Skin microbiome alters attractiveness to Anopheles mosquitoes

Abstract

Background: Some people produce specific body odours that make them more attractive than others to mosquitoes, and consequently are at higher risk of contracting vector-borne diseases. The skin microbiome can break down carbohydrates, fatty acids and peptides on the skin into volatiles that mosquitoes can differentiate.

Results: Here, we examined how skin microbiome composition of women differs in relation to level of attractiveness to Anopheles coluzzii mosquitoes, to identify volatiles in body odour and metabolic pathways associated with individuals that tend to be poorly-attractive to mosquitoes. We used behavioural assays to measure attractiveness of participants to An. coluzzii mosquitoes, 16S rRNA amplicon sequencing of the bacteria sampled from the skin and gas chromatography of volatiles in body odour. We found differences in skin microbiome composition between the poorly- and highly-attractive groups, particularly eight Amplicon Sequence Variants (ASVs) belonging to the Proteobacteria, Actinobacteria and Firmicutes phyla. Staphylococcus 2 ASVs are four times as abundant in the highly-attractive compared to poorly-attractive group. Associations were found between these ASVs and volatiles known to be attractive to Anopheles mosquitoes. Propanoic pathways are enriched in the poorly-attractive participants compared to those found to be highly-attractive.

Conclusions: Our findings suggest that variation in attractiveness of people to mosquitoes is related to the composition of the skin microbiota, knowledge that could improve odour-baited traps or other next generation vector control tools.

Keywords: Anopheles coluzzii; Body odour; Diversity; Human attractiveness; Malaria; Mosquitoes; Repellents; Skin microbiome.

Fig. 1

Differences in skin microbial composition (beta diversity) between the poorly- and highly-attractive groups. Each participant’s attractiveness to mosquitoes was measured using a bioassay, and differences in microbial composition between participants calculated using centralised log ratio transformed, DEICODE beta diversities from 16S rRNA data (see Methods). The sparse partial least squares discriminant analysis (sPLS-DA) sample plot shown was used to compare differences in microbial composition between the poorly-attractive group (orange) and highly-attractive group (blue). Individuals are presented by small triangles (poorly-attractive) or small circles (highly-attractive). Data were scaled (centred and standardised). The centroids (stars) represent the mean microbial composition on the first and second components for each group. The ellipse plots (large circles) represent 95% confidence intervals for the relative attractiveness groups

Fig. 2

Bacterial genera with the greatest contribution to differences in microbiome composition between poorly- and highly attractiveness groups. The loading plot represents the 10 bacterial genera contributing the most to differences between attractiveness group on A component 1 and B component 2 of the sPLS-DA. Bars represent the loading weights or correlation coefficients of each bacterial genus to the components of the sPLS-DA. The direction of the bars (left or right) relates to the direction of the loadings in Fig. 1. Orange and blue bars indicate a higher abundance in the poorly- or highly-attractive group respectively. There are two Staphylococcus genera belonging to different families: Staphylococcus 1 belongs to the Planococcaceae family and Staphylococcus 2 belongs to the Staphylococcaceae family

Fig. 3

Differentially abundant bacteria between poorly- and highly-attractive groups. Volcano plot of amplicon sequence variants (ASVs, black dots). DESEQ2 was used to calculate log 2 fold changes i.e. if bacterial ASVs are more or less abundant in the poorly-attractive compared to highly-attractive group. X axis represents log 2 fold change abundance in the poorly-attractive compared to highly-attractive groups, with the biggest changes furthest from the centre. Y axis indicates the negative log-10 transform of the nominal p-value, i.e. increasing significance away from the origin. Red line represents P = 0.05 for exploratory purposes. ASVs above the red line are nominally significance and considered differentially abundant. Where genus level taxonomy is available and the ASV is above the red line it is labelled with genus level taxonomy

Fig. 4

Heatmap correlation compounds from the literature and differentially abundant ASVs. Heatmap showing Pearson correlations between known compounds from the literature tentatively identified in our dataset and the differentially abundant ASVs identified, taxonomy assigned at family; genus level. Asterisks represent p-values from Pearson correlation, < 0.001 is represented as ** and < 0.05 as *

Fig. 5

Heatmap compounds of interest identified and bacteria. Heatmap showing Pearson correlations between compounds of interest identified from the pathways and the 25 genera of bacteria identified in our samples that could be classified to the genus level, taxonomy at family; genus level. Asterisks represent p-values from Pearson correlation, < 0.0001 is represented as ***, < 0.001 is represented as ** and < 0.05 as *

Fig. 6

Diagram of the dual choice olfactometer. A The trap in which the nylon sock was placed. B The dual choice olfactometer from the side. Socks were placed on a wire frame inside the trap, and air flowed through the sock, carrying the body odour into the tunnel