期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2020
卷号：117
期号：11
页码：6264-6273
DOI：10.1073/pnas.1910499117
出版社：The National Academy of Sciences of the United States of America
摘要：Auxotrophies constrain the interactions of bacteria with their environment, but are often difficult to identify. Here, we develop an algorithm (AuxoFind) using genome-scale metabolic reconstruction to predict auxotrophies and apply it to a series of available genome sequences of over 1,300 Gram-negative strains. We identify 54 auxotrophs, along with the corresponding metabolic and genetic basis, using a pangenome approach, and highlight auxotrophies conferring a fitness advantage in vivo. We show that the metabolic basis of auxotrophy is species-dependent and varies with 1) pathway structure, 2) enzyme promiscuity, and 3) network redundancy. Various levels of complexity constitute the genetic basis, including 1) deleterious single-nucleotide polymorphisms (SNPs), in-frame indels, and deletions; 2) single/multigene deletion; and 3) movement of mobile genetic elements (including prophages) combined with genomic rearrangements. Fourteen out of 19 predictions agree with experimental evidence, with the remaining cases highlighting shortcomings of sequencing, assembly, annotation, and reconstruction that prevent predictions of auxotrophies. We thus develop a framework to identify the metabolic and genetic basis for auxotrophies in Gram-negatives..
