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

Abstract

Open fractures are characterized by disruption of the skin and soft tissue, which allows for microbial contamination and colonization. Preventing infection-related complications of open fractures and other acute wounds remains an evolving challenge due to an incomplete understanding of how microbial colonization and contamination influence healing and outcomes. Culture-independent molecular methods are now widely used to study human-associated microbial communities without introducing culture biases. Using such approaches, the objectives of this study were to (1) define the long-term temporal microbial community dynamics of open fracture wounds and (2) examine microbial community dynamics with respect to clinical and demographic factors. Fifty-two subjects with traumatic open fracture wounds (32 blunt and 20 penetrating injuries) were enrolled prospectively and sampled longitudinally from presentation to the emergency department (ED) and at each subsequent inpatient or outpatient encounter. Specimens were collected from both the wound center and adjacent skin. Culture-independent sequencing of the 16S ribosomal RNA gene was employed to identify and characterize microbiota. Upon presentation to the ED and time points immediately following, sample collection site (wound or adjacent skin) was the most defining feature discriminating microbial profiles. Microbial composition of adjacent skin and wound center converged over time. Mechanism of injury most strongly defined the microbiota after initial convergence. Further analysis controlling for race, gender, and age revealed that mechanism of injury remained a significant discriminating feature throughout the continuum of care. We conclude that the microbial communities associated with open fracture wounds are dynamic in nature until eventual convergence with the adjacent skin community during healing, with mechanism of injury as an important feature affecting both diversity and composition of the microbiota. A more complete understanding of the factors influencing microbial contamination and/or colonization in open fractures is a critical foundation for identifying markers indicative of outcome and deciphering their respective contributions to healing and/or complication.

Figure 1. Microbial communities of open fractures and adjacent skin at presentation to the emergency department

(A) PCoA of the weighted UniFrac inter-sample distances. Each point represents a single patient specimen at the ED time point; one wound sample and one adjacent skin sample from each patient are displayed where data is available. Distance between points (specimens) is indicative of similarity/dissimilarity to other points (specimens). Color indicates sample type (green=skin; blue=wound center). Shown are the first 2 principle coordinates (PC1 and PC2), and percent variance explained by each coordinate is indicated in parentheses by the axis. (B) Comparison of median microbial diversity as measured by Faith’s Phylogenetic diversity metric (y-axis) in wound center and skin specimens (x-axis). A higher metric value indicates higher microbial diversity. Upper and lower box hinges correspond to first and third quartiles, and the distance between these quartiles is defined as the interquartile range (IQR). Lines within the box depict median, and whiskers extend to the highest and lowest values within 1.5 times the IQR. Outliers of the IQR are depicted as dots above or below the whiskers. ***p < 0.005. (C) Average relative abundance/proportion (y-axis) of genus-level taxa in open fracture wounds and adjacent skin (x-axis). Each color represents a different genus-level taxa (identified in the corresponding legend) and its average proportion in the overall sample type indicated. Significant increases are indicated as **q < 0.01; *q < 0.05.

Figure 2. Microbial communities of open fractures differs by mechanism of injury at presentation to the ED

(A) PCoA of the weighted UniFrac inter-sample distances. Each point represents a single patient specimen at the ED time point; one wound sample and one adjacent skin sample from each patient are displayed where data is available. Color indicates mechanism of injury (purple=penetrating; orange=blunt) and shape indicates the sample type (circle=wound center; triangle=adjacent skin). Shown are the first 2 principle coordinates, and percent variance explained by each coordinate is indicated in parentheses by the axis. (B) Alpha diversity, as measured by Faith’s Phylogenetic Diversity index (Y-axis). Blunt and penetrating injuries are depicted on the left and right panels, respectively. Boxplots were generated according to the methods outlined in Figure 1. **p<0.01. (C) Average relative abundance of bacterial genera (y-axis) by sampling site and mechanism of injury. Significant differences in adjacent skin microbiota according to mechanism of injury are indicated by black asterisks. Significant differences between adjacent skin and wound center samples within each injury category are indicated by white asterisks. *q<0.05; **q<0.01

Figure 3. Microbial communities diverge based on mechanism of injury

A PCoA of the weighted UniFrac distances between all samples collected at office follow up visits. Points are colored by mechanism of injury, and circles represent the wound center while triangles represent the adjacent skin. The centroids represent the average coordinates of samples falling within 4 categories (Blunt Wound, Blunt Adjacent Skin, Penetrating Wound, and Penetrating Adjacent Skin) and can be seen as larger points along with error bars displaying the variance of each grouping. Each centroid contains two error bars, representing the variances along both principal coordinate axes of each group of samples.

Figure 4. Microbial community convergence of the wound center and adjacent skin over time

(A) Scatterplot of weighted UniFrac distances between the wound center and adjacent skin microbial communities (Y-axis) over time (X-axis). Purple and orange dots indicate distances between each patient’s skin-wound pair for penetrating and blunt injuries, respectively. Similarly, purple and orange lines represent a Loess curve fit through the points of penetrating and blunt injuries, respectively. Shading represents 95% confidence intervals of the Loess curves. By 100 days following hospital admission for the injury, wound microbiota in penetrating wounds, became more similar in composition to adjacent skin microbiota. The average distance at presentation to the ED was 0.403, indicated as a dashed horizontal line. (B and C) Relative abundance of microbiota colonizing wound center (“C”) and adjacent skin (“S”) in (B) Subject 29 who suffered a blunt injury, and (C) Subject 43 who suffered a penetrating injury. X-axis depicts sequential time points at which microbiota was sampled. (D) Convergence between the adjacent skin and wound microbiota for subjects 29 and 43. Weighted UniFrac distances between skin and wound samples were calculated (y-axis) and plotted over time (x-axis) for each subject. Subject 29 (blunt injury) is depicted in orange; Subject 43 (penetrating injury) is depicted in purple.