Association of Serum Mannose With Acute Respiratory Distress Syndrome Risk and Survival

Key Points Question Are serum metabolites associated with acute respiratory distress syndrome (ARDS) risk and survival among critically ill patients? Findings This cohort study, a 2-sample mendelian randomization analysis of 1630 participants, found that genetically regulated high-level serum mannose is associated with reduced ARDS risk and improved ARDS survival. Meaning This metabolome-wide association study identified mannose as a potential biomarker and therapeutic candidate for treating critically ill patients with ARDS.


Metabolomics Testing Procedures and Platform in Metabolon Inc.
Sample Accessioning: Following receipt, samples were inventoried and immediately stored at -80°C. Each sample was accessioned into the Metabolon LIMS system and assigned a unique identifier associated with the original source identifier. This identifier was used to track all sample handling, tasks, results, etc. The samples (and all derived aliquots) were tracked by the LIMS system. All portions of any sample were automatically assigned their own unique identifiers by LIMS when a new task was created; the relationship of these samples was also tracked. All samples were maintained at -80°C until processing.
Sample Preparation: Samples were prepared using the automated MicroLab STAR® system from Hamilton Company. Several recovery standards were added before the first step in the extraction process for QC purposes. To remove protein and dissociate small molecules bound to protein or trapped in a precipitated protein matrix, proteins were precipitated with methanol under vigorous shaking for 2 min (Glen Mills QA/QC: Controls were analyzed in concert with experimental samples: a pooled matrix sample generated by a small volume of each experimental sample (or alternatively, a pool of well-characterized human plasma) served as a technical replicate throughout the data set; extracted water samples served as process blanks; and a cocktail of QC standards that were carefully chosen not to interfere with measurement of endogenous compounds was added to every analyzed sample, allowing instrument performance monitoring and chromatographic alignment. Instrument variability was determined by the median relative standard deviation (RSD) for standards added to each sample before injection into mass spectrometers. Overall process variability was determined by calculating median RSD for all endogenous metabolites (i.e., non-instrument standards) present in 100% of pooled matrix samples.

Ultrahigh Performance Liquid Chromatography-Tandem Mass Spectroscopy
(UPLC-MS/MS): All methods used a Waters ACQUITY ultra-performance liquid chromatography (UPLC) and Thermo Scientific Q-Exactive high resolution/accurate mass spectrometer interfaced with a heated electrospray ionization (HESI-II) source and Orbitrap mass analyzer operated at 35,000 mass resolution. Sample extract was dried and reconstituted in solvents compatible with each of the four methods. Each reconstitution solvent contained a series of standards at fixed concentrations to ensure injection and chromatographic consistency. One aliquot was analyzed using acidic positive ion conditions and chromatographically optimized for more hydrophilic compounds. Extracts were gradient-eluted from a C18 column (Waters UPLC BEH C18-2.1x100 mm, 1.7 µm) using water and methanol containing 0.05% perfluoropentanoic acid (PFPA) and 0.1% formic acid (FA). Another aliquot was analyzed using acidic positive ion conditions; however, it was chromatographically optimized for more hydrophobic compounds. Extracts were gradient eluted from the C18 column using methanol, acetonitrile, water, 0.05% PFPA, and 0.01% FA and operated at an overall higher organic content. Another aliquot was analyzed using basic negative ion optimized conditions using a separate dedicated C18 column. Basic extracts were gradient eluted from the column using methanol and water with 6.5 mM ammonium bicarbonate at pH 8. The fourth aliquot was analyzed by negative ionization following elution from a HILIC column (Waters UPLC BEH Amide 2.1x150 mm, 1.7 µm) using a gradient consisting of water and acetonitrile with 10 mM ammonium formate, pH 10.8. The MS analysis alternated between MS and data-dependent MSn scans using dynamic exclusion. The scan range varied slightly between methods but covered 70-1000 m/z.

Bioinformatics:
The informatics system consisted of four major components, the Laboratory Information Management System (LIMS), data extraction and peakidentification software, data processing tools for QC and compound identification, and a collection of information interpretation and visualization tools for use by data analysts.
The hardware and software foundations for these informatics components were the LAN backbone and a database server running Oracle 10.2.0.1 Enterprise Edition.

LIMS:
The purpose of the Metabolon LIMS system was to enable fully auditable laboratory automation through a secure, easy to use, and highly specialized system.
The scope of the Metabolon LIMS system encompasses sample accessioning, sample preparation and instrumental analysis, and reporting and advanced data analysis. All subsequent software systems are grounded in LIMS data structures. It has been modified to leverage and interface with in-house information extraction and data visualization systems as well as third-party instrumentation and data analysis software. Curation procedure: A variety of curation procedures were carried out to ensure that a high-quality dataset was available for statistical analyses and data interpretation. QC and curation processes were designed to ensure accurate and consistent identification of true chemical entities and to remove those representing system artifacts, mis-assignments, and background noise. Metabolon data analysts use proprietary visualization and interpretation software to confirm the consistency of peak identification among samples. Library matches for each compound were checked for each sample and corrected if necessary.

Data Extraction and Compound
Metabolite Quantification and Data Normalization: Peaks were quantified using area-under-the-curve. For studies spanning multiple days, a data normalization step was performed to correct variation resulting from instrument inter-day tuning differences. Essentially, each compound was corrected in run-day blocks by registering the medians to equal one (1.00) and normalizing each data point proportionately (termed "block correction"). For studies that did not require more than one day of analysis, no normalization was necessary, other than for data visualization. In certain instances, biochemical data may have been normalized to an additional factor (e.g., cell counts, total protein as determined by Bradford assay, osmolality) to account for differences in metabolite levels due to differences in amount of material present in each sample.