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Metabolite-biomarker investigations in the life cycle of and infection with Schistosoma

Published online by Cambridge University Press:  16 June 2010

C. Legido-Quigley*
Affiliation:
PSD, King's College London, Franklin-Wilkins Building, 150 Stamford Street, London SE1 9NH, UK
*
*Corresponding author: Tel: +44 2078484722. E-mail: cristina.legido_quigley@kcl.ac.uk
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Summary

Schistosome infection is endemic in many Third World countries and affects an estimated 200 million individuals. Over the last few years, a number of investigations have focused on small molecule biomarkers of this infection. These studies were aimed at discovering key molecules relating to the life cycle of the parasite or deciphering metabolic change in the host during infection. In this review these studies are further divided into targeted approaches to find compounds and fingerprinting techniques i.e. metabonomics. A species-specific metabolite or group of biomarkers of the infection have yet to be discovered. For this reason a critical discussion contrasting with established diagnostic methods and future prospects are also provided.

Information

Type
Research Article
Copyright
Copyright © Cambridge University Press 2010
Figure 0

Fig. 1. Matrix-assisted laser-desorption-ionization time-of-flight (MALDI-TOF) spectra of mAb 114-4D12-affinity-purified marker oligosaccharides from a pool of urine from heavily Schistosoma mansoni-infected individuals (A–C) and non-infected controls (D). The compositions of the marker oligosaccharides are indicated (A). H, hexose; N, N-acetylhexosamine; F, fucose. For quantification, the eluate of the S. mansoni-infected urine was spiked with 125 fmol (B) and 62·5 fmol (C) of the di-antennary N-glycan standard, H5N4 (m/z 1663·6, [M+Na]+). (Permission granted)

Figure 1

Fig. 2. Scores plot for the OPLS-DA model derived from CE-UV data. The control and infected mice in the last week of infection are shown in black boxes and red triangles respectively. R2 is the percentage of data explained by the model (near 95%) and Q2 is the estimation of a new data point/sample being predicted correctly (78%). (Permission granted)

Figure 2

Table 1. The main metabolites found following a metabonomics approach using two different analytical techniques, CE and NMR

Figure 3

Fig. 3. Scores mean trajectory derived from PCA of UPLC-MS data: Each point is a day in the life of the mice, representing the mean position for the whole group along the illness. (Ahead of publication)