摘要
出身背景
细菌感染的诊断仍然依赖于培养，这是一个缓慢的过程，在这个过程中，临床医生最多可以在采样48小时后获得潜在病原体的抗生素敏感性谱。最近，临床宏基因组学，即对样本进行宏基因组测序，目的是识别微生物并确定其对抗菌药物的易感性，已成为一种潜在的诊断工具，可能比培养更快。临床宏基因组学确实有潜力检测抗生素耐药性基因（ARGs）和与耐药性相关的突变。尽管如此，要想从宏基因组数据中快速推断抗生素易感性的表型，仍有许多挑战需要克服。
目标
这篇叙述性综述的目的是讨论宏基因组数据中抗生素易感性表型推断的挑战。
来源
我们使用国家生物技术信息中心PubMed数据库中发表的文章进行了叙述性综述。
所容纳之物
我们回顾了目前的ARG数据库，特别强调了那些现在提供与表型数据相关的数据库。接下来，我们将讨论旨在识别宏基因组中ARG的生物信息学工具。然后，我们报告了从基因组数据进行表型推断的性能以及预测ARGs表达的问题。最后，我们解决了将ARG连接到此主机的挑战。
启示
最近在ARG和表型的关联方面取得了显著的改进，并且从基因组数据中推断易感性已经在致病菌如葡萄球菌和肠杆菌中得到证实。然而，涉及基因表达的耐药性更具挑战性，从铜绿假单胞菌等物种推断易感性仍然很困难。未来的研究方向包括通过RNA测序和机器学习来考虑基因表达。
Abstract
Background
The diagnosis of bacterial infections continues to rely on culture, a slow process in which antibiotic susceptibility profiles of potential pathogens are made available to clinicians 48 hours after sampling, at best. Recently, clinical metagenomics, the metagenomic sequencing of samples with the purpose of identifying microorganisms and determining their susceptibility to antimicrobials, has emerged as a potential diagnostic tool that could prove faster than culture. Clinical metagenomics indeed has the potential to detect antibiotic resistance genes (ARGs) and mutations associated with resistance. Nevertheless, many challenges have yet to be overcome in order to make rapid phenotypic inference of antibiotic susceptibility from metagenomic data a reality.

Objectives
The objective of this narrative review is to discuss the challenges underlying the phenotypic inference of antibiotic susceptibility from metagenomic data.

Sources
We conducted a narrative review using published articles available in the National Center for Biotechnology Information PubMed database.

Content
We review the current ARG databases with a specific emphasis on those which now provide associations with phenotypic data. Next, we discuss the bioinformatic tools designed to identify ARGs in metagenomes. We then report on the performance of phenotypic inference from genomic data and the issue predicting the expression of ARGs. Finally, we address the challenge of linking an ARG to this host.

Implications
Significant improvements have recently been made in associating ARG and phenotype, and the inference of susceptibility from genomic data has been demonstrated in pathogenic bacteria such as Staphylococci and Enterobacterales. Resistance involving gene expression is more challenging however, and inferring susceptibility from species such as Pseudomonas aeruginosa remains difficult. Future research directions include the consideration of gene expression via RNA sequencing and machine learning.

https://www.sciencedirect.com/science/article/abs/pii/S1198743X22002294