bioRxiv Science⌕ Search

Biology subjects

Totemeyer, S.

Publications and source records attributed to Totemeyer, S..

2 recordsLinked to original sources

Sensitivity of Listeria monocytogenes to lysozyme predicts ability to proliferate in bovine caruncular epithelial cells

Listeria monocytogenes is an important foodborne pathogen in human and veterinary health, causing significant morbidity and mortality including abortion. It has a particular tropism for the gravid uterus, however, the route of infection in reproductive tissues of ruminants (i.e. placentome), is much less clear. In this study, we aimed to investigate a bovine caruncular epithelial cell (BCEC) line as a model for L. monocytogenes infection of the bovine reproductive tract. The BCEC infection model was used to assess the ability of 14 different L. monocytogenes isolates to infect these cells. Lysozyme sensitivity and bacterial survival in 580 {micro}g lysozyme/ml correlated with attenuated ability to proliferate in BCEC (p=0.004 and p=0.02, respectively). Four isolates were significantly attenuated compared to the control strain 10403S. One of these strains (AR008) showed evidence of compromised cell wall leading to increased sensitivity to {beta}-lactam antibiotics, and another (7644) had compromised cell membrane integrity leading to increased sensitivity to cationic peptides. Whole genome sequencing followed by Multi Locus Sequence Type analysis identified that five invasive isolates had the same sequence type, ST59, despite originating from three different clinical conditions. Virulence gene analysis showed that the attenuated isolate LM4 was lacking two virulence genes (uhpT, virR) known to be involved in intracellular growth and virulence. In conclusion, the BCEC model was able to differentiate between the infective potential of different isolates. Moreover, resistance to lysozyme correlated with the ability to invade and replicate within BCEC, suggesting co-selection for surviving challenging environments as the abomasum.

microbiology↗

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.

bioinformatics↗