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Background <p>Pulmonary infections, ranging from mild respiratory issues to severe multiorgan failure, pose a major global health threat. The immune response in community-acquired pneumonia (CAP) and COVID-19 influences disease severity and outcomes, but molecular pathogenesis differs across pathogens. Comparisons of plasma cytokine profiles between CAP and COVID-19 are limited. Analyzing these profiles with machine learning and bioinformatics could reveal subtle patterns and improve our understanding of immune responses in both conditions.</p> Methods <p>We conducted a novel case–control study to profile cytokine levels in patients with CAP and COVID-19. Age- and sex-matched cohorts included 39 patients with CAP, 39 with COVID-19, and 20 healthy controls. We measured 384 plasma cytokine levels using proximity extension assays and analyzed differences between cohorts with conventional statistical methods, bioinformatics and machine learning.</p> Results <p>Median ages of the cohorts were comparable (<i>P</i> = 0.797). COVID-19 patients exhibited a higher prevalence of hematologic disease (<i>P</i> = 0.047), increased corticosteroid use (<i>P</i> = 0.040), and reduced antibiotic use (<i>P</i> = 0.012). Clinical outcomes, including mortality, ICU admission, invasive mechanical ventilation, renal replacement therapy, acute respiratory distress syndrome, and acute kidney injury, were similar between groups. Both cohorts showed comparable absolute circulating cytokine profiles but distinct profiles relative to healthy controls. Machine learning identified a model of twelve cytokines that distinguished CAP from COVID-19 with a classification accuracy of 0.71 (SD 0.20). Gene ontology and enrichment analysis revealed differences in cytosolic and nuclear functions, intracellular signaling, stress responses, and cell cycle processes between patient cohorts and healthy controls. Enriched GO pathways showed that CAP pathways were positively associated with leukocyte counts and ARDS development, while COVID-19 pathways were negatively associated with ARDS and positively with platelet counts.</p> Conclusions <p>This case–control study provides insights into cytokine profiles related to CAP and COVID-19 pathogenesis. Although absolute circulating cytokine levels showed no significant differences between the groups, machine learning identified a model of twelve proteins that effectively distinguished the cohorts. Gene ontology and enrichment analyses also revealed distinct dysregulated pathways with differing associations with clinical variables in each cohort. These findings underscore the complexity and variability of cytokine responses in pulmonary infections.</p>

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Divergent biological pathways distinguish community-acquired pneumonia from COVID-19 despite similar plasma cytokine profiles

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月31日

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  • Douglas D. Fraser,
  • Logan R. Van Nynatten,
  • David Tweddell,
  • Mark Daley,
  • James A. Russell,
  • Karen C. Tran,
  • Pierre Asfar,
  • Matthew P. Cheng,
  • Julien Demiselle,
  • Joel Singer,
  • Terry Lee,
  • David Sweet,
  • John H. Boyd,
  • Keith R. Walley,
  • Greg Haljan,
  • Guillaume Geri,
  • Johann Auchabie,
  • Jean-Pierre Quenot,
  • Todd C. Lee,
  • Jennifer Tsang,
  • Ferhat Meziani,
  • Francois Lamontagne,
  • Vincent Dubee,
  • Sigismond Lasocki,
  • Daniel Ovakim,
  • Gordon Wood,
  • Alexis F. Turgeon,
  • Yves Cohen,
  • Eddy Lebas,
  • Marine Goudelin,
  • David Forrest,
  • Alastair Teale,
  • Jean-Paul Mira,
  • Robert Fowler,
  • Nick Daneman,
  • Neil K. J. Adhikari,
  • Marie Gousseff,
  • Pierre Leroy,
  • Gaetan Plantefeve,
  • Patrick Rispal,
  • Roxanne Courtois,
  • Brent W. Winston,
  • Steve Reynolds,
  • Peter Birks,
  • Boris Bienvenue,
  • Jean-Marc Tadie,
  • Jean-Phillipe Talarmin,
  • Severine Ansart,
  • Anusha Karna,
  • Julia Kong

摘要

Background

Pulmonary infections, ranging from mild respiratory issues to severe multiorgan failure, pose a major global health threat. The immune response in community-acquired pneumonia (CAP) and COVID-19 influences disease severity and outcomes, but molecular pathogenesis differs across pathogens. Comparisons of plasma cytokine profiles between CAP and COVID-19 are limited. Analyzing these profiles with machine learning and bioinformatics could reveal subtle patterns and improve our understanding of immune responses in both conditions.

Methods

We conducted a novel case–control study to profile cytokine levels in patients with CAP and COVID-19. Age- and sex-matched cohorts included 39 patients with CAP, 39 with COVID-19, and 20 healthy controls. We measured 384 plasma cytokine levels using proximity extension assays and analyzed differences between cohorts with conventional statistical methods, bioinformatics and machine learning.

Results

Median ages of the cohorts were comparable (P = 0.797). COVID-19 patients exhibited a higher prevalence of hematologic disease (P = 0.047), increased corticosteroid use (P = 0.040), and reduced antibiotic use (P = 0.012). Clinical outcomes, including mortality, ICU admission, invasive mechanical ventilation, renal replacement therapy, acute respiratory distress syndrome, and acute kidney injury, were similar between groups. Both cohorts showed comparable absolute circulating cytokine profiles but distinct profiles relative to healthy controls. Machine learning identified a model of twelve cytokines that distinguished CAP from COVID-19 with a classification accuracy of 0.71 (SD 0.20). Gene ontology and enrichment analysis revealed differences in cytosolic and nuclear functions, intracellular signaling, stress responses, and cell cycle processes between patient cohorts and healthy controls. Enriched GO pathways showed that CAP pathways were positively associated with leukocyte counts and ARDS development, while COVID-19 pathways were negatively associated with ARDS and positively with platelet counts.

Conclusions

This case–control study provides insights into cytokine profiles related to CAP and COVID-19 pathogenesis. Although absolute circulating cytokine levels showed no significant differences between the groups, machine learning identified a model of twelve proteins that effectively distinguished the cohorts. Gene ontology and enrichment analyses also revealed distinct dysregulated pathways with differing associations with clinical variables in each cohort. These findings underscore the complexity and variability of cytokine responses in pulmonary infections.