Biofilm formation by the anaerobic pathogen Clostridium difficile

Clostridium difficile is an obligate anaerobic, Gram-positive, endospore-forming bacterium. Although an opportunistic pathogen, it is one of the important causes of healthcare-associated infections. While toxins TcdA and TcdB are the main virulence factors of C. difficile, the factors or processes involved in gut colonization during infection remain unclear. The biofilm-forming ability of bacterial pathogens has been associated with increased antibiotic resistance and chronic recurrent infections. Little is known about biofilm formation by anaerobic gut species. Biofilm formation by C. difficile could play a role in virulence and persistence of C. difficile, as seen for other intestinal pathogens. We demonstrate that C. difficile clinical strains, 630, and the strain isolated in the outbreak, R20291, form structured biofilms in vitro. Biofilm matrix is made of proteins, DNA and polysaccharide. Strain R20291 accumulates substantially more biofilm. Employing isogenic mutants, we show that virulence-associated proteins, Cwp84, flagella and a putative quorum sensing regulator, LuxS, Spo0A, are required for maximal biofilm formation by C. difficile. Moreover we demonstrate that bacteria in C. difficile biofilms are more resistant to high concentrations of vancomycin, a drug commonly used for treatment of CDI, and that inhibitory and sub-inhibitory concentrations of the same antibiotic induce biofilm formation. Surprisingly, clinical C. difficile strains from the same out-break, but from different origin, show differences in biofilm formation. Genome sequence analysis of these strains showed presence of a single nucleoide polymorphism (SNP) in the anti-σ factor RsbW, which regulates the stress-induced alternative sigma factor B (σB). We further demonstrate that RsbW, a negative regulator of alternative sigma factor B, has a role in biofilm formation and sporulation of C. difficile. Our data suggest that biofilm formation by C. difficile is a complex multifactorial process and may be a crucial mechanism for clostridial persistence in the host.

Molecular variability of Meningococcal antigens in carriage and disease strains and in other Neisseria species

This PhD thesis is focused on the study of the molecular variability of some specific proteins, part of the outer membrane of the pathogen Neisseria meningitidis, and described as protective antigens and important virulence factors. These antigens have been employed as components of the vaccine developed by Novartis Vaccines against N. meningitidis of serogroup B, and their variability in the meningococcal population is a key aspect when the effect of the vaccine is evaluated. The PhD project has led to complete three major studies described in three different manuscritps, of which two have been published and the third is in preparation. The thesis is structured in three main chapters, each of them dedicated to the three studies. The first, described in Chapter 1, is specifically dedicated to the analysis of the molecular conservation of meningococcal antigens in the genomes of all species classified in the genus Neisseria (Conservation of Meningococcal Antigens in the Genus Neisseria. A. Muzzi et al.. 2013. mBio 4 (3)). The second study, described in Chapter 2, focuses on the analysis of the presence and conservation of the antigens in a panel of bacterial isolates obtained from cases of the disease and from healthy individuals, and collected in the same year and in the same geographical area (Conservation of fHbp, NadA, and NHBA in carrier and pathogenic isolates of Neisseria meningitidis collected in the Czech Republic in 1993. A. Muzzi et al.. Manuscript in preparation). Finally, Chapter 3 describes the molecular features of the antigens in a panel of bacterial isolates collected over a period of 50 years, and representatives of the epidemiological history of meningococcal disease in the Netherlands (An Analysis of the Sequence Variability of Meningococcal fHbp, NadA and NHBA over a 50-Year Period in the Netherlands. S. Bambini et al.. 2013. PloS one e65043).

Learning with Kernels on Graphs: DAG-based kernels, data streams and RNA function prediction.

In many application domains data can be naturally represented as graphs. When the application of analytical solutions for a given problem is unfeasible, machine learning techniques could be a viable way to solve the problem. Classical machine learning techniques are defined for data represented in a vectorial form. Recently some of them have been extended to deal directly with structured data. Among those techniques, kernel methods have shown promising results both from the computational complexity and the predictive performance point of view. Kernel methods allow to avoid an explicit mapping in a vectorial form relying on kernel functions, which informally are functions calculating a similarity measure between two entities. However, the definition of good kernels for graphs is a challenging problem because of the difficulty to find a good tradeoff between computational complexity and expressiveness. Another problem we face is learning on data streams, where a potentially unbounded sequence of data is generated by some sources. There are three main contributions in this thesis. The first contribution is the definition of a new family of kernels for graphs based on Directed Acyclic Graphs (DAGs). We analyzed two kernels from this family, achieving state-of-the-art results from both the computational and the classification point of view on real-world datasets. The second contribution consists in making the application of learning algorithms for streams of graphs feasible. Moreover,we defined a principled way for the memory management. The third contribution is the application of machine learning techniques for structured data to non-coding RNA function prediction. In this setting, the secondary structure is thought to carry relevant information. However, existing methods considering the secondary structure have prohibitively high computational complexity. We propose to apply kernel methods on this domain, obtaining state-of-the-art results.

Differential expression analysis for sequence count data via mixtures of negative binomials

The recent advent of Next-generation sequencing technologies has revolutionized the way of analyzing the genome. This innovation allows to get deeper information at a lower cost and in less time, and provides data that are discrete measurements. One of the most important applications with these data is the differential analysis, that is investigating if one gene exhibit a different expression level in correspondence of two (or more) biological conditions (such as disease states, treatments received and so on). As for the statistical analysis, the final aim will be statistical testing and for modeling these data the Negative Binomial distribution is considered the most adequate one especially because it allows for "over dispersion". However, the estimation of the dispersion parameter is a very delicate issue because few information are usually available for estimating it. Many strategies have been proposed, but they often result in procedures based on plug-in estimates, and in this thesis we show that this discrepancy between the estimation and the testing framework can lead to uncontrolled first-type errors. We propose a mixture model that allows each gene to share information with other genes that exhibit similar variability. Afterwards, three consistent statistical tests are developed for differential expression analysis. We show that the proposed method improves the sensitivity of detecting differentially expressed genes with respect to the common procedures, since it is the best one in reaching the nominal value for the first-type error, while keeping elevate power. The method is finally illustrated on prostate cancer RNA-seq data.

Modeling multiple state electrostatically formed nanowire transistors

The present thesis work proposes a new physical equivalent circuit model for a recently proposed semiconductor transistor, a 2-drain MSET (Multiple State Electrostatically Formed Nanowire Transistor). It presents a new software-based experimental setup that has been developed for carrying out numerical simulations on the device and on equivalent circuits. As of 2015, we have already approached the scaling limits of the ubiquitous CMOS technology that has been in the forefront of mainstream technological advancement, so many researchers are exploring different ideas in the realm of electrical devices for logical applications, among them MSET transistors. The idea that underlies MSETs is that a single multiple-terminal device could replace many traditional transistors. In particular a 2-drain MSET is akin to a silicon multiplexer, consisting in a Junction FET with independent gates, but with a split drain, so that a voltage-controlled conductive path can connect either of the drains to the source. The first chapter of this work presents the theory of classical JFETs and its common equivalent circuit models. The physical model and its derivation are presented, the current state of equivalent circuits for the JFET is discussed. A physical model of a JFET with two independent gates has been developed, deriving it from previous results, and is presented at the end of the chapter. A review of the characteristics of MSET device is shown in chapter 2. In this chapter, the proposed physical model and its formulation are presented. A listing for the SPICE model was attached as an appendix at the end of this document. Chapter 3 concerns the results of the numerical simulations on the device. At first the research for a suitable geometry is discussed and then comparisons between results from finite-elements simulations and equivalent circuit runs are made. Where points of challenging divergence were found between the two numerical results, the relevant physical processes are discussed. In the fourth chapter the experimental setup is discussed. The GUI-based environments that allow to explore the four-dimensional solution space and to analyze the physical variables inside the device are described. It is shown how this software project has been structured to overcome technical challenges in structuring multiple simulations in sequence, and to provide for a flexible platform for future research in the field.

Multimodal Registration Procedure for the Initial Spatial Alignment of a Retinal Video Sequence to a Retinal Composite Image

Accurate placement of lesions is crucial for the effectiveness and safety of a retinal laser photocoagulation treatment. Computer assistance provides the capability for improvements to treatment accuracy and execution time. The idea is to use video frames acquired from a scanning digital ophthalmoscope (SDO) to compensate for retinal motion during laser treatment. This paper presents a method for the multimodal registration of the initial frame from an SDO retinal video sequence to a retinal composite image, which may contain a treatment plan. The retinal registration procedure comprises the following steps: 1) detection of vessel centerline points and identification of the optic disc; 2) prealignment of the video frame and the composite image based on optic disc parameters; and 3) iterative matching of the detected vessel centerline points in expanding matching regions. This registration algorithm was designed for the initialization of a real-time registration procedure that registers the subsequent video frames to the composite image. The algorithm demonstrated its capability to register various pairs of SDO video frames and composite images acquired from patients.

Real-time polymerase chain-reaction detection of pathogens is feasible to supplement the diagnostic sequence for urinary tract infections

To evaluate, in a prospective pilot study, the feasibility of identifying pathogens in urine using real-time polymerase chain reaction (PCR), and to compare the results with the conventional urine culture-based procedures.

Next-state comfort in learning a vertical stick transportation sequence

Over the last decade, the end-state comfort effect (e.g., Rosenbaum et al., 2006) has received a considerable amount of attention. However, some of the underlying mechanisms are still to be investigated, amongst others, how sequential planning affects end-state comfort and how this effect develops over learning. In a two-step sequencing task, e.g., postural comfort can be planned on the intermediate position (next state) or on the actual end position (final state). It might be hypothesized that, in initial acquisition, next state’s comfort is crucial for action planning but that, in the course of learning, final state’s comfort is taken more and more into account. To test this hypothesis, a variant of Rosenbaum’s vertical stick transportation task was used. Participants (N = 16, right-handed) received extensive practice on a two-step transportation task (10,000 trials over 12 sessions). From the initial position on the middle stair of a staircase in front of the participant, the stick had to be transported either 20 cm upwards and then 40 cm downwards or 20 cm downwards and then 40 cm upwards (N = 8 per subgroup). Participants were supposed to produce fluid movements without changing grasp. In the pre- and posttest, participants were tested on both two-step sequencing tasks as well as on 20 cm single-step upwards and downwards movements (10 trials per condition). For the test trials, grasp height was calculated kinematographically. In the pretest, large end/next/final-state comfort effects for single-step transportation tasks and large next-state comfort effects for sequenced tasks were found. However, no change in grasp height from pre- to posttest could be revealed. Results show that, in vertical stick transportation sequences, the final state is not taken into account when planning grasp height. Instead, action planning seems to be solely based on aspects of the next action goal that is to be reached.