Metabolic Reprogramming and Macrophage Expansion Define ACPA-negative Rheumatoid Arthritis: Insights from Single-cell RNA Sequencing

Overview

Journal Front Immunol

Date 2025 Jan 20

PMID 39830504

Authors

Yafeng Jiang

Zhaolan Hu

Roujie Huang

Kaying Ho

Pengfei Wang

Jin Kang

Affiliations

Soon will be listed here.

Abstract

Background: Anti-citrullinated peptide antibodies (ACPA)-negative (ACPA-) rheumatoid arthritis (RA) presents significant diagnostic and therapeutic challenges due to the absence of specific biomarkers, underscoring the need to elucidate its distinctive cellular and metabolic profiles for more targeted interventions.

Methods: Single-cell RNA sequencing data from peripheral blood mononuclear cells (PBMCs) and synovial tissues of patients with ACPA- and ACPA+ RA, as well as healthy controls, were analyzed. Immune cell populations were classified based on clustering and marker gene expression, with pseudotime trajectory analysis, weighted gene co-expression network analysis (WGCNA), and transcription factor network inference providing further insights. Cell-cell communication was explored using CellChat and MEBOCOST, while scFEA enabled metabolic flux estimation. A neural network model incorporating key genes was constructed to differentiate patients with ACPA- RA from healthy controls.

Results: Patients with ACPA- RA demonstrated a pronounced increase in classical monocytes in PBMCs and C1QChigh macrophages (p < 0.001 and p < 0.05). Synovial macrophages exhibited increased heterogeneity and were enriched in distinct metabolic pathways, including complement cascades and glutathione metabolism. The neural network model achieved reliable differentiation between patients with ACPA- RA and healthy controls (AUC = 0.81). CellChat analysis identified CD45 and CCL5 as key pathways facilitating macrophage-monocyte interactions in ACPA- RA, prominently involving iron-mediated metabolite communication. Metabolic flux analysis indicated elevated beta-alanine and glutathione metabolism in ACPA- RA macrophages.

Conclusion: These findings underscore that ACPA-negative rheumatoid arthritis is marked by elevated classical monocytes in circulation and metabolic reprogramming of synovial macrophages, particularly in complement cascade and glutathione metabolism pathways. By integrating single-cell RNA sequencing with machine learning, this study established a neural network model that robustly differentiates patients with ACPA- RA from healthy controls, highlighting promising diagnostic biomarkers and therapeutic targets centered on immune cell metabolism.

Citing Articles

Unraveling the role of neuregulin-mediated astrocytes-OPCs axis in the pathogenesis of age-related macular degeneration and Parkinson's disease.

Zhang S, Zhang C, Zhang Y, Feng Y Sci Rep. 2025; 15(1):7352.

PMID: 40025106 PMC: 11873146. DOI: 10.1038/s41598-025-92103-8.

References

Macfarlane S, Seatter M, Kanke T, HUNTER G, Plevin R . Proteinase-activated receptors. Pharmacol Rev. 2001; 53(2):245-82. View

Firestein G . Evolving concepts of rheumatoid arthritis. Nature. 2003; 423(6937):356-61. DOI: 10.1038/nature01661. View

Qiu X, Mao Q, Tang Y, Wang L, Chawla R, Pliner H . Reversed graph embedding resolves complex single-cell trajectories. Nat Methods. 2017; 14(10):979-982. PMC: 5764547. DOI: 10.1038/nmeth.4402. View

Aibar S, Gonzalez-Blas C, Moerman T, Huynh-Thu V, Imrichova H, Hulselmans G . SCENIC: single-cell regulatory network inference and clustering. Nat Methods. 2017; 14(11):1083-1086. PMC: 5937676. DOI: 10.1038/nmeth.4463. View

Jin S, Guerrero-Juarez C, Zhang L, Chang I, Ramos R, Kuan C . Inference and analysis of cell-cell communication using CellChat. Nat Commun. 2021; 12(1):1088. PMC: 7889871. DOI: 10.1038/s41467-021-21246-9. View

Al-Abed Y, Vanpatten S . MIF as a disease target: ISO-1 as a proof-of-concept therapeutic. Future Med Chem. 2011; 3(1):45-63. DOI: 10.4155/fmc.10.281. View

Nishimura K, Sugiyama D, Kogata Y, Tsuji G, Nakazawa T, Kawano S . Meta-analysis: diagnostic accuracy of anti-cyclic citrullinated peptide antibody and rheumatoid factor for rheumatoid arthritis. Ann Intern Med. 2007; 146(11):797-808. DOI: 10.7326/0003-4819-146-11-200706050-00008. View

van Nies J, de Jong Z, van der Helm-van Mil A, Knevel R, le Cessie S, Huizinga T . Improved treatment strategies reduce the increased mortality risk in early RA patients. Rheumatology (Oxford). 2010; 49(11):2210-6. DOI: 10.1093/rheumatology/keq250. View

Su H, Na N, Zhang X, Zhao Y . The biological function and significance of CD74 in immune diseases. Inflamm Res. 2016; 66(3):209-216. DOI: 10.1007/s00011-016-0995-1. View

10.

Ronnelid J, Hansson M, Mathsson-Alm L, Cornillet M, Reed E, Jakobsson P . Anticitrullinated protein/peptide antibody multiplexing defines an extended group of ACPA-positive rheumatoid arthritis patients with distinct genetic and environmental determinants. Ann Rheum Dis. 2017; 77(2):203-211. DOI: 10.1136/annrheumdis-2017-211782. View

11.

Siddiqui A, Totonchian A, Jabar Ali J, Ahmad I, Kumar J, Shiwlani S . Risk Factors Associated With Non-Respondence to Methotrexate in Rheumatoid Arthritis Patients. Cureus. 2021; 13(9):e18112. PMC: 8527277. DOI: 10.7759/cureus.18112. View

12.

Peters L, Kuebler W, Simmons S . Sphingolipids in Atherosclerosis: Chimeras in Structure and Function. Int J Mol Sci. 2022; 23(19). PMC: 9570378. DOI: 10.3390/ijms231911948. View

13.

Zhang F, Wei K, Slowikowski K, Fonseka C, Rao D, Kelly S . Defining inflammatory cell states in rheumatoid arthritis joint synovial tissues by integrating single-cell transcriptomics and mass cytometry. Nat Immunol. 2019; 20(7):928-942. PMC: 6602051. DOI: 10.1038/s41590-019-0378-1. View

14.

Raza K, Falciani F, Curnow S, Ross E, Lee C, Akbar A . Early rheumatoid arthritis is characterized by a distinct and transient synovial fluid cytokine profile of T cell and stromal cell origin. Arthritis Res Ther. 2005; 7(4):R784-95. PMC: 1175027. DOI: 10.1186/ar1733. View

15.

Morabito S, Reese F, Rahimzadeh N, Miyoshi E, Swarup V . hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data. Cell Rep Methods. 2023; 3(6):100498. PMC: 10326379. DOI: 10.1016/j.crmeth.2023.100498. View

16.

Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z . clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation (Camb). 2021; 2(3):100141. PMC: 8454663. DOI: 10.1016/j.xinn.2021.100141. View

17.

Yang Z, Fujii H, Mohan S, Goronzy J, Weyand C . Phosphofructokinase deficiency impairs ATP generation, autophagy, and redox balance in rheumatoid arthritis T cells. J Exp Med. 2013; 210(10):2119-34. PMC: 3782046. DOI: 10.1084/jem.20130252. View

18.

Newsholme P, Curi R, Pithon Curi T, Murphy C, Garcia C, Pires de Melo M . Glutamine metabolism by lymphocytes, macrophages, and neutrophils: its importance in health and disease. J Nutr Biochem. 2004; 10(6):316-24. DOI: 10.1016/s0955-2863(99)00022-4. View

19.

Recalcati S, Locati M, Gammella E, Invernizzi P, Cairo G . Iron levels in polarized macrophages: regulation of immunity and autoimmunity. Autoimmun Rev. 2012; 11(12):883-9. DOI: 10.1016/j.autrev.2012.03.003. View

20.

Bantug G, Galluzzi L, Kroemer G, Hess C . The spectrum of T cell metabolism in health and disease. Nat Rev Immunol. 2017; 18(1):19-34. DOI: 10.1038/nri.2017.99. View