Harnessing Engineered Microbial Consortia for Xenobiotic Bioremediation: Integrating Multi-Omics and AI for Next-Generation Wastewater Treatment.

Abstract

The global increase in municipal and industrial wastewater generation has intensified the need for ecologically resilient and technologically advanced treatment systems. Although traditional biological treatment technologies are effective for organic load reduction, they often fail to remove recalcitrant xenobiotics such as pharmaceuticals, synthetic dyes, endocrine disruptors (EDCs), and microplastics (MPs). Engineered microbial consortia offer a promising and sustainable alternative owing to their metabolic flexibility, ecological resilience, and capacity for syntrophic degradation of complex pollutants. This review critically examines emerging strategies for enhancing microbial bioremediation in wastewater treatment systems (WWTS), focusing on co-digestion, biofilm engineering, targeted bioaugmentation, and incorporation of conductive materials to stimulate direct interspecies electron transfer (DIET). This review highlights how multi-omics platforms, including metagenomics, transcriptomics, and metabolomics, enable high-resolution community profiling and pathway reconstructions. The integration of artificial intelligence (AI) and machine learning (ML) algorithms into bioprocess diagnostics facilitates real-time system optimization, predictive modeling of antibiotic resistance gene (ARG) dynamics, and intelligent bioreactor control. Persistent challenges, such as microbial instability, ARG dissemination, reactor fouling, and the absence of region-specific microbial reference databases, are critically analyzed. This review concludes with a translational pathway for the development of next-generation WWTS that integrate synthetic microbial consortia, AI-mediated biosensors, and modular bioreactors within the One Health and Circular Economy framework.