A fast impedance-based antimicrobial susceptibility test

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.

Same-day antimicrobial susceptibility test using acoustic-enhanced flow cytometry visualized with supervised machine learning

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.

Case Study: Applying Rapid Flow Cytometry Analysis to CAPD Effluent

Peritoneal dialysis (PD) peritonitis cases require rapid clinical interventions to ensure the best possible patient outcomes. Culture-dependent microbiology tools are slow and cannot provide clinicians with evidence to guide antimicrobial prescription practices in an appropriate time frame. Genotypic methods have met with limited success for analyzing continuous ambulatory PD effluent, with most centers still relying on culture-dependent microbiology. We present a case study in which we apply flow cytometry techniques to antibiotic-compromised effluent. We demonstrate, with supporting evidence, direct enumeration of bacterial and human immune cells, with results reported within 2 hours of receiving the clinical specimen.

Analysis of early mesothelial cell responses to Staphylococcus epidermidis isolated from patients with peritoneal dialysis-associated peritonitis

The major complication of peritoneal dialysis (PD) is the development of peritonitis, an infection within the abdominal cavity, primarily caused by bacteria. PD peritonitis is associated with significant morbidity, mortality and health care costs. Staphylococcus epidermidis is the most frequently isolated cause of PD-associated peritonitis. Mesothelial cells are integral to the host response to peritonitis, and subsequent clinical outcomes, yet the effects of infection on mesothelial cells are not well characterised. We systematically investigated the early mesothelial cell response to clinical and reference isolates of S. epidermidis using primary mesothelial cells and the mesothelial cell line Met-5A. Using an unbiased whole genome microarray, followed by a targeted panel of genes known to be involved in the human antibacterial response, we identified 38 differentially regulated genes (adj. p-value < 0.05) representing 35 canonical pathways after 1 hour exposure to S. epidermidis. The top 3 canonical pathways were TNFR2 signaling, IL-17A signaling, and TNFR1 signaling (adj. p-values of 0.0012, 0.0012 and 0.0019, respectively). Subsequent qPCR validation confirmed significant differences in gene expression in a number of genes not previously described in mesothelial cell responses to infection, with heterogeneity observed between clinical isolates of S. epidermidis, and between Met-5A and primary mesothelial cells. Heterogeneity between different S. epidermidis isolates suggests that specific virulence factors may play critical roles in influencing outcomes from peritonitis. This study provides new insights into early mesothelial cell responses to infection with S. epidermidis, and confirms the importance of validating findings in primary mesothelial cells.

Multiple Osmotic Stress Responses in Acidihalobacter prosperus Result in Tolerance to Chloride Ions

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.