Metabolic signatures of regulation by phosphorylation and acetylation.
Metabolic signatures of regulation by phosphorylation and acetylation.
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DOI：
10.1016/j.isci.2021.103730
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发表时间：
2022-01-21
期刊：
影响因子：
5.8
通讯作者：
Chandrasekaran S
中科院分区：
文献类型：
--
作者：
Smith K;Shen F;Lee HJ;Chandrasekaran S
乙酰化和磷酸化是高度保守的翻译后修饰（PTMs），它们调节细胞代谢，但这些PTMs之间如何共享代谢控制尚不清楚。在这里，我们使用CAROM分析了大肠杆菌、酿酒葡萄球菌和哺乳动物细胞在不同条件下的转录组、蛋白质组、乙酰组和磷蛋白质组数据集。CAROM是一种利用基因组尺度代谢网络和机器学习对PTMs靶点进行分类的新方法。我们建立了一个单一的机器学习模型，该模型基于反应属性（AUC>0.8）预测了所有三种生物体中每种PTM的目标。我们的模型预测了哺乳动物细胞周期中的磷酸化酶，我们使用磷酸化蛋白质组学验证了这一点。利用博弈论解释机器学习模型揭示了酶的特性，包括网络连通性、必要性和条件特定因素，如区分磷酸化和乙酰化目标的最大通量。在这些ptm之间确定的保守和可预测的代谢调节分配可能使调节回路的合理重新布线。基于酶和反应性质的条件下，CAROM预测PTM靶标生长限制酶是乙酰化同工酶的优先靶标，无效循环与磷酸化有关。CAROM揭示了每个PTM的“分工”和独特的调节作用；代谢通量分析；代谢调节;组学;系统生物学
Acetylation and phosphorylation are highly conserved posttranslational modifications (PTMs) that regulate cellular metabolism, yet how metabolic control is shared between these PTMs is unknown. Here we analyze transcriptome, proteome, acetylome, and phosphoproteome datasets in E. coli, S. cerevisiae, and mammalian cells across diverse conditions using CAROM, a new approach that uses genome-scale metabolic networks and machine learning to classify targets of PTMs. We built a single machine learning model that predicted targets of each PTM in a condition across all three organisms based on reaction attributes (AUC>0.8). Our model predicted phosphorylated enzymes during a mammalian cell-cycle, which we validate using phosphoproteomics. Interpreting the machine learning model using game theory uncovered enzyme properties including network connectivity, essentiality, and condition-specific factors such as maximum flux that differentiate targets of phosphorylation from acetylation. The conserved and predictable partitioning of metabolic regulation identified here between these PTMs may enable rational rewiring of regulatory circuits. CAROM predicts PTM targets in a condition based on enzyme & reaction properties Growth-limiting enzymes are preferential targets of acetylation Isozymes and futile-cycles are associated with phosphorylation CAROM reveals a ‘division of labor’ and a unique regulatory role for each PTM Machine learning; Metabolic flux analysis; Metabolic regulation; Omics; Systems biology
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影响因子：
14.8
作者：
Becker, Scott A.;Feist, Adam M.;Herrgard, Markus J.
通讯作者：
Herrgard, Markus J.
DOI：
10.1534/g3.117.041277
发表时间：
2017-08-07
期刊：
G3 (Bethesda, Md.)
影响因子：
--
作者：
Hart T;Tong AHY;Chan K;Van Leeuwen J;Seetharaman A;Aregger M;Chandrashekhar M;Hustedt N;Seth S;Noonan A;Habsid A;Sizova O;Nedyalkova L;Climie R;Tworzyanski L;Lawson K;Sartori MA;Alibeh S;Tieu D;Masud S;Mero P;Weiss A;Brown KR;Usaj M;Billmann M;Rahman M;Constanzo M;Myers CL;Andrews BJ;Boone C;Durocher D;Moffat J
通讯作者：
Moffat J
影响因子：
8.8
作者：
Chandrasekaran S;Zhang J;Sun Z;Zhang L;Ross CA;Huang YC;Asara JM;Li H;Daley GQ;Collins JJ
通讯作者：
Collins JJ
DOI：
10.1073/pnas.0707476104
发表时间：
2007-10-02
影响因子：
11.1
作者：
Daran-Lapujade, Pascale;Rossell, Sergio;Bakker, Barbara M.
通讯作者：
Bakker, Barbara M.
影响因子：
28.2
作者：
Gimple, Ryan C.;Kidwell, Reilly L.;Rich, Jeremy N.
通讯作者：
Rich, Jeremy N.