Integration of Metabolomics and Gene Expression Profiling Elucidates IL4I1 as Modulator of Ibrutinib Resistance in ABC-Diffuse Large B Cell Lymphoma.
Integration of Metabolomics and Gene Expression Profiling Elucidates IL4I1 as Modulator of Ibrutinib Resistance in ABC-Diffuse Large B Cell Lymphoma.
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DOI：
10.3390/cancers13092146
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发表时间：
2021-04-29
期刊：
影响因子：
5.2
通讯作者：
Sehgal L
中科院分区：
文献类型：
--
作者：
Choueiry F;Singh S;Sircar A;Laliotis G;Sun X;Chavdoula E;Zhang S;Helmig-Mason J;Hart A;Epperla N;Tsichlis P;Baiocchi R;Alinari L;Zhu J;Sehgal L
在这项研究中，我们提出了一种工作流程，通过整合代谢组学和基因表达谱来了解ABC弥漫性大B细胞淋巴瘤中伊布鲁替尼耐药的调节因素，如图摘要所示。我们使用Q-Exactive高分辨率质谱仪进行了一项非靶向代谢组学分析，以剖析与获得性伊布鲁替尼耐药相关的代谢重编程，这些细胞系对伊布鲁替尼敏感和伊布鲁替尼耐药。此外，我们确定了共同的分母，整合了代谢组和转录组数据，确认了临床意义，整合了途径，并确定了驱动伊布鲁替尼抵抗和代谢重编程的候选基因。我们的工作表明，多组学方法可以是一种强大而公正的策略，以发现导致癌细胞代谢失调的基因和途径。弥漫性大B细胞淋巴瘤(DLBCL)是最常见的非霍奇金淋巴瘤(NHL)。B细胞NHL依赖于Bruton酪氨酸激酶(BTK)介导的B细胞受体信号来维持生存和疾病进展。然而，它们往往对BTK抑制剂产生抗药性，或者在药物暴露后很快获得抗药性，导致耐药形式。耐药克隆增殖更快，代谢活性增加，并转变为氧化磷酸化；然而，这种代谢编程是如何在耐药肿瘤中发生的还知之甚少。在这项研究中，我们首次使用结合代谢组学(使用高分辨率质谱学)和转录组学(基因表达分析)的多组学分析来探索伊布鲁替尼耐药激活B细胞(ABC)DLBCL的代谢调节机制。无偏倚的统计分析、遗传扰动和药物抑制的叠加被进一步用于确定对耐药克隆的代谢重新编程有贡献的关键因素。基因-代谢物整合揭示了白介素4诱导的1(IL4I1)在两条参与产生各种氨基酸的显着改变的代谢途径的交汇处。我们首次展示了耐药克隆经历了氧化磷酸化的代谢重编程，并通过BTK-PI3K-AKT-IL4I1轴进行调节。我们的报告展示了这些细胞在获得伊布鲁替尼耐药后如何依赖PI3K/AKT信号生存并转变为持续的氧化磷酸化；此外，我们还概述了可能调节耐药细胞中这种代谢重新编程的代偿途径。这些来自我们无偏见分析的发现突出了代谢重编程在耐药发展过程中的作用。我们的工作表明，多组学方法可以是一种强大而公正的策略，以发现驱动癌细胞代谢放松调控的基因和途径。
In this study, we present a workflow to understand the modulator of ibrutinib resistance in ABC diffuse large B cell lymphoma by integrating Metabolomics and Gene expression profiling as shown in the graphical abstract. We performed an untargeted metabolomics analysis using a Q-Exactive high-resolution mass spectrometer to dissect the metabolic reprogramming associated with acquired ibrutinib resistance in paired ibrutinib-sensitive and ibrutinib-resistant DLBCL cell lines. Further, we identified common denominators, integrating metabolome and transcriptome data, confirming clinical significance, integrating pathways, and identifying the candidate gene driving ibrutinib resistance and metabolic reprogramming. Our work demonstrates that a multi-omics approach can be a robust and impartial strategy to uncover genes and pathways that cause metabolic deregulation in cancer cells. Diffuse large B-cell lymphoma (DLBCL) is the most common non-Hodgkin lymphoma (NHL). B-cell NHLs rely on Bruton’s tyrosine kinase (BTK) mediated B-cell receptor signaling for survival and disease progression. However, they are often resistant to BTK inhibitors or soon acquire resistance after drug exposure resulting in the drug-tolerant form. The drug-tolerant clones proliferate faster, have increased metabolic activity, and shift to oxidative phosphorylation; however, how this metabolic programming occurs in the drug-resistant tumor is poorly understood. In this study, we explored for the first time the metabolic regulators of ibrutinib-resistant activated B-cell (ABC) DLBCL using a multi-omics analysis that integrated metabolomics (using high-resolution mass spectrometry) and transcriptomic (gene expression analysis). Overlay of the unbiased statistical analyses, genetic perturbation, and pharmaceutical inhibition was further used to identify the key players contributing to the metabolic reprogramming of the drug-resistant clone. Gene-metabolite integration revealed interleukin four induced 1 (IL4I1) at the crosstalk of two significantly altered metabolic pathways involved in producing various amino acids. We showed for the first time that drug-resistant clones undergo metabolic reprogramming towards oxidative phosphorylation and are modulated via the BTK-PI3K-AKT-IL4I1 axis. Our report shows how these cells become dependent on PI3K/AKT signaling for survival after acquiring ibrutinib resistance and shift to sustained oxidative phosphorylation; additionally, we outline the compensatory pathway that might regulate this metabolic reprogramming in the drug-resistant cells. These findings from our unbiased analyses highlight the role of metabolic reprogramming during drug resistance development. Our work demonstrates that a multi-omics approach can be a robust and impartial strategy to uncover genes and pathways that drive metabolic deregulation in cancer cells.
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影响因子：
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作者：
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作者：
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DOI：
10.1002/ajmg.c.30289
发表时间：
2011-02-15
影响因子：
3.1
作者：
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通讯作者：
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