br Materials and Methods br

Materials and Methods
Results
Discussion Our study demonstrates that applications of BCP extend beyond determination of antibiotic mechanism of action to determination of antibiotic susceptibility and identification of optimally effective combinatorial therapies. This single cell phenotype-driven approach provides a rapid susceptibility test that can be performed in parallel with traditional MIC testing, but which delivers a result within 1–2h, rather than 1–2days. The test is precise, yielding no major or minor errors in our blinded tests. Rather than relying on a single parameter (such as growth or permeability), BCP as implemented here assesses 22 different parameters that together capture the wide array of affects EZ Cap Reagent AG (3\' OMe) have on bacterial cells, including changes in cell length, width, permeability, and in chromosome number, compactness and shape (Lamsa et al., 2012; Nonejuie et al., 2013). This likely explains why BCP is more accurate (as well as more rapid) than other proposed phenotype-driven tests (Choi et al., 2014; Ligozzi et al., 2002). Indeed, phenotypic methods that rely only on lysis (Price et al., 2014; Kalashnikov et al., 2012; Choi et al., 2013) would have miscategorized the MRSAHL strains as MSSA, whereas methods that rely only on growth would have failed to identify the two classes of MRSA strains that differ in their susceptibility to combinatorial drug therapies. Our mechanistic studies have demonstrated that BCP works for all antibiotics, natural products and antimicrobial peptides tested so far, and for all species tested (S. aureus, Escherichia coli, Bacillus subtilis, E. faecium, Enterococcus faecalis, Streptococcus pneumoniae and A. baumannii) (Lamsa et al., 2012; Nonejuie et al., 2013; Sakoulas et al., 2015a, 2015b; Lin et al., 2015; Hindler et al., 2015; Werth et al., 2014). A major strength of BCP is that, in contrast to the rapid gene and genome-based susceptibility tests, it does not rely on prior knowledge of the mechanism of antibiotic resistance or prior identification of all possible genes and mutations that confer resistance. This is critical, even for drugs that bind well-defined targets, such as the ribosome, since the number of mutations that confer drug resistance in the clinic continues to expand (Wilson, 2014). It is even more critical for drugs such as daptomycin, for which several different mutations confer various levels of resistance (Diaz et al., 2014; Mishra et al., 2014; Bayer et al., 2015, 2013; Berti et al., 2015). The reliance of BCP on phenotype rather than genotype makes it highly adaptable, allowing future applications to new drug resistant pathogens and new antibiotics. BCP can be readily optimized to provide a more accurate test for different species or antibiotics. For example, the susceptibility test reported here relies on a different combination of fluorescent stains than used in our previous BCP assays. BCP provides a robust and rapid technique that is readily adapted to identify opportunities for antibiotic treatment synergy. Such synergies often derive from the additive effects of multiple genetic factors, making it difficult or impossible to detect them using a gene-based technique. This was found to be the case for the beta-lactam plus daptomycin synergy in E. faecium, which depends on multiple PBPs (Sakoulas et al., 2015a) and for daptomycin resistance in S. aureus and E. faecium for which multiple mutations contribute to resistance (Bayer et al., 2013) by modifying either the cell membrane or the cell wall (Humphries et al., 2013). Hence we propose that BCP could be used to guide treatment options including the usage of dual beta-lactam treatment or the substitution or addition of different class antibiotics to lower the chances of treatment failure. Identification of synergistic combinations in vitro followed by appropriate clinical correlation studies to determine the efficacy in vivo allows for the assessment of new treatment options using existing antibiotics.