Peroxynitrite, pumps and perivascular adipose tissue : studies across the physiological spectrum

The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.Title from PDF of title page (University of Missouri--Columbia, viewed on April 6, 2010).Vita.Thesis advisor: Mark Milanick"June 2008"Ph. D. University of Missouri-Columbia 2008.Peroxynitrite (ONOO-) is a reactive nitrogen species produced when nitric oxide (NO) and superoxide (O2-) react. In vivo studies suggest that reactive oxygen species and perhaps peroxynitrite can influence Na,K-ATPase (Na pump) function. However, the direct effects of peroxynitrite on Na,K-ATPase function remain unknown. We show that a single bolus addition of peroxynitrite inhibit purified renal Na, K - ATPase activity with an IC50 of 107 [plus or minus] 9 [Mu]M. Peroxynitrite treatment produced 3-nitrotyrosine residues on the [alpha], [beta] and FXYD subunits of the Na pump and also modified cysteine residues. Taken together these results show that peroxynitrite is a potent inhibitor of Na,K-ATPase activity and that peroxynitrite can induce specific amino acid modifications to the pump. We also investigated if the Na pump was a "target" of peroxynitrite in vivo under cellular conditions. Preliminary evidence suggests that LLC-PK1 cells do contain nitrated Na pumps and that tissue from sedentary high fat fed pigs contain nitrated proteins. A denitrase activity capable of modifying 3-nitrotyrosine back to tyrosine would have implications for cell signaling/repair mechanisms as well as overall 3-nitrotyrosine levels. The red blood cell, due to its lack of nucleus and long life span, represents an ideal cell type that may or may not contain a denitrase activity. Shown here are results of just two experiments that might suggest a denitrase activity in RBCs. However, other experiments (not shown) seemed to lack any denitrase activity and ultimately the results from all experiments neither clearly demonstrated nor ruled out an obvious denitrase activity in red blood cells. Also, presented here is a study investigating some basic aspects of Na pump functioning. Specifically, we demonstrate that terbium is a non-competitive inhibitor of rubidium uptake suggesting it does not bind to the outside transport site of the Na pump. In contrast we show that chrysoidine competes with sodium and potassium for ATPase activity suggesting it binds to the inside transport site. Together these results support that chrysoidine, but not terbium, might be a useful probe for the transport site. We also show that the outside transport site is very specific for monovalent cations over divalent cations. Also, presented here is a study investigating the effects of perivascular adipose tissue on coronary artery reactivity and the influence of diet and exercise. Results from this study suggest that perivascular adipose tissue blunts contraction induced by endothelin-1 in coronary arteries from normal fat and high fat fed pigs. While exercise abolished this effect normal fat fed pigs, exercise did not alter the anti-contractile effect in the high fat fed pigs.Includes bibliographical reference

Modelling Precipitation Intensities from X-Band Radar Measurements Using Artificial Neural Networks—A Feasibility Study for the Bavarian Oberland Region

Radar data may potentially provide valuable information for precipitation quantification, especially in regions with a sparse network of in situ observations or in regions with complex topography. Therefore, our aim is to conduct a feasibility study to quantify precipitation intensities based on radar measurements and additional meteorological variables. Beyond the well-established Z–R relationship for the quantification, this study employs Artificial Neural Networks (ANNs) in different settings and analyses their performance. For this purpose, the radar data of a station in Upper Bavaria (Germany) is used and analysed for its performance in quantifying in situ observations. More specifically, the effects of time resolution, time offsets in the input data, and meteorological factors on the performance of the ANNs are investigated. It is found that ANNs that use actual reflectivity as only input are outperforming the standard Z–R relationship in reproducing ground precipitation. This is reflected by an increase in correlation between modelled and observed data from 0.67 (Z–R) to 0.78 (ANN) for hourly and 0.61 to 0.86, respectively, for 10 min time resolution. However, the focus of this study was to investigate if model accuracy benefits from additional input features. It is shown that an expansion of the input feature space by using time-lagged reflectivity with lags up to two and additional meteorological variables such as temperature, relative humidity, and sunshine duration significantly increases model performance. Thus, overall, it is shown that a systematic predictor screening and the correspondent extension of the input feature space substantially improves the performance of a simple Neural Network model. For instance, air temperature and relative humidity provide valuable additional input information. It is concluded that model performance is dependent on all three ingredients: time resolution, time lagged information, and additional meteorological input features. Taking all of these into account, the model performance can be optimized to a correlation of 0.9 and minimum model bias of 0.002 between observed and modelled precipitation data even with a simple ANN architecture

Modelling precipitation intensities from x-band radar measurements using Artificial Neural Networks — a feasibility study for the Bavarian Oberland region

Radar data may potentially provide valuable information for precipitation quantification, especially in regions with a sparse network of in situ observations or in regions with complex topography. Therefore, our aim is to conduct a feasibility study to quantify precipitation intensities based on radar measurements and additional meteorological variables. Beyond the well-established Z–R relationship for the quantification, this study employs Artificial Neural Networks (ANNs) in different settings and analyses their performance. For this purpose, the radar data of a station in Upper Bavaria (Germany) is used and analysed for its performance in quantifying in situ observations. More specifically, the effects of time resolution, time offsets in the input data, and meteorological factors on the performance of the ANNs are investigated. It is found that ANNs that use actual reflectivity as only input are outperforming the standard Z–R relationship in reproducing ground precipitation. This is reflected by an increase in correlation between modelled and observed data from 0.67 (Z–R) to 0.78 (ANN) for hourly and 0.61 to 0.86, respectively, for 10 min time resolution. However, the focus of this study was to investigate if model accuracy benefits from additional input features. It is shown that an expansion of the input feature space by using time-lagged reflectivity with lags up to two and additional meteorological variables such as temperature, relative humidity, and sunshine duration significantly increases model performance. Thus, overall, it is shown that a systematic predictor screening and the correspondent extension of the input feature space substantially improves the performance of a simple Neural Network model. For instance, air temperature and relative humidity provide valuable additional input information. It is concluded that model performance is dependent on all three ingredients: time resolution, time lagged information, and additional meteorological input features. Taking all of these into account, the model performance can be optimized to a correlation of 0.9 and minimum model bias of 0.002 between observed and modelled precipitation data even with a simple ANN architecture

Spezifische Wärme von supraleitenden metallischen Gläsern bei tiefen Temperaturen

In der vorliegenden Arbeit wurden mit der Bestimmung der spezifischen Wärme supraleitender massiver metallischer Gläser (BMGs) im Temperaturbereich zwischen 25mK und 300K erstmalig deren thermodynamische Eigenschaften bei diesen Temperaturen detailliert untersucht. Die Messungen wurden mit Hilfe eines Aufbaus durchgeführt, welcher die etablierte Relaxationsmethode implementiert. Des Weiteren wurde ein neuartiger mikrostrukturierter Aufbau zur Untersuchung kleinster Proben mit Massen von nur wenigen Milligramm bis hinab zu Temperaturen von 5mK entwickelt. Die Temperatur dieser Plattform wird mit Hilfe eines metallischen paramagnetischen AgEr-Sensors bestimmt, welcher durch eine mikrostrukturierte, gradiometrische Mäanderspule aus Niob mit Hilfe eines dc-SQUIDs induktiv ausgelesen wird. Auf diese Weise konnte eine hervorragende Temperaturauflösung von unter 30nK/sqrt{Hz} und eine äußerst geringe Addenda von unter 200pJ/K bei 50mK erreicht werden. Durch die Verknüpfung gemessener spezifischer Wärmen mit Daten der Wärmeleitfähigkeit konnten die unterschiedlichen Freiheitsgrade der untersuchten BMGs sowie deren Wechselwirkungsmechanismen konsistent modelliert werden: Für T > 2K zeigt sich eine ausgeprägte Anomalie in der phononischen spezifischen Wärme, welche durch lokalisierte harmonische Vibrationen hervorgerufen wird. Unterhalb des Phasenübergangs in den supraleitenden Zustand, in dem Wechselwirkungen atomarer Tunnelsysteme mit Quasiteilchen berücksichtigt werden müssen, stehen die Beobachtungen im Einklang mit der BCS-Theorie. Bei tiefsten Temperaturen lassen sich die Ergebnisse im Rahmen des Standardtunnelmodells interpretieren

Molecular diagnostics of gliomas: state of the art

Modern neuropathology serves a key function in the multidisciplinary management of brain tumor patients. Owing to the recent advancements in molecular neurooncology, the neuropathological assessment of brain tumors is no longer restricted to provide information on a tumor’s histological type and malignancy grade, but may be complemented by a growing number of molecular tests for clinically relevant tissue-based biomarkers. This article provides an overview and critical appraisal of the types of genetic and epigenetic aberrations that have gained significance in the molecular diagnostics of gliomas, namely deletions of chromosome arms 1p and 19q, promoter hypermethylation of the O6-methylguanine-methyl-transferase (MGMT) gene, and the mutation status of the IDH1 and IDH2 genes. In addition, the frequent oncogenic aberration of BRAF in pilocytic astrocytomas may serve as a novel diagnostic marker and therapeutic target. Finally, this review will summarize recent mechanistic insights into the molecular alterations underlying treatment resistance in malignant gliomas and outline the potential of genome-wide profiling approaches for increasing our repertoire of clinically useful glioma markers

Integrative Genomic Analyses of Patient-Matched Intracranial and Extracranial Metastases Reveal a Novel Brain-Specific Landscape of Genetic Variants in Driver Genes of Malignant Melanoma

Background: Development of brain metastases in advanced melanoma patients is a frequent event that limits patients’ quality of life and survival. Despite recent insights into melanoma genetics, systematic analyses of genetic alterations in melanoma brain metastasis formation are lacking. Moreover, whether brain metastases harbor distinct genetic alterations beyond those observed at different anatomic sites of the same patient remains unknown. Experimental Design and Results: In our study, 54 intracranial and 18 corresponding extracranial melanoma metastases were analyzed for mutations using targeted next generation sequencing of 29 recurrently mutated driver genes in melanoma. In 11 of 16 paired samples, we detected nucleotide modifications in brain metastases that were absent in matched metastases at extracranial sites. Moreover, we identified novel genetic variants in ARID1A, ARID2, SMARCA4 and BAP1, genes that have not been linked to brain metastases before; albeit most frequent mutations were found in ARID1A, ARID2 and BRAF. Conclusion: Our data provide new insights into the genetic landscape of intracranial melanoma metastases supporting a branched evolution model of metastasis formation

Identification of a Rare 3 bp BRAF Gene Deletion in a Thyroid Nodule by Mutant Enrichment with 3'-Modified Oligonucleotides Polymerase Chain Reaction

Papillary thyroid carcinoma (PTC) is the most common malignant thyroid tumor, and 36-69% of PTC cases are caused by mutations in the BRAF gene. The substitution of a valine for a glutamic acid (V600E) comprises up to 95-100% of BRAF mutations; therefore, most diagnostic methods, including allele-specific PCR and real-time PCR, are designed to detect this mutation. Nevertheless, other mutations can also comprise the genetic background of PTC. Recently, a novel and sensitive technique called mutant enrichment with 3'-modified oligonucleotides (MEMO) PCR has been introduced. When we applied allelespecific PCR and MEMO-PCR for the detection of the BRAF V600E mutation, we found an unusual 3' bp deletion mutation (c.1799_1801delTGA) only when using MEMO-PCR. This deletion results in the introduction of a glutamic acid into the B-Raf activation segment (p.V600_K601delinsE), leading to an elevated basal kinase activity of BRAF. This is the first report of a rare 3 bp BRAF deletion in a PTC patient that could not be detected by allele-specific PCR