There is an urgent need to develop simple and fast antimicrobial susceptibility tests (ASTs) that allow informed prescribing of antibiotics. Here, we describe a label-free AST that can deliver results within an hour, using an actively dividing culture as starting material. The bacteria are incubated in the presence of an antibiotic for 30 min, and then approximately 105 cells are analysed one-by-one with microfluidic impedance cytometry for 2–3 min. The measured electrical characteristics reflect the phenotypic response of the bacteria to the mode of action of a particular antibiotic, in a 30-minute incubation window. The results are consistent with those obtained by classical broth microdilution assays for a range of antibiotics and bacterial species.

Extremely acidophilic microorganisms (pH optima for growth of ≤3) are utilized for the extraction of metals from sulfide minerals in the industrial biotechnology of “biomining.” A long term goal for biomining has been development of microbial consortia able to withstand increased chloride concentrations for use in regions where freshwater is scarce. However, when challenged by elevated salt, acidophiles experience both osmotic stress and an acidification of the cytoplasm due to a collapse of the inside positive membrane potential, leading to an influx of protons. In this study, we tested the ability of the halotolerant acidophile Acidihalobacter prosperus to grow and catalyze sulfide mineral dissolution in elevated concentrations of salt and identified chloride tolerance mechanisms in Ac. prosperus as well as the chloride susceptible species, Acidithiobacillus ferrooxidans. Ac. prosperus had optimum iron oxidation at 20 g L−1 NaCl while At. ferrooxidans iron oxidation was inhibited in the presence of 6 g L−1 NaCl. The tolerance to chloride in Ac. prosperus was consistent with electron microscopy, determination of cell viability, and bioleaching capability. The Ac. prosperus proteomic response to elevated chloride concentrations included the production of osmotic stress regulators that potentially induced production of the compatible solute, ectoine uptake protein, and increased iron oxidation resulting in heightened electron flow to drive proton export by the F0F1 ATPase. In contrast, At. ferrooxidans responded to low levels of Cl− with a generalized stress response, decreased iron oxidation, and an increase in central carbon metabolism. One potential adaptation to high chloride in the Ac. prosperus Rus protein involved in ferrous iron oxidation was an increase in the negativity of the surface potential of Rus Form I (and Form II) that could help explain how it can be active under elevated chloride concentrations. These data have been used to create a model of chloride tolerance in the salt tolerant and susceptible species Ac. prosperus and At. ferrooxidans, respectively.

The expanding global distribution of multi-resistant Klebsiella pneumoniae demands faster antimicrobial susceptibility testing (AST) to guide antibiotic treatment. Current ASTs rely on time-consuming differentiation of resistance and susceptibility after initial isolation of bacteria from a clinical specimen. Here we describe a flow cytometry workflow to determine carbapenem susceptibility from bacterial cell characteristics in an international K. pneumoniae isolate collection (n = 48), with a range of carbapenemases. Our flow cytometry-assisted susceptibility test (FAST) method combines rapid qualitative susceptible/non-susceptible classification and quantitative MIC measurement in a single process completed shortly after receipt of a primary isolate (54 and 158 minutes respectively). The qualitative FAST results and FAST-derived MIC (MICFAST) correspond closely with broth microdilution MIC (MICBMD, Matthew’s correlation coefficient 0.887), align with the international AST standard (ISO 200776-1; 2006) and could be used for rapid determination of antimicrobial susceptibility in a wider range of Gram negative and Gram positive bacteria.

Purpose. Antimicrobial susceptibility is slow to determine, taking several days to fully impact treatment. This proof-of-concept study assessed the feasibility of using machine-learning techniques for analysis of data produced by the flow cytometer-assisted antimicrobial susceptibility test (FAST) method we developed. Methods. We used machine learning to assess the effect of antimicrobial agents on bacteria, comparing FAST results with broth microdilution (BMD) antimicrobial susceptibility tests (ASTs). We used Escherichia coli (1), Klebsiella pneumoniae (1) and Staphylococcus aureus (2) strains to develop the machine-learning algorithm, an expanded panel including these plus E. coli (2), K. pneumoniae (3), Proteus mirabilis (1), Pseudomonas aeruginosa (1), S. aureus (2) and Enterococcus faecalis (1), tested against FAST and BMD (Sensititre, Oxoid), then two representative isolates directly from blood cultures. Results. Our data machines defined an antibiotic-unexposed population (AUP) of bacteria, classified the FAST result by antimicrobial concentration range, and determined a concentration-dependent antimicrobial effect (CDE) to establish a predicted inhibitory concentration (PIC). Reference strains of E. coli, K. pneumoniae and S. aureus tested with different antimicrobial agents demonstrated concordance between BMD results and machine-learning analysis (CA, categoric agreement of 91 %; EA, essential agreement of 100 %). CA was achieved in 35 (83 %) and EA in 28 (67 %) by machine learning on first pass in a challenge panel of 27 Gram-negative and 15 Gram-positive ASTs. Same-day AST results were obtained from clinical E. coli (1) and S. aureus (1) isolates. Conclusions. The combination of machine learning with the FAST method generated same-day AST results and has the potential to aid early antimicrobial treatment decisions, stewardship and detection of resistance.