Objective: The human immunodeficiency virus type 1 (HIV-1) currently infects over 30 million people worldwide. It uses one of two main co-receptors to infect cells. The primary objective of this thesis is to evaluate genotypic and phenotypic assays for co-receptor usage in the clinical setting and investigate approaches for improvement of these assays. Methods: The concordance of recombinant co-receptor phenotyping assays and the predictive ability of genotype-based methods including the ‘11/25’ rule, position specific scoring matrices (PSSMs), and support vector machines (SVMs) were evaluated in the clinical setting using patient-derived plasma samples. Samples and patient data were evaluated in cross-sectional analyses from a retrospective population-based cohort of HIV-infected individuals enrolled in the HIV/AIDS Drug Treatment Program in British Columbia, Canada. Results: Current implementations of HIV V3 region-based predictors for HIV co-receptor usage tested on patient derived samples are inadequate in the clinical setting, primarily due to low sensitivities as a result of difficult to detect minority species. Recombinant phenotype assays also show discordances when tested against each other on the same set of patient derived samples, raising doubts if any of these assays can truly be considered a ‘gold standard’. Significant associations between clinical progression, viral sequence-based predictors of co-receptor usage and the output of recombinant assays are observed, suggesting that sensitivity can be improved by incorporating CD4% into genotype-based predictors. This is verified with a SVM model which showed a 17% increase in sensitivity when CD4% was incorporated into training and testing. Conclusion: This work in this thesis has exposed the difficulty in determining the co-receptor phenotype in the clinical setting, primarily due to minority species. Although genotypic methods of screening for HIV co-receptor usage prior to the administration of CCR5 antagonists may reduce costs and increase turn-around time over phenotypic methods, they are currently inadequate for use in the clinical setting due to low sensitivities. Although the addition of clinical parameters such as CD4 count significantly increases the predictive ability of genotypic methods, the presence of low-levels of X4 virus continues to reduce the sensitivity of both genotypic and phenotypic methods. / Medicine, Faculty of / Medicine, Department of / Experimental Medicine, Division of / Graduate

Inferring regulatory relationships between genes, including the direction and the nature of influence between them, is the foremost problem in the field of genetics. One classical approach to this problem is epistasis analysis. Broadly speaking, epistasis analysis infers the regulatory relationships between a pair of genes in a genetic pathway by considering the patterns of change in an observable trait resulting from single and double deletion of genes. More specifically, a “surprising” situation occurs when the phenotype of a double mutant has a similar, aggravating or alleviating effect compared to the phenotype resulting from the single deletion of either one of the genes. As useful as this broad approach has been, there are limits to its ability to discriminate alternative pathway structures, meaning it is not always possible to infer the relationship between the genes. Here, we explore the possibility of dynamic epistasis analysis. In addition to performing genetic perturbations, we drive a genetic pathway with a dynamic, time-varying upstream signal, where the phenotypic consequence is measured at each time step. We explore the theoretical power of dynamic epistasis analysis by conducting an identifiability analysis of Boolean models of genetic pathways, comparing static and dynamic approaches. We also explore the identifiability of individual links in the pathway. Through these evaluations, we quantify how helpful the addition of dynamics is. We believe that a dynamic input in addition to epistasis analysis is a powerful tool to discriminate between different networks. Our primary findings show that the use of a dynamic input signal alone, without genetic perturbations, appears to be very weak in comparison with the more traditional genetic approaches based on the deletion of genes. However, the combination of dynamical input with genetic perturbations is far more powerful than the classical epistasis analysis approach. In all cases, we find that even relatively simple input dynamics with gene deletions greatly increases the power of epistasis analysis to discriminate alternative network structures and to confidently identify individual links in a network. Our positive results show the potential value of dynamics in epistasis analysis.

Geometric morphometrics, the science about the study of shape, has developed much in the last twenty years. In this thesis I first study the reliability of the phylogenies built using geometric morphometrics. The effect of different evolutionary models, branch-length combinations, dimensionality and degrees of integration is explored using computer simulations. Unfortunately in the most common situations (presence of stabilizing selection, short distance between internal nodes and presence of integration) the reliability of the phylogenies is very low. Different empirical studies are analysed to estimate the degree of evolutionary integration usually found in nature. This gives an idea about how powerful the effect of integration is over the reliability of the phylogenies in empirical studies. Evolutionary integration is studied looking at the decrease of variance in the principal components of the tangent shape space using the independent contrasts of shape. The results suggest that empirical data usually show strong degrees of integration in most of the organisms and structures analysed. These are bad news, since strong degree of integration has devastating effects over the phylogenetic reliability, as suggested by our simulations. However, we also propose the existence of other theoretical situations in which strong integration may not translate into convergence between species, like perpendicular orientation of the integration patterns or big total variance relative to the distance between species in the shape space. Finally, geometric morphometrics is applied to the study of the evolution of shape in proteins. There are reasons to think that, because of their modular nature and huge dimensionality, proteins may show different patterns of evolutionary integration. Unfortunately, proteins also show strong functional demands, which influence their evolution and that cause strong integration patterns. Integration is then confirmed as a widespread property in the evolution of shape, which causes poor phylogenetic estimates.

京都大学 / 新制・課程博士 / 博士(医学) / 甲第23379号 / 医博第4748号 / 新制||医||1052(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 石見 拓, 教授 戸口田 淳也, 教授 中山 健夫 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

In this dissertation, the emerging stimulated Raman scattering (SRS) microscopy in combination with various vibrational tags was extensively used to explore various aspects of biological systems. New techniques as well as new Raman active materials were also developed to facilitate the applications of SRS in biology. Chapter one introduces and comprehensively reviews vibrational tags that have been developed to date in combination with imaging techniques and their applications in biological sciences to investigate metabolism in living organisms. Chapter two studies lipotoxicity, a phenomenon that is well known but poorly understood. The study found phase separation can form on ER membrane in cells treated with long chain fatty acids due to the high transition temptation of their metabolites. It was also found that the phase separation severely disturbs normal distribution of ER membrane proteins because of hydrophobic mismatching. As the result, ER normal structure is disrupted, luminal space is collapsed, and interconnectivity of ER that ensures normal ER functions is lost. Additionally, ER stress sensor IRE1α was found to be activated directly by the formation of phase separation, which triggers apoptosis and ultimately leads to cell death. Chapter three describes the development of a new method termed as metabolic activity phenotyping (MAP) that acquires quantitative measurements of metabolic activities of individual cells, which is essential to understanding questions in diverse fields in biology. To achieve the goal, an automatic system was designed and built that improves the acquisition speed by more than 100 times compared to commercially available instruments. A set of vibrational probes with deuterium labeling was also carefully selected to enable accurate measurement of metabolic flux. Combining the merits of high throughput measurements and vibrational tags, MAP was applied to investigate the metabolic activity differences among various cancer cells, to study the heterogeneity of drug efficacy, and to facilitate breast cancer subtyping. Chapter four describes the development and application of a new class of Raman active nanoparticles, or Rdots. These Rdots were generated by non-covalently incorporating small molecule Raman probe into polymeric nanoparticles. The resulted Rdots are of compact size (~20 nm) and preserve all Raman spectral features of the small molecule probes used. Rdots were compared to other existing Raman active materials including SERS nanoparticles, and Rdots surpass all the other materials in terms of brightness. In addition, Rdots also possess narrow spectral linewidth (< 3 nm), making them ideal for multiplexed imaging. In the study, Rdots were used as immunostaining reporters to visualize cytoskeleton networks and surface markers in cell and tissue samples.

Indiana University-Purdue University Indianapolis (IUPUI) / This study investigated familial cases of cleft lip with or without cleft palate to determine whether the unaffected members of each family can be identified as gene carriers for the cleft trait. This research presumes that such carriers will have henotypic features identifiable by cephalometric analysis that are associated with an increased risk to cleft offspring. Using population genetics methodology, a pedigree analysis was made for each family member was assigned to one of four groups: (1) obligate normal, (2) affected, (3) carrier, and (4) unknown. LA and PA cephalographs were taken on each subject and a clinical oral-facial examination carried out on participating family members. Various anatomic landmarks located on the LA and PA films were digitized and from them, a total of 28 linear measurements were made. To eliminate the effect of sex and differential age responses, Z scores were calculated. Through univariate analysis, only one variable, NCR-MO, was shown to be significantly different between the two groups. This variable difference by itself is not adequate to differentiate those in the normal group from the carrier group. Even though only one variable was significant, other differences in the variables between these groups become obvious when the group variables were plotted as Z scores. Since Z scores are pure values with no limits (2--the number of standard deviations in a given variable differs from normal). Thereby, age-related growth differences were minimized. Further information is gained when these Z scores are plotted as pattern profiles, Figures 5-7. These profiles of mean Z scores for each variable pointed out areas of the face in which the differences were so great that specific anatomic areas appeared to be associated with one of the four groups. For example, gene carriers demonstrated specific alterations in facial height that might conceivably be used to discriminate that group from the other three groups. The family normals and carriers were then analyzed by using a stepwise multivariate analysis. By this approach, a discriminant function was generated consisting of six variables (three each from the lateral and frontal headplates), which proved to be significant in distinguishing an individual's phenotype. These variables define facial height, width and depth. The specific findings included a decrease in mid-facial height and depth along with an increased lower facial height and width in the gene carrier population as compared to the normals. The function then was used to predict group membership of the same two groups. Comparing this analytical prediction to that of the grouping system that resulted from the pedigree analysis, all but one individual was classified correctly in both the normal and carrier population. A discriminant score was also determined for the unknown population of family members which were defined as non-cleft blood relatives of cleft probands. Thus, they were a mixture of two types--those unaffected who carried a genetic liability for producing a cleft child and those unaffected who did not. A prediction of their placement into either the normal or carrier group was made with the discriminate function. One-third were classed in the normal group and two-thirds as gene carriers. The results of this study confirm that the phenotype of these unaffected family members designated as obligate gene carriers differs significantly from that of the family normals. This information is not only quite useful for genetic counselling but gives both a better understanding or the genetic control of clefting and can lead to molecular research to identify the specific gene in question.

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Health disparities are preventable differences in health status and outcomes that adversely affect certain populations, and are generally attributable to unjust social or environmental influences. Mitigating health disparities is crucial toward preventing unnecessary and avoidable human suffering, and as such there has been a significant increase in health disparities research and funding. However, existing health disparities publications are geographically-constrained to specific institutions or populations, and often rely on disease definitions that cannot be easily applied elsewhere. While more recent publications have begun identifying differences utilizing larger datasets, for most diseases, differences in prevalence, age of onset, and time to diagnosis differences remain unstudied and unknown. This dissertation leverages informatics solutions built atop observational health datasets to enable high-throughput, reproducible assessments of disparities across subgroups, conditions, and datasets. In the first aim, this dissertation examines the literature to identify how health disparities in disease diagnosis are measured, computed, and reported. It then proposes an iterative approach for generating fair phenotype definitions that are more inclusive of subgroups of interest by utilizing algorithmic fairness measurements translated to epidemiological measures. In the second aim, this dissertation conducts large-scale characterizations of disease diagnosis patterns across subgroups (gender and race), conditions, and datasets. In particular, this dissertation conducts a prevalence-based assessment of disease diagnosis by computing prevalence differences, risk ratios, and age of onset differences across diseases and datasets. The dissertation then conducts a scalable assessment of time to diagnosis differences across 122 disease phenotypes. Finally, in the third aim, this dissertation moves from quantifying differences to identifying disparities in diagnosis. To do so, the dissertation applies a framework for causal fairness to decompose observed time to diagnosis differences into direct, indirect, and spurious effects. In conclusion, this dissertation's primary contributions are providing a systematic, scalable approach for identifying health differences and then quantifying health disparities at-scale across large-scale observational health datasets. The dissertation (1) proposes an iterative approach for systematically assessing the fairness of phenotypes used in observational health research, (2) systematically characterizes differential patterns of disease diagnosis across diseases and observational datasets, and (3) causally decomposes differences into quantifiable effects that suggest the presence of potential health disparities.