Natural compounds Camptothecin and Triptolide: highly specific enzyme inhibitors and tools to dissect transcriptional functions

With this work I elucidated new and unexpected mechanisms of two strong and highly specific transcription inhibitors: Triptolide and Campthotecin. Triptolide (TPL) is a diterpene epoxide derived from the Chinese plant Trypterigium Wilfoordii Hook F. TPL inhibits the ATPase activity of XPB, a subunit of the general transcription factor TFIIH. In this thesis I found that degradation of Rbp1 (the largest subunit of RNA Polymerase II) caused by TPL treatments, is preceded by an hyperphosphorylation event at serine 5 of the carboxy-terminal domain (CTD) of Rbp1. This event is concomitant with a block of RNA Polymerase II at promoters of active genes. The enzyme responsible for Ser5 hyperphosphorylation event is CDK7. Notably, CDK7 downregulation rescued both Ser5 hyperphosphorylation and Rbp1 degradation triggered by TPL. Camptothecin (CPT), derived from the plant Camptotheca acuminata, specifically inhibits topoisomerase 1 (Top1). We first found that CPT induced antisense transcription at divergent CpG islands promoter. Interestingly, by immunofluorescence experiments, CPT was found to induce a burst of R loop structures (DNA/RNA hybrids) at nucleoli and mitochondria. We then decided to investigate the role of Top1 in R loop homeostasis through a short interfering RNA approach (RNAi). Using DNA/RNA immunoprecipitation techniques coupled to NGS I found that Top1 depletion induces an increase of R loops at a genome-wide level. We found that such increase occurs on the entire gene body. At a subset of loci R loops resulted particularly stressed after Top1 depletion: some of these genes showed the formation of new R loops structures, whereas other loci showed a reduction of R loops. Interestingly we found that new peaks usually appear at tandem or divergent genes in the entire gene body, while losses of R loop peaks seems to be a feature specific of 3’ end regions of convergent genes.

Kernel Methods for Tree Structured Data

Machine learning comprises a series of techniques for automatic extraction of meaningful information from large collections of noisy data. In many real world applications, data is naturally represented in structured form. Since traditional methods in machine learning deal with vectorial information, they require an a priori form of preprocessing. Among all the learning techniques for dealing with structured data, kernel methods are recognized to have a strong theoretical background and to be effective approaches. They do not require an explicit vectorial representation of the data in terms of features, but rely on a measure of similarity between any pair of objects of a domain, the kernel function. Designing fast and good kernel functions is a challenging problem. In the case of tree structured data two issues become relevant: kernel for trees should not be sparse and should be fast to compute. The sparsity problem arises when, given a dataset and a kernel function, most structures of the dataset are completely dissimilar to one another. In those cases the classifier has too few information for making correct predictions on unseen data. In fact, it tends to produce a discriminating function behaving as the nearest neighbour rule. Sparsity is likely to arise for some standard tree kernel functions, such as the subtree and subset tree kernel, when they are applied to datasets with node labels belonging to a large domain. A second drawback of using tree kernels is the time complexity required both in learning and classification phases. Such a complexity can sometimes prevents the kernel application in scenarios involving large amount of data. This thesis proposes three contributions for resolving the above issues of kernel for trees. A first contribution aims at creating kernel functions which adapt to the statistical properties of the dataset, thus reducing its sparsity with respect to traditional tree kernel functions. Specifically, we propose to encode the input trees by an algorithm able to project the data onto a lower dimensional space with the property that similar structures are mapped similarly. By building kernel functions on the lower dimensional representation, we are able to perform inexact matchings between different inputs in the original space. A second contribution is the proposal of a novel kernel function based on the convolution kernel framework. Convolution kernel measures the similarity of two objects in terms of the similarities of their subparts. Most convolution kernels are based on counting the number of shared substructures, partially discarding information about their position in the original structure. The kernel function we propose is, instead, especially focused on this aspect. A third contribution is devoted at reducing the computational burden related to the calculation of a kernel function between a tree and a forest of trees, which is a typical operation in the classification phase and, for some algorithms, also in the learning phase. We propose a general methodology applicable to convolution kernels. Moreover, we show an instantiation of our technique when kernels such as the subtree and subset tree kernels are employed. In those cases, Direct Acyclic Graphs can be used to compactly represent shared substructures in different trees, thus reducing the computational burden and storage requirements.

Mechanosensitivity in the myenteric plexus of the guinea pig ileum

The enteric nervous system regulates autonomously from the central nervous system all the reflex pathways that control blood flow, motility, water and electrolyte transport and acid secretion. The ability of the gut to function in isolation is one of the most intriguing phenomenons in neurogastroenterology. This requires coding of sensory stimuli by cells in the gut wall. Enteric neurons are prominent candidates to relay mechanosensitivity. Surprisingly, the identity of mechanosensitive neurons in the enteric nervous system as well as the appropriate stimulus modality is unknown despite the evidence that enteric neurons respond to sustained distension. Objectives: The aim of our study was to record from mechanosensitive neurons using physiological stimulus modalities. Identification of sensory neurons is of central importance to understand sensory transmission under normal conditions and in gut diseases associated with sensorimotor dysfunctions, such as Irritable Bowel Syndrome. Only then it will be possible to identify novel targets that help to normalise sensory functions. Methods: We used guinea-pig ileum myenteric plexus preparations and recorded responses of all neurons in a given ganglion with a fast neuroimaging technique based on voltage sensitive dyes. To evoke a mechanical response we used two different kinds of stimuli: firstly we applied a local mechanical distortion of the ganglion surface with von Frey hair. Secondarily we mimic the ganglia deformation during physiological movements of myenteric ganglia in a freely contracting ileal preparation. We were able to reliably and reproducibly mimic this distortion by intraganglionic injections of small volumes of oxygenated and buffered Krebs solution using stimulus parameters that correspond to single contractions. We also performed in every ganglion tested, electrical stimulations to evoke fast excitatory postsynaptic potentials. Immunohistochemistry reactions were done with antibodies against Calbindin and NeuN, considered markers for sensory neurons. Results: Recordings were performed in 46 ganglia from 31 guinea pigs. In every ganglion tested we found from 1 to 21 (from 3% to 62%) responding cells with a median value of 7 (24% of the total number of neurons). The response consisted of an almost instantaneous spike discharge that showed adaptation. The median value of the action potential frequency in the responding neurons was 2.0 Hz, with a recording time of 1255 ms. The spike discharge lasted for 302 ± 231 ms and occurred only during the initial deformation phase. During sustained deformation no spike discharge was observed. The response was reproducible and was a direct activation of the enteric neurons since it remained after synaptic blockade with hexamethonium or ω-conotoxin and after long time perfusion with capsaicin. Muscle tone appears not to be required for activation of mechanosensory neurons. Mechanosensory neurons showed a response to mechanical stimulation related to the stimulus strength. All mechanosensory neurons received fast synaptic inputs. There was no correlation between mechanosensitivity and Calbindin-IR and NeuN-IR (44% of mechanosensitive neurones Calb-IR-/NeuN-IR-). Conclusions: We identified mechanosensitive neurons in the myenteric plexus of the guinea pig ileum which responded to brief deformation. These cells appear to be rapidly accommodating neurons which respond to dynamic change. All mechanosensitive neurons received fast synaptic input suggesting that their activity can be highly modulated by other neurons and hence there is a low stimulus fidelity which allows adjusting the gain in a sensory network. Mechanosensitivity appears to be a common feature of many enteric neurons belonging to different functional classes. This supports the existence of multifunctional enteric neurons which may fulfil sensory, integrative and motor functions.

Molecular architecture of fur binding to iron-induced and - repressed genes in Helicobacter pylori

The ferric uptake regulator protein Fur regulates iron-dependent gene expression in bacteria. In the human pathogen Helicobacter pylori, Fur has been shown to regulate iron-induced and iron-repressed genes. Herein we investigate the molecular mechanisms that control this differential iron-responsive Fur regulation. Hydroxyl radical footprinting showed that Fur has different binding architectures, which characterize distinct operator typologies. On operators recognized with higher affinity by holo-Fur, the protein binds to a continuous AT-rich stretch of about 20 bp, displaying an extended protection pattern. This is indicative of protein wrapping around the DNA helix. DNA binding interference assays with the minor groove binding drug distamycin A, point out that the recognition of the holo-operators occurs through the minor groove of the DNA. By contrast, on the apo-operators, Fur binds primarily to thymine dimers within a newly identified TCATTn10TT consensus element, indicative of Fur binding to one side of the DNA, in the major groove of the double helix. Reconstitution of the TCATTn10TT motif within a holo-operator results in a feature binding swap from an holo-Fur- to an apo-Fur-recognized operator, affecting both affinity and binding architecture of Fur, and conferring apo-Fur repression features in vivo. Size exclusion chromatography indicated that Fur is a dimer in solution. However, in the presence of divalent metal ions the protein is able to multimerize. Accordingly, apo-Fur binds DNA as a dimer in gel shift assays, while in presence of iron, higher order complexes are formed. Stoichiometric Ferguson analysis indicates that these complexes correspond to one or two Fur tetramers, each bound to an operator element. Together these data suggest that the apo- and holo-Fur repression mechanisms apparently rely on two distinctive modes of operator-recognition, involving respectively the readout of a specific nucleotide consensus motif in the major groove for apo-operators, and the recognition of AT-rich stretches in the minor groove for holo-operators, whereas the iron-responsive binding affinity is controlled through metal-dependent shaping of the protein structure in order to match preferentially the major or the minor groove.