16S rRNA gene sequencing of DNA extracted from clinically uninfected hip and knee implant samples has revealed polymicrobial populations. However, previous studies assessed 16S rRNA gene sequencing as a technique for the diagnosis of periprosthetic joint infections, leaving the microbiota of presumed aseptic hip and knee implants largely unstudied. These communities of microorganisms might play important roles in aspects of host health, such as aseptic loosening. Therefore, this study sought to characterize the bacterial composition of presumed aseptic joint implant microbiota using next generation 16S rRNA gene sequencing, and it evaluated this method for future investigations. 248 samples were collected from implants of 41 patients undergoing total hip or knee arthroplasty revision for presumed aseptic failure. DNA was extracted using two methodologies-one optimized for high throughput and the other for human samples-and amplicons of the V4 region of the 16S rRNA gene were sequenced. Sequencing data were analyzed and compared with ancillary specific PCR and microbiological culture. Computational tools (SourceTracker and decontam) were used to detect and compensate for environmental and processing contaminants. Microbial diversity of patient samples was higher than that of open-air controls and differentially abundant taxa were detected between these conditions, possibly reflecting a true microbiota that is present in clinically uninfected joint implants. However, positive control-associated artifacts and DNA extraction methodology significantly affected sequencing results. As well, sequencing failed to identify Cutibacterium acnes in most culture- and PCR-positive samples. These challenges limited characterization of bacteria in presumed aseptic implants, but genera were identified for further investigation. In all, we provide further support for the hypothesis that there is likely a microbiota present in clinically uninfected joint implants, and we show that methods other than 16S rRNA gene sequencing may be ideal for its characterization. This work has illuminated the importance of further study of microbiota of clinically uninfected joint implants with novel molecular and computational tools to further eliminate contaminants and artifacts that arise in low bacterial abundance samples.

中文翻译：

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破译假定无菌髋关节和膝关节植入物的低丰度微生物群。

从临床未感染的髋关节和膝关节植入物样本中提取的 DNA 的 16S rRNA 基因测序揭示了多种微生物种群。然而，之前的研究将 16S rRNA 基因测序作为一种诊断假体周围关节感染的技术进行了评估，而对假定无菌的髋关节和膝关节植入物的微生物群进行了大量研究。这些微生物群落可能在宿主健康方面发挥重要作用，例如无菌松动。因此，本研究试图使用下一代 16S rRNA 基因测序来表征假定的无菌关节植入物微生物群的细菌组成，并评估该方法以用于未来的研究。从 41 名因假定无菌性失败而接受全髋或膝关节置换术的患者的植入物中收集了 248 个样本。使用两种方法提取 DNA - 一种针对高通量进行了优化，另一种针对人类样本进行了优化 - 并对 16S rRNA 基因的 V4 区域的扩增子进行了测序。对测序数据进行分析，并与辅助特异性 PCR 和微生物培养进行比较。计算工具（SourceTracker 和 decontam）用于检测和补偿环境和加工污染物。患者样本的微生物多样性高于露天对照，并且在这些条件之间检测到不同丰富的分类群，可能反映了临床未感染关节植入物中存在的真实微生物群。然而，阳性对照相关的伪影和 DNA 提取方法显着影响了测序结果。同样，测序未能在大多数培养和 PCR 阳性样品中识别出痤疮皮肤杆菌。这些挑战限制了对假定无菌植入物中细菌的表征，但确定了细菌属以进行进一步研究。总之，我们为临床未感染的关节植入物中可能存在微生物群的假设提供了进一步的支持，并且我们表明除 16S rRNA 基因测序之外的方法可能是其表征的理想选择。这项工作阐明了使用新型分子和计算工具进一步研究临床未感染关节植入物的微生物群的重要性，以进一步消除低细菌丰度样本中出现的污染物和伪影。但已确定属以进行进一步调查。总之，我们为临床未感染的关节植入物中可能存在微生物群的假设提供了进一步的支持，并且我们表明除 16S rRNA 基因测序之外的方法可能是其表征的理想选择。这项工作阐明了使用新型分子和计算工具进一步研究临床未感染关节植入物的微生物群的重要性，以进一步消除低细菌丰度样本中出现的污染物和伪影。但已确定属以进行进一步调查。总之，我们为临床未感染的关节植入物中可能存在微生物群的假设提供了进一步的支持，并且我们表明除 16S rRNA 基因测序之外的方法可能是其表征的理想选择。这项工作阐明了使用新型分子和计算工具进一步研究临床未感染关节植入物的微生物群的重要性，以进一步消除低细菌丰度样本中出现的污染物和伪影。