Spatial Mapping and Profiling of Metabolite Distributions During Germination

Germination is a highly complex process by which seeds begin to develop and establish themselves as viable organisms. In this paper, we utilize a combination of GC-MS, LC-fluorescence, and mass spectrometry imaging (MSI) approaches to profile and visualize the metabolic distributions of germinating seeds from two different inbreds of maize seeds, B73 and Mo17. GC and LC analyses demonstrate that the two inbreds are highly differentiated in their metabolite profiles throughout the course of germination, especially with regard to amino acids, sugar alcohols, and small organic acids. Crude dissection of the seed followed by GC-MS analysis of polar metabolites also revealed that many compounds were highly sequestered among the various seed tissue types. To further localize compounds, matrix-assisted laser desorption/ionization MSI is utilized to visualize compounds in fine detail in their native environments over the course of germination. Most notably, the fatty acyl chain-dependent differential localization of phospholipids and TAGs were observed within the embryo and radicle, showing correlation with the heterogeneous distribution of fatty acids. Other interesting observations include unusual localization of ceramides on the endosperm/scutellum boundary, and subcellular localization of ferulate in the aleurone

Gate-Controlled Ionization and Screening of Cobalt Adatoms on a Graphene Surface

We describe scanning tunneling spectroscopy (STS) measurements performed on individual cobalt (Co) atoms deposited onto backgated graphene devices. We find that Co adatoms on graphene can be ionized by either the application of a global backgate voltage or by the application of a local electric field from a scanning tunneling microscope (STM) tip. Large screening clouds are observed to form around Co adatoms ionized in this way, and we observe that some intrinsic graphene defects display a similar behavior. Our results provide new insight into charged impurity scattering in graphene, as well as the possibility of using graphene devices as chemical sensors.Comment: 19 pages, 4 figure

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

Technological development of high-performance MALDI mass spectrometry imaging for the study of metabolic biology

This thesis represents efforts made in technological developments for the study of metabolic biology in plants, specifically maize, using matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI). The first chapter gives a brief introduction on what MALDI-MSI is and how it works. The final chapter provides a summary of all works in this thesis as well as providing future directions that may be pursued based on this research. The second chapter encompasses research performed tracking metabolite distributions between two different inbreds of maize seeds throughout the course of germination utilizing GC-MS, LC-Fluorescence, and MSI analysis. GC and LC data provide quantitative information regarding a wide range of metabolites at crude localization levels, while MSI is able to track localizations of limited metabolites in fine spatial detail. Results demonstrate that metabolites are differentially localized throughout the seed depending on inbred, metabolite class, and germination time point. The third chapter demonstrates how MALDI-MSI can be used to acquire large, metabolomic scale datasets. Serial sections of a maize seed are coated with various matrices and analyzed in positive and negative ion mode using a multiplex imaging strategy. This strategy allows for visualization of metabolites through MSI as well as metabolite identification through the collection and analysis of high-quality MS/MS spectra. The fourth chapter outlines the development of a binary matrix comprised of 2,5-dihydroxybenzoic acid and iron oxide (Fe3O4) nanoparticles. The matrix is shown to alleviate the suppression of triacylglycerol species by phosphatidylcholine, both through standard analysis and on tissue analysis of maize seeds. The binary matrix also allows for the detection of more phospholipid classes in positive ion mode than either matrix on its own, while also demonstrating an apparent synergistic affect for large oligosaccharide type molecules. The fifth chapter represents efforts to reduce the laser spot size for our MALDI-MSI experiments through the modification of laser optics. This work demonstrates that the swapping of the beam expander component of the optical system allows for simplistic modification of the laser spot size resulting in an easily applicable multi-resolution setup with the ability to perform imaging at a resolution of 5 õm. However, the high-resolution spot size is severely limited by depth of focus issues. This multi-resolution setup is then applied on a single maize root section at resolutions of 5, 10, and 50 õm. The images and spectral characteristics of these analyses are then compared and analyzed

Cost-effectiveness models for chronic obstructive pulmonary disease: cross-model comparison of hypothetical treatment scenarios

Objectives: To compare different chronic obstructive pulmonary disease (COPD) cost-effectiveness models with respect to structure and input parameters and to cross-validate the models by running the same hypothetical treatment scenarios.&lt;p&gt;&lt;/p&gt; Methods: COPD modeling groups simulated four hypothetical interventions with their model and compared the results with a reference scenario of no intervention. The four interventions modeled assumed 1) 20% reduction in decline in lung function, 2) 25% reduction in exacerbation frequency, 3) 10% reduction in all-cause mortality, and 4) all these effects combined. The interventions were simulated for a 5-year and lifetime horizon with standardization, if possible, for sex, age, COPD severity, smoking status, exacerbation frequencies, mortality due to other causes, utilities, costs, and discount rates. Furthermore, uncertainty around the outcomes of intervention four was compared.&lt;p&gt;&lt;/p&gt; Results: Seven out of nine contacted COPD modeling groups agreed to participate. The 5-year incremental cost-effectiveness ratios (ICERs) for the most comprehensive intervention, intervention four, was €17,000/quality-adjusted life-year (QALY) for two models, €25,000 to €28,000/QALY for three models, and €47,000/QALY for the remaining two models. Differences in the ICERs could mainly be explained by differences in input values for disease progression, exacerbation-related mortality, and all-cause mortality, with high input values resulting in low ICERs and vice versa. Lifetime results were mainly affected by the input values for mortality. The probability of intervention four to be cost-effective at a willingness-to-pay value of €50,000/QALY was 90% to 100% for five models and about 70% and 50% for the other two models, respectively.&lt;p&gt;&lt;/p&gt; Conclusions: Mortality was the most important factor determining the differences in cost-effectiveness outcomes between models