Antibiotic-resistant Escherichia Coli from Retail Poultry Meat with Different Antibiotic Use Claims

Background We sought to determine if the prevalence of antibiotic-resistant Escherichia coli differed across retail poultry products and among major production categories, including organic, “raised without antibiotics”, and conventional. Results We collected all available brands of retail chicken and turkey—including conventional, “raised without antibiotic”, and organic products—every two weeks from January to December 2012. In total, E. coli was recovered from 91% of 546 turkey products tested and 88% of 1367 chicken products tested. The proportion of samples contaminated with E. coli was similar across all three production categories. Resistance prevalence varied by meat type and was highest among E. coli isolates from turkey for the majority of antibiotics tested. In general, production category had little effect on resistance prevalence among E. coli isolates from chicken, although resistance to gentamicin and multidrug resistance did vary. In contrast, resistance prevalence was significantly higher for 6 of the antibiotics tested—and multidrug resistance—among isolates from conventional turkey products when compared to those labelled organic or “raised without antibiotics”. E. coli isolates from chicken varied strongly in resistance prevalence among different brands within each production category. Conclusion The high prevalence of resistance among E. coli isolates from conventionally-raised turkey meat suggests greater antimicrobial use in conventional turkey production as compared to “raised without antibiotics” and organic systems. However, among E. coli from chicken meat, resistance prevalence was more strongly linked to brand than to production category, which could be caused by brand-level differences during production and/or processing, including variations in antimicrobial use

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

Supplementary Table 1

Bacterial reads assigned to the genus level in all samples analyzed, as identified by sample ID and subject ID. Samples have been pre-filtered for singleton OTUs and sub-sampled at a depth of 1,500 sequences.<br

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

Escherichia coli ST131-H22 as a Foodborne Uropathogen

E. coli ST131 is an important extraintestinal pathogen that can colonize the gastrointestinal tracts of humans and food animals. Here, we combined detection of accessory traits associated with avian adaptation (ColV plasmids) with high-resolution phylogenetics to quantify the portion of human infections caused by ST131 strains of food animal origin. Our results suggest that one ST131 sublineage—ST131-H22—has become established in poultry populations around the world and that meat may serve as a vehicle for human exposure and infection. ST131-H22 is just one of many E. coli lineages that may be transmitted from food animals to humans. Additional studies that combine detection of host-associated accessory elements with phylogenetics may allow us to quantify the total fraction of human extraintestinal infections attributable to food animal E. coli strains.Escherichia coli sequence type 131 (ST131) has emerged rapidly to become the most prevalent extraintestinal pathogenic E. coli clones in circulation today. Previous investigations appeared to exonerate retail meat as a source of human exposure to ST131; however, these studies focused mainly on extensively multidrug-resistant ST131 strains, which typically carry allele 30 of the fimH type 1 fimbrial adhesin gene (ST131-H30). To estimate the frequency of extraintestinal human infections arising from foodborne ST131 strains without bias toward particular sublineages or phenotypes, we conducted a 1-year prospective study of E. coli from meat products and clinical cultures in Flagstaff, Arizona. We characterized all isolates by multilocus sequence typing, fimH typing, and core genome phylogenetic analyses, and we screened isolates for avian-associated ColV plasmids as an indication of poultry adaptation. E. coli was isolated from 79.8% of the 2,452 meat samples and 72.4% of the 1,735 culture-positive clinical samples. Twenty-seven meat isolates were ST131 and belonged almost exclusively (n = 25) to the ST131-H22 lineage. All but 1 of the 25 H22 meat isolates were from poultry products, and all but 2 carried poultry-associated ColV plasmids. Of the 1,188 contemporaneous human clinical E. coli isolates, 24 were ST131-H22, one-quarter of which occurred in the same high-resolution phylogenetic clades as the ST131-H22 meat isolates and carried ColV plasmids. Molecular clock analysis of an international ST131-H22 genome collection suggested that ColV plasmids have been acquired at least six times since the 1940s and that poultry-to-human transmission is not limited to the United States

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

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 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

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