Trascriptome analysis and functional genomics of Neisseria meningitidis in human blood

Neisseria meningitidis (Nm) is the major cause of septicemia and meningococcal meningitis. During the course of infection, it must adapt to different host environments as a crucial factor for survival. Despite the severity of meningococcal sepsis, little is known about how Nm adapts to permit survival and growth in human blood. A previous time-course transcriptome analysis, using an ex vivo model of human whole blood infection, showed that Nm alters the expression of nearly 30% of ORFs of the genome: major dynamic changes were observed in the expression of transcriptional regulators, transport and binding proteins, energy metabolism, and surface-exposed virulence factors. Starting from these data, mutagenesis studies of a subset of up-regulated genes were performed and the mutants were tested for the ability to survive in human whole blood; Nm mutant strains lacking the genes encoding NMB1483, NalP, Mip, NspA, Fur, TbpB, and LctP were sensitive to killing by human blood. Then, the analysis was extended to the whole Nm transcriptome in human blood, using a customized 60-mer oligonucleotide tiling microarray. The application of specifically developed software combined with this new tiling array allowed the identification of different types of regulated transcripts: small intergenic RNAs, antisense RNAs, 5’ and 3’ untranslated regions and operons. The expression of these RNA molecules was confirmed by 5’-3’RACE protocol and specific RT-PCR. Here we describe the complete transcriptome of Nm during incubation in human blood; we were able to identify new proteins important for survival in human blood and also to identify additional roles of previously known virulence factors in aiding survival in blood. In addition the tiling array analysis demonstrated that Nm expresses a set of new transcripts, not previously identified, and suggests the presence of a circuit of regulatory RNA elements used by Nm to adapt to proliferate in human blood.

Lesion detection and classification in breast cancer: evaluation of approaches based on morphological features, tracer kinetic modelling and semi-quantitative parameters in MR functional imaging (DCE-MRI)

The diagnosis, grading and classification of tumours has benefited considerably from the development of DCE-MRI which is now essential to the adequate clinical management of many tumour types due to its capability in detecting active angiogenesis. Several strategies have been proposed for DCE-MRI evaluation. Visual inspection of contrast agent concentration curves vs time is a very simple yet operator dependent procedure, therefore more objective approaches have been developed in order to facilitate comparison between studies. In so called model free approaches, descriptive or heuristic information extracted from time series raw data have been used for tissue classification. The main issue concerning these schemes is that they have not a direct interpretation in terms of physiological properties of the tissues. On the other hand, model based investigations typically involve compartmental tracer kinetic modelling and pixel-by-pixel estimation of kinetic parameters via non-linear regression applied on region of interests opportunely selected by the physician. This approach has the advantage to provide parameters directly related to the pathophysiological properties of the tissue such as vessel permeability, local regional blood flow, extraction fraction, concentration gradient between plasma and extravascular-extracellular space. Anyway, nonlinear modelling is computational demanding and the accuracy of the estimates can be affected by the signal-to-noise ratio and by the initial solutions. The principal aim of this thesis is investigate the use of semi-quantitative and quantitative parameters for segmentation and classification of breast lesion. The objectives can be subdivided as follow: describe the principal techniques to evaluate time intensity curve in DCE-MRI with focus on kinetic model proposed in literature; to evaluate the influence in parametrization choice for a classic bi-compartmental kinetic models; to evaluate the performance of a method for simultaneous tracer kinetic modelling and pixel classification; to evaluate performance of machine learning techniques training for segmentation and classification of breast lesion.

A clustering method for robust and reliable large scale functional and structural protein sequence annotation

Bioinformatics, in the last few decades, has played a fundamental role to give sense to the huge amount of data produced. Obtained the complete sequence of a genome, the major problem of knowing as much as possible of its coding regions, is crucial. Protein sequence annotation is challenging and, due to the size of the problem, only computational approaches can provide a feasible solution. As it has been recently pointed out by the Critical Assessment of Function Annotations (CAFA), most accurate methods are those based on the transfer-by-homology approach and the most incisive contribution is given by cross-genome comparisons. In the present thesis it is described a non-hierarchical sequence clustering method for protein automatic large-scale annotation, called “The Bologna Annotation Resource Plus” (BAR+). The method is based on an all-against-all alignment of more than 13 millions protein sequences characterized by a very stringent metric. BAR+ can safely transfer functional features (Gene Ontology and Pfam terms) inside clusters by means of a statistical validation, even in the case of multi-domain proteins. Within BAR+ clusters it is also possible to transfer the three dimensional structure (when a template is available). This is possible by the way of cluster-specific HMM profiles that can be used to calculate reliable template-to-target alignments even in the case of distantly related proteins (sequence identity < 30%). Other BAR+ based applications have been developed during my doctorate including the prediction of Magnesium binding sites in human proteins, the ABC transporters superfamily classification and the functional prediction (GO terms) of the CAFA targets. Remarkably, in the CAFA assessment, BAR+ placed among the ten most accurate methods. At present, as a web server for the functional and structural protein sequence annotation, BAR+ is freely available at http://bar.biocomp.unibo.it/bar2.0.

Functional and Genetic characterization of new genomic islands from an E. coli strain associated with neonatal meningitis.

Enterobacteriaceae genomes evolve through mutations, rearrangements and horizontal gene transfer (HGT). The latter evolutionary pathway works through the acquisition DNA (GEI) modules of foreign origin that enhances fitness of the host to a given environment. The genome of E. coli IHE3034, a strain isolated from a case of neonatal meningitis, has recently been sequenced and its subsequent sequence analysis has predicted 18 possible GEIs, of which: 8 have not been previously described, 5 fully meet the pathogenic island definition and at least 10 that seem to be of prophagic origin. In order to study the GEI distribution of our reference strain, we screened for the presence 18 GEIs a panel of 132 strains, representative of E. coli diversity. Also, using an inverse nested PCR approach we identified 9 GEI that can form an extrachromosomal circular intermediate (CI) and their respective attachment sites (att). Further, we set up a qPCR approach that allowed us to determine the excision rates of 5 genomic islands in different growth conditions. Four islands, specific for strains appertaining to the sequence type complex 95 (STC95), have been deleted in order to assess their function in a Dictyostelium discoideum grazing assays. Overall, the distribution data presented here indicate that 16 IHE3034 GEIs are more associated to the STC95 strains. Also the functional and genetic characterization has uncovered that GEI 13, 17 and 19 are involved in the resistance to phagocitation by Dictyostelium d thus suggesting a possible role in the adaptation of the pathogen during certain stages of infection.

Identification and functional characterization of human cytomegalovirus Fc binding proteins

ABSTRACT Human cytomegalovirus (HCMV) employs many different mechanisms to escape and subvert the host immune system surveillance. Among these different mechanisms the role of human IgG Fc receptors (FcγR) in HCMV pathogenesis is still unclear. In mammalians, FcγRs are expressed on the surface of all haematopoietic cells and have a multifaceted role in regulating the activity of antibodies to generate a well-balanced immune response. Viral proteins with Fcγ binding ability are highly diffuse among herpesviruses. They interfere with the host receptors functions in order to counteract immune system recognition. So far, two human HCMV Fcγ binding proteins have been described: UL119 and RL11. This work was aimed to the identification and characterization of HCMV Fcγ binding proteins. The study is divided in two parts: first the characterization of UL119 and RL11; second the identification and characterization of novel HCMV Fcγ binding proteins. Regarding the first part, we demonstrated that both UL119 and RL11 internalize Fcγ fragments from transfected cells surface through a clathrin dependent pathway. In infected cells both proteins were found in the viral assembly complex and on virions surface as envelope associated glycoproteins. Moreover, internalized Fcγ in infected cells do not undergo lysosomal degradation but rather traffic in early endosomes up to the viral assembly complex. Regarding the second part, we were able to identify two novels Fcγ binding protein coded by CMV: RL12 and RL13. The latter was also further characterized as recombinant protein in terms of cellular localization, Fc binding site and IgG internalization ability. Finally binding specificity of both RL12 and RL13 seems to be confined to human IgG1 and IgG2. Taken together, these data show that HCMV codes for up to 4 FcγR and that they could have a double role both on virus and on infected cells.