Recent trends in molecular diagnostics of yeast infections : from PCR to NGS

The incidence of opportunistic yeast infections in humans has been increasing over recent years. These infections are difficult to treat and diagnose, in part due to the large number and broad diversity of species that can underlie the infection. In addition, resistance to one or several antifungal drugs in infecting strains is increasingly being reported, severely limiting therapeutic options and showcasing the need for rapid detection of the infecting agent and its drug susceptibility profile. Current methods for species and resistance identification lack satisfactory sensitivity and specificity, and often require prior culturing of the infecting agent, which delays diagnosis. Recently developed high-throughput technologies such as next generation sequencing or proteomics are opening completely new avenues for more sensitive, accurate and fast diagnosis of yeast pathogens. These approaches are the focus of intensive research, but translation into the clinics requires overcoming important challenges. In this review, we provide an overview of existing and recently emerged approaches that can be used in the identification of yeast pathogens and their drug resistance profiles. Throughout the text we highlight the advantages and disadvantages of each methodology and discuss the most promising developments in their path from bench to bedside

Computational Biology and Chemistry

The use of computers and software tools in biochemistry (biology) has led to a deep revolution in basic sciences and medicine. Bioinformatics and systems biology are the direct results of this revolution. With the involvement of computers, software tools, and internet services in scientific disciplines comprising biology and chemistry, new terms, technologies, and methodologies appeared and established. Bioinformatic software tools, versatile databases, and easy internet access resulted in the occurrence of computational biology and chemistry. Today, we have new types of surveys and laboratories including “in silico studies” and “dry labs” in which bioinformaticians conduct their investigations to gain invaluable outcomes. These features have led to 3-dimensioned illustrations of different molecules and complexes to get a better understanding of nature

Computational solutions for addressing heterogeneity in DNA methylation data

DNA methylation, a reversible epigenetic modification, has been implicated with various bi- ological processes including gene regulation. Due to the multitude of datasets available, it is a premier candidate for computational tool development, especially for investigating hetero- geneity within and across samples. We differentiate between three levels of heterogeneity in DNA methylation data: between-group, between-sample, and within-sample heterogeneity. Here, we separately address these three levels and present new computational approaches to quantify and systematically investigate heterogeneity. Epigenome-wide association studies relate a DNA methylation aberration to a phenotype and therefore address between-group heterogeneity. To facilitate such studies, which necessar- ily include data processing, exploratory data analysis, and differential analysis of DNA methy- lation, we extended the R-package RnBeads. We implemented novel methods for calculating the epigenetic age of individuals, novel imputation methods, and differential variability analysis. A use-case of the new features is presented using samples from Ewing sarcoma patients. As an important driver of epigenetic differences between phenotypes, we systematically investigated associations between donor genotypes and DNA methylation states in methylation quantitative trait loci (methQTL). To that end, we developed a novel computational framework –MAGAR– for determining statistically significant associations between genetic and epigenetic variations. We applied the new pipeline to samples obtained from sorted blood cells and complex bowel tissues of healthy individuals and found that tissue-specific and common methQTLs have dis- tinct genomic locations and biological properties. To investigate cell-type-specific DNA methylation profiles, which are the main drivers of within-group heterogeneity, computational deconvolution methods can be used to dissect DNA methylation patterns into latent methylation components. Deconvolution methods require pro- files of high technical quality and the identified components need to be biologically interpreted. We developed a computational pipeline to perform deconvolution of complex DNA methyla- tion data, which implements crucial data processing steps and facilitates result interpretation. We applied the protocol to lung adenocarcinoma samples and found indications of tumor in- filtration by immune cells and associations of the detected components with patient survival. Within-sample heterogeneity (WSH), i.e., heterogeneous DNA methylation patterns at a ge- nomic locus within a biological sample, is often neglected in epigenomic studies. We present the first systematic benchmark of scores quantifying WSH genome-wide using simulated and experimental data. Additionally, we created two novel scores that quantify DNA methyla- tion heterogeneity at single CpG resolution with improved robustness toward technical biases. WSH scores describe different types of WSH in simulated data, quantify differential hetero- geneity, and serve as a reliable estimator of tumor purity. Due to the broad availability of DNA methylation data, the levels of heterogeneity in DNA methylation data can be comprehensively investigated. We contribute novel computational frameworks for analyzing DNA methylation data with respect to different levels of hetero- geneity. We envision that this toolbox will be indispensible for understanding the functional implications of DNA methylation patterns in health and disease.DNA Methylierung ist eine reversible, epigenetische Modifikation, die mit verschiedenen biologischen Prozessen wie beispielsweise der Genregulation in Verbindung steht. Eine Vielzahl von DNA Methylierungsdatensätzen bildet die perfekte Grundlage zur Entwicklung von Softwareanwendungen, insbesondere um Heterogenität innerhalb und zwischen Proben zu beschreiben. Wir unterscheiden drei Ebenen von Heterogenität in DNA Methylierungsdaten: zwischen Gruppen, zwischen Proben und innerhalb einer Probe. Hier betrachten wir die drei Ebenen von Heterogenität in DNA Methylierungsdaten unabhängig voneinander und präsentieren neue Ansätze um die Heterogenität zu beschreiben und zu quantifizieren. Epigenomweite Assoziationsstudien verknüpfen eine DNA Methylierungsveränderung mit einem Phänotypen und beschreiben Heterogenität zwischen Gruppen. Um solche Studien, welche Datenprozessierung, sowie exploratorische und differentielle Datenanalyse beinhalten, zu vereinfachen haben wir die R-basierte Softwareanwendung RnBeads erweitert. Die Erweiterungen beinhalten neue Methoden, um das epigenetische Alter vorherzusagen, neue Schätzungsmethoden für fehlende Datenpunkte und eine differentielle Variabilitätsanalyse. Die Analyse von Ewing-Sarkom Patientendaten wurde als Anwendungsbeispiel für die neu entwickelten Methoden gewählt. Wir untersuchten Assoziationen zwischen Genotypen und DNA Methylierung von einzelnen CpGs, um sogenannte methylation quantitative trait loci (methQTL) zu definieren. Diese stellen einen wichtiger Faktor dar, der epigenetische Unterschiede zwischen Gruppen induziert. Hierzu entwickelten wir ein neues Softwarepaket (MAGAR), um statistisch signifikante Assoziationen zwischen genetischer und epigenetischer Variation zu identifizieren. Wir wendeten diese Pipeline auf Blutzelltypen und komplexe Biopsien von gesunden Individuen an und konnten gemeinsame und gewebespezifische methQTLs in verschiedenen Bereichen des Genoms lokalisieren, die mit unterschiedlichen biologischen Eigenschaften verknüpft sind. Die Hauptursache für Heterogenität innerhalb einer Gruppe sind zelltypspezifische DNA Methylierungsmuster. Um diese genauer zu untersuchen kann Dekonvolutionssoftware die DNA Methylierungsmatrix in unabhängige Variationskomponenten zerlegen. Dekonvolutionsmethoden auf Basis von DNA Methylierung benötigen technisch hochwertige Profile und die identifizierten Komponenten müssen biologisch interpretiert werden. In dieser Arbeit entwickelten wir eine computerbasierte Pipeline zur Durchführung von Dekonvolutionsexperimenten, welche die Datenprozessierung und Interpretation der Resultate beinhaltet. Wir wendeten das entwickelte Protokoll auf Lungenadenokarzinome an und fanden Anzeichen für eine Tumorinfiltration durch Immunzellen, sowie Verbindungen zum Überleben der Patienten. Heterogenität innerhalb einer Probe (within-sample heterogeneity, WSH), d.h. heterogene Methylierungsmuster innerhalb einer Probe an einer genomischen Position, wird in epigenomischen Studien meist vernachlässigt. Wir präsentieren den ersten Vergleich verschiedener, genomweiter WSH Maße auf simulierten und experimentellen Daten. Zusätzlich entwickelten wir zwei neue Maße um WSH für einzelne CpGs zu berechnen, welche eine verbesserte Robustheit gegenüber technischen Faktoren aufweisen. WSH Maße beschreiben verschiedene Arten von WSH, quantifizieren differentielle Heterogenität und sagen Tumorreinheit vorher. Aufgrund der breiten Verfügbarkeit von DNA Methylierungsdaten können die Ebenen der Heterogenität ganzheitlich beschrieben werden. In dieser Arbeit präsentieren wir neue Softwarelösungen zur Analyse von DNA Methylierungsdaten in Bezug auf die verschiedenen Ebenen der Heterogenität. Wir sind davon überzeugt, dass die vorgestellten Softwarewerkzeuge unverzichtbar für das Verständnis von DNA Methylierung im kranken und gesunden Stadium sein werden

Modelling prognostic trajectories in Alzheimer’s disease

Progression to dementia due to Alzheimer’s Disease (AD) is a long and protracted process that involves multiple pathways of disease pathophysiology. Predicting these dynamic changes has major implications for timely and effective clinical management in AD. There are two reasons why at present we lack appropriate tools to make such predictions. First, a key feature of AD is the interactive nature of the relationships between biomarkers, such as accumulation of β-amyloid -a peptide that builds plaques between nerve cells-, tau -a protein found in the axons of nerve cells- and widespread neurodegeneration. Current models fail to capture these relationships because they are unable to successfully reduce the high dimensionality of biomarkers while exploiting informative multivariate relationships. Second, current models focus on simply predicting in a binary manner whether an individual will develop dementia due to AD or not, without informing clinicians about their predicted disease trajectory. This can result in administering inefficient treatment plans and hindering appropriate stratification for clinical trials. In this thesis, we overcome these challenges by using applied machine learning to build predictive models of patient disease trajectories in the earliest stages of AD. Specifically, to exploit the multi-dimensionality of biomarker data, we used a novel feature generation methodology Partial Least Squares regression with recursive feature elimination (PLSr-RFE). This method applies a hybrid-feature selection and feature construction method that captures co-morbidities in cognition and pathophysiology, resulting in an index of Alzheimer’s disease atrophy from structural MRI. We validated our choice of biomarker and the efficacy of our methodology by showing that the learnt pattern of grey matter atrophy is highly predictive of tau accumulation in an independent sample. Next, to go beyond predicting binary outcomes to deriving individualised prognostic scores of cognitive decline due to AD, we used a novel trajectory modelling approach (Generalised Metric Learning Vector Quantization – Scalar projection) that mines multimodal data from large AD research cohorts. Using this approach, we derive individualised prognostic scores of cognitive decline due to AD, revealing interactive cognitive, and biological factors that improve prediction accuracy. Next, we extended our machine learning framework to classify and stage early AD individuals based on future pathological tau accumulation. Our results show that the characteristic spreading pattern of tau in early AD can be predicted by baseline biomarkers, particularly when stratifying groups using multimodal data. Further, we showed that our prognostic index predicts individualised rates of future tau accumulation with high accuracy and regional specificity in an independent sample of cognitively unimpaired individuals. Overall, our work used machine learning to combine continuous information from AD biomarkers predicting pathophysiological changes at different stages in the AD cascade. The approaches presented in this thesis provide an excellent framework to support personalised clinical interventions and guide effective drug discovery trials

Inferring Geodesic Cerebrovascular Graphs: Image Processing, Topological Alignment and Biomarkers Extraction

A vectorial representation of the vascular network that embodies quantitative features - location, direction, scale, and bifurcations - has many potential neuro-vascular applications. Patient-specific models support computer-assisted surgical procedures in neurovascular interventions, while analyses on multiple subjects are essential for group-level studies on which clinical prediction and therapeutic inference ultimately depend. This first motivated the development of a variety of methods to segment the cerebrovascular system. Nonetheless, a number of limitations, ranging from data-driven inhomogeneities, the anatomical intra- and inter-subject variability, the lack of exhaustive ground-truth, the need for operator-dependent processing pipelines, and the highly non-linear vascular domain, still make the automatic inference of the cerebrovascular topology an open problem. In this thesis, brain vessels’ topology is inferred by focusing on their connectedness. With a novel framework, the brain vasculature is recovered from 3D angiographies by solving a connectivity-optimised anisotropic level-set over a voxel-wise tensor field representing the orientation of the underlying vasculature. Assuming vessels joining by minimal paths, a connectivity paradigm is formulated to automatically determine the vascular topology as an over-connected geodesic graph. Ultimately, deep-brain vascular structures are extracted with geodesic minimum spanning trees. The inferred topologies are then aligned with similar ones for labelling and propagating information over a non-linear vectorial domain, where the branching pattern of a set of vessels transcends a subject-specific quantized grid. Using a multi-source embedding of a vascular graph, the pairwise registration of topologies is performed with the state-of-the-art graph matching techniques employed in computer vision. Functional biomarkers are determined over the neurovascular graphs with two complementary approaches. Efficient approximations of blood flow and pressure drop account for autoregulation and compensation mechanisms in the whole network in presence of perturbations, using lumped-parameters analog-equivalents from clinical angiographies. Also, a localised NURBS-based parametrisation of bifurcations is introduced to model fluid-solid interactions by means of hemodynamic simulations using an isogeometric analysis framework, where both geometry and solution profile at the interface share the same homogeneous domain. Experimental results on synthetic and clinical angiographies validated the proposed formulations. Perspectives and future works are discussed for the group-wise alignment of cerebrovascular topologies over a population, towards defining cerebrovascular atlases, and for further topological optimisation strategies and risk prediction models for therapeutic inference. Most of the algorithms presented in this work are available as part of the open-source package VTrails