Microbial Community Distribution and Core Microbiome in Successive Wound Grades of Individuals with Diabetic Foot Ulcers

Abstract

Diabetic foot ulcer (DFU) is a major complication of diabetes with high morbidity and mortality rates. The pathogenesis of DFUs is governed by a complex milieu of environmental and host factors. The empirical treatment is initially based on wound severity since culturing and profiling the antibiotic sensitivity of wound-associated microbes is time-consuming. Hence, a thorough and rapid analysis of the microbial landscape is a major requirement toward devising evidence-based interventions. Toward this, 122 wound (100 diabetic and 22 nondiabetic) samples were sampled for their bacterial community structure using both culture-based and next-generation 16S rRNA-based metagenomics approach. Both the approaches showed that the Gram-negative microbes were more abundant in the wound microbiome. The core microbiome consisted of bacterial genera, including Alcaligenes, Pseudomonas, Burkholderia, and Corynebacterium in decreasing order of average relative abundance. Despite the heterogenous nature and extensive sharing of microbes, an inherent community structure was apparent, as revealed by a cluster analysis based on Euclidean distances. Facultative anaerobes (26.5%) were predominant in Wagner grade 5, while strict anaerobes were abundant in Wagner grade 1 (26%). A nonmetric dimensional scaling analysis could not clearly discriminate samples based on HbA1c levels. Sequencing approach revealed the presence of major culturable species even in samples with no bacterial growth in culture-based approach. Our study indicates that (i) the composition of core microbial community varies with wound severity, (ii) polymicrobial species distribution is individual specific, and (iii) antibiotic susceptibility varies with individuals. Our study suggests the need to evolve better-personalized care for better wound management therapies.IMPORTANCE Chronic nonhealing diabetic foot ulcers (DFUs) are a serious complication of diabetes and are further exacerbated by bacterial colonization. The microbial burden in the wound of each individual displays diverse morphological and physiological characteristics with unique patterns of host-pathogen interactions, antibiotic resistance, and virulence. Treatment involves empirical decisions until definitive results on the causative wound pathogens and their antibiotic susceptibility profiles are available. Hence, there is a need for rapid and accurate detection of these polymicrobial communities for effective wound management. Deciphering microbial communities will aid clinicians to tailor their treatment specifically to the microbes prevalent in the DFU at the time of assessment. This may reduce DFUs associated morbidity and mortality while impeding the rise of multidrug-resistant microbes.

Keywords: 16S metagenomics; antibiotic resistance; diabetic foot ulcer; wound microbiome.

FIG 1

Microbial diversity and distribution across different cohorts. (A) Microbial diversity across the two cohorts based on phylum. Vertical bars represent relative abundances for different microbial phyla from binned OTUs in each sample. Only phyla with a relative abundance of >1% in more than one sample have been plotted. (B) Number of OTUs based on bacterial genera shared across the two cohorts; diabetic wounds (DW) and nondiabetic wounds (NDW). (C) Bacterial OTUs shared across the five different wound grades (WG1 to WG5) based on clinical severity of the wound. Genera with an abundance of >1% in at least one sample were considered. (D) Distribution of OTUs based on all genera across the five WGs stratified by oxygen requirement. (E) Average relative abundance (ARA) of bacteria in wound microbiome of diabetic and nondiabetic ulcer samples based on gender (only genera with a >1% average relative abundance in >50% of the samples in the respective DW and NDW cohorts were plotted).

FIG 2

Core microbiome of DW and NDW cohorts. (A) Dominant microbes among diabetic wound samples. (B) Dominant microbes among nondiabetic wound samples. The average relative abundance of each bacterial OTU (at genus level) was plotted against number of samples in which the OTU was present. Only genera with >1% ARA and present in >5 samples were plotted. The scatter plot was divided into four quadrants at the midpoints of the maximum and minimum value of each axis. Q1 represents genera with a high average relative abundance and high occurrence, Q2 represents genera with a low average relative abundance and high occurrence, Q3 represents genera with a high average relative abundance but low occurrence, and Q4 represents genera with a low average relative abundance and low occurrence.

FIG 3

Representation of the core microbiome members of each Wagner grade as a circular chord diagram (A) and bar plots (B) comparing the average relative abundance of the bacterial genera across each Wagner grade with significant differences (two-way ANOVA with Tukey post hoc test; P < 0.05 [*], P < 0.01 [**], and P < 0.001 [***]). Bacterial OTUs at the genus level with an average relative abundance of >1% in >50% of the total samples per Wagner grade were included for plotting the graph, and all other genera were included as “others.” The top right quadrant in the outer track represents Wagner grades; bacterial genera are represented in the inner track, and the scale is shown in the respective tracks. The scales for bacterial genera represent their contribution in each Wagner grade while the scales in Wagner grade represent the contribution by each genus toward the overall abundance. The width of the connecting chords indicates the abundance, while the shade represents the Wagner grade.

FIG 4

Variation of the diabetic wound microbiome based on Wagner grade. (A) Alpha diversity metrics were calculated for diabetic wound samples stratified by Wagner grade (WG1 to WG5). (B) NMDS with normalized data were performed for the samples, and the results are stratified based on Wagner grade.

FIG 5

Clustering of DW based on Euclidean distances of normalized abundances of OTUs. (A) Euclidean distances of 100 DW samples were subjected to clustering by partitioning around medoids (PAM). Each point represents one diabetic wound sample. The choice of centroids/clusters (k) was decided based on average silhouette score. The first two principal components, explaining 56.9% of the point variability, with clustering of k = 3 medoids is shown. The three Euclidean clusters (EUCs) and samples within these clusters are marked with different colors and symbols, respectively. The silhouette score for the above clusters was 0.34. (B) Alpha diversity metrics calculated for diabetic wound samples stratified by their representative Euclidean clusters. (C) Average relative abundance values for the four genera that varied significantly (P < 0.0001, two-way ANOVA) across these clusters are plotted.

FIG 6

Microbial distribution in diabetic wound samples based on HbA1c values. Bacterial OTUs were taxonomically classified to the phylum level. Samples were classified into good (<7.5% HbA1c) and poor (≥7.5% HbA1c) glucose control. (A) Average relative abundance of each OTU categorized by its phylum. (B) Alpha diversity indices for the two cohorts. (C) NMDS results with normalized data for samples and stratified based on glucose control.

FIG 7

Microbiome diversity in culture negative DW samples based on NGS data. A principal-component analysis (PCA) graph developed from normalized data was constructed using NGS-based identification, and culture-negative samples (based on culture results, n = 22) were identified. A PCA biplot identified Alcaligenes as the major contributor to the clustering.