Coccidioidomycosis Detection Using Targeted Plasma and Urine Metabolic Profiling

Paniz Jasbi, Natalie M. Mitchell, Xiaojian Shi, Thomas E. Grys, Yiping Wei, Li Liu, Douglas Lake, Haiwei Gu

Research output: Contribution to journalArticle

Abstract

Coccidioidomycosis, also known as Valley fever (VF), is a potentially lethal fungal infection that results in more than 200 deaths per year in the United States. Despite the important role of metabolic processes in the molecular pathogenesis of VF, robust metabolic markers to enable effective screening, rapid diagnosis, accurate surveillance, and therapeutic monitoring of VF are still lacking. We present a targeted liquid chromatography-tandem mass spectrometry-based metabolic profiling approach for identifying metabolic marker candidates that could enable rapid, highly sensitive, and specific VF detection. Using this targeted approach, 207 plasma metabolites and 231 urinary metabolites from many metabolic pathways of potential biological significance were reliably detected and monitored in 147 samples taken from two groups of subjects (48 VF patients and 99 non-VF controls). The results of our univariate significance testing and multivariate model development informed the construction of a three-metabolite panel of potential plasma biomarkers and a nine-metabolite panel of potential urinary biomarkers. Receiver operating characteristic curves generated based on orthogonal partial least-squares-discriminant analysis models showed excellent classification performance, with 94.4% sensitivity and 97.6% specificity for plasma metabolites. Urine metabolites were less accurate, demonstrating 89.7% sensitivity and 88.1% specificity. Enrichment, pathway, and network analyses revealed significant disturbances in glycine and serine metabolism, in both plasma and urine samples. To the best of our knowledge, this is the first study aiming to discover novel metabolite markers of VF, which could achieve accurate diagnosis within 24 h. The results expand the basic knowledge of the metabolome related to VF and potentially reveal pathways or markers that could be therapeutically targeted. This study also provides a promising basis for the development of larger multisite projects to validate our findings across population groups and further advance the development of better clinical care for VF patients.

Original languageEnglish (US)
JournalJournal of Proteome Research
DOIs
StatePublished - Jan 1 2019

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Coccidioidomycosis
Metabolites
Urine
Plasmas
Biomarkers
Liquid chromatography
Discriminant analysis
Sensitivity and Specificity
Metabolism
Glycine
Serine
Metabolome
Mycoses
Mass spectrometry
Discriminant Analysis
Screening
Tandem Mass Spectrometry
Metabolic Networks and Pathways
Least-Squares Analysis
Population Groups

Keywords

  • biomarker discovery
  • coccidioidomycosis
  • LC-MS/MS
  • metabolomics
  • Valley fever

ASJC Scopus subject areas

  • Biochemistry
  • Chemistry(all)

Cite this

Coccidioidomycosis Detection Using Targeted Plasma and Urine Metabolic Profiling. / Jasbi, Paniz; Mitchell, Natalie M.; Shi, Xiaojian; Grys, Thomas E.; Wei, Yiping; Liu, Li; Lake, Douglas; Gu, Haiwei.

In: Journal of Proteome Research, 01.01.2019.

Research output: Contribution to journalArticle

Jasbi, Paniz ; Mitchell, Natalie M. ; Shi, Xiaojian ; Grys, Thomas E. ; Wei, Yiping ; Liu, Li ; Lake, Douglas ; Gu, Haiwei. / Coccidioidomycosis Detection Using Targeted Plasma and Urine Metabolic Profiling. In: Journal of Proteome Research. 2019.
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abstract = "Coccidioidomycosis, also known as Valley fever (VF), is a potentially lethal fungal infection that results in more than 200 deaths per year in the United States. Despite the important role of metabolic processes in the molecular pathogenesis of VF, robust metabolic markers to enable effective screening, rapid diagnosis, accurate surveillance, and therapeutic monitoring of VF are still lacking. We present a targeted liquid chromatography-tandem mass spectrometry-based metabolic profiling approach for identifying metabolic marker candidates that could enable rapid, highly sensitive, and specific VF detection. Using this targeted approach, 207 plasma metabolites and 231 urinary metabolites from many metabolic pathways of potential biological significance were reliably detected and monitored in 147 samples taken from two groups of subjects (48 VF patients and 99 non-VF controls). The results of our univariate significance testing and multivariate model development informed the construction of a three-metabolite panel of potential plasma biomarkers and a nine-metabolite panel of potential urinary biomarkers. Receiver operating characteristic curves generated based on orthogonal partial least-squares-discriminant analysis models showed excellent classification performance, with 94.4{\%} sensitivity and 97.6{\%} specificity for plasma metabolites. Urine metabolites were less accurate, demonstrating 89.7{\%} sensitivity and 88.1{\%} specificity. Enrichment, pathway, and network analyses revealed significant disturbances in glycine and serine metabolism, in both plasma and urine samples. To the best of our knowledge, this is the first study aiming to discover novel metabolite markers of VF, which could achieve accurate diagnosis within 24 h. The results expand the basic knowledge of the metabolome related to VF and potentially reveal pathways or markers that could be therapeutically targeted. This study also provides a promising basis for the development of larger multisite projects to validate our findings across population groups and further advance the development of better clinical care for VF patients.",
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AB - Coccidioidomycosis, also known as Valley fever (VF), is a potentially lethal fungal infection that results in more than 200 deaths per year in the United States. Despite the important role of metabolic processes in the molecular pathogenesis of VF, robust metabolic markers to enable effective screening, rapid diagnosis, accurate surveillance, and therapeutic monitoring of VF are still lacking. We present a targeted liquid chromatography-tandem mass spectrometry-based metabolic profiling approach for identifying metabolic marker candidates that could enable rapid, highly sensitive, and specific VF detection. Using this targeted approach, 207 plasma metabolites and 231 urinary metabolites from many metabolic pathways of potential biological significance were reliably detected and monitored in 147 samples taken from two groups of subjects (48 VF patients and 99 non-VF controls). The results of our univariate significance testing and multivariate model development informed the construction of a three-metabolite panel of potential plasma biomarkers and a nine-metabolite panel of potential urinary biomarkers. Receiver operating characteristic curves generated based on orthogonal partial least-squares-discriminant analysis models showed excellent classification performance, with 94.4% sensitivity and 97.6% specificity for plasma metabolites. Urine metabolites were less accurate, demonstrating 89.7% sensitivity and 88.1% specificity. Enrichment, pathway, and network analyses revealed significant disturbances in glycine and serine metabolism, in both plasma and urine samples. To the best of our knowledge, this is the first study aiming to discover novel metabolite markers of VF, which could achieve accurate diagnosis within 24 h. The results expand the basic knowledge of the metabolome related to VF and potentially reveal pathways or markers that could be therapeutically targeted. This study also provides a promising basis for the development of larger multisite projects to validate our findings across population groups and further advance the development of better clinical care for VF patients.

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