VictorJuiz/thesis huggingface dataset:
Assessment and Application of Subclonal Reconstruction for Profiling Tumour Evolution

Tumour evolution is fundamental to cancer biology. As cells proliferate and successively acquire mutations, they can establish subpopulations which largely share a common genotype (subclones). Over time, successive mutation accumulation and selection can lead to tumourigenesis and intra-tumoural heterogeneity, which can have important clinical consequences. While many approaches exist for profiling subclonal heterogeneity, identifying subclones using the frequency of mutations in DNA-sequencing reads (subclonal reconstruction) can be an effective and broadly applicable method. This is a computationally ambiguous task and upstream errors from read alignment and variant calling can propagate into it. I co-developed a framework to assess the accuracy of subclonal reconstruction and characterized its error profiles. I then applied subclonal reconstruction and complementary approaches to understand the evolutionary impact of a common driver event. Characterizing algorithm accuracy and error is critical for effectively carrying out and interpreting subclonal reconstruction. To profile the accuracy of subclonal reconstruction algorithms, I first developed a pipeline to simulate a benchmark dataset of 50 realistic tumour BAMs with subclonal structure as well as a scoring framework. I then used a subset of these tumours, downsampled to five depths, to benchmark the accuracy of 576 mutation caller-depth-subclonal reconstruction algorithm pipelines and demonstrate the efficacy of a wide range of pipelines past a minimum effective read depth. I then evaluated 31 crowd-sourced algorithms on 50 simulated tumour BAMs. This analysis further highlighted the importance of effective read depth for subclonal reconstruction accuracy, emphasized the limitations of single sample data for phylogeny reconstruction and characterized common algorithmic biases. Finally, I applied subclonal reconstruction and comparative mutational landscape analysis to study the evolutionary consequences of the functional abrogation of RB1 through DNA mutations. I first contrasted the mutational profiles of RB1- and MYCN-driven retinoblastoma and then compared tumours with the early loss of one or both RB1 copies and RB1-wildtype tumours pan-cancer. In both analyses, I discovered weak, probabilistic contingencies of RB1 inactivation in shaping tumour evolution. Together, these studies provide a benchmark for subclonal reconstruction and a template for linking it to detailed tumour evolution studies from single sample NGS data.

Keywords: Genomics
Retinoblastoma
Subclonal reconstruction
Tumour evolution
Advisor: Boutros, Paul C.
Health Utilities in People with Chronic Hepatitis C

Background: Hepatitis C virus infection is associated with reduced quality of life. One way to measure the quality of life of hepatitis C patients is using health utilities. Health utilities provide insight into not only the state of a patient’s health but also the strength of preference for that health state. This is a meaningful metric that can be used to measure individual and population-level health as well as conduct cost-utility analysis. Methods: We conducted three studies on health utilities in hepatitis C patients: 1) a systematic review and meta-analysis; 2) a clinical study measuring hepatitis C patients’ health utilities in hospital and community health centre settings; and 3) a population-level health utility study using linked survey and healthcare administrative data. Results: The results of these three studies support the observation that hepatitis C is associated with a reduced quality of life. The meta-analysis found that hepatitis C virus infection is associated with impaired health utility. We observed that experimental study designs yield higher health utilities than observational study designs—an effect not previously documented. We also concluded that more research is needed in socioeconomically marginalized hepatitis C patients. The clinical study attempted to address this gap by recruiting marginalized hepatitis C patients from a community-based program. This study found that marginalized patients attending the community-based program had lower health utilities than patients recruited in hospital settings, where clinical research is most commonly conducted. Lastly, the study using administrative data found that the trends observed in the first two studies held true for a larger population-based sample from across Ontario: those with hepatitis C had a reduced quality of life as measured by health utilities, and socioeconomically marginalized individuals with hepatitis C had even lower health utilities. Conclusion: These three studies extend our understanding of health utilities and quality of life in hepatitis C patients, particularly in the context of marginalized populations who face a disproportionate burden. The utility estimates generated by these studies can be incorporated into cost-utility analyses of hepatitis C programs, including cost-utility analyses assessing different models of care for marginalized hepatitis C patients.

Keywords: health economics
health utilities
hepatitis C
patient preferences
patient-reported outcomes
quality of life
Advisor: Krahn, Murray
Wong, William WL
Training a Covert Speech Brain-Computer Interface Through the Passive Perception of Speech

Covert speech (CS), the mental imagery of speaking, is a ubiquitous trait of human cognition that has received increasing amounts of attention in brain-computer interface (BCI) research. However, collecting large amounts of data are challenging due to fatigue resulting from repeated mental rehearsal. Therefore, a more user-friendly paradigm is needed. Fortunately, investigations have revealed a linkage between CS and speech perception (SP) incurring spatiotemporal similarities. However, it remains unclear whether these correlations can be leveraged to decode CS based on data from SP during online BCI operation. Therefore, the overall goal of the thesis was to train a model to distinguish CS based on SP. Due to the lack of knowledge on how information is processed during CS, the first study compared the neural oscillatory characteristics between SP and CS using a studentized continuous wavelet transform (t-CWT) and phase-amplitude coupling (PAC). Across ten participants, the absence of within task PAC in CS and the presence of cross-task PAC between SP-CS suggested that θ activity was not the latent variable linking SP and CS, but rather that it is the γ-bands (30-60 Hz) that produce the shared features. Using the same data, in Study 2, this interaction was further investigated by measuring phase-coding, a theory of temporal encoding in the brain. Here, significant spatial correlations were observed in the γ-band synchrony profiles as well as in PAC, reinforcing the idea that the γ and θ-bands likely subserve, respectively, shared and distinct encoding processes across tasks. Given the identified correlations in how SP and CS temporally encode information, the objective of Study 3 was to produce an online BCI. Here, utilizing a unique dyadic protocol whereby SP and CS trials were presented in pairs, CS data were linearly transformed using SP and subsequently classified during online BCI operation. Across 10 participants, ternary online accuracies reached up to 93% and averaged at 75% ± 9.02 across all participants. Moreover, model performance correlated to γ-band correspondence between tasks. Collectively, the three studies demonstrated that the shared characteristics between SP and CS can be leveraged to train a CS BCI based on simply hearing words.

Keywords: Brain-computer interface
Machine learning
Speech neuroscience
Advisor: Chau, Tom
Using Integrative Unstructural’ Biology to Characterize the Gram-negative Lactoferrin Receptor

The mammalian innate immune system possesses several metalloproteins (including lactoferrin and transferrin) with the function of sequestering critical biometals from the microorganisms that colonize the mammalian host. To overcome this nutritional pressure, Gram-negative bacteria from the families Neisseriaceae and Moraxellaceae express lactoferrin- and transferrin-receptors on their cell surface. Upon binding, iron may be removed from the host protein and taken up into the bacterium for use in its own metabolic pathways. Mammalian lactoferrin also presents an additional pressure to bacterial survival apart from metal sequestration - the presence of a highly bactericidal N-terminal subdomain. In this work, integrated structural biology and biophysical methodologies including thermal stability measurements, circular dichroism, fluorescence/brightfield optical microscopy, nuclear magnetic resonance, protein crosslinking coupled with mass spectrometry, small angle X-ray scattering, molecular dynamics, and single particle cryo electron microscopy were employed in order to understand the mechanism of iron-hijacking and anti-microbial peptide sequestration by the lactoferrin receptor. The sum of these experimental observations describe a molecular mechanism by which the lactoferrin receptor is able to fulfil its role in immune evasion. This data reveals how pathogens leverage intrinsic disorder to develop a general defense mechanism against antimicrobial peptides, and how sequestration may occur via coacervation. This foundational work provides another target in our investigation to engineer therapeutics against emerging pathogens affecting humans and livestock.

Advisor: Moraes, Trevor F
Towards Automated Metabolic Model Reconstruction with Application to Parasite Evolution

An organism’s metabolic reactions, catalyzed by enzymes, produce key molecules like DNA and are thus key to its survival. Drugs targeting metabolic capacities of pathogens have been developed, and bacterial strains with desired bioproduction capacities have been engineered through wet lab experiments. Key to these applications is knowing the metabolic capacities of the organism of interest. Biochemical experiments being time-consuming, sequence analysis techniques have increasingly been applied to infer an organism’s metabolism. Moreover, since visually inspecting (complex) metabolic networks is impractical, mathematical optimizations through constraints-based simulations are instrumental in further propelling these studies. Sequence similarity, typically used to annotate a protein sequence with an enzymatic activity, has poor specificity. Indeed, the enzyme annotation tool DETECT, released in 2010, further attributed the results of these searches with confidence scores. In this thesis, I improve DETECT by further computing enzyme-specific cutoffs, whose use in annotation yields better tradeoff between precision and recall than its predecessor. However, DETECT can only predict large enzyme classes. Thus, I present Architect, a method which uses an ensemble approach that combines predictions from multiple enzyme annotation tools to predict a wider range of metabolic capacities with demonstrably higher accuracy than individual tools. Architect also uses these annotations to automatically produce metabolic models that can readily be used in constraints-based simulations. I show that while accurate prediction of a wider range of metabolic capacities can positively impact simulation results, the quality of these results also depends on other parameters (such as reaction directionality). Finally, I apply Architect in the metabolic reconstruction of four apicomplexan parasites and a sister species to understand the role of host environment on the evolution of the parasites' remarkably reduced metabolism. I develop a mixed-integer linear programming formulation which predicts possible parasitic networks (through minimal model computation) from an approximation of a free-living ancestor’s metabolism; I also employ various strategies to decrease the problem’s computational complexity. While predictions mostly do not overlap with the metabolism of extant parasites, I propose that this framework may serve as a stepping stone for answering further questions about these parasites as well as other organisms.

Keywords: Apicomplexa
Metabolic reconstruction
Mixed-integer linear programming
Parasite evolution
Sequence analysis
Advisor: Parkinson, John
Moses, Alan M
Heterogeneous Water Transport in Fuel Cell Materials

Current state-of-the-art fuel cell designs exhibit heterogeneity, e.g., in wettability and pore size distributions in gas diffusion layers (GDLs) or land-channel patterns in flow fields. This inherent heterogeneity impacts liquid water transport and fuel cell performance and durability and needs accurate characterization to enable next-generation designs. Here, we reveal novel insights on how heterogeneity affects a) formation of pore-scale water pathways within GDLs, b) distribution of operando membrane hydration and morphology, and c) fundamental contact angle variation.3-D pore-scale liquid water distribution within the cathode GDL was characterized via operando synchrotron X-ray tomography to capture the early appearance of liquid water pathways. Liquid water only partially fills certain GDL pores, which is attributed to heterogeneous pore sizes and wettability distribution. Liquid water is observed to preferentially flow along some GDL fibers, due to the hydrophilic nature of carbon fiber and the presence of pore-scale mixed wettability within the GDLs. Operando spatiotemporal distributions of six interacting fuel cell components are resolved in high contrast using a novel combination of simultaneous neutron and X-ray tomography (NeXT) and advanced image processing, enabling the highest reported pairwise contrast between these materials (average and median pairwise contrast-to-noise ratio of 9.5). Signal and contrast are enhanced by up to 10 times and 48 times, respectively compared to single-modality imaging with conventional image processing. These drastic contrast enhancements reveal subtle variation in operando microstructural morphology of the catalyst coated membrane and liquid water distribution with respect to flow-field land-channel patterns in fuel cells. Quantitative analysis reveals that local heterogeneity in membrane hydration and interfacial properties are dependent upon operating conditions and geometry of flow-field lands and channels and interfaces such as GDL-gasket interface. Bubble adhesion on a smooth surface is analyzed using a novel mechanics perspective to reveal the existence of a pinning force during thermodynamic non-equilibrium. A new definition proposed here extends the validity of Young and Young-Laplace equations to a wide range of experimentally observed contact angles and lends insights into the fundamental mechanism of contact angle evolution.

Keywords: Contact angle
Electrochemistry
Fuel cells
Heterogeneity
Tomography
Water management
Advisor: Bazylak, Aimy
Ultra-Flexible Piezoresistive Nanocomposite Foam Sensing Systems for Force Mapping

The growing interest in the fabrication of flexible nanocomposite sensors, along with the limitation of current technologies, prompted us to develop new types of nanocomposite foam structures that possess high sensitivity, repeatable piezoresistive behavior, ultra-flexibility, high compressibility, and high mechanical properties. In this context, a comprehensive study that investigates the effect of various polymer matrices, conductive filler contents, foam porosity, foam morphology, and different manufacturing methods on the piezoresistivity, sensitivity, and mechanical properties of nanocomposite foams was conducted. Moreover, for a better understanding of porous and nonporous nanocomposites’ mechanical behavior, micromechanical modeling approaches were used to predict their elastic modulus.The first phase of this research work was focused on the fabrication and characterization of mechanical and electrical properties of polydimethylsiloxane (PDMS)/multi-walled carbon nanotube (MWCNT) nanocomposite foams. Eshelby-Mori-Tanaka (EMT) and Halpin-Tsai (HT) micromechanical approaches and extended Gibson and Ashby’s approach were applied to theoretically predict the elastic modulus of nonporous and porous PDMS/MWCNT. The results indicated that optimal porosity (60%) and MWCNT content (0.5 wt.%) could guarantee improvement in pressure sensitivity and Young’s modulus of PDMS/MWCNT nanocomposite foams. In the second phase, we focused on the fabrication of thermoplastic polyurethane (TPU) based closed-cell nanocomposite foams through different cost-effective scalable manufacturing methods. Porous TPU/MWCNT nanocomposites foamed by implementing a chemical blowing agent (CBA) into the nanocomposite matrix through combined compounding-compression molding methods, demonstrated high elastic modulus (up to 6.2 ± 0.6 MPa) and compressibility (up to 68.5% compressive strain). According to the theoretical approaches, different modifications of HT employed in solid TPU/MWCNT, and Gibson and Ashby’s method employed in foam TPU/MWCNT were in good agreement with experimental results in predicting elastic modulus. To improve MWCNT dispersion, TPU/MWCNT nanocomposite foams were fabricated by solvent casting method using thermo-expandable blowing agents. The cyclic mechanical testing showed a repeatable piezoresistive behavior and deformation in each cycle with constant hysteresis for these materials. Results indicated that higher MWCNT and blowing agent contents reduced the electrical resistance of nanocomposite foams; however, in response to the compression deformation and formation of new conductive pathways, an optimum level of both parameters was necessary to meet higher pressure sensitivity.

Advisor: Behdinan, Kamran
Naguib, Hani E
Engineered Antibodies for Igsf8 and Tgfbr1 Modulate TGF-β Signals in Melanoma

Comprehensive understanding of molecular pathways will identify the mechanisms underpinning disease. Including cancer progression. TGF-β signals can behave opposingly depending on context, yet key molecular insights of this regulation are still lacking. It is increasingly recognized that functional effects of this pathway are regulated via protein interactions and modifications of peripheral, accessory proteins, in addition to the core TGF-β proteins. Clarifying the contextual regulation of TGF-β signals would support the innovation of life-changing therapies that target TGF-β, given the importance of this pathway in nearly all cancers. Metastatic melanoma is the most aggressive skin cancer, and its invasive progression is primarily driven by TGF-β signals. Recent evidence demonstrates that Igsf8 can negatively regulate TGF-β signals in melanoma, suggesting that Igsf8 may hold a regulatory role for TGF-β signaling that is specific to melanoma. Efforts to target TGF-β in cancer include small molecules, antisense oligonucleotides, and monoclonal antibodies. Antibodies offer numerous advantages including increased target specificity, dose-dependence and modular structure that enables virtually limitless modification via protein engineering methods. Here, I have developed a method to engineer synthetic antibodies targeting multiple protein domains without structural information, using a phage-displayed antibody library. I then characterize antibody specificity, affinity, stability and epitope bins and screen for cell-binding. The cumulation of this data was then used to identify the most promising antibody candidates for Igsf8 with the goal to target the multiple domains of this protein. I then assessed the functional effects of Igsf8 antibody treatment on melanoma cells by setting up a highly sensitive cell signaling assay that detected any TGF-β signaling changes in vitro using a highly metastatic cell line. This assay demonstrated that antibodies binding to the membrane-proximal domain of Igsf8 could influence TGF-β signals, identifying a domain-specific function of Igsf8 in its regulation of TGF-β signaling in melanoma. My work supports a versatile method to develop domain-specific antibodies that can be applied to any member of the proteome, provides the field of Igsf8 research with specific protein tools for this target and advances our current understanding of melanoma by exploring a novel avenue for potentially new melanoma treatments.

Keywords: Antibody engineering
Immune-oncology
Melanoma
Phage Display
Advisor: Sidhu, Sachdev S
The Role of Endothelial Cell Cis-acting DNA Elements in Regulating Hemodynamic-mediated Changes in Gene Expression

Endothelial cells (ECs) form the inner lining of blood vessels and are exposed to blood flow, which exerts a frictional hemodynamic force known as shear stress (SS). ECs are professional SS sensors, as they can sense and respond to the stimulus of blood flow, and gene expression in ECs is profoundly responsive to changes in SS. The expression of endothelial nitric oxide synthase (eNOS), which produces the vasodilator nitric oxide (NO), is highly restricted to arterial ECs. eNOS expression is decreased in ECs overlying human atherosclerotic lesions, occurring at bifurcations and curvatures in the arterial tree, and this expression is increased with laminar flow. We have identified two cis-elements in the eNOS gene promoter that mediate its flow effect, namely the Shear Stress Response Element (SSRE) and the Krüppel-like factor (KLF) binding element, both in vitro and in vivo. We generated insertional transgenic mice with a mutation at each element, using a murine eNOS promoter-β-galactosidase reporter. The SSRE mutation abolished eNOS transcription while the KLF mutation showed an integration site-specific decrease in transcription and both mutations were associated with hypermethylation at the eNOS proximal promoter. This data revealed that both elements are necessary, and neither element alone is sufficient for eNOS transcription in vivo and the maintenance of a transcriptionally permissive, hypomethylated eNOS promoter. Furthermore, like eNOS, other arterial EC genes are regulated by blood flow through these cis-elements and chromatin-based epigenetic mechanisms. The transcription factors (TFs) that bind to the SSRE remained unknown after one hour of SS. Using biotin-streptavidin affinity pulldowns with mass spectrometry (MS) and in silico analysis, we investigated three target proteins of interest, ZC3H18, RASIP1, and PPARg, observed to bind to the SSRE. Each protein exerts distinct effects on flow-responsive genes of interest, and the genes themselves are differentially regulated by SS. In conclusion, we describe the important in vivo function of SS-responsive cis-DNA elements and their connection to epigenetics. Additionally, we explore proteins that bind to the SSRE as a nucleoprotein complex. Understanding these TFs can provide insight into potential novel regulators of the EC response to SS and the pathogenesis of cardiovascular diseases.

Keywords: Atherosclerosis
Endothelial cells
Epigenomics
Hemodynamic forces
Nitric oxide synthase type III
Shear stress
Advisor: Marsden, Philip A