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. 2014 Jul;52(7):2583-94.
doi: 10.1128/JCM.00556-14. Epub 2014 May 14.

Microbial Profiling of Combat Wound Infection Through Detection Microarray and Next-Generation Sequencing

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Free PMC article

Microbial Profiling of Combat Wound Infection Through Detection Microarray and Next-Generation Sequencing

Nicholas A Be et al. J Clin Microbiol. .
Free PMC article

Abstract

Combat wound healing and resolution are highly affected by the resident microbial flora. We therefore sought to achieve comprehensive detection of microbial populations in wounds using novel genomic technologies and bioinformatics analyses. We employed a microarray capable of detecting all sequenced pathogens for interrogation of 124 wound samples from extremity injuries in combat-injured U.S. service members. A subset of samples was also processed via next-generation sequencing and metagenomic analysis. Array analysis detected microbial targets in 51% of all wound samples, with Acinetobacter baumannii being the most frequently detected species. Multiple Pseudomonas species were also detected in tissue biopsy specimens. Detection of the Acinetobacter plasmid pRAY correlated significantly with wound failure, while detection of enteric-associated bacteria was associated significantly with successful healing. Whole-genome sequencing revealed broad microbial biodiversity between samples. The total wound bioburden did not associate significantly with wound outcome, although temporal shifts were observed over the course of treatment. Given that standard microbiological methods do not detect the full range of microbes in each wound, these data emphasize the importance of supplementation with molecular techniques for thorough characterization of wound-associated microbes. Future application of genomic protocols for assessing microbial content could allow application of specialized care through early and rapid identification and management of critical patterns in wound bioburden.

Figures

FIG 1
FIG 1
Microbial species detected by LLMDA in combat wound tissue and effluent samples. Nucleic acid was extracted from combat wound samples and hybridized to a microbial detection microarray. Tissue biopsy and effluent samples were analyzed independently. The number of positive detection events for each microbial target is shown. For samples in which a non-species-specific microbial target was detected (e.g., Acinetobacter plasmid) and no other species-specific target was observed in that sample, the sample was classified according to genus (e.g., Acinetobacter sp.).
FIG 2
FIG 2
Clustering of samples from healed and failed combat wounds according to microbial species detected by microarray. Wound samples were ordered by hierarchical clustering. Samples were clustered according to their detected microbial profile, as determined by microarray detection. Individual patient samples are shown in columns and are labeled along the bottom horizontal axis. Patient samples are labeled according to the following scheme: patient number-wound number-extraction method (e.g., 9-1-WA). Sample extraction types are detailed in Materials and Methods. Detected microbial species are shown in rows and are labeled along the right vertical axis. As in the previous figure, when a non-species-specific microbial target was detected and no other species-specific target was observed in that sample, the sample was classified according to genus. Positive microbial detection is shown in light blue, and negative microbial detection is shown in dark blue. Wound outcome is indicated in a horizontal bar above the plot. Samples obtained from healed wounds are indicated in green, and samples from failed wounds are in red.
FIG 3
FIG 3
Quantity of next-generation sequence data mapped to microbial species by LMAT. Nucleic acid extracted from wound samples was processed via next-generation sequencing. Resultant reads were aligned to bacterial reference genomes using the Livermore Metagenomics Analysis Toolkit (LMAT). The abundance of reads mapped to microbial reference genomes is shown for each sample. Samples are shown along the horizontal axis, listed in numerical order by patient. The patient and wound from which each sample was derived are shown, along with sample extraction type (detailed in Materials and Methods). In some cases, multiple wounds from the same patient were analyzed. Metrics shown below the axis for each sample include wound clinical outcome (H, healed; D, dehiscence), LLMDA microbial detection status, and microbial culture status.
FIG 4
FIG 4
Microbial profiles in combat wound samples as determined by LMAT analysis of next-generation sequence data. Sequence data from wound samples were analyzed to determine the total quantity of reads mapping to each microbial species identified as present by LMAT. Microbial abundance within each sample, as measured by mapped reads, was used to order samples within a heat map using NMDS ordination by Phyloseq. Individual wound samples are given in columns. Labels below the horizontal axis show patient number, wound number, and sample extraction type (detailed in Materials and Methods). In some cases, more than one wound from the same patient was analyzed. Wound outcome is also indicated (H, healed; D, dehiscence). Microbial species are represented in rows and are shown along the vertical axis. (A) Heat map showing all microbial species detected by LMAT analysis. Individual species are not labeled due to the high number of total targets. (B) Heat map showing only those microbial species to which 1,000 or more total reads were assigned across all samples. (C) Heat map showing only those samples derived from patient 26, with samples ordered according to temporal collection point. Only those microbial species to which 1,000 or more total reads were mapped across all species are shown.

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