The rapid expansion of global aquaculture has led to wastewater enriched with nitrogen, phosphorus, organic matter, antibiotics, and heavy metals, posing serious risks such as eutrophication, ecological imbalance, and public health threats. Conventional physical, chemical, and biological treatments face limitations including high cost, secondary pollution, and insufficient efficiency, limiting sustainable wastewater management. Algal–bacterial symbiotic systems (ABSS) provide a sustainable alternative, coupling the metabolic complementarity of microalgae and bacteria for effective pollutant mitigation and concurrent biomass valorization. Immobilizing microbial consortia within carrier materials enhances system stability, tolerance to environmental changes, and scalability. This review systematically summarizes the pollution characteristics and ecological risks of aquaculture effluents, highlighting the limitations of conventional treatment methods. It focuses on the metabolic cooperation within ABSS, including nutrient cycling and pollutant degradation, the impact of environmental factors, and the role of immobilization carriers in enhancing system performance and biomass resource valorization. Despite their potential, ABSS still face challenges related to mass transfer limitations, complex microbial interactions, and difficulties in scale-up. Future research should focus on improving environmental adaptability, regulating microbial dynamics, designing intelligent and cost-effective carriers, and developing modular engineering systems to enable robust and scalable solutions for sustainable aquaculture wastewater treatment.

Introduction: Recirculating aquaculture systems (RAS) are increasingly used in intensive aquaculture because of their capacity to reduce nitrogen, phosphorus, and organic matter discharges. However, whether improved conventional effluent quality is accompanied by a reduced antibiotic resistance gene (ARG) burden remains unclear. Methods: During the mid-cultivation stage, effluents were collected from four RAS farms and four paved flow-through raceway systems (PFRS). Water quality parameters, including total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), chlorophyll-a (Chl-a), and dissolved oxygen (DO), were measured. Microbial community composition was characterized using 16S rRNA gene sequencing, while ARGs and mobile genetic elements (MGEs) were quantified using GeoChip 5.0. Multivariate statistics and co-occurrence network analyses were used to evaluate associations among environmental conditions, microbial communities, ARG profiles, and MGE profiles. Results: RAS effluents showed substantially lower nutrient and organic loads than PFRS, confirming improved conventional water quality. However, total ARG abundance was significantly higher in RAS than in PFRS, increasing from 35.39 ± 11.43 × 10 ⁶ to 63.99 ± 15.33 × 10 ⁶ , corresponding to a 1.81-fold increase. In contrast, total MGE abundance was numerically higher in RAS but did not differ significantly between systems. Microbial alpha diversity was lower in RAS, and beta diversity analysis showed distinct community structuring between cultivation modes, with both compositional differences and dispersion effects contributing to group separation. ARG class- and subtype-level profiles showed heterogeneous abundance patterns, with several major ARG categories remaining highly abundant in RAS, although most feature-level differences were not significant after FDR correction. Co-occurrence network analysis further revealed structured associations among microbial species, ARGs, and MGEs, with modular organization and highly connected resistance-associated nodes. Conclusion: This study demonstrates a “low-pollution but non-reduced ARG burden” pattern in RAS effluents. Improved removal of conventional pollutants did not correspond to a proportional reduction in ARG or MGE profiles. These findings indicate that conventional water-quality indicators alone are insufficient to evaluate resistance-associated genetic pollution in engineered aquaculture systems, and that ARGs, MGEs, microbial community structure, and species–ARG/MGE association patterns should be integrated into future RAS monitoring and management.

Fish flesh quality is a multidimensional phenotype shaped across the production–post-harvest continuum by interacting metabolic, structural, lipid, redox, and microbial processes. Conventional sensory, physicochemical, biochemical, and instrumental measurements remain essential for defining quality phenotypes, while foodomics and microbiome approaches increasingly resolve the biological mechanisms underlying quality formation and deterioration. This review integrates recent advances in fish flesh quality assessment, foodomics, microbiome science, intelligent sensing, machine learning, and explainable artificial intelligence from a mechanism-informed perspective. Particular emphasis is placed on energy metabolism, lipid remodeling, redox regulation, muscle-fiber and collagen organization, microbial metabolites, and spoilage-related microbial functions that connect biological state with measurable quality traits. We further examine the transition from current-state recognition toward prospective quality prediction, with emphasis on biological interpretability, transferability, external validation, and uncertainty. Building on this evidence, we propose a dual-stage framework in which a context-dependent harvest biological-state vector integrates energetic, structural, lipid/nutritional, redox, and product-associated microbial dimensions to connect pre-harvest quality formation with post-harvest deterioration trajectories. Together, these advances provide a basis for moving from quality detection toward prospective prediction and mechanism-informed intelligent management of fish flesh quality.