Consumer and environmental microbiome effects of domestic probiotic cleaning products

It has been reported that spores of Bacillus spp. delivered in cleaning products can germinate
on dry surfaces in the home, generating vegetative forms of bacteria, which can counteract the
growth of pathogens by competitive exclusion on treated surfaces (Caselli et al. 2016). We will
test to what extent MBCP application in domestic settings could: i) Lead to persistent Bacillus
colonisation of the home. ii) Alter the microbiota of the application surfaces. iii) Transfer to
consumers via direct contact (kitchen surfaces) or the airborne route. iv) Modify the human
(skin) microbiome through direct contact or via colonised surfaces.
Aim and Objectives: 1) To investigate the colonisation of domestic hard surface materials by
microorganisms within MBCP. This will include an assessment of spore numbers, viability,
surface colonisation and persistence in a simulated domestic environment during repeated
application of MBCP to surfaces. This will be done using a combination of high throughput
amplicon sequencing, qPCR and bacterial culture. 2) To evaluate the extent of the change in the
domestic surface microbiome when using MBPC, including the duration of any observed
changes, and their reversibility. Microbiome analyses will be done by high throughput
amplicon sequencing. Propidium monoazide treatment will be included to differentiate
between DNA derived from viable and non-viable bacteria (Marotz et al., 2018; Ni et al., 2019;
Sielaff et al., 2019). 3) To investigate the potential for spores within MBCP to transfer to
kitchen users. To be done using samples of kitchen surface materials within a controlled
laboratory environment. 4) To use data generated in (3) to design and conduct a human
volunteer study of the skin microbiome effects of both direct (during deployment) and indirect
(during food preparation) contact with MBPC. 5) To use data generated in (2) and (4) to
create co-occurrence network models, the impact of MBCPs on microbial community structure.
These will be analysed using an exponential random graph model framework (Lusher et al
2013). Such models can be insightful into the stability and ecological dynamics of microbiomes
(DeVries et al. 2018).