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The innate immune system eliminates bloodstream pathogens such as Escherichia coli in part through complement protein deposition and subsequent bacterial death (i.e., "serum killing"). Some E. coli strains have developed mechanisms to resist serum killing, though the extent of variation in serum killing among bloodstream infection (BSI) isolates and the clinical impact of this variation is not well understood. To address this issue, we developed a novel assay that uses flow cytometry to perform high throughput serum bactericidal assays (SBAs) with E. coli BSI isolates (n = 183) to define the proportion of surviving bacteria after exposure to serum. We further determined whether E. coli resistance to serum killing is associated with clinical outcomes (e.g., in-hospital attributable mortality, in-hospital total mortality, septic shock) and bacterial genotype in the corresponding patients with E. coli BSI. Our novel flow cytometry-based SBA performed similarly to a traditional SBA, though with significantly decreased hands-on bench work. Among E. coli BSI isolates, the mean proportion that survived exposure to 25% serum was 0.68 (Standard deviation 0.02, range 0.57-0.93). We did not identify associations between E. coli resistance to serum killing and clinical outcomes in our adjusted models. Together, this study describes a novel flow cytometry-based approach to the bacterial SBA that allowed for high-throughput testing of E. coli BSI isolates and identified high variability in resistance to serum killing among a large set of BSI isolates., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Poteete et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Clinical data characterizing invasive Escherichia coli disease (IED) are limited. We assessed the clinical presentation of IED and antimicrobial resistance (AMR) patterns of causative E. coli isolates in older adults., Methods: EXPECT-2 (NCT04117113) was a prospective, observational, multinational, hospital-based study conducted in patients with IED aged ≥ 60 years. IED was determined by the microbiological confirmation of E. coli from blood; or by the microbiological confirmation of E. coli from urine or an otherwise sterile body site in the presence of requisite criteria of systemic inflammatory response syndrome (SIRS), Sequential Organ Failure Assessment (SOFA), or quick SOFA (qSOFA). The primary outcomes were the clinical presentation of IED and AMR rates of E. coli isolates to clinically relevant antibiotics. Complications and in-hospital mortality were assessed through 28 days following IED diagnosis., Results: Of 240 enrolled patients, 80.4% had bacteremic and 19.6% had non-bacteremic IED. One-half of infections (50.4%) were community-acquired. The most common source of infection was the urinary tract (62.9%). Of 240 patients, 65.8% fulfilled ≥ 2 SIRS criteria, and 60.4% had a total SOFA score of ≥ 2. Investigator-diagnosed sepsis and septic shock were reported in 72.1% and 10.0% of patients, respectively. The most common complication was kidney dysfunction (12.9%). The overall in-hospital mortality was 4.6%. Of 299 E. coli isolates tested, the resistance rates were: 30.4% for trimethoprim-sulfamethoxazole, 24.1% for ciprofloxacin, 22.1% for levofloxacin, 16.4% for ceftriaxone, 5.7% for cefepime, and 4.3% for ceftazidime., Conclusions: The clinical profile of identified IED cases was characterized by high rates of sepsis. IED was associated with high rates of AMR to clinically relevant antibiotics. The identification of IED can be optimized by using a combination of clinical criteria (SIRS, SOFA, or qSOFA) and culture results., (© 2024. The Author(s).)

Plasmids are key vehicles of horizontal gene transfer (HGT), mobilizing antibiotic resistance, virulence, and other traits among bacterial populations. The environmental and genetic forces that drive plasmid transfer are poorly understood, however, due to the lack of definitive quantification coupled with genomic analysis. Here, we integrate conjugative phenotype with plasmid genotype to provide quantitative analysis of HGT in clinical Escherichia coli pathogens. We find a substantial proportion of these pathogens (>25%) able to readily spread resistance to the most common classes of antibiotics. Antibiotics of varied modes of action had less than a 5-fold effect on conjugation efficiency in general, with one exception displaying 31-fold promotion upon exposure to macrolides and chloramphenicol. In contrast, genome sequencing reveals plasmid incompatibility group strongly correlates with transfer efficiency. Our findings offer new insights into the determinants of plasmid mobility and have implications for the development of treatments that target HGT., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).)

OBJECTIVE To describe the epidemiology of extended-spectrum β-lactamase (ESBL)-producing Escherichia coli (ESBL-EC) and Klebsiella pneumoniae (ESBL-KP) infections DESIGN Retrospective cohort SETTING Inpatient care at community hospitals PATIENTS All patients with ESBL-EC or ESBL-KP infections METHODS ESBL-EC and ESBL-KP infections from 26 community hospitals were prospectively entered into a centralized database from January 2009 to December 2014. RESULTS A total of 925 infections caused by ESBL-EC (10.5 infections per 100,000 patient days) and 463 infections caused by ESBL-KP (5.3 infections per 100,000 patient days) were identified during 8,791,243 patient days of surveillance. The incidence of ESBL-EC infections increased from 5.28 to 10.5 patients per 100,000 patient days during the study period (P=.006). The number of community hospitals with ESBL-EC infections increased from 17 (65%) in 2009 to 20 (77%) in 2014. The median ESBL-EC infection rates among individual hospitals with ≥1 ESBL-EC infection increased from 11.1 infections/100,000 patient days (range, 2.2-33.9 days) in 2009 to 22.1 infections per 100,000 patient days (range, 0.66-134 days) in 2014 (P=.05). The incidence of ESBL-KP infections remained constant over the study period (P=.14). Community-associated and healthcare-associated ESBL-EC infections trended upward (P=.006 and P=.02, respectively), while hospital-onset infections remained stable (P=.07). ESBL-EC infections were more common in females (54% vs 44%, P<.001) and Caucasians (50% vs 40%, P<.0001), and were more likely to be isolated from the urinary tract (61% vs 52%, P<.0001) than ESBL-KP infections. CONCLUSIONS The incidence of ESBL-EC infection has increased in community hospitals throughout the southeastern United States, while the incidence of ESBL-KP infection has remained stable. Community- and healthcare-associated ESBL-EC infections are driving the upward trend. Infect. Control Hosp. Epidemiol. 2015;37(1):49-54.

Background: Escherichia coli is a common cause of bloodstream infections (BSIs) in infants and is associated with high mortality and morbidity among survivors. The clinical significance of antibiotic resistance and timing of appropriate antimicrobial therapy in this population is poorly understood., Methods: We identified all infants with E. coli BSIs discharged from 77 neonatal intensive care units managed by the Pediatrix Medical Group in 2012. We used multivariable logistic regression to evaluate the association between 30-day mortality and ampicillin-resistant E. coli BSI, as well as the number of active empiric antimicrobial agents administered, controlling for gestational age, small-for-gestational age status, early-onset versus late-onset BSI, oxygen requirement, ventilator support and inotropic support on the day of the first positive blood culture., Results: We identified 258 episodes of E. coli BSI, including 123 (48%) ampicillin-resistant isolates. Unadjusted 30-day mortality did not significantly differ between infants with ampicillin-resistant versus ampicillin-susceptible E. coli BSI [11 of 123 (9%) vs. 7 of 135 (5%); P = 0.33; adjusted odds ratio = 1.37 (95% confidence interval: 0.39, 4.77)]. Among ampicillin-resistant E. coli BSIs, 30-day mortality was not significantly lower for infants treated with at least one empiric antimicrobial active against ampicillin-resistant E. coli versus infants receiving no active empiric agent [adjusted odds ratio = 1.50 (0.07, 33.6)]., Conclusions: In this population of infants with E. coli BSI, ampicillin resistance was not associated with significantly increased mortality. Among the subset of infants with ampicillin-resistant E. coli, appropriate empirical antibiotic therapy was not associated with lower mortality.

Machine learning approaches offer the potential to systematically identify transcriptional regulatory interactions from a compendium of microarray expression profiles. However, experimental validation of the performance of these methods at the genome scale has remained elusive. Here we assess the global performance of four existing classes of inference algorithms using 445 Escherichia coli Affymetrix arrays and 3,216 known E. coli regulatory interactions from RegulonDB. We also developed and applied the context likelihood of relatedness (CLR) algorithm, a novel extension of the relevance networks class of algorithms. CLR demonstrates an average precision gain of 36% relative to the next-best performing algorithm. At a 60% true positive rate, CLR identifies 1,079 regulatory interactions, of which 338 were in the previously known network and 741 were novel predictions. We tested the predicted interactions for three transcription factors with chromatin immunoprecipitation, confirming 21 novel interactions and verifying our RegulonDB-based performance estimates. CLR also identified a regulatory link providing central metabolic control of iron transport, which we confirmed with real-time quantitative PCR. The compendium of expression data compiled in this study, coupled with RegulonDB, provides a valuable model system for further improvement of network inference algorithms using experimental data., Competing Interests: Competing interests. A portion of this work was conducted in collaboration with Cellicon Biotechnologies. JJC and TSG are founders and shareholders in the company. GC and JW are also shareholders in the company. All data, results, and algorithms from this collaboration have been made publicly available.