Stransky Stephanie, Aguilan Jennifer, Lachowicz Jake, Madrid-Aliste Carlos, Nieves Edward, Sidoli Simone
Department of Biochemistry, Albert Einstein College of Medicine, Bronx, NY 10461, USA.
Department of Pathology, Albert Einstein College of Medicine, Bronx, NY 10461, USA.
Biology (Basel). 2020 Jun 26;9(6):140. doi: 10.3390/biology9060140.
Chromatin accessibility is a major regulator of gene expression. Histone writers/erasers have a critical role in chromatin compaction, as they "flag" chromatin regions by catalyzing/removing covalent post-translational modifications on histone proteins. Anomalous chromatin decondensation is a common phenomenon in cells experiencing aging and viral infection. Moreover, about 50% of cancers have mutations in enzymes regulating chromatin state. Numerous genomics methods have evolved to characterize chromatin state, but the analysis of (in)accessible chromatin from the protein perspective is not yet in the spotlight. We present an overview of the most used approaches to generate data on chromatin accessibility and then focus on emerging methods that utilize mass spectrometry to quantify the accessibility of histones and the rest of the chromatin bound proteome. Mass spectrometry is currently the method of choice to quantify entire proteomes in an unbiased large-scale manner; accessibility on chromatin of proteins and protein modifications adds an extra quantitative layer to proteomics dataset that assist more informed data-driven hypotheses in chromatin biology. We speculate that this emerging new set of methods will enhance predictive strength on which proteins and histone modifications are critical in gene regulation, and which proteins occupy different chromatin states in health and disease.
染色质可及性是基因表达的主要调节因子。组蛋白书写酶/擦除酶在染色质压缩中起关键作用，因为它们通过催化/去除组蛋白上的共价翻译后修饰来“标记”染色质区域。异常的染色质解聚是衰老和病毒感染细胞中的常见现象。此外，约50%的癌症在调节染色质状态的酶中存在突变。已经发展出许多基因组学方法来表征染色质状态，但从蛋白质角度分析（不可）及染色质尚未成为研究热点。我们概述了生成染色质可及性数据最常用的方法，然后重点介绍利用质谱法定量组蛋白及其他染色质结合蛋白质组可及性的新兴方法。质谱法目前是无偏大规模定量整个蛋白质组的首选方法；蛋白质和蛋白质修饰在染色质上的可及性为蛋白质组学数据集增加了额外的定量层面，有助于在染色质生物学中提出更有依据的数据驱动假设。我们推测，这套新兴的新方法将增强对哪些蛋白质和组蛋白修饰在基因调控中至关重要，以及哪些蛋白质在健康和疾病状态下占据不同染色质状态的预测能力。
