MChip, a low density microarray, differentiates among seasonal human H1N1, North American swine H1N1, and the 2009 pandemic H1N1

BACKGROUND The MChip uses data from the hybridization of amplified viral RNA to 15 distinct oligonucleotides that target the influenza A matrix (M) gene segment. An artificial neural network (ANN) automates the interpretation of subtle differences in fluorescence intensity patterns from the microarray. The complete process from clinical specimen to identification including amplification of viral RNA can be completed in <8 hours for under US$10. OBJECTIVES The work presented here represents an effort to expand and test the capabilities of the MChip to differentiate influenza A/H1N1 of various species origin. METHODS The MChip ANN was trained to recognize fluorescence image patterns of a variety of known influenza A viruses, including examples of human H1N1, human H3N2, swine H1N1, 2009 pandemic influenza A H1N1, and a wide variety of avian, equine, canine, and swine influenza viruses. Robustness of the MChip ANN was evaluated using 296 blinded isolates. RESULTS Training of the ANN was expanded by the addition of 71 well-characterized influenza A isolates and yielded relatively high accuracy (little misclassification) in distinguishing unique H1N1 strains: nine human A/H1N1 (88·9% correct), 35 human A/H3N2 (97·1% correct), 31 North American swine A/H1N1 (80·6% correct), 14 2009 pandemic A/H1N1 (87·7% correct), and 23 negative samples (91·3% correct). Genetic diversity among the swine H1N1 isolates may have contributed to the lower success rate for these viruses. CONCLUSIONS The current study demonstrates the MChip has the capability to differentiate the genetic variations among influenza viruses with appropriate ANN training. Further selective enrichment of the ANN will improve its ability to rapidly and reliably characterize influenza viruses of unknown origin.