Back

Towards Biohybrid Lung Development? Inflammatory Conditions Disrupt Endothelial Layer Integrity on Gas Exchange Membranes

Cheremkhina, M.; Babendreyer, A.; Neullens, C. T.; Krapp, S.; Pabst, A.; Ohl, K.; Tenbrock, K.; Ruetten, S.; Ludwig, A.; Cornelissen, C. G.; Thiebes, A. L.; Jockenhoevel, S.

2023-11-02 bioengineering
10.1101/2023.10.31.564901 bioRxiv
Show abstract

Systemic inflammation presents a significant challenge to the long-term function of biohybrid implants. While endothelialisation of biohybrid implants has been shown to improve device hemocompatibility, its feasibility under the influence of patients inflammatory status remains largely unexplored. To investigate this, we developed a controlled in vitro model which allows to study endothelial dysfunction under inflammatory stress. Endothelial cells were cultured on polydimethylsiloxane under physiological shear stress and exposed to lipopolysaccharide (LPS)-activated peripheral blood mononuclear cells (PBMCs), simulating inflammatory conditions. Endothelial morphology and confluence was assessed using immunohistochemistry and scanning electron microscopy. Leukocyte adhesion was evaluated directly as well as indirectly, using flow cytometry to analyse cell adhesion molecules. Quantitative PCR was used for gene expression analysis of inflammatory mediators. Notably, neither LPS nor PBMCs alone induced endothelial disruption, whereas their combination significantly impaired endothelial confluence: Inflammatory activation led to substantial loss of endothelial confluence, increased leukocyte adhesion, and elevated expression of adhesion molecules ICAM-1, VCAM-1, and E-selectin. Gene expression analysis highlights the upregulation of inflammatory mediators, such as IL-6, IL-8, IL-10, and MCP-1. This study underscores the challenges of implementing endothelialisation in biohybrid devices, particularly in patients with systemic inflammation. By considering translational hurdles, this work contributes to the development of clinically viable biohybrid constructs and highlights the importance of considering inflammatory dynamics when designing next-generation implants.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

1
Advanced Healthcare Materials
71 papers in training set
Top 0.2%
9.8%
2
Biomaterials Advances
20 papers in training set
Top 0.1%
8.1%
3
Biomaterials
78 papers in training set
Top 0.1%
6.6%
4
Advanced Materials Technologies
27 papers in training set
Top 0.1%
6.2%
5
Materials Today Bio
18 papers in training set
Top 0.1%
6.2%
6
ACS Biomaterials Science & Engineering
37 papers in training set
Top 0.1%
6.1%
7
Acta Biomaterialia
85 papers in training set
Top 0.2%
6.1%
8
Biomaterials Science
21 papers in training set
Top 0.1%
4.7%
50% of probability mass above
9
Biofabrication
32 papers in training set
Top 0.2%
4.7%
10
Advanced Functional Materials
41 papers in training set
Top 0.8%
3.5%
11
Advanced Materials
53 papers in training set
Top 0.8%
3.5%
12
Bioactive Materials
18 papers in training set
Top 0.2%
3.5%
13
ACS Applied Bio Materials
21 papers in training set
Top 0.1%
3.5%
14
Bioengineering & Translational Medicine
21 papers in training set
Top 0.2%
2.6%
15
ACS Applied Materials & Interfaces
39 papers in training set
Top 0.4%
1.8%
16
PLOS ONE
4510 papers in training set
Top 51%
1.8%
17
Lab on a Chip
88 papers in training set
Top 0.7%
1.6%
18
Annals of Biomedical Engineering
34 papers in training set
Top 0.7%
1.6%
19
Advanced Science
249 papers in training set
Top 12%
1.6%
20
Advanced Materials Interfaces
10 papers in training set
Top 0.1%
1.2%
21
Frontiers in Bioengineering and Biotechnology
88 papers in training set
Top 2%
1.1%
22
Scientific Reports
3102 papers in training set
Top 72%
0.9%
23
Journal of Neural Engineering
197 papers in training set
Top 2%
0.9%
24
Cytotherapy
14 papers in training set
Top 0.3%
0.8%
25
Small
70 papers in training set
Top 1%
0.7%
26
Nature Communications
4913 papers in training set
Top 64%
0.7%
27
International Journal of Molecular Sciences
453 papers in training set
Top 16%
0.7%
28
Journal of Advanced Research
15 papers in training set
Top 0.9%
0.7%
29
ACS Nano
99 papers in training set
Top 4%
0.6%