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Understanding biofilm formation in acidophilic bioleaching microorganisms: Advances and challenges
Beatriz Salas1, Mauricio Díaz2, Mario Vera3
1Instituto de Ingeniería Biológica y Médica, Facultades de Ingeniería, Medicina y Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago, Chile; Departamento de Ingeniería Hidráulica y Ambiental, Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile.
Abstract:
Bioleaching of metal sulfides (MS) is driven by acidophilic chemolithotrophic microorganisms that oxidize ferrous ions and reduced inorganic sulfur compounds under extremely acidic conditions. These microorganisms form biofilms on mineral surfaces, influencing their metabolism and bioleaching activity. Studying biofilms in bioleaching acidophiles presents unique challenges, such as the dual role of MS surfaces as electron donors that generate reactive oxygen species, provide selective colonization sites, biofilm mineralization, and limited knowledge of inter- and intra-species interactions. This chapter reviews recent advances in bioleaching biofilm research, focusing on cell-to-cell communication through Quorum Sensing (QS), extracellular polymeric substances (EPS) studies using non-invasive cell labeling, as well as high-throughput image analysis for biofilm quantification. We discuss the effects of acyl homoserine lactone (AHLs)-based and diffusible signal factor (DSF)-based QS systems on the metabolic activity and biofilm formation of bioleaching bacteria. Additionally, we examine evidence suggesting that QS influences biofilm formation and MS bioleaching in mixed acidophilic cultures. We also review recent progress in visualizing acidophilic leaching biofilms by fluorescence microscopy techniques (Epifluorescence and Confocal Laser Scanning Microscopy) to study their establishment on mineral surfaces, EPS production, and interspecies interactions. Finally, we present an improved approach using Open-Source Software to overcome limitations in biofilm quantitative analysis, addressing biases caused by the low number of images frequently analyzed and the absence of robust statistical frameworks for cell and microcolony quantification.
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