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Quantitative Framework for Model Evaluation in Microbiology Research Using Pseudomonas aeruginosa and Cystic Fibrosis Infection as a Test Case.

以铜绿假单胞菌和囊性纤维化感染为试验病例的微生物学研究中模型评价的定量框架。

  • 影响因子:6.74700
  • DOI:10.1128/mBio.03042-19
  • 作者列表:"Cornforth DM","Diggle FL","Melvin JA","Bomberger JM","Whiteley M
  • 发表时间:2020-01-14
Abstract

:Laboratory models are a cornerstone of modern microbiology, but the accuracy of these models has not been systematically evaluated. As a result, researchers often choose models based on intuition or incomplete data. We propose a general quantitative framework to assess model accuracy from RNA sequencing data and use this framework to evaluate models of Pseudomonas aeruginosa cystic fibrosis (CF) lung infection. We found that an in vitro synthetic CF sputum medium model and a CF airway epithelial cell model had the highest genome-wide accuracy but underperformed on distinct functional categories, including porins and polyamine biosynthesis for the synthetic sputum medium and protein synthesis for the epithelial cell model. We identified 211 "elusive" genes that were not mimicked in a reference strain grown in any laboratory model but found that many were captured by using a clinical isolate. These methods provide researchers with an evidence-based foundation to select and improve laboratory models.IMPORTANCE Laboratory models have become a cornerstone of modern microbiology. However, the accuracy of even the most commonly used models has never been evaluated. Here, we propose a quantitative framework based on gene expression data to evaluate model performance and apply it to models of Pseudomonas aeruginosa cystic fibrosis lung infection. We discovered that these models captured different aspects of P. aeruginosa infection physiology, and we identify which functional categories are and are not captured by each model. These methods will provide researchers with a solid basis to choose among laboratory models depending on the scientific question of interest and will help improve existing experimental models.

摘要

: 实验室模型是现代微生物学的基石,但这些模型的准确性尚未得到系统评估。因此,研究人员经常选择基于直觉或不完整数据的模型。我们提出了一个通用的定量框架,从 RNA 测序数据中评估模型的准确性,并使用该框架评估铜绿假单胞菌囊性纤维化 (CF) 肺部感染的模型。我们发现体外合成 CF 痰液培养基模型和 CF 气道上皮细胞模型具有最高的全基因组准确性,但在不同的功能类别上表现不佳。包括用于合成痰培养基的孔蛋白和多胺生物合成以及用于上皮细胞模型的蛋白质合成。我们确定了 211 个 “难以捉摸” 的基因,这些基因在任何实验室模型中生长的参考菌株中都没有被模仿,但发现许多基因是通过使用临床分离株捕获的。这些方法为研究人员选择和改进实验室模型提供了循证基础,重要性实验室模型已经成为现代微生物学的基石。然而,即使是最常用的模型的准确性也从未被评估过。在此,我们提出了一个基于基因表达数据的定量框架来评估模型性能,并将其应用于铜绿假单胞菌囊性纤维化肺部感染的模型。我们发现这些模型捕获了铜绿假单胞菌感染生理学的不同方面,我们确定了每个模型捕获和不捕获哪些功能类别。这些方法将为研究人员提供坚实的基础,根据感兴趣的科学问题在实验室模型中进行选择,并有助于改进现有的实验模型。

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翻译标题与摘要 下载文献
影响因子:4.40
发表时间:2020-01-01
DOI:10.1007/s00262-019-02431-8
作者列表:["Shibaki, Ryota","Murakami, Shuji","Matsumoto, Yuji","Yoshida, Tatsuya","Goto, Yasushi","Kanda, Shintaro","Horinouchi, Hidehito","Fujiwara, Yutaka","Yamamoto, Nobuyuki","Kusumoto, Masahiko","Yamamoto, Noboru","Ohe, Yuichiro"]

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翻译标题与摘要 下载文献
影响因子:4.04
发表时间:2020-01-25
来源期刊:New biotechnology
DOI:10.1016/j.nbt.2019.08.006
作者列表:["Sousa SA","Soares-Castro P","Seixas AMM","Feliciano JR","Balugas B","Barreto C","Pereira L","Santos PM","Leitão JH"]

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