Temporal Stability in Chronic Wound Microbiota Is Associated With Poor Healing

Abstract

Microbial burden of chronic wounds is believed to play an important role in impaired healing and the development of infection-related complications. However, clinical cultures have little predictive value of wound outcomes, and culture-independent studies have been limited by cross-sectional design and small cohort size. We systematically evaluated the temporal dynamics of the microbiota colonizing diabetic foot ulcers, a common and costly complication of diabetes, and its association with healing and clinical complications. Dirichlet multinomial mixture modeling, Markov chain analysis, and mixed-effect models were used to investigate shifts in the microbiota over time and their associations with healing. Here we show, to our knowledge, previously unreported temporal dynamics of the chronic wound microbiome. Microbiota community instability was associated with faster healing and improved outcomes. Diabetic foot ulcer microbiota were found to exist in one of four community types that experienced frequent and nonrandom transitions. Transition patterns and frequencies were associated with healing time. Exposure to systemic antibiotics destabilized the wound microbiota, rather than altering overall diversity or relative abundance of specific taxa. This study provides evidence that the dynamic wound microbiome is indicative of clinical outcomes and may be a valuable guide for personalized management and treatment of chronic wounds.

Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Figures

Figure 1. The DFU microbiome clusters into four Community Types

(A) DFU samples partitioned into four clusters by Dirichlet multinomial mixture model. Mean relative abundances of bacterial taxa in DFU samples assigned to each Community Type. Relative abundance is shown on the Y-axis. Taxa are filtered to those with a mean abundance greater than 1%. (B) Sample similarity between DFU microbial communities were calculated using the Bray-Curtis distance and these distances were ordinated and visualized via non-metric multidimensional scaling (NMDS). Each taxonomic contribution to community differentiation is overlaid with black text and “x” indicating the exact location. The impacts of various metadata are depicted as vectors labeled with gray text. Success of NMDS ordination is represented by the stress score, which measures the agreement between the 2-D and multidimensional representations. Stress scores range from 0 to 1 and scores below 0.3 are considered good approximations. Samples, taxa, and metadata that are closer together are more related. Samples are color-coded based on community type.

Figure 2. DFU Community Types are dynamic

(A) Per patient illustration of Community Type switching grouped by outcome. Depicted on the X-axis is visit number. Each row on the Y-axis represents a subject with a DFU. Colored boxes illustrate which Community Type was colonizing the DFU at the indicated visit number. Empty tiles represent a missed visit, whereas gray tiles indicate that a sample was not collected or available for analysis at that time point. The black diamonds indicate that the patient received antibiotics since the last visit. Only subjects that participated in >1 study visit are shown. (B) Markov chain visualization depicting the differential transition probabilities between community types of DFUs that healed in 12 weeks or did not. Each node represents a Community Type, arrows indicate the transition direction and probability (thickness), node size represents number of samples. Annotated are the self-transition probabilities.

Figure 3

Inter-visit Weighted UniFrac distances associations with healing time for subjects that healed during the study. X axes represent the study visit; study visits were 2 weeks apart. (A) Inter-visit distances are shown for each subject and depict a negative trend over time. Line and point colors represent the number of study visits that the ulcer persisted (red = 1, green = 8). Ulcers stabilize at a rate of −0.024/visit, but start at a lower rate in those ulcers that require more time to heal (−0.036 per visit required to heal). (B) Inter-visit distances between baseline and first study visit as a function of number of visits until healing. A negative correlation is found even within this initial comparison (R2 = 0.1601, p<0.0001).

Figure 4

Effects of antibiotics on microbial communities in DFUs. (A) Boxplot showing the inter-visit Weighted UniFrac distances of subjects during exposure to antibiotics split by indication. Antibiotics given for the ulcer being studied produces greater community disruption than antibiotics given for other ulcers or other infections. Antibiotic class did not yield more information. (B) Boxplot showing the inter-visit distances of all samples binned by event type (complication, antibiotics, both, or none). Antibiotics and ulcer complications both disrupt the microbiota, and their combined effect is additive.