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Cantilever Sensors for Rapid Optical Antimicrobial Sensitivity TestingClick to copy article linkArticle link copied!
- Isabel BennettIsabel BennettLondon Centre for Nanotechnology, University College London, 17-19 Gordon Street, London WC1H 0AH, United KingdomDivision of Medicine, University College London, Cruciform Building, Gower Street, London WC1E 6BT, United KingdomMore by Isabel Bennett
- Alice L. B. Pyne*Alice L. B. Pyne*Email: [email protected]London Centre for Nanotechnology, University College London, 17-19 Gordon Street, London WC1H 0AH, United KingdomDepartment of Materials Science and Engineering, Sir Robert Hadfield Building, University of Sheffield, Sheffield S1 3JD, United KingdomMore by Alice L. B. Pyne
- Rachel A. McKendry*Rachel A. McKendry*Email: [email protected]London Centre for Nanotechnology, University College London, 17-19 Gordon Street, London WC1H 0AH, United KingdomDivision of Medicine, University College London, Cruciform Building, Gower Street, London WC1E 6BT, United KingdomMore by Rachel A. McKendry
Abstract
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Growing antimicrobial resistance (AMR) is a serious global threat to human health. Current methods to detect resistance include phenotypic antibiotic sensitivity testing (AST), which measures bacterial growth and is therefore hampered by a slow time to obtain results (∼12–24 h). Therefore, new rapid phenotypic methods for AST are urgently needed. Nanomechanical cantilever sensors have recently shown promise for rapid AST but challenges of bacterial immobilization can lead to variable results. Herein, a novel cantilever-based method is described for detecting phenotypic antibiotic resistance within ∼45 min, capable of detecting single bacteria. This method does not require complex, variable bacterial immobilization and instead uses a laser and detector system to detect single bacterial cells in media as they pass through the laser focus. This provides a simple readout of bacterial antibiotic resistance by detecting growth (resistant) or death (sensitive), much faster than the current methods. The potential of this technique is demonstrated by determining the resistance in both laboratory and clinical strains of Escherichia coli (E. coli), a key species responsible for clinically burdensome urinary tract infections. This work provides the basis for a simple and fast diagnostic tool to detect antibiotic resistance in bacteria, reducing the health and economic burdens of AMR.
Copyright © 2020 American Chemical Society
Introduction
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Antimicrobial resistance (AMR) is steadily increasing and poses a major threat to global health, with estimates of AMR leading to 10 million deaths per year and costing the global economy $100tn by 2050. (1,2) The increase in AMR has been caused by several factors including the overuse of antibiotics. (3) Despite the growth of AMR, methods for antibiotic susceptibility testing (AST) have remained relatively unchanged for several decades.
In common AST methods, bacterial growth is used as a measure of sensitivity to antibiotics, determined directly by an increase in media turbidity (the number of bacteria) or indirectly by the release of fluorescent metabolites. These phenotypic methods provide in vitro confirmation of resistances in isolated bacterial species, which are inferred from known resistance genes in genetic methods. However, phenotypic methods are inherently limited by the speed of bacterial growth (e.g., the doubling time of Escherichia coli(E. coli) is 20 min, whereas Mycobacterium tuberculosis (M. tuberculosis) is 12–15 h), meaning these methods require long culture times (12–24 h, or longer for some species) for an observable change to occur. These delays result in empirical prescribing of antibiotics for patients instead of targeted treatment, which has been shown to increase mortality from sepsis fivefold, (4) in addition to being a driver of resistance. Having access to the identity and antibiogram of the pathogen just a few hours earlier could avoid unnecessary costs associated with inappropriate prescribing, increase patient welfare, and reduce the effects of AMR. (5,6) Therefore, to reduce the damaging effects of AMR, we require solutions in the form of novel diagnostic tools to detect resistance and improve antibiotic stewardship, surveillance, and patient management. (7,8)
Recent developments in this field have exploited single-cell methods for faster and more sensitive detection of antibiotic resistance. This has been achieved by miniaturizing the volume observed using microfluidics, (9−12) measuring mass or mechanical changes, (11,13,14) or by exploiting machine learning techniques for video tracking analysis of single cells. (15−17) Despite advances in the detection limit and speed of testing, these are mostly complex setups, which remain far from point of care.
Recently, a nanomechanical method for detecting the viability of bacterial cells immobilized on soft cantilevers was reported by Longo et al., (18−21) which has attracted much attention by virtue of its ability to detect AST within minutes. Here, we exploit an optical signal as an alternative method to the nanomechanical method reported by Longo et al., with the advantage that it does not require complex immobilization chemistries and optimization of bacterial seeding densities (Figure 1). This novel optical method is able to rapidly detect antibiotic resistance in bacterial solutions with single-cell resolution.
Figure 1
Figure 1. Principle of the rapid optical AST method. (a) Illustration of bacterial cells inoculated in growth media with antibiotic molecules, with laser reflecting off the cantilever surface onto a photodiode detector. Bacteria in the solution move through the laser beam, which can be observed as peaks in the photodiode signal. The photodiode signal measured from the media solution decreases after the addition of the antibiotic for sensitive strains. (b–d) Photodiode signal (b) without bacterial inoculant, (c) with bacteria in solution, and (d) 45 min after addition of the antibiotic.
Results and Discussion
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In the nanomechanical method, a nonspecific linker molecule was used to coat the cantilever surface with hundreds of bacterial cells, and the motion of the cantilever was monitored using a laser before and after the application of an antibiotic. However, there remains speculation as to the origin of the nanomechanical signal. Herein, when we initially sought to reproduce the Longo method and apply it to clinical samples, a significant issue was found in obtaining consistent bacterial immobilization on the cantilever surface. Bacterial immobilization numbers were found to vary from cantilever to cantilever, from zero/low numbers to very high clumpy immobilization (Figure S1). Efforts to identify the source of variability by testing different immobilization conditions resulted in significant variation across conditions (from 90 to >1000 cells, Figure S2) with no clear pattern identified. For example, out of 60 cantilevers functionalized with bacterial cells, only 28 achieved measurable bacterial immobilization of which only five had “optimal coverage” of 500–600 cells. Additionally, no significant difference was found between the mechanical cantilever motion for preantibiotic and postantibiotic treatments for these five cantilevers (P = 0.4569, Figure 2). However, many large “peaks” observed in the raw data were found to be correlated temporally and spatially with the bacterial cells in solution passing through the laser path.
Figure 2
Figure 2. Data analysis of initial mechanical signal experiments. (a, b) Subtraction of linear regression from raw data and large peaks not caused by mechanical motion of the cantilever identified (*). (c, d) Averaging of variance over 10 s segments and (e) removing large peaks from the average variance calculation for one experiment. (f) Average variance for n = 5 experiments, pre-treatment (green, pre-amp) and 15 min post-treatment (red, post-amp) with 125 μg/mL ampicillin for optimal immobilization count cantilever D experiments. P = 0.4569. Cantilever D: k = 0.06 N/m, fres = 4 kHz.
We exploited this observed optical signal as an alternative method to the above-mentioned nanomechanical method. This optical method uses a cantilever, a laser, and a sensitive photodetector to measure the effect of antibiotics on bacterial growth, as briefly described here. A reflective surface (a small stiff cantilever) is immersed in filtered growth media (4 mL) in a small Petri dish, off which a laser is reflected onto a photodiode detector (Figure 1a). Stiff cantilevers (AC160 TS, k = 26 N/m) were selected to disentangle the effect of the optical signal from the nanomechanical motion of the cantilever. In the bacterial growth media (LB media) before inoculation with bacterial cells, no variation in the laser signal was observed (Figure 1b). On inoculation with bacterial cells, the free bacteria in the growth media were observed to move through the path of the laser. This cell movement in solution interferes with the laser beam, causing it to shift on the detector, observable as peaks in the signal (Figure 1c). On addition of an antibiotic to the media, cell death of antibiotic-sensitive bacteria occurs, and fewer bacteria are detected passing through the laser. This results in a decrease in the number of peaks after ∼45 min (Figure 1d).
To determine the origin of the signal, the bacterial concentration in solution was reduced to ∼105 CFU (colony forming units, a standard measure of bacterial concentration). At this concentration, individual bacterial crossing events can be observed as peaks within the optical signal (Figure 3a). When a single bacterium is tracked optically crossing the path of the laser (Figure 3b, blue circle), a corresponding peak in the signal can be observed in the data (Figure 3c). These peaks are of varying width and amplitude, due to differing angle and distance at which the bacteria pass through the laser. These single bacterial cell crossing events give this system single-cell resolution at this low bacterial concentration. However, the limit of detection has not been measured. As bacteria replicate and cell numbers increase in the system (i.e., increasing CFU), the number of peaks in the signal also increases (Figure 3d), indicating that it is the bacteria that give rise to the signal. In addition, this suggests that bacterial growth leads to an increase in the signal over time (further shown in Figure S3).
Figure 3
Figure 3. Signal caused by bacteria crossing the laser path decreases after 45 min from antibiotic addition. (a) At a low bacterial inoculant concentration, individual peaks can be identified within the signal. Combined optical tracking and signal measurement shows (a) of single bacterium (blue circle) passing through the laser path (b, optical images) as a single peak in the signal (c). (d) Bacterial concentration (CFU, × 105) correlates with the number of bacterial crossings.
The number of peaks observed in the raw signal is linked to the number of viable bacteria in solution, which can exploited to determine the antibiotic resistance. If the number of peaks (or bacterial crossings) is measured at distinct time points during an experiment (e.g., “media only” (gray box), “inoculated media” (black box), and “inoculated media containing an antibiotic” (green box)) (Figure 4), a distinct trend appears where bacterial crossings increase on addition of bacteria to the system and decrease around 45 min (about two replication cycles for E. coli) after the addition of an antibiotic in the case of sensitive strains. The two peaks observed in the signal correspond to the addition of bacteria and an antibiotic (Figure 5a, yellow and dark blue dotted lines, respectively) and occur due to mixing of the system. These peaks settle to a baseline and are observed in control experiments (Figure S3, points “3” and “4”). This trend is not observed in a control where the growth media is added without an antibiotic as the number of crossings continues to rise over time due to cell replication (Figure S3). Here, an exponential curve is observed over time, correlating to the expected exponential growth of bacteria in the system.
Figure 4
Figure 4. Peak identifying and counting analysis. (a) Number of bacterial crossings in 800 s was calculated and plotted over the course of the experiment. (b) Raw data traces for points at “media only” (gray box), “inoculated media” (black box), and “inoculated media containing an antibiotic” (green box). Peaks identified (blue triangles) as ±0.5 nm from previous peaks. Each point in (a) is the total number of peaks identified in 800 s.
Figure 5
Figure 5. Systematic analysis of antibiotic susceptibility in clinical and laboratory strains of E. coli. (a) Susceptibility of BL21-WT (S, green) and BL21-ampR E. coli (R, red) to 125 μg/mL ampicillin. Addition of bacteria (yellow dotted line) and antibiotic solution (dark blue dotted line) to the system cause large fluctuations in the signal as the liquid is mixed, which dissipate within ∼800 s. The number of bacterial crossings in a given time period, here 800 s, is plotted. The number of bacterial crossings shows a decrease in 45 min after antibiotic addition. (b) Determination of the resistance profile, with sensitivity readout (rsensitivity). rsensitivity was calculated from the ratio of crossings postantibiotic and preantibiotic treatments at set time points marked in blue in (a). Strains were determined to be sensitive (S) if rsensitivity < 1 (green) or resistant (R) if rsensitivity ⩾ 1 (red), cut off (rsensitivity = 1) shown as a blue dashed line, shown for five concentrations of ampicillin and BL21 E. coli. (c) Susceptibility of a clinical isolate of E. coli, determined to be resistant to both ampicillin (purple line) and trimethoprim (blue line). (d) Determination of resistance profile. rsensitivity for repeats of clinical isolate with 125 μg/mL trimethoprim and ampicillin. Antibiotic concentrations are given in μg/mL.
Using this method, sensitive and resistant strains of E. coli can be differentiated. As described above, a reduction in the signal after addition of an antibiotic for sensitive strains is seen (Figure 5a, green); for resistant strains, there is an increase in signal (Figure 5a, red). Though the trend remains the same, the magnitude of the signal change can vary (Figure S4a) based on several factors, which effect growth rates, including inoculant concentration, strain, and temperature, for example. The data were therefore normalized to the baseline taken before the addition of the antibiotic when comparing between experiments (Sbaseline) (Figure S4b,c).
To obtain a systematic readout of antibiotic sensitivity across experiments, including multiple strains and antibiotics, a normalized measure of bacterial growth was determined as follows. Antibiotic sensitivity (rsensitivity) is defined as the ratio of Sbaseline and 45 min postantibiotic treatment (Santibiotic), shaded blue in Figure 5a. rsensitivity provides a binary readout of sensitivity, rsensitivity ≤ 1 indicates cell death or inhibition of bacterial growth and sensitivity to the antibiotic in solution; rsensitivity > 1 indicates bacterial growth and therefore resistance to the antibiotic used. This method allows both bactericidal and bacteriostatic antibiotics to be used as rsensitivity < 1 indicates a decrease in cell number or cell death (bactericidal); rsensitivity = 1 would indicate inhibition of growth but little cell death (bacteriostatic). As shown in Figure 5a, for ampicillin, rsensitivity = 0.5 for the green strain (sensitive) and rsensitivity = 1.1 for the red strain (resistant). For kanamycin, rsensitivity = 0.92 for a sensitive strain and rsensitivity = 2.0 for a resistant strain (green and red, respectively, Figure S5).
Having shown that rsensitivity can be used as a measure of bacterial sensitivity, this method was applied across a range of concentrations of ampicillin to determine the minimum inhibitory concentration (MIC) for the E.coli strain BL21 (Figure 5b). The MIC value is defined as the lowest concentration of an antibiotic that will inhibit the visible growth of a bacterial strain (22) and is used to determine clinical breakpoints and provide patient-dose information for prescribing treatment. At low ampicillin concentrations (0–12.5 μg/mL), rsensitivity > 1, and at increased ampicillin concentrations (50–125 μg/mL), rsensitivity < 1. This indicates an MIC of 12.5–50 μg/mL ampicillin for E.coli BL21. This result is within the range determined by broth microdilution, the gold-standard method (8–16 μg/mL). Despite difficulties in measuring MICs, (23,24) these values are used by clinicians when making decisions about patient care (antibiotic selection and dosage), and hence, are an important result for any new diagnostic tool for accurate measurement.
Uropathogenic E. coli (UPEC) is the leading cause of urinary tract infections (UTIs) (25) and is clinically burdensome across the globe. AMR has increased in UTIs and hence represents an excellent clinical target for a new diagnostic tool. The potential of this rapid optical AST method was demonstrated by testing an E. coli clinical isolate. As shown in Figure 5c, treatment of the clinical isolate with 125 μg/mL ampicillin and trimethoprim resulted in no decrease in signal and gave rsensitivity > 1 within 45 min (Figure 5d). This was confirmed by broth microdilution (resistance > 256 μg/mL ampicillin and trimethoprim). These detected resistances agreed with the resistance spectrum obtained from the hospital (Great Ormond Street Hospital, London) measured using the gold-standard method in a clinical laboratory (Table S1). This study demonstrates the ability of this method to successfully carry out an AST for a strain of bacteria isolated from a patient within 45 min of the addition of the antibiotic, faster than traditional methods of AST measurements (24 h).
To conclude, in the face of AMR, novel rapid methods to detect resistance in bacteria are needed to prevent their further spread and development. This study has shown that this optical AST method can rapidly differentiate between resistant and sensitive phenotypes in laboratory and clinical strains of E. coli and determine MIC values in the same range as the current gold-standard methods. A readout of bacterial sensitivity was obtained within ∼45 min of the addition of the antibiotic. This method lends itself to miniaturization and automation, for example, requiring a small stable reflective surface, which could be immersed within a 96-well plate for automated reading, with a laser and a photodetector readout. Further miniaturization to a microfluidic environment would be advantageous as the observed volume would be significantly smaller, allowing for even faster detection time and increased sensitivity. This method can be exploited as a new rapid phenotypic method for AST to provide these time-critical results to determine patient care and antibiotic stewardship.
Experimental Section
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Experimental Method
A stiff AC160 TS cantilever (k = 26 N/m; Olympus, Japan) was loaded onto an AFM head (JPK Nanowizard 3 ULTRA Speed; JPK Instruments, Germany) and immersed in filtered Luria Broth (LB; Sigma-Aldrich, USA) in a 35 mm diameter glass-bottom Petri dish (WillCo Wells, Netherlands). The cantilever spring constant was calibrated using the thermal noise method with JPK software to convert vertical deflection from volts to nm. The cantilever was allowed to equilibrate for 15 min, during which time the vertical deflection of the laser was measured. The LB media was then inoculated with bacteria to a constant concentration (∼105 CFU) and recording was started again for another 40 min to obtain a preantibiotic baseline. An antibiotic solution was then added directly to the LB + bacteria solution to a desired final concentration, and deflection recording was then measured.
During experiments, only the real-time scan function was used to monitor the vertical deflection of the laser. Experiments were conducted at 28 °C in an acoustic isolation hood. Prior to the start of the experiments, the AFM laser was left on for ∼2 h to ensure the laser had warmed up fully and to reduce laser power fluctuations, which would affect the drift of the signal.
Reagents
Luria broth (LB) and antibiotics (ampicillin, kanamycin, and trimethoprim) were all supplied by Sigma-Aldrich (USA).
Bacterial Strains
E. coli BL21(DE3)pLysS competent cells (Promega, UK) were selected for their suitability for transformation with a plasmid containing ampicillin resistance (pRSET/EmGFP plasmid; Invitrogen, UK).
A clinical isolate of E. coli was obtained from the microbiology repository of the Great Ormond Street Hospital (London, UK).
Bacterial Preparation
An LB media (Sigma-Aldrich) plate was streaked with BL21 E. coli (Promega) or clinical isolate E. coli (obtained from the Great Ormond Street Hospital) from frozen stocks in a sterile hood. These were grown up overnight at 37 °C. A single colony was used to inoculate 4 mL of LB media, which was incubated at 37 °C for 2 h (225 rpm. shaking), to obtain a mid-log phase growth. The OD600 of the culture was measured using a Nanodrop One C (Thermo Scientific), and the final OD600 for bacterial inoculation for experimental measurements was adjusted to keep as constant as possible.
Bacterial Transformation with Ampicillin Resistance
An aliquot of a competent bacterial stock was thawed in ice for 20–30 min. A volume of 1–5 μL (10 pg–100 ng) of pRSET–EmGFP plasmid (Invitrogen, CA, USA) was mixed with 25 μL of thawed bacterial solution and incubated for 5–10 min in ice followed by a heat shock treatment at 42 °C for 40 s and freezing for further two minutes. A volume of 500 μL of warmed SOC media was added, and this was incubated at 37 °C at 225 rpm. for 1 h. A volume of 50 μL was plated onto an agar plate, which contained 50 μg/mL of a nafcillin/ampicillin mixture. This plate was incubated overnight at 37 °C, and colonies used were made into frozen stocks for experimental use.
Data Analysis
Vertical deflection data (nm) were recorded on JPK Nanowizard 3 software at 20 kHz sampling frequency. The raw data (Figure S6a) were then processed in 800 s “chunks” using an analysis code written in Matlab. This code applied a Savitzky–Golay finite impulse response (FIR) smoothing filter of polynomial order 2 to the data, with a filtering frequency of 101 Hz (Figure S6b). The Savitzky–Golay smoothing filter was chosen as this function can filter noisy data effectively without removing the high-frequency data.
To identify the number of bacterial crossings, both local maxima and minima were identified, as bacteria moving through the laser were observed to cause both peaks and dips in the signal (Figure S6c, peaks labeled with blue triangles). A “Peak Finder” function was used to identify the local minima/maxima in the signal, where a “peak” was defined as having a threshold drop of at least 0.5 nm on each side. This was to ensure that only the larger peaks were counted, which correspond to bacteria moving across the laser. Smaller “noise” seen in the signal was not attributed to actual bacterial crossings but could be due to partial crossings or a change of orientation of bacteria within the laser during a crossing. This threshold peak prominence value of 0.5 nm was applied empirically across all files when carrying out the analysis to remove any bias of identifying peaks in the signal.
Across the experiment, the number of peaks was calculated for a subsampled time frame to increase the resolution of the data from 800 to 267 s and plotted across the experimental conditions of LB media, addition of bacteria, and addition of the antibiotic (Figure S6d).
To calculate the antibiotic sensitivity (rsensitivity), the ratio of the signal preantibiotic addition, Sbaseline, and 45 min postantibiotic addition, Santibiotic was used (Figure S6d). rsensitivity provides a binary readout of sensitivity; rsensitivity ≤ 1 indicates cell death or inhibition of bacterial growth and sensitivity to the antibiotic in solution; rsensitivity > 1 indicates bacterial growth and therefore resistance to the antibiotic used.
Supporting Information
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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acssensors.0c01216.
Replication of the nanomechanical method; representative optical images of the range of bacterial coverage; investigation of bacterial immobilization conditions; growth of bacteria over time; baseline normalization and magnitude variability between experiments; data of kanamycin resistant and sensitive strain; data analysis steps applied to raw data; and resistance spectrum of patient isolate from the Great Ormond Street Hospital (PDF).
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Author Information
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- Alice L. B. Pyne - London Centre for Nanotechnology, University College London, 17-19 Gordon Street, London WC1H 0AH, United Kingdom; Department of Materials Science and Engineering, Sir Robert Hadfield Building, University of Sheffield, Sheffield S1 3JD, United Kingdom;
http://orcid.org/0000-0002-2658-8987;
- Rachel A. McKendry - London Centre for Nanotechnology, University College London, 17-19 Gordon Street, London WC1H 0AH, United Kingdom; Division of Medicine, University College London, Cruciform Building, Gower Street, London WC1E 6BT, United Kingdom;http://orcid.org/0000-0003-2018-6829;
- Isabel Bennett - London Centre for Nanotechnology, University College London, 17-19 Gordon Street, London WC1H 0AH, United Kingdom; Division of Medicine, University College London, Cruciform Building, Gower Street, London WC1E 6BT, United Kingdom;http://orcid.org/0000-0002-4847-4470
I.B., A.L.B.P., and R.A.M. designed the study. I.B. performed the experiments. I.B. and A.L.B.P. analyzed the data. I.B. and A.L.B.P. drafted the paper. All authors discussed the results and edited the manuscript.
Acknowledgments
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This work was supported by i-sense EPSRC IRC in Early Warning Sensing Systems in Infectious Disease (EP/K031953/1), the European Metrology Programme for Innovation and Research (EMPIR) joint research project [HLT07] “AntiMicroResist”, which has received funding from the EMPIR program cofinanced by the Participating States and the European Union’s Horizon 2020 Research and Innovation program, i-sense EPSRC IRC in Agile Early Warning Sensing Systems for Infectious Diseases and Antimicrobial Resistance (EP/R00529X/1), the EPSRC Royal Society Wolfson Research Merit Award, and by the UKRI/MRC Rutherford Innovation Fellowship (MR/R024871/1). I.B. was funded by the EPSRC UCL Impact Award Grant affiliated to i-sense. The authors would like to thank E. Gray (UCL) for providing knowledge on microbiology, K. Harris and R. Doyle (GOSH) for providing the clinical isolate, and T. Evans and B. Miller for assistance with data analysis (UCL).
References
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This article references 25 other publications.
- 1The Review on Antimicrobial Resistance Chaired by Jim O’Neill. Antimicrobial Resistance: Tackling a Crisis for the Future Health and Wealth of Nations; 2014.Google ScholarThere is no corresponding record for this reference.
- 2Department of Health; Annual Report of the Chief Medical Officer: Volume Two Infections and the rise of antimicrobial resistance ; 2011.Google ScholarThere is no corresponding record for this reference.
- 3Chatterjee, A.; Modarai, M.; Naylor, N. R.; Boyd, S. E.; Atun, R.; Barlow, J.; Holmes, A. H.; Johnson, A.; Robotham, J. V. Quantifying drivers of antibiotic resistance in humans: a systematic review. Lancet Infect. Dis. 2018, 18, e368– e378, DOI: 10.1016/S1473-3099(18)30296-2Google Scholar3Quantifying drivers of antibiotic resistance in humans: a systematic reviewChatterjee, Anuja; Modarai, Maryam; Naylor, Nichola R.; Boyd, Sara E.; Atun, Rifat; Barlow, James; Holmes, Alison H.; Johnson, Alan; Robotham, Julie V.Lancet Infectious Diseases (2018), 18 (12), e368-e378CODEN: LIDABP; ISSN:1473-3099. (Elsevier Ltd.)A review. Mitigating the risks of antibiotic resistance requires a horizon scan linking the quality with the quantity of data reported on drivers of antibiotic resistance in humans, arising from the human, animal, and environmental reservoirs. We did a systematic review using a One Health approach to survey the key drivers of antibiotic resistance in humans. Two sets of reviewers selected 565 studies from a total of 2819 titles and abstrs. identified in Embase, MEDLINE, and Scopus (2005-18), and the European Center for Disease Prevention and Control, the US Centers for Disease Control and Prevention, and WHO (One Health data). Study quality was assessed in accordance with Cochrane recommendations. Previous antibiotic exposure, underlying disease, and invasive procedures were the risk factors with most supporting evidence identified from the 88 risk factors retrieved. The odds ratios of antibiotic resistance were primarily reported to be between 2 and 4 for these risk factors when compared with their resp. controls or baseline risk groups. Food-related transmission from the animal reservoir and water-related transmission from the environmental reservoir were frequently quantified. Uniformly quantifying relationships between risk factors will help researchers to better understand the process by which antibiotic resistance arises in human infections.
- 4Kumar, A.; Ellis, P.; Arabi, Y.; Roberts, D.; Light, B.; Parrillo, J. E.; Dodek, P.; Wood, G.; Simon, D.; Peters, C.; Ahsan, M.; Chateau, D. Initiation of inappropriate antimicrobial therapy results in a fivefold reduction of survival in human septic shock. Chest 2009, 136, 1237– 1248, DOI: 10.1378/chest.09-0087Google Scholar4Initiation of inappropriate antimicrobial therapy results in a fivefold reduction of survival in human septic shockKumar Anand; Ellis Paul; Arabi Yaseen; Roberts Dan; Light Bruce; Peters Cheryl; Ahsan Muhammad; Parrillo Joseph E; Dodek Peter; Wood Gordon; Kumar Aseem; Simon David; Chateau DanChest (2009), 136 (5), 1237-1248 ISSN:.OBJECTIVE: Our goal was to determine the impact of the initiation of inappropriate antimicrobial therapy on survival to hospital discharge of patients with septic shock. METHODS: The appropriateness of initial antimicrobial therapy, the clinical infection site, and relevant pathogens were retrospectively determined for 5,715 patients with septic shock in three countries. RESULTS: Therapy with appropriate antimicrobial agents was initiated in 80.1% of cases. Overall, the survival rate was 43.7%. There were marked differences in the distribution of comorbidities, clinical infections, and pathogens in patients who received appropriate and inappropriate initial antimicrobial therapy (p < 0.0001 for each). The survival rates after appropriate and inappropriate initial therapy were 52.0% and 10.3%, respectively (odds ratio [OR], 9.45; 95% CI, 7.74 to 11.54; p < 0.0001). Similar differences in survival were seen in all major epidemiologic, clinical, and organism subgroups. The decrease in survival with inappropriate initial therapy ranged from 2.3-fold for pneumococcal infection to 17.6-fold with primary bacteremia. After adjustment for acute physiology and chronic health evaluation II score, comorbidities, hospital site, and other potential risk factors, the inappropriateness of initial antimicrobial therapy remained most highly associated with risk of death (OR, 8.99; 95% CI, 6.60 to 12.23). CONCLUSIONS: Inappropriate initial antimicrobial therapy for septic shock occurs in about 20% of patients and is associated with a fivefold reduction in survival. Efforts to increase the frequency of the appropriateness of initial antimicrobial therapy must be central to efforts to reduce the mortality of patients with septic shock.
- 5Doern, G. V.; Vautour, R.; Gaudet, M.; Levy, B. Clinical impact of rapid in vitro susceptibility testing and bacterial identification. J. Clin. Microbiol. 1994, 32, 1757– 1762, DOI: 10.1128/JCM.32.7.1757-1762.1994Google Scholar5Clinical impact of rapid in vitro susceptibility testing and bacterial identificationDoern G V; Vautour R; Gaudet M; Levy BJournal of clinical microbiology (1994), 32 (7), 1757-62 ISSN:0095-1137.During the past decade, a variety of instrument-assisted bacterial identification and antimicrobial susceptibility test systems have been developed which permit provision of test results in a matter of hours rather than days, as has been the case with traditional overnight procedures. These newer rapid techniques are much more expensive than older methods. It has been presumed but not proven that the clinical benefits of rapid testing to patients with infection offset the added cost. The intent of this study was to objectively define the clinical impact of rapid bacterial identification and antimicrobial susceptibility testing. A 1-year study was performed in which infected, hospitalized patients in a tertiary-care, teaching, medical center were randomly assigned to one of two groups: patients for whom identification and susceptibility testing was performed by using a semi-automated, rapid, same-day procedure and those for whom testing was accomplished by using traditional overnight techniques. The two groups were compared with respect to numerous demographic descriptors, and then patients were monitored prospectively through the end of their hospitalization with the aim of determining whether there existed objectively defineable differences in management and outcome between the two groups. The mean lengths of time to provision of susceptibility and identification test results in the rapid test group were 11.3 and 9.6 h, respectively. In the overnight test group, these values were 19.6 and 25.9 h, respectively (P < 0.0005). There were 273 evaluable patients in the first group and 300 in the second group. Other than the length of time required to provide susceptibility and identification test results, no significant differences were noted between the two groups with respect to > 100 demographic descriptors. With regard to measures of outcome, the mean lengths of hospitalization were also the same in both groups. Mortality rates were however, lower in the rapid test group (i.e., 8.8% versus 15.3%). Similarly, statistically significantly fewer laboratory studies, imaging procedures, days of intubation, and days in an intensive or intermediate-care area were observed with patients in the rapid test group. Rapid testing was also associated with significantly shortened lengths of elapsed time prior to alterations in antimicrobial therapy. Lastly, patient costs for hospitalization were significantly lower in the rapid test group. The results of this study indicate the rapid same-day bacterial identification and susceptibility testing in the microbiology laboratory can have a major impact on the care and outcome of hospitalized patients with infection.
- 6Barenfanger, J.; Drake, C.; Kacich, G. Clinical and financial benefits of rapid bacterial identification and antimicrobial susceptibility testing. J. Clin. Microbiol. 1999, 37, 1415– 1418, DOI: 10.1128/JCM.37.5.1415-1418.1999Google Scholar6Clinical and financial benefits of rapid bacterial identification and antimicrobial susceptibility testingBarenfanger J; Drake C; Kacich GJournal of clinical microbiology (1999), 37 (5), 1415-8 ISSN:0095-1137.To assess the expected clinical and financial benefits of rapid reporting of microbiology results, we compared patients whose cultured samples were processed in the normal manner to patients whose samples were processed more rapidly due to a minor change in work flow. For the samples tested in the rapid-reporting time period, the vast majority of bacterial identification and antimicrobial susceptibility testing (AST) results were verified with the Vitek system on the same day that they were available. This time period was called rapid AST (RAST). For RAST, a technologist on the evening shift verified the data that became available during that shift. For the control time period, cultures were processed in the normal manner (normal AST [NAST]), which did not include evening-shift verification. For NAST, the results for approximately half of the cultures were verified on the first day that the result was available. The average turnaround time for the reporting of AST results was 39.2 h for RAST and 44.4 h for NAST (5.2 h faster for RAST [P = 0.001]). Subsequently, physicians were able to initiate appropriate antimicrobial therapy sooner for patients whose samples were tested as part of RAST (P = 0.006). The mortality rates were 7. 9 and 9.6% for patients whose samples were tested as part of RAST and NAST, respectively (P = 0.45). The average length of stay was 10. 7 days per patient for RAST and 12.6 days for NAST, a difference of 2.0 days less for RAST (P = 0.006). The average variable cost was $4, 927 per patient for RAST and $6,677 for NAST, a difference of $1,750 less per patient for RAST (P = 0.001). This results in over $4 million in savings in variable costs per year in our hospital.
- 7The Review on Antimicrobial Resistance Chaired by Jim O’Neill. Tackling Drug-Resistant Infections Globally: Final Report and Recommendations; 2016.Google ScholarThere is no corresponding record for this reference.
- 8Doern, C. D. The Slow March toward Rapid Phenotypic Antimicrobial Susceptibility Testing: Are We There Yet?. J. Clin. Microbiol. 2018, 56, e01999, DOI: 10.1128/JCM.01999-17Google ScholarThere is no corresponding record for this reference.
- 9Pitruzzello, G.; Thorpe, S.; Johnson, S.; Evans, A.; Gadêlha, H.; Krauss, T. F. Multiparameter antibiotic resistance detection based on hydrodynamic trapping of individual E. coli. Lab Chip 2019, 19, 1417– 1426, DOI: 10.1039/C8LC01397GGoogle Scholar9Multiparameter antibiotic resistance detection based on hydrodynamic trapping of individual E. coliPitruzzello, Giampaolo; Thorpe, Stephen; Johnson, Steven; Evans, Adrian; Gadelha, Hermes; Krauss, Thomas F.Lab on a Chip (2019), 19 (8), 1417-1426CODEN: LCAHAM; ISSN:1473-0189. (Royal Society of Chemistry)There is an urgent need to develop novel methods for assessing the response of bacteria to antibiotics in a timely manner. Antibiotics are traditionally assessed via their effect on bacteria in a culture medium, which takes 24-48 h and exploits only a single parameter, i.e. growth. Here, we present a multiparameter approach at the single-cell level that takes approx. an hour from spiking the culture to correctly classify susceptible and resistant strains. By hydrodynamically trapping hundreds of bacteria, we simultaneously monitor the evolution of motility and morphol. of individual bacteria upon drug administration. We show how this combined detection method provides insights into the activity of antimicrobials at the onset of their action which single parameter and traditional tests cannot offer. Our observations complement the current growth-based methods and highlight the need for future antimicrobial susceptibility tests to take multiple parameters into account.
- 10Boedicker, J. Q.; Li, L.; Kline, T. R.; Ismagilov, R. F. Detecting bacteria and determining their susceptibility to antibiotics by stochastic confinement in nanoliter droplets using plug-based microfluidics. Lab on a Chip 2008, 8, 1265– 1272, DOI: 10.1039/b804911dGoogle Scholar10Detecting bacteria and determining their susceptibility to antibiotics by stochastic confinement in nanoliter droplets using plug-based microfluidicsBoedicker, James Q.; Li, Liang; Kline, Timothy R.; Ismagilov, Rustem F.Lab on a Chip (2008), 8 (8), 1265-1272CODEN: LCAHAM; ISSN:1473-0197. (Royal Society of Chemistry)This article describes plug-based microfluidic technol. that enables rapid detection and drug susceptibility screening of bacteria in samples, including complex biol. matrixes, without pre-incubation. Unlike conventional bacterial culture and detection methods, which rely on incubation of a sample to increase the concn. of bacteria to detectable levels, this method confines individual bacteria into droplets nanoliters in vol. When single cells are confined into plugs of small vol. such that the loading is less than one bacterium per plug, the detection time is proportional to plug vol. Confinement increases cell d. and allows released mols. to accumulate around the cell, eliminating the pre-incubation step and reducing the time required to detect the bacteria. The authors refer to this approach as stochastic confinement'. Using the microfluidic hybrid method, this technol. was used to det. the antibiogram - or chart of antibiotic sensitivity - of methicillin-resistant Staphylococcus aureus (MRSA) to many antibiotics in a single expt. and to measure the minimal inhibitory concn. (MIC) of the drug cefoxitin (CFX) against this strain. In addn., this technol. was used to distinguish between sensitive and resistant strains of S. aureus in samples of human blood plasma. High-throughput microfluidic techniques combined with single-cell measurements also enable multiple tests to be performed simultaneously on a single sample contg. bacteria. This technol. may provide a method of rapid and effective patient-specific treatment of bacterial infections and could be extended to a variety of applications that require multiple functional tests of bacterial samples on reduced timescales.
- 11Etayash, H.; Khan, M. F.; Kaur, K.; Thundat, T. Microfluidic cantilever detects bacteria and measures their susceptibility to antibiotics in small confined volumes. Nat. Commun. 2016, 7, 12947, DOI: 10.1038/ncomms12947Google Scholar11Microfluidic cantilever detects bacteria and measures their susceptibility to antibiotics in small confined volumesEtayash, Hashem; Khan, M. F.; Kaur, Kamaljit; Thundat, ThomasNature Communications (2016), 7 (), 12947CODEN: NCAOBW; ISSN:2041-1723. (Nature Publishing Group)In the fight against drug-resistant bacteria, accurate and high-throughput detection is essential. Here, a bimaterial microcantilever with an embedded microfluidic channel with internal surfaces chem. or phys. functionalized with receptors selectively captures the bacteria passing through the channel. Bacterial adsorption inside the cantilever results in changes in the resonance frequency (mass) and cantilever deflection (adsorption stress). The excitation of trapped bacteria using IR radiation (IR) causes the cantilever to deflect in proportion to the IR absorption of the bacteria, providing a nanomech. IR spectrum for selective identification. We demonstrate the in situ detection and discrimination of Listeria monocytogenes at a concn. of single cell per μl. Trapped Escherichia coli in the microchannel shows a distinct nanomech. response when exposed to antibiotics. This approach, which combines enrichment with three different modes of detection, can serve as a platform for the development of a portable, high-throughput device for use in the real-time detection of bacteria and their response to antibiotics.
- 12Baltekin, Ö.; Boucharin, A.; Tano, E.; Andersson, D. I.; Elf, J. Point-of-care antibiotic susceptibility test. PNAS 2017, 114, 201708558, DOI: 10.1073/pnas.1708558114Google ScholarThere is no corresponding record for this reference.
- 13Bermingham, C. R.; Murillo, I.; Payot, A. D. J.; Balram, K. C.; Kloucek, M. B.; Hanna, S.; Redmond, N. M.; Baxter, H.; Oulton, R.; Avison, M. B.; Antognozzi, M. Imaging of sub-cellular fluctuations provides a rapid way to observe bacterial viability and response to antibiotics. bioRxiv 2018, 460139, DOI: 10.1101/460139Google ScholarThere is no corresponding record for this reference.
- 14Ramos, D.; Tamayo, J.; Mertens, J.; Calleja, M.; Villanueva, L. G.; Zaballos, A. Detection of bacteria based on the thermomechanical noise of a nanomechanical resonator: origin of the response and detection limits. Nanotechnology 2008, 19, 035503 DOI: 10.1088/0957-4484/19/03/035503Google Scholar14Detection of bacteria based on the thermomechanical noise of a nanomechanical resonator: origin of the response and detection limitsRamos, D.; Tamayo, J.; Mertens, J.; Calleja, M.; Villanueva, L. G.; Zaballos, A.Nanotechnology (2008), 19 (3), 035503/1-035503/9CODEN: NNOTER; ISSN:0957-4484. (Institute of Physics Publishing)We have measured the effect of bacteria adsorption on the resonant frequency of microcantilevers as a function of the adsorption position and vibration mode. The resonant frequencies were measured from the Brownian fluctuations of the cantilever tip. We found that the sign and amt. of the resonant frequency change is detd. by the position and extent of the adsorption on the cantilever with regard to the shape of the vibration mode. To explain these results, a theor. one-dimensional model is proposed. We obtain anal. expressions for the resonant frequency that accurately fit the data obtained by the finite element method. More importantly, the theory data shows a good agreement with the expts. Our results indicate that there exist two opposite mechanisms that can produce a significant resonant frequency shift: the stiffness and the mass of the bacterial cells. Based on the thermomech. noise, we analyze the regions of the cantilever of lowest and highest sensitivity to the attachment of bacteria. The combination of high vibration modes and the confinement of the adsorption to defined regions of the cantilever allows the detection of single bacterial cells by only measuring the Brownian fluctuations. This study can be extended to smaller cantilevers and other biol. systems such as proteins and nucleic acids.
- 15Choi, J.; Yoo, J.; Lee, M.; Kim, E. G.; Lee, J. S.; Lee, S.; Joo, S.; Song, S. H.; Kim, E. C.; Lee, J. C.; Kim, H. C.; Jung, Y. G.; Kwon, S. A rapid antimicrobial susceptibility test based on single-cell morphological analysis. Sci. Transl. Med. 2014, 6, 267ra174, DOI: 10.1126/scitranslmed.3009650Google ScholarThere is no corresponding record for this reference.
- 16Syal, K.; Iriya, R.; Yang, Y.; Yu, H.; Wang, S.; Haydel, S. E.; Chen, H. Y.; Tao, N. Antimicrobial Susceptibility Test with Plasmonic Imaging and Tracking of Single Bacterial Motions on Nanometer Scale. ACS Nano 2016, 10, 845– 852, DOI: 10.1021/acsnano.5b05944Google Scholar16Antimicrobial Susceptibility Test with Plasmonic Imaging and Tracking of Single Bacterial Motions on Nanometer ScaleSyal, Karan; Iriya, Rafael; Yang, Yunze; Yu, Hui; Wang, Shaopeng; Haydel, Shelley E.; Chen, Hong-Yuan; Tao, NongjianACS Nano (2016), 10 (1), 845-852CODEN: ANCAC3; ISSN:1936-0851. (American Chemical Society)Antimicrobial susceptibility tests (ASTs) are important for confirming susceptibility to empirical antibiotics and detecting resistance in bacterial isolates. Currently, most ASTs performed in clin. microbiol. labs. are based on bacterial culturing, which take days to complete for slowly growing microorganisms. A faster AST will reduce morbidity and mortality rates and help healthcare providers administer narrow spectrum antibiotics at the earliest possible treatment stage. The authors report the development of a nonculture-based AST using a plasmonic imaging and tracking (PIT) technol. The authors track the motion of individual bacterial cells tethered to a surface with nanometer (nm) precision and correlate the phenotypic motion with bacterial metab. and antibiotic action. Antibiotic action significantly slows down bacterial motion, which can be quantified for development of a rapid phenotypic-based AST.
- 17Yu, H.; Jing, W.; Iriya, R.; Yang, Y.; Syal, K.; Mo, M.; Grys, T. E.; Haydel, S. E.; Wang, S.; Tao, N. Phenotypic Antimicrobial Susceptibility Testing with Deep Learning Video Microscopy. Anal. Chem. 2018, 90, 6314– 6322, DOI: 10.1021/acs.analchem.8b01128Google Scholar17Phenotypic Antimicrobial Susceptibility Testing with Deep Learning Video MicroscopyYu, Hui; Jing, Wenwen; Iriya, Rafael; Yang, Yunze; Syal, Karan; Mo, Manni; Grys, Thomas E.; Haydel, Shelley E.; Wang, Shaopeng; Tao, NongjianAnalytical Chemistry (Washington, DC, United States) (2018), 90 (10), 6314-6322CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)Timely detn. of antimicrobial susceptibility for a bacterial infection enables precision prescription, shortens treatment time, and helps minimize the spread of antibiotic resistant infections. Current antimicrobial susceptibility testing (AST) methods often take several days and thus impede these clin. and health benefits. Here, we present an AST method by imaging freely moving bacterial cells in urine in real time and analyzing the videos with a deep learning algorithm. The deep learning algorithm dets. if an antibiotic inhibits a bacterial cell by learning multiple phenotypic features of the cell without the need for defining and quantifying each feature. We apply the method to urinary tract infection, a common infection that affects millions of people, to det. the min. inhibitory concn. of pathogens from both bacteria spiked urine and clin. infected urine samples for different antibiotics within 30 min and validate the results with the gold std. broth macrodilution method. The deep learning video microscopy-based AST holds great potential to contribute to the soln. of increasing drug-resistant infections.
- 18Longo, G.; Alonso-Sarduy, L.; Rio, L. M.; Bizzini, A.; Trampuz, A.; Notz, J.; Dietler, G.; Kasas, S. Rapid detection of bacterial resistance to antibiotics using AFM cantilevers as nanomechanical sensors. Nat. Nanotechnol. 2013, 8, 522– 526, DOI: 10.1038/nnano.2013.120Google Scholar18Rapid detection of bacterial resistance to antibiotics using AFM cantilevers as nanomechanical sensorsLongo, G.; Alonso-Sarduy, L.; Rio, L. Marques; Bizzini, A.; Trampuz, A.; Notz, J.; Dietler, G.; Kasas, S.Nature Nanotechnology (2013), 8 (7), 522-526CODEN: NNAABX; ISSN:1748-3387. (Nature Publishing Group)The widespread misuse of drugs has increased the no. of multiresistant bacteria, and this means that tools that can rapidly detect and characterize bacterial response to antibiotics are much needed in the management of infections. Various techniques, such as the resazurin-redn. assays, the mycobacterial growth indicator tube or polymerase chain reaction-based methods, have been used to investigate bacterial metab. and its response to drugs. However, many are relatively expensive or unable to distinguish between living and dead bacteria. Here we show that the fluctuations of highly sensitive at. force microscope cantilevers can be used to detect low concns. of bacteria, characterize their metab. and quant. screen (within minutes) their response to antibiotics. We applied this methodol. to Escherichia coli and Staphylococcus aureus, showing that live bacteria produced larger cantilever fluctuations than bacteria exposed to antibiotics. Our preliminary expts. suggest that the fluctuation is assocd. with bacterial metab.
- 19Stupar, P.; Opota, O.; Longo, G.; Prod’hom, G.; Dietler, G.; Greub, G.; Kasas, S. Nanomechanical sensor applied to blood culture pellets: a fast approach to determine the antibiotic susceptibility against agents of bloodstream infections. Clin Microbiol Infect 2017, 23, 400, DOI: 10.1016/j.cmi.2016.12.028Google Scholar19Nanomechanical sensor applied to blood culture pellets: a fast approach to determine the antibiotic susceptibility against agents of bloodstream infectionsStupar, P.; Opota, O.; Longo, G.; Prod'hom, G.; Dietler, G.; Greub, G.; Kasas, S.Clinical Microbiology and Infection (2017), 23 (6), 400-405CODEN: CMINFM; ISSN:1198-743X. (Elsevier Ltd.)The management of bloodstream infection, a life-threatening disease, largely relies on early detection of infecting microorganisms and accurate detn. of their antibiotic susceptibility to reduce both mortality and morbidity. Recently we developed a new technique based on at. force microscopy capable of detecting movements of biol. samples at the nanoscale. Such sensor is able to monitor the response of bacteria to antibiotics pressure, allowing a fast and versatile susceptibility test. Furthermore, rapid prepn. of a bacterial pellet from a pos. blood culture can improve downstream characterization of the recovered pathogen as a result of the increased bacterial concn. obtained. Using artificially inoculated blood cultures, we combined these two innovative procedures and validated them in double-blind expts. to det. the susceptibility and resistance of Escherichia coli strains (ATCC 25933 as susceptible and a characterized clin. isolate as resistant strain) towards a selection of antibiotics commonly used in clin. settings. On the basis of the variance of the sensor movements, we were able to pos. discriminate the resistant from the susceptible E. coli strains in 16 of 17 blindly investigated cases. Furthermore, we defined a variance change threshold of 60% that discriminates susceptible from resistant strains. By combining the nanomotion sensor with the rapid prepn. method of blood culture pellets, we obtained an innovative, rapid and relatively accurate method for antibiotic susceptibility test directly from pos. blood culture bottles, without the need for bacterial subculture.
- 20Villalba, M. I.; Stupar, P.; Chomicki, W.; Bertacchi, M.; Dietler, G.; Arnal, L.; Vela, M. E.; Yantorno, O.; Kasas, S. Nanomotion Detection Method for Testing Antibiotic Resistance and Susceptibility of Slow-Growing Bacteria. Small 2018, 14, 1702671, DOI: 10.1002/smll.201702671Google ScholarThere is no corresponding record for this reference.
- 21Kasas, S.; Ruggeri, F. S.; Benadiba, C.; Maillard, C.; Stupar, P.; Tournu, H.; Dietler, G.; Longo, G. Detecting nanoscale vibrations as signature of life. Proc Nat Acad Sci 2015, 112, 378– 381, DOI: 10.1073/pnas.1415348112Google Scholar21Detecting nanoscale vibrations as signature of lifeKasas, Sandor; Ruggeri, Francesco Simone; Benadiba, Carine; Maillard, Caroline; Stupar, Petar; Tournu, Helene; Dietler, Giovanni; Longo, GiovanniProceedings of the National Academy of Sciences of the United States of America (2015), 112 (2), 378-381CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)The existence of life in extreme conditions, in particular in extraterrestrial environments, is certainly one of the most intriguing scientific questions of our time. In this report, we demonstrate the use of an innovative nanoscale motion sensor in life-searching expts. in Earth-bound and interplanetary missions. This technique exploits the sensitivity of nanomech. oscillators to transduce the small fluctuations that characterize living systems. The intensity of such movements is an indication of the viability of living specimens and conveys information related to their metabolic activity. Here, we show that the nanomotion detector can assess the viability of a vast range of biol. specimens and that it could be the perfect complement to conventional chem. life-detection assays. Indeed, by combining chem. and dynamical measurements, we could achieve an unprecedented depth in the characterization of life in extreme and extraterrestrial environments.
- 22Andrews, J. M. Determination of minimum inhibitory concentrations. J. Antimicrob. Chemother. 2001, 48, 5– 16, DOI: 10.1093/jac/48.suppl_1.5Google Scholar22Determination of minimum inhibitory concentrationsAndrews, Jennifer M.Journal of Antimicrobial Chemotherapy (2001), 48 (Suppl. S1), 5-16CODEN: JACHDX; ISSN:0305-7453. (Oxford University Press)A review with 3 refs. Min. inhibitory concns. (MICs) are defined as the lowest concn. of an antimicrobial that will inhibit the visible growth of a microorganism after overnight incubation, and min. bactericidal concns. (MBCs) as the lowest concn. of antimicrobial that will prevent the growth of an organism after subculture on to antibiotic-free media. MICs are used by diagnostic labs. mainly to confirm resistance, but most often as a research tool to det. the in vitro activity of new antimicrobials, and data from such studies have been used to det. MIC breakpoints. MBC detns. are undertaken less frequently and their major use has been reserved for isolates from the blood of patients with endocarditis. Standardized methods for detg. MICs and MBCs are described in this paper. Like all standardized procedures, the method must be adhered to and may not be adapted by the user. The method gives information on the storage of std. antibiotic powder, prepn. of stock antibiotic solns., media, prepn. of inocula, incubation conditions, and reading and interpretation of results. Tables giving expected MIC ranges for control NCTC and ATCC strains are also supplied.
- 23Annis, D. H.; Craig, B. A. The effect of interlaboratory variability on antimicrobial susceptibility determination. Diagn Microbiol Infect Dis 2005, 53, 61– 64, DOI: 10.1016/j.diagmicrobio.2005.03.012Google Scholar23The effect of interlaboratory variability on antimicrobial susceptibility determinationAnnis, David H.; Craig, Bruce A.Diagnostic Microbiology and Infectious Disease (2005), 53 (1), 61-64CODEN: DMIDDZ; ISSN:0732-8893. (Elsevier Inc.)In the min. inhibitory concn. (MIC) test literature, discussion concerning the effect of lab.-to-lab. variation is lacking. We present 2 sets of drug diln. test quality control data that illustrate considerable lab. differences in measured MIC. In both isolates (Escherichia coli, ATCC 25922; Staphylococcus aureus, ATCC 29213) the lab.-to-lab. variability accounts for approx. half of the total variability. We illustrate the impact of this variability on the probability of correctly classifying the susceptibility level of an isolate and on the estn. of resistance prevalence. For example, we show that lab. differences in the probability of correctly classifying the isolate (specifically near the lower breakpoint) can vary up to 80%.
- 24Mouton, J. W.; Meletiadis, J.; Voss, A.; Turnidge, J. Variation of MIC measurements: the contribution of strain and laboratory variability to measurement precision. J Antimicrob Chemother 2018, 73, 2374– 2379, DOI: 10.1093/jac/dky232Google Scholar24Variation of MIC measurements: the contribution of strain and laboratory variability to measurement precisionMouton, Johan W.; Meletiadis, Joseph; Voss, Andreas; Turnidge, JohnJournal of Antimicrobial Chemotherapy (2018), 73 (9), 2374-2379CODEN: JACHDX; ISSN:1460-2091. (Oxford University Press)Although testing of antimicrobial agents for susceptibility has inherent variability like any assay, it is generally held that there are also real differences in susceptibility between strains. In the routine lab., variability of the MIC measurement may be sufficient to mask real strain differences. We detd. which factors contributed to the variability, using linezolid against Staphylococcus aureus as one example. Twenty-five S. aureus strains were sent to five different labs. in quadruplicate in a blinded fashion. Labs. detd. MICs of linezolid using Etest. Results of 22 strains corresponding to 440 observations were available for anal. Sources of variability were explored and quantified using an ANOVA approach. The overall geometric mean MIC was 1.8 mg/L, comparable to that of the published WT distribution of 1.7 mg/L. The total variation amounted to ∼1.3 2-fold dilns. for a one-sided CI of 95% and two 2-fold dilns. for a CI of 99%. Variation between labs. and variation between strains contributed 10% and 48%, and in a subset anal. averaging 17% and 26%, resp. Strain-to-strain variation (biol. variation) could not be reliably detd., even with four replicates. This anal. serves as an example of an approach to discerning various sources of MIC variation. Here, at best, a single measurement of an MIC may provide an indication of whether it likely belongs to the WT distribution. Only repeated measurements of MICs for individual strains within one lab. may provide an indication of differences in susceptibility between strains.
- 25Flores-Mireles, A. L.; Walker, J. N.; Caparon, M.; Hultgren, S. J. Urinary tract infections: epidemiology, mechanisms of infection and treatment options. Nat Rev Microbiol 2015, 13, 269– 284, DOI: 10.1038/nrmicro3432Google Scholar25Urinary tract infections: epidemiology, mechanisms of infection and treatment optionsFlores-Mireles, Ana L.; Walker, Jennifer N.; Caparon, Michael; Hultgren, Scott J.Nature Reviews Microbiology (2015), 13 (5), 269-284CODEN: NRMACK; ISSN:1740-1526. (Nature Publishing Group)Urinary tract infections (UTIs) are a severe public health problem and are caused by a range of pathogens, but most commonly by Escherichia coli, Klebsiella pneumoniae, Proteus mirabilis, Enterococcus faecalis and Staphylococcus saprophyticus. High recurrence rates and increasing antimicrobial resistance among uropathogens threaten to greatly increase the economic burden of these infections. In this Review, we discuss how basic science studies are elucidating the mol. details of the crosstalk that occurs at the host-pathogen interface, as well as the consequences of these interactions for the pathophysiol. of UTIs. We also describe current efforts to translate this knowledge into new clin. treatments for UTIs.
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Abstract
Figure 1
Figure 1. Principle of the rapid optical AST method. (a) Illustration of bacterial cells inoculated in growth media with antibiotic molecules, with laser reflecting off the cantilever surface onto a photodiode detector. Bacteria in the solution move through the laser beam, which can be observed as peaks in the photodiode signal. The photodiode signal measured from the media solution decreases after the addition of the antibiotic for sensitive strains. (b–d) Photodiode signal (b) without bacterial inoculant, (c) with bacteria in solution, and (d) 45 min after addition of the antibiotic.
Figure 2
Figure 2. Data analysis of initial mechanical signal experiments. (a, b) Subtraction of linear regression from raw data and large peaks not caused by mechanical motion of the cantilever identified (*). (c, d) Averaging of variance over 10 s segments and (e) removing large peaks from the average variance calculation for one experiment. (f) Average variance for n = 5 experiments, pre-treatment (green, pre-amp) and 15 min post-treatment (red, post-amp) with 125 μg/mL ampicillin for optimal immobilization count cantilever D experiments. P = 0.4569. Cantilever D: k = 0.06 N/m, fres = 4 kHz.
Figure 3
Figure 3. Signal caused by bacteria crossing the laser path decreases after 45 min from antibiotic addition. (a) At a low bacterial inoculant concentration, individual peaks can be identified within the signal. Combined optical tracking and signal measurement shows (a) of single bacterium (blue circle) passing through the laser path (b, optical images) as a single peak in the signal (c). (d) Bacterial concentration (CFU, × 105) correlates with the number of bacterial crossings.
Figure 4
Figure 4. Peak identifying and counting analysis. (a) Number of bacterial crossings in 800 s was calculated and plotted over the course of the experiment. (b) Raw data traces for points at “media only” (gray box), “inoculated media” (black box), and “inoculated media containing an antibiotic” (green box). Peaks identified (blue triangles) as ±0.5 nm from previous peaks. Each point in (a) is the total number of peaks identified in 800 s.
Figure 5
Figure 5. Systematic analysis of antibiotic susceptibility in clinical and laboratory strains of E. coli. (a) Susceptibility of BL21-WT (S, green) and BL21-ampR E. coli (R, red) to 125 μg/mL ampicillin. Addition of bacteria (yellow dotted line) and antibiotic solution (dark blue dotted line) to the system cause large fluctuations in the signal as the liquid is mixed, which dissipate within ∼800 s. The number of bacterial crossings in a given time period, here 800 s, is plotted. The number of bacterial crossings shows a decrease in 45 min after antibiotic addition. (b) Determination of the resistance profile, with sensitivity readout (rsensitivity). rsensitivity was calculated from the ratio of crossings postantibiotic and preantibiotic treatments at set time points marked in blue in (a). Strains were determined to be sensitive (S) if rsensitivity < 1 (green) or resistant (R) if rsensitivity ⩾ 1 (red), cut off (rsensitivity = 1) shown as a blue dashed line, shown for five concentrations of ampicillin and BL21 E. coli. (c) Susceptibility of a clinical isolate of E. coli, determined to be resistant to both ampicillin (purple line) and trimethoprim (blue line). (d) Determination of resistance profile. rsensitivity for repeats of clinical isolate with 125 μg/mL trimethoprim and ampicillin. Antibiotic concentrations are given in μg/mL.
References
This article references 25 other publications.
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- 4Kumar, A.; Ellis, P.; Arabi, Y.; Roberts, D.; Light, B.; Parrillo, J. E.; Dodek, P.; Wood, G.; Simon, D.; Peters, C.; Ahsan, M.; Chateau, D. Initiation of inappropriate antimicrobial therapy results in a fivefold reduction of survival in human septic shock. Chest 2009, 136, 1237– 1248, DOI: 10.1378/chest.09-00874Initiation of inappropriate antimicrobial therapy results in a fivefold reduction of survival in human septic shockKumar Anand; Ellis Paul; Arabi Yaseen; Roberts Dan; Light Bruce; Peters Cheryl; Ahsan Muhammad; Parrillo Joseph E; Dodek Peter; Wood Gordon; Kumar Aseem; Simon David; Chateau DanChest (2009), 136 (5), 1237-1248 ISSN:.OBJECTIVE: Our goal was to determine the impact of the initiation of inappropriate antimicrobial therapy on survival to hospital discharge of patients with septic shock. METHODS: The appropriateness of initial antimicrobial therapy, the clinical infection site, and relevant pathogens were retrospectively determined for 5,715 patients with septic shock in three countries. RESULTS: Therapy with appropriate antimicrobial agents was initiated in 80.1% of cases. Overall, the survival rate was 43.7%. There were marked differences in the distribution of comorbidities, clinical infections, and pathogens in patients who received appropriate and inappropriate initial antimicrobial therapy (p < 0.0001 for each). The survival rates after appropriate and inappropriate initial therapy were 52.0% and 10.3%, respectively (odds ratio [OR], 9.45; 95% CI, 7.74 to 11.54; p < 0.0001). Similar differences in survival were seen in all major epidemiologic, clinical, and organism subgroups. The decrease in survival with inappropriate initial therapy ranged from 2.3-fold for pneumococcal infection to 17.6-fold with primary bacteremia. After adjustment for acute physiology and chronic health evaluation II score, comorbidities, hospital site, and other potential risk factors, the inappropriateness of initial antimicrobial therapy remained most highly associated with risk of death (OR, 8.99; 95% CI, 6.60 to 12.23). CONCLUSIONS: Inappropriate initial antimicrobial therapy for septic shock occurs in about 20% of patients and is associated with a fivefold reduction in survival. Efforts to increase the frequency of the appropriateness of initial antimicrobial therapy must be central to efforts to reduce the mortality of patients with septic shock.
- 5Doern, G. V.; Vautour, R.; Gaudet, M.; Levy, B. Clinical impact of rapid in vitro susceptibility testing and bacterial identification. J. Clin. Microbiol. 1994, 32, 1757– 1762, DOI: 10.1128/JCM.32.7.1757-1762.19945Clinical impact of rapid in vitro susceptibility testing and bacterial identificationDoern G V; Vautour R; Gaudet M; Levy BJournal of clinical microbiology (1994), 32 (7), 1757-62 ISSN:0095-1137.During the past decade, a variety of instrument-assisted bacterial identification and antimicrobial susceptibility test systems have been developed which permit provision of test results in a matter of hours rather than days, as has been the case with traditional overnight procedures. These newer rapid techniques are much more expensive than older methods. It has been presumed but not proven that the clinical benefits of rapid testing to patients with infection offset the added cost. The intent of this study was to objectively define the clinical impact of rapid bacterial identification and antimicrobial susceptibility testing. A 1-year study was performed in which infected, hospitalized patients in a tertiary-care, teaching, medical center were randomly assigned to one of two groups: patients for whom identification and susceptibility testing was performed by using a semi-automated, rapid, same-day procedure and those for whom testing was accomplished by using traditional overnight techniques. The two groups were compared with respect to numerous demographic descriptors, and then patients were monitored prospectively through the end of their hospitalization with the aim of determining whether there existed objectively defineable differences in management and outcome between the two groups. The mean lengths of time to provision of susceptibility and identification test results in the rapid test group were 11.3 and 9.6 h, respectively. In the overnight test group, these values were 19.6 and 25.9 h, respectively (P < 0.0005). There were 273 evaluable patients in the first group and 300 in the second group. Other than the length of time required to provide susceptibility and identification test results, no significant differences were noted between the two groups with respect to > 100 demographic descriptors. With regard to measures of outcome, the mean lengths of hospitalization were also the same in both groups. Mortality rates were however, lower in the rapid test group (i.e., 8.8% versus 15.3%). Similarly, statistically significantly fewer laboratory studies, imaging procedures, days of intubation, and days in an intensive or intermediate-care area were observed with patients in the rapid test group. Rapid testing was also associated with significantly shortened lengths of elapsed time prior to alterations in antimicrobial therapy. Lastly, patient costs for hospitalization were significantly lower in the rapid test group. The results of this study indicate the rapid same-day bacterial identification and susceptibility testing in the microbiology laboratory can have a major impact on the care and outcome of hospitalized patients with infection.
- 6Barenfanger, J.; Drake, C.; Kacich, G. Clinical and financial benefits of rapid bacterial identification and antimicrobial susceptibility testing. J. Clin. Microbiol. 1999, 37, 1415– 1418, DOI: 10.1128/JCM.37.5.1415-1418.19996Clinical and financial benefits of rapid bacterial identification and antimicrobial susceptibility testingBarenfanger J; Drake C; Kacich GJournal of clinical microbiology (1999), 37 (5), 1415-8 ISSN:0095-1137.To assess the expected clinical and financial benefits of rapid reporting of microbiology results, we compared patients whose cultured samples were processed in the normal manner to patients whose samples were processed more rapidly due to a minor change in work flow. For the samples tested in the rapid-reporting time period, the vast majority of bacterial identification and antimicrobial susceptibility testing (AST) results were verified with the Vitek system on the same day that they were available. This time period was called rapid AST (RAST). For RAST, a technologist on the evening shift verified the data that became available during that shift. For the control time period, cultures were processed in the normal manner (normal AST [NAST]), which did not include evening-shift verification. For NAST, the results for approximately half of the cultures were verified on the first day that the result was available. The average turnaround time for the reporting of AST results was 39.2 h for RAST and 44.4 h for NAST (5.2 h faster for RAST [P = 0.001]). Subsequently, physicians were able to initiate appropriate antimicrobial therapy sooner for patients whose samples were tested as part of RAST (P = 0.006). The mortality rates were 7. 9 and 9.6% for patients whose samples were tested as part of RAST and NAST, respectively (P = 0.45). The average length of stay was 10. 7 days per patient for RAST and 12.6 days for NAST, a difference of 2.0 days less for RAST (P = 0.006). The average variable cost was $4, 927 per patient for RAST and $6,677 for NAST, a difference of $1,750 less per patient for RAST (P = 0.001). This results in over $4 million in savings in variable costs per year in our hospital.
- 7The Review on Antimicrobial Resistance Chaired by Jim O’Neill. Tackling Drug-Resistant Infections Globally: Final Report and Recommendations; 2016.There is no corresponding record for this reference.
- 8Doern, C. D. The Slow March toward Rapid Phenotypic Antimicrobial Susceptibility Testing: Are We There Yet?. J. Clin. Microbiol. 2018, 56, e01999, DOI: 10.1128/JCM.01999-17There is no corresponding record for this reference.
- 9Pitruzzello, G.; Thorpe, S.; Johnson, S.; Evans, A.; Gadêlha, H.; Krauss, T. F. Multiparameter antibiotic resistance detection based on hydrodynamic trapping of individual E. coli. Lab Chip 2019, 19, 1417– 1426, DOI: 10.1039/C8LC01397G9Multiparameter antibiotic resistance detection based on hydrodynamic trapping of individual E. coliPitruzzello, Giampaolo; Thorpe, Stephen; Johnson, Steven; Evans, Adrian; Gadelha, Hermes; Krauss, Thomas F.Lab on a Chip (2019), 19 (8), 1417-1426CODEN: LCAHAM; ISSN:1473-0189. (Royal Society of Chemistry)There is an urgent need to develop novel methods for assessing the response of bacteria to antibiotics in a timely manner. Antibiotics are traditionally assessed via their effect on bacteria in a culture medium, which takes 24-48 h and exploits only a single parameter, i.e. growth. Here, we present a multiparameter approach at the single-cell level that takes approx. an hour from spiking the culture to correctly classify susceptible and resistant strains. By hydrodynamically trapping hundreds of bacteria, we simultaneously monitor the evolution of motility and morphol. of individual bacteria upon drug administration. We show how this combined detection method provides insights into the activity of antimicrobials at the onset of their action which single parameter and traditional tests cannot offer. Our observations complement the current growth-based methods and highlight the need for future antimicrobial susceptibility tests to take multiple parameters into account.
- 10Boedicker, J. Q.; Li, L.; Kline, T. R.; Ismagilov, R. F. Detecting bacteria and determining their susceptibility to antibiotics by stochastic confinement in nanoliter droplets using plug-based microfluidics. Lab on a Chip 2008, 8, 1265– 1272, DOI: 10.1039/b804911d10Detecting bacteria and determining their susceptibility to antibiotics by stochastic confinement in nanoliter droplets using plug-based microfluidicsBoedicker, James Q.; Li, Liang; Kline, Timothy R.; Ismagilov, Rustem F.Lab on a Chip (2008), 8 (8), 1265-1272CODEN: LCAHAM; ISSN:1473-0197. (Royal Society of Chemistry)This article describes plug-based microfluidic technol. that enables rapid detection and drug susceptibility screening of bacteria in samples, including complex biol. matrixes, without pre-incubation. Unlike conventional bacterial culture and detection methods, which rely on incubation of a sample to increase the concn. of bacteria to detectable levels, this method confines individual bacteria into droplets nanoliters in vol. When single cells are confined into plugs of small vol. such that the loading is less than one bacterium per plug, the detection time is proportional to plug vol. Confinement increases cell d. and allows released mols. to accumulate around the cell, eliminating the pre-incubation step and reducing the time required to detect the bacteria. The authors refer to this approach as stochastic confinement'. Using the microfluidic hybrid method, this technol. was used to det. the antibiogram - or chart of antibiotic sensitivity - of methicillin-resistant Staphylococcus aureus (MRSA) to many antibiotics in a single expt. and to measure the minimal inhibitory concn. (MIC) of the drug cefoxitin (CFX) against this strain. In addn., this technol. was used to distinguish between sensitive and resistant strains of S. aureus in samples of human blood plasma. High-throughput microfluidic techniques combined with single-cell measurements also enable multiple tests to be performed simultaneously on a single sample contg. bacteria. This technol. may provide a method of rapid and effective patient-specific treatment of bacterial infections and could be extended to a variety of applications that require multiple functional tests of bacterial samples on reduced timescales.
- 11Etayash, H.; Khan, M. F.; Kaur, K.; Thundat, T. Microfluidic cantilever detects bacteria and measures their susceptibility to antibiotics in small confined volumes. Nat. Commun. 2016, 7, 12947, DOI: 10.1038/ncomms1294711Microfluidic cantilever detects bacteria and measures their susceptibility to antibiotics in small confined volumesEtayash, Hashem; Khan, M. F.; Kaur, Kamaljit; Thundat, ThomasNature Communications (2016), 7 (), 12947CODEN: NCAOBW; ISSN:2041-1723. (Nature Publishing Group)In the fight against drug-resistant bacteria, accurate and high-throughput detection is essential. Here, a bimaterial microcantilever with an embedded microfluidic channel with internal surfaces chem. or phys. functionalized with receptors selectively captures the bacteria passing through the channel. Bacterial adsorption inside the cantilever results in changes in the resonance frequency (mass) and cantilever deflection (adsorption stress). The excitation of trapped bacteria using IR radiation (IR) causes the cantilever to deflect in proportion to the IR absorption of the bacteria, providing a nanomech. IR spectrum for selective identification. We demonstrate the in situ detection and discrimination of Listeria monocytogenes at a concn. of single cell per μl. Trapped Escherichia coli in the microchannel shows a distinct nanomech. response when exposed to antibiotics. This approach, which combines enrichment with three different modes of detection, can serve as a platform for the development of a portable, high-throughput device for use in the real-time detection of bacteria and their response to antibiotics.
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- 14Ramos, D.; Tamayo, J.; Mertens, J.; Calleja, M.; Villanueva, L. G.; Zaballos, A. Detection of bacteria based on the thermomechanical noise of a nanomechanical resonator: origin of the response and detection limits. Nanotechnology 2008, 19, 035503 DOI: 10.1088/0957-4484/19/03/03550314Detection of bacteria based on the thermomechanical noise of a nanomechanical resonator: origin of the response and detection limitsRamos, D.; Tamayo, J.; Mertens, J.; Calleja, M.; Villanueva, L. G.; Zaballos, A.Nanotechnology (2008), 19 (3), 035503/1-035503/9CODEN: NNOTER; ISSN:0957-4484. (Institute of Physics Publishing)We have measured the effect of bacteria adsorption on the resonant frequency of microcantilevers as a function of the adsorption position and vibration mode. The resonant frequencies were measured from the Brownian fluctuations of the cantilever tip. We found that the sign and amt. of the resonant frequency change is detd. by the position and extent of the adsorption on the cantilever with regard to the shape of the vibration mode. To explain these results, a theor. one-dimensional model is proposed. We obtain anal. expressions for the resonant frequency that accurately fit the data obtained by the finite element method. More importantly, the theory data shows a good agreement with the expts. Our results indicate that there exist two opposite mechanisms that can produce a significant resonant frequency shift: the stiffness and the mass of the bacterial cells. Based on the thermomech. noise, we analyze the regions of the cantilever of lowest and highest sensitivity to the attachment of bacteria. The combination of high vibration modes and the confinement of the adsorption to defined regions of the cantilever allows the detection of single bacterial cells by only measuring the Brownian fluctuations. This study can be extended to smaller cantilevers and other biol. systems such as proteins and nucleic acids.
- 15Choi, J.; Yoo, J.; Lee, M.; Kim, E. G.; Lee, J. S.; Lee, S.; Joo, S.; Song, S. H.; Kim, E. C.; Lee, J. C.; Kim, H. C.; Jung, Y. G.; Kwon, S. A rapid antimicrobial susceptibility test based on single-cell morphological analysis. Sci. Transl. Med. 2014, 6, 267ra174, DOI: 10.1126/scitranslmed.3009650There is no corresponding record for this reference.
- 16Syal, K.; Iriya, R.; Yang, Y.; Yu, H.; Wang, S.; Haydel, S. E.; Chen, H. Y.; Tao, N. Antimicrobial Susceptibility Test with Plasmonic Imaging and Tracking of Single Bacterial Motions on Nanometer Scale. ACS Nano 2016, 10, 845– 852, DOI: 10.1021/acsnano.5b0594416Antimicrobial Susceptibility Test with Plasmonic Imaging and Tracking of Single Bacterial Motions on Nanometer ScaleSyal, Karan; Iriya, Rafael; Yang, Yunze; Yu, Hui; Wang, Shaopeng; Haydel, Shelley E.; Chen, Hong-Yuan; Tao, NongjianACS Nano (2016), 10 (1), 845-852CODEN: ANCAC3; ISSN:1936-0851. (American Chemical Society)Antimicrobial susceptibility tests (ASTs) are important for confirming susceptibility to empirical antibiotics and detecting resistance in bacterial isolates. Currently, most ASTs performed in clin. microbiol. labs. are based on bacterial culturing, which take days to complete for slowly growing microorganisms. A faster AST will reduce morbidity and mortality rates and help healthcare providers administer narrow spectrum antibiotics at the earliest possible treatment stage. The authors report the development of a nonculture-based AST using a plasmonic imaging and tracking (PIT) technol. The authors track the motion of individual bacterial cells tethered to a surface with nanometer (nm) precision and correlate the phenotypic motion with bacterial metab. and antibiotic action. Antibiotic action significantly slows down bacterial motion, which can be quantified for development of a rapid phenotypic-based AST.
- 17Yu, H.; Jing, W.; Iriya, R.; Yang, Y.; Syal, K.; Mo, M.; Grys, T. E.; Haydel, S. E.; Wang, S.; Tao, N. Phenotypic Antimicrobial Susceptibility Testing with Deep Learning Video Microscopy. Anal. Chem. 2018, 90, 6314– 6322, DOI: 10.1021/acs.analchem.8b0112817Phenotypic Antimicrobial Susceptibility Testing with Deep Learning Video MicroscopyYu, Hui; Jing, Wenwen; Iriya, Rafael; Yang, Yunze; Syal, Karan; Mo, Manni; Grys, Thomas E.; Haydel, Shelley E.; Wang, Shaopeng; Tao, NongjianAnalytical Chemistry (Washington, DC, United States) (2018), 90 (10), 6314-6322CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)Timely detn. of antimicrobial susceptibility for a bacterial infection enables precision prescription, shortens treatment time, and helps minimize the spread of antibiotic resistant infections. Current antimicrobial susceptibility testing (AST) methods often take several days and thus impede these clin. and health benefits. Here, we present an AST method by imaging freely moving bacterial cells in urine in real time and analyzing the videos with a deep learning algorithm. The deep learning algorithm dets. if an antibiotic inhibits a bacterial cell by learning multiple phenotypic features of the cell without the need for defining and quantifying each feature. We apply the method to urinary tract infection, a common infection that affects millions of people, to det. the min. inhibitory concn. of pathogens from both bacteria spiked urine and clin. infected urine samples for different antibiotics within 30 min and validate the results with the gold std. broth macrodilution method. The deep learning video microscopy-based AST holds great potential to contribute to the soln. of increasing drug-resistant infections.
- 18Longo, G.; Alonso-Sarduy, L.; Rio, L. M.; Bizzini, A.; Trampuz, A.; Notz, J.; Dietler, G.; Kasas, S. Rapid detection of bacterial resistance to antibiotics using AFM cantilevers as nanomechanical sensors. Nat. Nanotechnol. 2013, 8, 522– 526, DOI: 10.1038/nnano.2013.12018Rapid detection of bacterial resistance to antibiotics using AFM cantilevers as nanomechanical sensorsLongo, G.; Alonso-Sarduy, L.; Rio, L. Marques; Bizzini, A.; Trampuz, A.; Notz, J.; Dietler, G.; Kasas, S.Nature Nanotechnology (2013), 8 (7), 522-526CODEN: NNAABX; ISSN:1748-3387. (Nature Publishing Group)The widespread misuse of drugs has increased the no. of multiresistant bacteria, and this means that tools that can rapidly detect and characterize bacterial response to antibiotics are much needed in the management of infections. Various techniques, such as the resazurin-redn. assays, the mycobacterial growth indicator tube or polymerase chain reaction-based methods, have been used to investigate bacterial metab. and its response to drugs. However, many are relatively expensive or unable to distinguish between living and dead bacteria. Here we show that the fluctuations of highly sensitive at. force microscope cantilevers can be used to detect low concns. of bacteria, characterize their metab. and quant. screen (within minutes) their response to antibiotics. We applied this methodol. to Escherichia coli and Staphylococcus aureus, showing that live bacteria produced larger cantilever fluctuations than bacteria exposed to antibiotics. Our preliminary expts. suggest that the fluctuation is assocd. with bacterial metab.
- 19Stupar, P.; Opota, O.; Longo, G.; Prod’hom, G.; Dietler, G.; Greub, G.; Kasas, S. Nanomechanical sensor applied to blood culture pellets: a fast approach to determine the antibiotic susceptibility against agents of bloodstream infections. Clin Microbiol Infect 2017, 23, 400, DOI: 10.1016/j.cmi.2016.12.02819Nanomechanical sensor applied to blood culture pellets: a fast approach to determine the antibiotic susceptibility against agents of bloodstream infectionsStupar, P.; Opota, O.; Longo, G.; Prod'hom, G.; Dietler, G.; Greub, G.; Kasas, S.Clinical Microbiology and Infection (2017), 23 (6), 400-405CODEN: CMINFM; ISSN:1198-743X. (Elsevier Ltd.)The management of bloodstream infection, a life-threatening disease, largely relies on early detection of infecting microorganisms and accurate detn. of their antibiotic susceptibility to reduce both mortality and morbidity. Recently we developed a new technique based on at. force microscopy capable of detecting movements of biol. samples at the nanoscale. Such sensor is able to monitor the response of bacteria to antibiotics pressure, allowing a fast and versatile susceptibility test. Furthermore, rapid prepn. of a bacterial pellet from a pos. blood culture can improve downstream characterization of the recovered pathogen as a result of the increased bacterial concn. obtained. Using artificially inoculated blood cultures, we combined these two innovative procedures and validated them in double-blind expts. to det. the susceptibility and resistance of Escherichia coli strains (ATCC 25933 as susceptible and a characterized clin. isolate as resistant strain) towards a selection of antibiotics commonly used in clin. settings. On the basis of the variance of the sensor movements, we were able to pos. discriminate the resistant from the susceptible E. coli strains in 16 of 17 blindly investigated cases. Furthermore, we defined a variance change threshold of 60% that discriminates susceptible from resistant strains. By combining the nanomotion sensor with the rapid prepn. method of blood culture pellets, we obtained an innovative, rapid and relatively accurate method for antibiotic susceptibility test directly from pos. blood culture bottles, without the need for bacterial subculture.
- 20Villalba, M. I.; Stupar, P.; Chomicki, W.; Bertacchi, M.; Dietler, G.; Arnal, L.; Vela, M. E.; Yantorno, O.; Kasas, S. Nanomotion Detection Method for Testing Antibiotic Resistance and Susceptibility of Slow-Growing Bacteria. Small 2018, 14, 1702671, DOI: 10.1002/smll.201702671There is no corresponding record for this reference.
- 21Kasas, S.; Ruggeri, F. S.; Benadiba, C.; Maillard, C.; Stupar, P.; Tournu, H.; Dietler, G.; Longo, G. Detecting nanoscale vibrations as signature of life. Proc Nat Acad Sci 2015, 112, 378– 381, DOI: 10.1073/pnas.141534811221Detecting nanoscale vibrations as signature of lifeKasas, Sandor; Ruggeri, Francesco Simone; Benadiba, Carine; Maillard, Caroline; Stupar, Petar; Tournu, Helene; Dietler, Giovanni; Longo, GiovanniProceedings of the National Academy of Sciences of the United States of America (2015), 112 (2), 378-381CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)The existence of life in extreme conditions, in particular in extraterrestrial environments, is certainly one of the most intriguing scientific questions of our time. In this report, we demonstrate the use of an innovative nanoscale motion sensor in life-searching expts. in Earth-bound and interplanetary missions. This technique exploits the sensitivity of nanomech. oscillators to transduce the small fluctuations that characterize living systems. The intensity of such movements is an indication of the viability of living specimens and conveys information related to their metabolic activity. Here, we show that the nanomotion detector can assess the viability of a vast range of biol. specimens and that it could be the perfect complement to conventional chem. life-detection assays. Indeed, by combining chem. and dynamical measurements, we could achieve an unprecedented depth in the characterization of life in extreme and extraterrestrial environments.
- 22Andrews, J. M. Determination of minimum inhibitory concentrations. J. Antimicrob. Chemother. 2001, 48, 5– 16, DOI: 10.1093/jac/48.suppl_1.522Determination of minimum inhibitory concentrationsAndrews, Jennifer M.Journal of Antimicrobial Chemotherapy (2001), 48 (Suppl. S1), 5-16CODEN: JACHDX; ISSN:0305-7453. (Oxford University Press)A review with 3 refs. Min. inhibitory concns. (MICs) are defined as the lowest concn. of an antimicrobial that will inhibit the visible growth of a microorganism after overnight incubation, and min. bactericidal concns. (MBCs) as the lowest concn. of antimicrobial that will prevent the growth of an organism after subculture on to antibiotic-free media. MICs are used by diagnostic labs. mainly to confirm resistance, but most often as a research tool to det. the in vitro activity of new antimicrobials, and data from such studies have been used to det. MIC breakpoints. MBC detns. are undertaken less frequently and their major use has been reserved for isolates from the blood of patients with endocarditis. Standardized methods for detg. MICs and MBCs are described in this paper. Like all standardized procedures, the method must be adhered to and may not be adapted by the user. The method gives information on the storage of std. antibiotic powder, prepn. of stock antibiotic solns., media, prepn. of inocula, incubation conditions, and reading and interpretation of results. Tables giving expected MIC ranges for control NCTC and ATCC strains are also supplied.
- 23Annis, D. H.; Craig, B. A. The effect of interlaboratory variability on antimicrobial susceptibility determination. Diagn Microbiol Infect Dis 2005, 53, 61– 64, DOI: 10.1016/j.diagmicrobio.2005.03.01223The effect of interlaboratory variability on antimicrobial susceptibility determinationAnnis, David H.; Craig, Bruce A.Diagnostic Microbiology and Infectious Disease (2005), 53 (1), 61-64CODEN: DMIDDZ; ISSN:0732-8893. (Elsevier Inc.)In the min. inhibitory concn. (MIC) test literature, discussion concerning the effect of lab.-to-lab. variation is lacking. We present 2 sets of drug diln. test quality control data that illustrate considerable lab. differences in measured MIC. In both isolates (Escherichia coli, ATCC 25922; Staphylococcus aureus, ATCC 29213) the lab.-to-lab. variability accounts for approx. half of the total variability. We illustrate the impact of this variability on the probability of correctly classifying the susceptibility level of an isolate and on the estn. of resistance prevalence. For example, we show that lab. differences in the probability of correctly classifying the isolate (specifically near the lower breakpoint) can vary up to 80%.
- 24Mouton, J. W.; Meletiadis, J.; Voss, A.; Turnidge, J. Variation of MIC measurements: the contribution of strain and laboratory variability to measurement precision. J Antimicrob Chemother 2018, 73, 2374– 2379, DOI: 10.1093/jac/dky23224Variation of MIC measurements: the contribution of strain and laboratory variability to measurement precisionMouton, Johan W.; Meletiadis, Joseph; Voss, Andreas; Turnidge, JohnJournal of Antimicrobial Chemotherapy (2018), 73 (9), 2374-2379CODEN: JACHDX; ISSN:1460-2091. (Oxford University Press)Although testing of antimicrobial agents for susceptibility has inherent variability like any assay, it is generally held that there are also real differences in susceptibility between strains. In the routine lab., variability of the MIC measurement may be sufficient to mask real strain differences. We detd. which factors contributed to the variability, using linezolid against Staphylococcus aureus as one example. Twenty-five S. aureus strains were sent to five different labs. in quadruplicate in a blinded fashion. Labs. detd. MICs of linezolid using Etest. Results of 22 strains corresponding to 440 observations were available for anal. Sources of variability were explored and quantified using an ANOVA approach. The overall geometric mean MIC was 1.8 mg/L, comparable to that of the published WT distribution of 1.7 mg/L. The total variation amounted to ∼1.3 2-fold dilns. for a one-sided CI of 95% and two 2-fold dilns. for a CI of 99%. Variation between labs. and variation between strains contributed 10% and 48%, and in a subset anal. averaging 17% and 26%, resp. Strain-to-strain variation (biol. variation) could not be reliably detd., even with four replicates. This anal. serves as an example of an approach to discerning various sources of MIC variation. Here, at best, a single measurement of an MIC may provide an indication of whether it likely belongs to the WT distribution. Only repeated measurements of MICs for individual strains within one lab. may provide an indication of differences in susceptibility between strains.
- 25Flores-Mireles, A. L.; Walker, J. N.; Caparon, M.; Hultgren, S. J. Urinary tract infections: epidemiology, mechanisms of infection and treatment options. Nat Rev Microbiol 2015, 13, 269– 284, DOI: 10.1038/nrmicro343225Urinary tract infections: epidemiology, mechanisms of infection and treatment optionsFlores-Mireles, Ana L.; Walker, Jennifer N.; Caparon, Michael; Hultgren, Scott J.Nature Reviews Microbiology (2015), 13 (5), 269-284CODEN: NRMACK; ISSN:1740-1526. (Nature Publishing Group)Urinary tract infections (UTIs) are a severe public health problem and are caused by a range of pathogens, but most commonly by Escherichia coli, Klebsiella pneumoniae, Proteus mirabilis, Enterococcus faecalis and Staphylococcus saprophyticus. High recurrence rates and increasing antimicrobial resistance among uropathogens threaten to greatly increase the economic burden of these infections. In this Review, we discuss how basic science studies are elucidating the mol. details of the crosstalk that occurs at the host-pathogen interface, as well as the consequences of these interactions for the pathophysiol. of UTIs. We also describe current efforts to translate this knowledge into new clin. treatments for UTIs.
Supporting Information
Supporting Information
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acssensors.0c01216.
Replication of the nanomechanical method; representative optical images of the range of bacterial coverage; investigation of bacterial immobilization conditions; growth of bacteria over time; baseline normalization and magnitude variability between experiments; data of kanamycin resistant and sensitive strain; data analysis steps applied to raw data; and resistance spectrum of patient isolate from the Great Ormond Street Hospital (PDF).
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