Insight into the molecular and functional determinants of HP1043, the essential master regulator of Helicobacter pylori.

Helicobacter pylori is one of the most widespread and successful human pathogens, colonizing half of the population stomach mucosa and causing gastric malignancies in 1% of carriers. Due to the increasing number of antimicrobial-resistant strains, in 2017 the WHO included H. pylori among pathogens that pose a major threat for humankind. In this study, we propose as a molecular target for novel antimicrobial strategies HP1043, an orphan response regulator essential for the viability of H. pylori as it orchestrates all the most important cellular processes. Amino acids most relevant for HP1043 dimerization and target DNA recognition were identified and used to guide an in-silico protein-DNA docking and generate a high-resolution structural model of the interacting HP1043 dimer and its target DNA. The model was experimentally validated and exploited to carry out a virtual screening of small molecule libraries, identifying 8 compounds potentially able to interfere with HP1043 function and likely block H. pylori infection. A second line of research aimed at the characterization of the regulatory function of HP1043 and the tight mechanisms of regulation of hp1043 gene expression. In particular, we proved a direct interaction between HP1043 and the housekeeping sigma80 factor of the RNA polymerase. A conditional mutant H. pylori strain overexpressing a synthetic copy of the hp1043 gene altered in nucleotide sequence yet encoding the wild-type protein was generated, achieving increased intracellular levels of HP1043. However, overexpression of HP1043 did not result in an upregulation of target genes transcription nor modulation of hp1043 transcript levels, pinpointing the existence of multiple overlayed mechanisms of regulation that affect both protein levels and functionality as well as maintain steady the amount of hp1043 transcript. Finally, we proposed that a mechanism of post-transcriptional regulation could depend on an antisense transcript to the hp1043 gene which was validated in two different strains.

Characterization of DNA sequence properties through network and statistical approaches

In this thesis we will see that the DNA sequence is constantly shaped by the interactions with its environment at multiple levels, showing footprints of DNA methylation, of its 3D organization and, in the case of bacteria, of the interaction with the host organisms. In the first chapter, we will see that analyzing the distribution of distances between consecutive dinucleotides of the same type along the sequence, we can detect epigenetic and structural footprints. In particular, we will see that CG distance distribution allows to distinguish among organisms of different biological complexity, depending on how much CG sites are involved in DNA methylation. Moreover, we will see that CG and TA can be described by the same fitting function, suggesting a relationship between the two. We will also provide an interpretation of the observed trend, simulating a positioning process guided by the presence and absence of memory. In the end, we will focus on TA distance distribution, characterizing deviations from the trend predicted by the best fitting function, and identifying specific patterns that might be related to peculiar mechanical properties of the DNA and also to epigenetic and structural processes. In the second chapter, we will see how we can map the 3D structure of the DNA onto its sequence. In particular, we devised a network-based algorithm that produces a genome assembly starting from its 3D configuration, using as inputs Hi-C contact maps. Specifically, we will see how we can identify the different chromosomes and reconstruct their sequences by exploiting the spectral properties of the Laplacian operator of a network. In the third chapter, we will see a novel method for source clustering and source attribution, based on a network approach, that allows to identify host-bacteria interaction starting from the detection of Single-Nucleotide Polymorphisms along the sequence of bacterial genomes.