Differences in local immune cell landscape between Q fever and atherosclerotic abdominal aortic aneurysms identified by multiplex immunohistochemistry
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.
-
Vascular Q fever inflammationDryad Digital Repository, doi:10.5061/dryad.bzkh189b4.
Article and author information
Author details
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.
Reviewing Editor
- Christina L Stallings, Washington University School of Medicine, United States
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).
Version history
- Received: July 26, 2021
- Preprint posted: August 28, 2021 (view preprint)
- Accepted: February 3, 2022
- Accepted Manuscript published: February 9, 2022 (version 1)
- Version of Record published: February 24, 2022 (version 2)
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.
Metrics
-
- 515
- views
-
- 80
- downloads
-
- 2
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Medicine
Caesarean section scar diverticulum (CSD) is a significant cause of infertility among women who have previously had a Caesarean section, primarily due to persistent inflammatory exudation associated with this condition. Even though abnormal bacterial composition is identified as a critical factor leading to this chronic inflammation, clinical data suggest that a long-term cure is often unattainable with antibiotic treatment alone. In our study, we employed metagenomic analysis and mass spectrometry techniques to investigate the fungal composition in CSD and its interaction with bacteria. We discovered that local fungal abnormalities in CSD can disrupt the stability of the bacterial population and the entire microbial community by altering bacterial abundance via specific metabolites. For instance, Lachnellula suecica reduces the abundance of several Lactobacillus spp., such as Lactobacillus jensenii, by diminishing the production of metabolites like Goyaglycoside A and Janthitrem E. Concurrently, Clavispora lusitaniae and Ophiocordyceps australis can synergistically impact the abundance of Lactobacillus spp. by modulating metabolite abundance. Our findings underscore that abnormal fungal composition and activity are key drivers of local bacterial dysbiosis in CSD.
-
- Medicine
- Neuroscience
Gait is impaired in musculoskeletal conditions, such as knee arthropathy. Gait analysis is used in clinical practice to inform diagnosis and to monitor disease progression or intervention response. However, clinical gait analysis relies on subjective visual observation of walking, as objective gait analysis has not been possible within clinical settings due to the expensive equipment, large-scale facilities, and highly trained staff required. Relatively low-cost wearable digital insoles may offer a solution to these challenges. In this work, we demonstrate how a digital insole measuring osteoarthritis-specific gait signatures yields similar results to the clinical gait-lab standard. To achieve this, we constructed a machine learning model, trained on force plate data collected in participants with knee arthropathy and controls. This model was highly predictive of force plate data from a validation set (area under the receiver operating characteristics curve [auROC] = 0.86; area under the precision-recall curve [auPR] = 0.90) and of a separate, independent digital insole dataset containing control and knee osteoarthritis subjects (auROC = 0.83; auPR = 0.86). After showing that digital insole derived gait characteristics are comparable to traditional gait measurements, we next showed that a single stride of raw sensor time series data could be accurately assigned to each subject, highlighting that individuals using digital insoles can be identified by their gait characteristics. This work provides a framework for a promising alternative to traditional clinical gait analysis methods, adds to the growing body of knowledge regarding wearable technology analytical pipelines, and supports clinical development of at-home gait assessments, with the potential to improve the ease, frequency, and depth of patient monitoring.