A novel and accurate diagnostic test for human African trypanosomiasis.
BACKGROUND
Human African trypanosomiasis (sleeping sickness) affects up to half a million people every year in sub-Saharan Africa. Because current diagnostic tests for the disease have low accuracy, we sought to develop a novel test that can diagnose human African trypanosomiasis with high sensitivity and specificity.


METHODS
We applied serum samples from 85 patients with African trypanosomiasis and 146 control patients who had other parasitic and non-parasitic infections to a weak cation exchange chip, and analysed with surface-enhanced laser desorption-ionisation time-of-flight mass spectrometry. Mass spectra were then assessed with three powerful data-mining tools: a tree classifier, a neural network, and a genetic algorithm.


RESULTS
Spectra (2-100 kDa) were grouped into training (n=122) and testing (n=109) sets. The training set enabled data-mining software to identify distinct serum proteomic signatures characteristic of human African trypanosomiasis among 206 protein clusters. Sensitivity and specificity, determined with the testing set, were 100% and 98.6%, respectively, when the majority opinion of the three algorithms was considered. This novel approach is much more accurate than any other diagnostic test.


CONCLUSIONS
Our report of the accurate diagnosis of an infection by use of proteomic signature analysis could form the basis for diagnostic tests for the disease, monitoring of response to treatment, and for improving the accuracy of patient recruitment in large-scale epidemiological studies.
Papadopoulos
Marios C
MC
Department of Cellular and Molecular Sciences, St George's Hospital Medical School, London, UK.
Abel
Paulo M
PM
Agranoff
Dan
D
Stich
August
A
Tarelli
Edward
E
Bell
B Anthony
BA
Planche
Timothy
T
Loosemore
Alison
A
Saadoun
Samira
S
Wilkins
Peter
P
Krishna
Sanjeev
S
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000810:Angola; D000818:Animals; D003663:Decision Trees; D005260:Female; D006801:Humans; D007239:Infections / Q000175:diagnosis; D008297:Male; D008875:Middle Aged; D008957:Models, Genetic; D016571:Neural Networks, Computer; D020543:Proteome / Q000032:analysis*; D015800:Protozoan Proteins / Q000032:analysis; D012680:Sensitivity and Specificity; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; D014345:Trypanosoma / Q000737:chemistry; D014353:Trypanosomiasis, African / Q000175:diagnosis*
D020543:Proteome; D015800:Protozoan Proteins