Rapid Etiological Classification of Meningitis by NMR Spectroscopy Based on Metabolite Profiles and Host Response

22 Background: Gastric cancer (GC) has 70-75% mortality, attributable to delayed diagnosis. There is no standard screening in North America. Metabolomics is a systems biology approach to measure low molecular weight chemicals (metabolites) in body fluids or tissues to provide a phenotypic “fingerprint” of disease etiology. In this preliminary study it was hypothesized that metabolic profiling of urine samples using 1H-NMR spectroscopy could discriminate between resectable gastric adenocarcinoma (GC), benign gastric disease (BN), and healthy (HE) patients (pts). Methods: Midstream urine samples were collected, processed, and biobanked at -80°C, from 30 BN, 30 HE and 16 of 29 GC pts visiting three Edmonton clinics from August 2013 – January 2014. Thirteen of 29 samples were retrieved from a 2009-13 GC biobank. Samples were matched on age, gender and BMI. Using a validated standard operating procedure each sample was analyzed using high resolution 1H-NMR spectroscopy. Resulting spectral traces were converted into annotated and quantified metabolite profiles of 58 metabolites. Univariate and multivariate statistical analysis uncovered a disease specific biomarker profile. Partial Least Squares Discriminant Analysis (PLS-DA) developed a GC vs. HE discriminative model. A Receiver Operator Characteristic (ROC) curve was constructed. Results: There was no significant difference in metabolite profiles between GC and BN pts. However, univariate analysis revealed 13 metabolites that differed significantly between GC and HE (p<0.05). Correlation analysis, followed by PLS-DA produced a discriminative model with an area under ROC curve of 0.996, such that for a specificity of 100% the corresponding sensitivity was 93%. Conclusions: GC pts have a distinct urinary metabolite profile compared to HE controls; however in this study metabolic profiling was unable to discriminate GC from BN pts. This was probably due to sample size and phenotypic heterogeneity of BN patients. This preliminary study shows clinical potential for metabolic profiling for early GC detection.

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Fluoropyrimidine chemotherapy in a rat model: comparison of fluorouracil metabolite profiles determined by high-field19F-NMR spectroscopy of tissuesex vivo with therapy response and toxicity for locoregional vs systemic infusion protocols

NMR in Biomedicine ◽

10.1002/nbm.880 ◽

2004 ◽

Vol 17 (3) ◽

pp. 101-131 ◽

Cited By ~ 11

Author(s):

Norbert W. Lutz ◽

Belal Naser-Hijazi ◽

Squire Koroma ◽

Martin R. Berger ◽

William E. Hull

Keyword(s):

Nmr Spectroscopy ◽

Rat Model ◽

Model Comparison ◽

Therapy Response ◽

Metabolite Profiles

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A new method for classification of wines based on proton and carbon-13 NMR spectroscopy in combination with pattern recognition techniques

Chemometrics and Intelligent Laboratory Systems ◽

10.1016/0169-7439(93)89017-5 ◽

1993 ◽

Vol 21 (2-3) ◽

pp. 249-258 ◽

Cited By ~ 34

Author(s):

J.T.W.E. Vogels ◽

A.C. Tas ◽

F. van den Berg ◽

J. van der Greef

Keyword(s):

Pattern Recognition ◽

Nmr Spectroscopy ◽

New Method ◽

Pattern Recognition Techniques

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Cerebrospinal fluid proteome maps detect pathogen-specific host response patterns in meningitis

eLife ◽

10.7554/elife.64159 ◽

2021 ◽

Vol 10 ◽

Author(s):

Anahita Bakochi ◽

Tirthankar Mohanty ◽

Paul Theodor Pyl ◽

Carlos Alberto Gueto-Tettay ◽

Lars Malmström ◽

...

Keyword(s):

Cerebrospinal Fluid ◽

Bacterial Meningitis ◽

Host Response ◽

Viral Meningitis ◽

Acute Bacterial Meningitis ◽

Tissue Cell ◽

Response Patterns ◽

Lasso Regression ◽

Specific Host

Meningitis is a potentially life-threatening infection characterized by the inflammation of the leptomeningeal membranes. Many different viral and bacterial pathogens can cause meningitis, with differences in mortality rates, risk of developing neurological sequelae and treatment options. Here we constructed a compendium of digital cerebrospinal fluid (CSF) proteome maps to define pathogen-specific host response patterns in meningitis. The results revealed a drastic and pathogen-type specific influx of tissue-, cell- and plasma proteins in the CSF, where in particular a large increase of neutrophil derived proteins in the CSF correlated with acute bacterial meningitis. Additionally, both acute bacterial and viral meningitis result in marked reduction of brain-enriched proteins. Generation of a multi-protein LASSO regression model resulted in an 18-protein panel of cell and tissue associated proteins capable of classifying acute bacterial meningitis and viral meningitis. The same protein panel also enabled classification of tick-borne encephalitis, a subgroup of viral meningitis, with high sensitivity and specificity. The work provides insights into pathogen specific host response patterns in CSF from different disease etiologies to support future classification of pathogen-type based on host response patterns in meningitis.

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