Context matters:the power of single-cell analyses in identifying context-dependent effects on gene expression in blood immune cells

The human immune system is a complex system that we still do not fully understand. No two humans react in the same way to attacks by bacteria, viruses or fungi. Factors such as genetics, the type of pathogen or previous exposure to the pathogen may explain this diversity in response. Single-cell RNA sequencing (scRNA-seq) is a new technique that enables us to study the gene expression of each cell individually, allowing us to study immune diversity in much greater detail. This increased resolution helps us discern how disease-associated genetic variants actually contribute to disease. In this thesis, I studied the relation between disease-associated genetic variants and gene expression levels in the context of different cell types and pathogen exposures in order to gain insight into the working mechanisms of these variants. For many variants we learnt in which cell types and under which pathogen exposures they affect gene expression, and we were even able to identify changes in gene co-expression, suggesting that disease-associated variants change how our genes interact with each other. With the single-cell field being so new, much of my work was showing the feasibility of using scRNA-seq to study the interplay between genetics and gene expression. To set up future research, we created guidelines for these analyses and established a consortium that brings together many major scientists in the field to enable large-scale studies across an even wider variety of contexts. This final work helps inform current and future large-scale scRNA-seq research

Comparative genomics of recent adaptation in Candida pathogens

[eng] Fungal infections pose a serious health threat, affecting >1,000 million people and causing ~1.5 million deaths each year. The problem is growing due to insufficient diagnostic and therapeutic options, increased number of susceptible patients, expansion of pathogens partly linked to climate change and the rise of antifungal drug resistance. Among other fungal pathogens, Candida species are a major cause of severe hospital-acquired infections, with high mortality in immunocompromised patients. Various Candida pathogens constitute a public health issue, which require further efforts to develop new drugs, optimize currently available treatments and improve diagnostics. Given the high dynamism of Candida genomes, a promising strategy to improve current therapies and diagnostics is to understand the evolutionary mechanisms of adaptation to antifungal drugs and to the human host. Previous work using in vitro evolution, population genomics, selection inferences and Genome Wide Association Studies (GWAS) have partially clarified such recent adaptation, but various open questions remain. In the three research articles that conform this PhD thesis we addressed some of these gaps from the perspective of comparative genomics. First, we addressed methodological issues regarding the analysis of Candida genomes. Studying recent adaptation in these pathogens requires adequate bioinformatic tools for variant calling, filtering and functional annotation. Among other reasons, current methods are suboptimal due to limited accuracy to identify structural variants from short read sequencing data. In addition, there is a need for easy-to-use, reproducible variant calling pipelines. To address these gaps we developed the “personalized Structural Variation detection” pipeline (perSVade), a framework to call, filter and annotate several variant types, including structural variants, directly from reads. PerSVade enables accurate identification of structural variants in any species of interest, such as Candida pathogens. In addition, our tool automatically predicts the structural variant calling accuracy on simulated genomes, which informs about the reliability of the calling process. Furthermore, perSVade can be used to analyze single nucleotide polymorphisms and copy number-variants, so that it facilitates multi-variant, reproducible genomic studies. This tool will likely boost variant analyses in Candida pathogens and beyond. Second, we addressed open questions about recent adaptation in Candida, using perSVade for variant identification. On the one hand, we investigated the evolutionary mechanisms of drug resistance in Candida glabrata. For this, we used a large-scale in vitro evolution experiment to study adaptation to two commonly-used antifungals: fluconazole and anidulafungin. Our results show rapid adaptation to one or both drugs, with moderate fitness costs and through few mutations in a narrow set of genes. In addition, we characterize a novel role of ERG3 mutations in cross-resistance towards fluconazole in anidulafungin-adapted strains. These findings illuminate the mutational paths leading to drug resistance and cross-resistance in Candida pathogens. On the other hand, we reanalyzed ~2,000 public genomes and phenotypes to understand the signs of recent selection and drug resistance in six major Candida species: C. auris, C. glabrata, C. albicans, C. tropicalis, C. parapsilosis and C. orthopsilosis. We found hundreds of genes under recent selection, suggesting that clinical adaptation is diverse and complex. These involve species-specific but also convergently affected processes, such as cell adhesion, which could underlie conserved adaptive mechanisms. In addition, using GWAS we predicted known drivers of antifungal resistance alongside potentially novel players. Furthermore, our analyses reveal an important role of generally-overlooked structural variants, and suggest an unexpected involvement of (para)sexual recombination in the spread of resistance. Taken together, our findings provide novel insights on how Candida pathogens adapt to human-related environments and suggest candidate genes that deserve future attention. In summary, the results of this thesis improve our knowledge about the mechanisms of recent adaptation in Candida pathogens, which may enable improved therapeutic and diagnostic applications.[cat] Les infeccions fúngiques representen una greu amenaça per a la salut, afectant a més de 1.000 milions de persones i causant aproximadament 1,5 milions de morts cada any. El problema està augmentant a causa d’unes opcions terapèutiques i diagnòstiques insuficients, l'increment del nombre de pacients susceptibles, l'expansió dels patògens parcialment vinculada al canvi climàtic i l'augment de la resistència als fàrmacs antifúngics. D’entre diversos fongs patògens, els llevats del gènere Candida són una causa important d'infeccions nosocomials, amb una alta mortalitat en pacients immunodeprimits. Diverses espècies de Candida constitueixen un problema de salut pública, cosa que requereix més esforços per a desenvolupar nous medicaments, optimitzar els tractaments disponibles i millorar els diagnòstics. Tenint en compte el dinamisme genòmic d’aquests patògens, una estratègia prometedora per millorar les teràpies i diagnòstics actuals és comprendre els mecanismes evolutius d'adaptació als fàrmacs antifúngics i a l’hoste humà. Treballs anteriors utilitzant l'evolució in vitro, la genòmica de poblacions, les inferències de selecció i els estudis d'associació de genoma complet (GWAS, per les sigles en anglès) han aclarit parcialment aquesta adaptació recent, però encara hi ha diverses preguntes obertes. En els tres articles que conformen aquesta tesi doctoral, hem abordat algunes d'aquestes preguntes des de la perspectiva de la genòmica comparativa. En primer lloc, hem abordat qüestions metodològiques relatives a l'anàlisi dels genomes de les espècies Candida. L'estudi de l'adaptació recent en aquests patògens requereix eines bioinformàtiques adequades per a la detecció, filtratge i anotació funcional de variants genètiques. Entre altres raons, els mètodes actuals són subòptims a causa de la limitada precisió per identificar variants estructurals a partir de dades de seqüenciació amb lectures curtes. A més, hi ha una necessitat d’eines computacionals per a la detecció de variants que siguin senzilles d'utilitzar i reproduibles. Per abordar aquestes mancances, hem desenvolupat el mètode bioinformàtic "personalized Structural Variation detection" (perSVade), una eina que permet la detecció, filtratge i anotació de diversos tipus de variants, incloent-hi les variants estructurals, directament des de les lectures. PerSVade permet la identificació precisa de les variants estructurals en qualsevol espècie d'interès, com ara els patògens Candida. A més, la nostra eina prediu automàticament la precisió de la detecció d’aquestes variants en genomes simulats, la qual cosa informa sobre la fiabilitat del procés. Finalment, perSVade es pot utilitzar per analitzar altres tipus de variants, com els polimorfismes de nucleòtid únic o els canvis en el nombre de còpies, facilitant així estudis genòmics integrals i reproduibles. Aquesta eina probablement impulsarà les anàlisis genòmiques en els patògens Candida i també en altres espècies. En segon lloc, hem abordat algunes de les preguntes obertes sobre l'adaptació recent en els llevats Candida, utilitzant perSVade per a la identificació de variants. D'una banda, hem investigat els mecanismes evolutius de resistència als fàrmacs antifúngics en Candida glabrata. Per a això, hem utilitzat un experiment d'evolució in vitro a gran escala per estudiar l'adaptació a dos antifúngics comuns: el fluconazol i l’anidulafungina. Els nostres resultats mostren una adaptació ràpida a un o ambdós fàrmacs, amb un cost per al creixement moderat i a través de poques mutacions en un nombre reduït de gens. A més, hem caracteritzat un paper nou de les mutacions en ERG3 en la resistència creuada al fluconazol en soques adaptades a anidulafungina. Aquests descobriments aclareixen els processos mutacionals que condueixen a la resistència als fàrmacs i a la resistència creuada en els patògens Candida. D'altra banda, hem re-analitzat aproximadament 2.000 genomes i fenotips disponibles en repositoris públics per a comprendre els senyals genòmics de selecció recent i de resistència a fàrmacs antifúngics, en sis espècies rellevants de Candida: C. auris, C. glabrata, C. albicans, C. tropicalis, C. parapsilosis i C. orthopsilosis. Hem trobat centenars de gens sota selecció recent, suggerint que l'adaptació clínica és diversa i complexa. Aquests gens estan relacionats amb funcions específiques de cada espècie, però també trobem processos alterats de manera similar en diferents patògens, com per exemple l’adhesió cel·lular, cosa que indica fenòmens d’adaptació conservats. A part, utilitzant GWAS hem predit mecanismes esperats de resistència a antifúngics i també possibles nous factors. A més, les nostres anàlisis revelen un paper important de les variants estructurals, generalment poc estudiades, i suggereixen una implicació inesperada de la recombinació (para)sexual en la propagació de la resistència. En conjunt, els nostres descobriments proporcionen noves perspectives sobre com els patògens Candida s'adapten als entorns humans, i suggereixen gens candidats que mereixen investigacions futures. En resum, els resultats d’aquesta tesi milloren el nostre coneixement sobre els mecanismes d'adaptació recent en els patògens Candida, cosa que pot permetre el disseny de noves teràpies i diagnòstics

A journey from molecule to physiology and in silico tools for drug discovery targeting the transient receptor potential vanilloid type 1 (TRPV1) channel

The heat and capsaicin receptor TRPV1 channel is widely expressed in nerve terminals of dorsal root ganglia (DRGs) and trigeminal ganglia innervating the body and face, respectively, as well as in other tissues and organs including central nervous system. The TRPV1 channel is a versatile receptor that detects harmful heat, pain, and various internal and external ligands. Hence, it operates as a polymodal sensory channel. Many pathological conditions including neuroinflammation, cancer, psychiatric disorders, and pathological pain, are linked to the abnormal functioning of the TRPV1 in peripheral tissues. Intense biomedical research is underway to discover compounds that can modulate the channel and provide pain relief. The molecular mechanisms underlying temperature sensing remain largely unknown, although they are closely linked to pain transduction. Prolonged exposure to capsaicin generates analgesia, hence numerous capsaicin analogs have been developed to discover efficient analgesics for pain relief. The emergence of in silico tools offered significant techniques for molecular modeling and machine learning algorithms to indentify druggable sites in the channel and for repositioning of current drugs aimed at TRPV1. Here we recapitulate the physiological and pathophysiological functions of the TRPV1 channel, including structural models obtained through cryo-EM, pharmacological compounds tested on TRPV1, and the in silico tools for drug discovery and repositioning

FAIR and bias-free network modules for mechanism-based disease redefinitions

Even though chronic diseases are the cause of 60% of all deaths around the world, the underlying causes for most of them are not fully understood. Hence, diseases are defined based on organs and symptoms, and therapies largely focus on mitigating symptoms rather than cure. This is also reflected in the most commonly used disease classifications. The complex nature of diseases, however, can be better defined in terms of networks of molecular interactions. This research applies the approaches of network medicine – a field that uses network science for identifying and treating diseases – to multiple diseases with highly unmet medical need such as stroke and hypertension. The results show the success of this approach to analyse complex disease networks and predict drug targets for different conditions, which are validated through preclinical experiments and are currently in human clinical trials

Systems Analytics and Integration of Big Omics Data

A “genotype"" is essentially an organism's full hereditary information which is obtained from its parents. A ""phenotype"" is an organism's actual observed physical and behavioral properties. These may include traits such as morphology, size, height, eye color, metabolism, etc. One of the pressing challenges in computational and systems biology is genotype-to-phenotype prediction. This is challenging given the amount of data generated by modern Omics technologies. This “Big Data” is so large and complex that traditional data processing applications are not up to the task. Challenges arise in collection, analysis, mining, sharing, transfer, visualization, archiving, and integration of these data. In this Special Issue, there is a focus on the systems-level analysis of Omics data, recent developments in gene ontology annotation, and advances in biological pathways and network biology. The integration of Omics data with clinical and biomedical data using machine learning is explored. This Special Issue covers new methodologies in the context of gene–environment interactions, tissue-specific gene expression, and how external factors or host genetics impact the microbiome

Phylogenomic, Biogeographic, and Evolutionary Research Trends in Arachnology

This book focuses on systematics, biogeography, and evolution of arachnids, a group of ancient chelicerate lineages that have taken on terrestrial lifestyles. The book opens with the questions of what arachnology represents, and where the field should go in the future. Twelve original contributions then dissect the current state-of-the-art in arachnological research. These papers provide innovative phylogenomic, evolutionary and biogeographic analyses and interpretations of new data and/or synthesize our knowledge to offer new directions for the future of arachnology

Analyzing epigenomic data in a large-scale context

While large amounts of epigenomic data are publicly available, their retrieval in a form suitable for downstream analysis is a bottleneck in current research. In a typical analysis, users are required to download huge files that span the entire genome, even if they are only interested in a small subset (e.g., promoter regions) or an aggregation thereof. Moreover, complex operations on genome-level data are not always feasible on a local computer due to resource limitations. The DeepBlue Epigenomic Data Server mitigates this issue by providing a robust server that affords a powerful API for searching, filtering, transforming, aggregating, enriching, and downloading data from several epigenomic consortia. Furthermore, its main component implements scalable data storage and Manipulation methods that scale with the increasing amount of epigenetic data, thereby making it the ideal resource for researchers that seek to integrate epigenomic data into their analysis workflow. This work also presents companion tools that utilize the DeepBlue API to enable users not proficient in scripting or programming languages to analyze epigenomic data in a user-friendly way: (i) an R/Bioconductor package that integrates DeepBlue into the R analysis workflow. The extracted data are automatically converted into suitable R data structures for downstream analysis and visualization within the Bioconductor frame- work; (ii) a web portal that enables users to search, select, filter and download the epigenomic data available in the DeepBlue Server. This interface provides elements, such as data tables, grids, data selections, developed for empowering users to find the required epigenomic data in a straightforward interface; (iii) DIVE, a web data analysis tool that allows researchers to perform large-epigenomic data analysis in a programming-free environment. DIVE enables users to compare their datasets to the datasets available in the DeepBlue Server in an intuitive interface, which summarizes the comparison of hundreds of datasets in a simple chart. Furthermore, these tools are integrated, being capable of sharing results among themselves, creating a powerful large-scale epigenomic data analysis environment. The DeepBlue Epigenomic Data Server and its ecosystem was well received by the International Human Epigenome Consortium and already attracted much attention by the epigenomic research community with currently 160 registered users and more than three million anonymous workflow processing requests since its release.Während große Mengen epigenomischer Daten öffentlich verfügbar sind, ist ihre Abfrage in einer für die Downstream-Analyse geeigneten Form ein Engpass in der aktuellen Forschung. Bei einer typischen Analyse müssen Benutzer riesige Dateien herunterladen, die das gesamte Genom umfassen, selbst wenn sie nur an einer kleinen Teilmenge (z.B., Promotorregionen) oder einer Aggregation davon interessiert sind. Darüber hinaus sind komplexe Vorgänge mit Daten auf Genomebene aufgrund von Ressourceneinschränkungen auf einem lokalen Computer nicht immer möglich. Der DeepBlue Epigenomic Data Server behebt dieses Problem, indem er eine leistungsstarke API zum Suchen, Filtern, Umwandeln, Aggregieren, Anreichern und Herunterladen von Daten verschiedener epigenomischer Konsortien bietet. Darüber hinaus implementiert der DeepBlue-Server skalierbare Datenspeicherungs- und manipulationsmethoden, die der zunehmenden Menge epigenetischer Daten gerecht werden. Dadurch ist der DeepBlue Server ideal für Forscher geeignet, die die aktuellen epigenomischen Ressourcen in ihren Analyse-Workflow integrieren möchten. In dieser Arbeit werden zusätzlich Begleittools vorgestellt, die die DeepBlue-API verwenden, um Benutzern, die sich mit Scripting oder Programmiersprachen nicht auskennen, die Möglichkeit zu geben, epigenomische Daten auf benutzerfreundliche Weise zu analysieren: (i) ein R/ Bioconductor-Paket, das DeepBlue in den R-Analyse-Workflow integriert. Die extrahierten Daten werden automatisch in geeignete R-Datenstrukturen für die Downstream-Analyse und Visualisierung innerhalb des Bioconductor-Frameworks konvertiert; (ii) ein Webportal, über das Benutzer die auf dem DeepBlue Server verfügbaren epigenomischen Daten suchen, auswählen, filtern und herunterladen können. Diese Schnittstelle bietet Elemente wie Datentabellen, Raster, Datenselektionen, mit denen Benutzer die erforderlichen epigenomischen Daten in einer einfachen Schnittstelle finden können; (iii) DIVE, ein Webdatenanalysetool, mit dem Forscher umfangreiche epigenomische Datenanalysen in einer programmierungsfreien Umgebung durchführen können. Mit DIVE können Benutzer ihre Datensätze mit den im Deep- Blue Server verfügbaren Datensätzen in einer intuitiven Benutzeroberfläche vergleichen. Dabei kann der Vergleich hunderter Datensätze in einem Diagramm ausgedrückt werden. Aufgrund der großen Datenmenge, die in DIVE verfügbar ist, werden Methoden bereitgestellt, mit denen die ähnlichsten Datensätze für eine vergleichende Analyse vorgeschlagen werden können. Alle zuvor genannten Tools sind miteinander integriert, so dass sie die Ergebnisse untereinander austauschen können, wodurch eine leistungsstarke Umgebung für die Analyse epigenomischer Daten entsteht. Der DeepBlue Epigenomic Data Server und sein Ökosystem wurden vom International Human Epigenome Consortium äußerst gut aufgenommen und erreichten seit ihrer Veröffentlichung große Aufmerksamkeit bei der epigenomischen Forschungsgemeinschaft mit derzeit 160 registrierten Benutzern und mehr als drei Millionen anonymen Verarbeitungsanforderungen

An Informatics Roadmap Toward a FAIR Understanding of Mitochondrial Biology and Rare Mitochondrial Disease

Mitochondrial biology is integral to our fundamental understanding of human health and many diseases. They exist in every human cell type except for red blood cells and have critical functions in metabolism, oxidative phosphorylation, oxidation-reduction, and as signaling hubs responsible for mediating protective mechanisms. Rare mitochondrial diseases (RMDs) are devastating and complex, affect multiple organ systems, and disproportionately impact young children. Despite copious existing knowledge and increased public interest, the knowledge is fragmented and difficult to access. Clinical case reports (CCRs) on RMDs contain valuable clinical insights, but they are scarce and lack the metadata necessary to facilitate their discovery among the two million CCRs on PubMed. The unstructured text data of CCRs is also ill-suited to computational approaches, limiting our ability to derive the knowledge contained within.To address these issues, I assembled all available informatics tools and resources with mitochondrial components and used them to contribute to Gene Wiki pages that enable easy access to mitochondrial knowledge for researchers, students, clinicians, and patients. Through these efforts, I made mitochondrial gene, protein, and disease knowledge widely accessible with contributions of over 4MB of content across 541 Gene Wiki pages. Concurrently, I used Gene Wiki as an educational platform to train over 50 students in the biosciences and pre-medical studies in mitochondrial biology and disease, as well as instilling effective research and writing methods in biomedicine.To impose structure on CCRs and render them FAIR (Findable, Accessible, Interoperable, Reusable), I developed and applied a standardized metadata template to RMD CCRs and codified patient symptomology with the International Statistical Classification of Disease and Related Health Problems (ICD) system. I created the open-source, cloud-based MitoCases RMD Knowledge Platform (http://mitocases.org/) to house data on 384 RMD CCRs, including 4,561 instances of 952 unique ICD codes. Supplementing CCRs with structured metadata amplifies machine-readable information content and provides a distinct improvement in searching for CCRs as compared to indexing by title and abstract. Finally, I employed these resources to conduct a thorough review of Barth syndrome and characterized the diversity of presentations, range of genetic etiologies, and treatment paradigms