Biology subjects

Keith, M.

Publications and source records attributed to Keith, M..

5 recordsLinked to original sources

bioRxiv2026

Analysis of a detoxified Escherichia coli strain for bacteriophage production

Phage therapeutics are re-emerging as adjuncts or alternatives to antibiotics and their clinical translation will be enhanced with production methods that minimise downstream processing. We evaluated whether an endotoxin-reduced E. coli strain developed for production of recombinant proteins, ClearColi(R), can serve as a useful, safe phage production host without compromising yield and whether targeted receptor complementation can expand its utility. The parent strain BL21(DE3), and its lipid A modified derivative, ClearColi(R), were compared with respect to infection and generation of phage. Across a panel of 31 phage, a similar host range was observed between BL21(DE3) and ClearColi(R). To expand host range ompC was genetically engineered into the chromosome of ClearColi(R), thereby adding OmpC-dependent phage to its production capacity. Production metrics were broadly comparable between the hosts; efficiency of plating and final titres for representative phage were not significantly different; burst size varied by phage but without consistent host bias. Endotoxin activity in ClearColi(R)-propagated lysates was reduced by over 1000-fold relative to BL21(DE3), reaching the low hundreds of endotoxin units (EU) versus hundreds of thousands for BL21(DE3). Intravesical administration of ClearColi(R)-derived phage (LUC4) into pigs elicited no clinical abnormalities and no significant increases in circulating cytokines up to 48 hours after administration. ClearColi(R) allows efficient production of diverse phage with low endotoxin, reducing the requirement for downstream processing. Although its minimal LPS reduces its capacity for producing some LPS-dependent phage and its growth is slower than BL21(DE3), requiring optimisation for maximal phage titre, the safety and simplified manufacturing process support further development of endotoxin modified strains for phage production. Impact statementAntibiotic resistance is a current global problem and treatments based on phage and phage products already have a proven track record with particular bacterial infections, especially in the urinary tract. While progress is being made on in vitro phage synthesis, large scale bacteriophage preparations require a bacterial host for production, consequently toxic components in the initial lysate need to be removed or significantly diluted for safe clinical use. This is a study of the potential to utilise an endotoxin-reduced E. coli strain, ClearColi(R), to produce safer phage therapeutics. Such endotoxin modified strains should minimise the processing steps required and reduce overall production costs of a phage preparation. The research demonstrates that the endotoxin-reduced strain was able to produce a wide range of phage and for studied examples at phage titres equivalent to the more toxic parent strain. We also show that the strain can be modified to increase its host range and confirm the very low endotoxicity of basic phage lysates produced by the strain. Replicating this process to engineer additional low-toxicity bacterial production strains will accelerate the development of safer, more cost-effective phage therapeutics.

microbiology↗

bioRxiv2026

A systematic comparison of tools for predicting antimicrobial resistance from nanopore sequence data

Antimicrobial resistance (AMR) presents a pressing need to ensure that the right antimicrobials are used to target the right microbes at the right time. Ideally, the appropriate antimicrobial is selected after patient samples have been cultured and assessed with antimicrobial sensitivity testing (AST). However, the time needed for culture-based diagnosis leads to immediate empirical treatment, often with broad-spectrum and/or high-tier antimicrobials. Direct nanopore metagenomic whole genome sequencing to identify pathogens and predict their antimicrobial resistance is a rapid and patient-side alternative. A limitation of this approach is potential inconsistencies in in silico predicted AMR phenotypes. Here, we benchmarked the current performance of in silico AMR prediction strategies for nanopore-generated long read data. Using nanopore data paired with AST phenotyping for 201 samples representing 27 bacterial species, we assessed the impact of basecalling mode, data volume, and assembly strategy, and compared the performance of eight in silico AMR prediction tools with seven AMR databases. We found that basecalling accuracy mode does not significantly affect the overall accuracy of in silico AMR predictions, but assembly strategy and data volume both do. Prediction tools using the ResFinder database scored best for balanced accuracy (0.80 {+/-} 0.02 for both ResFinder and ABRicate), whilst DeepARG scored best for sensitivity (0.65 {+/-} 0.03); predictions were more accurate for some antibiotic classes and genera than others. However, even the best performing in silico AMR prediction strategy missed some resistance identified by lab-based AST. We conclude therefore that, currently, in silico AMR prediction can supplement lab-based AST, but cannot yet replace it.

microbiology↗

bioRxiv2025

MOPRs in mouse islets of Langerhans modulate cell signaling and secretion

Article highlightsO_LIMu opioid receptors are expressed on multiple islets of Langerhans cell types C_LIO_LIMu opioid receptors on islets engage canonical Gi signaling cascades in islets C_LIO_LIMu opioid receptors on islets modulate calcium influx and oscillations C_LIO_LIMu opioid receptors on islets modulate insulin and glucagon secretion. C_LI Most clinically and recreationally used opioids drugs act on the endogenous mu opioid receptor (MOPR). While MOPR is typically studied in the context of addiction and analgesia, decades of evidence indicates that they have a strong modulatory role on metabolism and glycemia. However, whether these effects are directly driven by MOPR actions on pancreatic islets remains poorly understood. Here we sought to comprehensively profile MOPRs on islets to assess how their activity shapes cellular physiology and secretion. First, we used RNA-seq, fluorescent in situ hybridization, and immunoblotting approaches to map islet expression. We observed robust expression of MOPR across multiple cell types in islets. Next, using a FRET-based approach, we show that MOPRs recruit canonical inhibitory pathways, reducing cAMP accumulation. Correspondingly, islets from constitutive MOPR knockout mice showed increased calcium influx and oscillations. However, MOPR knockout had no effect on insulin secretion, instead increase glucagon secretion. Surprisingly, while MOPR antagonism increased overall calcium, it reduced calcium oscillations and suppressed insulin secretion. By contrast MOPR agonism suppressed calcium, increased oscillations, and had no effect on overall hormone secretion. Collectively, these results suggest that MOPR can profoundly shape islet activity, with these effects likely driven by their actions on distinct cell types.

pharmacology and toxicology↗

bioRxiv2023

Predictive phage therapy for Escherichia coli urinary tract infections: cocktail selection for therapy based on machine learning models

This study supports the development of predictive bacteriophage (phage) therapy: the concept of phage cocktail selection to treat a bacterial infection based on machine learning models (MLM). For this purpose, MLM were trained on thousands of measured interactions between a panel of phage and sequenced bacterial isolates. The concept was applied to Escherichia coli (E. coli) associated with urinary tract infections. This is an important common infection in humans and companion animals from which multi-drug resistant (MDR) bloodstream infections can originate. The global threat of MDR infection has reinvigorated international efforts into alternatives to antibiotics including phage therapy. E. coli exhibit extensive genome-level variation due to horizontal gene transfer via phage and plasmids. Associated with this, phage selection for E. coli is difficult as individual isolates can exhibit considerable variation in phage susceptibility due to differences in factors important to phage infection including phage receptor profiles and resistance mechanisms. The activity of 31 phage were measured on 314 isolates with growth curves in artificial urine. Random Forest models were built for each phage from bacterial genome features and the more generalist phage, acting on over 20% of the bacterial population, exhibited F1 scores of >0.6 and could be used to predict phage cocktails effective against previously untested strains. The study demonstrates the potential of predictive models which integrate bacterial genomics with phage activity datasets allowing their use on data derived from direct sequencing of clinical samples to inform rapid and effective phage therapy. Significance StatementWith the growing challenge of antimicrobial resistance there is an urgency for alternative treatments for common bacterial diseases including urinary tract infections (UTIs). Escherichia coli is the main causative agent of UTIs in both humans and companion animals with multidrug resistant strains such as the globally disseminated ST131 becoming more common. Bacteriophage (phage) are natural predators of bacteria and potentially an alternative therapy. However, a major barrier for phage therapy is the specificity of phage on target bacteria and therefore difficulty efficiently selecting the appropriate phage. Here, we demonstrate a genomics driven approach using machine learning prediction models combined with phage activity clustering to select phage cocktails based only on the genome sequence of the infecting bacterial strain.

microbiology↗

bioRxiv2019

Natural Killer cells dampen the pathogenic features of recall responses to influenza infection.

Despite evidence of augmented Natural Killer (NK) cell responses after influenza vaccination, the role of these cells in vaccine-induced immunity remains unclear. Here, we hypothesized that NK cells might increase viral clearance but possibly at the expense of increased severity of pathology. On the contrary, we found that NK cells serve a homeostatic role during influenza virus infection of vaccinated mice, allowing viral clearance with minimal pathology. Using a diphtheria toxin receptor transgenic mouse model, we were able to specifically deplete NKp46+ NK cells through the administration of diphtheria toxin. Using this model, we assessed the effect of NK cell depletion prior to influenza challenge in vaccinated and unvaccinated mice. NK-depleted, vaccinated animals lost significantly more weight after viral challenge than vaccinated NK intact animals, indicating that NK cells ameliorate disease in vaccinated animals. However, there was also a significant reduction in viral load in NK-depleted, unvaccinated animals indicating that NK cells also constrain viral clearance. Depletion of NK cells after vaccination, but 21 days before infection, did not affect viral clearance or weight loss - indicating that it is the presence of NK cells during the infection itself that promotes homeostasis. Further work is needed to identify the mechanism(s) by which NK cells regulate adaptive immunity in influenza-vaccinated animals to allow efficient and effective virus control whilst simultaneously minimizing inflammation and pathology.

immunology↗