PDF(Pubmed)
Abstract：
BACKGROUND: Non-surgical chronic wounds, including diabetes-related foot diseases (DRFD), pressure injuries (PIs) and venous leg ulcers (VLU), are common hard-to-heal wounds. Wound evolution partly depends on microbial colonisation or infection, which is often confused by clinicians, thereby hampering proper management. Current routine microbiology investigation of these wounds is based on in vitro culture, focusing only on a limited panel of the most frequently isolated bacteria, leaving a large part of the wound microbiome undocumented.
METHODS: A literature search was conducted on original studies published through October 2022 reporting metagenomic next generation sequencing (mNGS) of chronic wound samples. Studies were eligible for inclusion if they applied 16 S rRNA metagenomics or shotgun metagenomics for microbiome analysis or diagnosis. Case reports, prospective, or retrospective studies were included. However, review articles, animal studies, in vitro model optimisation, benchmarking, treatment optimisation studies, and non-clinical studies were excluded. Articles were identified in PubMed, Google Scholar, Web of Science, Microsoft Academic, Crossref and Semantic Scholar databases.
RESULTS: Of the 3,202 articles found in the initial search, 2,336 articles were removed after deduplication and 834 articles following title and abstract screening. A further 14 were removed after full text reading, with 18 articles finally included. Data were provided for 3,628 patients, including 1,535 DRFDs, 956 VLUs, and 791 PIs, with 164 microbial genera and 116 species identified using mNGS approaches. A high microbial diversity was observed depending on the geographical location and wound evolution. Clinically infected wounds were the most diverse, possibly due to a widespread colonisation by pathogenic bacteria from body and environmental microbiota. mNGS data identified the presence of virus (EBV) and fungi (Candida and Aspergillus species), as well as Staphylococcus and Pseudomonas bacteriophages.
CONCLUSIONS: This study highlighted the benefit of mNGS for time-effective pathogen genome detection. Despite the majority of the included studies investigating only 16 S rDNA, ignoring a part of viral, fungal and parasite colonisation, mNGS detected a large number of bacteria through the included studies. Such technology could be implemented in routine microbiology for hard-to-heal wound microbiota investigation and post-treatment wound colonisation surveillance.
METHODS: A literature search was conducted on original studies published through October 2022 reporting metagenomic next generation sequencing (mNGS) of chronic wound samples. Studies were eligible for inclusion if they applied 16 S rRNA metagenomics or shotgun metagenomics for microbiome analysis or diagnosis. Case reports, prospective, or retrospective studies were included. However, review articles, animal studies, in vitro model optimisation, benchmarking, treatment optimisation studies, and non-clinical studies were excluded. Articles were identified in PubMed, Google Scholar, Web of Science, Microsoft Academic, Crossref and Semantic Scholar databases.
RESULTS: Of the 3,202 articles found in the initial search, 2,336 articles were removed after deduplication and 834 articles following title and abstract screening. A further 14 were removed after full text reading, with 18 articles finally included. Data were provided for 3,628 patients, including 1,535 DRFDs, 956 VLUs, and 791 PIs, with 164 microbial genera and 116 species identified using mNGS approaches. A high microbial diversity was observed depending on the geographical location and wound evolution. Clinically infected wounds were the most diverse, possibly due to a widespread colonisation by pathogenic bacteria from body and environmental microbiota. mNGS data identified the presence of virus (EBV) and fungi (Candida and Aspergillus species), as well as Staphylococcus and Pseudomonas bacteriophages.
CONCLUSIONS: This study highlighted the benefit of mNGS for time-effective pathogen genome detection. Despite the majority of the included studies investigating only 16 S rDNA, ignoring a part of viral, fungal and parasite colonisation, mNGS detected a large number of bacteria through the included studies. Such technology could be implemented in routine microbiology for hard-to-heal wound microbiota investigation and post-treatment wound colonisation surveillance.
摘要：
背景：非手术性慢性伤口，包括糖尿病相关的足部疾病（DRFD），压力性损伤（PI）和静脉性腿部溃疡（VLU），是常见的难以愈合的伤口。伤口演变部分取决于微生物定植或感染，这经常被临床医生混淆，从而阻碍了适当的管理。目前对这些伤口的常规微生物学研究是基于体外培养，只关注有限的一组最常分离的细菌，留下伤口微生物组的很大一部分没有记录。
方法：对2022年10月发表的报告慢性伤口样本的宏基因组下一代测序（mNGS）的原始研究进行了文献检索。如果研究将16SrRNA宏基因组学或鸟枪宏基因组学应用于微生物组分析或诊断，则研究有资格纳入。病例报告,prospective,或纳入回顾性研究.然而,评论文章,动物研究,体外模型优化，基准测试,治疗优化研究，非临床研究被排除.文章在PubMed中确定，谷歌学者，WebofScience,MicrosoftAcademic,Crossref和语义学者数据库。
结果：在最初搜索中找到的3,202篇文章中，重复数据删除后删除了2,336篇文章，标题和摘要筛选后删除了834篇文章。全文阅读后又删除了14个，最后有18篇文章。提供了3,628名患者的数据，包括1,535个DRFD，956个VLU，和791个PI，使用MNGS方法鉴定了164个微生物属和116个物种。根据地理位置和伤口演变，观察到高度的微生物多样性。临床感染的伤口最多样化，可能是由于来自身体和环境微生物群的病原菌广泛定植。mNGS数据确定了病毒（EBV）和真菌（念珠菌和曲霉属）的存在，以及葡萄球菌和假单胞菌噬菌体。
结论：这项研究强调了mNGS在时间有效的病原体基因组检测中的益处。尽管大多数纳入的研究只调查了16SrDNA，忽略病毒的一部分，真菌和寄生虫定植，通过纳入的研究，mNGS检测到大量细菌。这种技术可以在常规微生物学中实施，用于难以愈合的伤口微生物群调查和治疗后的伤口定植监测。
方法：对2022年10月发表的报告慢性伤口样本的宏基因组下一代测序（mNGS）的原始研究进行了文献检索。如果研究将16SrRNA宏基因组学或鸟枪宏基因组学应用于微生物组分析或诊断，则研究有资格纳入。病例报告,prospective,或纳入回顾性研究.然而,评论文章,动物研究,体外模型优化，基准测试,治疗优化研究，非临床研究被排除.文章在PubMed中确定，谷歌学者，WebofScience,MicrosoftAcademic,Crossref和语义学者数据库。
结果：在最初搜索中找到的3,202篇文章中，重复数据删除后删除了2,336篇文章，标题和摘要筛选后删除了834篇文章。全文阅读后又删除了14个，最后有18篇文章。提供了3,628名患者的数据，包括1,535个DRFD，956个VLU，和791个PI，使用MNGS方法鉴定了164个微生物属和116个物种。根据地理位置和伤口演变，观察到高度的微生物多样性。临床感染的伤口最多样化，可能是由于来自身体和环境微生物群的病原菌广泛定植。mNGS数据确定了病毒（EBV）和真菌（念珠菌和曲霉属）的存在，以及葡萄球菌和假单胞菌噬菌体。
结论：这项研究强调了mNGS在时间有效的病原体基因组检测中的益处。尽管大多数纳入的研究只调查了16SrDNA，忽略病毒的一部分，真菌和寄生虫定植，通过纳入的研究，mNGS检测到大量细菌。这种技术可以在常规微生物学中实施，用于难以愈合的伤口微生物群调查和治疗后的伤口定植监测。
