Metabolic screening as a tool for assessing the pathogenesis and course of psoriasis

Cover Page

Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription Access

Abstract

Psoriasis is a chronic, autoinflammatory/autoimmune systemic skin disease. The etiology and pathogenesis of the disease are still unclear. However, Th17/IL-17 activation and abnormalities in the Th17/Treg balance axis are observed in psoriasis, but this pathomechanism does not fully explain the frequent occurrence of metabolic disorders. Therefore, it is necessary to search for better biomarkers in the diagnosis, prognosis and monitoring of comorbid disorders and therapeutic effects in psoriasis.

Metabolomics is a new technology that allows to identify a set of small molecular chemicals involved in metabolism. This method has traditionally been studied with the aim of identifying biomarkers in the diagnosis and prognosis of the disease. Metabolic screening is essential for clinical diagnosis, therapeutic monitoring, predicting the efficacy of psoriasis treatment, and further discovery of new metabolic-based therapeutic targets.

Pharmacometabolomics is aimed at predicting individual differences in response to treatment and in the development of side effects associated with specific drugs.

This review summarizes studies that show responses to drug treatment based on their metabolic profiles obtained before, during, or after therapeutic intervention.

About the authors

Olga Yu. Olisova

I.M. Sechenov First Moscow State Medical University

Author for correspondence.
Email: olisovaolga@mail.ru
ORCID iD: 0000-0003-2482-1754
SPIN-code: 2500-7989

MD, Dr. Sci. (Med.), Professor

Russian Federation, Moscow

Vladimir G. Kukes

I.M. Sechenov First Moscow State Medical University

Email: elmed@yandex.ru
ORCID iD: 0000-0002-5112-6928
SPIN-code: 8498-3521

MD, Dr. Sci. (Med.), Professor, Academician of the Russian Academy of Sciences

Russian Federation, Moscow

Ilya V. Kukes

I.M. Sechenov First Moscow State Medical University

Email: ilyakukes@gmail.com
ORCID iD: 0000-0003-1449-8711
SPIN-code: 1166-3569

MD, Cand. Sci. (Med.), Research Associate

Russian Federation, Moscow

Dmitry V. Ignatiev

I.M. Sechenov First Moscow State Medical University

Email: dmitrywork@list.ru
ORCID iD: 0000-0001-8751-3965
SPIN-code: 6743-7960

MD

Russian Federation, Moscow

Veronika V. Rogacheva

I.M. Sechenov First Moscow State Medical University

Email: rogacheva-90@mail.ru
ORCID iD: 0000-0003-4200-7887
SPIN-code: 6164-1817

Graduate Student

Russian Federation, Moscow

References

  1. Yang G, Xia Y, Ren W. Glutamine metabolism in Th17/Treg cell fate: applications in Th17 cell-associated diseases. Sci China Life Sci. 2021;64(2):221–233. doi: 10.1007/s11427-020-1703-2
  2. De Berardinis RJ, Mancuso A, Daikhin E, et al. Beyond aerobic glycolysis: transformed cells can engage in glutamine metabolism that exceeds the requirement for protein and nucleotide synthesis. Proc Natl Acad Sci USA. 2007;104(49):19345–19350. doi: 10.1073/pnas.0709747104
  3. Carr EL, Kelman A, Wu GS, et al. Glutamine uptake and metabolism are coordinately regulated by ERK/MAPK during T lymphocyte activation. J Immunol. 2010;185(2):1037–1044. doi: 10.4049/jimmunol.0903586
  4. Johnson MO, Wolf MM, Madden MZ, et al. Distinct regulation of Th17 and Th1 cell differentiation by glutaminase-dependent metabolism. Cell. 2018;175(7):1780–1795.e19. doi: 10.1016/j.cell.2018.10.001
  5. Klysz D, Tai X, Robert PA, et al. Glutamine-dependent α-ketoglutarate production regulates the balance between T helper 1 cell and regulatory T cell generation. Sci Signal. 2015;8(396):ra97. doi: 10.1126/scisignal.aab2610
  6. Lian G, Gnanaprakasam JR, Wang T, et al. Glutathione de novo synthesis but not recycling process coordinates with glutamine catabolism to control redox homeostasis and directs murine T cell differentiation. Elife. 2018;7:e36158. doi: 10.7554/eLife.36158
  7. Lian N, Shi LQ, Hao ZM, Chen M. Research progress and perspective in metabolism and metabolomics of psoriasis. Chin Med J. 2020;133(24):2976–2986. doi: 10.1097/CM9.0000000000001242
  8. Gauza-Włodarczyk M, Kubisz L, Włodarczyk D. Amino acid composition in determination of collagen origin and assessment of physical factors effects. Int J Biol Macromol. 2017;104(Pt A):987–991. doi: 10.1016/j.ijbiomac.2017.07.013
  9. Smith RJ, Phang JM. The importance of ornithine as a precursor for proline in mammalian cells. J Cell Physiol. 1979;98(3):475–481. doi: 10.1002/jcp.1040980306
  10. Suskova VS, Pinson IY, Olisova OY. Immunopathogenesis of psoriasis. Clin Dermatology Venereology. 2006;(1):68–70. (In Russ).
  11. Tashiro T, Sawada Y. Psoriasis and systemic inflammatory disorders. Int J Mol Sci. 2022;23(8):4457. doi: 10.3390/ijms23084457
  12. Bilgiç Ö, Altınyazar HC, Baran H, Ünlü A. Serum homocysteine, asymmetric dimethyl arginine (ADMA) and other arginine-NO pathway metabolite levels in patients with psoriasis. Arch Dermatol Res. 2015;307(5):439–444. doi: 10.1007/s00403-015-1553-3
  13. Kamleh M, Snowden S, Grapov D, et al. LC-MS metabolomics of psoriasis patients reveals disease severity-dependent increases in circulating amino acids that are ameliorated by anti-TNFα treatment. J Proteome Res. 2015;14(1):557–566. doi: 10.1021/pr500782g
  14. Kapoor SR, Filer A, Fitzpatrick MA, et al. Metabolic profiling predicts response to anti-tumor necrosis factor α therapy in patients with rheumatoid arthritis. Arthritis Rheum. 2013;65(6):1448–1456. doi: 10.1002/art.37921
  15. Kang H, Li X, Zhou Q, et al. Exploration of candidate biomarkers for human psoriasis based on gc-ms serum metabolomics. Br J Dermatol. 2017;176(3):713–722. doi: 10.1111/bjd.15008
  16. Madsen RK, Lundstedt T, Gabrielsson J, et al. Diagnostic properties of metabolic perturbations in rheumatoid arthritis. Arthritis Res Ther. 2011;13(1):R19. doi: 10.1186/ar3243
  17. Ottas A, Fishman D, Okas TL, et al. The metabolic analysis of psoriasis identifies the associated metabolites while providing computational models for the monitoring of the disease. Arch Dermatol Res. 2017;309(7):519–528. doi: 10.1007/s00403-017-1760-1
  18. Souto-Carneiro M, Tóth L, Behnisch R, et al. Differences in the serum metabolome and lipidome identify potential biomarkers for seronegative rheumatoid arthritis versus psoriatic arthritis. Ann Rheum Dis. 2020;79(4):499–506. doi: 10.1136/annrheumdis-2019-216374

Copyright (c) 2022 Olga Yu. Olisova, Vladimir G. Kukes, Ilya V. Kukes, Dmitry V. Ignatiev, Veronika V. Rogacheva

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
 


This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies