Authors: Shaohua Gu, Jiqi Shao, Ruolin He, Guanyue Xiong, Zeyang Qu, Yuanzhe Shao, Linlong Yu, Di Zhang, Fanhao Wang, Ruichen Xu, Peng Guo, Ningbo Xi, Yinxiang Li, Yanzhao Wu, Zhong Wei, Zhiyuan Li.
Journal: Quantitative Biology
DOI: 10.1002/qub2.84
Link: https://onlinelibrary.wiley.com/doi/10.1002/qub2.84
Published: Jan 10, 2025
Document Type: Perspective
Abstract:
Iron is a critical yet limited nutrient for microbial growth. To scavenge iron, most microbes produce siderophores—diverse small molecules with high iron affinities. Different siderophores are specifically recognized and uptaken by corresponding recognizers, enabling targeted interventions and intriguing cheater-producer dynamics. We propose constructing a comprehensive iron interaction network, or “iron-net”, across the microbial world. Such a network offers the potential for precise manipulation of the microbiota, with conceivable applications in medicine, agriculture, and industry as well as advancing microbial ecology and evolution theories. Previously, our successful construction of an iron-net in the Pseudomonas genus demonstrated the feasibility of coevolution-inspired digital siderophore-typing. Enhanced by machine learning techniques and expanding sequencing data, forging such an iron-net calls for multidisciplinary collaborations and holds significant promise in addressing critical challenges in microbial communities.