Tracing the transition of methicillin resistance in sub-populations of Staphylococcus aureus, using SELDI-TOF Mass Spectrometry and Artificial Neural Network Analysis.
Strains (n=99) of Staphylococcus aureus isolated from a large number of clinical sources and tested for methicillin sensitivity were analysed by MALDI-TOF-MS using the Weak Cation Exchange (CM10) ProteinChip Array (designated SELDI-TOF-MS). The profile data generated was analysed using Artificial Neural Network (ANN) Analysis modelling techniques. Seven key ions identified by the ANNs that were predictive of MRSA and MSSA were validated by incorporation into a model. This model exhibited an area under the ROC curve value of 0.9147 indicating the potential application of this approach for rapidly characterising MRSA and MSSA isolates. Nearly all strains (n=97) were correctly assigned to the correct group, with only two aberrant MSSA strains being misclassified. However, approximately 21% of the strains appeared to be in a process of transition as resistance to methicillin was being acquired.
Health Protection Agency, 61 Colindale Avenue, London NW95EQ, UK. haroun.shah@hpa.org.uk
Haroun N
HN
Shah
Lakshani
L
Rajakaruna
Graham
G
Ball
Raju
R
Misra
Ali
A
Al-Shahib
Min
M
Fang
Saheer E
SE
Gharbia
D001426:Bacterial Proteins / Q000032:analysis*; D024881:Drug Resistance, Bacterial; D055624:Methicillin-Resistant Staphylococcus aureus / Q000737:chemistry* / Q000145:classification*; D016571:Neural Networks, Computer; D040081:Protein Array Analysis / Q000379:methods; D012680:Sensitivity and Specificity; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods*
D001426:Bacterial Proteins