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Background

Heteroresistance (HR) is a significant type of antibiotic resistance observed for several bacterial species and antibiotic classes where a susceptible main population contains small subpopulations of resistant cells. Mathematical models, animal experiments and clinical studies associate HR with treatment failure. Currently used susceptibility tests do not detect heteroresistance reliably, which can result in misclassification of heteroresistant isolates as susceptible which might lead to treatment failure. Here we examined if whole genome sequence (WGS) data and machine learning (ML) can be used to detect bacterial HR.

Methods

We classified 467 Escherichia coli clinical isolates as HR or non-HR to the often used β-lactam/inhibitor combination piperacillin-tazobactam using pre-screening and Population Analysis Profiling tests. We sequenced the isolates, assembled the whole genomes and created a set of predictors based on current knowledge of HR mechanisms. Then we trained several machine learning models on 80% of this data set aiming to detect HR isolates. We compared performance of the best ML models on the remaining 20% of the data set with a baseline model based solely on the presence of β-lactamase genes. Furthermore, we sequenced the resistant sub-populations in order to analyse the genetic mechanisms underlying HR.

Findings

The best ML model achieved 100% sensitivity and 84.6% specificity, outperforming the baseline model. The strongest predictors of HR were the total number of β-lactamase genes, β-lactamase gene variants and presence of IS elements flanking them. Genetic analysis of HR strains confirmed that HR is caused by an increased copy number of resistance genes via gene amplification or plasmid copy number increase. This aligns with the ML model's findings, reinforcing the hypothesis that this mechanism underlies HR in Gram-negative bacteria.

Interpretation

We demonstrate that a combination of WGS and ML can identify HR in bacteria with perfect sensitivity and high specificity. This improved detection would allow for better-informed treatment decisions and potentially reduce the occurrence of treatment failures associated with HR.

Background

Antibiotic heteroresistance is a common bacterial phenotype characterised by the presence of small resistant subpopulations within a susceptible population. During antibiotic exposure, these resistant subpopulations can be enriched and potentially lead to treatment failure. In this study, we examined the prevalence, misclassification, and clinical effect of heteroresistance in Escherichia coli bloodstream infections for the clinically important antibiotics cefotaxime, gentamicin, and piperacillin–tazobactam.

Methods

We conducted a retrospective cohort analysis of patients (n=255) admitted to in-patient care and treated for E coli bloodstream infections within the Uppsala region in Sweden between Jan 1, 2014, and Dec 31, 2015. Patient inclusion criteria were admission to hospital on suspicion of infection, starting systemic antibiotics at the time of admission, positive blood cultures for the growth of E coli upon admission, and residency in the Uppsala health-care region at the time of admission. Exclusion criteria were growth of an additional pathogen than E coli in blood cultures taken at admission or previous inclusion of the patients in the study for another bloodstream infection. Antibiotic susceptibility of preserved blood culture isolates of E coli was assessed for cefotaxime, gentamicin, and piperacillin–tazobactam by disk diffusion and breakpoint crossing heteroresistance (BCHR) was identified using population analysis profiling. The clinical outcome parameters were obtained from patient records. The primary outcome variable was length of hospital stay due to the E coli bloodstream infection, defined as the time between admission and discharge from inpatient care as noted on the physician’s notes. Secondary outcomes were time to fever resolution, admission to intermediary care unit or intensive care unit during time in hospital, switching or adding another intravenous antibiotic treatment, re-admission to hospital within 30 days of original admission, recurrent E coli infection within 30 days of admission to hospital, and all-cause mortality within 90 days of admission.

Findings

A total of 255 participants with a corresponding E coli isolate (out of 500 screened for eligibility) met the inclusion criteria, with 135 female patients and 120 male patients. One (<1%) of 255 strains was BCHR for cefotaxime, 109 (43%) of 255 strains were BCHR for gentamicin, and 22 (9%) of 255 strains were BCHR for piperacillin–tazobactam. Clinical susceptibility testing misclassified 120 (96%) of 125 heteroresistant bacterial strains as susceptible. The BCHR phenotypes had no correlation to length of hospital stay due to the E coli bloodstream infection. However, patients with piperacillin–tazobactam BCHR strains who received piperacillin–tazobactam had 3·1 times higher odds for admittance to the intermediate care unit (95% CI 1·1–9·6, p=0·041) than the remainder of the cohort, excluding those treated with gentamicin. Similarly, those infected with gentamicin BCHR who received gentamicin showed higher odds for admittance to the intensive care unit (5·6 [1·1–42·0, p=0·043]) and mortality (7·1 [1·2–49·2, p=0·030]) than patients treated with gentamicin who were infected with non-gentamicin BCHR E coli.

Interpretation

In a cohort of patients with E coli bloodstream infections, heteroresistance is common and frequently misidentified in routine clinical testing. Several negative effects on patient outcomes are associated with heteroresistant strains.

Background

Piperacillin/tazobactam is a β-lactam/β-lactamase inhibitor combination with a broad spectrum of activity that is often used as empirical and/or targeted therapy among hospitalized patients. Heteroresistance (HR) is a form of antibiotic resistance in which a minority population of resistant cells coexists with a majority susceptible population that has been found to be a cause of antibiotic treatment failure in murine models.

Objectives

To determine the prevalence of HR and mechanisms of HR to piperacillin/tazobactam among Klebsiella pneumoniae bloodstream infection (BSI) isolates.

Materials

From July 2018 to June 2021, K. pneumoniae piperacillin/tazobactam-susceptible BSI isolates were collected from two tertiary hospitals in Atlanta, GA, USA. Only first isolates from each patient per calendar year were included. Population analysis profiling (PAP) and WGS were performed to identify HR and its mechanisms.

Results

Among 423 K. pneumoniae BSI isolates collected during the study period, 6% (25/423) were found to be HR with a subpopulation surviving above the breakpoint. WGS of HR isolates grown in the presence of piperacillin/tazobactam at concentrations 8-fold that of the MIC revealed copy number changes of plasmid-located β-lactamase genes bla_CTX-M-15, bla_SHV33, bla_OXA-1 and bla_TEM-1 by tandem gene amplification or plasmid copy number increase.

Conclusions

Prevalence of HR to piperacillin/tazobactam among bloodstream isolates was substantial. The HR phenotype appears to be caused by tandem amplification of β-lactamase genes found on plasmids or plasmid copy number increase. This raises the possibility of dissemination of HR through horizontal gene transfer and requires further study.

Heteroresistance (HR) is an enigmatic phenotype where, in a main population of susceptible cells, small subpopulations of resistant cells exist. This is a cause for concern, as this small subpopulation is difficult to detect by standard antibiotic susceptibility tests, and upon antibiotic exposure the resistant subpopulation may increase in frequency and potentially lead to treatment complications or failure. Here, we determined the prevalence and mechanisms of HR for 40 clinical Staphylococcus aureus isolates, against 6 clinically important antibiotics: daptomycin, gentamicin, linezolid, oxacillin, teicoplanin, and vancomycin. High frequencies of HR were observed for gentamicin (69.2%), oxacillin (27%), daptomycin (25.6%), and teicoplanin (15.4%) while none of the isolates showed HR toward linezolid or vancomycin. Point mutations in various chromosomal core genes, including those involved in membrane and peptidoglycan/teichoic acid biosynthesis and transport, tRNA charging, menaquinone and chorismite biosynthesis and cyclic-di-AMP biosynthesis, were the mechanisms responsible for generating the resistant subpopulations. This finding is in contrast to gram-negative bacteria, where increased copy number of bona fide resistance genes via tandem gene amplification is the most prevalent mechanism. This difference can be explained by the observation that S. aureus has a low content of resistance genes and absence of the repeat sequences that allow tandem gene amplification of these genes as compared to gram-negative species.

The fast-progressing landscape of the bioprocessing industry emphasizes innovation and efficiency enhancement, propelled by the integration of advanced solutions. Additive manufacturing technologies, particularly laserbased powder bed fusion with polypropylene, are pivotal in this industrial metamorphosis. However, despite the substantial scientific effort in the field, a significant gap exists in comprehending the surface characteristics of new surfaces and their implications for bacterial attachment and biofilm formation. This arises, in part, due to the absence of comprehensive and universally applicable topographical characterization analysis specifically designed for additively manufactured-fabricated surfaces. Typically, researchers tend to rely on the commonly used roughness parameter, Sa, that primarily quantifies the average height variation across a surface. Addressing this limitation is crucial for understanding the connection between surface characteristics and bacterial attachment dynamics. Here, we propose an innovative approach using surface analysis including confocal microscopy, advanced roughness measurements, and multivariate statistical analysis to uncover the connections between bacterial attachment for Gram negative Escherichia coli and Gram positive Staphylococcus aureus in early biofilm formation with surfaces produced by standardized and additively manufactured techniques. Finally, we advocate for the adoption of a set of roughness parameters that specifically describe the dale region of the surfaces. By doing so, we intend to establish direct links between surface texture and bacterial adhesion, thus contributing significantly to the advancement of both bioprocessing and additive manufacturing research domains.

Heteroresistance is a medically relevant phenotype where small antibiotic-resistant subpopulations coexist within predominantly susceptible bacterial populations. Heteroresistance reduces treatment efficacy across diverse bacterial species and antibiotic classes, yet its genetic and physiological mechanisms remain poorly understood. Here, we investigated a multi-resistant Klebsiella pneumoniae isolate and identified three primary drivers of gene dosage-dependent heteroresistance for several antibiotic classes: tandem amplification, increased plasmid copy number, and transposition of resistance genes onto cryptic plasmids. All three mechanisms imposed fitness costs and were genetically unstable, leading to fast reversion to susceptibility in the absence of antibiotics. We used a mouse gut colonization model to show that heteroresistance due to elevated resistance-gene dosage can result in antibiotic treatment failures. Importantly, we observed that the three mechanisms are prevalent among Escherichia coli bloodstream isolates. Our findings underscore the necessity for treatment strategies that address the complex interplay between plasmids, resistance cassettes, and transposons in bacterial populations.

The use and misuse of antibiotics have resulted in the selection of difficult-to-treat resistant bacteria. Two key parameters that influence the selection of resistant bacteria are the minimal selective concentration (MSC) and the fitness cost of resistance, both of which have been measured during planktonic growth in several studies. However, bacterial growth most often occurs in biofilms, and it is unclear if and how these parameters differ under these two growth conditions. To address this knowledge gap, we compared a selection of several types of antibiotic-resistant Escherichia coli mutants during planktonic and biofilm growth to determine the fitness costs and MSCs. Biofilm-forming Escherichia coli strains are commonly found in catheter-associated and recurrent urinary tract infections. Isogenic strains of a biofilm-forming E. coli strain, differing only in the resistance mechanisms and the fluorescent markers, were constructed, and susceptible and resistant bacteria were grown in head-to-head competitions at various concentrations of antibiotics under planktonic and biofilm conditions. Mutants with resistance to five different antibiotics were studied. The results show that during both planktonic and biofilm growth, selection for the resistant mutants occurred for all antibiotics at sub-MICs far below the MIC of the antibiotic. Even though differences were seen, the MSC values and the fitness costs did not differ systematically between planktonic and biofilm growth, implying that despite the different growth modes, the basic selection parameters are similar. These findings highlight the risk that resistant mutants may, similarly to planktonic growth, also be selected at sub-MICs of antibiotics in biofilms.

IMPORTANCE Our understanding of how and where antibiotic resistance is selected in response to antibiotic exposure is still limited, and this is particularly true for selective processes when bacteria are growing in biofilms, arguably the most significant mode of growth of bacteria in human and animal infections as well as in other settings. In this study, we compared how different types of resistant E. coli strains were selected in response to antibiotic exposure during planktonic and biofilm growth. Determination of the minimal selective concentrations (MSCs) and fitness costs of resistance showed that they were comparable under these two different conditions, even though some differences were observed. Importantly, the MSCs were far below the MICs for all mutants under both planktonic and biofilm growth, emphasizing the significance of low antibiotic concentrations in driving the emergence and enrichment of resistant bacteria. Our understanding of how and where antibiotic resistance is selected in response to antibiotic exposure is still limited, and this is particularly true for selective processes when bacteria are growing in biofilms, arguably the most significant mode of growth of bacteria in human and animal infections as well as in other settings. In this study, we compared how different types of resistant E. coli strains were selected in response to antibiotic exposure during planktonic and biofilm growth.

Medical device-related biofilms are a major cause of hospital-acquired infections, especially chronic infections. Numerous diverse models to study surface-associated biofilms have been developed; however, their usability varies. Often, a simple method is desired without sacrificing throughput and biological relevance. Here, we present an in-house developed 3D-printed device (FlexiPeg) for biofilm growth, conceptually similar to the Calgary Biofilm device but aimed at increasing ease of use and versatility. Our device is modular with the lid and pegs as separate units, enabling flexible assembly with up- or down-scaling depending on the aims of the study. It also allows easy handling of individual pegs, especially when disruption of biofilm populations is needed for downstream analysis. The pegs can be printed in, or coated with, different materials to create surfaces relevant to the study of interest. We experimentally validated the use of the device by exploring the biofilms formed by clinical strains of Escherichia coli and Klebsiella pneumoniae, commonly associated with device-related infections. The biofilms were characterized by viable cell counts, biomass staining, and scanning electron microscopy (SEM) imaging. We evaluated the effects of different additive manufacturing technologies, 3D printing resins, and coatings with, for example, silicone, to mimic a medical device surface. The biofilms formed on our custom-made pegs could be clearly distinguished based on species or strain across all performed assays, and they corresponded well with observations made in other models and clinical settings, for example, on urinary catheters. Overall, our biofilm device is a robust, easy-to-use, and relevant assay, suitable for a wide range of applications in surface-associated biofilm studies, including materials testing, screening for biofilm formation capacity, and antibiotic susceptibility testing.