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The rising prevalence of obesity in Saudi Arabia is a major contributor to the nation's high levels of cardiometabolic diseases such as type 2 diabetes. To assess the impact of obesity on the diabetic metabolic phenotype presented in young Saudi Arabian adults, participants ( n = 289, aged 18-40 years) were recruited and stratified into four groups: healthy weight (BMI 18.5-24.99 kg/m 2 ) with ( n = 57) and without diabetes ( n = 58) or overweight/obese (BMI > 24.99 kg/m 2 ) with ( n = 102) and without diabetes ( n = 72). Distinct plasma metabolic phenotypes associated with high BMI and diabetes were identified using nuclear magnetic resonance spectroscopy and ultraperformance liquid chromatography mass spectrometry. Increased plasma glucose and dysregulated lipoproteins were characteristics of obesity in individuals with and without diabetes, but the obesity-associated lipoprotein phenotype was partially masked in individuals with diabetes. Although there was little difference between diabetics and nondiabetics in the global plasma LDL cholesterol and phospholipid concentration, the distribution of lipoprotein particles was altered in diabetics with a shift toward denser and more atherogenic LDL5 and LDL6 particles, which was amplified in the presence of obesity. Further investigation is warranted in larger Middle Eastern populations to explore the dysregulation of metabolism driven by interactions between obesity and diabetes in young adults.

Integration of multiple datasets can greatly enhance bioanalytical studies, for example, by increasing power to discover and validate biomarkers. In liquid chromatography-mass spectrometry (LC-MS) metabolomics, it is especially hard to combine untargeted datasets since the majority of metabolomic features are not annotated and thus cannot be matched by chemical identity. Typically, the information available for each feature is retention time (RT), mass-to-charge ratio ( m / z ), and feature intensity (FI). Pairs of features from the same metabolite in separate datasets can exhibit small but significant differences, making matching very challenging. Current methods to address this issue are too simple or rely on assumptions that cannot be met in all cases. We present a method to find feature correspondence between two similar LC-MS metabolomics experiments or batches using only the features' RT, m / z , and FI. We demonstrate the method on both real and synthetic datasets, using six orthogonal validation strategies to gauge the matching quality. In our main example, 4953 features were uniquely matched, of which 585 (96.8%) of 604 manually annotated features were correct. In a second example, 2324 features could be uniquely matched, with 79 (90.8%) out of 87 annotated features correctly matched. Most of the missed annotated matches are between features that behave very differently from modeled inter-dataset shifts of RT, MZ, and FI. In a third example with simulated data with 4755 features per dataset, 99.6% of the matches were correct. Finally, the results of matching three other dataset pairs using our method are compared with a published alternative method, metabCombiner, showing the advantages of our approach. The method can be applied using M2S (Match 2 Sets), a free, open-source MATLAB toolbox, available at https://github.com/rjdossan/M2S.

Purpose: Independent calculations of proton therapy plans are an important quality control procedure in treatment planning. When using custom Monte Carlo (MC) models of the beamline, deploying the calculations can be laborious, time consuming, and require in-depth knowledge of the computational environment. We developed an automated framework to remove these barriers and integrate our MC model into the clinical workflow., Materials and Methods: The Eclipse Scripting Application Programming Interface was used to initiate the automation process. A series of MATLAB scripts were then used for preprocessing of input data and postprocessing of results. Additional scripts were used to monitor the calculation process and appropriately deploy calculations to an institutional high-performance computing facility. The automated framework and beamline models were validated against 160 patient specific QA measurements from an ionization chamber array and using a ±3%/3 mm gamma criteria., Results: The automation reduced the human-hours required to initiate and run a calculation to 1-2 min without leaving the treatment planning system environment. Validation comparisons had an average passing rate of 99.4% and were performed at depths ranging from 1 to 15 cm., Conclusion: An automated framework for running MC calculations was developed which enables the calculation of dose and linear energy transfer within a clinically relevant workflow and timeline. The models and framework were validated against patient specific QA measurements and exhibited excellent agreement. Before this implementation, execution was prohibitively complex for an untrained individual and its use restricted to a research environment., (© 2020 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.)

Importance: Chinese women have the highest rate of lung cancer among female never-smokers in the world, and the etiology is poorly understood., Objective: To assess the association between metabolomics and lung cancer risk among never-smoking women., Design, Setting, and Participants: This nested case-control study included 275 never-smoking female patients with lung cancer and 289 never-smoking cancer-free control participants from the prospective Shanghai Women's Health Study recruited from December 28, 1996, to May 23, 2000. Validated food frequency questionnaires were used for the collection of dietary information. Metabolomic analysis was conducted from November 13, 2015, to January 6, 2016. Data analysis was conducted from January 6, 2016, to November 29, 2018., Exposures: Untargeted ultra-high-performance liquid chromatography-tandem mass spectrometry and nuclear magnetic resonance metabolomic profiles were characterized using prediagnosis urine samples. A total of 39 416 metabolites were measured., Main Outcomes and Measures: Incident lung cancer., Results: Among the 564 women, those who developed lung cancer (275 participants; median [interquartile range] age, 61.0 [52-65] years) and those who did not develop lung cancer (289 participants; median [interquartile range] age, 62.0 [53-66] years) at follow-up (median [interquartile range] follow-up, 10.9 [9.0-11.7] years) were similar in terms of their secondhand smoke exposure, history of respiratory diseases, and body mass index. A peak metabolite, identified as 5-methyl-2-furoic acid, was significantly associated with lower lung cancer risk (odds ratio, 0.57 [95% CI, 0.46-0.72]; P < .001; false discovery rate = 0.039). Furthermore, this peak was weakly correlated with self-reported dietary soy intake (ρ = 0.21; P < .001). Increasing tertiles of this metabolite were associated with lower lung cancer risk (in comparison with first tertile, odds ratio for second tertile, 0.52 [95% CI, 0.34-0.80]; and odds ratio for third tertile, 0.46 [95% CI, 0.30-0.70]), and the association was consistent across different histological subtypes and follow-up times. Additionally, metabolic pathway analysis found several systemic biological alterations that were associated with lung cancer risk, including 1-carbon metabolism, nucleotide metabolism, oxidative stress, and inflammation., Conclusions and Relevance: This prospective study of the untargeted urinary metabolome and lung cancer among never-smoking women in China provides support for the hypothesis that soy-based metabolites are associated with lower lung cancer risk in never-smoking women and suggests that biological processes linked to air pollution may be associated with higher lung cancer risk in this population.

Purpose: A good Monte Carlo model with an accurate head shielding model is important in estimating the long-term risks of unwanted radiation exposure during radiation therapy. The aim of this paper was to validate the Monte Carlo simulation of a TrueBeam linear accelerator (linac) head shielding model. We approach this by evaluating the accuracy of out-of-field dose predictions at extended distances which are comprised of scatter from within the patient and treatment head leakage and thus reflect the accuracy of the head shielding model. We quantify the out-of-field dose of a TrueBeam linac for low-energy photons, 6X and 6X-FFF beams, and compare measurements to Monte Carlo simulations using Varian VirtuaLinac that include a realistic head shielding model, for a variety of jaw sizes and angles up to a distance of 100 cm from the isocenter, in both positive and negative directions. Given the high value and utility of the VirtuaLinac model, it is critical that this model is validated thoroughly and the results be available to the medical physics community., Materials and Method: Simulations were done using VirtuaLinac, the GEANT4-based Monte Carlo model of the TrueBeam treatment head from Varian Medical Systems, and an in-house GEANT4-based code. VirtuaLinac included a detailed model of the treatment head shielding and was run on the Amazon Web Services cloud to generate spherical phase space files surrounding the treatment head. These phase space files were imported into the in-house code, which modeled the measurement setup with a solid water buildup, the carbon fiber couch, and the gantry stand. For each jaw size (2 × 2 cm 2 , 4 × 4 cm 2 , 10 × 10 cm 2 , and 20 × 20 cm 2 ) and angular setting (0°, 90°, 45°, 135°), the dose was calculated at intervals of 5 cm along each measurement direction., Results: For the 10 × 10 cm 2 jaw size, both 6X and 6X-FFF showed very good agreement between simulation and measurement in both in-plane directions, with no apparent systematic bias. The percentage deviations for these settings were as follows: (mean, STDEV, maximum) (8.34, 6.44, 24.84) for 6X and (13.21, 8.93, 35.56) for 6X-FFF. For all jaw sizes, simulation agreed well in the in-plane direction going away from the gantry, but, some deviations were observed moving toward the gantry at larger distances. At larger distances, for the jaw sizes smaller than 10 × 10 cm 2 , the simulation underestimates the dose compared with measurement, while for jaw sizes larger than 10 × 10 cm 2 , it overestimates dose. For all comparisons between ±50 cm from isocenter, average absolute agreement between simulation and measurement was better than 28%., Conclusion: We have validated the Varian VirtuaLinac's head shielding model via out-of-field doses and quantified the differences between TrueBeam head shielding model created out-of-field doses and measurements for an extended distance of 100 cm., (© 2018 American Association of Physicists in Medicine.)

Introduction: Differences in the metabolite profiles between serum and plasma are incompletely understood., Objectives: To evaluate metabolic profile differences between serum and plasma and among plasma sample subtypes., Methods: We analyzed serum, platelet rich plasma (PRP), platelet poor plasma (PPP), and platelet free plasma (PFP), collected from 8 non-fasting apparently healthy women, using untargeted standard 1D and CPMG 1 H NMR and reverse phase and hydrophilic (HILIC) UPLC-MS. Differences between metabolic profiles were evaluated using validated principal component and orthogonal partial least squares discriminant analysis., Results: Explorative analysis showed the main source of variation among samples was due to inter-individual differences with no grouping by sample type. After correcting for inter-individual differences, lipoproteins, lipids in VLDL/LDL, lactate, glutamine, and glucose were found to discriminate serum from plasma in NMR analyses. In UPLC-MS analyses, lysophosphatidylethanolamine (lysoPE)(18:0) and lysophosphatidic acid(20:0) were higher in serum, and phosphatidylcholines (PC)(16:1/18:2, 20:3/18:0, O-20:0/22:4), lysoPC(16:0), PE(O-18:2/20:4), sphingomyelin(18:0/22:0), and linoleic acid were lower. In plasma subtype analyses, isoleucine, leucine, valine, phenylalanine, glutamate, and pyruvate were higher among PRP samples compared with PPP and PFP by NMR while lipids in VLDL/LDL, citrate, and glutamine were lower. By UPLC-MS, PE(18:0/18:2) and PC(P-16:0/20:4) were higher in PRP compared with PFP samples., Conclusions: Correction for inter-individual variation was required to detect metabolite differences between serum and plasma. Our results suggest the potential importance of inter-individual effects and sample type on the results from serum and plasma metabolic phenotyping studies.

Numerous metabolic profiling pipelines have been developed to characterize the composition of human biofluids and tissues, the vast majority of these being for studies in adults. To accommodate limited sample volume and to take into account the compositional differences between adult and infant biofluids, we developed and optimized sample handling and analytical procedures for studying urine from newborns. A robust pipeline for metabolic profiling using NMR spectroscopy was established, encompassing sample collection, preparation, spectroscopic measurement, and computational analysis. Longitudinal samples were collected from five infants from birth until 14 months of age. Methods of extraction and effects of freezing and sample dilution were assessed, and urinary contaminants from breakdown of polymers in a range of diapers and cotton wool balls were identified and compared, including propylene glycol, acrylic acid, and tert-butanol. Finally, assessment of urinary profiles obtained over the first few weeks of life revealed a dramatic change in composition, with concentrations of phenols, amino acids, and betaine altering systematically over the first few months of life. Therefore, neonatal samples require more stringent standardization of experimental design, sample handling, and analysis compared to that of adult samples to accommodate the variability and limited sample volume.

Mutations in fukutin-related protein (FKRP) are responsible for a common group of muscular dystrophies ranging from adult onset limb girdle muscular dystrophies to severe congenital forms with associated structural brain involvement. The defining feature of this group of disorders is the hypoglycosylation of α-dystroglycan and its inability to effectively bind extracellular matrix ligands such as laminin α2. However, α-dystroglycan has the potential to interact with a number of laminin isoforms many of which are basement membrane/tissue specific and developmentally regulated. To further investigate this we evaluated laminin α-chain expression in the cerebral cortex and eye of our FKRP knock-down mouse (FKRP(KD)). These mice showed a marked disturbance in the deposition of laminin α-chains including α1, α2, α4, and α5, although only laminin α1- and γ1-chain mRNA expression was significantly upregulated relative to controls. Moreover, there was a diffuse pattern of laminin deposition below the pial surface which correlated with an abrupt termination of many of the radial glial cells. This along with the pial basement membrane defects, contributed to the abnormal positioning of both early- and late-born neurons. Defects in the inner limiting membrane of the eye were associated with a reduction of laminin α1 demonstrating the involvement of the α-dystroglycan:laminin α1 axis in the disease process. These observations demonstrate for the first time that a reduction in Fkrp influences the ability of tissue-specific forms of α-dystroglycan to direct the deposition of several laminin isoforms in the formation of different basement membranes.

Muscular dystrophies are a clinically and genetically heterogeneous group of disorders. Until recently most of the proteins associated with muscular dystrophies were believed to be proteins of the sarcolemma associated with reinforcing the plasma membrane or in facilitating its re-sealing following injury. In the last few years a novel and frequent pathogenic mechanism has been identified that involves the abnormal glycosylation of alpha-dystroglycan (ADG). This peripheral membrane protein undergoes complex and crucial glycosylation steps that enable it to interact with LG domain containing extracellular matrix proteins such as laminins, agrin and perlecan. Mutations in six genes (POMT1, POMT2, POMGnT1, fukutin, FKRP and LARGE) have been identified in patients with reduced glycosylation of ADG. While initially a clear correlation between gene defect and phenotype was observed for each of these 6 genes (for example, Walker Warburg syndrome was associated with mutations in POMT1 and POMT2, Fukuyama congenital muscular dystrophy associated with fukutin mutations, and Muscle Eye Brain disease associated with POMGnT1 mutations), we have recently demonstrated that allelic mutations in each of these 6 genes can result in a much wider spectrum of clinical conditions. Thus, the crucial aspect in determining the phenotypic severity is not which gene is primarily mutated, but how severely the mutation affects the glycosylation of ADG. Systematic mutation analysis of these 6 glycosyltransferases in patients with a dystroglycan glycosylation disorder identifies mutations in approximately 65% suggesting that more genes have yet to be identified.

Mutations in the LMNA gene, which encodes nuclear lamins A and C by alternative splicing, can give rise to Emery-Dreifuss muscular dystrophy. The mechanism by which lamins A and C separately contribute to this molecular phenotype is unknown. To address this question we examined ten LMNA mutations exogenously expressed as lamins A and C in COS-7 cells. Eight of the mutations when expressed in lamin A, exhibited a range of nuclear mislocalisation patterns. However, two mutations (T150P and delQ355) almost completely relocated exogenous lamin A from the nuclear envelope to the cytoplasm, disrupted nuclear envelope reassembly following cell division and altered the protein composition of the mid-body. In contrast, exogenously expressed DsRed2-tagged mutant lamin C constructs were only inserted into the nuclear lamina if co-expressed with any EGFP-tagged lamin A construct, except with one carrying the T150P mutation. The T150P, R527P and L530P mutations reduced the ability of lamin A, but not lamin C from binding to emerin. These data identify specific functional roles for the emerin-lamin C- and emerin-lamin A- containing protein complexes and is the first report to suggest that the A-type lamin mutations may be differentially dysfunctional for the same LMNA mutation.