Dysregulated functional and metabolic response in multiple sclerosis patient macrophages correlate with a more inflammatory state, reminiscent of trained immunity
Fransson, J.; Bachelin, C.; Deknuydt, F.; Ichou, F.; Guillot-Noel, L.; Ponnaiah, M.; Gloaguen, A.; Maillart, E.; Stankoff, B.; Tenenhaus, A.; Mochel, F.; Fontaine, B.; Louapre, C.; Zujovic, V.
Show abstract
In multiple sclerosis (MS), immune cells invade the central nervous system and destroy myelin. Macrophages contribute to demyelination and myelin repair, and their role in each process depends on their ability to acquire specific phenotypes in response to external signals. Here, we assess whether defects in MS patient macrophage responses may lead to increased inflammation or lack of neuro-regenerative effects. To test this hypothesis, CD14+CD16- monocytes from MS patients and healthy controls were activated in vitro to obtain homeostatic-like, pro-inflammatory and pro-regenerative macrophages. Myelin phagocytic capacity and surface molecule expression of CD14, CD16 and HLA-DR were evaluated with flow cytometry. In parallel, macrophages were assessed through RNA sequencing and metabolomics. We observed that MS patient monocytes ex vivo recapitulate their preferential activation toward a CD16+ phenotype, a subset of pro-inflammatory cells present in MS lesions. Even in the absence of pro-inflammatory stimuli, MS patient macrophages exhibit a pro-inflammatory transcriptomic profile with higher levels of cytokine/chemokine suggesting increased recruitment capacities. Interestingly, MS patient macrophages exhibit a specific metabolic signature with a mitochondrial energy metabolism blockage resulting in a shift from oxidative phosphorylation to glycolysis. Furthermore, we observe a failure to up-regulate apoptosis effector genes in the pro inflammatory state suggesting a longer-lived pro-inflammatory macrophage population. Our results highlight an intrinsic defect of MS patient macrophages that provide evidence of innate immune cell memory in MS.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Peripheral Myeloid-Derived Suppressor Cells are good biomarkers of the efficacy of Fingolimod in Multiple Sclerosis 96%
- Senolytic treatment depletes microglia and decreases severity of experimental autoimmune encephalomyelitis. 96%
- RGS10 Attenuates Systemic Immune Dysregulation Induced by Chronic Inflammatory Stress 94%
Similar papers in this journal
- A New Serological Autoantibody Signature Associated with Multiple Sclerosis 96%
- A Deep Transcriptome Meta-Analysis Reveals Sex-based Molecular Differences in Multiple Sclerosis 96%
- Circulating Myeloid-Derived Suppressor Cell load and disease severity are associated to an enhanced oligodendroglial production in a murine model of multiple sclerosis 95%
Similar papers in this journal
- Persons with multiple sclerosis reveal distinct kynurenine pathway metabolite patterns: a multinational cross-sectional study 95%
- CSF of SARS-CoV-2 patients with neurological syndromes reveals hints to understand pathophysiology 95%
- Dynamics of spinal fluid immune cell alterations following cladribine tablet treatment in multiple sclerosis 94%
Similar papers in this journal
- Whole exome sequencing in multi-incident families identifies novel candidate genes for multiple sclerosis 95%
- Gene expression and alternative splicing analysis in a large-scale Multiple Sclerosis study 95%
- Dynamics of central remyelination and treatment evolution in a model of Multiple Sclerosis with Optic Coherence Tomography 94%
Similar papers in this journal
- Profiling of microglia nodules in multiple sclerosis reveals propensity for lesion formation 96%
- A comparative transcriptomic analysis of mouse demyelination models and Multiple Sclerosis lesions 95%
- Translocator protein is a marker of activated microglia in rodent models but not human neurodegenerative diseases 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.