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. 2021 Feb 25;17(2):e1009369.
doi: 10.1371/journal.ppat.1009369. eCollection 2021 Feb.

Bacterial virulence plays a crucial role in MRSA sepsis

Affiliations

Bacterial virulence plays a crucial role in MRSA sepsis

Gordon Y C Cheung et al. PLoS Pathog. .

Abstract

Bacterial sepsis is a major global cause of death. However, the pathophysiology of sepsis has remained poorly understood. In industrialized nations, Staphylococcus aureus represents the pathogen most commonly associated with mortality due to sepsis. Because of the alarming spread of antibiotic resistance, anti-virulence strategies are often proposed to treat staphylococcal sepsis. However, we do not yet completely understand if and how bacterial virulence contributes to sepsis, which is vital for a thorough assessment of such strategies. We here examined the role of virulence and quorum-sensing regulation in mouse and rabbit models of sepsis caused by methicillin-resistant S. aureus (MRSA). We determined that leukopenia was a predictor of disease outcome during an early critical stage of sepsis. Furthermore, in device-associated infection as the most frequent type of staphylococcal blood infection, quorum-sensing deficiency resulted in significantly higher mortality. Our findings give important guidance regarding anti-virulence drug development strategies for the treatment of staphylococcal sepsis. Moreover, they considerably add to our understanding of how bacterial sepsis develops by revealing a critical early stage of infection during which the battle between bacteria and leukocytes determines sepsis outcome. While sepsis has traditionally been attributed mainly to host factors, our study highlights a key role of the invading pathogen and its virulence mechanisms.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Mouse sepsis model.
(A) Scheme of experimental setup and survival experiment. Survival of female C57BL/6NCrl mice following intravenous challenge with ~2–3 × 108 CFU of S. aureus LAC or its isogenic agr deletion strain (n = 10/group) was recorded. Animals were monitored for survival up to 52 hours. Statistical analysis is by log-rank (Mantel-Cox) test. (B) Survival experiment under cyclophosphamide (CY) treatment. Scheme of experimental setup and survival data. Survival of female C57BL/6NCrl mice following intravenous challenge with 106 CFU of S. aureus LAC or its isogenic Δagr deletion strain (n = 10/group) was recorded. Statistical analysis is by log-rank (Mantel-Cox) test. Mouse picture is from openclipart.org.
Fig 2
Fig 2. Rabbit sepsis models–experimental setup and survival.
(A) Experimental setup of catheter- and non-catheter-associated model. The models were set up essentially in the same fashion, except that in the catheter-associated model, a silastic CVC was inserted 7 days before injection of 108 CFU of S. aureus LAC or its isogenic Δagr deletion strain and additional 16- and 20- h samples (dashed circles) were only taken in the catheter-associated model. (B) Survival curve in the non-catheter associated (n = 9/group) and (C) of the catheter-associated model (n = 12/group). The non-catheter-associated model was performed two independent times (n = 4/group and n = 5/group, respectively; total, n = 9 animal/group). The catheter-associated model was performed two independent times with n = 6 animals/group each (total, n = 12 animals/group). Data obtained with the two batches were combined in both models. Statistical analysis is by the indicated tests. Rabbit picture is from openclipart.org.
Fig 3
Fig 3. Rabbit non-catheter-associated sepsis model–CFU in blood and organs.
(A) Bacterial CFU in the blood over time. (B) Bacterial CFU at the time of death or the end of the experiment in the indicated organs. Empty symbols represent animals that died during the observation period.
Fig 4
Fig 4. Rabbit catheter-associated sepsis model–CFU in blood, organs, and on catheters.
(A) Bacterial CFU in the blood over time. (B) Bacterial CFU at the time of death or the end of the experiment in the indicated organs. (C) Bacterial CFU in the lumina of implanted catheters. Data are from the second batch of rabbits only (n = 6/group). The dotted line depicts the threshold above which CFU values were considered to reflect biofilm formation in both batches of rabbits. (A-C) Rabbits that developed catheter biofilms are marked by open symbols and light-colored borders.
Fig 5
Fig 5. Rabbit sepsis models–leukocyte numbers.
(A-D) WBC and neutrophil numbers over time in the non-catheter-associated (A,B) and catheter-associated (C,D) model. (C,D) Rabbits that developed catheter biofilms are marked by open symbols and light-colored borders. (E-H) Analysis of death versus survival outcome based on the averages of leukocyte numbers of every animal in the time window 6–24 h (grey shading) in panels A-D. (I-L) Analysis of impact of Agr status on the averages of WBC and neutrophil numbers in the time window 6–24 h (grey shading) in panels A through D. (E-L) Statistical analysis is by unpaired two-tailed t-tests. Error bars show the mean ± SD. NS, not significant (p≥0.05). See S2 Fig for monocyte and lymphocyte analyses.
Fig 6
Fig 6. Rabbit sepsis model–cytokine levels.
(A-C) Fold-changes of the indicated cytokines over time in the non-catheter-associated model. (D-F) Analysis of death versus survival outcome based on average cytokine fold-changes in the time window 6–24 h (grey shading) in panels A-C. (G-I) Fold-changes of the indicated cytokines over time in the catheter-associated model. (J-L) Analysis of death versus survival outcome based on average cytokine fold-changes in the time window 6–24 h (grey shading) in panels G-I. (D-F, J-L) Statistical analysis is by unpaired two-tailed t-tests. Error bars show the geometric mean and geometric SD. NS, not significant (p≥0.05). See S3 Fig for the corresponding analysis of Agr status for both rabbit models.

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References

    1. Rudd KE, Johnson SC, Agesa KM, Shackelford KA, Tsoi D, Kievlan DR, et al.. Global, regional, and national sepsis incidence and mortality, 1990–2017: analysis for the Global Burden of Disease Study. Lancet. 2020;395(10219):200–11. 10.1016/S0140-6736(19)32989-7 - DOI - PMC - PubMed
    1. Liu V, Escobar GJ, Greene JD, Soule J, Whippy A, Angus DC, et al.. Hospital deaths in patients with sepsis from 2 independent cohorts. JAMA. 2014;312(1):90–2. 10.1001/jama.2014.5804 - DOI - PubMed
    1. Torio CM, Moore BJ. National Inpatient Hospital Costs: The Most Expensive Conditions by Payer, 2013: Statistical Brief #204. Healthcare Cost and Utilization Project (HCUP) Statistical Briefs. Rockville (MD)2006. - PubMed
    1. Cohen J. The immunopathogenesis of sepsis. Nature. 2002;420(6917):885–91. 10.1038/nature01326 - DOI - PubMed
    1. Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al.. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016;315(8):801–10. 10.1001/jama.2016.0287 - DOI - PMC - PubMed

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