Adam Blanchard

A distinct bacterial dysbiosis associated skin inflammation in ovine footrot.

Ovine footrot is a highly prevalent bacterial disease caused by Dichelobacter nodosus and characterised by the separation of the hoof horn from the underlying skin. The role of innate immune molecules and other bacterial communities in the development of footrot lesions remains unclear. This study shows a significant association between the high expression of IL1β and high D. nodosus load in footrot samples. Investigation of the microbial population identified distinct bacterial populations in the different disease stages and also depending on the level of inflammation. Treponema (34%), Mycoplasma (29%) and Porphyromonas (15%) were the most abundant genera associated with high levels of inflammation in footrot. In contrast, Acinetobacter (25%), Corynebacteria (17%) and Flavobacterium (17%) were the most abundant genera associated with high levels of inflammation in healthy feet. This demonstrates for the first time there is a distinct microbial community associated with footrot and high cytokine expression.

Importance of the Difference in Surface Pressures of the Cell Membrane in Doxorubicin Resistant Cells That do not Express Pgp and ABCG2

P-glycoprotein (Pgp) represents the archetypal mechanism of drug resistance. But Pgp alone cannot expel drugs. A small but growing body of works has demonstrated that the membrane biophysical properties are central to Pgp-mediated drug resistance. For example, a change in the membrane surface pressure is expected to support drug–Pgp interaction. An interesting aspect from these models is that under specific conditions, the membrane is predicted to take over Pgp concerning the mechanism of drug resistance especially when the surface pressure is high enough, at which point drugs remain physically blocked at the membrane level. However it remains to be determined experimentally whether the membrane itself could, on its own, affect drug entry into cells that have been selected by a low concentration of drug and that do not express transporters. We demonstrate here that in the case of the drug doxorubicin, alteration of the surface pressure of membrane leaflets drive drug resistance.

The applied development of a tiered multilocus sequence typing (MLST) scheme for Dichelobacter nodosus

Dichelobacter nodosus (D. nodosus) is the causative pathogen of ovine footrot, a disease that has a significant welfare and financial impact on the global sheep industry. Previous studies into the phylogenetics of D. nodosus have focused on Australia and Scandinavia, meaning the current diversity in the United Kingdom (U.K.) population and its relationship globally, is poorly understood. Numerous epidemiological methods are available for bacterial typing; however, few account for whole genome diversity or provide the opportunity for future application of new computational techniques. Multilocus sequence typing (MLST) measures nucleotide variations within several loci with slow accumulation of variation to enable the designation of allele numbers to determine a sequence type. The usage of whole genome sequence data enables the application of MLST, but also core and whole genome MLST for higher levels of strain discrimination with a negligible increase in experimental cost. An MLST database was developed alongside a seven loci scheme using publically available whole genome data from the sequence read archive. Sequence type designation and strain discrimination was compared to previously published data to ensure reproducibility. Multiple D. nodosus isolates from U.K. farms were directly compared to populations from other countries. The U.K. isolates define new clades within the global population of D. nodosus and predominantly consist of serogroups A, B and H, however serogroups C, D, E, and I were also found. The scheme is publically available at https://pubmlst.org/dnodosus/.

A novel 3D skin explant model to study anaerobic bacterial infection

Skin infection studies are often limited by financial and ethical constraints, and alternatives, such as monolayer cell culture, do not reflect many cellular processes limiting their application. For a more functional replacement, 3D skin culture models offer many advantages such as the maintenance of the tissue structure and the cell types present in the host environment. A 3D skin culture model can be set up using tissues acquired from surgical procedures or post slaughter, making it a cost effective and attractive alternative to animal experimentation. The majority of 3D culture models have been established for aerobic pathogens, but currently there are no models for anaerobic skin infections. Footrot is an anaerobic bacterial infection which affects the ovine interdigital skin causing a substantial animal welfare and financial impact worldwide. Dichelobacter nodosus is a Gram-negative anaerobic bacterium and the causative agent of footrot. The mechanism of infection and host immune response to D. nodosus is poorly understood. Here we present a novel 3D skin ex vivo model to study anaerobic bacterial infections using ovine skin explants infected with D. nodosus. Our results demonstrate that D. nodosus can invade the skin explant, and that altered expression of key inflammatory markers could be quantified in the culture media. The viability of explants was assessed by tissue integrity (histopathological features) and cell death (DNA fragmentation) over 76 h showing the model was stable for 28 h. D. nodosus was quantified in all infected skin explants by qPCR and the bacterium was visualized invading the epidermis by Fluorescent in situ Hybridization. Measurement of pro-inflammatory cytokines/chemokines in the culture media revealed that the explants released IL1β in response to bacteria. In contrast, levels of CXCL8 production were no different to mock-infected explants. The 3D skin model realistically simulates the interdigital skin and has demonstrated that D. nodosus invades the skin and triggered an early cellular inflammatory response to this bacterium. This novel model is the first of its kind for investigating an anaerobic bacterial infection.

PIMMS (Pragmatic Insertional Mutation Mapping System) Laboratory Methodology a Readily Accessible Tool for Identification of Essential Genes in Streptococcus

The Pragmatic Insertional Mutation Mapping (PIMMS) laboratory protocol was developed alongside various bioinformatics packages (Blanchard et al., 2015) to enable detection of essential and conditionally essential genes in Streptococcus and related bacteria. This extended the methodology commonly used to locate insertional mutations in individual mutants to the analysis of mutations in populations of bacteria. In Streptococcus uberis, a pyogenic Streptococcus associated with intramammary infection and mastitis in ruminants, the mutagen pGhost9:ISS1 was shown to integrate across the entire genome. Analysis of >80,000 mutations revealed 196 coding sequences, which were not be mutated and a further 67 where mutation only occurred beyond the 90th percentile of the coding sequence. These sequences showed good concordance with sequences within the database of essential genes and typically matched sequences known to be associated with basic cellular functions. Due to the broad utility of this mutagen and the simplicity of the methodology it is anticipated that PIMMS will be of value to a wide range of laboratories in functional genomic analysis of a wide range of Gram positive bacteria (Streptococcus, Enterococcus, and Lactococcus) of medical, veterinary, and industrial significance.

Transposon insertion mapping with PIMMS - Pragmatic Insertional Mutation Mapping System

The PIMMS (Pragmatic Insertional Mutation Mapping System) pipeline has been developed for simple conditionally essential genome discovery experiments in bacteria. Capable of using raw sequence data files alongside a FASTA sequence of the reference genome and GFF file, PIMMS will generate a tabulated output of each coding sequence with corresponding mapped insertions accompanied with normalized results enabling streamlined analysis. This allows for a quick assay of the genome to identify conditionally essential genes on a standard desktop computer prioritizing results for further investigation. Availability: The PIMMS script, manual and accompanying test data is freely available at https://github.com/ADAC-UoN/PIMMS

Novel inflammatory cell infiltration scoring system to investigate healthy and footrot affected ovine interdigital skin

Ovine footrot is a degenerative disease of sheep feet leading to the separation of hoof-horn from the underlying skin and lameness. This study quantitatively examined histological features of the ovine interdigital skin as well as their relationship with pro-inflammatory cytokine (IL-1β) and virulent Dichelobacter nodosus in footrot. From 55 healthy and 30 footrot ovine feet, parallel biopsies (one fixed for histology) were collected post-slaughter and analysed for lesions and histopathological analysis using haematoxylin and eosin and Periodic Acid-Schiff. Histological lesions were similar in both conditions while inflammatory scores mirror IL-1β expression levels. Increased inflammatory score corresponded with high virulent D. nodosus load and was significant (p < 0.0001) in footrot feet with an inflammatory score of 3 compared to scores 1 and 2. In addition, in contrast to healthy tissues, localisation of eubacterial load extended beyond follicular depths in footrot samples. The novel inflammatory cell infiltration scoring system in this study may be used to grade inflammatory response in the ovine feet and demonstrated an association between severity of inflammatory response and increased virulent D. nodosus load.

DirtyGenes: significance testing for gene or bacterial population composition changes using the Dirichlet distribution

High throughput sequencing, and quantitative polymerase chain reaction (qPCR), can detect changes in bacterial communities or the genes that they carry, between different environments or treatments. These methods are applied widely to microbiomes in humans, animals, soil and water; an important application is for changes in antimicrobial resistance genes (ARGs). However, at present, there is no statistical method to determine whether observed changes in the overall composition are significant, or result from random variations between samples. Therefore researchers are limited to graphical descriptions. We describe a novel statistical method to determine whether or not observed differences in bacterial populations or their genes are significant. It can be used with data from shotgun metagenomics, 16S characterisation or qPCR. It can also be used for experimental design, to calculate the number of samples needed in future experiments. We show its application to two example data sets. The first is published data on bacterial communities and ARGs in the environment, in which we show that there are significant changes in both ARG and community composition. The second is a new data set on seasonality in bacterial communities and ARGs in hooves from four sheep. While the observed differences are not significant, we show that a minimum group size of eight sheep in a future experiment would provide sufficient power to observe significant changes, should the already observed changes be true. This method has broad uses for statistical testing and experimental design in experiments on changing microbiomes, including for studies on antimicrobial resistance.