Immunopathogenic features of hemorrhagic fever with renal syndrome as criteria for early immunodiagnostics

Cover Page

Cite item

Full Text

Abstract

Relevance. Hemorrhagic fever with renal syndrome (HFRS) is a natural focal viral infection with a high probability of severe course, the possibility of death, a long recovery period after infection, low effectiveness of therapy and vaccine prevention. In the Russian Federation, HFRS is most often caused by the Puumala orthohantavirus. The aim of the study — to evaluate the immunophenotypic composition of lymphocytes and cytokine profile in the blood of patients with hemorrhagic fever with renal syndrome in comparison with acute respiratory viral infections and with the prospect of developing immunological criteria for early diagnosis of HFRS. Matherials and Methods. There were examined the blood of 24 patients with a verified diagnosis of HFRS who were hospitalized in the infectious diseases department of the Samara Medical University Clinics and admitted in the first days of the disease, 18 patients with acute respiratory viral infections of established etiology, as well as 15 healthy people. Results and Discussion. Analysis of the results of lymphocyte phenotyping and cytokine levels in the blood revealed that the percentage of B lymphocytes in the blood was >12.6 %, cytotoxic CD8+ T lymphocytes expressing the activating lectin receptor NKG2D (CD3+CD8+CD314+), >25 %, regulatory T cells with CD3+CD4+FoxP3+ phenotypes >7.8 % and CD3+CD8+FoxP3+ >9.5 %, as well as IL-6 >24 pg/ml, TNFß >55 pg/ml, IL-10 <11.3 pg/ml with high diagnostic significance, judging by the results of ROC analysis, indicates in favor of GLPS, but not ARVI. Conclusion. The results obtained can be used as criteria for early immunodiagnosis of HFRS. The development of a new hypothesis on the mechanism of CD8+  immunological memory formation may contribute to the discovery of new potential targets for HFRS immunotherapy and the creation of new principles for the production of vaccine preparations for the prevention of this disease.

Full Text

Table 1 Relative blood abundance of different phenotype lymphocytes in the early stages of HFRS and in comparison groups

Lymphocyte phenotypic parameters (%)

Median (minimum; maximum)

p1
p2
p3

HFRS рatients n = 24

ARVI рatients (comparison group 1) n = 18

Healthy people (comparison group 2) n = 15

B cells, CD19+

13.6
(5.0; 25.0)

11.3
(6.9; 17.4)

10.5
(2.5; 15.7)

0.039*

0.044*

0.046*

Т cells, CD3+

68.0
(49.7; 87.0)

73.5
(52.0; 89.5)

75.0
(62.0; 87.0)

0.338

0.053

0.881

Activated Т cells,

CD3+CD25+

4.8
(1.1; 27.0)

4.0
(1.6; 7.4)

7.5
(2.6; 7.8)

0.026*

0.164

0.707

T helper cells, CD3+CD4+

36.8
(16.0; 67.0)

40.0
(20.5; 61.2)

41.0
(14.0; 57.0)

0.737

0.289

0.858

Cytotoxic T-lymphocytes (CTLs), CD3+CD8+

26.0
(10.4; 78.0)

28.3
(13.3; 37.5)

28.0
(16.0; 71.0)

0.950

0.754

0.929

NKG2D+ CTLs, CD3+CD8+CD314+

30.6
(8.3; 58.9)

21.8
(10.2; 31.5)

12.6
(9.6; 27.0)

0.031*

<0.001***

0.003**

CD4+ regulatory T cells, CD3+CD4+FoxP3+

10.7
(4.9; 16.3)

7.7
(5.0; 18.0)

3.05
(2.3; 8.1)

0.034*

<0.001***

0.044*

CD8+ regulatory T cells, CD3+CD8+FoxP3+

13.0
(3.5; 23.9)

7.3
(6.2; 23.0)

0.45
(0.1; 4.4)

0.008**

<0.001***

0.001**

NКТ‑like cells,

CD3+CD56+

4.7
(1.5; 30.6)

5.0
(2.3; 20.0)

3.4
(2.3; 5.0)

0.231

0.169

0.233

Natural killer cells (NK),

CD3–СD16+CD56+

17.0
(7.7; 53.0)

21.5
(4.0; 34.4)

12.9
(9.5; 27.7)

0.022*

0.001**

0.049*

NKG2D+ NК, CD16+CD56+CD314+

6.7
(1.0; 16.3)

10.4
(1.0; 15.0)

9.6
(7.7; 21.6)

0.068

0.118

0.254

Note: n — the number of persons in the group; p1 — probability of differences in HFRS and healthy people groups; p2 — probability of differences in ARVI and healthy people groups; p3 — probability of differences in HFRS and ARVI groups; significance of Mann-­Whitney differences: * at p<0.05, ** at p<0.01, *** at p < 0.001.

Fig. 1. 95 % confidence intervals of B cell and natural killer cell percentages among blood lymphocytes in the study groups
and ROC curves of their prognostic value

Fig. 2. 95 % confidence intervals of NKG2D+ CTL, СD4+ and CD8+ regulatory T cell percentages among blood lymphocytes in the study groups and ROC curves of their prognostic value

Table 2 Serum levels of pro-inflammatory and anti-inflammatory cytokines in the early stages of HFRS and in comparison groups

Cytokines tested (pg/mL)

Median (minimum; maximum)

p1
p2
p3

HFRS рatients n = 24

ARVI рatients (comparison group 1) n = 18

Healthy people (comparison group 2) n = 15

IL-4

1,5
(1,5; 1,8)

2,0
(1,6; 2,8)

2,2
(1,3; 4,2)

0,002**

0,284

0,053

IL-12

12,1
(10,9; 13,7)

12,0
(9,5; 12,7)

9,1
(7,8; 14,4)

0,036*

0,041*

0,918

IFNγ

86,5
(72,2; 101,0)

81,4
(76,1; 119,7)

40,8
(27,5; 51,5)

<0,001***

0,004**

0,703

IL-1β

2,4
(2,0; 2,9)

2,6
(2,1; 3,0)

3,8
(2,50; 5,2)

0,003**

0,008**

0,237

IL-6

25,9
(20,0; 32,3)

20,6
(16,2; 28,0)

6,2
(2,7; 9,2)

<0,001***

0,003**

0,047*

TNFα

3,0
(2,7; 3,9)

2,9
(2,60; 3,4)

2,0
(0,8; 2,8)

0,021*

0,043*

0,513

TNFβ

52,3
(48,0; 82,1)

48,4
(45,1; 55,6)

1,4
(0,6; 2,9)

<0,001***

<0,001***

0,041*

IL-10

14,8
(11,2; 40,5)

9,0
(7,5; 25,2)

6,8
(3,4; 9,1)

<0,001***

0,005**

0,026*

Note: n — the number of persons in the group; p1 — probability of differences in HFRS and healthy people groups; p2 — probability of differences in ARVI and healthy people groups; p3 — probability of differences in HFRS and ARVI groups; significance of Mann-Whitney differences: * at p<0.05, ** at p<0.01, *** at p<0.001.

Fig. 3. 95 % confidence intervals of IL‑6, TNFβ, IL‑10 levels (pg/ml) in the serums of study group patients and ROC curves of their prognostic value

 

Fig. 4. Significant correlations between informative immunological signs in the early stages of HFRS and in the comparison group (ARVI)

×

About the authors

Michail F. Ivanov

Samara State Medical Uniiversity

Author for correspondence.
Email: m.f.ivanov@samsmu.ru
ORCID iD: 0000-0002-2528-0091
SPIN-code: 2195-3768
Samara, Russian Federation

Irina P. Balmasova

Russian University of Medcine

Email: m.f.ivanov@samsmu.ru
ORCID iD: 0000-0001-8194-2419
SPIN-code: 8025-8611
Moscow, Russian Federation

Elena S. Malova

Reaviz Medical University

Email: m.f.ivanov@samsmu.ru
ORCID iD: 0000-0001-5710-3076
SPIN-code: 8207-7835
Samara, Russian Federation

Dmitriy Yu. Konstantinov

Samara State Medical Uniiversity

Email: m.f.ivanov@samsmu.ru
ORCID iD: 0000-0002-6177-8487
SPIN-code: 3061-8265
Samara, Russian Federation

References

  1. Borodina ZhI, Tsarenko OE, Monakhov KM, Bagautdinova LI. Hemorrhagic fever with renal syndrome - a problem of modernity. Archive of Internal Medicine, 2019;6:419-427. doi: 10.20514/2226-6704-2019-9-6-419-427. (In Russian).
  2. Avsic-­Zupanc T, Saksida A, Korva M. Hantavirus infections. Clin Microbiol Infect. 2019;21S:6-16. doi: 10.1111/1469-0691.12291
  3. Tkachenko E, Kurashova S, Balkina A, Ivanov A, Egorova M, Leonovich O, Popova Yu, Teodorovich R, Belyakova A, Tkachenko P, Trankvilevsky D, Blinova E, Ishmukhametov A, Dzagurova T. Cases of hemorrhagic fever with renal syndrome in Russia during 2000-2022. Viruses. 2023;15(7):1537. doi: 10.3390/v15071537
  4. Supotnitskiy MV. Viral hemorrhagic fevers. In: Biological War. Introduction to the Epidemiology of Artificial Epidemic Processes and Biological Lesions. Moscow: Chair, Russian Panorama. 2013:887-927. (In Russian).
  5. Jiang H, Wang LM, Wang PZ, Bai XF. Hemorrhagic fever with renal syndrome: pathogenesis and clinical picture. Front Cell Infect Microbiol. 2016;6:1-14. doi: 10.3389/fcimb.2016.00001
  6. Liu R, Ma H, Shu J, Zhang Q, Han M, Liu Z, Jin X, Zhang F, Wu X. Vaccines and Therapeutics Against Hantaviruses. Front Microbiol. 2020;10:2989. doi: 10.3389/fmicb.2019.02989
  7. Guterres A, de Oliveira CR, Fernandes J, de Lemos RSE. The mystery of the phylogeographic structural pattern in rodent-­borne hantaviruses. Mol Phylogenet Evol. 2019;136:35-43. doi: 10.1016/j.ympev.2019.03.020
  8. Morozov VG, Ishmukhametov AA, Dzagurova TK, Tkachenko EA. Clinical features of hemorrhagic fever with renal syndrome in Russia. Infectious diseases. 2017;(5):156-161. doi: 10.21518/2079-701x-2017-5-156-161. (In Russian).
  9. Manigold T, Vial Р. Human hantavirus infections: epidemiology, clinical features, pathogenesis and immunology. Swiss Med Wkly. 2014;144:13937-13955. doi: 10.4414/smw.2014.13937
  10. Klingström J, Smed-­Sörensen A, Maleki KT, Solà-­Riera C, Ahlm C, Björkström NK, Ljunggren HG. Innate and adaptive immune responses against human Puumala virus infection: immunopathogenesis and suggestions for novel treatment strategies for severe hantavirus-­associated syndromes. J Intern Med. 2019;285(5):510-523. doi: 10.1111/joim.12876
  11. Terajima M, Hendershot 3rd, JD, Kariwa H, Koster FT, Hjelle B, Goade D, DeFronzo MC, Ennis FA. High levels of viremia in patients with the hantavirus pulmonary syndrome. J Infect Dis. 1999;180(6):2030-2034. doi: 10.1086/315153
  12. Vereta LA, Elisova TD, Voronkova GM, Mzhelskaya TV. Method of early diagnosis of hemorrhagic fever with renal syndrome. Patent RU 2683955, publ. 2019-04-03. (In Russian).
  13. Ivanov MF, Balmasova IP, Zhestkov AV, Konstantinov DYu, Malova ES. Expression of NKG2D by cytotoxic T-lymphocytes as a possible mechanism of immunopathogenesis of hemorrhagic fever with renal syndrome. Immunologiya. 2023;44(1):93-102. doi: 10.33029/0206-4952-2023-43-1-93-102. (In Russian). [
  14. Habibzadeh F, Habibzadeh P, Yadollahie M. On determining the most appropriate test cut-off value: the case of tests with continuous results. Biochem Med (Zagreb). 2016;26(3):297-307. doi: 10.11613/BM.2016.034
  15. Scholz S, Baharom F, Rankin G, Maleki KT, Gupta S, Vangeti S, Pourazar J, Discacciati A, Höijer J, Bottai M, Björkström NK, Rasmuson J, Evander M, Blomberg A, Ljunggren H-G, Klingström J, Ahlm C, Smed-­Sörensen A. Human hantavirus infection elicits pronounced redistribution of mononuclear phagocytes in peripheral blood and airways. PLoS Pathog. 2017;13(6): e1006462. doi: 10.1371/journal.ppat.1006462
  16. Stoltz M, Ahlm C, Lundkvist A, Klingström J. Lambda interferon (IFN-lambda) in serum is decreased in hantavirus-­infected patients, and in vitro-­established infection is insensitive to treatment with all IFNs and inhibits IFN-gamma-­induced nitric oxide production. J Virol. 2007;81(16):8685-8691. doi: 10.1128/JVI.00415-07
  17. Linderholm M, Ahlm C, Settergren B, Waage A, Tärnvik A. Elevated plasma levels of tumor necrosis factor (TNF)-alpha, soluble TNF receptors, interleukin (IL)-6, and IL-10 in patients with hemorrhagic fever with renal syndrome. J Infect Dis. 1996;173(1):38-43. doi: 10.1093/infdis/173.1.38
  18. Flippe L, Bézie S, Anegon I, Guillonneau C. Future prospects for CD8+ regulatory T cells in immune tolerance. Immunol Rev. 2019;292(1):209-224. doi: 10.1111/imr.12812
  19. Maes P, Clement J, Groeneveld PH, Colson P, Huizinga TWJ, van Ranst M. Tumor necrosis factor-α genetic predisposing factors can influence clinical severity in nephropathia epidemica. Viral Immunol. 2006;19(3):558-564. doi: 10.1089/vim.2006.19.558
  20. Koivula TT, Tuulasvaara A, Hetemäki L, Mäkelä SM, Mustonen J, Sironen T, Vaheri A, Arstila TP. Regulatory T cell response correlates with the severity of human hantavirus infection. J Infec. 2014;68(4):387-394. doi: 10.1016/j.jinf.2013.11.007
  21. Arpaia N, Green JA, Moltedo B, Arvey A, Hemmers S, Yuan S, Treuting PM, Rudensky AY. A distinct function of regulatory T cells in tissue protection. Cell. 2015;162(5):1078-1089. doi: 10.1016/j.cell.2015.08.021
  22. Fan W, Liu X, Yue J. Determination of urine tumor necrosis factor, IL-6, IL-8, and serum IL-6 in patients with hemorrhagic fever with renal syndrome. Braz J Infect Dis. 2012;16(6):527-530. doi: 10.1016/j.bjid.2012.10.002
  23. Lee G-Y, Kim W-K, No JS, Yi Y, Park HC, Jung J, Cho S, Lee J, Lee S-H, Park K, Kim J, Song J-W. Clinical and immunological predictors of hemorrhagic fever with renal syndrome outcome during the early phase. Viruses. 2022;14(3):595. doi: 10.3390/v14030595

Supplementary files

Supplementary Files
Action
1. Fig. 1. 95 % confidence intervals of B cell and natural killer cell percentages among blood lymphocytes in the study groups and ROC curves of their prognostic value

Download (68KB)
Indexing metadata
2. Fig. 2. 95 % confidence intervals of NKG2D+ CTL, СD4+ and CD8+ regulatory T cell percentages among blood lymphocytes in the study groups and ROC curves of their prognostic value

Download (90KB)
Indexing metadata
3. Fig. 3. 95 % confidence intervals of IL‑6, TNFβ, IL‑10 levels (pg/ml) in the serums of study group patients and ROC curves of their prognostic value

Download (79KB)
Indexing metadata
4. Fig. 4. Significant correlations between informative immunological signs in the early stages of HFRS and in the comparison group (ARVI)

Download (51KB)
Indexing metadata

Copyright (c) 2024 Ivanov M.F., Balmasova I.P., Malova E.S., Konstantinov D.Y.

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

This website uses cookies

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

About Cookies