Metabolomics-based discovery of diagnostic biomarkers for onchocerciasis - PubMed (original) (raw)

Metabolomics-based discovery of diagnostic biomarkers for onchocerciasis

Judith R Denery et al. PLoS Negl Trop Dis. 2010.

Abstract

Background: Development of robust, sensitive, and reproducible diagnostic tests for understanding the epidemiology of neglected tropical diseases is an integral aspect of the success of worldwide control and elimination programs. In the treatment of onchocerciasis, clinical diagnostics that can function in an elimination scenario are non-existent and desperately needed. Due to its sensitivity and quantitative reproducibility, liquid chromatography-mass spectrometry (LC-MS) based metabolomics is a powerful approach to this problem.

Methodology/principal findings: Analysis of an African sample set comprised of 73 serum and plasma samples revealed a set of 14 biomarkers that showed excellent discrimination between Onchocerca volvulus-positive and negative individuals by multivariate statistical analysis. Application of this biomarker set to an additional sample set from onchocerciasis endemic areas where long-term ivermectin treatment has been successful revealed that the biomarker set may also distinguish individuals with worms of compromised viability from those with active infection. Machine learning extended the utility of the biomarker set from a complex multivariate analysis to a binary format applicable for adaptation to a field-based diagnostic, validating the use of complex data mining tools applied to infectious disease biomarker discovery and diagnostic development.

Conclusions/significance: An LC-MS metabolomics-based diagnostic has the potential to monitor the progression of onchocerciasis in both endemic and non-endemic geographic areas, as well as provide an essential tool to multinational programs in the ongoing fight against this neglected tropical disease. Ultimately this technology can be expanded for the diagnosis of other filarial and/or neglected tropical diseases.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Figure 1

Figure 1. Schematic diagram of the LC-MS based metabolomic workflow.

Wherein the multi-region serum and plasma samples are extracted and analyzed within a single sequence on the ESI-TOF in positive mode. Mass spectral data is preprocessed with XCMS software and multivariate statistical analysis and machine learning classification algorithms are used to distinguish patterns in the data and provide a binary output to the classification of samples. ROC curves are used to quantify the relationship between sensitivity and specificity for a given test. Ultimately, this information can be used in an iterative fashion to interrogate larger datasets and provide necessary diagnostic information to better characterize the disease status of clinical samples from a variety of geographic regions.

Figure 2

Figure 2. PCA factor score plots of MS peak intensity values for the 14 candidate onchocerciasis biomarkers.

Mass feature intensity values were extracted through ESI-TOF+/XCMS analysis of (A) African blood serum and plasma samples from 55 O. volvulus infected individuals compared against 18 healthy controls. (B) A sample set including 76 O. volvulus infected individuals compared against 56 O. volvulus negative controls (including healthy and those infected with other tropical diseases). (C) An extraction of only those data points representing the 21 Guatemala O. volvulus infected individuals compared against 18 healthy controls. Individual data points are symbolized using the following code for country of origin and disease status: “blue diamond” = Cameroon _Ov_−, “blue circle” = Guatemala _Ov_−, “blue astrisk” = Scripps _Ov_−, “green circle” = Leishmaniasis _Ov_−, “green square” = Chagas _Ov_−, “green triangle” = LF _Ov_−, “pink diamond” = Cameroon Ov+, “pink circle” = Guatemala Ov+, “pink square” = Ghana _Ov_+ (1986, 1991 and 2003 samples), “pink triangle” = Liberia Ov+, “orange diamond” = Cameroon Ov?.

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