Molecular and functional characterization of the chemotactic genes in the PCBs-degrader Pseudomonas pseudoalcaligenes KF707

Although bacteria represent the simplest form of life on Earth, they have a great impact on all living beings. For example the degrader bacterium Pseudomonas pseudoalcaligenes KF707 is used in bioremediation procedures for the recovery of polluted sites. Indeed, KF707 strain is know for its ability to degrade biphenyl and polychlorinated biphenyls - to which is chemotactically attracted - and to tolerate the oxydative stress due to toxic metal oxyanions such as tellurite and selenite. Moreover, in bioremediation processes, target compounds can be easily accessible to KF707 through biofilm formation. All these considerations suggest that KF707 is such a unique microorganism and this Thesis work has been focused on determining the molecular nature of some of the peculiar physiological traits of this strain. The genome project provided a large set of informations: putative genes involved in the degradation of aromatic and toxic compounds and associated to stress response were identified. Notably, multiple chemotactic operons and cheA genes were also found. Deleted mutants in the cheA genes were constructed and their role in motility, chemotaxis and biofilm formation were assessed and compared to those previously attributed to a cheA1 gene in a KF707 mutant constructed by a mini-Tn5 transposon insertion and which was impaired in motility and biofilm development. The results of this present Thesis work, taken together, were interpreted to suggest that in Pseudomonas pseudoalcaligenes KF707 strain, multiple factors are involved in these networks and they might play different roles depending on the environmental conditions. The ability of KF707 strain to produce signal molecules possibly involved in cell-to-cell communication, was also investigated: lack of a lux-like QS system - which is conversely widely present in Gram negative bacteria – keeps open the question about the actual molecular nature of KF707 quorum sensing mechanism.

Machine learning tools for protein annotation: the cases of transmembrane β-barrel and myristoylated proteins

Biology is now a “Big Data Science” thanks to technological advancements allowing the characterization of the whole macromolecular content of a cell or a collection of cells. This opens interesting perspectives, but only a small portion of this data may be experimentally characterized. From this derives the demand of accurate and efficient computational tools for automatic annotation of biological molecules. This is even more true when dealing with membrane proteins, on which my research project is focused leading to the development of two machine learning-based methods: BetAware-Deep and SVMyr. BetAware-Deep is a tool for the detection and topology prediction of transmembrane beta-barrel proteins found in Gram-negative bacteria. These proteins are involved in many biological processes and primary candidates as drug targets. BetAware-Deep exploits the combination of a deep learning framework (bidirectional long short-term memory) and a probabilistic graphical model (grammatical-restrained hidden conditional random field). Moreover, it introduced a modified formulation of the hydrophobic moment, designed to include the evolutionary information. BetAware-Deep outperformed all the available methods in topology prediction and reported high scores in the detection task. Glycine myristoylation in Eukaryotes is the binding of a myristic acid on an N-terminal glycine. SVMyr is a fast method based on support vector machines designed to predict this modification in dataset of proteomic scale. It uses as input octapeptides and exploits computational scores derived from experimental examples and mean physicochemical features. SVMyr outperformed all the available methods for co-translational myristoylation prediction. In addition, it allows (as a unique feature) the prediction of post-translational myristoylation. Both the tools here described are designed having in mind best practices for the development of machine learning-based tools outlined by the bioinformatics community. Moreover, they are made available via user-friendly web servers. All this make them valuable tools for filling the gap between sequential and annotated data.