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最小翻译机制:我们能从自然和实验性简化基因组中学到什么。

The Minimal Translation Machinery: What We Can Learn From Naturally and Experimentally Reduced Genomes.

作者信息

Garzón María José, Reyes-Prieto Mariana, Gil Rosario

机构信息

Departament de Genètica, Universitat de València, Burjassot, Spain.

Institute for Integrative Systems Biology, Universitat de València-Consejo Superior de Investigaciones Científicas, Paterna, Spain.

出版信息

Front Microbiol. 2022 Apr 11;13:858983. doi: 10.3389/fmicb.2022.858983. eCollection 2022.

Abstract

The current theoretical proposals of minimal genomes have not attempted to outline the essential machinery for proper translation in cells. Here, we present a proposal of a minimal translation machinery based on (1) a comparative analysis of bacterial genomes of insects' endosymbionts using a machine learning classification algorithm, (2) the empiric genomic information obtained from JCVI-syn3.0 the first minimal bacterial genome obtained by design and synthesis, and (3) a detailed functional analysis of the candidate genes based on essentiality according to the DEG database ( and ) and the literature. This proposed minimal translational machinery is composed by 142 genes which must be present in any synthetic prokaryotic cell designed for biotechnological purposes, 76.8% of which are shared with JCVI-syn3.0. Eight additional genes were manually included in the proposal for a proper and efficient translation.

摘要

目前关于最小基因组的理论提议尚未尝试勾勒出细胞中正确翻译所需的基本机制。在此,我们基于以下几点提出了一种最小翻译机制的提议:(1)使用机器学习分类算法对昆虫内共生菌的细菌基因组进行比较分析;(2)从通过设计和合成获得的首个最小细菌基因组JCVI-syn3.0获取的经验性基因组信息;(3)根据DEG数据库(以及)和文献,基于必要性对候选基因进行详细的功能分析。这个提议的最小翻译机制由142个基因组成,这些基因必须存在于任何为生物技术目的而设计的合成原核细胞中,其中76.8%与JCVI-syn3.0共享。为了实现正确且高效的翻译,提议中还手动纳入了另外八个基因。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ded/9035817/63371c643e08/fmicb-13-858983-g001.jpg

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