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. 2023 Jun 15;11(3):e0118822.
doi: 10.1128/spectrum.01188-22. Epub 2022 Dec 12.

Haze Exposure Changes the Skin Fungal Community and Promotes the Growth of Talaromyces Strains

Affiliations

Haze Exposure Changes the Skin Fungal Community and Promotes the Growth of Talaromyces Strains

Dong Yan et al. Microbiol Spectr. .

Abstract

Haze pollution has been a public health issue. The skin microbiota, as a component of the first line of defense, is disturbed by environmental pollutants, which may have an impact on human health. A total of 74 skin samples from healthy students were collected during haze and nonhaze days in spring and winter. Significant differences of skin fungal community composition between haze and nonhaze days were observed in female and male samples in spring and male samples in winter based on unweighted UniFrac distance analysis. Phylogenetic diversity whole-tree indices and observed features were significantly increased during haze days in male samples in winter compared to nonhaze days, but no significant difference was observed in other groups. Dothideomycetes, Capnodiales, Mycosphaerellaceae, etc. were significantly enriched during nonhaze days, whereas Trichocomaceae, Talaromyces, and Pezizaceae were significantly enriched during haze days. Thus, five Talaromyces strains were isolated, and an in vitro culture experiment revealed that the growth of representative Talaromyces strains was increased at high concentrations of particulate matter, confirming the sequencing results. Furthermore, during haze days, the fungal community assembly was better fitted to a niche-based assembly model than during nonhaze days. Talaromyces enriched during haze days deviated from the neutral assembly process. Our findings provided a comprehensive characterization of the skin fungal community during haze and nonhaze days and elucidated novel insights into how haze exposure influences the skin fungal community. IMPORTANCE Skin fungi play an important role in human health. Particulate matter (PM), the main haze pollutant, has been a public environmental threat. However, few studies have assessed the effects of air pollutants on skin fungi. Here, haze exposure influenced the diversity and composition of the skin fungal community. In an in vitro experiment, a high concentration of PM promoted the growth of Talaromyces strains. The fungal community assembly is better fitted to a niche-based assembly model during haze days. We anticipate that this study may provide new insights on the role of haze exposure disturbing the skin fungal community. It lays the groundwork for further clarifying the association between the changes of the skin fungal community and adverse health outcomes. Our study is the first to report the changes in the skin fungal community during haze and nonhaze days, which expands the understanding of the relationship between haze and skin fungi.

Keywords: community composition; diversity; haze; particulate matter; skin fungi.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

FIG 1
FIG 1
The information of sample sites, date, and haze level. (A) Map showing the information of the sample site in Xinxiang, Henan, China. (B) The levels of PM2.5, PM10, and AQI on various sample days.
FIG 2
FIG 2
Changes in skin fungal community composition during haze and nonhaze days. (A) Principal-coordinate analysis (PCoA) indicating changes in the skin fungal community between spring and winter based on unweighted and weighted UniFrac distance. (B) Canonical correspondence analysis (CCA) between skin fungal community and skin factors (gender, pH, oil content, and water content) in spring and winter. (C) PCoA indicating changes in the skin fungal community between haze and nonhaze days in spring and winter based on unweighted and weighted UniFrac distance. (D) PCoA indicating changes in the skin fungal community from females between haze and nonhaze days in spring and winter based on unweighted and weighted UniFrac distance. (E) PCoA indicating changes in the skin fungal community from males between haze and nonhaze days in spring and winter based on unweighted and weighted UniFrac distance.
FIG 3
FIG 3
Diversity and composition of the skin fungal community at the genus level. (A) Violin plot of skin fungal diversity based on Shannon, PD whole-tree, and observed features indices. (B) Bar plot indicating the relative abundance of dominant genera (>0.02%).
FIG 4
FIG 4
The fungal genera with differences between haze and nonhaze days. (A) Heatmap analysis indicating relative abundances of dominant genera (>0.02%). (B) LEfSe analysis illustrating differentially abundant fungal genera among samples between haze and nonhaze days. (C) Variations in relative abundances of genera with significant differences (Talaromyces, Mycosphaerella, Cladosporium, Schizophyllum).
FIG 5
FIG 5
High levels of PM pollutants promoted the growth of Talaromyces representative strains. (A) Phylogenetic tree of isolated Talaromyces strains based on ITS sequences using neighbor-joining methods (the closest strain is listed after each strain number). (B) High levels of PM pollutants promoted the growth of representative Talaromyces strains, especially in XSF7, XSF10, and XSF103. The Talaromyces strains were cultured with PM at 0 mg/mL (control), 0.08 mg/mL (LC group), 0.64 mg/mL (MC group), and 5.12 mg/mL (HC group).
FIG 6
FIG 6
Sloan neutral model predictions of the skin fungal community. (A) AIC score comparing the ability of the neutral, binomial, and Poisson models in explaining the skin fungal assembly process. (B) Sloan neutral model prediction in spring and winter during haze and non-haze days. Species are represented by data point and colored according to whether the taxon fit above (green), within (blue), or below (red) the 95% confidence interval (dotted lines). R2 values (measurement of fit to neutral assembly process) and Nm values (estimated migration rate) are indicated for each prediction. (C) Proportion of species within each of the main PM pollutant-associated fungal taxa according to their Sloan neutral model prediction.

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