Predicting network activity from high throughput metabolomics.
Predicting network activity from high throughput metabolomics.
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DOI：
10.1371/journal.pcbi.1003123
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发表时间：
2013
影响因子：
4.3
通讯作者：
Pulendran B
中科院分区：
文献类型：
--
作者：
Li S;Park Y;Duraisingham S;Strobel FH;Khan N;Soltow QA;Jones DP;Pulendran B
代谢物的鉴定是一项繁琐且具有挑战性的任务，这阻碍了质谱法对高通量代谢组学的功能解释。我们提出了一套计算算法，利用代谢途径和网络的集体力量，预测功能活性直接从光谱特征表没有先验识别的代谢产物。该算法在天然免疫细胞的激活上进行了实验验证。基于质谱的非靶向代谢组学现在可以同时分析数千种代谢物。然而，在从数据中得出任何生物学意义之前，必须鉴定这些代谢物。代谢物鉴定是一个具有挑战性和低通量的过程，因此成为该领域的瓶颈。我们在这里报告了一种新的方法来预测生物活性直接从质谱数据没有先验识别的代谢产物。通过将网络分析和代谢物预测统一在同一计算框架下，代谢网络和途径的组织有助于在很大程度上解决代谢物预测中的模糊性。我们通过一组先天免疫细胞的激活实验验证了我们的算法。通过基因表达和代谢产物鉴定证实了预测的活性。这种方法将大大加速高通量代谢组学的应用，因为在我们的计算预测之后，可以将识别数百种代谢物的繁琐任务转移到少数验证实验中。
The functional interpretation of high throughput metabolomics by mass spectrometry is hindered by the identification of metabolites, a tedious and challenging task. We present a set of computational algorithms which, by leveraging the collective power of metabolic pathways and networks, predict functional activity directly from spectral feature tables without a priori identification of metabolites. The algorithms were experimentally validated on the activation of innate immune cells. Mass spectrometry based untargeted metabolomics can now profile several thousand of metabolites simultaneously. However, these metabolites have to be identified before any biological meaning can be drawn from the data. Metabolite identification is a challenging and low throughput process, therefore becomes the bottleneck of the filed. We report here a novel approach to predict biological activity directly from mass spectrometry data without a priori identification of metabolites. By unifying network analysis and metabolite prediction under the same computational framework, the organization of metabolic networks and pathways helps resolve the ambiguity in metabolite prediction to a large extent. We validated our algorithms on a set of activation experiment of innate immune cells. The predicted activities were confirmed by both gene expression and metabolite identification. This method shall greatly accelerate the application of high throughput metabolomics, as the tedious task of identifying hundreds of metabolites upfront can be shifted to a handful of validation experiments after our computational prediction.