Differences in local immune cell landscape between Q fever and atherosclerotic abdominal aortic aneurysms identified by multiplex immunohistochemistry
- Radboud Institute for Molecular Life Sciences, Netherlands
- Radboud University Medical Centre, Netherlands
- Jeroen Bosch Ziekenhuis, Netherlands
- Radboud University Medical Center, Netherlands
Abstract
Background: Chronic Q fever is a zoonosis caused by the bacterium Coxiella burnetii which can manifest as infection of an abdominal aortic aneurysm (AAA). Antibiotic therapy often fails, resulting in severe morbidity and high mortality. Whereas previous studies have focused on inflammatory processes in blood, the aim of this study was to investigate local inflammation in aortic tissue.
Methods: Multiplex immunohistochemistry was used to investigate local inflammation in Q fever AAAs compared to atherosclerotic AAAs in aorta tissue specimen. Two six-plex panels were used to study both the innate and adaptive immune system.
Results: Q fever AAAs and atherosclerotic AAAs contained similar numbers of CD68+ macrophages and CD3+ T cells. However, in Q fever AAAs the number of CD68+CD206+ M2 macrophages was increased, while expression of GM-CSF was decreased compared to atherosclerotic AAAs. Furthermore, Q fever AAAs showed an increase in both the number of CD8+ cytotoxic T cells and CD3+CD8-FoxP3+ regulatory T cells. Lastly, Q fever AAAs did not contain any well-defined granulomas.
Conclusions: These findings demonstrate that despite the presence of pro-i is associated with an immune suppressed micro environment.
Funding: This work was supported by SCAN consortium: European Research Area - CardioVascualar Diseases (ERA-CVD) grant [JTC2017-044] and TTW-NWO open technology grant [STW-14716].
Data availability
All data generated or analyzed during this study are included in the manuscript and uploaded to Dryad (http://dx.doi.org/10.5061/dryad.bzkh189b4).Figure 3 - Source data 3; Figure 5 - Source data 5; Figure 6 - Source figure 6; Figure 7 - Source figure 7 contain numerical data used to generate the figures.
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Vascular Q fever inflammationDryad Digital Repository, doi:10.5061/dryad.bzkh189b4.
Article and author information
Author details
Kimberley RG Cortenbach
Roland RJ Van Kimmenade
Funding
European Research Area - Cardiovascular Diseases (JTC2017-044)
- Kimberley RG Cortenbach
TTW-NWO Open Technology (STW-14716)
- Alexander HJ Staal
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: The medical ethics committees of the institutions approved the study, in line with the principlesoutlined in the Declaration of Helsinki (Radboudumc: 2017-3196; Jeroen Bosch Hospital:2019.05.02.01).
Copyright
© 2022, Cortenbach et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Differences in local immune cell landscape between Q fever and atherosclerotic abdominal aortic aneurysms identified by multiplex immunohistochemistry
eLife 11:e72486.
https://doi.org/10.7554/eLife.72486
Further reading
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Background:
Identification of individuals with prediabetes who are at high risk of developing diabetes allows for precise interventions. We aimed to determine the role of nuclear magnetic resonance (NMR)-based metabolomic signature in predicting the progression from prediabetes to diabetes.
Methods:
This prospective study included 13,489 participants with prediabetes who had metabolomic data from the UK Biobank. Circulating metabolites were quantified via NMR spectroscopy. Cox proportional hazard (CPH) models were performed to estimate the associations between metabolites and diabetes risk. Supporting vector machine, random forest, and extreme gradient boosting were used to select the optimal metabolite panel for prediction. CPH and random survival forest (RSF) models were utilized to validate the predictive ability of the metabolites.
Results:
During a median follow-up of 13.6 years, 2525 participants developed diabetes. After adjusting for covariates, 94 of 168 metabolites were associated with risk of progression to diabetes. A panel of nine metabolites, selected by all three machine-learning algorithms, was found to significantly improve diabetes risk prediction beyond conventional risk factors in the CPH model (area under the receiver-operating characteristic curve, 1 year: 0.823 for risk factors + metabolites vs 0.759 for risk factors, 5 years: 0.830 vs 0.798, 10 years: 0.801 vs 0.776, all p < 0.05). Similar results were observed from the RSF model. Categorization of participants according to the predicted value thresholds revealed distinct cumulative risk of diabetes.
Conclusions:
Our study lends support for use of the metabolite markers to help determine individuals with prediabetes who are at high risk of progressing to diabetes and inform targeted and efficient interventions.
Funding:
Shanghai Municipal Health Commission (2022XD017). Innovative Research Team of High-level Local Universities in Shanghai (SHSMU-ZDCX20212501). Shanghai Municipal Human Resources and Social Security Bureau (2020074). Clinical Research Plan of Shanghai Hospital Development Center (SHDC2020CR4006). Science and Technology Commission of Shanghai Municipality (22015810500).
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