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Background <p><i>Staphylococcus aureus</i> bacteremia (SAB) is a prevalent life-threatening infection often caused by methicillin-resistant <i>S. aureus</i> (MRSA). Up to 30% of SAB patients fail to clear infection even with gold-standard anti-MRSA antibiotics. This phenomenon is termed antibiotic-persistent MRSA bacteremia (APMB). The mechanisms driving APMB are complex and involve host phenotypes significantly impacting the immune response. Thus, defining early immune signatures and clinical phenotypes that differentiate APMB from antibiotic resolving (AR)MB could aid therapeutic success.</p> Methods <p>We assessed 38 circulating cytokines and chemokines using affinity proteomics in 74 matched pairs of vancomycin-treated SAB cases identified as ARMB or APMB after 5 days of blood culture.</p> Results <p>Unsupervised hierarchical clustering segregated APMB from ARMB based on differential levels of IL-10, IL-12p40, IL-13, CCL4, and TGFα. Additionally, CXCL1, CCL22 and IL-17A significantly differed between APMB and ARMB when correlated with diabetes, dialysis, metastatic infection, or cardiac vegetation. Combining immune signatures with these relevant clinical phenotypes sharply increased accuracy of discriminating APMB outcome to 79.1% via logistic regression modeling. Finally, classification-regression tree analysis revealed explicit analyte thresholds associated with APMB outcome at presentation especially in patients with metastatic infection.</p> Conclusions <p>Collectively, this study identifies previously unrecognized cytokine and chemokine signatures that distinguish APMB and ARMB at presentation and in the context of host clinical characteristics associated with increased disease severity. Validation of a biomarker signature that accurately predicts outcomes could guide early therapeutic strategies and interventions to reduce risks of persistent SAB that are associated with worsened morbidity and mortality.</p>

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Early cytokine signatures and clinical phenotypes discriminate persistent from resolving MRSA bacteremia

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月18日

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  • Kristina V. Bergersen,
  • Ying Zheng,
  • Maura Rossetti,
  • Felicia Ruffin,
  • Harry Pickering,
  • Rajesh Parmar,
  • Gemalene Sunga,
  • Liana C. Chan,
  • David Gjertson,
  • Vance G. Fowler,
  • Michael R. Yeaman,
  • Elaine F. Reed,
  • Alexander Hoffmann,
  • Felix Medie,
  • Batu Sharma,
  • Joshua Thaden

摘要

Background

Staphylococcus aureus bacteremia (SAB) is a prevalent life-threatening infection often caused by methicillin-resistant S. aureus (MRSA). Up to 30% of SAB patients fail to clear infection even with gold-standard anti-MRSA antibiotics. This phenomenon is termed antibiotic-persistent MRSA bacteremia (APMB). The mechanisms driving APMB are complex and involve host phenotypes significantly impacting the immune response. Thus, defining early immune signatures and clinical phenotypes that differentiate APMB from antibiotic resolving (AR)MB could aid therapeutic success.

Methods

We assessed 38 circulating cytokines and chemokines using affinity proteomics in 74 matched pairs of vancomycin-treated SAB cases identified as ARMB or APMB after 5 days of blood culture.

Results

Unsupervised hierarchical clustering segregated APMB from ARMB based on differential levels of IL-10, IL-12p40, IL-13, CCL4, and TGFα. Additionally, CXCL1, CCL22 and IL-17A significantly differed between APMB and ARMB when correlated with diabetes, dialysis, metastatic infection, or cardiac vegetation. Combining immune signatures with these relevant clinical phenotypes sharply increased accuracy of discriminating APMB outcome to 79.1% via logistic regression modeling. Finally, classification-regression tree analysis revealed explicit analyte thresholds associated with APMB outcome at presentation especially in patients with metastatic infection.

Conclusions

Collectively, this study identifies previously unrecognized cytokine and chemokine signatures that distinguish APMB and ARMB at presentation and in the context of host clinical characteristics associated with increased disease severity. Validation of a biomarker signature that accurately predicts outcomes could guide early therapeutic strategies and interventions to reduce risks of persistent SAB that are associated with worsened morbidity and mortality.