Biosurfactants are a class of functional molecules produced and secreted by microorganisms, which play important roles in cell physiology such as flagellum-dependent or -independent bacterial spreading, cell signaling, and biofilm formation. They are amphipathic compounds and comprise a variety of chemical structures, including rhamnolipids, typically produced by Pseudomonas spp. and also reported within other bacterial genera. The present study is focused on Burkholderia kururiensis KP23(T), a trichloroethylene (TCE)-degrading, N-fixing, and plant growth-promoting bacterium. Herein, we describe the production of rhamnolipids by B. kururiensis, and its characterization by LTQ-Orbitrap Hybrid Mass Spectrometry, a powerful tool that allowed efficient identification of molecular subpopulations, due to its high selectivity, mass accuracy, and resolving power. The population of rhamnolipids produced by B. kururiensis revealed molecular species commonly observed in Pseudomonas spp. and/or Burkholderia spp. In addition, this strain was used as a platform for expression of two Pseudomonas aeruginosa biosynthetic enzymes: RhlA, which directly utilizes β-hydroxydecanoyl-ACP intermediates in fatty acid synthesis to generate the HAA, and RhlB, the rhamnosyltransferase 1, which catalyzes the transfer of dTDP-L-rhamnose to β-hydroxy fatty acids in the biosynthesis of rhamnolipids. We show that rhamnolipid production by the engineered B. kururiensis was increased over 600 % when compared to the wild type. Structural analyses demonstrated a molecular population composed mainly of monorhamnolipids, as opposed to wild-type B. kururiensis and P. aeruginosa in which dirhamnolipids are predominant. We conclude that B. kururiensis is a promising biosurfactant-producing organism, with great potential for environmental and biotechnological applications due to its non-pathogenic characteristics and efficiency as a platform for metabolic engineering and production of tailor-made biosurfactants.

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The paper introduces the Oil-Slick Hub (OSH), a computational platform to facilitate the data visualization of a large database of petroleum signatures observed on the surface of the ocean with synthetic aperture radar (SAR) measurements. This Internet platform offers an information search and retrieval system of a database resulting from >20 years of scientific projects that interpreted ~15 thousand offshore mineral oil “slicks”: natural oil “seeps” versus operational oil “spills”. Such a Digital Mega-Collection Database consists of satellite images and oil-slick polygons identified in the Gulf of Mexico (GMex) and the Brazilian Continental Margin (BCM). A series of attributes describing the interpreted slicks are also included, along with technical reports and scientific papers. Two experiments illustrate the use of the OSH to facilitate the selection of data subsets from the mega collection (GMex variables and BCM samples), in which artificial intelligence techniques—machine learning (ML)—classify slicks into seeps or spills. The GMex variable dataset was analyzed with simple linear discriminant analyses (LDAs), and a three-fold accuracy performance pattern was observed: (i) the least accurate subset (~65%) solely used acquisition aspects (e.g., acquisition beam mode, date, and time, satellite name, etc.); (ii) the best results (>90%) were achieved with the inclusion of location attributes (i.e., latitude, longitude, and bathymetry); and (iii) moderate performances (~70%) were reached using only morphological information (e.g., area, perimeter, perimeter to area ratio, etc.). The BCM sample dataset was analyzed with six traditional ML methods, namely naive Bayes (NB), K-nearest neighbors (KNN), decision trees (DT), random forests (RF), support vector machines (SVM), and artificial neural networks (ANN), and the most effective algorithms per sample subsets were: (i) RF (86.8%) for Campos, Santos, and Ceará Basins; (ii) NB (87.2%) for Campos with Santos Basins; (iii) SVM (86.9%) for Campos with Ceará Basins; and (iv) SVM (87.8%) for only Campos Basin. The OSH can assist in different concerns (general public, social, economic, political, ecological, and scientific) related to petroleum exploration and production activities, serving as an important aid in discovering new offshore exploratory frontiers, avoiding legal penalties on oil-seep events, supporting oceanic monitoring systems, and providing valuable information to environmental studies.