Mari L. DeMarco

Beyond Identification: Emerging and Future Uses for MALDI-TOF Mass Spectrometry in the Clinical Microbiology Laboratory

The routine use of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has revolutionized microorganism identification in the clinical microbiology laboratory. Building from these now common microorganism identification strategies, this review explores future clinical applications of MALDI-TOF MS. This includes practical approaches for laboratorians interested in implementing direct identification processing methods for MALDI-TOF detection of microbes in bloodstream infection (BSI) and urinary tract infection (UTI), as well as, post-analytical approaches for classifying MALDI-TOF spectral data to detect characteristics other and species-level identification (e.g. strain-level classification, typing, and resistance mechanisms).

Biomarker Development for Chronic Obstructive Pulmonary Disease. From Discovery to Clinical Implementation

Chronic obstructive pulmonary disease (COPD) is one of the major causes of morbidity and mortality in the world. Regrettably, there are no biomarkers to objectively diagnose COPD exacerbations, which are the major drivers of hospitalization and deaths from COPD. Moreover, there are no biomarkers to guide therapeutic choices or to risk stratify patients for imminent exacerbations and no objective biomarkers of disease activity or disease progression. Although there has been a tremendous investment in COPD biomarker discovery over the past 2 decades, clinical translation and implementation have not matched these efforts. In this article, we outline the challenges of biomarker development in COPD and provide an overview of a developmental pipeline that may be able to surmount these challenges and bring novel biomarker solutions to accelerate therapeutic discoveries and to improve the care and outcomes of the millions of individuals worldwide with COPD.

Biomarker Development in COPD: Moving From P Values to Products to Impact Patient Care

&NA; There is a great interest in developing biomarkers to enable precision medicine and improve health outcomes of patients with COPD. However, biomarker development is extremely challenging and expensive, and translation of research endeavors to date has been largely unsuccessful. In most cases, biomarkers fail because of poor replication of initial promising results in independent cohorts and/or inability to transfer the biomarker from a discovery platform to a clinical assay. Ultimately, new biomarker assays must address 5 questions for optimal clinical translation. They include the following: is the biomarker likely to be (1) superior (will the test outperform current standards?); (2) actionable (will the test change patient management?); (3) valuable (will the test improve patient outcomes?); (4) economical (will the implementation of the biomarker in the target population be cost‐saving or cost‐effective?); and (5) clinically deployable (is there a pathway for the biomarker and analytical technology to be implemented in a clinical laboratory?)? In this article we review some of the major barriers to biomarker development in COPD and provide possible solutions to overcome these limitations, enabling translation of promising biomarkers from discovery experiments to clinical implementation.

Sweating the small stuff: Adequacy and accuracy in sweat chloride determination ☆

OBJECTIVES Sweat chloride testing is the gold standard for diagnosis of cystic fibrosis (CF). Our objectives were to: 1) describe variables that determine sweat rate; 2) determine the analytic and diagnostic capacity of sweat chloride analysis across the range of observed sweat rates; and 3) determine the biologic variability of sweat chloride concentration. METHODS A retrospective analysis was performed using data from all sweat chloride tests performed at St. Louis Childrens Hospital over a 21-month period. RESULTS A total of 1397 sweat chloride tests (1155 sufficient [≥75 mg], 242 insufficient [<75 mg]), were performed on 904 individuals. The sweat weight collected from forearms was statistically greater than that collected from legs. There was a negligible correlation between sweat weight and chloride concentration (r=-0.06). The mean individual biologic CV calculated from individuals with two or more sweat collections ≥75 mg was 13.1% (95% CI: 11.3-14.9%; range 0-88%) yielding a reference change value of 36%. Using 60 mmol/L as the diagnostic chloride cutoff, 100% of CF cases were detected whether a minimum sweat weight of 75, 40, or 20 mg was required. CONCLUSIONS 1) Collection of sweat from forearms is preferable to upper legs, particularly in very young infants; 2) sweat chloride concentrations are not highly dependent upon sweat rate; 3) a change in sweat chloride concentration exceeding 36% may be considered a clinically significant response to cystic fibrosis transmembrane receptor targeted therapy, and 4) sweat collections of less than 75 mg provide clinically accurate information.

Renal leukocyte chemotactic factor 2 (LECT2) amyloidosis in First Nations people in Northern British Columbia, Canada: a report of 4 cases.

Leukocyte chemotactic factor 2 (LECT2) amyloidosis is a recently identified type of amyloidosis that may represent an underdiagnosed cause of chronic kidney disease. LECT2 amyloidosis typically is reported as being renal limited and, in the United States, more prevalent in Hispanic patients. We add to the epidemiologic data of this condition by describing 4 First Nations people from Northern British Columbia, Canada, who presented with slowly progressive chronic kidney disease that was found to be due to LECT2 amyloidosis.

Resolution of Spurious Immunonephelometric IgG Subclass Measurement Discrepancies by LC-MS/MS

BACKGROUND The Binding Site immunonephelometric (IN) IgG subclass reagents (IgG1, IgG2, IgG3, IgG, BSIN) are used for assessment of both immunodeficiency and IgG4-related disease (IgG4-RD). In our laboratory, suspected analytic errors were noted in patients with increases in IgG4: The sum of the individual IgG subclasses was substantially greater than the measured total IgG concentrations (unlike samples with normal IgG4), and the IgG4 concentration was always less than the IgG2 concentration. METHODS We developed a tryptic digest LC-MS/MS method to quantify IgG1, IgG2, IgG3, and IgG4 in serum. Samples with IgG4 concentrations ranging from <0.03 g/L to 32 g/L were reanalyzed by LC-MS/MS, and a subset was also reanalyzed by Siemens IN (SIN) subclass measurements. RESULTS Multivariate linear regression identified 3 subclass tests with multiple predictors of the measured subclass concentration. For these 3 subclasses, the predominant predictors were (in terms of LC-MS/MS IgG subclass measurement coefficients) BSIN IgG1 = 0.89·IgG1 + 0.4·IgG4; BSIN IgG2 = 0.94·IgG4 + 0.89·IgG2; and SIN IgG2 = 0.72·IgG2 + 0.24·IgG4. CONCLUSIONS There is apparent IgG4 cross-reactivity with select IN subclass measurements affecting tests from both vendors tested. These findings can be explained either by direct cross-reactivity of the IN reagents with the IgG4 subclass or unique physicochemical properties of IgG4 that permit nonspecific binding of IgG4 heavy chain to other IgG immunoglobulin heavy chains. Irrespective of the mechanism, the observed intermethod discrepancies support the use of LC-MS/MS as the preferred method for measurement of IgG subclasses when testing patients with suspected IgG4-RD.

C-reactive protein and N-terminal prohormone brain natriuretic peptide as biomarkers in acute exacerbations of COPD leading to hospitalizations

There are currently no accepted and validated blood tests available for diagnosing acute exacerbations of chronic obstructive pulmonary disease (AECOPD). In this study, we sought to determine the discriminatory power of blood C-reactive protein (CRP) and N-terminal prohormone brain natriuretic peptide (NT-proBNP) in the diagnosis of AECOPD requiring hospitalizations. The study cohort consisted of 468 patients recruited in the COPD Rapid Transition Program who were hospitalized with a primary diagnosis of AECOPD, and 110 stable COPD patients who served as controls. Logistic regression was used to build a classification model to separate AECOPD from convalescent or stable COPD patients. Performance was assessed using an independent validation set of patients who were not included in the discovery set. Serum CRP and whole blood NT-proBNP concentrations were highest at the time of hospitalization and progressively decreased over time. Of the 3 classification models, the one with both CRP and NT-proBNP had the highest AUC in discriminating AECOPD (cross-validated AUC of 0.80). These data were replicated in a validation cohort with an AUC of 0.88. A combination of CRP and NT-proBNP can reasonably discriminate AECOPD requiring hospitalization versus clinical stability and can be used to rapidly diagnose patients requiring hospitalization for AECOPD.

Phenotyping COPD exacerbations using imaging and blood-based biomarkers

Rationale Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are caused by a variety of different etiologic agents. Our aim was to phenotype COPD exacerbations using imaging (chest X-ray [CXR] and computed tomography [CT]) and to determine the possible role of the blood tests (C-reactive protein [CRP], the N-terminal prohormone brain natriuretic peptide [NT-proBNP]) as diagnostic biomarkers. Materials and methods Subjects who were hospitalized with a primary diagnosis of AECOPD and who had had CXRs, CT scans, and blood collection for CRP and NT-proBNP were assessed in this study. Radiologist blinded to the clinical and laboratory characteristics of the subjects interpreted their CXRs and CT images. ANOVA and Spearman’s correlation were performed to test for associations between these imaging parameters and the blood-based biomarkers NT-proBNP and CRP; logistic regression models were used to assess the performance of these biomarkers in predicting the radiological parameters. Results A total of 309 subjects were examined for this study. Subjects had a mean age of 65.6±11.1 years, 66.7% of them were males, and 62.4% were current smokers, with a mean FEV1 54.4%±21.5% of predicted. Blood NT-proBNP concentrations were associated with cardiac enlargement (area under the curve [AUC] =0.72, P<0.001), pulmonary edema (AUC =0.63, P=0.009), and pleural effusion on CXR (AUC =0.64, P=0.01); whereas on CT images, NT-proBNP concentrations were associated with pleural effusion (AUC =0.71, P=0.002). Serum CRP concentrations, on the other hand, were associated with consolidation on CT images (AUC =0.75, P<0.001), ground glass opacities (AUC =0.64, P=0.028), and pleural effusion (AUC =0.72, P<0.001) on CT images. A serum CRP sensitivity-oriented cutoff point of 11.5 mg/L was selected for the presence of consolidation on CT images in subjects admitted as cases of AECOPD, which has a sensitivity of 91% and a specificity of 53% (P<0.001). Conclusion Elevated CRP may indicate the presence of pneumonia, while elevated NT-proBNP may indicate cardiac dysfunction. These readily available blood-based biomarkers may provide more accurate phenotyping of AECOPD and enable the discovery of more precise therapies.

Identifying Molecular Mechanisms of the Late-Phase Asthmatic Response by Integrating Cellular, Gene, and Metabolite Levels in Blood

RATIONALE Individuals with allergic asthma respond differently, but reproducibly, to allergen inhalation challenge. Some individuals develop an isolated early response (early responders) (ERs), whereas others go on to develop a late response (dual responders) (DRs). It is not understood why late responses do not develop in all sensitized individuals. OBJECTIVES The aim of this study was to identify blood biomarkers that can discriminate ERs and DRs using cellular frequencies and gene and metabolite expression from whole blood. METHODS Thirty-two individuals participated in the allergen inhalation challenge as part of the AllerGen Clinical Investigator Collaborative. Fifteen participants were classified as ERs and 17 as DRs. Blood samples were collected before (pre) and 2 hours after (post) the allergen challenge. Cell counts were obtained using a hematolyzer, gene transcript relative levels using RNA sequencing, and metabolite concentrations using tandem mass spectrometry. An integrative ensemble algorithm that was based on canonical correlation analysis was used to classify ERs and DRs using all three data sets, adjusting for age and sex. The objective of this algorithm was to identify a correlated subset of molecules from each data set that best discriminated ERs from DRs. Gene set enrichment analysis was performed using Enrichr (Chen et al., BMC Bioinformatics 2013;128). MEASUREMENTS AND MAIN RESULTS The pre-challenge multisignature classifier (error = 30%) outperformed the post-challenge multisignature classifier (error = 50%) in separating ERs from DRs. The cells selected in the prechallenge multisignature panel included eosinophils, lymphocytes, and neutrophils. The selected metabolites were enriched for glycerophospholipids. The subset of gene transcripts in the multisignature panel was enriched for the T-cell receptor and costimulatory signaling pathway (P = 3.4 × 10(-6)) (Wikipathways) and positive regulation of antigen receptor-mediated signaling pathway (P = 5.7 × 10(-4)) (GO Ontology). CONCLUSIONS This study provides a systems perspective on the deregulated molecular processes between early and dual responses in whole blood. The integrative biomarker analysis suggests that a molecular signature that is predictive of the late-phase response can be identified. The variability in the onset of the late response may explain the poor predictive performance of the postchallenge multiomic biomarker signature. Replication of the prechallenge biomarker signature in additional independent samples is required to validate this panel.

Molecular Dynamics Simulations of Membrane- and Protein-Bound Glycolipids Using GLYCAM

This chapter outlines protocols for the preparation, execution, and analysis of molecular dynamics (MD) simulations of glycolipids in biologically relevant environments, i.e., imbedded in lipid bilayers or bound to proteins, with the goal of generating biologically relevant structural and dynamic information. Also included is a description of ensemble average (EA) charge set development consistent with the GLYCAM06 force field and its implementation using the AMBER molecular dynamics software suite.