Pollution and edaphic factors shape bacterial community structure and functionality in historically contaminated soils.

Studies about biodegradation potential in soils often refer to artificially contaminated and simplified systems, overlooking the complexity associated with contaminated sites in a real context. This work aims to provide a holistic view on microbiome assembly and functional diversity in the model site SIN Brescia-Caffaro (Italy), characterized by historical and uneven contamination by organic and inorganic compounds. Here, physical and chemical analyses and microbiota characterization were applied on one-hundred-twenty-seven soil samples to unravel the environmental factors driving bacterial community assembly and biodegradation potential in three former agricultural fields. Chemical analyses showed a patchy distribution of metals, metalloids and polychlorinated biphenyls (PCB) and allowed soil categorization according to depth and area of collections. Likewise, the bacterial community structure, described by molecular fingerprinting and 16S rRNA gene analyses, was significantly different according to collection site and depth. Pollutant concentrations (i.e., hexachloro-biphenyls, arsenic and mercury), nitrogen content and parameters related to soil texture were identified as main drivers of microbiota assembly, being significantly correlated to bacterial community composition. Moreover, bacteria putatively involved in the aerobic degradation of PCBs were enriched over the total bacterial community in topsoils, where the highest activity was recorded using fluorescein hydrolysis as proxy. Metataxonomic analyses revealed the presence of bacteria having metabolic pathways related to PCB degradation and tolerance to heavy metals and metalloids in the topsoil samples collected in all areas. Overall, the provided dissection of soil microbiota structure and its degradation potential in the SIN Brescia-Caffaro can contribute to target specific areas for rhizoremediation implementation. Metagenomics studies could be implemented in the future to understand if specific degradative pathways are present in historically polluted sites characterized by the co-occurrence of multiple classes of contaminants.