An in vivo critically colonised wound model with dysbiotic wound microbiota

Abstract

In critically colonised wounds, many of the signs of infection are often absent, and delayed healing may be the only clinical sign. The prevention of critical colonisation is important, but its pathophysiology has not yet been elucidated. We have previously reported that dysbiotic microbiota dissimilar to the peri-wound skin microbiota may develop in critically colonised wounds. To investigate the role of dysbiotic microbiota, this study aimed to develop a critically colonised wound model by transplantation of dysbiotic microbiota. To transplant microbiota, a bacterial solution (dysbiosis group) or with Luria-Bertani medium (commensal group) was inoculated to full-thickness wounds of rats. The bacterial solution was prepared by anaerobically culturing bacteria from donor rats on an artificial dermis in Luria-Bertani medium for 72 hours. As a result, the degree of the change in the microbial similarity between pre- and post-transplantation of microbiota was significantly higher in the dysbiosis group (P < .001). No signs of infection were observed in any rat in either group. The wound area in the dysbiosis group was significantly larger (P < .001), and there was a significant infiltration of neutrophils (P < .001). All rats of the dysbiosis group represented the clinical features of critically colonised wounds. Furthermore, there were significantly fewer regulatory T cells in the wounds of the dysbiosis group. This is the first study to develop a novel animal model that represents the clinical features of critically colonised wounds and will be useful in investigating the pathogenesis of critical colonisation via regulatory T cells.

Keywords: 16S ribosomal RNA; chronic wounds; hard-to-heal wounds; microbiome; wound infection.

FIGURE 1

Experimental protocols and animal groups. LB medium, Luria‐Bertani medium; PWD, post‐wounding day

FIGURE 2

Reproducibility of dysbiotic microbiota in the bacterial solution. (A) Bacterial count was estimated from the copy number of tuf gene. The error bar represents the SE. (B) Microbial composition was shown using the relative abundance of bacteria classified at the species level (top 40). (C) Alpha diversity was evaluated using the Phylogenetic Diversity index. The error bar represents the SE. (D) Beta diversity was evaluated using the weighted UniFrac dissimilarity index

FIGURE 3

Composition and diversity of the microbiota. Swab samples collected from skin and wounds were used for the 16S rRNA gene analysis to identify the microbiota. (A) One case from each of the commensal and dysbiosis groups was presented. The bar graph shows the composition of the microbiota at each site. The line graph shows the temporal change of microbial similarity between the skin sample collected on post‐wounding day 1 and the wound sample at each time point. Black triangles indicated the time point of microbiota transplantation. (B) The difference in the values of the weighted UniFrac dissimilarity index between the post‐wounding day 1 and 2 values (before and after microbiota transplantation) was calculated. S, skin samples collected on PWD1; W, wound samples

FIGURE 4

Alpha diversity of wound and peri‐wound skin microbiota. (A) The wound appearance was recorded daily. The scale bar indicates 1 cm in the photograph. (B) The erythema index was quantified, based on the digital images taken on post‐wounding day 4. Results are expressed as the mean ± SE. (C) Temporal change in relative wound area adjusted by the value of post‐wounding day 1 was shown. Results are expressed as mean ± SE. PWD, post‐wounding day

FIGURE 5

Haematoxylin and eosin staining of the wound tissue. Pathophysiological analysis was performed using haematoxylin and eosin staining of paraffin sections prepared from post‐wounding day 4 wound tissue. (A,C) Lower magnification images of the commensal and dysbiosis groups. Regions enclosed in the dotted line are at higher magnification (B,D). (f,g,n) The fibrin membrane, granulation tissue, and necrotic tissue, respectively. Magnification: ×4.0. Scale bar = 300 μm. (B,D) Higher magnification images of the granulation tissue. Magnification: ×20. Scale bar = 50 μm

FIGURE 6

Evaluation of wound inflammation based on the infiltration of neutrophils. Wound tissue was stained with anti‐myeloperoxidase heavy chain antibody to confirm infiltration of neutrophils. The images of granulation tissue in the commensal group (A) and dysbiosis group (B) were shown. Region enclosed in solid box in lower magnification (×4.0, Scale bar = 300 μm) is at higher magnification images (×20, Scale bar = 50 μm). (C) Data show the number of rats assigned to each class. *Wilcoxon rank sum test. (D,E) The bacterial count of the wound surface and wound tissue was determined based on the copy number of tuf gene. Each error bar represents the SE

FIGURE 7

Localization of FOXP3 in tissues and expression level for Treg‐related genes. Wound tissue was stained with an anti‐FOXP3 antibody to confirm the localization of regulatory T cells. The images of granulation tissue in the commensal group (A) and dysbiosis group (B) were shown. Region enclosed in solid box in lower magnification (×4.0, Scale bar = 300 μm) is at higher magnification images (×20, Scale bar = 50 μm). Black arrows: regulatory T cells. (C) The expression level was quantified by a real‐time reverse transcription‐polymerase chain reaction. Each error bar represents the SE. FOXP3, Forkhead box P3.