III族窒化物半導体分極制御構造による太陽光エネルギー変換に関する研究

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 杉山 正和, 東京大学教授 中野 義昭, 東京大学教授 田畑 仁, 東京大学准教授 八井 崇, 東京大学准教授 種村 拓夫, 北九州市立大学教授 藤井 克司University of Tokyo(東京大学

Antibiotic resistance genes in an urban river as impacted by bacterial community and physicochemical parameters

Antibiotic resistance genes (ARGs) in urban rivers are a serious public health concern in regions with poorly planned, rapid development. To gain insights into the predominant factors affecting the fate of ARGs in a highly polluted urban river in eastern China, a total of 285 ARGs, microbial communities, and 20 physicochemical parameters were analyzed for 17 sites. A total of 258 unique ARGs were detected using high-throughput qPCR, and the absolute abundance of total ARGs was positively correlated with total organic carbon and total dissolved nitrogen concentrations (P < 0.01). ARG abundance and diversity were greatly altered by microbial community structure. Variation partitioning analysis showed that the combined effects of multiple factors contributed to the profile and dissemination of ARGs, and variation of microbial communities was the major factor affecting the distribution of ARGs. The disparate distribution of some bacteria, including Bacteroides from mammalian gastrointestinal flora, Burkholderia from zoonotic infectious diseases, and Zoogloea from wastewater treatment, indicates that the urban river was strongly influenced by point-source pollution. Results imply that microbial community shifts caused by changes in water quality may lead to the spread of ARGs, and point-source pollution in urban rivers requires greater attention to control the transfer of ARGs between environmental bacteria and pathogens

Trace levels of sewage effluent are sufficient to increase class 1 integron prevalence in freshwater biofilms without changing the core community.

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.Most river systems are impacted by sewage effluent. It remains unclear if there is a lower threshold to the concentration of sewage effluent that can significantly change the structure of the microbial community and its mobile genetic elements in a natural river biofilm. We used novel in situ mesocosms to conduct replicated experiments to study how the addition of low-level concentrations of sewage effluent (nominally 2.5 ppm) affects river biofilms in two contrasting Chalk river systems, the Rivers Kennet and Lambourn (high/low sewage impact, respectively). 16S sequencing and qPCR showed that community composition was not significantly changed by the sewage effluent addition, but class 1 integron prevalence (Lambourn control 0.07% (SE ± 0.01), Lambourn sewage effluent 0.11% (SE ± 0.006), Kennet control 0.56% (SE ± 0.01), Kennet sewage effluent 1.28% (SE ± 0.16)) was significantly greater in the communities exposed to sewage effluent than in the control flumes (ANOVA, F = 5.11, p = 0.045) in both rivers. Furthermore, the difference in integron prevalence between the Kennet control (no sewage effluent addition) and Kennet sewage-treated samples was proportionally greater than the difference in prevalence between the Lambourn control and sewage-treated samples (ANOVA (interaction between treatment and river), F = 6.42, p = 0.028). Mechanisms that lead to such differences could include macronutrient/biofilm or phage/bacteria interactions. Our findings highlight the role that low-level exposure to complex polluting mixtures such as sewage effluent can play in the spread of antibiotic resistance genes. The results also highlight that certain conditions, such as macronutrient load, might accelerate spread of antibiotic resistance genes.This work was funded by the NERC Centre for Ecology & Hydrology National Capability funding through the CEH Thames Initiative (NEC04877) and the NERCeKnowledge Transfer (PREPARE) Initiative contract NE/F009216/1. Thanks to Heather Wickham, Linda Armstrong and Sarah Harman for providing nutrient levels, Gareth Old and Colin Roberts for providing the Boxford data as part of the NERC funded CEH Lambourn Observatory, to John Hounslow (Action for the River Kennet) for access to his land and to Alan Grafen for advice on statistical methods

An inter-laboratory study to investigate the impact of the bioinformatics component on microbiome analysis using mock communities

Despite the advent of whole genome metagenomics, targeted approaches (such as 16S rRNA gene amplicon sequencing) continue to be valuable for determining the microbial composition of samples. Amplicon microbiome sequencing can be performed on clinical samples from a normally sterile site to determine the aetiology of an infection (usually single pathogen identification) or samples from more complex niches such as human mucosa or environmental samples where multiple microorganisms need to be identified. The methodologies are frequently applied to determine both presence of micro-organisms and their quantity or relative abundance. There are a number of technical steps required to perform microbial community profiling, many of which may have appreciable precision and bias that impacts final results. In order for these methods to be applied with the greatest accuracy, comparative studies across different laboratories are warranted. In this study we explored the impact of the bioinformatic approaches taken in different laboratories on microbiome assessment using 16S rRNA gene amplicon sequencing results. Data were generated from two mock microbial community samples which were amplified using primer sets spanning five different variable regions of 16S rRNA genes. The PCR-sequencing analysis included three technical repeats of the process to determine the repeatability of their methods. Thirteen laboratories participated in the study, and each analysed the same FASTQ files using their choice of pipeline. This study captured the methods used and the resulting sequence annotation and relative abundance output from bioinformatic analyses. Results were compared to digital PCR assessment of the absolute abundance of each target representing each organism in the mock microbial community samples and also to analyses of shotgun metagenome sequence data. This ring trial demonstrates that the choice of bioinformatic analysis pipeline alone can result in different estimations of the composition of the microbiome when using 16S rRNA gene amplicon sequencing data. The study observed differences in terms of both presence and abundance of organisms and provides a resource for ensuring reproducible pipeline development and application. The observed differences were especially prevalent when using custom databases and applying high stringency operational taxonomic unit (OTU) cut-off limits. In order to apply sequencing approaches with greater accuracy, the impact of different analytical steps needs to be clearly delineated and solutions devised to harmonise microbiome analysis results