Differential producibility analysis (DPA) of transcriptomic data with metabolic networks: deconstructing the metabolic response of M. tuberculosis.
Differential producibility analysis (DPA) of transcriptomic data with metabolic networks: deconstructing the metabolic response of M. tuberculosis.
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DOI：
10.1371/journal.pcbi.1002060
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发表时间：
2011-06
影响因子：
4.3
通讯作者：
McFadden J
中科院分区：
文献类型：
--
作者：
Bonde BK;Beste DJ;Laing E;Kierzek AM;McFadden J
对结核分枝杆菌在宿主环境中的代谢状态的知识的普遍缺乏是阻碍抗结核新药开发的主要因素。目前的实验方法不允许直接测定细菌病原体在体内的整体代谢状态，但所有编码基因的转录活性已在许多微阵列研究中进行了研究。我们描述了一种新的算法，差分生产率分析（DPA），使用代谢网络从转录组数据中提取代谢信号。该方法利用通量平衡分析（FBA）来识别影响网络中产生每种代谢物的能力的基因集。随后，秩积分析用于鉴定预测受转录信号影响最大的那些代谢物。我们首先应用DPA研究了大肠杆菌的代谢反应。大肠杆菌的厌氧生长和灭活FNR全局调节。DPA成功地提取了与实验数据相对应的代谢信号，并提供了新的代谢见解。我们接下来应用DPA研究M的代谢反应。结核病对巨噬细胞环境、人痰和一系列体外环境扰动的影响。该分析揭示了M.结核病对巨噬细胞环境的影响：影响中枢代谢代谢物的基因下调，同时影响细胞壁组分和毒力因子合成的基因上调。DPA表明，结核杆菌对细胞内环境的反应的一个重要特征是将资源引导到其细胞包膜的重塑，可能是为宿主防御的攻击做准备。DPA可用于阐明M.结核病和其他病原体，并且可以具有从其他“组学”数据提取代谢信号的一般应用。 结核分枝杆菌引起结核病，每年导致数百万人死亡。治疗需要6个月或更长时间，导致患者缺乏依从性并出现耐药性。病原体需要很长时间才能杀死，因为它能够进入休眠/潜伏/持续状态，对药物不敏感。目前迫切需要开发针对休眠/持久/潜伏生物体的新抗生素。大多数抗生素靶向代谢过程，但很难直接在宿主或宿主细胞内检查病原体的代谢。当然，通过转录组学来鉴定哪些基因是活跃的是可能的，但是还没有建立和验证的方法来使用转录组数据来预测代谢。我们在这里描述这种方法的发展，称为DPA。用E.大肠杆菌数据，然后使用DPA预测宿主细胞内生长的结核病病原体和结核病痰样本的代谢。DPA表明，结核杆菌重塑其细胞以应对宿主环境，可能是为了增加病原体对宿主免疫系统的防御。发现这种重塑的代谢细节可能会发现脆弱的代谢反应，这些反应可能是新的结核病药物的目标。
A general paucity of knowledge about the metabolic state of Mycobacterium tuberculosis within the host environment is a major factor impeding development of novel drugs against tuberculosis. Current experimental methods do not allow direct determination of the global metabolic state of a bacterial pathogen in vivo, but the transcriptional activity of all encoded genes has been investigated in numerous microarray studies. We describe a novel algorithm, Differential Producibility Analysis (DPA) that uses a metabolic network to extract metabolic signals from transcriptome data. The method utilizes Flux Balance Analysis (FBA) to identify the set of genes that affect the ability to produce each metabolite in the network. Subsequently, Rank Product Analysis is used to identify those metabolites predicted to be most affected by a transcriptional signal. We first apply DPA to investigate the metabolic response of E. coli to both anaerobic growth and inactivation of the FNR global regulator. DPA successfully extracts metabolic signals that correspond to experimental data and provides novel metabolic insights. We next apply DPA to investigate the metabolic response of M. tuberculosis to the macrophage environment, human sputum and a range of in vitro environmental perturbations. The analysis revealed a previously unrecognized feature of the response of M. tuberculosis to the macrophage environment: a down-regulation of genes influencing metabolites in central metabolism and concomitant up-regulation of genes that influence synthesis of cell wall components and virulence factors. DPA suggests that a significant feature of the response of the tubercle bacillus to the intracellular environment is a channeling of resources towards remodeling of its cell envelope, possibly in preparation for attack by host defenses. DPA may be used to unravel the mechanisms of virulence and persistence of M. tuberculosis and other pathogens and may have general application for extracting metabolic signals from other “-omics” data. Mycobacterium tuberculosis causes tuberculosis, leading to millions of deaths each year. Treatment takes 6 months or more, leading to lack of patient compliance and emergence of drug resistance. The pathogen takes so long to kill because it is able to enter a state of dormancy/latency/persistence where it is insensitive to drugs. There is an urgent unmet need to develop new antibiotics that target dormant/persistent/latent organisms. Most antibiotics target metabolic processes but it is difficult to examine the metabolism of the pathogen directly inside the host or host cells. It is of course possible to identify which genes are active by transcriptomics but there are no established and validated methods to use transcriptome data to predict metabolism. We here describe the development of such a method, called DPA. We validate the method with E. coli data and then use DPA to predict the metabolism of the TB pathogen growing inside host cells and from TB sputum samples. DPA demonstrates that the TB bacillus remodels its cells in response to the host environment, possibly to increase the pathogen's defenses against the host immune system. Discovering the metabolic details of this remodeling may identify vulnerable metabolic reactions that may be targeted with new TB drugs.
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影响因子：
--
作者：
Jamshidi, Neema;Palsson, Bernhard O
通讯作者：
Palsson, Bernhard O
影响因子：
3.9
作者：
KACSER, H;BURNS, JA;FELL, DA
通讯作者：
FELL, DA
影响因子：
3.2
作者：
Beste, D. J. V.;Laing, E.;McFadden, J. J.
通讯作者：
McFadden, J. J.
DOI：
10.1142/s0219720005001442
发表时间：
2005-10-01
影响因子：
1
作者：
Breitling, Rainer;Herzyk, Pawel
通讯作者：
Herzyk, Pawel
影响因子：
3
作者：
Jeffery, Ian B.;Higgins, Desmond G.;Culhane, Aedin C.
通讯作者：
Culhane, Aedin C.