NRG-P0074 Viral Sample RU1 from Unclassified Mosigvirus Genomic Characterization and Host Range Analysis.

IF 1.8
PHAGE (New Rochelle, N.Y.) Pub Date : 2025-09-12 eCollection Date: 2025-09-01 DOI:10.1089/phage.2025.0003
Rachel M Carson, Ranee K Anderson, Shijie Qin, Brenda Werner, Sam R Nugen
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Abstract

Background: Machine learning models for phage-host range prediction and design require comprehensive training data on phage genomes and host ranges to predict phage-host interactions effectively.

Materials and methods: This study characterizes phage sample NRG-P0074 viral sample RU1 from unclassified Mosigvirus, originally isolated by the Betty Kutter. The complete genome of NRG-P0074 was sequenced, annotated, and analyzed using various bioinformatic tools. Host range analysis was conducted using the Escherichia coli Reference (ECOR) Library and nine Escherichia coli (E. coli) K12 strains (Keio Knockout Collection) with single nonessential gene deletions.

Results: The genome of NRG-P0074 spans 168,357 base pairs with a guanine-cytosine (GC) content of 37.5%. NRG-P0074 exhibited permissiveness in 15.28% of the ECOR isolates and all 9 Keio knockout strains. Comparative genomic analysis revealed that NRG-P0074 is closely related to E. coli phage a20. Its genome is comprised of 270 coding sequences, 153 known genes, 16 terminators, 3 ribosomal-binding sites, 0 tRNAs, and 117 hypothetical proteins.

Conclusions: This research provides valuable data for developing machine learning models to predict phage-host interactions, aiding the development of targeted phage therapies against antibiotic-resistant bacteria.

非分类莫西病毒NRG-P0074病毒样本RU1的基因组特征和宿主范围分析。
背景:用于噬菌体-宿主范围预测和设计的机器学习模型需要噬菌体基因组和宿主范围的综合训练数据来有效预测噬菌体-宿主相互作用。材料和方法:本研究从未分类的Mosigvirus中提取噬菌体样本NRG-P0074病毒样本RU1,该病毒最初由Betty Kutter分离。对NRG-P0074全基因组进行测序、注释,并使用各种生物信息学工具进行分析。利用大肠杆菌参考文库(ECOR)和9株非必需基因缺失的大肠杆菌(E. coli) K12菌株(Keio敲除收集)进行宿主范围分析。结果:NRG-P0074基因组全长168,357个碱基对,鸟嘌呤-胞嘧啶(GC)含量为37.5%。NRG-P0074在15.28%的ECOR分离株和所有9株Keio敲除菌株中表现出许可性。对比基因组分析显示,NRG-P0074与大肠杆菌噬菌体a20亲缘关系密切。它的基因组由270个编码序列、153个已知基因、16个终止子、3个核糖体结合位点、0个trna和117个假设的蛋白质组成。结论:本研究为开发预测噬菌体-宿主相互作用的机器学习模型提供了有价值的数据,有助于开发针对抗生素耐药细菌的靶向噬菌体疗法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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