Speaker
Description
Sulfate-reducing bacterial (SRB) biofilms are prevalent across natural and engineered environments, mediating biogeochemical sulfur cycling while accelerating biofouling and microbiologically influenced corrosion (MIC). To uncover surface-dependent biofilm adaptations, we performed a comparative transcriptomic analysis of Oleidesulfovibrio alaskensis G20 biofilms formed on copper and polycarbonate surfaces. RNA sequencing revealed 1,255 differentially expressed genes, with copper-grown biofilms exhibiting upregulation of Dde_1570 (flagellin; log₂FC 2.31) and Dde_0831 (polysaccharide chain length determinant; log₂FC 1.15), indicative of enhanced motility and extracellular polymeric substance production. In contrast, stress-related genes, including Dde_0132 (Cu/Zn efflux transporter; log₂FC -3.37), were downregulated on copper, reflecting metabolic adaptation to heavy metal exposure. Morphological characterization via SEM and AFM revealed denser biofilm clustering on copper surfaces and significant surface roughness increases (4.6-fold and 3.8-fold on copper and polycarbonate, respectively). Protein-protein interaction analysis underscored the importance of ribosomal synthesis, folate metabolism, and quorum sensing in biofilm resilience. Functional annotation further identified novel biofilm regulators, including Dde_4025 and Dde_3288.
Building on these results, we propose an artificial intelligence (AI)-driven framework to predict and optimize biofilm responses on engineered surfaces. By integrating transcriptomic profiles, morphological descriptors, and functional annotations into machine learning models, surface-specific biofilm behaviors can be classified and forecasted. Feature importance analysis using explainable AI methods will pinpoint critical genes and morphological features that govern microbial-material interactions. This predictive pipeline will accelerate the discovery and rational design of novel antifouling materials, including corrosion-resistant alloys, engineered polymer composites, nanopatterned metals, and smart stimuli-responsive surfaces, thereby offering sustainable solutions to mitigate MIC and enhance material longevity.
