使用线性样条回归比较几种暴露于不同浓度乳铁蛋白的大肠杆菌菌株的生长。

Camilla Sekse, Jon Bohlin, Eystein Skjerve, Gerd E Vegarud
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引用次数: 22

摘要

背景:我们想比较13株大肠杆菌菌株在两种不同类型的肉汤(Syncase和Luria-Bertani (LB))中暴露于不同浓度的生长抑制剂乳铁蛋白的生长差异。为了实现这一点,我们提出了一个简单的统计程序,将自然随机扰动引起的微生物生长曲线和更可能由生物差异引起的微生物生长曲线分开。利用光密度数据(OD)测定细菌生长,记录三次,每个菌株在620 nm下生长18小时。每个生成的生长曲线被分成三个间隔相等的区间。我们提出了一个程序,使用线性样条回归与两个结点,以计算在细菌生长曲线的每个区间的斜率。这些斜率随后用于估计基于适当统计分布的95%置信区间。置信区间外的斜率被认为与置信区间内的斜率显著不同。我们还演示了使用相关但更先进的方法,统称为广义加性模型(GAMs)来模拟生长。除了具有相应置信区间的令人印象深刻的曲线拟合能力外,GAM还允许计算导数,即相对于每个时间点的增长率估计。结果:实验结果与观测数据吻合良好。结果表明,大肠杆菌菌株之间存在显著的生长差异。与Syncase相比,大多数菌株在富含营养的LB肉汤中表现出更好的生长。乳铁蛋白的抑制作用在不同菌株之间存在差异。非典型肠致病性菌株eiec -2在两种培养液中的平均生长速度均快于其他菌株,而肠侵袭性菌株EIEC-6和EIEC-7的生长速度较慢。产肠毒素菌株ec -5在Syncase肉汤中表现出异常的生长,但在LB肉汤中生长较慢。结论:我们的研究结果没有显示病原菌群之间或致病性与非致病性大肠杆菌之间的明显生长差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Growth comparison of several Escherichia coli strains exposed to various concentrations of lactoferrin using linear spline regression.

Growth comparison of several Escherichia coli strains exposed to various concentrations of lactoferrin using linear spline regression.

Growth comparison of several Escherichia coli strains exposed to various concentrations of lactoferrin using linear spline regression.

Growth comparison of several Escherichia coli strains exposed to various concentrations of lactoferrin using linear spline regression.

Background: We wanted to compare growth differences between 13 Escherichia coli strains exposed to various concentrations of the growth inhibitor lactoferrin in two different types of broth (Syncase and Luria-Bertani (LB)). To carry this out, we present a simple statistical procedure that separates microbial growth curves that are due to natural random perturbations and growth curves that are more likely caused by biological differences.Bacterial growth was determined using optical density data (OD) recorded for triplicates at 620 nm for 18 hours for each strain. Each resulting growth curve was divided into three equally spaced intervals. We propose a procedure using linear spline regression with two knots to compute the slopes of each interval in the bacterial growth curves. These slopes are subsequently used to estimate a 95% confidence interval based on an appropriate statistical distribution. Slopes outside the confidence interval were considered as significantly different from slopes within. We also demonstrate the use of related, but more advanced methods known collectively as generalized additive models (GAMs) to model growth. In addition to impressive curve fitting capabilities with corresponding confidence intervals, GAM's allow for the computation of derivatives, i.e. growth rate estimation, with respect to each time point.

Results: The results from our proposed procedure agreed well with the observed data. The results indicated that there were substantial growth differences between the E. coli strains. Most strains exhibited improved growth in the nutrient rich LB broth compared to Syncase. The inhibiting effect of lactoferrin varied between the different strains. The atypical enteropathogenic aEPEC-2 grew, on average, faster in both broths than the other strains tested while the enteroinvasive strains, EIEC-6 and EIEC-7 grew slower. The enterotoxigenic ETEC-5 strain, exhibited exceptional growth in Syncase broth, but slower growth in LB broth.

Conclusions: Our results do not indicate clear growth differences between pathogroups or pathogenic versus non-pathogenic E. coli.

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