Temporal dynamics of relative abundances and bacterial succession in chronic wound communities

Abstract

Polymicrobial bacterial infection is an important factor contributing to wound chronicity. Consequently, clinicians frequently adopt a biofilm-based wound care approach, in which wounds are treated utilizing DNA sequencing information about microbial communities. While more successful than treatment not using community information, there is little information about temporal dynamics of wound communities and optimal approaches over the course of treatment. To characterize these dynamics, temporal analysis over three sampling points was conducted for 167 chronic wounds. Across sampling intervals, wound communities from the same patients changed in composition, and most commonly shared less than 50% of observed species. There was a significant relationship between community similarity and time between sampling. Classifying wounds into state types, we found that communities frequently transitioned from Pseudomonas or Staphylococcus dominated, into a highly variable state type. Although low abundance microbial species are typically disregarded due to uncertainty of biological importance, we found that 80% of wound microbiomes included common or dominant species at subsequent time points that were in low abundance in earlier samples. Moreover, these species were often those known to frequently infect wounds. Results document compositional shifts through the course of treatment and suggest that routine consideration of low abundance species may improve biofilm-based wound care. Moreover, findings indicate that integrating ecological modeling to understand wound microbiome succession may lead to more informed therapy.

The skin microbiome of wound scars and unaffected skin in patients with moderate to severe burns in the subacute phase

“…Among the top four out of the 16 microbial phyla discovered in samples associated with burn patients, no substantial differences in the relative abundance were found between the index (wound) and control sites (all p > 0.05, Figure A). According to the cutoff values proposed by Tipton et al in classifying microbial abundance, two phyla dominated (> 10%) the sample sequence reads in both patient groups: proteobacteria and firmicutes (Figure B). Yet, proteobacteria was significantly more abundant in control patients' samples (median: 82.6% vs. 58.8%) whereas firmicutes were more frequently observed in burn swabs (14.9% vs. 25.6%).…”

The microbiota of traumatic, open fracture wounds is associated with mechanism of injury

“…The nature of open fractures and the inherent continuum of care requiring patient follow‐up enable longitudinal study designs. In other types of wounds, longitudinal profiling revealed temporally instable microbial communities, suggesting a major limitation of cross‐sectional study designs . Understanding the temporal dynamics of all components of the acute wound environment, including the microbiota, will improve our ability to effectively manage and treat them.…”

The role of the microbiome in nonhealing diabetic wounds

“…That is, the these wounds became stalled in these community types, which suggests that the more stable a wound microbiome becomes, the more likely that the wound will remain unhealed. 52,103 This pioneering work pro-vides further evidence to support the hypothesis that establishment of a biofilm is the tipping point between normal and stalled healing. Mature microbial biofilms are stable and can persist over long periods due to their recalcitrance toward perturbations, including antibiotic exposure.…”

“…This has allowed researchers to apply community ecology principles to measure the stability of wound microbiomes over time and in response to perturbations and physiological markers. The effects of antibiotic use, mechanical debridement, blood glucose levels, or wound deterioration events on the microbiome can be monitored . By shifting the focus from taxonomic profiling, in an attempt to identify causative agents, toward mathematical modeling of diversity and community dynamics within a single wound niche, it has been discovered that community stability is the best marker for poor healing.…”

“…The effects of antibiotic use, mechanical debridement, blood glucose levels, or wound deterioration events on the microbiome can be monitored. 52,86,103 By shifting the focus from taxonomic profiling, in an attempt to identify causative agents, toward mathematical modeling of diversity and community dynamics within a single wound niche, it has been discovered that community stability is the best marker for poor healing. This was discovered by applying a Dirichlet multinomial mixture model approach to assign community types to DFU microbiota.…”