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生物信息学分析利用深度学习的光学显微镜揭示了细菌形态与抗生素耐药性之间的关联。

Bioinformatic analysis reveals the association between bacterial morphology and antibiotic resistance using light microscopy with deep learning.

作者信息

Ikebe Miki, Aoki Kota, Hayashi-Nishino Mitsuko, Furusawa Chikara, Nishino Kunihiko

机构信息

SANKEN (Institute of Scientific and Industrial Research), Osaka University, Osaka, Japan.

Graduate School of Pharmaceutical Sciences, Osaka University, Suita, Japan.

出版信息

Front Microbiol. 2024 Sep 19;15:1450804. doi: 10.3389/fmicb.2024.1450804. eCollection 2024.

DOI:10.3389/fmicb.2024.1450804
PMID:39364166
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11446759/
Abstract

Although it is well known that the morphology of Gram-negative rods changes on exposure to antibiotics, the morphology of antibiotic-resistant bacteria in the absence of antibiotics has not been widely investigated. Here, we studied the morphologies of 10 antibiotic-resistant strains of and used bioinformatics tools to classify the resistant cells under light microscopy in the absence of antibiotics. The antibiotic-resistant strains showed differences in morphology from the sensitive parental strain, and the differences were most prominent in the quinolone-and β-lactam-resistant bacteria. A cluster analysis revealed increased proportions of fatter or shorter cells in the antibiotic-resistant strains. A correlation analysis of morphological features and gene expression suggested that genes related to energy metabolism and antibiotic resistance were highly correlated with the morphological characteristics of the resistant strains. Our newly proposed deep learning method for single-cell classification achieved a high level of performance in classifying quinolone-and β-lactam-resistant strains.

摘要

虽然众所周知革兰氏阴性杆菌在接触抗生素后形态会发生变化，但在无抗生素情况下抗生素耐药菌的形态尚未得到广泛研究。在此，我们研究了10株抗生素耐药菌株的形态，并使用生物信息学工具在无抗生素条件下通过光学显微镜对耐药细胞进行分类。抗生素耐药菌株与敏感亲代菌株在形态上存在差异，喹诺酮和β-内酰胺耐药菌中的差异最为显著。聚类分析显示，抗生素耐药菌株中较胖或较短细胞的比例增加。形态特征与基因表达的相关性分析表明，与能量代谢和抗生素耐药相关的基因与耐药菌株的形态特征高度相关。我们新提出的用于单细胞分类的深度学习方法在对喹诺酮和β-内酰胺耐药菌株进行分类时表现出很高的性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/130a/11446759/b5b1a7242a64/fmicb-15-1450804-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/130a/11446759/ee49a3ea884b/fmicb-15-1450804-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/130a/11446759/eeceb7cf4d9f/fmicb-15-1450804-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/130a/11446759/c2a22634c9fc/fmicb-15-1450804-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/130a/11446759/3abc8c87491c/fmicb-15-1450804-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/130a/11446759/e3d23f127ad5/fmicb-15-1450804-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/130a/11446759/b5b1a7242a64/fmicb-15-1450804-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/130a/11446759/ee49a3ea884b/fmicb-15-1450804-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/130a/11446759/eeceb7cf4d9f/fmicb-15-1450804-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/130a/11446759/c2a22634c9fc/fmicb-15-1450804-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/130a/11446759/3abc8c87491c/fmicb-15-1450804-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/130a/11446759/e3d23f127ad5/fmicb-15-1450804-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/130a/11446759/b5b1a7242a64/fmicb-15-1450804-g006.jpg

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