Diabetic foot infections (DFIs) represent a frequent complication of diabetes and a major cause of amputations. This study aimed to evaluate the utility of 16S rRNA gene sequencing for the rapid microbiological diagnosis of DFIs and to consistently characterize the microbiome of chronic diabetic foot ulcers (DFUs) and intact skin. Wound samples were collected by ulcer swabbing and tissue biopsy, and paired swabs of intact skin were collected from 10 patients with DFIs (five were moderately infected, and the other five were severely infected). Samples were analyzed by conventional culture and using Personal Genome Machine (PGM) 16S rRNA sequencing technology. The results showed that PGM technology detected significantly more bacterial genera (66.1 vs. 1.5 per wound sample, < 0.001); more obligate anaerobes (52.5 vs. 0%, < 0.001) and more polymicrobial infections (100.0 vs. 55.0%, < 0.01) than conventional cultures. There was no statistically significant difference in bacterial richness, diversity or composition between the wound swabs and tissues ( > 0.05). The bacterial community on intact skin was significantly more diverse than that in DFUs (Chao1 value, < 0.05; Shannon index value, < 0.001). Gram-positive bacteria (67.6%) and aerobes (59.2%) were predominant in contralateral intact skin, while Gram-negative bacteria (63.3%) and obligate anaerobes (50.6%) were the most ubiquitous in DFUs. The most differentially abundant taxon in skin was , while was the bacterial taxon most representative of DFUs. Moreover, (ρ = 0.80, < 0.01) and (ρ = 0.78, < 0.01) were significantly correlated with the duration of DFIs. In conclusion, PGM 16S rRNA sequencing technology could be a potentially useful technique for the rapid microbiological diagnosis of DFIs. Wound swabbing may be sufficient for sampling bacterial pathogens in DFIs compared with biopsy which is an invasive technique. The empirical use of broad-spectrum antibiotics covering Gram-negative obligate anaerobes should be considered for the treatment of moderate or severe DFIs.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9582933PMC
http://dx.doi.org/10.3389/fmicb.2022.1021955DOI Listing

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