Establishing hospital-specific background microbial libraries to reduce false positives in mNGS diagnosis of...

Due to the high sensitivity of metagenomic next-generation sequencing (mNGS), trace amounts of nucleic acid contamination can lead to false positives, posing challenges for result interpretation. This study is the first to experimentally identify and establish background microbial libraries (BML) related to periprosthetic joint infection (PJI) across different medical institutions, aiming to demonstrate the necessity of institution-specific BMLs to improve mNGS diagnostic accuracy.

Samples were taken from 3 different acetabular reamer for hip arthroplasty in 7 different hospitals. The whole process was strictly aseptic, mNGS was performed according to standard operating procedures. The sterility of instruments was confirmed by culture method. The sequencing results of specimens from different hospitals were compared to analyze the difference of background bacteria. Bioinformatics analysis and visualization were presented through R language.

A total of 26 samples (24 instrument swabs and 2 negative controls) generated 254 million reads, of which 1.13% matched microbial genomes. The proportion of microbial reads (1.13%) falls within ranges typically observed for contamination in low-biomass metagenomic sequencing studies. Among these, bacteria accounted for 87.48%, fungi 11.18%, parasites 1.26%, and viruses 0.06%. The most abundant bacterial genera included Cutibacterium, Staphylococcus, and Acinetobacter. Principal component analysis revealed distinct bacterial compositions among the seven hospitals, and clustering analysis showed significant inter-hospital variation (p < 0.05). Liaocheng People's Hospital exhibited the highest species richness (340 species), followed by Guanxian County People's Hospital (169 species).

The composition and abundance of residual bacterial DNA vary markedly among institutions, underscoring the necessity of establishing hospital-specific BMLs. Incorporating such libraries into clinical mNGS interpretation can effectively reduce false positives and enhance the diagnostic accuracy of PJI. arthroplasty, bacterial culture, next-generation sequencing, joint replacement, periprosthetic joint infection, background microbial libraries.