Pressure ulcers microbiota dynamics and wound evolution

Catherine Dunyach-Remy, Florian Salipante, Jean-Philippe Lavigne, Maxime Brunaud, Christophe Demattei, Alex Yahiaoui-Martinez, Sophie Bastide, Claire Palayer, Albert Sotto, Anthony Gélis, Catherine Dunyach-Remy, Florian Salipante, Jean-Philippe Lavigne, Maxime Brunaud, Christophe Demattei, Alex Yahiaoui-Martinez, Sophie Bastide, Claire Palayer, Albert Sotto, Anthony Gélis

Abstract

Bacterial species and their role in delaying the healing of pressure ulcers (PU) in spinal cord injury (SCI) patients have not been well described. This pilot study aimed to characterise the evolution of the cutaneous microbiota of PU in SCI cohort. Twenty-four patients with SCI from a French neurological rehabilitation centre were prospectively included. PU tissue biopsies were performed at baseline (D0) and 28 days (D28) and analysed using 16S rRNA gene-based sequencing analysis of the V3-V4 region. At D0, if the overall relative abundance of genus highlighted a large proportion of Staphylococcus, Anaerococcus and Finegoldia had a significantly higher relative abundance in wounds that stagnated or worsened in comparison with those improved at D28 (3.74% vs 0.05%; p = 0.015 and 11.02% versus 0.16%; p = 0.023, respectively). At D28, Proteus and Morganella genera were only present in stagnated or worsened wounds with respectively 0.02% (p = 0.003) and 0.01% (p = 0.02). Moreover, Proteus, Morganella, Anaerococcus and Peptoniphilus were associated within the same cluster, co-isolated from biopsies that had a poor evolution. This pathogroup could be a marker of wound degradation and Proteus could represent a promising target in PU management.

Conflict of interest statement

The authors declare no competing interests.

© 2021. The Author(s).

Figures

Figure 1
Figure 1
Description of bacterial communities isolated from wounds at D0. (a) Barplot of overall repartition of phyla in wounds at D0 for total population (n = 24). (b) Barplot of average relative frequencies of genera. Only genera > 1% of OTUs in at least one wound and in at least one time (D0 or/and D28) were represented. The remaining genera are added to the group “other”. (c) Boxplot of genus relative frequencies gathered in respiratory phenotype. (d) Histogram of OTU’s number by wounds at D0.
Figure 2
Figure 2
Evolution of bacterial microbiota isolated from wounds over time. (a) Boxplot of relative frequencies differences (D28-D0) of phyla (the group “Other” represents unclassified bacteria at phylum level). (b) Boxplot of relative frequencies differences (D28-D0) of bacteria genera. Only genera > 1% of OTUs in at least one wound and in at least one time (D0 or/and D28) and belonging to Firmicutes are represented.
Figure 3
Figure 3
Heatmap of the PU microbiota’s standardised composition at D0 ranked according to wound evolution. Standardised relative frequencies of genera are used in order to see variations between groups even for low-abundant bacteria. Genera are classified according to agglomerative hierarchical clustering with complete linkage.
Figure 4
Figure 4
Heatmap of the PU microbiota’s standardised composition at D28 ranked according to wound evolution. Standardised relative frequencies of genus are used to see variations between groups even for low-abundant bacteria. Genera are classified according to agglomerative hierarchical clustering with complete linkage.
Figure 5
Figure 5
Principal Component Analyses (PCA) of the most discriminant genus. The principal component analysis is based on standardised data for a selection of genera with the best ability to separate wounds according to their evolution (“Improved” “Stagnated” and “Worsened”). The criterion for the genus selection is an Area Under the ROC Curve greater than 0.65. The two first components are shown, representing 80% of total information.

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