The preadolescent acne microbiome: A prospective, randomized, pilot study investigating characterization and effects of acne therapy.

BACKGROUND/OBJECTIVES: Acne, a common pediatric disease, tends to be more comedonal in preadolescents, whereas older individuals are more likely to have inflammatory lesions in addition to comedones. Thus the microbiome of preadolescents may be different. In this pilot study we aimed to characterize the preadolescent acne microbiome, compare the microbiome in preadolescents with and without acne, and investigate changes in the microbiome after topical treatment with benzoyl peroxide or a retinoid in a small cohort of preadolescents. METHODS: Participants were 7-10 years of age with (intervention group) or without (control group) acne and were recruited during routine outpatient dermatology visits. Baseline questionnaires, physical examination, and pore strip application were performed for all participants. Intervention group participants were randomized to receive topical therapy with benzoyl peroxide 5% gel or cream or tretinoin 0.025% cream. Participants with acne were followed up 8-10 weeks later and pore strip application was repeated. RESULTS: Preadolescents with acne were colonized with a greater diversity of cutaneous bacteria than controls and the most commonly identified bacterium was Streptococcus. The number of bacterial species and phylogenetic diversity decreased after treatment with benzoyl peroxide and tretinoin. CONCLUSION: The predominant bacteria in microbiome studies of adult acne is Propionibacterium, whereas in this pediatric population we saw a lot of Streptococcus bacteria. After treatment, the microbiomes of intervention group participants more closely resembled those of control group participants

Commensal microbiota modulate gene expression in the skin

Abstract Background The skin harbors complex communities of resident microorganisms, yet little is known of their physiological roles and the molecular mechanisms that mediate cutaneous host-microbe interactions. Here, we profiled skin transcriptomes of mice reared in the presence and absence of microbiota to elucidate the range of pathways and functions modulated in the skin by the microbiota. Results A total of 2820 genes were differentially regulated in response to microbial colonization and were enriched in gene ontology (GO) terms related to the host-immune response and epidermal differentiation. Innate immune response genes and genes involved in cytokine activity were generally upregulated in response to microbiota and included genes encoding toll-like receptors, antimicrobial peptides, the complement cascade, and genes involved in IL-1 family cytokine signaling and homing of T cells. Our results also reveal a role for the microbiota in modulating epidermal differentiation and development, with differential expression of genes in the epidermal differentiation complex (EDC). Genes with correlated co-expression patterns were enriched in binding sites for the transcription factors Klf4, AP-1, and SP-1, all implicated as regulators of epidermal differentiation. Finally, we identified transcriptional signatures of microbial regulation common to both the skin and the gastrointestinal tract. Conclusions With this foundational approach, we establish a critical resource for understanding the genome-wide implications of microbially mediated gene expression in the skin and emphasize prospective ways in which the microbiome contributes to skin health and disease

Additional file 9: Figure S4. of Commensal microbiota modulate gene expression in the skin

Analysis of skin immune cell populations supports gene expression findings. (A) Toluidine blue staining for mast cells. (B) Immunofluorescence staining of CD3, a pan T cell marker. Significance testing was performed on an aggregate of three experiments with n = 3 GF and SPF mice each. (C) Flow cytometry analysis of GF and SPF (n = 5 each) of IL-1α and IL-1β production by cell subset. Comparisons that are significantly different with a p value < 0.05 are denoted with * and those with a p value < 0.01 with **. (D) Barplots showing normalized gene expression values for IL-1α and IL-1β. Lines depict standard error and padj represents the FDR-corrected p value (1-prob) calculated by NOISeqBio. (E) Boxplot of normalized gene expression of terminal differentiation markers Krt1 and Krt14, with padj indicating the FDR-corrected p value (1-prob) calculated by NOISeqBio. (EPS 85855 kb

Additional file 10: Figure S5. of Commensal microbiota modulate gene expression in the skin

DGCA analysis identified significantly correlated DEGs that share potential transcription factor binding sites. Analysis with oppossum3 identified enriched transcription factors in positively correlated DGCA gene sets, using Z scores to assess significance. The y-axis identifies significant transcription factors, while x-axis represents the significance metric, with higher values indicating greater significance, and the shape indicating whether the metric score was 1 or 2 standard deviations (SD) above the mean. Z scores are significant when greater than 2 SD above the mean. Size of each point reflects the percentage of all DGCA +/+ DEGs containing a binding region for each TF and color indicates colonization status of the DGCA +/+ DEGs. (EPS 1582 kb

Additional file 2: Figure S1. of Commensal microbiota modulate gene expression in the skin

Quality control of RNA-sequencing data. (A) Mean quality score per base for each of the 16 samples. (B) Number of reads mapping to the mouse reference genome for each sample. (C) Relative abundance of reads mapping to each biotype. (D) Percentage of the genome covered by mapped reads per sample. (EPS 1354 kb

Le Cri du peuple : journal politique quotidien

13 octobre 18881888/10/13 (A5,N1815)-1888/10/13

Additional file 7: Table S2. of Commensal microbiota modulate gene expression in the skin

WGCNA gene module characterization. Top 5 significantly enriched biological process gene ontology terms (Bonferroni p < 0.05) associated with each WGCNA module. (XLSX 47 kb

Additional file 3: of Commensal microbiota modulate gene expression in the skin

Dataset S1. Results from differential expression analysis. Rows contain the 15,448 features analyzed. Columns contain Ensembl feature id, mean expression of GF samples, mean expression of SPF samples, the NOISeq differential expression statistic theta, the probability of differential expression (equal to 1-FDR-corrected p value when using NOISeqBio, DEGs defined as those with prob. > 0.9), the log2 fold change in expression (upregulated in GF > 0, downregulated in GF < 0), feature length, chromosome, feature start and end coordinates, feature biotype, and feature symbol. (XLSX 2289 kb

Additional file 1: Table S1. of Commensal microbiota modulate gene expression in the skin

Sample summary statistics. Rows contain the 16 samples analyzed; with columns containing associated sequencing statistics and metadata. (XLSX 56 kb

Le Cri du peuple : journal politique quotidien

04 février 18901890/02/04 (A7,N1996)-1890/02/04