Short-term genome stability of serial Clostridium difficile ribotype 027 isolates in an experimental gut model and recurrent human disease

Copyright: © 2013 Eyre 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 creditedClostridium difficile whole genome sequencing has the potential to identify related isolates, even among otherwise indistinguishable strains, but interpretation depends on understanding genomic variation within isolates and individuals.Serial isolates from two scenarios were whole genome sequenced. Firstly, 62 isolates from 29 timepoints from three in vitro gut models, inoculated with a NAP1/027 strain. Secondly, 122 isolates from 44 patients (2–8 samples/patient) with mostly recurrent/on-going symptomatic NAP-1/027 C. difficile infection. Reference-based mapping was used to identify single nucleotide variants (SNVs).Across three gut model inductions, two with antibiotic treatment, total 137 days, only two new SNVs became established. Pre-existing minority SNVs became dominant in two models. Several SNVs were detected, only present in the minority of colonies at one/two timepoints. The median (inter-quartile range) [range] time between patients’ first and last samples was 60 (29.5–118.5) [0–561] days. Within-patient C. difficile evolution was 0.45 SNVs/called genome/year (95%CI 0.00–1.28) and within-host diversity was 0.28 SNVs/called genome (0.05–0.53). 26/28 gut model and patient SNVs were non-synonymous, affecting a range of gene targets.The consistency of whole genome sequencing data from gut model C. difficile isolates, and the high stability of genomic sequences in isolates from patients, supports the use of whole genome sequencing in detailed transmission investigations.Peer reviewe

Addition of macrolide antibiotics for hospital treatment of community-acquired pneumonia

Background: Current guidelines recommend combining a macrolide with a β-lactam antibiotic for the empirical treatment of moderate-to-high severity community-acquired pneumonia (CAP); however macrolide use is associated with potential adverse events and antimicrobial resistance. // Methods: We analysed electronic health data from 8,872 adults in Oxfordshire, UK, hospitalised with CAP between 01-January-2016 and 19-March-2024, who received either amoxicillin or co-amoxiclav as initial treatment. We examined the effects of adjunctive macrolides on 30-day all-cause mortality, time to hospital discharge, and changes in Sequential Organ Failure Assessment (SOFA) score, using inverse probability treatment weighting to address confounding by baseline severity. Subgroup analyses by severity and sensitivity analyses with missing covariates imputed were performed. // Results: There was no evidence of an association between the use of additional macrolides and 30-day mortality, with marginal odds ratios of 1.05 (95%CI 0.75-1.47) for amoxicillin with vs. without macrolide, and 1.12 (0.93-1.34) for co-amoxiclav with vs. without macrolide. No evidence of difference was found in time to discharge from additional macrolides to amoxicillin (restricted mean days lost +1.76 [-1.66,+5.19]), or co-amoxiclav (+0.44 [-1.63,+2.51]). There was also no evidence that macrolide use was associated with SOFA score decreases. Results were consistent across stratified analyses by pneumonia severity, and remained broadly similar in sensitivity analyses with missing data imputed. // Conclusions: At a population level, the addition of macrolides was not associated with improved clinical outcomes for CAP patients. The potential advantages of combining macrolides with a β-lactam antibiotic in CAP treatment should be balanced against the risks of adverse effects and antimicrobial resistance

Changes in plasma levels of N-arachidonoyl ethanolamine and N-palmitoylethanolamine following bariatric surgery in morbidly obese females with impaired glucose homeostasis

Aim: We examined endocannabinoids (ECs) in relation to bariatric surgery and the association between plasma ECs and markers of insulin resistance. Methods: A study of 20 participants undergoing bariatric surgery. Fasting and 2-hour plasma glucose, lipids, insulin, and C-peptide were recorded preoperatively and 6 months postoperatively with plasma ECs (AEA, 2-AG) and endocannabinoid-related lipids (PEA, OEA). Results: Gender-specific analysis showed differences in AEA, OEA, and PEA preoperatively with reductions in AEA and PEA in females postoperatively. Preoperatively, AEA was correlated with 2-hour glucose (r = 0.55, P = 0.01), HOMA-IR (r = 0.61, P = 0.009), and HOMA %S (r = -0.71, P = 0.002). OEA was correlated with weight (r = 0.49, P = 0.03), waist circumference (r = 0.52, P = 0.02), fasting insulin (r = 0.49, P = 0.04), and HOMA-IR (r = 0.48, P = 0.05). PEA was correlated with fasting insulin (r = 0.49, P = 0.04). 2-AG had a negative correlation with fasting glucose (r = -0.59, P = 0.04). Conclusion: Gender differences exist in circulating ECs in obese subjects. Females show changes in AEA and PEA after bariatric surgery. Specific correlations exist between different ECs and markers of obesity and insulin and glucose homeostasis

Algorithmic fairness and bias mitigation for clinical machine learning with deep reinforcement learning

As models based on machine learning continue to be developed for healthcare applications, greater effort is needed to ensure that these technologies do not reflect or exacerbate any unwanted or discriminatory biases that may be present in the data. Here we introduce a reinforcement learning framework capable of mitigating biases that may have been acquired during data collection. In particular, we evaluated our model for the task of rapidly predicting COVID-19 for patients presenting to hospital emergency departments and aimed to mitigate any site (hospital)-specific and ethnicity-based biases present in the data. Using a specialized reward function and training procedure, we show that our method achieves clinically effective screening performances, while significantly improving outcome fairness compared with current benchmarks and state-of-the-art machine learning methods. We performed external validation across three independent hospitals, and additionally tested our method on a patient intensive care unit discharge status task, demonstrating model generalizability

Constraining the Milky Way potential with a 6-D phase-space map of the GD-1 stellar stream

The narrow GD-1 stream of stars, spanning 60 deg on the sky at a distance of ~10 kpc from the Sun and ~15 kpc from the Galactic center, is presumed to be debris from a tidally disrupted star cluster that traces out a test-particle orbit in the Milky Way halo. We combine SDSS photometry, USNO-B astrometry, and SDSS and Calar Alto spectroscopy to construct a complete, empirical 6-dimensional phase-space map of the stream. We find that an eccentric orbit in a flattened isothermal potential describes this phase-space map well. Even after marginalizing over the stream orbital parameters and the distance from the Sun to the Galactic center, the orbital fit to GD-1 places strong constraints on the circular velocity at the Sun's radius V_c=224 \pm 13 km/s and total potential flattening q_\Phi=0.87^{+0.07}_{-0.04}. When we drop any informative priors on V_c the GD-1 constraint becomes V_c=221 \pm 18 km/s. Our 6-D map of GD-1 therefore yields the best current constraint on V_c and the only strong constraint on q_\Phi at Galactocentric radii near R~15 kpc. Much, if not all, of the total potential flattening may be attributed to the mass in the stellar disk, so the GD-1 constraints on the flattening of the halo itself are weak: q_{\Phi,halo}>0.89 at 90% confidence. The greatest uncertainty in the 6-D map and the orbital analysis stems from the photometric distances, which will be obviated by Gaia.Comment: 16 pages, 18 figures; accepted to ApJ; full resolution version is available at http://www.ast.cam.ac.uk/~koposov/files/gd1_fullres.pd

Relationship between bacterial strain type, host biomarkers, and mortality in clostridium difficile infection

Background: Despite substantial interest in biomarkers, their impact on clinical outcomes and variation with bacterial strain has rarely been explored using integrated databases. Methods: From September 2006 to May 2011, strains isolated from Clostridium difficile toxin enzyme immunoassay (EIA)-positive fecal samples from Oxfordshire, United Kingdom (approximately 600 000 people) underwent multilocus sequence typing. Fourteen-day mortality and levels of 15 baseline biomarkers were compared between consecutive C. difficile infections (CDIs) from different clades/sequence types (STs) and EIA-negative controls using Cox and normal regression adjusted for demographic/clinical factors. Results: Fourteen-day mortality was 13% in 2222 adults with 2745 EIA-positive samples (median, 78 years) vs 5% in 20 722 adults with 27 550 EIA-negative samples (median, 74 years) (absolute attributable mortality, 7.7%; 95% CI, 6.4%-9.0%). Mortality was highest in clade 5 CDIs (25% [16 of 63]; polymerase chain reaction (PCR) ribotype 078/ST 11), then clade 2 (20% [111 of 560]; 99% PCR ribotype 027/ST 1) versus clade 1 (12% [137 of 1168]; adjusted P <. 0001). Within clade 1, 14-day mortality was only 4% (3 of 84) in ST 44 (PCR ribotype 015) (adjusted P =. 05 vs other clade 1). Mean baseline neutrophil counts also varied significantly by genotype: 12.4, 11.6, and 9.5 × 109 neutrophils/L for clades 5, 2 and 1, respectively, vs 7.0 × 109 neutrophils/L in EIA-negative controls (P <. 0001) and 7.9 × 109 neutrophils/L in ST 44 (P =. 08). There were strong associations between C. difficile-type-specific effects on mortality and neutrophil/white cell counts (rho = 0.48), C-reactive-protein (rho = 0.43), eosinophil counts (rho =-0.45), and serum albumin (rho =-0.47). Biomarkers predicted 30%-40% of clade-specific mortality differences. Conclusions: C. difficile genotype predicts mortality, and excess mortality correlates with genotype-specific changes in biomarkers, strongly implicating inflammatory pathways as a major influence on poor outcome after CDI. PCR ribotype 078/ST 11 (clade 5) leads to severe CDI; thus ongoing surveillance remains essential

The Effect of Transposable Element Insertions on Gene Expression Evolution in Rodents

Background:Many genomes contain a substantial number of transposable elements (TEs), a few of which are known to be involved in regulating gene expression. However, recent observations suggest that TEs may have played a very important role in the evolution of gene expression because many conserved non-genic sequences, some of which are know to be involved in gene regulation, resemble TEs. Results:Here we investigate whether new TE insertions affect gene expression profiles by testing whether gene expression divergence between mouse and rat is correlated to the numbers of new transposable elements inserted near genes. We show that expression divergence is significantly correlated to the number of new LTR and SINE elements, but not to the numbers of LINEs. We also show that expression divergence is not significantly correlated to the numbers of ancestral TEs in most cases, which suggests that the correlations between expression divergence and the numbers of new TEs are causal in nature. We quantify the effect and estimate that TE insertion has accounted for ~20% (95% confidence interval: 12% to 26%) of all expression profile divergence in rodents. Conclusions:We conclude that TE insertions may have had a major impact on the evolution of gene expression levels in rodents

Machine learning and statistical inference in microbial population genomics

The availability of large genome datasets has changed the microbiology research landscape. Analyzing such data requires computationally demanding analyses, and new approaches have come from different data analysis philosophies. Machine learning and statistical inference have overlapping knowledge discovery aims and approaches. However, machine learning focuses on optimizing prediction, whereas statistical inference focuses on understanding the processes relating variables. In this review, we outline the different aspirations, precepts, and resulting methodologies, with examples from microbial genomics. Emphasizing complementarity, we argue that the combination and synthesis of machine learning and statistics has potential for pathogen research in the big data era

Machine learning and clinician predictions of antibiotic resistance in Enterobacterales bloodstream infections

BACKGROUND: Patients with Gram-negative bloodstream infections are at risk of serious adverse outcomes without active treatment, but identifying who has antimicrobial resistance (AMR) to target empirical treatment is challenging. METHODS: We used XGBoost machine learning models to predict antimicrobial resistance to seven antibiotics in patients with Enterobacterales bloodstream infection. Models were trained using hospital and community data from Oxfordshire, UK, for patients with positive blood cultures between 01-January-2017 and 31-December-2021. Model performance was evaluated by comparing predictions to final microbiology results in test datasets from 01-January-2022 to 31-December-2023 and to clinicians’ prescribing. FINDINGS: 4709 infection episodes were used for model training and evaluation; antibiotic resistance rates ranged from 7–67%. In held-out test data, resistance prediction performance was similar for the seven antibiotics (AUCs 0.680 [95%CI 0.641–0.720] to 0.737 [0.674–0.797]). Performance improved for most antibiotics when species identifications (available ∼24 h later) were included as model inputs (AUCs 0.723 [0.652–0.791] to 0.827 [0.797–0.857]). In patients treated with a beta-lactam, clinician prescribing led to 70% receiving an active beta-lactam: 44% were over-treated (broader spectrum treatment than needed), 26% optimally-treated (narrowest spectrum active agent), and 30% under-treated (inactive beta-lactam). Model predictions without species data could have led to 79% of patients receiving an active beta-lactam: 45% over-treated, 34% optimally-treated, and 21% under-treated. CONCLUSIONS: Predicting AMR in bloodstream infections is challenging for both clinicians and models. Despite modest performance, machine learning models could still increase the proportion of patients receiving active empirical treatment by up to 9% over current clinical practice in an environment prioritising antimicrobial stewardship

Informed Conditioning on Clinical Covariates Increases Power in Case-Control Association Studies

Genetic case-control association studies often include data on clinical covariates, such as body mass index (BMI), smoking status, or age, that may modify the underlying genetic risk of case or control samples. For example, in type 2 diabetes, odds ratios for established variants estimated from low–BMI cases are larger than those estimated from high–BMI cases. An unanswered question is how to use this information to maximize statistical power in case-control studies that ascertain individuals on the basis of phenotype (case-control ascertainment) or phenotype and clinical covariates (case-control-covariate ascertainment). While current approaches improve power in studies with random ascertainment, they often lose power under case-control ascertainment and fail to capture available power increases under case-control-covariate ascertainment. We show that an informed conditioning approach, based on the liability threshold model with parameters informed by external epidemiological information, fully accounts for disease prevalence and non-random ascertainment of phenotype as well as covariates and provides a substantial increase in power while maintaining a properly controlled false-positive rate. Our method outperforms standard case-control association tests with or without covariates, tests of gene x covariate interaction, and previously proposed tests for dealing with covariates in ascertained data, with especially large improvements in the case of case-control-covariate ascertainment. We investigate empirical case-control studies of type 2 diabetes, prostate cancer, lung cancer, breast cancer, rheumatoid arthritis, age-related macular degeneration, and end-stage kidney disease over a total of 89,726 samples. In these datasets, informed conditioning outperforms logistic regression for 115 of the 157 known associated variants investigated (P-value = 1×$10^{−9}$). The improvement varied across diseases with a 16% median increase in χ2 test statistics and a commensurate increase in power. This suggests that applying our method to existing and future association studies of these diseases may identify novel disease loci