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链霉菌转录组的机器学习分析揭示了一个包含生物合成基因簇的转录调控网络。

Machine-Learning Analysis of Streptomyces coelicolor Transcriptomes Reveals a Transcription Regulatory Network Encompassing Biosynthetic Gene Clusters.

机构信息

Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea.

Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA.

出版信息

Adv Sci (Weinh). 2024 Nov;11(41):e2403912. doi: 10.1002/advs.202403912. Epub 2024 Sep 12.

Abstract

Streptomyces produces diverse secondary metabolites of biopharmaceutical importance, yet the rate of biosynthesis of these metabolites is often hampered by complex transcriptional regulation. Therefore, a fundamental understanding of transcriptional regulation in Streptomyces is key to fully harness its genetic potential. Here, independent component analysis (ICA) of 454 high-quality gene expression profiles of the model species Streptomyces coelicolor is performed, of which 249 profiles are newly generated for S. coelicolor cultivated on 20 different carbon sources and 64 engineered strains with overexpressed sigma factors. ICA of the transcriptome dataset reveals 117 independently modulated groups of genes (iModulons), which account for 81.6% of the variance in the dataset. The genes in each iModulon are involved in specific cellular responses, which are often transcriptionally controlled by specific regulators. Also, iModulons accurately predict 25 secondary metabolite biosynthetic gene clusters encoded in the genome. This systemic analysis leads to reveal the functions of previously uncharacterized genes, putative regulons for 40 transcriptional regulators, including 30 sigma factors, and regulation of secondary metabolism via phosphate- and iron-dependent mechanisms in S. coelicolor. ICA of large transcriptomic datasets thus enlightens a new and fundamental understanding of transcriptional regulation of secondary metabolite synthesis along with interconnected metabolic processes in Streptomyces.

摘要

链霉菌产生具有生物制药重要性的多种次级代谢产物,但这些代谢产物的生物合成速度常常受到复杂的转录调控的阻碍。因此,深入了解链霉菌中的转录调控是充分发挥其遗传潜力的关键。在这里,对模式物种变铅青链霉菌的 454 个高质量基因表达谱进行了独立成分分析 (ICA),其中 249 个是新生成的变铅青链霉菌在 20 种不同碳源上培养的表达谱,以及 64 个过表达 sigma 因子的工程菌株的表达谱。对转录组数据集的 ICA 揭示了 117 个独立调节的基因群 (iModulons),它们占数据集方差的 81.6%。每个 iModulon 中的基因参与特定的细胞反应,这些反应通常由特定的调节剂转录控制。此外,iModulons 可以准确预测基因组中编码的 25 个次级代谢产物生物合成基因簇。这种系统分析揭示了先前未表征基因的功能、40 个转录调节剂的假定调控子,包括 30 个 sigma 因子,以及变铅青链霉菌中通过磷酸盐和铁依赖性机制调节次级代谢的作用。因此,对大型转录组数据集进行 ICA 揭示了链霉菌中次级代谢物合成以及相互关联的代谢过程的转录调控的新的基本理解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb19/11538686/e38a879bb588/ADVS-11-2403912-g001.jpg

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