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Biofilm in group A streptococcal necrotizing soft tissue infections
Nikolai Siemens, … , Mattias Svensson, Anna Norrby-Teglund
Nikolai Siemens, … , Mattias Svensson, Anna Norrby-Teglund
Published July 7, 2016
Citation Information: JCI Insight. 2016;1(10):e87882. https://doi.org/10.1172/jci.insight.87882.
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Research Article Infectious disease

Biofilm in group A streptococcal necrotizing soft tissue infections

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Abstract

Necrotizing fasciitis caused by group A streptococcus (GAS) is a life-threatening, rapidly progressing infection. At present, biofilm is not recognized as a potential problem in GAS necrotizing soft tissue infections (NSTI), as it is typically linked to chronic infections or associated with foreign devices. Here, we present a case of a previously healthy male presenting with NSTI caused by GAS. The infection persisted over 24 days, and the surgeon documented the presence of a “thick layer biofilm” in the fascia. Subsequent analysis of NSTI patient tissue biopsies prospectively included in a multicenter study revealed multiple areas of biofilm in 32% of the patients studied. Biopsies associated with biofilm formation were characterized by massive bacterial load, a pronounced inflammatory response, and clinical signs of more severe tissue involvement. In vitro infections of a human skin tissue model with GAS NSTI isolates also revealed multilayered fibrous biofilm structures, which were found to be under the control of the global Nra gene regulator. The finding of GAS biofilm formation in NSTIs emphasizes the urgent need for biofilm to be considered as a potential complicating microbiological feature of GAS NSTI and, consequently, emphasizes reconsideration of antibiotic treatment protocols.

Authors

Nikolai Siemens, Bhavya Chakrakodi, Srikanth Mairpady Shambat, Marina Morgan, Helena Bergsten, Ole Hyldegaard, Steinar Skrede, Per Arnell, Martin B. Madsen, Linda Johansson, INFECT Study Group, Julius Juarez, Lidija Bosnjak, Matthias Mörgelin, Mattias Svensson, Anna Norrby-Teglund

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Figure 1

Group A streptococcal (GAS) infections in 3D skin tissue result in biofilm formation.

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Group A streptococcal (GAS) infections in 3D skin tissue result in biofi...
(A) Quantitative analyses of biofilm formation of clinical GAS necrotizing soft tissue infection isolates on uncoated or fibronectin-coated polystyrene surfaces at indicated time points. The box and whisker plots show median values with min to max distribution (n = 4). (B) Immunofluorescence images of 5626 strain forming biofilm (wheat germ agglutinin [WGA], DAPI, Nile red positive) on uncoated (left panel) and fibronectin-coated (right panel) glass surfaces (original magnification, ×60). Individual stainings are shown in Supplemental Figure 1A. Representative images from 1 of 4 experiments are shown. (C) Representative immunofluorescence images of GAS 800 infected tissue models (original magnification, ×40). GAS is shown in green, and bacterial aggregations are indicated by arrows. Mean fluorescent intensity (MFI) of stained tissue (D), and CFU counts of bacteria recovered from skin models (E). The data represent the mean values ± SD (n ≥ 3). (F) Blinded scoring of tissue pathology of the skin model after infection. Histological severity scoring was performed in a blinded fashion using the following criteria: 0, unaffected; 0.5–1, mild injury with minor epithelial loosening; 1.5–2, moderate injury with some epithelial disruption; 2.5–3, severe injury with continuous epithelial disruption and some detachment; and > 4, extensive injury, massive epithelial disruption, and detachment. Each symbol represents one independent experiment. Horizontal lines denote median values (n = 4). (G) 3D reconstruction of immunostained biofilm in 8157 infected model (original magnification, ×60). GAS-specific antibody, WGA, and Nile red were used. Arrows indicate multilayered bacterial aggregations. (H) SEM images of bacterial biofilm in the skin tissue model 48 hours after infection with indicated clinical isolates (original magnification, ×10,000). The level of significance was determined using 1-way ANOVA with Dunnett’s multiple comparison test.

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