Reconstructing promoter activity from Lux bioluminescent reporters.
Reconstructing promoter activity from Lux bioluminescent reporters.
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DOI：
10.1371/journal.pcbi.1005731
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发表时间：
2017-09
影响因子：
4.3
通讯作者：
Stekel DJ
中科院分区：
文献类型：
--
作者：
Iqbal M;Doherty N;Page AML;Qazi SNA;Ajmera I;Lund PA;Kypraios T;Scott DJ;Hill PJ;Stekel DJ
细菌Lux系统用作基因表达报告基因。它具有快速、灵敏和非破坏性的特点，可实现高频测量。最初是为细菌细胞开发的，它也适用于真核细胞，并可用于全细胞生物传感器，或在真实的时间与活动物，而不需要安乐死。然而，生物发光数据的正确解释是有限的：由于Lux系统的非线性分子和酶动力学，生物发光与基因表达不同。我们已经开发出一种计算方法，首次允许Lux测定的用户从光输出推断基因转录水平。这种方法是基于一个新的数学模型的Lux活动，其中包括LuxAB，LuxEC和Free的行动，与所有反应的改进机制，以及合成和周转的Lux蛋白。该模型是校准与新的实验数据的LuxAB和Fre反应的Photorhabdus发光的来源，现代勒克斯发光二极管，而文献数据已被用于LuxEC。重要的是，数据显示了先前未报告的LuxAB反应产物抑制的明确证据。模型模拟表明，预测的生物发光谱可能与基因表达的变化非常不同，光输出的瞬时峰值与一些实验数据集中看到的光输出非常相似。通过将校准的模型纳入贝叶斯推理方案，我们可以从生物发光中反向工程启动子活性。我们显示的例子中，生物发光的减少将更好地解释为关闭的启动子，或在生物发光的增加将更好地解释为一个较长的基因表达期。这种方法可以使Lux技术的所有用户受益。生物发光报告基因作为快速、灵敏和非破坏性的基因表达检测方法，广泛应用于生物学的许多领域。它们已经被开发用于细菌，现在适用于其他种类的生物体，最近被用于全细胞生物传感器，并用于实时活体动物模型，而无需安乐死。然而，Lux技术的用户依赖于与他们希望测量的基因表达相似的光输出。我们表明，这是不是这样的。相反，两者之间存在非线性关系：光输出可能会产生误导，因此限制了解释此类数据的方式。我们开发了一种新的计算方法，首次允许Lux报告者的用户从生物发光数据推断准确的基因转录水平。我们展示了一些例子，其中光的小幅减少将更好地解释为启动子被关闭，或者光的增加将更好地解释为启动子活性持续更长时间。
The bacterial Lux system is used as a gene expression reporter. It is fast, sensitive and non-destructive, enabling high frequency measurements. Originally developed for bacterial cells, it has also been adapted for eukaryotic cells, and can be used for whole cell biosensors, or in real time with live animals without the need for euthanasia. However, correct interpretation of bioluminescent data is limited: the bioluminescence is different from gene expression because of nonlinear molecular and enzyme dynamics of the Lux system. We have developed a computational approach that, for the first time, allows users of Lux assays to infer gene transcription levels from the light output. This approach is based upon a new mathematical model for Lux activity, that includes the actions of LuxAB, LuxEC and Fre, with improved mechanisms for all reactions, as well as synthesis and turn-over of Lux proteins. The model is calibrated with new experimental data for the LuxAB and Fre reactions from Photorhabdus luminescens—the source of modern Lux reporters—while literature data has been used for LuxEC. Importantly, the data show clear evidence for previously unreported product inhibition for the LuxAB reaction. Model simulations show that predicted bioluminescent profiles can be very different from changes in gene expression, with transient peaks of light output, very similar to light output seen in some experimental data sets. By incorporating the calibrated model into a Bayesian inference scheme, we can reverse engineer promoter activity from the bioluminescence. We show examples where a decrease in bioluminescence would be better interpreted as a switching off of the promoter, or where an increase in bioluminescence would be better interpreted as a longer period of gene expression. This approach could benefit all users of Lux technology. Bioluminescent reporters are used in many areas of biology as fast, sensitive and non-destructive measures of gene expression. They have been developed for bacteria, adapted now for other kinds of organisms, and recently been used for whole cell biosensors, and for real-time live animal models for infection without the need for euthanasia. However, users of Lux technologies rely on the light output being similar to the gene expression they wish to measure. We show that this is not the case. Rather, there is a nonlinear relationship between the two: light output can be misleading and so limits the way that such data can be interpreted. We have developed a new computational method that, for the first time, allows users of Lux reporters to infer accurate gene transcription levels from bioluminescent data. We show examples where a small decrease in light would be better interpreted as promoter being switched off, or where an increase in light would be better interpreted as promoter activity for a longer time.
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DOI：
10.3390/s120100732
发表时间：
2012
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Close D;Xu T;Smartt A;Rogers A;Crossley R;Price S;Ripp S;Sayler G
通讯作者：
Sayler G
DOI：
10.1007/978-1-4939-3515-4_7
发表时间：
2016-01-01
期刊：
BACTERIAL THERAPY OF CANCER: METHODS AND PROTOCOLS
影响因子：
--
作者：
Cronin, Michelle;Akin, Ali R.;Tangney, Mark
通讯作者：
Tangney, Mark
影响因子：
3.1
作者：
Qazi, S;Middleton, B;Williams, P
通讯作者：
Williams, P
DOI：
10.1007/s11693-006-9001-5
发表时间：
2007-03-01
期刊：
Systems and synthetic biology
影响因子：
--
作者：
Allen, Michael S;Wilgus, John R;Simpson, Michael L
通讯作者：
Simpson, Michael L
影响因子：
5.6
作者：
Burton, Neil A.;Johnson, Matthew D.;Lund, Peter A.
通讯作者：
Lund, Peter A.