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Idiopathic pulmonary fibrosis (IPF) is a disease of unknown pathogenesis characterized by scarring of the lung and declining respiratory function. Originating from bone marrow, circulating monocytes can be recruited into the lung tissue and polarized toward the alternatively activated (M2) profibrotic macrophage phenotype. Recent literature has shown that cluster of differentiation 14 positive (CD14+) monocytes are more abundant in IPF patient blood and are associated with disease outcome and acute exacerbation. Additionally, a 52-gene risk profile from peripheral blood mononuclear cells for outcome prediction in IPF was recently published. Here, we began by characterizing macrophages in human IPF lung tissue. We then assembled a biobank and examined transcriptomic characteristics of blood-derived circulating monocytes from IPF patients. Various histological assessments were completed on a tissue microarray including lung biopsies from 24 IPF patients and 17 controls, to characterize M2 macrophage expression in human tissue. Whole blood samples were collected from 50 IPF patients and 12 control subjects. CD14+ monocytes were isolated and mRNA was extracted for bulk RNA sequencing. Data were analyzed for differential expression (DE), and Gene Set Enrichment Analysis (GSEA) was performed to examine enrichment of the previously published 52-gene risk profile in our dataset. We found that M2 macrophage expression was increased in IPF lung tissue compared to controls. CD14+ monocyte levels were significantly elevated in IPF patients in our cohort compared to control participants, and was negatively correlated with forced vital capacity (FVC). DE analysis comparing IPF and control monocytes yielded a 35-gene signature, with 16 up-regulated genes and 19 down-regulated genes. When comparing the signature related to long transplant-free survival from the published dataset to our data, GSEA demonstrated that this signature is enriched in donors from our dataset, supporting concurrence between the meanings of the two datasets. Overall, these results provide insight to identify targets to modulate monocyte/macrophage function in IPF and potentially affect progressive disease. / Thesis / Master of Science (MSc) / Idiopathic pulmonary fibrosis (IPF) is a disease of unknown cause that results in excessive scarring of the lungs and progressive impairment in lung function. We believe that white blood cells called monocytes and macrophages play a key role in the development and progression of this disease. Overall, it is thought that monocytes, which circulate in the blood, enter the lung tissue and become macrophages. These macrophages then lead to the formation of scar tissue, which is characteristic to IPF. In order to better understand how these cells contribute to IPF, we studied their properties in blood and lung biopsies from IPF patients. We found significant differences between monocytes/macrophages in IPF than those in healthy controls, that may help explain disease progression. We hope that these findings will provide insight into causes of the IPF, and potential avenues for therapeutic intervention.

Peanut (Arachis hypogaea L.) is an important food crop in the USA and worldwide with high net returns but yield in excess of 4500 kg ha-1 is needed to offset the production costs. Because yield is limited by biotic and abiotic stresses, cultivars with stress tolerance are needed to optimize yield. The U.S. peanut mini-core germplasm collection is a valuable resource that breeders can use to improve stress tolerance in peanut. Phenotyping for plant height, leaf area, and leaf wilting have been used as proxies for the desired tolerance traits. However, proximal data collection, i.e. measurements are taken on individual plants or in the proximity, is slow. Remote data collection and machine learning techniques for analysis offer a high-throughput phenotyping (HTP) alternative to manual measurements that could help breeding for stress tolerance. The objectives of this study were to 1) develop HTP methods using aerial remote sensing; 2) evaluate the mini-core collection in SE Virginia; and 3) perform a detailed physiological analysis on a sub-set of 28 accessions from the mini-core collection under drought stress, i.e. the sub-set was selected based on contrasting differences under drought in three states, Virginia, Texas, and Oklahoma. To address these objectives, replicated experiments were performed in the field at the Tidewater Agricultural Research and Extension Center in Suffolk, VA, in 2017, 2018, and 2019, under rainfed, irrigated, and controlled conditions using rainout shelters to induce drought. Proximal data collection involved physiological, morphological, and yield measurements. Remote data collection was performed aerially and included collection of red-green-blue (RGB) images and canopy reflectance in the visible, near infra-red, and infra-red spectra. This information was used to estimate plant characteristics related to growth and drought tolerance. Under objective 1), we developed HTP for plant height with 85-95% accuracy, LAI with 85-88% accuracy, and wilting with 91-99% accuracy; this was done with significant reduction of time as compared to proximal data collection. Under objectives 2) and 3), we determined that shorter genotypes were more drought tolerant than taller genotypes; and identified CC650 less wilted and with increased carbon assimilation, electron transport, quantum efficiency, and yield than other accessions. / Doctor of Philosophy / Peanut is a profitable food crop in the USA but has high input costs. Pod yield over 4500 kg ha-1 is required for a profitable production, which is challenging in dry and hot years, and under disease pressure. Varieties tolerant to dry weather conditions (drought) and disease presence are required to sustain production. A collection of 112 peanut varieties is available for researchers to study the mechanisms of tolerance to drought and disease, and identify tolerant varieties to these stresses. Plant characteristics including height, leaf area, and leaf wilting can be used as proxies to estimate stress tolerance and yield, and identify tolerant varieties. How to measure these characteristics is very important. We think that using images collected by a drone and automated analysis by specific computer programs is the easiest, fastest, and most accurate way. Therefore, the objectives of my study were to 1) use drones and cameras to collect images, and computer programs to derive plant characteristics from these images, 2) evaluate the peanut collection to identify varieties with tolerance to drought and disease, and 3) evaluate in depth a sub-set of 28 varieties from this collection under controlled drought conditions to further learn about peanut mechanisms of tolerance to drought and diseases. Field experiments were conducted in 2017, 2018, and 2019, at the Tidewater Agricultural Research and Extension Center in Suffolk, VA. For some tests, we used rainout shelters to mimic drought. We measured plant height, leaf area, color, and wilting, canopy temperature, photosynthesis, and pod yield. From a drone, we collected images in the visible and invisible radiation and, using specific computer programs, estimated plant characteristics with 95% accuracy for height, 88% for leaf area, and 91% for leaf wilting under drought. We concluded that taller varieties were more susceptible to drought than shorter varieties. Peanut varieties CC650 and CC068 had higher end of season yield. The study showed that drought reduced several key mechanisms of photosynthesis including electron transport; and reduced the end of season yield. Variety CC650 performed better under drought than other varieties of the collection.

Quantitative genetics aims to bridge the genome to phenome gap. The advent of high-throughput genotyping technologies has accelerated the progress of genome to phenome mapping, but a challenge remains in phenotyping. Various high-throughput phenotyping (HTP) platforms have been developed recently to obtain economically important phenotypes in an automated fashion with less human labor and reduced costs. However, the effective way of designing HTP has not been investigated thoroughly. In addition, high-dimensional HTP data bring up a big challenge for statistical analysis by increasing computational demands. A new strategy for modeling high-dimensional HTP data and elucidating the interrelationships among these phenotypes are needed. Previous studies used pedigree-based connectetdness statistics to study the design of phenotyping. The availability of genetic markers provides a new opportunity to evaluate connectedness based on genomic data, which can serve as a means to design HTP. This dissertation first discusses the utility of connectedness spanning in three studies. In the first study, I introduced genomic connectedness and compared it with traditional pedigree-based connectedness. The relationship between genomic connectedness and prediction accuracy based on cross-validation was investigated in the second study. The third study introduced a user-friendly connectedness R package, which provides a suite of functions to evaluate the extent of connectedness. In the last study, I proposed a new statistical approach to model high-dimensional HTP data by leveraging the combination of confirmatory factor analysis and Bayesian network. Collectively, the results from the first three studies suggested the potential usefulness of applying genomic connectedness to design HTP. The statistical approach I introduced in the last study provides a new avenue to model high-dimensional HTP data holistically to further help us understand the interrelationships among phenotypes derived from HTP. / Doctor of Philosophy / Quantitative genetics aims to bridge the genome to phenome gap. With the advent of genotyping technologies, the genomic information of individuals can be included in a quantitative genetic model. A new challenge is to obtain sufficient and accurate phenotypes in an automated fashion with less human labor and reduced costs. The high-throughput phenotyping (HTP) technologies have emerged recently, opening a new opportunity to address this challenge. However, there is a paucity of research in phenotyping design and modeling high-dimensional HTP data. The main themes of this dissertation are 1) genomic connectedness that could potentially be used as a means to design a phenotyping experiment and 2) a novel statistical approach that aims to handle high-dimensional HTP data. In the first three studies, I first compared genomic connectedness with pedigree-based connectedness. This was followed by investigating the relationship between genomic connectedness and prediction accuracy derived from cross-validation. Additionally, I developed a connectedness R package that implements a variety of connectedness measures. The fourth study investigated a novel statistical approach by leveraging the combination of dimension reduction and graphical models to understand the interrelationships among high-dimensional HTP data.

Hyperspectral imaging has become one of the most popular technologies in plant phenotyping because it can efficiently and accurately predict numerous plant physiological features such as plant biomass, leaf moisture content, and chlorophyll content. Various hyperspectral imaging systems have been deployed in both greenhouse and field phenotyping activities. However, the hyperspectral imaging quality is severely affected by the continuously changing environmental conditions such as cloud cover, temperature and wind speed that induce noise in plant spectral data. Eliminating these environmental effects to improve imaging quality is critically important. In this thesis, two approaches were taken to address the imaging noise issue in greenhouse and field separately. First, a computational simulation model was built to simulate the greenhouse microclimate changes (such as the temperature and radiation distributions) through a 24-hour cycle in a research greenhouse. The simulated results were used to optimize the movement of an automated conveyor in the greenhouse: the plants were shuffled with the conveyor system with optimized frequency and distance to provide uniform growing conditions such as temperature and lighting intensity for each individual plant. The results showed the variance of the plants’ phenotyping feature measurements decreased significantly (i.e., by up to 83% in plant canopy size) in this conveyor greenhouse. Secondly, the environmental effects (i.e., sun radiation) on <a>aerial </a>hyperspectral images in field plant phenotyping were investigated and modeled. <a>An artificial neural network (ANN) method was proposed to model the relationship between the image variation and environmental changes. Before the 2019 field test, a gantry system was designed and constructed to repeatedly collect time-series hyperspectral images with 2.5 minutes intervals of the corn plants under varying environmental conditions, which included sun radiation, solar zenith angle, diurnal time, humidity, temperature and wind speed. Over 8,000 hyperspectral images of </a>corn (<i>Zea mays </i>L.) were collected with synchronized environmental data throughout the 2019 growing season. The models trained with the proposed ANN method were able to accurately predict the variations in imaging results (i.e., 82.3% for NDVI) caused by the changing environments. Thus, the ANN method can be used by remote sensing professionals to adjust or correct raw imaging data for changing environments to improve plant characterization.

Master of Science / Department of Agronomy / William T. Schapaugh Jr / Crops research moving forward faces many challenges to improve crop performance. In breeding programs, phenotyping has time and economic constraints requiring new phenotyping techniques to be developed to improve selection efficiency and increase germplasm entering the pipeline. The objectives of these studies were to examine the changes in spectral reflectance with soybean breeding from 1923 to 2010, evaluate band regions most significantly contributing to yield estimation, evaluate spectral reflectance data for yield estimation modeling across environments and growth stages and to evaluate the usefulness of spectral data as an optimized phenotyping technique in breeding programs. Twenty maturity group III (MGIII) and twenty maturity group IV (MGIV) soybeans, arranged in a randomized complete block design, were grown in Manhattan, KS in 2011 and 2012. Spectral reflectance data were collected over the growing season in a total of six irrigated and water- stressed environments. Partial least squares and multiple linear regression were used for spectral variable selection and yield estimation model building. Significant differences were found between genotypes for yield and spectral reflectance data, with the visible (VI) having greater differences between genotypes than the near-infrared (NIR). This study found significant correlations with year of release (YOR) in the VI and NIR portions of the spectra, with newer released cultivars tending to have lower reflectance in the VI and high reflectance in the NIR. Spectral reflectance data accounted for a large portion of variability for seed yield between genotypes using the red edge and NIR portions of the spectra. Irrigated environments tended to explain a larger portion of seed yield variability than water-stressed environments. Growth stages most useful for yield estimation was highly dependent upon the environment as well as maturity group. This study found that spectral reflectance data is a good candidate for exploration into optimized phenotyping techniques and with further research and validation datasets, may be a suitable indirect selection technique for breeding programs.

La vigueur initiale (accumulation de biomasse aérienne) est déterminante pour un rapide établissement de la culture et l'accès aux ressources, contribuant ainsi à un évitement du stress hydrique. Cette thèse vise à caractériser la diversité phénotypique chez le riz (Oriza Sativa.L) des traits constituant la vigueur initiale et sa plasticité sous stress hydrique. L'étude à démontrée que la vigueur initiale dépend de caractères relatifs aux forces de puits et à la demande en assimilats carbonés, tels que le taux de développement (DR), le tallage et la taille potentielle des feuilles. Une relation négative entre DR et la taille des feuilles a été observée et reliée à des différences d'utilisation des sucres par la plante au niveau des organes source et puits. En particulier des plantes à fort DR ont montré la tendance à stocker très peu d'amidon dans les feuilles source, inversement aux génotypes à grande feuilles. Sous stress hydrique des faibles tolérances à la sécheresse ont été liés à des réductions des activités des organes puits. Cette étude a montré l'existence d'une grande diversité génétique pour des trais liés à la tolérance au stress hydrique chez le riz. De plus des fortes réductions de croissance sous stress ont été observées pour les génotypes vigoureux. En effet de forts DR étaient aussi associés à une forte sensibilité du taux de transpiration foliaire (fermeture stomatique) et à une faible efficience d'utilisation de l'eau sous stress, de plus les génotypes à grandes feuilles ont montré un fort taux de sénescence foliaire. La diversité phénotypique observée dans le panel des riz Japonica est prometteuse pour des analyses génétiques d'association permettant l'amélioration de la tolérance au stress hydrique du riz ; cependant, les éventuelles limitations génétiques liées aux relations négatives observées entre vigueur initiale et tolérance au stress hydrique et donc, la facilité d'une co-sélection pour ces deux caractères complexes, devront être explorées. / Early vigour (ie.shoot biomass accumulation) is essential for rapid crop establishment, resource acquisition and can thus contribute to drought avoidance. This work aims at characterizing the diversity of component traits constituting early vigor and its plasticity under drought for rice (Oriza Sativa L.). This study demonstrated that sink dynamics: Developmental Rate (DR, inverse of phyllochron, in °C.d-1); tillering capacity and potential leaf size which together constitute incremental demand for assimilates are mayor drivers of early vigor. A tradeoff between DR and leaf size was explained by differences in carbon concentrations in source and sink leaves, in particular high DR genotypes stored low starch in source leaves compared to large leaf genotypes under well watered conditions. Low drought tolerance was related to a reduction in sink activity under drought. This study demonstrates that rice has a great genetic diversity in terms of drought tolerance. Under drought both high DR and large leaves vigorous genotypes had the strongest growth reduction. Indeed, DR was associated to high stomatal sensibility to drought and low WUE, while large leaves genotypes showed high leaf senescence rates. Finally, the phenotypic diversity observed within the studied japonica panel is promising for genetic association studies in order to improve rice drought resistance. The genetic limitations of the negative, phenotypic linkages observed between early vigor and drought tolerance, and thus the easiness to co-select for both traits will have to be explored.

Chronic respiratory diseases (CRDs) include chronic diseases involving the airways and other structures of the lung. In the current circumstance of Vietnam, people are exposed to numerous risk factors of CRD, such as heavy smoking, high frequency of pulmonary tuberculosis, chronic helminthiasis, allergic factors, migration and urbanization (the last associated with traffic-related pollution). The phenotype diagnoses should take into account the risk factors of each individual besides the clinical features, while the differential diagnoses mostly depend on the available techniques in each healthcare center. Our aim was to improve the differential diagnoses of the 3 most frequent CRDs: chronic obstructive pulmonary disease (COPD), asthma and COPD – asthma overlap syndrome (ACOS), in Vietnam. In the first part, we evaluated the prevalence of the allergen sensitization among patients with CRD, in regard to the urban and rural area in the South of Vietnam. House dust mites and cockroach droppings were the most frequent sensitizer. Compared with participants born in the urban setting, those born in the rural environment were less frequently sensitized and this protective effect disappeared in the case of migration from rural to urban areas. In the second part, we evaluated skin prick test as a method to screen dust mite sensitization in CRD in southern Vietnam. The data suggested that, in the present circumstance, skin prick test can be used to screen mite sensitization. In the third part, we evaluated the risk of mite sensitization in the native and migrant population, in regard to several environmental factors. Consistently with the hygiene hypothesis, compared to urban, exposure to high endotoxin concentration in rural was a protective factor against allergic sensitization. We reported for the first time that this effect was reversible among the migrants from rural to urban setting in association with lower endotoxin exposure. In the fourth part, we have defined asthma, COPD and ACOS based on clinical symptoms, cumulative smoking and airway expiratory flow with reversibility, on one side, and the age-related of the different phenotypes, on the other side. We hypothesized that the cumulative exposure to noxious particles should increase the age-related prevalence of COPD, while due to the immunosenescence process, the prevalence of IgE-mediated asthma should decrease with age, and ACOS prevalence being not related to age due to the combined mechanisms.  In conclusion, we showed in the South of Vietnam that:1) mites and cockroach allergens were the most frequent sensitizer in chronic respiratory diseases;2) the skin prick test to mite has been validated to screen mite sensitization;3) associated with a reduced level of endotoxin level, migration from rural to the urban setting was a risk factor of mite sensitization in chronic respiratory diseases;4) based on the clinical symptoms, spirometric values, and cumulative smoking, the diagnosis of asthma, COPD and ACOS have been made and their prevalence were 25, 42 and 33%, respectively. / Doctorat en Sciences biomédicales et pharmaceutiques (Médecine) / info:eu-repo/semantics/nonPublished

Doctor of Philosophy / Genetics Interdepartmental Program / Jesse A. Poland / A central question in modern biology is to understand the genotype-to-phenotype (G2P) link, that is, how the genetics of an organism results in specific characteristics. However, prediction of phenotypes from genotypes is a difficult problem due to the complex nature of genomes, the environment, and their interactions. While the recent advancements in genome sequencing technologies have provided almost unlimited access to high-density genetic markers, large-scale rapid and accurate phenotyping of complex plant traits remains a major bottleneck. Here, we demonstrate field-based complex trait assessment approaches using a commercially available light-weight Unmanned Aerial Systems (UAS). By deploying novel data acquisition and processing pipelines, we quantified lodging, ground cover, and crop growth rate of 1745 advanced spring wheat lines at multiple time-points over the course of three field seasons at three field sites in South Asia. High correlations of digital measures to visual estimates and superior broad-sense heritability demonstrate these approaches are amenable for reproducible assessment of complex plant traits in large breeding nurseries. Using these validated high-throughput measurements, we applied genome-wide association and prediction models to assess the underlying genetic architecture and genetic control. Our results suggest a diffuse genetic architecture for lodging and ground cover in wheat, but heritable genetic variation for prediction and selection in breeding programs. The logistic regression-derived parameters of dynamic plant height exhibited strong physiological linkages with several developmental and agronomic traits, suggesting the potential targets of selection and the associated tradeoffs. Taken together, our highly reproducible approaches provide a proof-of-concept application of UAS-based phenomics that is scalable to tens-of-thousands of plots in breeding and genetic studies as will be needed to understand the G2P and increase the rate of gain for complex traits in crop breeding.