Diversity of navel microbiome in young adults

Microbiota in Umbilical Dirt and Its Relationship with Odor

The umbilicus accumulates more dirt than other body surfaces and is difficult to clean. Hygiene in this area is vital, particularly for surgery, because of its proximity to the laparotomy site. Although microorganisms in the umbilicus have been extensively examined, those in umbilical dirt have not due to the lack of an efficient method of collection. We previously established a technique to extract umbilical dirt using the anchor effect of polymers, which are injected into the umbilicus. In the present study, we applied this technique for the first time to investigate umbilical dirt. The results obtained revealed an abundance of Corynebacterium among various bacteria, whereas Cutibacterium and Staphylococcus, which are abundant at other skin sites, were rare. The relationships between the microbiota and issues related to the umbilicus were investigated and some covariates, including the odor score and several bacteria, were identified. A detailed ana-lysis of the genera associated with odor revealed no correlation with Corynebacterium; however, some minor anaerobic bacteria, such as Mobiluncus, Arcanobacterium, and Peptoniphilus, were more abundant in the high odor score group. Therefore, this technique to collect umbilical dirt provided insights into the microbiota in umbilical dirt and suggested functions for minor anaerobes. Furthermore, since various pathogenic microorganisms were detected, their control may contribute to the prevention of both odor production and infectious diseases caused by these microorganisms.

Candida albicans Infections: a novel porcine wound model to evaluate treatment efficacy

Candida albicans is a common cause of opportunistic mycoses worldwide and a major contributor in wound infections. The purpose of this study was to establish a fungal wound model and analyze the effects of a common antifungal agent against the proliferation of three C. albicans strains. Second degree burns were created, and then inoculated with one of three different C. albicans ATCC strains: 10261 reference strain, 64550 fluconazole resistant and 26310 fluconazole sensitive. After fungal inoculation, every wound was covered with dressings for 4 h to allow fungal colonization on every wound bed. After 4 h, the dressings were removed, and each wound was treated either once or twice daily with a topical terbinafine hydrochloride or left untreated. On days 2, 4 and 7 post inoculation, three wounds from each treatment group were scrub cultured and quantified. On day 2, wounds infected with the sensitive strains 26310 and 10261 and treated twice showed a significant reduction when compared against those infected wounds receiving once daily treatment. On day 4, wounds which were infected with C. albicans fluconazole sensitive (ATCC 26310) showed a significant reduction in fungal cell counts with treatment applied twice daily. A significant reduction in the colony counts was exhibited in all three strains at the seventh day with active as compared to the non-treated wounds. Twice daily treatment resulted in a lower fungal count than once daily treatment. Neither treatment was able to entirely eradicate C. albicans during the duration of this study. Establishing a reliable fungal wound model will help in the translational goal of identifying new antifungal that could be used clinically by wound care providers.

Benchmarking of 16S rRNA gene databases using known strain sequences

16S rRNA gene analysis is the most convenient and robust method for microbiome studies. Inaccurate taxonomic assignment of bacterial strains could have deleterious effects as all downstream analyses rely heavily on the accurate assessment of microbial taxonomy. The use of mock communities to check the reliability of the results has been suggested. However, often the mock communities used in most of the studies represent only a small fraction of taxa and are used mostly as validation of sequencing run to estimate sequencing artifacts. Moreover, a large number of databases and tools available for classification and taxonomic assignment of the 16S rRNA gene make it challenging to select the best-suited method for a particular dataset. In the present study, we used authentic and validly published 16S rRNA gene type strain sequences (full length, V3-V4 region) and analyzed them using a widely used QIIME pipeline along with different parameters of OTU clustering and QIIME compatible databases. Data Analysis Measures (DAM) revealed a high discrepancy in ratifying the taxonomy at different taxonomic hierarchies. Beta diversity analysis showed clear segregation of different DAMs. Limited differences were observed in reference data set analysis using partial (V3-V4) and full-length 16S rRNA gene sequences, which signify the reliability of partial 16S rRNA gene sequences in microbiome studies. Our analysis also highlights common discrepancies observed at various taxonomic levels using various methods and databases.