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Wilcox, M.

Publications and source records attributed to Wilcox, M..

6 recordsLinked to original sources

The Quorum Sensing regulated sRNA Lrs1 is involved in the adaptation to low iron in Pseudomonas aeruginosa

Iron is an essential nutrient for microbial growth. The opportunistic pathogen Pseudomonas aeruginosa can survive under diverse conditions, including iron-depleted environments with the aid of small non-coding RNAs (sRNAs). P. aeruginosa also uses three quorum sensing (QS) systems Las, Rhl, and Pqs to coordinate virulence and infection establishment at the population level. This study links the sRNA Lrs1, which is located within the promoter of the Pqs biosynthetic operon pqsABCDE, to iron uptake regulation in the P. aeruginosa strain PAO1-L. Transcriptomics and phenotypic assays indicate that Lrs1 downregulates the production of the siderophore pyochelin but not pyoverdine, and that lrs1 regulation itself is dependent on iron availability. Although Lrs1 has been implicated in a positive feedback loop with the transcriptional regulator LasR in the strain PA14, the present findings indicate that this is not the case in PAO1-L in the tested conditions. Transcription of Lrs1 is dependent on quorum sensing, predominantly on RhlR with an auxiliary effect by PqsE. Furthermore, the Pqs system and phenazine production are modulated by Lrs1 only under iron limitation. This study identifies Lrs1 as a new QS-dependent post-transcriptional regulator of iron uptake and virulence highlighting its importance in environmental adaptation in P. aeruginosa. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=74 SRC="FIGDIR/small/627364v1_ufig1.gif" ALT="Figure 1"> View larger version (12K): org.highwire.dtl.DTLVardef@1585ca5org.highwire.dtl.DTLVardef@2a7d55org.highwire.dtl.DTLVardef@18a712eorg.highwire.dtl.DTLVardef@c84d2_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

MiGut: a scalable in vitro platform for simulating the human gut microbiome - development, validation, and simulation of antibiotic induced dysbiosis

In vitro models of the human colon have been used extensively in developing understanding of the human gut microbiome and how internal and external factors affect the residing bacterial populations. Such models can be highly predictive of in vivo effects of antibiotics, and indeed more so than animal models. The complexity required by current in vitro models to closely mimic the physiology of the colon poses practical limits on their scalability. MiGut allows considerable expansion of model runs, increasing the capacity to test reproducibility or parameters under investigation. The MiGut platform has been assessed against a well-studied triple-stage chemostat model in a demanding nine-week study, with exposure to multiple antibiotics inducing a state of dysbiosis in the microbiome. A good correlation is found, both between individual MiGut models and against the triple-stage chemostat. Together with high-throughput molecular techniques for sample analysis, it is now conceivable that tens of in vitro models could be run simultaneously, allowing complex microbiome-xenobiotic interactions to be explored in far greater detail. MiGut is a unique platform whereby multiple colonic models can be run simultaneously with minimal added resource and complexity to support our understanding of the cause-effect relationships that govern the gut microbiome. This model system expands the capacity to generate clinically relevant data that can optimize interventions which target the gut microbiome directly or indirectly.

microbiology↗

K-mer based prediction of Clostridioides difficile relatedness and ribotypes

Comparative analysis of Clostridioides difficile whole-genome sequencing (WGS) data enables fine scaled investigation of transmission and is increasingly becoming part of routine surveillance. However, these analyses are constrained by the computational requirements of the large volumes of data involved. By decomposing WGS reads or assemblies into k-mers and using the dimensionality reduction technique MinHash, it is possible to rapidly approximate genomic distances without alignment. Here we assessed the performance of MinHash, as implemented by sourmash, in predicting single nucleotide differences between genomes (SNPs) and C. difficile ribotypes (RTs). For a set of 1,905 diverse C. difficile genomes (differing by 0-168,519 SNPs), using sourmash to screen for closely related genomes, at a sensitivity of 100% for pairs [≤]10 SNPs, sourmash reduced the number of pairs from 1,813,560 overall to 161,934, i.e., by 91%, with a positive predictive value of 32% to correctly identify pairs [≤]10 SNPs (maximum SNP distance 4,144). At a sensitivity of 95%, pairs were reduced by 94% to 108,266 and PPV increased to 45% (maximum SNP distance 1,009). Increasing the MinHash sketch size above 2000 produced minimal performance improvement. We also explored a MinHash similarity-based ribotype prediction method. Genomes with known ribotypes (n=3,937) were split into a training set (2,937) and test set (1,000) randomly. The training set was used to construct a sourmash index against which genomes from the test set were compared. If the closest 5 genomes in the index had the same ribotype this was taken to predict the searched genomes ribotype. Using our MinHash ribotype index, predicted ribotypes were correct in 780/1000 (78%) genomes, incorrect in 20 (2%), and indeterminant in 200 (20%). Relaxing the classifier to 4/5 closest matches with the same RT improved the correct predictions to 87%. Using MinHash it is possible to subsample C. difficile genome k-mer hashes and use them to approximate small genomic differences within minutes, significantly reducing the search space for further analysis. Impact statementThe genetic code, or DNA, of bacteria is increasingly used to track how infection spreads and to guide infection control interventions, as similar or identical DNA sequences are expected in samples from pair of individuals related by transmission. While obtaining the DNA sequence for bacteria is increasingly straightforward, comparing thousands or even millions of sequences requires substantial computing power and time using current approaches. Here we describe how a method for summarising sequencing data, MinHash, can be used to rapidly reduce the number of possible close sequence matches in Clostridioides difficile, an important healthcare-associated pathogen. It can also be used to approximate traditional schemes used to classify C. difficile into smaller subgroups in transmission analyses, such as ribotyping. Data summaryThe authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files.

genomics↗

Insights into the regulatory mechanisms of Clostridioides difficile biofilm formation

Mucosal 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.

microbiology↗

Trends in Escherichia coli bloodstream infection, urinary tract infections and antibiotic susceptibilities in Oxfordshire, 1998-2016: an observational study

BackgroundThe incidence of Escherichia coli bloodstream infections (EC-BSIs), particularly those caused by antibiotic-resistant strains, is increasing in the UK and internationally. This is a major public health concern but the evidence base to guide interventions is limited.\n\nMethodsIncidence of EC-BSIs and E. coli urinary tract infections (EC-UTIs) in one UK region (Oxfordshire) were estimated from anonymised linked microbiological and hospital electronic health records, and modelled using negative binomial regression based on microbiological, clinical and healthcare exposure risk factors. Infection severity, 30-day allcause mortality, and community and hospital co-amoxiclav use were also investigated.\n\nFindingsFrom 1998-2016, 5706 EC-BSIs occurred in 5215 patients, and 228376 EC-UTIs in 137075 patients. 1365(24%) EC-BSIs were nosocomial (onset >48h post-admission), 1863(33%) were community (>365 days post-discharge), 1346(24%) were quasi-community (31-365 days post-discharge), and 1132(20%) were quasi-nosocomial ([&le;]30 days postdischarge). 1413(20%) EC-BSIs and 36270(13%) EC-UTIs were co-amoxiclav-resistant (41% and 30%, respectively, in 2016). Increases in EC-BSIs were driven by increases in community (10%/year (95% CI:7%-13%)) and quasi-community (8%/year (95% CI:7%-10%)) cases. Changes in EC-BSI-associated 30-day mortality were at most modest (p>0{middle dot}03), and mortality was substantial (14-25% across groups). By contrast, co-amoxiclav-resistant EC-BSIs increased in all groups (by 11%-19%/year, significantly faster than susceptible EC-BSIs, pheterogeneity<0{middle dot}001), as did co-amoxiclav-resistant EC-UTIs (by 13%-29%/year, pheterogeneity*0{middle dot}001). Co-amoxiclav use in primary-care facilities was associated with subsequent co-amoxiclav-resistant EC-UTIs (p=0{middle dot}03) and all EC-UTIs (p=0{middle dot}002).\n\nInterpretationCurrent increases in EC-BSIs in Oxfordshire are primarily community-associated, with high rates of co-amoxiclav resistance, nevertheless not impacting mortality. Interventions should target primary-care facilities with high co-amoxiclav usage.\n\nFundingNational Institute for Health Research.\n\nResearch in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed for publications from inception up until October 26, 2017, with the terms \"Escherichia coli\", \"E. coli\", \"bacteraemia\", \"bloodstream infection\", restricting the search to English language articles, and also reviewed references from retrieved articles. Escherichia coli (E. coli) is the most common cause of bloodstream infection, and the incidence of E. coli bloodstream infection, and particularly antibiotic-resistant infections, is increasing in the UK and internationally. Although the UK government aims to reduce healthcare-associated E. coli bloodstream infection, there is only limited evidence to inform appropriate interventions.\n\nAdded value of this studyWe investigated potential drivers for these increases in incidence by exploiting available linked electronic health records over 19 years for ~5200 patients with E. coli bloodstream infection and ~140000 with E. coli urinary tract infection, together with community antimicrobial prescribing data for the most recent six years. Our study identified several findings with significant implications for health policy and patient care: O_LIIncreases in the incidence of E. coli bloodstream infections were driven mainly by non-hospital-associated cases; however, neither patients with previous urinary tract infections nor having previously had urine specimens sent from catheters appeared to be driving the increases\nC_LIO_LICo-amoxiclav-resistant bloodstream infections rose significantly faster than co-amoxiclav-susceptible bloodstream infections, with the greatest number of co-amoxiclav-resistant bloodstream infections in 2016 being in patients discharged more than a month previously (i.e. community-associated)\nC_LIO_LIHigher co-amoxiclav use in primary care was associated with higher rates of both co-amoxiclav-resistant E. coli urinary tract infections and E. coli urinary tract infections overall, supporting drives to reduce broad-spectrum and inappropriate antibiotic use in primary care\nC_LIO_LIDespite substantial increases in co-amoxiclav-resistant bloodstream infections there was no evidence that mortality was increasing in these cases; this does not support moving to broader empiric antibiotic prescribing in hospitals (i.e. carbapenems, piperacillin-tazobactam)\nC_LI\n\nImplications of all available adviceThis suggests that government strategies to effectively reduce E. coli bloodstream infections should target community settings, as well as healthcare-associated settings. The absence of an increased mortality signal suggests that co-amoxiclav resistant E. coli infections are either being successfully treated by dual empiric therapy in severe cases (e.g. with concomitant gentamicin), can be \"rescued\" once isolate susceptibilities become available, or currently deployed phenotypic susceptibility testing breakpoints do not adequately correlate with clinical outcome.

epidemiology↗

Identifying the diamond in the rough: a study of allelic diversity underlying flowering time adaptation in maize landraces

Landraces (traditional varieties) of crop species are a reservoir of useful genetic diversity, yet remain untapped due to the genetic linkage between the few useful alleles with hundreds of undesirable alleles1. We integrated two approaches to characterize the genetic diversity of over 3000 maize landraces from across the Americas. First, we mapped the genomic regions controlling latitudinal and altitudinal adaptation, identifying 1498 genes. Second, we developed and used F-One Association Mapping (FOAM) to directly map genes controlling flowering time across 22 environments, identifying 1,005 genes. In total 65% of the SNPs associated with altitude were also associated with flowering time. In particular, we observed many of the significant SNPs were contained in large structural variants (inversions, centromeres, and pericentromeric regions): 29.4% for flowering time, 58.4% for altitude and 13.1% for latitude. The combined mapping results indicate that while floral regulatory network genes contribute substantially to field variation, over 90% of contributing genes likely have indirect effects. Our strategy can be used to harness the diversity of maize and other plant and animal species.

genetics↗