科學家首次繪製出大腸桿菌的功能蛋白質圖譜
作者：
小柯機器人發布時間：2020/12/10 13:03:13
德國歐洲分子生物學實驗室Mikhail M. Savitski、Athanasios Typas等研究人員合作首次繪製出大腸桿菌的功能蛋白質圖譜。相關論文於2020年12月9日在線發表在《自然》雜誌上。
通過使用高含量的生化讀數、熱蛋白質組圖譜，研究人員在大腸桿菌中測量了應對121中遺傳擾動時蛋白質組範圍的蛋白質豐度和熱穩定性。結果表明，熱穩定性以及必需蛋白質的狀態和相互作用通常受到調節，從而提高了研究蛋白質組的可能性。研究人員發現，功能相關的蛋白質由於它們的共同調節和物理相互作用（與蛋白質、代謝物或輔因子），在整個擾動中具有協調的豐度和熱穩定性變化。最後，研究人員提供對已知生長表型的機制見解。這些數據能夠作為推測蛋白質功能和相互作用的豐富資源。
據悉，高通量反向遺傳學的最新進展徹底改變了人們定位基因功能和相互作用的能力。這些方法的能力取決於它們識別功能相關基因的能力，這些基因在敲除後會在幾種擾動（化學、環境或遺傳）中引發相似的表型變化。但是，由於存在大量擾動，這些方法僅限於生長或形態學讀數。
附：英文原文
Title: The functional proteome landscape of Escherichia coli
Author: Andr Mateus, Johannes Hevler, Jacob Bobonis, Nils Kurzawa, Malay Shah, Karin Mitosch, Camille V. Goemans, Dominic Helm, Frank Stein, Athanasios Typas, Mikhail M. Savitski
Issue&Volume: 2020-12-09
Abstract: Recent developments in high-throughput reverse genetics1,2 have revolutionized our ability to map gene function and interactions3,4,5,6. The power of these approaches depends on their ability to identify functionally associated genes, which elicit similar phenotypic changes across several perturbations (chemical, environmental or genetic) when knocked out7,8,9. However, owing to the large number of perturbations, these approaches have been limited to growth or morphological readouts10. Here we use a high-content biochemical readout, thermal proteome profiling11, to measure the proteome-wide protein abundance and thermal stability in response to 121 genetic perturbations in Escherichia coli. We show that thermal stability, and therefore the state and interactions of essential proteins, is commonly modulated, raising the possibility of studying a protein group that is particularly inaccessible to genetics. We find that functionally associated proteins have coordinated changes in abundance and thermal stability across perturbations, owing to their co-regulation and physical interactions (with proteins, metabolites or cofactors). Finally, we provide mechanistic insights into previously determined growth phenotypes12 that go beyond the deleted gene. These data represent a rich resource for inferring protein functions and interactions.
DOI: 10.1038/s41586-020-3002-5
Source: https://www.nature.com/articles/s41586-020-3002-5