Application of 16S rRNA metagenomics to analyze bacterial communities at a respiratory care centre in Taiwan.

In this study, we applied a 16S ribosomal RNA (rRNA) metagenomics approach to survey inanimate hospital environments (IHEs) in a respiratory care center (RCC). A total of 16 samples, including 9 from medical devices and 7 from workstations, were analyzed. Besides, clinical isolates were retrospectively analyzed during the sampling period in the RCC. A high amount of microbial diversity was detected, with an average of 1,836 phylotypes per sample. In addition to Acinetobacter, more than 60 % of the bacterial communities present among the top 25 abundant genera were dominated by skin-associated bacteria. Differences in bacterial profiles were restricted to individual samples. Furthermore, compliance with hand hygiene guidelines may be unsatisfactory among hospital staff according to a principal coordinate analysis that indicated clustering of bacterial communities between devices and workstations for most of the sampling sites. Compared to the high incidence of clinical isolates in the RCC, only Staphylococcus and Acinetobacter were highly abundant in the IHEs. Despite Acinetobacter was the most abundant genus present in IHEs of the RCC, potential pathogens, e.g., Acinetobacter baumannii, might remain susceptible to carbapenem. This study is the first in Taiwan to demonstrate a high diversity of human-associated bacteria in the RCC via 16S rRNA metagenomics, which allows for new assessment of potential health risks in RCCs, aids in the evaluation of existing sanitation protocols, and furthers our understanding of the development of healthcare-associated infections.

The role of Mss11 in Candida albicans biofilm formation.

Candida albicans is an opportunistic human pathogen that can form a biofilm on biotic or inert surfaces such as epithelia and clinical devices. In this study, we examine the formation of C. albicans biofilm by establishing a key gene-centered network based on protein-protein interaction (PPI) and gene expression datasets. Starting from C. albicans Cph1 and Efg1, transcription factors associated with morphogenesis of biofilm formation, a network elucidates the complex cellular process and predicts potential unknown components related to biofilm formation. Subsequently, we analyzed the functions of Mss11 among these identified proteins to test the efficiency of the proposed computational approach. MSS11-deleted mutants were compared with a wild-type strain, indicating that the mutant is defective in forming a mature biofilm and partially attenuates the virulence of C. albicans in an infected mouse model. Finally, a DNA microarray analysis was conducted to identify the potential target genes of C. albicans Mss11. The findings of this study clarify complex gene or protein interaction during the biofilm formation process of C. albicans, supporting the application of a systems biology approach to study fungal pathogenesis.

Sample size estimation of multiregional clinical trials with heterogeneous variability across regions.

In recent years, global collaboration has become a conventional strategy for new drug development. To accelerate the development process and shorten approval time, the design of multiregional clinical trials (MRCTs) incorporates subjects from many countries around the world under the same protocol. After showing the overall efficacy of a drug in all global regions, one can also simultaneously evaluate the possibility of applying the overall trial results to all regions and subsequently support drug registration in each of them. Several statistical methods have been proposed for the design and evaluation of MRCTs. Most of these approaches, however, assume a common variability of the primary endpoint across regions. In practice, this assumption may not be true, due to differences across regions (e.g., differences in ethnic factors and/or medical culture/practice). In this article, we use a random-effect model for modeling heterogeneous variability across regions for the design and evaluation of MRCTs. We also address consideration on the determination of the number of subjects in a specific region to establish the consistency of treatment effects between the specific region and the entire group.

Subgingival microbiota in individuals with severe chronic periodontitis.

BACKGROUND/PURPOSE: Subgingival microorganisms are potentially associated with periodontal diseases. However, the correlation between the variance in the periodontal microbiome and the prevalence and severity of periodontitis remains unclear. The aim of this study was to determine the subgingival microbiota in Taiwanese individuals with severe chronic periodontitis (SP). METHODS: The composition of the subgingival microbiota in healthy and diseased individuals was compared using a 16S rRNA metagenomic approach and quantitative polymerase chain reaction (qPCR). A total of 20 samples, including 10 from healthy individuals and 10 from SP patients, were analyzed. RESULTS: We found high microbial diversity, with an average of 774 classified phylotypes per sample and a total of six bacterial phyla across all samples. Cluster analysis by principal component analysis and heat map showed that the bacterial communities were different in the two groups. Streptococcus dominated across all the healthy samples, whereas Prevotella, Porphyromonas, and Treponema were highly abundant across all diseased samples. At least 13 bacterial genera were conserved among all the samples. Only eight genera, including Lautropia, Parvimonas, Actinomyces, Capnocytophaga, Paludibacter, Streptococcus, Haemophilus, and Corynebacterium, were significantly enriched in the healthy group, and six genera, including Porphyromonas, Treponema, Tannerella, Aggregatibacter, Peptostreptococcus, and Filifactor, were significantly enriched in the diseased group. Furthermore, a trend of abundance of bacteria at the species level measured by qPCR in all samples was consistent with the 16S rRNA metagenomics results. CONCLUSION: This study is the first in Taiwan to provide a picture of the microbiome in SP via 16S rRNA metagenomics.