d78be4b2-a423-4b05-a435-37424b437034
Metagenomics Next Generation Sequencing (mNGS)-based Approaches for Microbiota Analysis in Biopsies of Chronic Wounds
Background: Chronic wounds (CWs) are wounds that do not heal within six weeks. In most developed countries, CWs affect 2% of the population, creating a severe economic burden. Microbial colonization is one of the main causes of CWs nonhealing. Therefore, timely identification of the microbial composition of CWs is essential to promote wound healing.  Methods: The aim of this study was to compare the results of conventional testing (CT) and metagenomics Next Generation Sequencing (mNGS). Tissue biopsy samples were obtained from 18 patients with CWs for analysis. We used CT and mNGS to identify the microbial composition and characteristics of CWs. Results: A total of 26 unique genera were detected using mNGS, and one of these was Anaerobic cocci; three of these were fungi and one was a virus. Seven unique genera were identified using CT. None of these were fungi or virus. The positive and negative consistencies for mNGS and CT were 90% and 25%, respectively. Overall, mNGS found more microbes per patient (mean, 2.83) than CT (mean, 0.83). Conclusions: CWs are often colonized by microorganisms that affect healing. mNGS is a robust and efficient diagnostic tool for detecting the microbial composition of CWs compared to CT. The high sensitivity and specificity of mNGS technology has the potential to identify pathogens that are difficult to culture and further guide clinical treatment.
Background: Chronic wounds (CWs) are wounds that do not heal within six weeks. In most developed countries, CWs affect 2% of the population, creating a severe economic burden. Microbial colonization is one of the main causes of CWs nonhealing. Therefore, timely identification of the microbial composition of CWs is essential to promote wound healing.  Methods: The aim of this study was to compare the results of conventional testing (CT) and metagenomics Next Generation Sequencing (mNGS). Tissue biopsy samples were obtained from 18 patients with CWs for analysis. We used CT and mNGS to identify the microbial composition and characteristics of CWs. Results: A total of 26 unique genera were detected using mNGS, and one of these was Anaerobic cocci; three of these were fungi and one was a virus. Seven unique genera were identified using CT. None of these were fungi or virus. The positive and negative consistencies for mNGS and CT were 90% and 25%, respectively. Overall, mNGS found more microbes per patient (mean, 2.83) than CT (mean, 0.83). Conclusions: CWs are often colonized by microorganisms that affect healing. mNGS is a robust and efficient diagnostic tool for detecting the microbial composition of CWs compared to CT. The high sensitivity and specificity of mNGS technology has the potential to identify pathogens that are difficult to culture and further guide clinical treatment.