Unlocking the metabolic potential of endophytic fungi through epigenetics: a paradigm shift for natural product discovery and plant-microbe interactions.

范式转换 天然产物 生物 表观遗传学 内生真菌在植物防御中的应用 计算生物学 植物代谢 植物 遗传学 基因 物理 生物化学 量子力学 核糖核酸
作者
Rui Liu,Xiao-Ping Peng,David Newman,Diane Purchase,Gang Li,Souvik Kusari
出处
期刊:PubMed
标识
DOI:10.1039/d5np00028a
摘要

Covering: up to December 2024Microbial metabolic pathways, including those of endophytic fungi, offer significant potential for synthesizing secondary metabolites, regardless of their ecological niche. These pathways can be modulated at the molecular level through genome and epigenome manipulation. The metabolic activation of fungal endophytes using epigenetics presents an exciting frontier in science, paving the way for advanced biotechnological applications and enhancing our understanding of these microorganisms' roles in ecosystems. This review examines the significant role of epigenetics in the biosynthesis of secondary metabolites from fungal endophytes, which is vital for drug discovery. Our primary focus centers on studies that explore the epigenetic modulation of endophytic fungi up until December 2024. Acknowledging the rapidly evolving landscape of epigenetic research in this field, which has limited examples for endophytic fungi, we provide crucial foundational insights into fungal epigenetics and relate these insights to the broader context of plant-microbe interactions and endophytic fungal epigenetics, supported by relevant examples. Key mechanisms, such as histone acetylation, histone methylation, and DNA methylation, are discussed alongside recent advances in small-molecule epigenetic modulators that can activate silent biosynthetic gene clusters (BGCs). Further, chromatin-dependent regulation of these BGCs and methods for probing chromatin modifications and secondary metabolism in fungi are discussed. The role of CRISPR-Cas9 genome editing, combined with epigenetic strategies, is highlighted, showcasing its ability to alter the metabolite profiles of fungal endophytes. Finally, we explore how artificial intelligence (AI), machine learning (ML), and deep learning (DL) innovations are transforming research in chemical epigenomics at the plant-microbe interface.
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