testing, thereby reducing reliance on animal trials and adhering to the principles of Replacement, Reduction, and Refinement (11). Subsequent in vivo validation using controlled feeding trials coupled with multi omics analyses allows for mechanistic elucidation of functional effects, discovery of predictive biomarkers, and assessment of translational relevance in target species. Despite the promise of omics-driven feed design, several challenges persist, including the management of complex multidimensional datasets, high costs of instrumentation and skilled labor, and the need for standardization and large-scale validation. Emerging solutions such as standardized protocols, publicly accessible omics databases, and artificial intelligence-based data integration offer pathways to overcome these barriers, enabling precision nutrition strategies tailored to genetic lines, life stages, and environmental conditions (12). Collectively, these advances omics driven functional feed development at the forefront of sustainable and targeted animal nutrition, offering the potential for improved health, productivity, and environmental outcomes.

2 Omics-based characterization of raw materials

2.1 Metabolomics for phytochemical and anti-nutritional factor profiling

The integration of untargeted and targeted metabolomic approaches represents a cornerstone in the advanced characterization of feed raw materials. This analytical paradigm employs high resolution separation techniques, primarily Liquid Chromatography and Gas Chromatography coupled to high resolution Mass Spectrometry (LC-/GC-HRMS), to generate comprehensive metabolite fingerprints (5). This methodology provides an unparalleled, systems-level view of the small molecule composition, far exceeding the capabilities of traditional chemical assays that often target compound classes rather than specific molecules. For a novel plant protein source, this sophisticated analytical pipeline enables the precise identification and quantification of a vast array of metabolites critical for assessing its nutritional and functional value. Metabolomics facilitates the deconvolution of complex phytochemical mixtures. Beyond merely detecting the presence of phenolic compounds, it allows for the specific identification of sub-classes such as hydroxycinnamic acids (e.g., ferulic acid, sinapic acid), flavanols (e.g., quercetin derivatives), and condensed tannins (proanthocyanidins). The identification of these specific molecules is crucial, as their bioactivity including antioxidant capacity, immunomodulatory potential, and antimicrobial effects is highly structure-dependent (6). This specificity enables formulators to select ingredients based on a known profile of bioactives with defined mechanistic actions, moving beyond empirical selection criteria. The value of metabolomics is particularly evident in its ability to delineate the precise spectrum of ANFs present. Rather than reporting cumulative saponin content, LC-MS can identify and quantify specific saponin variants (e.g., soyasaponin Aa vs. Ab), each with distinct implications for palatability and membrane integrity (7). Similarly, it can differentiate between various protease inhibitors (e.g., Kunitz vs. Bowman-Birk inhibitors in legumes), alkaloids (e.g., gramine in lupins), and glucosinolates (e.g., sinigrin in rapeseed), each requiring specific mitigation strategies due to their unique modes of action (13). This molecular-level resolution is a prerequisite for developing targeted and efficient processing protocols. In, European Sea Bass (Dicentrarchus labrax) analysis of plant-based diets with transcriptomics and metabolomics has shown better metabolic flexibility, of superior genotypes, which showcases the efficient use of alternative plant and insect meals (14), similarly faba beans inclusion in the diet of Nile Tilapia (Oreochromis niloticus) has upregulated the chac1 gene, driving hyperplastic muscle growth, coordinating lipid/collagen accumulation, and reducing taurine (15).

In recent decade using soybean meal (SBM), remains the most studied plant protein ingredient in fish feed formulation. One of the most biologically robust and universally recognized application from omics is the molecular characterization of Soybean Meal-Induced Enteritis (SBMIE) in salmonids. But the ANF burden from soybean meal is higher which includes soy isoflavones (genistein, daidzein), saponins (groups A and B), trypsin inhibitors (Kunitz and Bowman-Birk types), phytic acid, lectins, and non-starch polysaccharides, each capable of eliciting distinct pathological responses in different fish species and tissues (16). Application of metabolomics in the intestinal mucosa of mirror carp fed with SBM showed disruptions in purine metabolism, this study illustrates how metabolomics not only identifies which metabolites change but also maps the biological pathway consequences of those changes at the intestinal interface (17). Also, transcriptomic signature reveals the severe downregulation of genes responsible for maintaining mucosal barrier integrity, active nutrient transport, and supranuclear vacuolation. Transcript levels of specific genes such as MAP3K1, LYG, NOD1, STAT1, and HSP90AB have emerged as highly reliable indicators of this pathology. This robust, reproducible molecular signature has not only proven that SBMIE is an ideal, standardized in vivo model for studying diet-induced mucosal inflammation across teleosts, but it has also provided the industry with reliable transcriptomic biomarkers for screening the efficacy of feed additives (such as novel prebiotics, probiotics, or specialized processing techniques) designed to mitigate enteritis prior to long-term growth trials (18). Table 1 represents the Key Metabolite Classes Identified via Metabolomics in Plant-Based Feed Ingredients.

Table 1. Key metabolite classes identified via metabolomics in plant-based feed ingredients.

2.2 Nutrient precursors and quality markers

The technology provides a simultaneous quantitative profile of nutritional constituents, including free and protein-bound amino acids, vitamin isoforms, and fatty acid profiles. Furthermore, it can detect and quantify metabolites indicative of processing history or spoilage, such as oxidation products of lipids (e.g., hydroxyl and hydroperoxy fatty acids) and Maillard reaction products (e.g., furosine, advanced glycation end-products), which are critical markers for protein quality and bioavailability (8). The power of this detailed molecular profile lies in its application to rational feed design. By knowing the exact identity and concentration of both detrimental ANFs and beneficial bioactives, nutritionists can make strategic decisions. For instance, if a specific heat labile protease inhibitor is identified as the primary ANF, a precise thermal processing regimen can be designed to inactivate it without degrading valuable, heat-sensitive vitamins or antioxidant pigments. The strategic application of specific molecular precursors in aquafeed fundamentally alters intestinal architecture, systemic redox homeostasis, and translational initiation. The precursors can be of three levels molecular level (amino acid and fatty acid precursors), tissue level (micronutrient precursors and fillet quality makers, organismal level (phytogenic modulators as biochemical markers). At molecular level, in cobia (Rachycentron canadum), dietary methionine functions beyond simple protein accretion, which acts as a specific molecular precursor for lipid metabolism, its concentration is monitored by the organism through the General Control Nonderepressible 2 (GCN2) pathway, establishing GCN2 transcription as a highly sensitive intracellular biomarker for amino acid availability and subsequent lipid regulation (27). In tissue level makers, micronutrients act as a direct precursor to textural and organoleptic fillet quality, in grass carp (Ctenopharyngodon idella), dietary inclusion of folic acid and vitamin A enhances muscle quality, mechanistically, these vitamins act as upstream regulators that augment muscle antioxidant status strictly through the activation of Nrf2/Keap1a signaling pathway, establishing muscle oxidative resilience as a primary marker of tissue quality (28). In organismal level, exogenous botanical extracts introduce complex secondary metabolites that alter the conventional biochemical markers, in the large yellow croaker (Larimichthys crocea), the administration of tea polyphenols specifically alters the fatty acids profile and the transcription of lipid metabolism related genes (29),

3 Proteomics for protein quality assessment

Proteomics provides a high-resolution framework for evaluating dietary protein quality by characterizing molecular changes in protein expression in response to specific bioactive peptides and amino acid formulations (30). The structural configuration of dietary amino acids, whether they are administered as intact protein-bound amino acids (PBAA) or crystalline amino acids (CAA) dictates fundamentally divergent intracellular proteomic and enzymatic response. In juvenile hybrid grouper (Epinephelus fuscoguttatus ♀ × E. lanceolatus ♂, 12.0 g), the administration of CAA-based methionine and isoleucine diets triggers rapid, transient postprandial absorption that exceeds the organism’s synchronous protein synthesis capacity. This imbalance drives excess free amino acids into oxidative catabolic pathways, establishing elevated hepatic glutamate-pyruvic transaminase (GPT) and glutamic-oxaloacetic transaminase (GOT) activities as highly sensitive, enzyme-level quality markers for evaluating true amino acid bioavailability (31). Traditional methods of protein analysis, such as crude protein measurement, give only a general estimate of nitrogen content and do not provide insights into the quality, digestibility, or biological functions of proteins (32). Proteomics the large-scale study of proteins and their modifications offers a far more precise approach to assessing protein quality in feed and food systems (33). It allows researchers to evaluate not only the presence and abundance of proteins but also their structural features, functional roles, and changes induced during processing. The amino acid sequence and three-dimensional structure of proteins largely determine how efficiently digestive enzymes can hydrolyze them. Proteomics helps in mapping cleavage sites susceptible to pepsin, trypsin, and chymotrypsin, predicting digestibility in vivo (34). Structural features such as disulfide bonds, hydrophobic cores, or repetitive domains can make certain proteins resistant to digestion, influencing their nutritional value. For instance, protease inhibitors in legumes can reduce digestibility, but proteomic profiling helps track their presence and residual activity after processing (35). Some proteins, even in trace amounts, act as allergens by triggering immune responses (e.g., storage proteins in soy, caseins in milk, parvalbumin in fish). Proteomics enables the detection, identification, and quantification of such allergenic proteins, which is critical for food safety, labeling compliance, and consumer protection. Advanced techniques like LC-MS/MS (liquid chromatography–tandem mass spectrometry) can distinguish isoforms of allergenic proteins and evaluate their stability during cooking or fermentation (36). Thermal treatments (roasting, extrusion, autoclaving) and chemical reactions during storage can alter protein quality. One critical modification is the Maillard reaction, in which reducing sugars react with amino groups of proteins (notably the ϵ-amino group of lysine), leading to nutritionally unavailable lysine and formation of advanced glycation end products (AGEs) (8). Proteomic approaches allow the detection of these modifications, tracking the extent of protein cross-linking, aggregation, and glycation, thereby linking processing conditions with changes in nutritional value. This is especially important in aquafeeds and livestock feeds, where excessive heat treatment can reduce protein bioavailability despite high crude protein content. Despite the theoretical capacity of proteomics to evaluate complete feed metabolomics and trace physiological response at the individual level, severe operational constraints impede widespread industrial application in precise formulation.

4 Data-driven formulation and high-resolution in vitro screening

The comprehensive omics derived molecular profiles of raw materials provide the foundational data for a rational, hypothesis driven formulation process (37). This stage transitions from descriptive characterization to predictive modeling, leveraging in silico and in vitro tools to pre-emptively evaluate the functional efficacy and bioavailability of designed feed compositions before committing to costly and ethically complex in vivo trials. Armed with deep compositional data, feed formulation evolves from a least cost optimization based on macro nutrients to a multi parametric exercise that incorporates functional properties. Advanced formulation software can now integrate (38). Databases enriched with omics data provide precise values for amino acid profiles, vitamin isoforms, fatty acid chains, and specific bioactive concentrations, moving beyond tabulated average values. The precise concentrations of identified ANFs (e.g., specific saponins, trypsin inhibitors) are incorporated as constraints. The formulation algorithm can be tasked with minimizing these below known threshold levels for the target species (3). Formulations can be designed to exploit potential synergies between bioactive from different ingredients (e.g., the combined antioxidant effect of tocopherols from plant oils and flavonoids from plant meals) (12). This approach allows for the creation of experimental functional feeds (EFFs) with a predicted mode of action for instance, a diet designed specifically to enhance antioxidant capacity or modulate the gut microbiome based on its known molecular composition. Figure 1 represents the Pipeline for Data Driven Feed Formulation and omics integration in aquaculture.

4.1 High fidelity in vitro gastrointestinal simulation

Specific in vitro cell lines serve as high-resolution models for evaluating the dietary absorption bottlenecks of alternative protein sources. rainbow trout (Oncorhynchus mykiss) intestinal RTgutGC and hepatic RTH-149 cell lines function as specialized in vitro platforms to study the absorption constraints of cationic amino acids (CAA) associated with plant-based diets (39). To quantify prebiotic metabolomic outputs independent of host gastrointestinal absorption constraints, in vitro fermentation models are deployed. Utilizing extracted glycosaminoglycans (e-GAGs) from mackerel fish waste, in vitro anaerobic fermentations utilizing Lactobacillus plantarum (LP) and Bifidobacterium bifidum (BB) (inoculated at 8 × 109 cells/100 mL) trace highly specific metabolic shifts over 48 hours at 37 °C (40).

To validate these predictions, sophisticated in vitro gastrointestinal models are employed. While static models (simple pH-controlled incubations) offer a preliminary screen, dynamic, multi-compartmental systems like the TNO Gastro-Intestinal Model (TIM-1) provide a physiologically realistic simulation of the animal gut (11). Also, scanning electron microscope (SEM) of these in vitro environments provides structural validation of substrate utilization. SEM imaging visualizes the physical degradation of prebiotic e-GAG particles into smaller structures that actively entrap viable probiotic bacteria, verifying that these complexes charged polysaccharides are utilized for microbial growth without exerting toxicity. Concurrently, the in vitro media demonstrates severe localized acidification, dropping from a pH of approximately 6.2 down to 3.3, acting as a measurable proxy for successful SCFA biosynthesis (40).

These systems meticulously replicate:

• Gastric and Intestinal Transit: Timed passage of the digesta through successive compartments (stomach, duodenum, jejunum, ileum).
• Dynamic Secretion: Computer-controlled addition of enzymes (pepsin, pancreatin), bile salts, and electrolytes at rates and concentrations mimicking in vivo secretions.
• pH Gradients: Accurate pH profiles, from the acidic stomach to the neutral ileum.
• Absorption: Continuous removal of water and digested nutrients through semi-permeable membranes, simulating passive absorption.

4.2 Omics enhanced analysis of digestae (digestome)

The true power of this integrated approach is realized through the application of omics technologies to the effluents (“digestae”) collected from the in vitro system (41). This analysis, often referred to as the study of the “digestome,” provides an unprecedented level of insight:

• Metabolomics of bio accessibility: LC-MS/MS analysis of the bio accessible fraction reveals not only how much of a nutrient is released but which specific molecular forms are liberated. For example, it can show whether carotenoids are released in free form or still esterified, or which specific phenolic compounds survive gastric conditions and become available in the intestine (42). This directly predicts which molecules the animal will actually encounter.
• Proteomics of protein digestibility: Analysis of the peptide profiles in the digestae using proteomics techniques identifies resistant protein fragments and incomplete hydrolysis products, providing a mechanistic understanding of protein quality and pinpointing enzymatic barriers to digestion (9).
• Prebiotic potential assessment: The digestae can be used as a substrate for in vitro batch culture fermentation with fecal or cecal microbiota from the target species. Subsequent microbiomics (16S rRNA sequencing) and metabolomic (SCFA quantification) analysis of these cultures predicts the prebiotic potential of the feed i.e., its capacity to selectively promote beneficial gut bacteria and stimulate the production of health-promoting metabolites like butyrate (43). This multi-tiered in vitro screening platform acts as a high-resolution filter. It identifies promising EFFs with a high probability of in vivo success, while simultaneously eliminating formulations with poor bio accessibility, unintended negative interactions, or low functional potential. This drastically reduces the number of animal trials required, accelerating the development cycle, reducing costs, and adhering to the principles of the 3Rs (Replacement, Reduction, and Refinement) in animal research.

4.3 In vivo validation and mechanistic elucidation

This stage represents the most critical phase of functional feed evaluation, where hypotheses generated from in vitro and ex vivo models are tested in living organisms. In vivo validation ensures that the biological effects of the experimental functional feed (EFF) are not only observable under controlled laboratory conditions but are also physiologically relevant and sustainable in the whole organism (44). In common carp (Cyprinus carpio), in vivo administration of the probiotic Lactococcus lactis Z-2 physically validates the upregulation of a broad suite of intestinal nutrient transporters, specifically Sglt1, Glut2, Pept1, and rBAT. (45). Beyond structural transporters, in vivo chronobiological interventions elucidate how nutrient timing affects cellular physiology. In the red swamp crayfish (Procambarus clarkii), dietary melatonin supplementation mechanistically alters the diurnal variation of non-specific immunity and digestive enzyme activity by actively modulating circadian clock-related gene expression (30).

By integrating omics technologies, researchers can move beyond descriptive endpoints to uncover mechanistic pathways and causal relationships. Performance indicators such as growth rate, feed conversion ratio (FCR), survival, and reproduction are monitored. Physiological responses (immune status, stress tolerance, gut health, and metabolic efficiency) are evaluated to capture the holistic benefits or drawbacks of the EFF. Transcriptomics (RNA-Seq): Reveals gene expression changes triggered by EFF, highlighting pathways related to metabolism, immunity, or stress response (46). Proteomics, provides insight into actual protein abundance, post-translational modifications, and bioavailability of dietary proteins (28) Metabolomics, identifies shifts in metabolic signatures (e.g., amino acids, fatty acids, short-chain fatty acids) reflecting nutrient utilization and energy balance. Metagenomics/Microbiome Analysis, captures EFF-induced changes in gut microbial community structure and function, linking diet to host health outcomes. Multi-omics data integration helps to construct a cause–effect network, mapping how EFF components influence molecular targets, signaling pathways, and physiological outcomes (47). A prebiotic-rich feed may enhance butyrate production (metabolomics), which upregulates tight junction proteins (proteomics/transcriptomics), thereby improving gut barrier integrity and reducing pathogen invasion. A protein-rich functional ingredient may activate mTOR signaling (transcriptomics), leading to enhanced muscle growth and feed efficiency. Such mechanistic insights go beyond traditional growth and survival data, providing biological explanations for efficacy. Despite the success of these in vivo validations, a prominent inconsistency remains in isolating the primary driver of these physiological improvements. It remains methodologically unresolved whether the observed enhancements in muscle growth are a direct cellular consequence of systematic hormonal activation (48). Figure 2 represents the multi-omics integration to develop functional feed.

5 Identifying biomarkers for efficacy

One of the most valuable outcomes of applying a multi-omics framework in functional feed studies is the discovery of biomarkers that reliably indicate the efficacy of the dietary intervention. Biomarkers are measurable biological signals such as a gene, protein, metabolite, or microbial taxon that are consistently correlated with improved performance, resilience, or health outcomes in the target species (49). A biomarker is a quantifiable molecular indicator of a biological state or condition. Biomarkers can be of many types based on their application. Genomic/Transcriptomic Biomarkers: Differentially expressed genes (e.g., lysozyme, cyp1a, hsp70) that indicate enhanced immunity or stress tolerance. Proteomic Biomarkers: Protein abundance or post-translational modifications (e.g., elevated glutathione S-transferase confirming detoxification) (50). Metabolomic Biomarkers: Small molecules (e.g., elevated butyrate, reduced malondialdehyde [MDA]) reflecting metabolic efficiency or reduced oxidative stress. Microbiomic Biomarkers: Specific microbial taxa (e.g., enrichment of Lactobacillus or Bifidobacterium) associated with gut health and immune modulation (51). The Biomarker Discovery Pipeline includes Data Mining: Multi-omics datasets are screened to identify molecular features consistently different between experimental functional feed (EFF) and control diet (CD) groups (52). Candidate biomarkers are statistically linked with key performance indicators (KPIs) such as growth rate, feed conversion ratio (FCR), survival, or disease resistance (53). Selected biomarkers are validated across independent trials or conditions to confirm their reliability. Once validated, biomarkers can be measured via rapid assays (e.g., qPCR for gene expression, ELISA for proteins, targeted LC-MS for metabolites). Table 2 represents the overview of biomarkers and their applications in important aquaculture groups.

Table 2. Overview of biomarkers and their applications in important aquaculture groups.

6 Species diversity and biological specificity in omics-guided aquafeed research

Teleosts comprise more than 30,000 species and span a range of physiological strategies, namely carnivore, omnivore, and herbivore; freshwater, marine, and diadromous life histories; cold-water and tropical thermal environments that fundamentally affect every aspect of how omics data should be generated, normalized, and interpreted. This species diversity is not merely academic; it has direct practical consequences for aquafeed design and the selection of appropriate reference datasets (60). Trophic level exerts a strong organizing influence on gut microbiome composition. Liu et al. (61) demonstrated, using 16S rRNA sequencing of eight fish species representing four trophic categories collected simultaneously from the same water body, that carnivorous and herbivorous fish formed distinctly separated clusters in principal coordinates analysis of microbial community composition. Herbivorous fish harbored higher gut microbial diversity, with cellulose-degrading bacteria (Clostridium, Citrobacter, Leptotrichia) dominating, while carnivorous fish showed lower diversity with protease-producing and fermentative genera (Cetobacterium, Halomonas) predominating (61). Correspondingly, gut content amylase and cellulase activities were significantly higher in herbivores, while trypsin activity was higher in carnivores, confirming that the enzymatic profile of the microbiome reflects the host’s trophic adaptation. This means that a microbiome dataset from rainbow trout cannot serve as a reference for evaluating feed effects in tilapia, and that microbiomics studies should define species- and trophic-group-appropriate baseline microbiome compositions before interpreting treatment effects (62). Habitat, particularly the freshwater–marine distinction, imposes additional constraints on microbiome composition and on the environmental metabolome that seeps into the gut lumen. Kim et al. (63), analyzing 227 individual fish representing 85 species across 14 orders, demonstrated that host habitat was the major determinant of gut bacterial community structure, with seawater fish harboring Enterobacteriaceae-enriched communities and freshwater species showing distinct Firmicutes profiles.

Gut morphology is equally species-specific and affects the design of in vivo validation experiments in ways that go beyond microbiomics. Carnivorous teleost’s such as salmonids have a true acid-secreting stomach and a set of pyloric caeca that increase the absorptive surface area of the proximal intestine, while cyprinids lack an acid stomach and depend entirely on pancreatic and intestinal enzymes for protein digestion (64). Transcriptomics of the intestinal epithelium of cyprinids in response to dietary change must therefore be interpreted in the context of a fundamentally different digestive physiology than that described in salmonid-dominated aquaculture omics literature (65). Furthermore, the gastric transcriptome of salmonids, including genes encoding pepsinogen isoforms, the proton pump (H+/K+-ATPase), and intrinsic factor (66), is an important component of the dietary response signature that is entirely absent in cyprinids, making direct comparisons of intestinal gene expression data across these taxa unreliable without taxonomic annotation of the biological process being measured. Table 3 represents the Representative Omics-Guided Feeding Studies in Teleost Fish: Species, Dietary Factor, Platform, and Key Findings.

Table 3. Representative omics-guided feeding studies in teleost fish: species, dietary factor, platform, and key findings.

7 Nutriepigenomics: an emerging frontier in aquafeed innovation

The epigenome comprises DNA methylation patterns, histone tail modifications, and non-coding RNA populations, providing a mechanistic layer between the fixed genotype and the dynamic transcriptome, and is increasingly recognized as a target of nutritional programming in fish (73). Nutriepigenomics investigates how dietary components, particularly methyl-group donors (methionine, choline, folate), bioactive polyphenols, and fatty acids, alter epigenetic marks in ways that adjust gene expression in a sustained, and in some cases trans generationally inherited, manner (74).

In aquaculture, this has profound practical implications because early nutritional interventions during the larval or early juvenile stage can ‘program’ the epigenome to better accept alternative proteins later in life, without genetic modification of the animal (75). Whole-genome bisulfite sequencing (WGBS) has enabled the identification of differentially methylated regions (DMRs) associated with dietary change at single-nucleotide resolution in fish. Li et al. (69), used WGBS combined with RNA-seq in crisp grass carp (‘crispened’ by faba bean feeding) to identify 34 genes with correlated DMRs and differential expression in muscle tissue that were associated with increased muscle hardness, providing a direct mechanistic link between dietary programming, DNA methylation, and a commercially relevant product quality trait. Bravo-Ruiz et al. (76) demonstrated in gilthead sea bream that broodstock nutritional programming differentially modified the hepatic transcriptome and genome-wide DNA methylome of offspring in a manner dependent on the genetic background of the family, establishing that epigenetic markers are family-specific and that interaction between genetic and epigenetic variation must be accounted for in precision feeding strategies. Despite the profound implications of transgenerational metabolic programming, the assessment of plant-based aquafeeds remains overwhelmingly focused on short term somatic growth rather than long-term epigenetic stability. Current feed formulations utilizing high levels of untreated plant protein risks inducing adverse epigenetic reprogramming that could cumulatively degrade the robustness of cultured populations (77). Optimizing formulations to actively minimize the adverse epigenetic reprogramming is an obligatory step for achieving sustainable and resilient aquaculture production under escalating climatic constraints (78).

8 Commercialization and precision application

Translating multi-omics discoveries from research-scale feeding trials into commercial aquafeed applications requires bridging two timescales: the near-term application of validated molecular biomarkers in rapid feed quality screening, and the longer-term integration of nutrigenomic findings into genetic improvement programs through marker-assisted selection. The former is technically the more immediate challenge. Validated biomarkers for feed efficacy such as hepatic GPx or Nrf2 target gene panels for antioxidant response, or microbiome diversity indices and specific taxon abundances for gut health can in principle be incorporated into multiplex qPCR panels, targeted LC-MS metabolomics assays, or biosensor-based platforms for near-farm monitoring. The precondition is that the same biomarker must have been validated across multiple independent feeding trials, ideally in the same genetic background, and must demonstrate acceptable sensitivity and specificity relative to the performance endpoint it predicts.

For genetic improvement programs, the most direct pathway from omics to commercial application is the use of candidate genes identified by nutrigenomic studies as markers in genomic selection. The ARS-KO rainbow trout strain study (67) identified specific metabolic gene modules correlated with plant-diet tolerance in the absence of enteritis; if polymorphisms in these genes (or in their regulatory regions) are shown to segregate with plant-diet tolerance QTL, they could be incorporated into genomic selection indexes to accelerate breeding for alternative-protein utilization. Similarly, the cumulative genetic improvement in FCR documented by Kause et al. (79) across eight generations of national rainbow trout breeding driven by correlated selection for growth and body composition without direct selection on FCR suggests that the genetic architecture of feed efficiency in salmonids is accessible to omics-guided dissection, potentially revealing regulatory genes whose effects have not been captured by phenotypic selection alone.

Precision feeding strategies, which tailor feed composition to the genetic background, life stage, health status, and environmental conditions of the fish population, are the ultimate objective of integrating omics with aquaculture production (80). This requires operational readiness at the farm level to collect biological samples, generate molecular data, and adjust feed formulation in near-real-time, a challenge that currently exceeds the practical and economic capacity of most aquaculture operations. However, the rapid cost reduction of targeted sequencing, multiplex protein assays, and portable near-infrared spectrometers for metabolite estimation is narrowing this gap (81). The development of species-specific reference databases for teleost transcriptomes, proteomes, and metabolomes, analogous to the well-developed resources for Salmo salar and Oncorhynchus mykiss, for additional commercially important species such as European sea bass (Dicentrarchus labrax), cobia (Rachycentron canadum), and snakehead (Channa spp.) will be a critical enabling step for extending precision aquafeed innovation beyond salmonid aquaculture. Table 4, represents the interventions made through integration of multi-omics and potential commercial outcome.

Table 4. Interventions made through integration of multi-omics and potential commercial outcome.

9 Challenges and future perspectives

The application of omics technologies in functional feed development and precision nutrition has opened transformative avenues for animal agriculture, yet several challenges must be addressed to fully realize its potential. At the same time, emerging innovations provide promising future directions. Despite the progress in omics technology in aqua feed research, several challenges at molecular level are still a huge concern (88). The primary constraint is the lack of omics database for commercially cultured species. While cold-water fish like rainbow trout and Atlantic salmon communities have well annotated reference genomes, to support omics data usage, high economic warm water species from Asia, Africa, and Latin America have insufficient data to support omics integration (89). Omics platforms generate large, multidimensional datasets including transcriptomes, proteomes, metabolomes, and microbiomes, that are highly complex and interconnected (90–92). Integrating these datasets to extract meaningful biological insights requires advanced bioinformatics tools, statistical models, and machine learning approaches (93–95). Without proper analytical pipelines, there is a risk of false positives/false discoveries and misinterpretation of the molecular signatures associated with feed efficacy (96). Acquisition of high-throughput omics equipment (e.g., LC-MS/MS, NGS sequencers) and the associated reagents can be expensive, making widespread adoption challenging for smaller operations. Skilled personnel trained in both wet-lab omics techniques and computational analyses are critical, further adding to operational costs (97). While laboratory and pilot-scale studies provide valuable mechanistic insights, translating these findings to commercial-scale production systems is not trivial. Feed effects can be influenced by environmental factors, husbandry practices, genetic variability, and seasonal changes, necessitating robust, multi-location trials to validate omics-derived biomarkers and functional feed performance (98). Variability in sample collection, storage, extraction protocols, and analytical workflows can compromise reproducibility across studies. Standardization of omics workflows across research groups and geographies is a second major challenge. Variability in tissue sampling protocols, RNA extraction methods, library preparation kits, sequencing depth targets, and bioinformatic pipelines introduces inter-study technical variation that can dwarf biologically relevant signals, making meta-analyses and cross-study comparisons unreliable (99). Establishing guidelines for sample preparation, sequencing/proteomics/metabolomics workflows, and data analysis pipelines will improve reproducibility and accelerate adoption. Standardization will facilitate sharing of omics data across laboratories and species, enabling comparative studies and benchmarking (100). Creation of comprehensive, publicly accessible databases for transcriptomes, proteomes, metabolomes, and microbiomes of livestock, aquaculture, and poultry species will serve as reference libraries. Such databases will accelerate biomarker discovery, functional annotation, and cross-study validation, reducing redundancy in experimental efforts (101). AI can integrate omics data with genetic information, phenotypic performance, management practices, and environmental conditions to model feed responses at an unprecedented scale. Predictive models can identify which animals or populations are most likely to benefit from specific functional feeds, optimizing precision nutrition strategies and reducing waste. For example, machine learning models can predict feed efficiency or disease resilience based on microbiome composition, metabolic signatures, and gene expression profiles (53). By combining omics data with genetic and environmental information, nutritionists can develop tailored feeds for specific genotypes, life stages, or environmental stressors. This approach reduces inefficiencies, enhances health outcomes, and supports sustainability by minimizing nutrient wastage and environmental impact (102). Technological advances, miniaturization of sequencing platforms, and cloud-based bioinformatics pipelines are gradually reducing costs and making omics accessible to a broader range of research and commercial operations. Affordable high-throughput screening and biomarker validation will enable smaller feed companies and research institutions to participate in precision feed development (103). Table 5 represents the Comparative Overview of Nutritional Omics in Key Aquaculture Groups.

Table 5. Comparative overview of nutritional omics in key aquaculture groups.

10 Conclusion

The integration of high-throughput omics technologies into aquatic animal nutrition research has transformed the depth and molecular resolution at which scientists interrogate the vital nutrient-gene-environment interface. These technologies have illuminated the complex, interconnected molecular mechanisms regulating the diet adaption, immune response, and metabolic homeostasis across diverse aquaculture organisms. From the precise, actionable mapping of molecular pathogenesis, in commercially vital species to successful integration for the genomic selection of fish lines with superior essential fatty acids retention, the robust, validated success of omics, are paving the way for more sustainable, efficient, and health-promoting aquafeeds.

However, the field requires a careful approach that identifies and removes methodological biases, environmental cofounders, and prospective over exploitations. The over reliance on only the transcriptomic data without phenotypic or proteomic data validation leads to false conclusions regarding organisms’ true metabolic capacity. The profound vulnerability of fish gut microbiomics to random environmental colonization and tank effects of certain culture system affect the microbial community shifts. Furthermore, transgenerational epigenetic inheritance must be grounded to experimental designs that extend beyond immediate, cofounding maternal provisioning effects. By focusing on these aspects and making future efforts, researchers can successfully bridge the current translational gap. Only through critical evaluation, omics can be transformed into descriptive, hypothesis generating academic exercise into a practical and economical viable asset for the aquaculture production industry.

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Footnotes

Edited by: Showkat Ahmad Dar, Bihar Animal Sciences University, India

Reviewed by: Anoop Anand Malik, University of Georgia, United States

Mohd Ashraf Malik, Birsa Agricultural University, India

Pallath Muhammed Nuzaiba, ICAR-Indian Agricultural Research Institute, Jharkhand, Hazaribagh, India