Differential Proteome and Interactome Analysis Reveal the Basis of Pleiotropy Associated With the Histidine Methyltransferase Hpm1p.
Differential Proteome and Interactome Analysis Reveal the Basis of Pleiotropy Associated With the Histidine Methyltransferase Hpm1p.
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DOI：
10.1016/j.mcpro.2022.100249
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发表时间：
2022-07
影响因子：
7
通讯作者：
Wilkins, Marc. R.
中科院分区：
文献类型：
--
作者：
Bartolec, Tara K.;Hamey, Joshua J.;Keller, Andrew;Chavez, Juan D.;Bruce, James E.;Wilkins, Marc. R.
组氨酸的甲基化是一种翻译后修饰，其功能知之甚少。甲基转移酶组氨酸蛋白甲基转移酶1（Hpm 1 p）使核糖体蛋白Rpl 3 p中的H243单甲基化，并且代表酿酒酵母中唯一已知的组氨酸甲基转移酶。有趣的是，hpm 1缺失菌株是高度多效性的，具有许多核糖体外表型，包括在替代碳源中提高的生长速率。在这里，我们研究了组氨酸甲基转移酶Hpm 1 p的丢失如何导致不同的表型，通过使用靶向质谱（MS），生长测定，定量蛋白质组学和差异交联MS。我们证实了H243甲基化位点的定位和化学计量，发现了未报道的Δ hpm 1酵母对非核糖体应激因子的敏感性，并鉴定了在HPM 1敲除后具有明显与糖代谢协调相关的差异丰度蛋白。我们适应了新兴的技术，定量大规模的稳定同位素标记的氨基酸在细胞培养交联MS酵母，这导致在1267独特的体内赖氨酸-赖氨酸交联的鉴定。通过在WT和Δ hpm 1中重复监测超过350个，我们检测到核糖体、膜蛋白、染色质和线粒体中蛋白质结构或蛋白质-蛋白质相互作用的变化。重要的是，这些发生独立于蛋白质丰度的变化，可以解释Δ hpm 1的许多表型，而不是通过表达分析来解决。除此之外，一些表型仅从蛋白质结构或相互作用的变化中预测，并且可以通过正交技术进行验证。总之，这些研究揭示了Hpm 1 p在酵母中的广泛作用，并说明了交联MS将如何成为理解复杂表型的重要工具。Hpm 1 p缺失时发现的新的核糖体外功能表型进行大规模体内定量和比较交联分析。在结构和相互作用中检测到的变化，与蛋白质丰度无关。通过交联分析可以解释或预测表型。酵母组胺酸甲基转移酶Hpm 1 p靶向核糖体底物，但具有许多无法解释的核糖体外表型。为了理解这些，我们使用蛋白质表达分析和定量交联质谱来比较WT和Δ hpm 1细胞。这揭示了Hpm 1 p在代谢、线粒体和膜中的作用。交联质谱检测蛋白质结构和相互作用的变化，是了解表型的有力手段。洞察力与表情分析不同，使得这些技术具有巨大的潜力。
The methylation of histidine is a post-translational modification whose function is poorly understood. Methyltransferase histidine protein methyltransferase 1 (Hpm1p) monomethylates H243 in the ribosomal protein Rpl3p and represents the only known histidine methyltransferase in Saccharomyces cerevisiae. Interestingly, the hpm1 deletion strain is highly pleiotropic, with many extraribosomal phenotypes including improved growth rates in alternative carbon sources. Here, we investigate how the loss of histidine methyltransferase Hpm1p results in diverse phenotypes, through use of targeted mass spectrometry (MS), growth assays, quantitative proteomics, and differential crosslinking MS. We confirmed the localization and stoichiometry of the H243 methylation site, found unreported sensitivities of Δhpm1 yeast to nonribosomal stressors, and identified differentially abundant proteins upon hpm1 knockout with clear links to the coordination of sugar metabolism. We adapted the emerging technique of quantitative large-scale stable isotope labeling of amino acids in cell culture crosslinking MS for yeast, which resulted in the identification of 1267 unique in vivo lysine–lysine crosslinks. By reproducibly monitoring over 350 of these in WT and Δhpm1, we detected changes to protein structure or protein–protein interactions in the ribosome, membrane proteins, chromatin, and mitochondria. Importantly, these occurred independently of changes in protein abundance and could explain a number of phenotypes of Δhpm1, not addressed by expression analysis. Further to this, some phenotypes were predicted solely from changes in protein structure or interactions and could be validated by orthogonal techniques. Taken together, these studies reveal a broad role for Hpm1p in yeast and illustrate how crosslinking MS will be an essential tool for understanding complex phenotypes. Novel extraribosomal functional phenotypes found on loss of Hpm1p. Large-scale in vivo quantitative and comparative crosslinking analysis performed. Changes detected in structures and interactions, independent of protein abundance. Phenotypes could be explained or predicted through crosslinking analysis. The yeast histidine methyltransferase Hpm1p targets a ribosomal substrate but has many unexplained extraribosomal phenotypes. To understand these, we used protein expression analysis and quantitative cross-linking mass spectrometry to compare WT and Δhpm1 cells. This revealed a role of Hpm1p in metabolism, mitochondria, and membranes. Cross-linking mass spectrometry detected changes in protein structures and interactions and was a powerful means of understanding phenotype. Insights were different to those gained from expression analysis, making these techniques of great potential.
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影响因子：
14.8
作者：
Gietz, R. Daniel;Schiestl, Robert H.
通讯作者：
Schiestl, Robert H.
影响因子：
16.6
作者：
Davydova E;Shimazu T;Schuhmacher MK;Jakobsson ME;Willemen HLDM;Liu T;Moen A;Ho AYY;Małecki J;Schroer L;Pinto R;Suzuki T;Grønsberg IA;Sohtome Y;Akakabe M;Weirich S;Kikuchi M;Olsen JV;Dohmae N;Umehara T;Sodeoka M;Siino V;McDonough MA;Eijkelkamp N;Schofield CJ;Jeltsch A;Shinkai Y;Falnes PØ
通讯作者：
Falnes PØ
DOI：
10.1016/j.bbrc.2016.01.107
发表时间：
2016-02-12
影响因子：
3.1
作者：
Al-Hadid, Qais;White, Jonelle;Clarke, Steven
通讯作者：
Clarke, Steven
影响因子：
2.6
作者：
Bengtsson, Oskar;Jeppsson, Marie;Gorwa-Grauslund, Mane-F.
通讯作者：
Gorwa-Grauslund, Mane-F.
影响因子：
2.7
作者：
Boca, Simina M.;Leek, Jeffrey T.
通讯作者：
Leek, Jeffrey T.