Design and implementation of bioinformatics tools for large scale genome annotation

The continuous increase of genome sequencing projects produced a huge amount of data in the last 10 years: currently more than 600 prokaryotic and 80 eukaryotic genomes are fully sequenced and publically available. However the sole sequencing process of a genome is able to determine just raw nucleotide sequences. This is only the first step of the genome annotation process that will deal with the issue of assigning biological information to each sequence. The annotation process is done at each different level of the biological information processing mechanism, from DNA to protein, and cannot be accomplished only by in vitro analysis procedures resulting extremely expensive and time consuming when applied at a this large scale level. Thus, in silico methods need to be used to accomplish the task. The aim of this work was the implementation of predictive computational methods to allow a fast, reliable, and automated annotation of genomes and proteins starting from aminoacidic sequences. The first part of the work was focused on the implementation of a new machine learning based method for the prediction of the subcellular localization of soluble eukaryotic proteins. The method is called BaCelLo, and was developed in 2006. The main peculiarity of the method is to be independent from biases present in the training dataset, which causes the over‐prediction of the most represented examples in all the other available predictors developed so far. This important result was achieved by a modification, made by myself, to the standard Support Vector Machine (SVM) algorithm with the creation of the so called Balanced SVM. BaCelLo is able to predict the most important subcellular localizations in eukaryotic cells and three, kingdom‐specific, predictors were implemented. In two extensive comparisons, carried out in 2006 and 2008, BaCelLo reported to outperform all the currently available state‐of‐the‐art methods for this prediction task. BaCelLo was subsequently used to completely annotate 5 eukaryotic genomes, by integrating it in a pipeline of predictors developed at the Bologna Biocomputing group by Dr. Pier Luigi Martelli and Dr. Piero Fariselli. An online database, called eSLDB, was developed by integrating, for each aminoacidic sequence extracted from the genome, the predicted subcellular localization merged with experimental and similarity‐based annotations. In the second part of the work a new, machine learning based, method was implemented for the prediction of GPI‐anchored proteins. Basically the method is able to efficiently predict from the raw aminoacidic sequence both the presence of the GPI‐anchor (by means of an SVM), and the position in the sequence of the post‐translational modification event, the so called ω‐site (by means of an Hidden Markov Model (HMM)). The method is called GPIPE and reported to greatly enhance the prediction performances of GPI‐anchored proteins over all the previously developed methods. GPIPE was able to predict up to 88% of the experimentally annotated GPI‐anchored proteins by maintaining a rate of false positive prediction as low as 0.1%. GPIPE was used to completely annotate 81 eukaryotic genomes, and more than 15000 putative GPI‐anchored proteins were predicted, 561 of which are found in H. sapiens. In average 1% of a proteome is predicted as GPI‐anchored. A statistical analysis was performed onto the composition of the regions surrounding the ω‐site that allowed the definition of specific aminoacidic abundances in the different considered regions. Furthermore the hypothesis that compositional biases are present among the four major eukaryotic kingdoms, proposed in literature, was tested and rejected. All the developed predictors and databases are freely available at: BaCelLo http://gpcr.biocomp.unibo.it/bacello eSLDB http://gpcr.biocomp.unibo.it/esldb GPIPE http://gpcr.biocomp.unibo.it/gpipe

The Domon Family of Plant Plasma Membrane B-Type Cytochromes: Heterologous Expression, Biochemical Characterization and Physiological Roles in Vivo

The DOMON domain is a domain widespread in nature, predicted to fold in a β-sandwich structure. In plants, AIR12 is constituted by a single DOMON domain located in the apoplastic space and is GPI-modified for anchoring to the plasma membrane. Arabidopsis thaliana AIR12 has been heterologously expressed as a recombinant protein (recAtAIR12) in Pichia pastoris. Spectrophotometrical analysis of the purified protein showed that recAtAir12 is a cytochrome b. RecAtAIR12 is highly glycosylated, it is reduced by ascorbate, superoxide and naftoquinones, oxidised by monodehydroascorbate and oxygen and insensitive to hydrogen peroxide. The addition of recAtAIR12 to permeabilized plasma membranes containing NADH, FeEDTA and menadione, caused a statistically significant increase in hydroxyl radicals as detected by electron paramagnetic resonance. In these conditions, recAtAIR12 has thus a pro-oxidant role. Interestingly, AIR12 is related to the cytochrome domain of cellobiose dehydrogenase which is involved in lignin degradation, possibly via reactive oxygen species (ROS) production. In Arabidopsis the Air12 promoter is specifically activated at sites where cell separations occur and ROS, including •OH, are involved in cell wall modifications. air12 knock-out plants infected with Botrytis cinerea are more resistant than wild-type and air12 complemented plants. Also during B. cinerea infection, cell wall modifications and ROS are involved. Our results thus suggest that AIR12 could be involved in cell wall modifying reactions by interacting with ROS and ascorbate. CyDOMs are plasma membrane redox proteins of plants that are predicted to contain an apoplastic DOMON fused with a transmembrane cytochrome b561 domain. CyDOMs have never been purified nor characterised. The trans-membrane portion of a soybean CyDOM was expressed in E. coli but purification could not be achieved. The DOMON domain was expressed in P. pastoris and shown to be itself a cytochrome b that could be reduced by ascorbate.

Attenuation of Eph Receptor Kinase Activation in Cancer Cells by Coexpressed Ephrin Ligands

The Eph receptor tyrosine kinases mediate juxtacrine signals by interacting “in trans” with ligands anchored to the surface of neighboring cells via a GPI-anchor (ephrin-As) or a transmembrane segment (ephrin-Bs), which leads to receptor clustering and increased kinase activity. Additionally, soluble forms of the ephrin-A ligands released from the cell surface by matrix metalloproteases can also activate EphA receptor signaling. Besides these trans interactions, recent studies have revealed that Eph receptors and ephrins coexpressed in neurons can also engage in lateral “cis” associations that attenuate receptor activation by ephrins in trans with critical functional consequences. Despite the importance of the Eph/ephrin system in tumorigenesis, Eph receptor-ephrin cis interactions have not been previously investigated in cancer cells. Here we show that in cancer cells, coexpressed ephrin-A3 can inhibit the ability of EphA2 and EphA3 to bind ephrins in trans and become activated, while ephrin-B2 can inhibit not only EphB4 but also EphA3. The cis-inhibition of EphA3 by ephrin-B2 implies that in some cases ephrins that cannot activate a particular Eph receptor in trans can nevertheless inhibit its signaling ability through cis association. We also found that an EphA3 mutation identified in lung cancer enhances cis interaction with ephrin-A3. These results suggest a novel mechanism that may contribute to cancer pathogenesis by attenuating the tumor suppressing effects of Eph receptor signaling pathways activated by ephrins in trans (Falivelli et al. 2013).