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Title: A designed experiments approach to optimizing MALDI-TOF MS spectrum processing parameters enhances detection of antibiotic resistance in Campylobacter jejuni

item CHRISTIAN, PENNY - Luxembourg Institute Of Science & Technology
item GROTHENDICK, BEAU - Arizona State University
item ZHANG, LIN - Arizona State University
item BORROR, CONNIE - Arizona State University
item BARBANO, DUANE - Arizona State University
item CORNELIUS, ANGELA - Institute Of Environmental Science And Research
item GILPIN, BRENT - Institute Of Environmental Science And Research
item Fagerquist, Clifton - Keith
item Zaragoza, William
item LASTOVICA, ALBERT - University Of The Western Cape
item CAUCHIE, HENRY-MICHEL - Luxembourg Institute Of Science & Technology
item SANDRIN, TODD - Arizona State University

Submitted to: Frontiers in Microbiology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/13/2016
Publication Date: 5/31/2016
Citation: Christian, P., Grothendick, B., Zhang, L., Borror, C.M., Barbano, D., Cornelius, A.J., Gilpin, B.J., Fagerquist, C.K., Zaragoza, W.J., Lastovica, A.J., Cauchie, H., Sandrin, T.R. 2016. A designed experiments approach to optimizing MALDI-TOF MS spectrum processing parameters enhances detection of antibiotic resistance in Campylobacter jejuni. Frontiers in Microbiology. doi: 10.3389/fmicb.2016.00818.

Interpretive Summary: Antibiotic resistance is increasing among pathogenic bacteria, including foodborne pathogens, presenting unique challenges to public health, agriculture and the food industry. It has been estimated that Campylobacter jejuni, a major foodborne pathogen, causes more than 1.3 million infections worldwide every year. Antibiotic resistance have been found in some C. jejuni strains. Methods are needed to rapidly identify and characterize foodborne pathogens including their susceptibility to antibiotic treatment. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) is increasingly used to identify and characterize microorganisms, e.g. viruses, bacteria and fungi. A large collection of C. jejuni strains (from four continents and varied sources) were analyzed by MALDI-TOF-MS to determine whether it can be used distinguish antibiotic resistance across C. jejuni strains. The results suggest that post-acquisition spectrum processing parameters can be optimized to allow for distinct clustering of beta-lactam resistant versus beta-lactam sensitive strains.

Technical Abstract: MALDI-TOF MS has been utilized as a reliable and rapid tool for microbial fingerprinting at the genus and species levels. Recently, there has been keen interest in using MALDI-TOF MS beyond the genus and species levels to rapidly identify antibiotic resistant strains of bacteria. The purpose of this study was to enhance strain level resolution for Campylobacter jejuni through the optimization of spectrum processing parameters using a series of designed experiments. A collection of 173 strains of C. jejuni were collected from Luxembourg, New Zealand, North America, and South Africa, consisting of four groups of antibiotic resistant isolates. The groups included: 1) 65 strains resistant to cefoperazone 2) five resistant to cefoperazone and beta-lactams 3) 26 strains resistant to cefoperazone, beta-lactams, and tetracycline, and 4) 76 strains resistant to cefoperazone, teicoplanin, amphotericin B and cephalothin. An additional second group was created from the collection consisting of 31 strains resistant to beta-lactams and 142 strains sensitive to beta-lactams. Initially, a model set of 16 strains (three biological replicates and three technical replicates per isolate, yielding a total of 144 spectra) of C. jejuni was subjected to each designed experiment to enhance detection of antibiotic resistance. The most optimal parameters were applied to the larger collection of 173 isolates (two biological replicates and three technical replicates per isolate, yielding a total of 1,035 spectra). We observed an increase in antibiotic resistance detection whenever either a curve based similarity coefficient (Pearson or ranked Pearson) was applied rather than a peak based (Dice) and/or the optimized preprocessing parameters were applied. Increases in antibiotic detection were scored using the jackknife maximum similarity technique following cluster analysis. Applying optimal preprocessing parameters, beta-lactam resistance detection was increased by 34 percent. From the first four groups of antibiotic resistant isolates, the optimized preprocessing parameters increased detection respective to the aforementioned groups by: 1) five percent 2) nine percent 3) ten percent, and 4) two percent. These results suggest that spectrum processing parameters, which are rarely optimized or adjusted, affect the performance of MALDI-TOF MS-based detection of antibiotic resistance and can be optimized to enhance performance. Future work may include the addition of whole genomic data for supplementation of the antibiotic resistance profiles described.