Colo-Pro: a pilot randomised controlled trial to compare standard bolus-dosed cefuroxime prophylaxis to bolus-continuous infusion–dosed cefuroxime prophylaxis for the prevention of infections after colorectal surgery

Standard bolus-dosed antibiotic prophylaxis may not inhibit growth of antibiotic resistant colonic bacteria, a cause of SSIs after colorectal surgery. An alternative strategy is continuous administration of antibiotic throughout surgery, maintaining concentrations of antibiotics that inhibit growth of resistant bacteria. This study is a pilot comparing bolus-continuous infusion with bolus-dosed cefuroxime prophylaxis in colorectal surgery. This is a pilot randomised controlled trial in which participants received cefuroxime bolus-infusion (intervention arm) targeting free serum cefuroxime concentrations of 64 mg/L, or 1.5 g cefuroxime as a bolus dose four-hourly (standard arm). Patients in both arms received metronidazole (500 mg intravenously). Eligible participants were adults undergoing colorectal surgery expected to last for over 2 h. Results were analysed on an intention-to-treat basis. The study was successfully piloted, with 46% (90/196) of eligible patients recruited and 89% (80/90) of participants completing all components of the protocol. A trialled bolus-continuous dosing regimen was successful in maintaining free serum cefuroxime concentrations of 64 mg/L. No serious adverse reactions were identified. Rates of SSIs (superficial and deep SSIs) were lower in the intervention arm than the standard treatment arm (24% (10/42) vs. 30% (13/43)), as were infection within 30 days of operation (41% (17/43) vs 51% (22/43)) and urinary tract infections (2% (1/42) vs. 9% (4/43)). These infection rates can be used to power future clinical trials. This study demonstrates the feasibility of cefuroxime bolus-continuous infusion of antibiotic prophylaxis trials, and provides safety data for infusions targeting free serum cefuroxime concentrations of 64 mg/L. Trial registration: NCT02445859

Identifying Bacterial Markers of Antimicrobial-Mediated Intestinal Dysbiosis

Background: The gastrointestinal tract microbiome has significant functional roles in maintaining host health, maintained through a delicate balance. Disruptions to this balance may lead to diseases such as antibiotic-associated diarrhoea and Clostridioides difficile infection. Taxonomic analyses can only partially explain this dysbiotic states due to the inter-individual microbiome variability. Functional and metabolic analyses may provide more a unified description of the microbiome and dysbiosis. Aims: Identify bacterial markers of antibiotic induced dysbiosis utilising in-vitro gut models. Methods: In-vitro gut models (Leeds Human Gut Model, MiGut) were used to simulate antibiotic-induced dysbiosis using three clinically reflective broad spectrum antibiotic dosing regimens (Amoxicillin, Ciprofloxacin, Piperacillin/Tazobactam). Gut models were challenged with two pathogens (Clostridioides difficile (027), KPC-positive Klebsiella pneumoniae) to demonstrate post-antibiotic functional dysbiosis. Shotgun metagenomic analysis was used to taxonomically and functionally profile the microbiome. Liquid Chromatography-Mass Spectrometry was used to identify metabolic markers of dysbiosis. Results: Cultural analysis: Antibiotic instillation resulted in wide scale disruptions to the microbiome coinciding with antibiotic therapy. This resolved post-antibiotic instillation with monitored taxa returning to similar abundance and composition pre-antibiotic instillation. However, functional dysbiosis was demonstrated by the colonisation and proliferation of Clostridioides difficile and Klebsiella pneumoniae within the models. Metagenomic analysis: Reductions in species diversity and a loss of key genera was associated with short chain fatty acid (SCFA) production. KEGG pathway analysis identified changes in abundance to pathways associated with SCFA metabolism for acetate, propanoate and butyrate. SCFA producers Faecalibacterium and Coprococcus were reduced in abundance following antibiotic instillation. Metabolomic analysis: Reduced SCFA levels within the metabolome coincided with antibiotic instillation that persisted beyond recovery of key microbial genera. Putative Metabolic Markers; Butyrate, Valerate, Lactate, 2-Hydroxyglutarate, 2-Oxoglutarate. Acetate and Propanoate. Conclusions: A putative panel of nine biomarkers of dysbiosis, consisting of 2 taxonomic species and 7 metabolites, were identified. In vitro models are effective tools to screen for marker of antibiotic induced dysbiosis

From the hospital toilet to the ward: A pilot study on microbe dispersal to multiple hospital surfaces following hand drying using a jet air dryer versus paper towels

AbstractUsing a bacteriophage to represent microbial contamination, we investigated virus transmission to the hospital environment following hand drying. The use of paper towels resulted in lower rates of virus contamination on hands and clothing compared with a jet air dryer and, consequently, lower contamination of multiple hospital surfaces.</jats:p

Sessile Clostridioides difficilecontribute towards recurrent C. difficileinfection

C. difficile, an anaerobic spore-forming intestinal pathogen, produces up to three toxins that cause host cell damage resulting in disease, C. difficileinfection (CDI). Therapies include antibiotic treatment; however, up to 30% of cases fail primary therapy, resulting in recurrent disease, which increases patient morbidity and places a burden on worldwide healthcare systems. We have little understanding of why these therapies fail. Using a clinically validated in vitro gut model, we assess the contribution of biofilms towards recurrent disease and to investigate biofilm microbiota-C. difficile interactions. During induction of simulated CDI, C. difficile spores and vegetative cells became associated with the colonic biofilm microbiota. Vancomycin treatment did not effectively remove the biofilm C. difficile cells and recurrent infection was observed. Additionally, vancomycin therapy followed by faecal microbiota transplant did resolve the recurrent infection, but the biofilm C. difficile cells remained unaffected. In a biofilm transfer experiment, we showed that transferring biofilm encased C. difficile cells into a C. difficile naïve (but CDI susceptible model) induced CDI. Furthermore, we show that members of the biofilm community can impact C. difficile biofilm formation either acting in an antagonistic or synergistic manner. We highlight the importance of biofilms as a reservoir for C. difficile, which can be a cause for recurrent infections.</jats:p

Insights into the regulatory mechanisms of <i>Clostridioides difficile</i> biofilm formation

AbstractMucosal biofilms play an important role in intestinal health; however, the mucosal bacterial community has been implicated in persistent infections. Clostridioides difficile is an important nosocomial pathogen, with an unacceptable high rate of recurrence following antibiotic treatment. As C. difficile is a known biofilm producer, a property which may contribute to this suboptimal therapeutic response, we have investigated the transcriptional changes and regulatory pathways during the transition from planktonic to biofilm mode of growth. Widespread metabolic reprogramming during biofilm formation was detected, characterised by an increased usage of glycine metabolic pathways to yield key metabolites, which are used for energy production and synthesis of short chain fatty acids. We detected the expression of 107 small non-coding RNAs that appear to, in some part, regulate these pathways; however, 25 of these small RNAs were specifically expressed during biofilm formation, indicating they may play a role in regulating biofilm-specific genes. Similar to Bacillus subtilis, biofilm formation is a multi-regulatory process and SinR negatively regulates biofilm formation independently of other known mechanisms. This comprehensive analysis furthers our understanding of biofilm formation in C. difficile, identifies potential targets for anti-virulence factors, and provides evidence of the link between metabolism and virulence traits.</jats:p

MiGut: a scalable in vitro platform for simulating the human gut microbiome – collated bacterial data

MiGut is a novel scalable platform whereby multtiple triple-stage in vitro models which simulate the human colon can be run in parallel with minimal additional resource requirement. This dataset contains the data used during a validation study whereby MiGut was run alongside a clinically relevant triple-stage gut model. The data include bacterial quantification of selected groups, as detailed in prior work, and the resulting Bray Curtis dissimilarity matrices, which were used in the associated publication

Method comparison for the direct enumeration of bacterial species using a chemostat model of the human colon

AbstractBackgroundClostridioides difficileinfection (CDI) has a high recurrent infection rate. Faecal microbiota transplantation (FMT) has been used successfully to treat recurrent CDI, but much remains unknown about the human gut microbiota response to replacement therapies. In this study, antibiotic-mediated dysbiosis of gut microbiota and bacterial growth dynamics were investigated by two quantitative methods: real-time quantitative PCR (qPCR) and direct culture enumeration, in triple-stage chemostat models of the human colon. Threein vitromodels were exposed to clindamycin to induce simulated CDI. All models were treated with vancomycin, and two received an FMT. Populations of total bacteria,Bacteroidesspp.,Lactobacillusspp.,Enterococcusspp.,Bifidobacteriumspp.,C. difficile,and Enterobacteriaceae were monitored using both methods. Total clostridia were monitored by selective culture. Using qPCR analysis, we additionally monitored populations ofPrevotellaspp.,Clostridium coccoidesgroup, andClostridium leptumgroup.ResultsBoth methods showed an exacerbation of disruption of the colonic microbiota following vancomycin (and earlier clindamycin) exposure, and a quicker recovery (within 4 days) of the bacterial populations in the models that received the FMT.C. difficileproliferation, consistent with CDI, was also observed by both qPCR and culture. Pearson correlation coefficient showed an association between results varying from 98% forBacteroidesspp., to 62% for Enterobacteriaceae.ConclusionsGenerally, a good correlation was observed between qPCR and bacterial culture. Overall, the molecular assays offer results in real-time, important for treatment efficacy, and allow the monitoring of additional microbiota groups. However, individual quantification of some genera (e.g. clostridia) might not be possible without selective culture.</jats:sec

Profiling the effects of rifaximin on the healthy human colonic microbiota using a chemostat model

AbstractRifaximin is a low solubility antibiotic with activity against a wide range of bacterial pathogens. It accumulates in the intestine and is suitable for prolonged use. Three chemostat models (A, B and C) were used to investigate the effects of three rifaximin formulations (α, β and κ, respectively) on the gut microbiome. Bacterial populations were monitored by bacterial culture and 16S rRNA gene amplicon (16S) sequencing. Limited disruption of bacterial populations was observed for rifaximin α, β and κ. All formulations caused declines in total spores (∼2 log10 cfu ml-1), Enterococcus spp. (∼2 log10 cfu ml-1 in models A and C, and ∼1 log10 cfu ml-1 in model B), and Bacteroides spp. populations (∼3 log10 cfu ml-1 in models A and C, and ∼4 log10 cfu ml-1 in model B). Bacterial populations fully recovered during antibiotic dosing in model C, and before the end of the experiment in models A and B. According to the taxonomic analysis, prior to rifaximin exposure, Bifidobacteriaceae, Ruminococcaceae, Acidaminococcaceae, Lachnospiraceae and Rikenellaceae families represented &gt;92% of the total relative abundance, in all models. Within these families, 15 bacterial genera represented &gt;99% of the overall relative abundance. Overall, the 16S sequencing and culture data showed similar variations in the bacterial populations studied. Among the three formulations, rifaximin κ appeared to have the least disruptive effect on the colonic microbiota, with culture populations showing recovery in a shorter period and the taxonomic analysis revealing the least global variation in relative abundance of prevalent groups.</jats:p

MOESM1 of Method comparison for the direct enumeration of bacterial species using a chemostat model of the human colon

Additional file 1: Figure S1. Mean gut microbiota populations of C. difficile based on the housekeeping gene gluD in vessel 3 of model A, B and C at the different stages of the experiment. Bar graphs represent the levels in log10 copies/μL measured by qPCR. CD, C. difficile; rCDI, recurrent CDI; FMT, faecal microbiota transplantation. Asterisks represent significant variations by qPCR between time points: *correspond to p < 0.05, and ***correspond to p < 0.0005. (TIF 619 Kb

MiGut: A scalable in vitro platform for simulating the human gut microbiome—Development, validation and simulation of antibiotic‐induced dysbiosis

Abstract In vitro models of the human colon have been used extensively in understanding the human gut microbiome (GM) and evaluating how internal and external factors affect the residing bacterial populations. Such models have been shown to be highly predictive of in vivo outcomes and have a number of advantages over animal models. The complexity required by in vitro models to closely mimic the physiology of the colon poses practical limits on their scalability. The scalable Mini Gut (MiGut) platform presented in this paper allows considerable expansion of model replicates and enables complex study design, without compromising on in vivo reflectiveness as is often the case with other model systems. MiGut has been benchmarked against a validated gut model in a demanding 9‐week study. MiGut showed excellent repeatability between model replicates and results were consistent with those of the benchmark system. The novel technology presented in this paper makes it conceivable that tens of models could be run simultaneously, allowing complex microbiome‐xenobiotic interactions to be explored in far greater detail, with minimal added resources or complexity. This platform expands the capacity to generate clinically relevant data to support our understanding of the cause‐effect relationships that govern the GM