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Cited by 156Wound Healing and TreatmentsDiabetic Foot Ulcer Assessment and ManagementMicrobial Community Ecology and Physiology

The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.

A Comparison of Bacterial Composition in Diabetic Ulcers and Contralateral Intact Skin

Open access
Gontcharova, Viktoria, Youn, Eunseog, Sun, Yan, Wolcott, Randall D, Dowd, Scot E·2010·The Open Microbiology Journal
DOI 10.2174/1874285801004010008PMC PMC2866239
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Abstract

An extensive portion of the healthcare budget is allocated to chronic human infection. Chronic wounds in particular are a major contributor to this financial burden. Little is known about the types of bacteria which may contribute to the chronicity, biofilm and overall bioburden of the wound itself. In this study we compare the bacteriology of wounds and associated intact skin. Wound and paired intact skin swabs (from a contralateral location) were collected. The bacterial diversity was determined using bacterial Tag-encoded FLX amplicon pyrosequencing (bTEFAP). Diversity analysis showed intact skin to be significantly more diverse than wounds on both the species and genus levels (3% and 5% divergence). Furthermore, wounds show heightened levels of anaerobic bacteria, like Peptoniphilus , Finegoldia , and Anaerococcus , and other detrimental genera such as Corynebacterium and Staphylococcus . Although some of these and other bacterial genera were found to be common between intact skin and wounds, notable opportunistic wound pathogens were found at lower levels in intact skin. Principal Component Analysis demonstrated a clear separability of the two groups. The findings of the study not only greatly support the hypothesis of differing bacterial composition of intact skin and wounds, but also contribute additional insight into the ecology of skin and wound microflora. The increased diversity and lowered levels of opportunistic pathogens found in skin make the system highly distinguishable from wounds.

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Crossref · 10.2174/1874285801004010008

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Figures

Fig. (1) PCA for intact skin and wound data. The figure display the three main principal components to which the data was reduced to. The axes represent the values for principal components 1, 2 and 3. Points lying in the negative portion of an axis indicate a negative correlation between the principal component and the sample. The two groups are denoted by different colors to demonstrate the separation between classes (intact skin vs. wounds). The ability to linearly separate the classes within the PCA figures indicates intact skin samples are different from wound samples.
Fig. (1) PCA for intact skin and wound data. The figure display the three main principal components to which the data was reduced to. The axes represent the values for principal components 1, 2 and 3. Points lying in the negative portion of an axis indicate a negative correlation between the principal component and the sample. The two groups are denoted by different colors to demonstrate the separation between classes (intact skin vs. wounds). The ability to linearly separate the classes within the PCA figures indicates intact skin samples are different from wound samples.CC BY-NC · PubMed Central
Fig. (2) Hierarchical clustering of healthy skin and wound data. This figure provides further support for the separability between the two classes of samples, healthy skin and wounds. The figure also indicates low correlation between healthy skin and wound samples for the same individual, indicating lack of contamination or other factors possibly affecting bacterial similarity.
Fig. (2) Hierarchical clustering of healthy skin and wound data. This figure provides further support for the separability between the two classes of samples, healthy skin and wounds. The figure also indicates low correlation between healthy skin and wound samples for the same individual, indicating lack of contamination or other factors possibly affecting bacterial similarity.CC BY-NC · PubMed Central
Fig. (3) Correlation Distances for paired intact skin against wound samples. Pearson Correlation distances, ranging from 0 to 2 were normalized to a scale of 0 to 1 where 1 represents the furthest distance, or the least similar samples.
Fig. (3) Correlation Distances for paired intact skin against wound samples. Pearson Correlation distances, ranging from 0 to 2 were normalized to a scale of 0 to 1 where 1 represents the furthest distance, or the least similar samples.CC BY-NC · PubMed Central
Fig. (4) Correlation Distances for all against all samples. Pearson Correlation distances, ranging from 0 to 2 were normalized to a scale of 0 to 1 where 1 represents the furthest distance, or the least similar samples.
Fig. (4) Correlation Distances for all against all samples. Pearson Correlation distances, ranging from 0 to 2 were normalized to a scale of 0 to 1 where 1 represents the furthest distance, or the least similar samples.CC BY-NC · PubMed Central

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Research works printed but not linked: 1

  1. Theodoridis S, Koutroumbas K. Pattern Recognition. 2nd. San Diego, Ca: Academic Press 2008.

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Metadata

Authors
Gontcharova, V., Youn, E., Sun, Y., Wolcott, R. D., & Dowd, S. E.
Affiliations
  • Texas Tech University (US)
  • Southwest Regional Wound Care Center (US)
Corresponding author
Gontcharova, Viktoria
ORCID
Venue
The Open Microbiology Journal
Publisher
Bentham Science Publishers Ltd.
Published
2010-03-17
Volume
4
Issue
1
Pages
8-19
ISSN
1874-2858 (ISSN-L)
DOI
10.2174/1874285801004010008
PMC
PMC2866239
OpenAlex
W2096279745
Cited by
156
References
57
Citations by year
2026: 6 · 2025: 5 · 2024: 5 · 2023: 10 · 2022: 11 · 2021: 7 · 2020: 14 · 2019: 4 · 2018: 9 · 2017: 14 · 2016: 11 · 2015: 11 · 2014: 9 · 2013: 11 · 2012: 12
FWCI
12.98
Type
journal article
Language
en
Open access
Licence
Locations
  • The Open Microbiology Journal (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Surgery › Wound Healing and Treatments
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetic Foot Ulcer Assessment and Management
  • Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
Keywords
diabetic ulcers, chronic wounds, skin microbiome, bacterial diversity, anaerobic bacteria, Peptoniphilus, Finegoldia, Anaerococcus, Corynebacterium, Staphylococcus, opportunistic pathogens, wound biofilm, microbial load

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