Applications of Boolean modelling to study and stratify dynamics of a complex disease

Interpretation of omics data is needed to form meaningful hypotheses about disease mechanisms. Pathway databases give an overview of disease-related processes, while mathematical models give qualitative and quantitative insights into their complexity. Similarly to pathway databases, mathematical models are stored and shared on dedicated platforms. Moreover, community-driven initiatives such as disease maps encode disease-specific mechanisms in both computable and diagrammatic form using dedicated tools for diagram biocuration and visualisation. To investigate the dynamic properties of complex disease mechanisms, computationally readable content can be used as a scaffold for building dynamic models in an automated fashion. The dynamic properties of a disease are extremely complex. Therefore, more research is required to better understand the complexity of molecular mechanisms, which may advance personalized medicine in the future. In this study, Parkinson’s disease (PD) is analyzed as an example of a complex disorder. PD is associated with complex genetic, environmental causes and comorbidities that need to be analysed in a systematic way to better understand the progression of different disease subtypes. Studying PD as a multifactorial disease requires deconvoluting the multiple and overlapping changes to identify the driving neurodegenerative mechanisms. Integrated systems analysis and modelling can enable us to study different aspects of a disease such as progression, diagnosis, and response to therapeutics. Therefore, more research is required to better understand the complexity of molecular mechanisms, which may advance personalized medicine in the future. Modelling such complex processes depends on the scope and it may vary depending on the nature of the process (e.g. signalling vs metabolic). Experimental design and the resulting data also influence model structure and analysis. Boolean modelling is proposed to analyse the complexity of PD mechanisms. Boolean models (BMs) are qualitative rather than quantitative and do not require detailed kinetic information such as Petri nets or Ordinary Differential equations (ODEs). Boolean modelling represents a logical formalism where available variables have binary values of one (ON) or zero (OFF), making it a plausible approach in cases where quantitative details and kinetic parameters 9 are not available. Boolean modelling is well validated in clinical and translational medicine research. In this project, the PD map was translated into BMs in an automated fashion using different methods. Therefore, the complexity of disease pathways can be analysed by simulating the effect of genomic burden on omics data. In order to make sure that BMs accurately represent the biological system, validation was performed by simulating models at different scales of complexity. The behaviour of the models was compared with expected behavior based on validated biological knowledge. The TCA cycle was used as an example of a well-studied simple network. Different scales of complex signalling networks were used including the Wnt-PI3k/AKT pathway, and T-cell differentiation models. As a result, matched and mismatched behaviours were identified, allowing the models to be modified to better represent disease mechanisms. The BMs were stratified by integrating omics data from multiple disease cohorts. The miRNA datasets from the Parkinson’s Progression Markers Initiative study (PPMI) were analysed. PPMI provides an important resource for the investigation of potential biomarkers and therapeutic targets for PD. Such stratification allowed studying disease heterogeneity and specific responses to molecular perturbations. The results can support research hypotheses, diagnose a condition, and maximize the benefit of a treatment. Furthermore, the challenges and limitations associated with Boolean modelling in general were discussed, as well as those specific to the current study. Based on the results, there are different ways to improve Boolean modelling applications. Modellers can perform exploratory investigations, gathering the associated information about the model from literature and data resources. The missing details can be inferred by integrating omics data, which identifies missing components and optimises model accuracy. Accurate and computable models improve the efficiency of simulations and the resulting analysis of their controllability. In parallel, the maintenance of model repositories and the sharing of models in easily interoperable formats are also important

Faculty of Mathematics and Science 1st Graduate Research Day Conference, 2022

FMS Graduate Research Day (FMS GRaD) is an academic conference open to all FMS students with a mandate to celebrate and communicate Brock University research and teaching. The FMS GRaD 2022 conference was hosted by the Dean’s office of the Faculty of Mathematics and Science and Graduate Mathematics and Science Society at Brock University. With 57 presenters and over 300 attendees this first FMS GRaD held on September 16th 2022 strengthened the STEM research community and highlight the research and profile of FMS graduate student research programs

The role of N1-Src regulated splicing in neuronal differentiation

Alternative splicing (AS) is one of the main contributors to transcriptome diversity and functional complexity involved in the process of neuronal development. Evidence suggests that many splicing regulators and alternative splicing events are neuron-specific and aberrations in the regulation of these events have been linked to various neurodevelopmental disorders. N1-Src is an evolutionarily conserved neuronal splice variant of the ubiquitous tyrosine kinase c-Src. It has been implicated in neural development and as a prognostic indicator in neuroblastoma, a childhood cancer that is caused by failure of neural crest cells to differentiate. Results from knockdown experiments where N1 exon inclusion was prevented with splice-blocking antisense morpholino oligos revealed that N1-Src is a key regulator of primary neurogenesis in Xenopus. Preliminary short and long read RNA-Seq data from Xenopus embryos suggest a role for N1-Src in regulation of an alternative splicing programme during early neurogenesis, with transcripts encoding the splicing/RNA processing machinery themselves being the most spliced targets. This study aimed to further describe the N1-Src-regulated splicing network in the developing Xenopus nervous system using bioinformatic analysis of various publicly available and Evans/Isaacs lab RNASeq datasets. A differential splicing (DS) analysis pipeline was developed to detect and quantify alternative splicing events that occur during early stages of Xenopus embryo development relevant to neurogenesis. By correlating alternative splicing quantifications with RNA-binding protein motif enrichment analysis, this project proposed mechanisms for Src regulation of alternative splicing

Algorithms and Applications for non-coding RNAs in Aging

Gene expression is a complex molecular process governing fate and function of most eukaryotic cells. The fundamental mechanism, namely that genetic material of a cell is compactly stored on chromosomal DNA and at times being transcribed into messenger-RNA to facilitate on-demand protein biosynthesis, is widely known. However, the interplay of biochemical regulatory pathways underlying an individual’s disease phenotype development remains incompletely understood. Intriguingly, the ∼ 20.000 protein-coding genes only account for 2% of the human genome, triggering profound questions on the purpose of remaining segments. In recent years it became apparent that non-coding RNAs essentially tune the observed gene expression circuits. In particular the small non-coding RNAs such as microRNAs, turned out to be regulatory players by switching on and off protein translation of target messenger-RNAs. Several thousand mammalian microRNAs have been discovered so far but little is known about their impact on the transcriptome, which likely depends on contextual variables like cell type identity, cellular and tissue environment or phase of activation. Previous efforts demonstrated that gene expression programs in human and mouse undergo gradual changes along the life trajectory with amplification at higher ages. In parallel, age-related diseases are currently accumulating in our globally aging population, posing a serious challenge to our society and healthcare systems. Neurodegenerative disorders such as Alzheimer’s disease and Parkinson’s disease show steadily rising incidence rates with several million people already affected. Both are caused by pathological protein accumulation in selectively vulnerable neurons and brain regions. Notably, these neurological disorders do not appear all of a sudden in an individual but are believed to originate after long asymptomatic phases of subtle aberrant changes on the cellular level, turning early diagnosis into an intricate affair. Yet, no single comprehensive model to explain aging associated changes in gene expression exists and certainly any such model must take into account the role of microRNAs and other important non-coding RNAs. With the advent of ultra-high-throughput sequencing techniques and unprecedented computational power, the screening of microRNAs and their targets from human biofluids and tissues became not only affordable but scalable. To deal with the increasing complexity of molecular studies, novel bioinformatics-driven approaches are needed to generate reproducible and comprehensive conclusions from large-scale data sets. Here, the role of small non-coding RNAs in governing gene expression changes observed in complex age-related diseases is explored with the aid of new methods and databases as well as several thousand RNA profiling samples. This cumulative doctoral thesis comprises eight peer-reviewed publications. Basic research covers a comprehensive review on most target prediction tools and a novel experimental and computational workflow for microRNA-target pathway identification. In addition, with miRPathDB 2.0 the so-far largest database on enriched microRNA pathways for human and mouse is presented. Moreover, the new versatile web tool miEAA 2.0 allows rapid annotation of statistically enriched molecular properties and functions for large lists of microRNAs from ten species. The lessons learned from web-based tool development were condensed in an invited summary and survey article on scientific web server availability along with best practices for developers. The here presented toolkit was used in three applied research studies to investigate the association between microRNAs and their target pathways in the context of aging as well as the to date largest Parkinson’s disease biomarker discovery framework. Circulating microRNAs obtained low-invasively from whole-blood samples bear diagnostic and prognostic value in Alzheimer’s and Parkinson’s disease patients, which was discovered using machine learning models. Furthermore, selected microRNA families were found to systematically target entire signaling pathways as to effectively silence gene expression. Indeed, these pathways are affected in prevalent neurodegenerative disorders. Taken together, the published candidate signatures and validated targets are pivotal for subsequent experimental perturbation in microRNA or gene knockout studies. In future efforts, large-scale single-cell studies will be required to further dissect disease and cell-type specificity of aging disease biomarker candidates and their long-term effect on gene expression, possibly indicating early neuropathological hallmarks.Genexpression ist ein komplexer molekularer Prozess, der das Überleben und die Funktion der meisten eukaryotischen Zellen entscheidend beeinflusst. Der zugrunde liegende Mechanismus, nämlich, dass das genetische Material einer Zelle kompakt in chromosomaler DNA vorliegt und je nach Bedarf in messenger-RNA zur Proteinbiosynthese genutzt wird, ist weitgehend bekannt. Allerdings ist das Zusammenspiel der regulatorischen Pfade im Hintergrund der phenotypischen Veränderungen von erkrankten Individuen nur wenig verstanden. Interessanterweise machen die fast 20.000 protein-kodierenden Gene nur in etwa 2% des menschlichen Erbgutes aus. In den letzten Jahren hat man festgestellt, dass nicht-kodierende RNAs eine essentielle Rolle bei der Einstellung der beobachteten Genexpressionsschaltkreise spielen. Insbesondere kleine nicht-kodierende RNAs wie microRNAs, stellten sich als zuvor unterschätzte regulatorische Einheiten heraus, die die Translation von Ziel-messenger-RNA in Proteine an und ausschalten. Mehrere tausend microRNAs wurden bisher bei Säugetieren entdeckt, trotzdem ist immer noch wenig über ihren Einfluss auf das Transkriptom bekannt, ein Zusammenhang der wahrscheinlich vom Kontext wie Zelltypidentität, dem zelluären Umfeld sowie dem umgebenden Gewebe, und den Aktivierungsphasen abhängt. Frühere Forschungsarbeiten haben bereits gezeigt, dass das Genexpressionsprogramm im Menschen und in der Maus sukzessiven Änderungen im Laufe des Lebens unterworfen ist, welche sich im höheren Alter verstärken. Zur gleichen Zeit akkumulieren Fälle von altersbedingten Krankheiten in unserer immer älter werdenden, globalen Population, was ernstzunehmende Herausforderungen für unsere Gesellschaft sowie unser Gesundheitssystem mit sich bringt. Neurodegenerative Krankheiten wie Morbus Alzheimer und Morbus Parkinson zeigen eine kontinuierlich ansteigende Inzidenz, wobei bereits mehrere millionen Menschen weltweit betroffen sind. Besonders für diese Krankheiten ist, dass sie bei einem Menschen nicht spontan oder plötzlich entstehen, sondern vermutlich nach langer Zeit der asymptomatischen Phase aufgrund schleichender, abnormaler Veränderungen auf zellulärer Ebene entstehen, was eine frühe Diagnose überaus schwierig gestaltet. Bisher existiert noch kein verständliches Modell das die altersassoziierten Veränderungen der Genexpression erklären kann, wobei jedes darauf ausgerichtete Modell mit Bestimmtheit die Rolle der microRNAs und anderen wichtigen nicht-kodierenden RNAs zwangsläufig in Betracht ziehen muss. Mit dem Aufkommen der Sequenzierung im Ultrahochdurchsatzverfahren und der unübertroffenen Leistung moderner Computersysteme, wurde die Untersuchung von microRNAs und ihren Zielgenen anhand von Proben menschlicher Flüssigkeiten und Geweben nicht nur möglich gemacht, sondern kann entsprechend hochskaliert werden. Um mit der zunehmenden Komplexität molekularer Studien Schritt zu halten, braucht es neue Ansätze der Bioinformatik um reproduzierbare und nachvollziehbare Schlüsse aus großen Datensätzen gewinnen zu können. Im Rahmen dieser Arbeit wurden kleine nicht-kodierende RNAs hinsichtlich ihrer Rolle der Genregulation in komplexen altersbedingten Krankheiten anhand neuer Methoden und Datenbanken sowie mehreren tausend Proben der RNA-Sequenzierung untersucht. Diese kumulative Dissertationsarbeit umfasst acht von unabhängigen Experten begutachtete (peer-reviewed), wissenschaftliche Publikationen. Die Grundlagenforschung enthält einen umfassenden Übersichtsartikel zu fast allen Methoden der Vorhersage von microRNA Zielgenen sowie ein neuartiges Protokoll bestehend aus Labormethoden und computergestützen Berechnungen zur Identifikation von durch microRNAs regulierte Genpfade. Zusätzlich wird mit miRPathDB 2.0 die bisher größte Datenbank zu signifikant angereicherten microRNA Zielpfaden präsentiert. Des Weiteren, bietet die neue und vielseitige, web-basierte Software miEAA 2.0 die Möglichkeit der rasanten Annotation statistisch angereicherter, molekularer Eigenschaften sowie bekannter Funktionen einer gegebenen Liste an microRNAs von zehn Spezies. Die durch web-basierte Softwareentwicklung zuvor angelernten Fähigkeiten sowie daraus resultierende Empfehlungen für nachfolgende Entwickler wurden kurz und bündig in einem eingeladenen Übersichtsartikel zum Thema Verfügbarkeit wissenschaftlicher Software im Internet veröffentlicht. Die hier präsentierten Werkzeuge wurden gezielt in drei Studien zur angewandten Forschung genutzt um die Assoziation zwischen microRNAs und ihren Zielpfaden im Kontext der allgemeinen Altersforschung sowie im Rahmen der bisher größten Studie zur Entdeckung von Biomarkern der Parkinson Krankheit zu untersuchen. Im Blutkreislauf zirkulierende microRNAs, die anhand von Vollblutproben extrahiert wurden, zeigen diagnostisches und prognostisches Potential bei Alzheimer und Parkinson Patienten, was mit Methoden des maschinellen Lernens entdeckt werden konnte. Überdies konnte herausgefunden werden, dass bestimmte microRNA Familien systematisch Signalwege blockieren können, um die Genexpression herunterzufahren. Tatsächlich sind diese Pfade auch in neurodegenerativen Krankheiten betroffen. Insgesamt sind die hier publizierten Signaturen von Kandidaten-microRNAs und einiger validierter Zielgene herausragend dazu geeignet in weiteren Studien anhand von gezielter Ausschaltung im Labor genauer untersucht zu werden. In zukünftigen Forschungsprojekten sollten groß angelegte Untersuchungen vieler einzelner Zellen im Vordergrund stehen, um zu verstehen wie spezifisch für Krankheit oder Zelltyp die hier genannten Biomarker-Kandidaten für altersbedingte Krankheiten sind. Auch wird es wichtig sein die Langzeiteffekte von dysregulierten microRNAs auf die Genexpression zu verstehen, die möglicherweise frühzeitig neuropathologische Kennzeichen widerspiegeln

Targets, Tracers and Translation – Novel Radiopharmaceuticals Boost Nuclear Medicine

This is the fourth Special Issue in Pharmaceuticals within the last six years dealing with aspects of radiopharmaceutical sciences. It demonstrates the significant interest and increasing relevance to ameliorate nuclear medicine imaging with PET or SPECT, and also radiotherapeutical procedures.Numerous targets and mechanisms have been identified and have been under investigation over the previous years, covering many fields of medical and clinical research. This development is well illustrated by the articles in the present issue, including 13 original research papers and one review, covering a broad range of actual research topics in the field of radiopharmaceutical sciences

Exercise protection of vascular endothelial cells against breast cancer chemotherapy toxicity: Evidence from in vitro serological studies

5-fluorouracil, epirubicin, cyclophosphamide, and docetaxel is an effective chemotherapy regimen for early-stage breast cancer (BC). However, these drugs associate with a 5% incidence of heart failure (HF). To attenuate the cardiovascular toxicity of chemotherapy, exercise has been proposed as a potential preventative measure. There is now emerging evidence for protective effects of exercise on the heart but there is a lack of evidence for vascular effects, despite vascular endothelial dysfunction being an initiating step in cardiovascular disease (CVD) development. This study aimed to determine if there are protective effects of habitual physical activity, a single acute exercise session, and an exercise training intervention on chemotherapyinduced vascular endothelial cell toxicity. It was hypothesised that serological factors in active women can alleviate vascular toxicity from chemotherapy; and an acute exercise session and an exercise training intervention can alleviate toxicity in previously sedentary women. To investigate protective effects of exercise, a novel ex vivo method was used. Endothelial cell cultures were preconditioned with serum from active and sedentary woman; woman pre- and post-acute exercise bout; and woman pre- and post-exercise intervention. After 24-hours of serum preconditioning, endothelial cells were exposed to physiological concentrations of 5-fluorouracil, epirubicin, cyclophosphamide, and docetaxel. Cell viability and function, and wound repair were assessed using flow cytometry and scratch assays (to simulate a wound), respectively. Overall, results confirm that FEC-T chemotherapy drugs, commonly used in early-stage BC treatment, elicit significant damage and dysfunction of endothelial cells. Exercise serum preconditioning from active women, serum collected after an acute exercise session, and serum collected after an exercise training intervention, elicited some protection of endothelial cells against the usual toxicity of 5-fluorouracil, epirubicin, cyclophosphamide, and docetaxel, when compared to control serum preconditioning from inactive women, serum collected prior to an acute exercise session, and serum collected prior to an exercise training intervention, respectively

Probabilistic Approaches to Modeling Uncertainty in Biological Pathway Dynamics

Ph.DDOCTOR OF PHILOSOPH