Proteomics in aging research: A roadmap to clinical, translational research.
Proteomics in aging research: A roadmap to clinical, translational research.
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DOI：
10.1111/acel.13325
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发表时间：
2021-04
期刊：
影响因子：
7.8
通讯作者：
Ferrucci L
中科院分区：
文献类型：
--
作者：
Moaddel R;Ubaida-Mohien C;Tanaka T;Lyashkov A;Basisty N;Schilling B;Semba RD;Franceschi C;Gorospe M;Ferrucci L
识别随年龄系统变化的血浆蛋白，并独立于实际年龄预测健康加速衰退是一个不断扩大的研究领域。循环蛋白是理想的翻译“组学”，因为它们是生理途径的最终效应者，而且医生习惯于使用血浆蛋白信息作为诊断、预后和跟踪治疗效果的生物标志物。最近的技术进步，包括基于质谱学(MS)的蛋白质组学、使用修饰适配子的多重蛋白质组学分析(SOMAscan)和邻近延伸分析(PEA，O-Link)，使得对血浆或其他生物基质中数千种蛋白质的评估成为可能，这些蛋白质可能被翻译成新的临床生物标志物，并为衰老与健康恶化和功能衰退的机制提供新的线索。我们使用多个平台对不同基质(血浆、血清、尿液、唾液、组织)和物种进行了详细的蛋白质组学研究。在这里，我们在研究中确定了232种与年龄相关的蛋白质。对232个AGE相关蛋白的富集分析揭示了先前在动物模型和人类中与生物衰老相关的代谢途径，其中最显著的是胰岛素样生长因子(IGF)信号转导、丝裂原激活蛋白激酶(MAPK)、低氧诱导因子1(HIF1)、细胞因子信号转导、Forkhead Box O(FOXO)代谢途径、叶酸代谢、糖基化终末产物(AGE)和受体AGE(RAGE)代谢途径。关于这些年龄相关蛋白的信息可能在纵向研究中得到扩展和验证，并在机制研究中进行检验，这将对患者分层和旨在改善健康预期的新治疗方法的开发至关重要。对评估生物体液或组织的微量样本中蛋白质的研究综述表明，大量蛋白质随着年龄的增长而发生系统变化。这些蛋白质是开发临床工具的有力候选者，这些工具旨在测量生物衰老并预测健康期限的变化。
The identification of plasma proteins that systematically change with age and, independent of chronological age, predict accelerated decline of health is an expanding area of research. Circulating proteins are ideal translational “omics” since they are final effectors of physiological pathways and because physicians are accustomed to use information of plasma proteins as biomarkers for diagnosis, prognosis, and tracking the effectiveness of treatments. Recent technological advancements, including mass spectrometry (MS)‐based proteomics, multiplexed proteomic assay using modified aptamers (SOMAscan), and Proximity Extension Assay (PEA, O‐Link), have allowed for the assessment of thousands of proteins in plasma or other biological matrices, which are potentially translatable into new clinical biomarkers and provide new clues about the mechanisms by which aging is associated with health deterioration and functional decline. We carried out a detailed literature search for proteomic studies performed in different matrices (plasma, serum, urine, saliva, tissues) and species using multiple platforms. Herein, we identified 232 proteins that were age‐associated across studies. Enrichment analysis of the 232 age‐associated proteins revealed metabolic pathways previously connected with biological aging both in animal models and in humans, most remarkably insulin‐like growth factor (IGF) signaling, mitogen‐activated protein kinases (MAPK), hypoxia‐inducible factor 1 (HIF1), cytokine signaling, Forkhead Box O (FOXO) metabolic pathways, folate metabolism, advance glycation end products (AGE), and receptor AGE (RAGE) metabolic pathway. Information on these age‐relevant proteins, likely expanded and validated in longitudinal studies and examined in mechanistic studies, will be essential for patient stratification and the development of new treatments aimed at improving health expectancy. A review of studies that assessed proteins in micro‐specimens of biological fluids or tissues revealed that a substantial group of proteins change systematically with aging. These proteins are strong candidate for developing clinical tools aimed at measuring biological aging and predict changes in health span.
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影响因子：
4.6
作者：
Baldensperger, Tim;Eggen, Michael;Glomb, Marcus A.
通讯作者：
Glomb, Marcus A.
影响因子：
16.6
作者：
Angelidis, Ilias;Simon, Lukas M.;Schiller, Herbert B.
通讯作者：
Schiller, Herbert B.
影响因子：
5.2
作者：
Bell-Temin, Harris;Yousefzadeh, Matthew J.;Yates, Nathan A.
通讯作者：
Yates, Nathan A.
DOI：
10.1093/bioinformatics/btp101
发表时间：
2009-04-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Bindea G;Mlecnik B;Hackl H;Charoentong P;Tosolini M;Kirilovsky A;Fridman WH;Pagès F;Trajanoski Z;Galon J
通讯作者：
Galon J
通过与RNA-Seq深入集成，对高通量蛋白质组学数据的同工型级解释。
影响因子：
4.4
作者：
Carlyle BC;Kitchen RR;Zhang J;Wilson RS;Lam TT;Rozowsky JS;Williams KR;Sestan N;Gerstein MB;Nairn AC
通讯作者：
Nairn AC