Inter-study and time-dependent variability of metabolite abundance in cultured red blood cells.
Inter-study and time-dependent variability of metabolite abundance in cultured red blood cells.
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DOI：
10.1186/s12936-021-03780-5
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发表时间：
2021-07-02
期刊：
影响因子：
3
通讯作者：
Wallqvist A
中科院分区：
文献类型：
--
作者：
Tewari SG;Rajaram K;Swift RP;Kwan B;Reifman J;Prigge ST;Wallqvist A
培养的人红细胞（RBC）提供了一个强大的离体检测平台，以研究血液阶段的疟疾感染和传播。近年来，高分辨率代谢组学方法已经定量了寄生虫感染的RBC培养物在各种干扰下的数百种代谢产物。在这种情况下，未感染培养系统的相应对照样品也可用于检查这些扰动对RBC代谢本身的影响及其对献血者的依赖性（研究间差异）。生成并分析了来自五项独立研究的时程数据集，在最初为寄生虫培养物设计的条件下，将未感染的RBC（uRBC）维持在2%红细胞压积48小时。使用相同的实验方案，在六个时间点收集四倍体样品，并对uRBC培养物的沉淀部分采用总体代谢组学。在每个数据集中总共检查了约500种代谢物，以量化RBC代谢的研究间变异性，代谢网络建模增强了分析，以表征RBC的代谢状态和通量。为了最大限度地减少与RBC代谢无关的研究间差异，确定了一种内标代谢物（磷脂酰乙醇胺C18：0/20：4），其丰度随时间推移和每个数据集的所有样本的变化极小，以标准化数据。尽管大部分标准化数据显示研究间高度一致，但观察到个体供体代谢物水平的变化和变异。因此，总共有24种代谢物与48小时培养时间窗中的显著变化相关，其中最大的变化涉及糖酵解和谷胱甘肽合成中的代谢物。代谢网络分析用于确定培养的红细胞中超氧自由基的产生，其通过谷胱甘肽氧化还原酶的活性和通过磷酸戊糖途径合成还原当量来抵消。肽降解发生的速率与中心碳通量相当，与高铁血红蛋白的活性降解一致，该过程通常也与RBC中的储存病变相关。大部分数据显示研究间的一致性很高。所收集的数据、RBC代谢物的预期丰度变化的定量以及RBC中高度可变代谢物的子集的表征将有助于识别代谢丰度的非特异性变化，这些变化可能会模糊恶性疟原虫和其他血液传播病原体的准确代谢组学分析。在线版本包含补充材料，可通过10.1186/s12936-021-03780-5获得。
Cultured human red blood cells (RBCs) provide a powerful ex vivo assay platform to study blood-stage malaria infection and propagation. In recent years, high-resolution metabolomic methods have quantified hundreds of metabolites from parasite-infected RBC cultures under a variety of perturbations. In this context, the corresponding control samples of the uninfected culture systems can also be used to examine the effects of these perturbations on RBC metabolism itself and their dependence on blood donors (inter-study variations). Time-course datasets from five independent studies were generated and analysed, maintaining uninfected RBCs (uRBC) at 2% haematocrit for 48 h under conditions originally designed for parasite cultures. Using identical experimental protocols, quadruplicate samples were collected at six time points, and global metabolomics were employed on the pellet fraction of the uRBC cultures. In total, ~ 500 metabolites were examined across each dataset to quantify inter-study variability in RBC metabolism, and metabolic network modelling augmented the analyses to characterize the metabolic state and fluxes of the RBCs. To minimize inter-study variations unrelated to RBC metabolism, an internal standard metabolite (phosphatidylethanolamine C18:0/20:4) was identified with minimal variation in abundance over time and across all the samples of each dataset to normalize the data. Although the bulk of the normalized data showed a high degree of inter-study consistency, changes and variations in metabolite levels from individual donors were noted. Thus, a total of 24 metabolites were associated with significant variation in the 48-h culture time window, with the largest variations involving metabolites in glycolysis and synthesis of glutathione. Metabolic network analysis was used to identify the production of superoxide radicals in cultured RBCs as countered by the activity of glutathione oxidoreductase and synthesis of reducing equivalents via the pentose phosphate pathway. Peptide degradation occurred at a rate that is comparable with central carbon fluxes, consistent with active degradation of methaemoglobin, processes also commonly associated with storage lesions in RBCs. The bulk of the data showed high inter-study consistency. The collected data, quantification of an expected abundance variation of RBC metabolites, and characterization of a subset of highly variable metabolites in the RBCs will help in identifying non-specific changes in metabolic abundances that may obscure accurate metabolomic profiling of Plasmodium falciparum and other blood-borne pathogens. The online version contains supplementary material available at 10.1186/s12936-021-03780-5.
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影响因子：
3.7
作者：
Sana TR;Gordon DB;Fischer SM;Tichy SE;Kitagawa N;Lai C;Gosnell WL;Chang SP
通讯作者：
Chang SP
影响因子：
4.3
作者：
King ZA;Dräger A;Ebrahim A;Sonnenschein N;Lewis NE;Palsson BO
通讯作者：
Palsson BO
影响因子：
30.3
作者：
Olszewski KL;Morrisey JM;Wilinski D;Burns JM;Vaidya AB;Rabinowitz JD;Llinás M
通讯作者：
Llinás M
影响因子：
1.6
作者：
Lee, Joomi;Park, Jeonghyeon;Yoon, Young-Ran
通讯作者：
Yoon, Young-Ran
影响因子：
6.4
作者：
Cobbold, Simon A.;Chua, Hwa H.;McConville, Malcolm J.
通讯作者：
McConville, Malcolm J.