NMR Precision Metabolomics: Dynamic Peak Sum Thresholding and Navigators for Highly Standardized and Reproducible Metabolite Profiling of Clinical Urine Samples.

Metabolomics, especially urine-based studies, offers incredible promise for the discovery and development of clinically impactful biomarkers. However, due to the unique challenges of urine, a highly precise and reproducible workflow for NMR-based urine metabolomics is lacking. Using 1D and 2D non-uniform sampled (NUS) 1H-13C NMR spectroscopy, we systematically explored how changes in hydration or specific gravity (SG) and pH can impact biomarker discovery. Further, we examined additional sources of error in metabolomics studies and identified Navigator molecules that could monitor for those biases. Adjustment of SG to 1.002-1.02 coupled with a dynamic sum-based peak thresholding eliminates false positives associated with urine hydration and reduces variation in chemical shift. We identified Navigator molecules that can effectively monitor for inconsistencies in sample processing, SG, protein contamination, and pH. The workflow described provides quality assurance and quality control tools to generate high-quality urine metabolomics data, which is the first step in biomarker discovery.

Olaris Global Panel (OGP): A Highly Accurate and Reproducible Triple Quadrupole Mass Spectrometry-Based Metabolomics Method for Clinical Biomarker Discovery.

Mass spectrometry (MS)-based clinical metabolomics is very promising for the discovery of new biomarkers and diagnostics. However, poor data accuracy and reproducibility limit its true potential, especially when performing data analysis across multiple sample sets. While high-resolution mass spectrometry has gained considerable popularity for discovery metabolomics, triple quadrupole (QqQ) instruments offer several benefits for the measurement of known metabolites in clinical samples. These benefits include high sensitivity and a wide dynamic range. Here, we present the Olaris Global Panel (OGP), a HILIC LC-QqQ MS method for the comprehensive analysis of ~250 metabolites from all major metabolic pathways in clinical samples. For the development of this method, multiple HILIC columns and mobile phase conditions were compared, the robustness of the leading LC method assessed, and MS acquisition settings optimized for optimal data quality. Next, the effect of U-13C metabolite yeast extract spike-ins was assessed based on data accuracy and precision. The use of these U-13C-metabolites as internal standards improved the goodness of fit to a linear calibration curve from r2 < 0.75 for raw data to >0.90 for most metabolites across the entire clinical concentration range of urine samples. Median within-batch CVs for all metabolite ratios to internal standards were consistently lower than 7% and less than 10% across batches that were acquired over a six-month period. Finally, the robustness of the OGP method, and its ability to identify biomarkers, was confirmed using a large sample set.

A Spatio-temporal Model for Detecting the Effect of Cocaine Use Disorder on Functional Connectivity.

Drug addiction can lead to many health-related problems and social concerns. Researchers are interested in the association between long-term drug usage and abnormal functional connectivity. Functional connectivity obtained from functional magnetic resonance imaging data promotes a variety of fundamental understandings in such association. Due to the complex correlation structure and large dimensionality, the modeling and analysis of the functional connectivity from neuroimage are challenging. By proposing a spatio-temporal model for multi-subject neuroimage data, we incorporate voxel-level spatio-temporal dependencies of whole-brain measurements to improve the accuracy of statistical inference. To tackle large-scale spatio-temporal neuroimage data, we develop a computational efficient algorithm to estimate the parameters. Our method is used to first identify functional connectivity, and then detect the effect of cocaine use disorder (CUD) on functional connectivity between different brain regions. The functional connectivity identified by our spatio-temporal model matches existing studies on brain networks, and further indicates that CUD may alter the functional connectivity in the medial orbitofrontal cortex subregions and the supplementary motor areas.

Chemerin/ChemR23 axis promotes inflammation of glomerular endothelial cells in diabetic nephropathy.

Diabetic nephropathy (DN) is characterized by inflammation of renal tissue. Glomerular endothelial cells (GEnCs) play an important role in inflammation and protein leakage in urine in DN patients. Chemerin and its receptor ChemR23 are inducers of inflammation. The aim of this study was to investigate the function of chemerin/ChemR23 in GEnCs of DN patients. Immunohistochemical staining and qRT-PCR were used to measure the expression of chemerin, ChemR23 and inflammatory factors in renal tissues of DN patients. Db/db mice were used as animal model. ChemR23 of DN mice was knocked down by injecting LV3-shRNA into tail vein. Inflammation, physiological and pathological changes in each group was measured. GEnCs were cultured as an in vitro model to study potential signalling pathways. Results showed that expression of chemerin, ChemR23 and inflammatory factors increased in DN patients and mice. LV3-shRNA alleviated renal damage and inflammation in DN mice. GEnCs stimulated by glucose showed increased chemerin, ChemR23 and inflammatory factors and decreased endothelial marker CD31. Both LV3-shRNA and SB203580 (p38 MAPK inhibitor) attenuated chemerin-induced inflammation and injury in GEnCs. Taken together, chemerin/ChemR23 axis played an important role in endothelial injury and inflammation in DN via the p38 MAPK signalling pathway. Suppression of ChemR23 alleviated DN damage.