L’exposition au régime maternel durant la période périnatale, induit des processus d’empreintes orientant à long terme le phénotype et la santé des individus. De plus, les orientations alimentaires, telles que celles concernant les protéines, évoluent quantitativement et qualitativement. Ces deux constats, encouragent la communauté scientifique à s’interroger sur les conséquences de ces variations de consommation en protéines sur les générations futures. Ce projet de thèse vise à évaluer chez la descendance femelle rat, les effets d’une alimentation maternelle variant par la teneur (riche versus normal) et la qualité (sources animales versus végétales) en protéines sur la modification des préférences alimentaires et sur les risques métaboliques.Deux études ont été réalisées chez le rat. Une première étude a évalué l’impact de l’excès de protéines à travers un régime hyperprotéique (HP) à base de protéines de lait pendant la gestation. Une seconde étude a évalué les effets d’un régime HP de source protéique spécifique (lait, pois ou dinde) pendant la gestation et d’un régime de source protéique spécifique (lait, pois ou dinde) pendant l’allaitement. Une fois sevrés et jusqu’à l’âge adulte (étude 1 : 15 semaines ; étude 2 : 10 semaines), les ratons femelles ont été soumis à des modèles de « dietary self-selection » (DSS) leur laissant la possibilité de choisir la composition en macronutriments, le niveau de consommation alimentaire et la source protéique (étude 2 uniquement). Indépendamment du régime maternel, ces deux études ont montré que lorsque les sources en macronutriments étaient séparées dans le modèle DSS, les ratons présentaient une hyperphagie liée à une consommation accrue de lipides au détriment des glucides.De plus, les résultats de la seconde étude ont montré que les ratons n’orientaient pas spécifiquement leur consommation de protéines vers la source protéique à laquelle ils avaient été exposés via le régime maternel périnatal. En revanche, les deux études ont montré que la consommation d’un régime HP pendant la gestation, quelle que soit la qualité des protéines le composant, induisait une augmentation de l’adiposité chez la descendance femelle adulte. Cette augmentation était majorée lorsque la descendance avait été soumise au régime de choix (DSS), leur permettant d’augmenter leur consommation de lipides au détriment des glucides.En conclusion, l’exposition périnatale à un régime HP de qualité variable en protéines augmente la sensibilité au surpoids chez la descendance femelle adulte rat. Nous avons évalué les relations entre ces données et : la sensibilité des voies centrales du contrôle de la prise alimentaire et de la récompense, la sensibilité des voies de contrôle du métabolisme énergétique périphérique et la composition et l’activité du microbiote de l’intestin.Ces travaux apportent un grand nombre de nouvelles données indiquant clairement qu’une alimentation équilibrée en quantité et en qualité de protéines pendant la grossesse, à travers le ratio protéines/glucides et le profil en acides aminés, pourrait jouer un rôle clé sur des paramètres phénotypiques de la descendance notamment lorsqu’elle est soumise à des choix alimentaires augmentés. / Abstract : Perinatal exposure to maternal diet induces programming processes of later individual phenotype and health. Additionally, food orientations like for protein, change in terms of quantity and quality. These observations enhance scientific community to evaluate consequences of protein consumption changes on future generations.This thesis project aims to determine the consequences of modifying protein quantity and quality in maternal diets on food preferences and metabolic risks in female rat offspring.Two studies were conducted in rats. The first study evaluated the impact of protein excess in the maternal diet during gestation, through a high-protein (HP) diet composed with cow milk protein. The second study evaluated effects of (i) a HP diet composed with different protein sources (cow milk, pea, or turkey) during gestation and (ii) these different protein sources (cow milk, pea, or turkey-derived) during lactation. From weaning to adulthood (study 1: 15 weeks after birth; study 2; 10 weeks after birth), female pups were subjected to “dietary self-selection” (DSS), which allowed them to choose their own macronutrient compositions, level of food intake and protein sources (second study only).Regardless of the maternal diet, these two studies showed that when DSS was composed with separate macronutrients, rats exhibited overfeeding and increased lipid intake coupled with a decreased carbohydrate intake. Moreover, the results of the second study indicated that rats did not orient their protein intake towards the maternal protein source to which they were exposed during perinatal period. Nevertheless, the two studies showed that the maternal HP diet during gestation caused an increased adiposity in female adult offspring, regardless of the maternal protein source. This increase was stronger when offspring were subjected to DSS condition, which allowed them to increase lipid intake and decrease carbohydrate intake.In conclusion, perinatal exposure to a HP diet varying in protein quantity and quality increases the risk of becoming overweight in female rat adult offspring. We assess the relationship between these data and the the sensitivity of central pathways of food intake and reward control, the sensitivity of energetic and peripheral metabolic pathways, and the gut microbiota composition and activity.This work provides new data indicating that a balanced diet in protein quantity and quality during gestation, through a protein/carbohydrate ratio and amino acid profile, could play a key role on offspring phenotypic parameters, especially when submitted to increased dietary options.

T cells can fight cancer, but an immunosuppressive tumor microenvironment (TME) disallows them from carrying out their function over time. Upregulation of inhibitory checkpoint molecules such as programmed cell death protein 1 (PD1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA4) can lead to such an immunosuppressive TME. Despite their widespread use, immune checkpoint blockade (ICB) antibodies targeting checkpoint molecules remain ineffective in most cancer patients. We do not understand why some patients respond to ICB better than others. To understand the heterogeneity of ICB response, we must understand the heterogeneity of the T cell subsets acted upon by such therapies. Here, we ask how T cell subsets change in the presence and absence of ICB. We track T cell clones through their T cell receptor sequences and link phenotypes with T cell receptor specificities. Through multiplexed single cell TCR sequencing, single cell RNA sequencing, and the use of cell- surface CITE-seq antibodies, coupled with surgical biopsy, we longitudinally tracked the fate of individual T cell clones within tumors at baseline and in response to ICB in an immunogenic mouse tumor model. Furthermore, computational clustering of T cells solely based on their gene expression profiles may ignore upstream regulatory mechanisms that control T cell gene expression. Hence, we employed Virtual Inference of Protein-activity by Enriched Regulon (VIPER) analysis to cluster CD8+ and CD4+ T cell phenotypes. VIPER leverages inference of gene regulatory networks to allow full quantitative characterization of protein activity for transcription factors, co-factors, and signaling molecules by assessing the enrichment of their transcriptional targets cell-by-cell among expressed genes. This gave us a window into the transcriptional states and their inferred protein activity. We next developed a computational analysis toolkit to study TCR clonality incorporating sub-sampling of TCR clonotypes, forward and back tracing of shared clones between timepoints, and in turn, inferred shared clonal evolution. We employed the above workflow to MC38 tumor-infiltrating and tumor-draining lymph node-derived CD8+ and CD4+ T cells. We found that T cell phenotypes are highly dynamic within tumors at baseline, in the absence of ICB, particularly within the window that they are responsive to therapy. In the absence of ICB, effector phenotype of CD8+ T cells diminished, while the exhaustion phenotype was enhanced as tumors progressed. Within the CD4+ population, a heterogenous subset of regulatory CD4+ T cells (Tregs) changed phenotype over time, and CD4+ Th1 like effectors, along with stem like progenitor CD4+ showed distinct dynamism. Next, by analyzing responses to therapy within his context, we found that both anti-PD1 and anti-CTLA4 act through distinct mechanisms on CD8+ and CD4+ T cells. Anti-PD1 acted upon intra-tumoral effector CD8+ T cells to slow their progression to terminally differentiated exhausted cells, i.e., increased their persistence within tumors. Anti-CTLA4 therapy increased recruitment of novel effector CD8+ T cell clones to tumors from lymph nodes while diminishing tumor-infiltrating Tregs. ICB also potentiated CD4+ Th1 like phenotype. These results uncovered a behavior pattern of CD8+ and CD4+ T cells within tumors at baseline tumor progression, and then in the presence of ICB. We believe these findings have added to our understanding of the subtleties of T cell phenotypes in tumors, specifically in response to ICB. This will provide a practical framework for designing and validating novel checkpoint blockade therapies in the future.

京都大学 / 0048 / 新制・課程博士 / 博士(人間・環境学) / 甲第19903号 / 人博第789号 / 新制||人||190(附属図書館) / 28||人博||789(吉田南総合図書館) / 32980 / 京都大学大学院人間・環境学研究科相関環境学専攻 / (主査)准教授 三浦 智行, 教授 川本 卓男, 教授 宮下 英明 / 学位規則第4条第1項該当 / Doctor of Human and Environmental Studies / Kyoto University / DFAM

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Indiana University-Purdue University Indianapolis (IUPUI) / The differentiation of naïve CD4+ T cells into subsets of T helper cells (Th) is an essential process that impacts host defense and the pathogenesis of immunemediated diseases. Signal transducers and activators of transcription (STAT) proteins, activated downstream of instructive cytokines, dictate and perpetuate the lineage decision of Th cells through both positive and negative effects. This is accomplished by regulating transcription factors, surface receptors and promoting epigenetic changes in gene expression through chromatin remodeling. Transforming growth factor-β1 (TGF-β1) can induce Foxp3 in developing Th cells and these Foxp3-expressing adaptive T regulatory cells (aTregs) are able to suppress inflammation in vitro and in vivo. To define the mechanism by which STAT proteins regulate Th cell pro- and anti-inflammatory phenotypes, we examined T cells deficient in Stat3, Stat4, and Stat6 as well as T cells expressing two STAT4 isoforms after being cultured in the presence or absence of TGF-β1 and cytokines known to be instructive in Th cell development. The negative effects of STAT proteins are demonstrated by our results indicating STAT3, STAT4 and STAT6 proteins activated downstream of the instructive cytokines IL- 6, IL-12 and IL-4, respectively, negatively regulate the development of TGF-β induced Foxp3 and aTreg development. STAT3, STAT4, and STAT6 utilize a vi Mark H. Kaplan, Ph.D., Chair common mechanism to inhibit aTreg generation by inhibiting STAT5, a positive regulator of Foxp3 expression, from binding to the Foxp3 gene. STAT proteins positively effecting inflammatory immunity are demonstrated by our analysis of STAT4 isoforms and their ability to regulate the production of proinflammatory cytokines downstream of IL-12. STAT4β, a STAT4 splice isoform that lacks a Cterminal domain, and STAT4α, a full-length isoform are both capable of mediating inflammatory cell development. However, STAT4β promotes greater inflammation in vivo than STAT4α independent of its ability to repress Foxp3. Instead, the inflammation correlates with STAT4 isoform-dependent expression of inflammatory cytokines. Thus, cytokine-stimulated STAT proteins orchestrate T helper cell pro- and anti-inflammatory cell phenotypes.

Indiana University-Purdue University Indianapolis (IUPUI) / This dissertation presents a body of research that attempts to tackle the ‘overfitting’ problem for gene signature and biomarker development in two different aspects (mechanistically and computationally). In achievement of a deeper understanding of cancer molecular mechanisms, this study presents new approaches to derive gene signatures for various biological phenotypes, including breast cancer, in the context of well-defined and mechanistically associated biological pathways. We identified the pattern of gene expression in the cell cycle pathway can indeed serve as a powerful biomarker for breast cancer prognosis. We further built a predictive model for prognosis based on the cell cycle gene signature, and found our model to be more accurate than the Amsterdam 70-gene signature when tested with multiple gene expression datasets generated from several patient populations. Aside from demonstrating the effectiveness of dimensionality reduction, phenotypic dissection, and prognostic or diagnostic prediction, this approach also provides an alternative to the current methodology of identifying gene expression markers that links to biological mechanism. This dissertation also presents the development of a novel feature selection algorithm called Predictive Power Estimate Analysis (PPEA) to computationally tackle on overfitting. The algorithm iteratively apply a two-way bootstrapping procedure to estimate predictive power of each individual gene, and make it possible to construct a predictive model from a much smaller set of genes with the highest predictive power. Using DrugMatrix™ rat liver data, we identified genomic biomarkers of hepatic specific injury for inflammation, cell death, and bile duct hyperplasia. We demonstrated that the signature genes were mechanistically related to the phenotype the signature intended to predict (e.g. 17 out of top 20 genes for inflammation selected by PPEA were members of NF-kB pathway, which is a key pre-inflammatory pathway for a xenobiotic response). The top 4 gene signature for BDH has been further validated by QPCR in a toxicology lab. This is important because our results suggest that the PPEA model not largely deters the over-fitting problem, but also has the capability to elucidate mechanism(s) of drug action and / or of toxicity.

No description available.

Alzheimer’s disease (AD) affects tens of millions of people worldwide. Its cause is unknown, with no cure, and disease-modifying treatment options have only recently become available. Emerging research has made a strong case for the involvement of immune cells, such as microglia and T cells, in modulating AD pathology. Newer technologies in RNA-sequencing have detailed specific phenotypic changes to microglia and T cells over the course of neurodegenerative disease. Some researchers have also used whole-genome sequencing to correlate genetic variants with changes in gene expression. However, no studies thus far have conducted this type of genotype-phenotype correlation in immune cells from aged individuals or AD patients. We have collected gene expression data from four sorted T cell subtypes in peripheral blood samples from 96 subjects in ROSMAP, a cohort of AD patients and age-matched controls. 78 of these subjects also have whole-genome sequencing data, which we used to detect genetic variants associated with changes in T cell gene expression. These are known as expression quantitative trait loci (eQTL). We found genes related to T cell cytotoxicity and immunosenescence in gene co-expression modules, among the eQTL, and in correlation with AD neuropathological traits or risk variants for several disease traits. We extended our findings related to disease association by calculating polygenic risk scores (PRSs) in our cohort from whole-genome sequencing data for 19 traits related to immune function and disease, including AD. Genes associated with the PRS for one or more disease traits often were in biological pathways related to downstream cytokine signaling, regulation of T cell receptor signaling, and T cell migration and trafficking. Overall, our findings indicate that the use of aged and AD patients in T cell genotype-phenotype correlation studies highlights genetic variants and differentially expressed genes that are not seen in studies using young, healthy individuals.

Stormwater sewer infrastructure is at risk due to ageing, structural deterioration, population growth, and climate change. Since the consequences of the sewer system failure can adversely impact the community safety, environment and economy, a resilient infrastructure system is of essential importance. However, limited reinvestment budget and insufficient asset management practices impact the rehabilitation of urban sewerage systems. Therefore, an effective and efficient rehabilitation plan is needed to help proper investment decisions. An effective rehabilitation plan will maximize hydraulic performance while minimizing the overall failure risk within a limited budget. The current study aims to address this issue through designing a risk-informed methodology in three steps. First, the hydraulic risk index (obtained using the SWMM model) was combined with the ageing pipe index. The framework uses multi-objective optimization technique to generate solutions under specific sewerage conditions. We named this new framework as Hydraulics and Risk Combined Model (HRCM). Several scenarios including high hydraulic risk, high ageing risk, hydraulic risk and ageing risk (combined problems), and limited budget problems, are used to test the performance of the proposed methodology. The results show that the proposed model could provide a satisfactory solution. Then, in order to increase the calculation speed and improve the accuracy, sensitivity and cost-effectiveness analyses were also conducted for the proposed methodology with different algorithms. The results show that different algorithms offer various benefits. A new calculation method was offered by combining the advantages of the previous methods. Finally, a new optimization method named Phenotype Searching Method, which was enlightened by sexual selection processes, was offered. This method can enhance the selection processes to specific phenotypes (pipes) so that it can increase the convergence speed and increase the performance of the HRCM model.

Objectives: To determine if temporomandibular joint (TMJ) compressive stresses during incisor biting (1) differed between growing children over time, and (2) were correlated with Frankfort Horizontal-mandibular plane angle (FHMPA, 8) and ramus length (Condylion-Gonion (Co-Go), mm). Materials and Methods: Three-dimensional anatomical geometries, FHMPA and Co-Go, were measured longitudinally from lateral and posteroanterior cephalographs1 of children aged 6 (T1), 12 (T2), and 18 (T3) years. Geometries were used in numerical models to estimate subject-specific TMJ eminence shape and forces for incisor bite-forces of 3, 5, and 8 Newtons at T1, T2, and T3, respectively. TMJ compressive stresses were estimated via two steps: First, TMJ force divided by age-dependent mandibular condylar dimensions, and second, modified by loading surfaces' congruency. Analysis of variance and Tukey honest significant difference post-hoc tests, plus repeated measures and mixed effects model analyses were used to evaluate differences in variables between facial groups. Regression analyses tested for correlation between agedependent compressive stresses, FHMPA, and Co-Go. Results: Sixty-five of 842 potential subjects had T1-T3 cephalographs and were grouped by FHMPA at T3. Dolichofacial (FHMPA ≥ 27°, n=36) compared to meso-brachyfacial (FHMPA, 27°, n=29) subjects had significantly larger FHMPA at T1-T3, shorter Co-Go at T2 and T3 (all P < .01), and larger increases in TMJ compressive stresses with age (P < .0001). Higher compressive stresses were correlated with larger FHMPA (all R2 ≥ 0.41) and shorter Co-Go (all R2 ≥ 0.49). Conclusions: Estimated TMJ compressive stress increases from ages 6 to 18 years were significantly larger in dolichofacial compared to meso-brachyfacial subjects and correlated to FHMPA and Co-Go.