Strong increase in the autofluorescence of cells signals struggle for survival.

Prokaryotic and eukaryotic cells exhibit an intrinsic natural fluorescence due to the presence of fluorescent cellular structural components and metabolites. Therefore, cellular autofluorescence (AF) is expected to vary with the metabolic states of cells. We examined how exposure to the different stressors changes the AF of Escherichia coli cells. We observed that bactericidal treatments increased green cellular AF, and that de novo protein synthesis was required for the observed AF increase. Excitation and emission spectra and increased expression of the genes from the flavin biosynthesis pathway, strongly suggested that flavins are major contributors to the increased AF. An increased expression of genes encoding diverse flavoproteins which are involved in energy production and ROS detoxification, indicates a cellular strategy to cope with severe stresses. An observed increase in AF under stress is an evolutionary conserved phenomenon as it occurs not only in cells from different bacterial species, but also in yeast and human cells.

Enhanced detection of carbapenemase-producing Enterobacteriaceae by an optimized phenol red assay.

Screening for the detection of carbapenemase-producing bacteria still encounters issues related to workflow, limit of detection, or qualitative interpretation. We developed a spectrophotometry-based version of the Carba NP phenol red assay (Nordmann et al., 2012) in a microtiter plate format, compatible with low bacterial cell counts. We were able to detect highly active carbapenemases such as KPC and IMP in 30min. A wider range of carbapenemases including OXA-48 were detected using higher inocula, still being competitive compared with currently available phenol red assays. Validation experiments of our test with a panel of 81 Enterobacteriaceae showed good performance with 93% of sensitivity and 92% of specificity. The compatibility of our routine-friendly protocol with automation offers great perspectives for high throughput screening in outbreak situations and/or in big laboratories.

Antibiotic Susceptibility Testing of the Gram-Negative Bacteria Based on Flow Cytometry.

Rapidly treating infections with adequate antibiotics is of major importance. This requires a fast and accurate determination of the antibiotic susceptibility of bacterial pathogens. The most frequently used methods are slow because they are based on the measurement of growth inhibition. Faster methods, such as PCR-based detection of determinants of antibiotic resistance, do not always provide relevant information on susceptibility, particularly that which is not genetically based. Consequently, new methods, such as the detection of changes in bacterial physiology caused by antibiotics using flow cytometry and fluorescent viability markers, are being explored. In this study, we assessed whether Alexa Fluor® 633 Hydrazide (AFH), which targets carbonyl groups, can be used for antibiotic susceptibility testing. Carbonylation of cellular macromolecules, which increases in antibiotic-treated cells, is a particularly appropriate to assess for this purpose because it is irreversible. We tested the susceptibility of clinical isolates of Gram-negative bacteria, Escherichia coli and Pseudomonas aeruginosa, to antibiotics from the three classes: ß-lactams, aminoglycosides, and fluoroquinolones. In addition to AFH, we used TO-PRO®-3, which enters cells with damaged membranes and binds to DNA, and DiBAC4 (3), which enters cells with depolarized membranes. We also monitored antibiotic-induced morphological alterations of bacterial cells by analyzing light scattering signals. Although all tested dyes and light scattering signals allowed for the detection of antibiotic-sensitive cells, AFH proved to be the most suitable for the fast and reliable detection of antibiotic susceptibility.

Automatic identification of mixed bacterial species fingerprints in a MALDI-TOF mass-spectrum.

MOTIVATION: Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry has been broadly adopted by routine clinical microbiology laboratories for bacterial species identification. An isolated colony of the targeted microorganism is the single prerequisite. Currently, MS-based microbial identification directly from clinical specimens can not be routinely performed, as it raises two main challenges: (i) the nature of the sample itself may increase the level of technical variability and bring heterogeneity with respect to the reference database and (ii) the possibility of encountering polymicrobial samples that will yield a 'mixed' MS fingerprint. In this article, we introduce a new method to infer the composition of polymicrobial samples on the basis of a single mass spectrum. Our approach relies on a penalized non-negative linear regression framework making use of species-specific prototypes, which can be derived directly from the routine reference database of pure spectra. RESULTS: A large spectral dataset obtained from in vitro mono- and bi-microbial samples allowed us to evaluate the performance of the method in a comprehensive way. Provided that the reference matrix-assisted laser desorption/ionization time-of-flight mass spectrometry fingerprints were sufficiently distinct for the individual species, the method automatically predicted which bacterial species were present in the sample. Only few samples (5.3%) were misidentified, and bi-microbial samples were correctly identified in up to 61.2% of the cases. This method could be used in routine clinical microbiology practice.