Abstract
Tick bite is traditionally viewed in clinical practice primarily as an event associated with the risk of pathogen transmission. Although this interpretation is epidemiologically justified, it does not capture the molecular layer that develops within the first hours after tick attachment and largely determines the subsequent trajectory of the host response. At the level of the skin and systemic circulation, an early immunometabolic response is initiated, involving changes in lipid mediators, shifts in the tryptophan-kynurenine pathway, alterations in nitric oxide-related regulation, redox imbalance, and activation of the hepatic acute-phase response.
The aim of this review is to analyze the molecular, biochemical, and metabolomic foundations for constructing an interpretable digital twin of the early immunometabolic response after tick bite. Such a twin would integrate LC-MS/MS profiling of lipid mediators, kynurenine-pathway metabolites, and redox markers with a QSP model of systemic inflammation, the hepatic acute-phase response, and the functional endothelial compartment. The central premise of the paper is that tick bite should be regarded not only as an epidemiologic entry point for infection, but also as a model of an early molecular event that triggers an individualized network of chemically defined mediators and metabolites.
Results. The literature indicates that the key components of such a platform have already been established, although they still exist largely in isolation and have not yet been integrated into a single interpretable system. There is substantial evidence of the pronounced immunomodulatory effects of tick saliva and early remodeling of the cutaneous immune landscape. A considerable body of research has also accumulated on metabolic and protein-mediated changes in early Lyme borreliosis, tick-borne encephalitis, and other tick-associated conditions; these studies analyze eicosanoids, kynurenine-pathway metabolites, acute-phase proteins, and oxidative stress markers. An analytical basis has emerged for targeted LC-MS/MS measurement of oxylipins, kynurenines, oxidized lipids, and glutathione-related parameters. In parallel, QSP approaches and digital twin concepts are developing rapidly across biomedicine and increasingly support personalized interpretation of molecular data. However, an integrated platform linking the early metabolomic signature of the response to tick bite with an interpretable QSP model and personalized phenotype stratification is still lacking.
Conclusion. It is concluded that a digital twin of the early response to tick bite should be viewed not as a tool for infection diagnosis, but as a bioinformatic and molecular medicine platform for integrating individual lipidomic, metabolomic, and baseline laboratory signatures into an interpretable model of systemic inflammation. In this form, it is consistent with the logic of personalized medicine and may be used for stratifying early immunometabolic phenotypes, selecting the depth of molecular profiling, and providing a scientific basis for monitoring during seasonal increases in healthcare utilization. The present paper is conceived as a conceptual review that systematizes current evidence, identifies molecular gaps, and outlines a research trajectory for future original studies.
Authors and affiliations
https://elibrary.ru/xbsxeg
V.P. Kutsenko(1), A.V. Nosov(1), I.E. Ushal(2), P.V. Seliverstov(3), E.I. Papina(4), A.G. Chopalaeva(5), A.F. Nuritdinova(6), I.I. Isupov(6)
1-Saint-Petersburg State Pediatric Medical University, Ministry of Health of Russia, Litovskaya St., 2, Saint-Petersburg, 194100,
Russian Federation; 2-Medical Interlaboratory Comparative Tests and Reference Materials, D.I. Mendeleev All-Russian Scientific Research Institute of Metrology, Moskovsky Ave., 19, Saint-Petersburg, 190005, Russian Federation; 3-S.M. Kirov Military Academy, Ministry of Defence of the Russian Federation, Lebedeva St., 6, Saint-Petersburg, 194044, Russian Federation; 4-Almazov National Medical Research Center
of the Ministry of Health of the Russian Federation, Akkuratova str., 2, Saint-Petersburg, 197341, Russian Federation; 5Dagestan State
Medical University, Ministry of Health of the Russian Federation, Lenin Square, 1, Makhachkala, Republic of Dagestan, 367005,
Russian Federation; 6-Peoples’ Friendship University of Russia named after Patrice Lumumba (RUDN University),
Miklukho-Maklaya St., 6, Moscow, 117198, Russian Federation
References
1. Centers for Disease Control and Prevention. Tick Bite Data Tracker [Internet]. Atlanta (GA): CDC; 2024 [cited 2026 Mar 29]. Available from: https://www.cdc.gov/ticks/data-research/facts-stats/tick-bite-data-tracker.html
2. Nalçaci M. Mysterious Allergy Caused by Tick Bite: Alpha-Gal Syndrome. Turkiye Parazitol Derg. 2024; 48 (3): 195–207. DOI: 10.4274/tpd.galenos.2024.97720.
3. Kotál J., Langhansová H., Lieskovská J., Andersen J.F., Francischetti I.M.B., Chavakis T. et al. Modulation of host immunity by tick saliva. J. Proteomics. 2015; 128: 58–68.
4. Kazimirová M., Štibrániová I. Tick salivary compounds: their role in modulation of host defences and pathogen transmission. Front Cell Infect Microbiol. 2013; 3: 43.
5. Aounallah H., Bensaoud C., M’ghirbi Y., Faria F., Chmelar J., Kopecký J. Tick salivary compounds for targeted immunomodulatory therapy. Front Immunol. 2020; 11: 583845.
6. Ali A., Parizi L.F., Guizzo M.G., Tirloni L., Seixas A., Vaz ID Jr. Host immune responses to salivary components: a critical facet of tick-host interactions. Front Cell Infect Microbiol. 2022; 12: 809052.
7. Kleissl L., Muendler V., Strobl J. et al. Ticks’ tricks: immunomodulatory effects of ixodid tick saliva at the cutaneous tick-host interface. Front Immunol. 2025; 16: 1520665.
8. Strobl J., Müller S., Derdak S. et al. Tick feeding modulates the human skin immune landscape to facilitate tick-borne pathogen transmission. J. Clin. Invest. 2022; 132 (21): e161188.
9. Lee W. et al. Molecular analysis of human tick-bitten skin yields signatures associated with distinct spatial and temporal trajectories: a proof-of-concept study. Heliyon. 2024; 10 (13): e33600.
10. Paine D.N. et al. Early transcriptomic changes at the skin interface during tick feeding and Powassan virus transmission. Front Immunol. 2025; 16: 1511132.
11. Strobl J. et al. Human epidermal Langerhans cells induce tolerance and hamper T cell function upon tick-borne pathogen transmission. Nat Commun. 2025; 16 (1): 11715. DOI: 10.1038/s41467-025-66821-6.
12. Cui Y., Hart C.E., Narasimhan S. et al. Metabolomic changes associated with acquired resistance to tick bites. Ticks Tick Borne Dis. 2024; 15 (1): 102315.
13. Bowman A.S., Dillwith J.W., Sauer J.R. Tick salivary prostaglandins: presence, origin and significance. Parasitol Today. 1996; 12 (10): 388–96.
14. Aljamali M.N., Bowman A.S., Dillwith J.W., Tucker J.S., Yates G.W., Essenberg R.C. et al. Identity and synthesis of prostaglandins in the lone star tick, Amblyomma americanum, as assessed by radioimmunoassay and gas chromatography-mass spectrometry. Insect Biochem Mol Biol. 2002; 32 (3): 331–41.
15. Poole N.M., Mamidanna G., Smith R.A., Coons L.B. Prostaglandin E2 in tick saliva regulates macrophage cell migration and cytokine profile. Parasit Vectors. 2013; 6: 261.
16. Fitzgerald B.L., Molins C.R., Islam M.N. et al. Host metabolic response in early Lyme disease. J Proteome Res. 2020; 19 (2): 610–23.
17. Molins C.R., Ashton L.V., Wormser G.P., Hess A.M., Delorey M.J., Mahapatra S. et al. Development of a metabolic biosignature for detection of early Lyme disease. Clin Infect Dis. 2015; 60 (12): 1767–75.
18. Molins C.R., Delorey M.J., Ashton L.V., Wormser G.P., Hess A.M., Schriefer M.E. et al. Metabolic differentiation of early Lyme disease from Southern tick-associated rash illness. Sci Transl Med. 2017; 9 (403): eaal2717.
19. Kehoe E.R., Henao R., Isaza C.E. et al. Biomarker selection and a prospective metabolite-based diagnostic test for Lyme disease. Sci Rep. 2022; 12: 1623.
20. Bockenstedt L.K., Belperron A.A. Insights From Omics in Lyme Disease. J. Infect Dis. 2024; 230 (1): 18–26. DOI: 10.1093/infdis/jiae250.
21. Kuukkanen I., Pietikäinen A., Rissanen T. et al. UHPLC-MS/MS-based untargeted metabolite profiling of Lyme neuroborreliosis. Sci Rep. 2025;15 (1): 8442. DOI: 10.1038/s41598-025-92189-0. PMID: 40069240; PMCID: PMC11897164.
22. Wickström R., Nilsson A. et al. The kynurenine pathway is differentially activated in children with Lyme neuroborreliosis and tick-borne encephalitis. Brain Behav Immun Health. 2021; 13: 100184.
23. Dobrzyńska M., Gęgotek A., Skrzydlewska E. Metabolic response to tick-borne encephalitis virus infection and bacterial co-infection in patients’ blood. Pathogens. 2022; 11 (4): 384.
24. Groth M., Gęgotek A., Skrzydlewska E. Redox imbalance and its metabolic consequences in tick-borne diseases. Int. J. Mol. Sci. 2022; 23 (14): 7855.
25. Groth M., Dobrzyńska M., Gęgotek A., Skrzydlewska E. Lipid mediators of cerebrospinal fluid in response to tick-borne encephalitis and bacterial co-infections. Free Radic Biol Med. 2023; 204: 1–12.
26. Du Y.D. et al. Proteomic and metabolomic analysis of the serum composition in patients with tick-borne encephalitis. J. Proteomics. 2024; 296: 105081.
27. Biernacki M., Groth M., Gęgotek A., Skrzydlewska E. Assessment of lipid mediators in the urine of patients with Lyme disease, tick-borne encephalitis and human granulocytic anaplasmosis. Sci Rep. 2026; 16: 40464.
28. Fredriksson T. et al. Diagnostic patterns of serum inflammatory protein markers in children with suspected Lyme neuroborreliosis. Ticks Tick Borne Dis. 2024; 15 (5): 102388.
29. Uhde M., Ajamian M., Li X., Wormser G.P., Marques A., Alaedini A. Expression of C-reactive protein and serum amyloid A in early to late manifestations of Lyme disease. Clin Infect Dis. 2016; 63 (10): 1399–404.
30. Khanapure S.P., Garvey D.S., Janero D.R., Letts L.G. Eicosanoids in inflammation: biosynthesis, pharmacology, and therapeutic frontiers. Curr Top Med Chem. 2007; 7 (3): 311–40.
31. Park W.H. Eicosanoids and inflammation: a delicate balance of pro-inflammatory and pro-resolving mediators. Biochem Pharmacol. 2026; 245: 117662.
32. Tsikas D. Quantitative GC-MS/MS in the clinical analysis of eicosanoids: a critical review and discussion, a 40-years historical retrospect, and possible implications for LC-MS/MS. J. Chromatogr B Analyt Technol Biomed Life Sci. 2026; 1270: 124911. DOI: 10.1016/j.jchromb.2025.124911.
33. Chhonker Y.S. et al. Simultaneous quantitation of lipid biomarkers for inflammation and oxidative stress by LC-MS/MS. Antioxidants (Basel). 2021; 10 (3): 463.
34. Hossen M.M., Fleiss B., Zakaria R. The current state in liquid chromatography-mass spectrometry methods for quantifying kynurenine pathway metabolites in biological samples: a systematic review. Crit Rev Clin Lab Sci. 2025; 62 (6): 437–53. DOI: 10.1080/10408363.2025.2495160.
35. Contella L. et al. LC-MS/MS detection of tryptophan, kynurenine, kynurenic acid, and quinolinic acid in human urine. Metabolites. 2025; 15 (11): 749.
36. Bhattacharyya D., LeVatte M.A., Wishart D.S. Towards Automated Testing of Kynurenine for Point-of-Care Metabolomics. Methods Protoc. 2025; 8 (3): 56. DOI: 10.3390/mps8030056.
37. Ünal K., İbrahimkhanli L., Erol M.E., İbrahimkhanli N., Altintop S.E., Cerit M.N., Cerit E.T., Şendur H.N. Altered tryptophan-kynurenine pathway and low-grade inflammation in metabolic dysfunction-associated steatotic liver disease (MASLD): insights from LC-MS/MS-based metabolite profiling. Int. J. Tryptophan Res. 2026; 19: 11786469261423510. DOI: 10.1177/11786469261423510.
38. Kim M.E. et al. Advances in the regulation of inflammatory mediators in nitric oxide synthases. Int. J. Mol. Sci. 2025; 26 (4): 1732.
39. Zaborova V., Budanova E., Kryuchkova K., Rybakov V., Shestakov D., Isaikin A. et al. Nitric oxide: a gas transmitter in healthy and diseased skin. Med Gas Res. 2025; 15 (4): 520–8. DOI: 10.4103/mgr.MEDGASRES-D-24-00144.
40. Miller M.J.S., Sandoval M. Nitric oxide as a mediator of inflammation? Mol. Aspects Med. 1999; 20 (1–2): 69–99.
41. Bellanti F. et al. Redox imbalance in inflammation: the interplay of oxidative stress and reductive stress. Antioxidants (Basel). 2025; 14 (6): 745.
42. Krishnamurthy H.K. et al. Oxidative stress: fundamentals and advances in biomarkers. Front Chem. 2024; 12: 1470458.
43. Fanti F. et al. LC-MS/MS-based analytical strategies for the detection of oxidative stress lipidic biomarkers. Biomed Chromatogr. 2025; 39 (2): e7001.
44. Yamaguchi H., Yamaguchi H. Oxidative and Glycation Stress Biomarkers: Advances in Detection Technologies and Point-of-Care Clinical Applications. Molecules. 2025; 30 (21): 4286. DOI: 10.3390/molecules30214286.
45. Wasinger V.C., Bustamante S., Najib N., Diwan A., Jayasena T., Chowdhury N.S. et al. Enzymes Drive Glutathione Shunt to Explain Oxidative State Using an In-Parallel Multi-Omic Method. Int. J. Mol. Sci. 2025; 26 (8): 3632. DOI: 10.3390/ijms26083632.
46. Gonçalves M., Barata P., Vale N. From Population-Based PBPK to Individualized Virtual Twins: Clinical Validation and Applications in Medicine. J. Clin. Med. 2026; 15 (3): 1210. DOI: 10.3390/jcm15031210.
47. Kaddi C., Tao M., Bergeler S., George K., Geerts H., van der Graaf P.H. et al. Quantitative Systems Pharmacology-Based Digital Twins Approach Supplements Clinical Trial Data for Enzyme Replacement Therapies in Pompe Disease. Clin Pharmacol Ther. 2025; 117 (2): 579–88. DOI: 10.1002/cpt.3498. Epub 2024 Dec 4.
48. Feigelman J. What Is a Digital Twin in QSP, and Are We Doing It Right? CPT Pharmacometrics Syst Pharmacol. 2026; 15 (3): e70229. DOI: 10.1002/psp4.70229.
49. Sun Z. et al. Conceptual quantitative systems pharmacology framework for immune-mediated diseases. Front Immunol. 2025; 16: 1714438.
50. Dai W. et al. A prototype QSP model of the immune response to SARS-CoV-2 for community development. CPT Pharmacometrics Syst Pharmacol. 2021; 10 (1): 18–29.
51. Khoshfekr Rudsari H. et al. Digital twins in healthcare: a comprehensive review and future prospects. Front Digit Health. 2025; 7: 1633539.
52. Dette C.M.U. et al. Advancing rare disease therapeutics through digital twins: Opportunities in drug development and precision dosing. Comput Struct Biotechnol J. 2025; 28: 592–608. DOI: 10.1016/j.csbj.2025.11.047.
53. Maharjan R. et al. Transformative roles of digital twins from drug discovery to development and precision medicine. Drug Discov Today. 2025; 30 (9): 104561.
54. Venkatapurapu S.P. et al. Digital twins for accelerating drug discovery and development. Drug Discov Today. 2026; 31 (2): 104883.
55. Akbarialiabad H. et al. Enhancing randomized clinical trials with digital twins. NPJ Syst Biol Appl. 2025; 11: 110.
56. Tudor B.H., Shargo R., Gray G.M. et al. A scoping review of human digital twins in healthcare applications and usage patterns. NPJ Digit Med. 2025; 8: 587.