No abstract

The bacterial pathogen Yersinia pestis, the cause of plague in humans and animals, normally has a sylvatic lifestyle, cycling between fleas and mammals. In contrast, laboratory-grown Y. pestis experiences a more constant environment and conditions that it would not normally encounter. The transition from the natural environment to the laboratory results in a vastly different set of selective pressures, and represents what could be considered domestication. Understanding the kinds of adaptations Y. pestis undergoes as it becomes domesticated will contribute to understanding the basic biology of this important pathogen.In this study, we performed a parallel serial passage experiment (PSPE) to explore the mechanisms by which Y. pestis adapts to laboratory conditions, hypothesizing that cells would undergo significant changes in virulence and nutrient acquisition systems. Two wild strains were serially passaged in 12 independent populations each for ~750 generations, after which each population was analyzed using whole-genome sequencing, LC-MS/MS proteomic analysis, and GC/MS metabolomics. We observed considerable parallel evolution in the endpoint populations, detecting multiple independent mutations in ail, pepA, and zwf, suggesting that specific selective pressures are shaping evolutionary responses. Complementary LC-MS/MS proteomic data provide physiological context to the observed mutations, and reveal regulatory changes not necessarily associated with specific mutations, including changes in amino acid metabolism and cell envelope biogenesis. Proteomic data support hypotheses generated by genomic data in addition to suggesting future mechanistic studies, indicating that future whole-genome sequencing studies be designed to leverage proteomics as a critical complement.

There is little published data on the performance of biological indicator tests and immunoassays that could be used by first responders to determine if a suspicious powder contains a potential biothreat agent. We evaluated a range of biological indicator tests, including 3 protein tests, 2 ATP tests, 1 DNA test, and 1 FTIR spectroscopy instrument for their ability to screen suspicious powders for Bacillus anthracis (B. anthracis) spores and ricin. We also evaluated 12 immunoassays (mostly lateral flow immunoassays) for their ability to screen for B. anthracis and ricin. We used a cost-effective, statistically based test plan that allows instruments to be evaluated at performance levels ranging from 0.85 to 0.95 lower confidence bound of the probability of detection at confidence levels of 80% to 95%. We also assessed interference with 22 common suspicious powders encountered in the field. The detection reproducibility for the biological indicators was evaluated at 108 B. anthracis spores and 62.5 μg ricin, and the immunoassay detection reproducibility was evaluated at 107 spores/mL (B. anthracis) and 0.1 μg/mL (ricin). Seven out of 12 immunoassays met our most stringent criteria for B. anthracis detection, while 9 out of 12 met our most stringent test criteria for ricin detection. Most of the immunoassays also detected ricin in 3 different crude castor seed preparations. Our testing results varied across products and sample preparations, indicating the importance of reviewing performance data for specific instruments and sample types of interest for the application in order to make informed decisions regarding the selection of biodetection equipment for field use.

The detection limit of a field chemical sensor under realistic operating conditions is determined by receiver operator characteristic (ROC) curves. The chemical sensor is an ion mobility spectrometry (IMS) device used to detect a chemical marker in diesel fuel. The detection limit is the lowest concentration of the marker in diesel fuel that obtains the desired true-positive probability (TPP) and false-positive probability (FPP). A TPP of 0.90 and a FPP of 0.10 were selected as acceptable levels for the field sensor in this study. The detection limit under realistic operating conditions is found to be between 2 to 4 ppm (w/w). The upper value is the detection limit under challenging conditions. The ROC-based detection limit is very reliable because it is determined from multiple and repetitive sensor analyses under realistic circumstances. ROC curves also clearly illustrate and gauge the effects data preprocessing and sampling environments have on the sensor's detection limit.