Arginase and Arginine Dysregulation in Asthma

In recent years, evidence has accumulated indicating that the enzyme arginase, which converts L-arginine into L-ornithine and urea, plays a key role in the pathogenesis of pulmonary disorders such as asthma through dysregulation of L-arginine metabolism and modulation of nitric oxide (NO) homeostasis. Allergic asthma is characterized by airway hyperresponsiveness, inflammation, and remodeling. Through substrate competition, arginase decreases bioavailability of L-arginine for nitric oxide synthase (NOS), thereby limiting NO production with subsequent effects on airway tone and inflammation. By decreasing L-arginine bioavailability, arginase may also contribute to the uncoupling of NOS and the formation of the proinflammatory oxidant peroxynitrite in the airways. Finally, arginase may play a role in the development of chronic airway remodeling through formation of L-ornithine with downstream production of polyamines and L-proline, which are involved in processes of cellular proliferation and collagen deposition. Further research on modulation of arginase activity and L-arginine bioavailability may reveal promising novel therapeutic strategies for asthma

Characterizing Sensitive Cardiac Substructure Excursion Due to Respiration

PURPOSE: Whole-heart dose metrics are not as strongly linked to late cardiac morbidities as radiation doses to individual cardiac substructures. Our aim was to characterize the excursion and dosimetric variation throughout respiration of sensitive cardiac substructures for future robust safety margin design. METHODS AND MATERIALS: Eleven patients with cancer treatments in the thorax underwent 4-phase noncontrast 4-dimensional computed tomography (4DCT) with T2-weighted magnetic resonance imaging in end-exhale. The end-exhale phase of the 4DCT was rigidly registered with the magnetic resonance imaging and refined with an assisted alignment surrounding the heart from which 13 substructures (chambers, great vessels, coronary arteries, etc) were contoured by a radiation oncologist on the 4DCT. Contours were deformed to the other respiratory phases via an intensity-based deformable registration for radiation oncologist verification. Measurements of centroid and volume were evaluated between phases. Mean and maximum dose to substructures were evaluated across respiratory phases for the breast (n = 8) and thoracic cancer (n = 3) cohorts. RESULTS: Paired t tests revealed reasonable maintenance of geometric and anatomic properties (P \u3c .05 for 4/39 volume comparisons). Maximum displacements \u3e5 mm were found for 24.8%, 8.5%, and 64.5% of the cases in the left-right, anterior-posterior, and superior-inferior axes, respectively. Vector displacements were largest for the inferior vena cava and the right coronary artery, with displacements up to 17.9 mm. In breast, the left anterior descending artery D(mean) varied 3.03 ± 1.75 Gy (range, 0.53-5.18 Gy) throughout respiration whereas lung showed patient-specific results. Across all patients, whole heart metrics were insensitive to breathing phase (mean and maximum dose variations \u3c0.5 Gy). CONCLUSIONS: This study characterized the intrafraction displacement of the cardiac substructures through the respiratory cycle and highlighted their increased dosimetric sensitivity to local dose changes not captured by whole heart metrics. Results suggest value of cardiac substructure margin generation to enable more robust cardiac sparing and to reduce the effect of respiration on overall treatment plan quality

The role of asymmetric interactions on the effect of habitat destruction in mutualistic networks

Plant-pollinator mutualistic networks are asymmetric in their interactions: specialist plants are pollinated by generalist animals, while generalist plants are pollinated by a broad involving specialists and generalists. It has been suggested that this asymmetric ---or disassortative--- assemblage could play an important role in determining the equal susceptibility of specialist and generalist plants under habitat destruction. At the core of the argument lies the observation that specialist plants, otherwise candidates to extinction, could cope with the disruption thanks to their interaction with generalist pollinators. We present a theoretical framework that supports this thesis. We analyze a dynamical model of a system of mutualistic plants and pollinators, subject to the destruction of their habitat. We analyze and compare two families of interaction topologies, ranging from highly assortative to highly disassortative ones, as well as real pollination networks. We found that several features observed in natural systems are predicted by the mathematical model. First, there is a tendency to increase the asymmetry of the network as a result of the extinctions. Second, an entropy measure of the differential susceptibility to extinction of specialist and generalist species show that they tend to balance when the network is disassortative. Finally, the disappearance of links in the network, as a result of extinctions, shows that specialist plants preserve more connections than the corresponding plants in an assortative system, enabling them to resist the disruption.Comment: 14 pages, 7 figure

Comparison of collapsing methods for the statistical analysis of rare variants

Novel technologies allow sequencing of whole genomes and are considered as an emerging approach for the identification of rare disease-associated variants. Recent studies have shown that multiple rare variants can explain a particular proportion of the genetic basis for disease. Following this assumption, we compare five collapsing approaches to test for groupwise association with disease status, using simulated data provided by Genetic Analysis Workshop 17 (GAW17). Variants are collapsed in different scenarios per gene according to different minor allele frequency (MAF) thresholds and their functionality. For comparing the different approaches, we consider the family-wise error rate and the power. Most of the methods could maintain the nominal type I error levels well for small MAF thresholds, but the power was generally low. Although the methods considered in this report are common approaches for analyzing rare variants, they performed poorly with respect to the simulated disease phenotype in the GAW17 data set

Fukushima Daiichi-derived radionuclides in the ocean: Transport, fate, and impacts

The events that followed the Tohoku earthquake and tsunami on March 11, 2011, included the loss of power and overheating at the Fukushima Daiichi nuclear power plants, which led to extensive releases of radioactive gases, volatiles, and liquids, particularly to the coastal ocean. The fate of these radionuclides depends in large part on their oceanic geochemistry, physical processes, and biological uptake. Whereas radioactivity on land can be resampled and its distribution mapped, releases to the marine environment are harder to characterize owing to variability in ocean currents and the general challenges of sampling at sea. Five years later, it is appropriate to review what happened in terms of the sources, transport, and fate of these radionuclides in the ocean. In addition to the oceanic behavior of these contaminants, this review considers the potential health effects and societal impacts

Reversal of infall in SgrB2(M) revealed by Herschel/HIFI observations of HCN lines at THz frequencies

To investigate the accretion and feedback processes in massive star formation, we analyze the shapes of emission lines from hot molecular cores, whose asymmetries trace infall and expansion motions. The high-mass star forming region SgrB2(M) was observed with Herschel/HIFI (HEXOS key project) in various lines of HCN and its isotopologues, complemented by APEX data. The observations are compared to spherically symmetric, centrally heated models with density power-law gradient and different velocity fields (infall or infall+expansion), using the radiative transfer code RATRAN. The HCN line profiles are asymmetric, with the emission peak shifting from blue to red with increasing J and decreasing line opacity (HCN to H$^{13}$CN). This is most evident in the HCN 12--11 line at 1062 GHz. These line shapes are reproduced by a model whose velocity field changes from infall in the outer part to expansion in the inner part. The qualitative reproduction of the HCN lines suggests that infall dominates in the colder, outer regions, but expansion dominates in the warmer, inner regions. We are thus witnessing the onset of feedback in massive star formation, starting to reverse the infall and finally disrupting the whole molecular cloud. To obtain our result, the THz lines uniquely covered by HIFI were critically important.Comment: A&A, HIFI special issue, accepte

Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample

Proteomic, biomechanical and functional analyses define neutrophil heterogeneity in systemic lupus erythematosus

Funder: NHLI FoundationFunder: NIHR Imperial Biomedical Research Centre; FundRef: http://dx.doi.org/10.13039/501100013342Funder: National Heart Lung and Blood InstituteFunder: Medical Research Council; FundRef: http://dx.doi.org/10.13039/501100000265Funder: National Institute of Biomedical Imaging and Bioengineering; FundRef: http://dx.doi.org/10.13039/100000070Funder: Gates Cambridge ScholarshipFunder: NIH/OXCAM FellowshipObjectives: Low-density granulocytes (LDGs) are a distinct subset of proinflammatory and vasculopathic neutrophils expanded in systemic lupus erythematosus (SLE). Neutrophil trafficking and immune function are intimately linked to cellular biophysical properties. This study used proteomic, biomechanical and functional analyses to further define neutrophil heterogeneity in the context of SLE. Methods: Proteomic/phosphoproteomic analyses were performed in healthy control (HC) normal density neutrophils (NDNs), SLE NDNs and autologous SLE LDGs. The biophysical properties of these neutrophil subsets were analysed by real-time deformability cytometry and lattice light-sheet microscopy. A two-dimensional endothelial flow system and a three-dimensional microfluidic microvasculature mimetic (MMM) were used to decouple the contributions of cell surface mediators and biophysical properties to neutrophil trafficking, respectively. Results: Proteomic and phosphoproteomic differences were detected between HC and SLE neutrophils and between SLE NDNs and LDGs. Increased abundance of type 1 interferon-regulated proteins and differential phosphorylation of proteins associated with cytoskeletal organisation were identified in SLE LDGs relative to SLE NDNs. The cell surface of SLE LDGs was rougher than in SLE and HC NDNs, suggesting membrane perturbances. While SLE LDGs did not display increased binding to endothelial cells in the two-dimensional assay, they were increasingly retained/trapped in the narrow channels of the lung MMM. Conclusions: Modulation of the neutrophil proteome and distinct changes in biophysical properties are observed alongside differences in neutrophil trafficking. SLE LDGs may be increasingly retained in microvasculature networks, which has important pathogenic implications in the context of lupus organ damage and small vessel vasculopathy

HAPRAP: a haplotype-based iterative method for statistical fine mapping using GWAS summary statistics

Motivation Fine mapping is a widely used approach for identifying the causal variant(s) at disease-associated loci. Standard methods (e.g. multiple regression) require individual level genotypes. Recent fine mapping methods using summary-level data require the pairwise correlation coefficients (r2 ) of the variants. However, haplotypes rather than pairwise r2 , are the true biological representation of linkage disequilibrium (LD) among multiple loci. In this article, we present an empirical iterative method, HAPlotype Regional Association analysis Program (HAPRAP), that enables fine mapping using summary statistics and haplotype information from an individual-level reference panel. Results Simulations with individual-level genotypes show that the results of HAPRAP and multiple regression are highly consistent. In simulation with summary-level data, we demonstrate that HAPRAP is less sensitive to poor LD estimates. In a parametric simulation using Genetic Investigation of ANthropometric Traits height data, HAPRAP performs well with a small training sample size (N < 2000) while other methods become suboptimal. Moreover, HAPRAP’s performance is not affected substantially by single nucleotide polymorphisms (SNPs) with low minor allele frequencies. We applied the method to existing quantitative trait and binary outcome meta-analyses (human height, QTc interval and gallbladder disease); all previous reported association signals were replicated and two additional variants were independently associated with human height. Due to the growing availability of summary level data, the value of HAPRAP is likely to increase markedly for future analyses (e.g. functional prediction and identification of instruments for Mendelian randomization)