Entropy of human leukocyte antigen and killer-cell immunoglobulin-like receptor systems in immune-mediated disorders: A pilot study on multiple sclerosis


Autoři: Maurizio Melis aff001;  Roberto Littera aff002;  Eleonora Cocco aff003;  Jessica Frau aff003;  Sara Lai aff002;  Elena Congeddu aff001;  Paola Ragatzu aff001;  Maria Serra aff002;  Valentina Loi aff002;  Roberta Maddi aff002;  Roberta Pitzalis aff003;  Sandro Orrù aff001;  Luchino Chessa aff004;  Andrea Perra aff005;  Carlo Carcassi aff001
Působiště autorů: Medical Genetics, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy aff001;  Complex Structure of Medical Genetics, R. Binaghi Hospital, ASSL Cagliari, ATS Sardegna, Italy aff002;  Multiple Sclerosis Center, R. Binaghi Hospital, University of Cagliari/ATS Sardegna, Cagliari, Italy aff003;  Center for the Study of Liver Diseases, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy aff004;  Unit of Oncology and Molecular Pathology, Department of Biomedical Sciences, University of Cagliari, Cagliari, Italy aff005
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: 10.1371/journal.pone.0226615

Souhrn

Background

Entropy is a thermodynamic variable statistically correlated with the disorder of a system. The hypothesis that entropy can be used to identify potentially unhealthy conditions was first suggested by Schrödinger, one of the founding fathers of quantum mechanics. Shannon later defined entropy as the quantity of information stored in a system. Shannon’s entropy has the advantage of being adaptable across a variety of disciplines, including genetic studies on complex immunogenetic systems such as the human leukocyte antigen (HLA) and killer-cell immunoglobulin-like receptor (KIR) systems.

Methods

In our study, entropy associated to the HLA and KIR systems was compared between a cohort of 619 Sardinian healthy controls and a group of 270 patients affected by multiple sclerosis (MS), the latter stratified into 81 patients with primary progressive multiple sclerosis (PPMS) and 189 patients with relapsing remitting multiple sclerosis (RRMS).

Results

The entropy associated to HLA four-loci haplotypes (A, B, C, DR) and combinations of two inhibitory KIR genes was significantly higher in patients affected by RRMS than in healthy controls. No significant differences were observed for patients with PPMS. By calculating the total HLA and KIR entropy ratio in each subject, it was possible to determine the individual risk of developing MS, particularly RRMS.

Conclusions

In addition to the standard statistical methods used to evaluate immunogenetic parameters associated to immune-mediated disease, the analysis of entropy measures the global disorder status deriving from these parameters. This innovative approach may represent a useful complementary tool to the risk assessment of immune-mediated disorders. Improved risk assessment is particularly important for family members of patients with MS. However, further investigation is warranted to confirm our findings and to evaluate the validity of the entropy-based method in other types of immune-mediated disorders.

Klíčová slova:

Antigens – Disabilities – Entropy – Genetics of disease – Haplotypes – Immune response – Information entropy – Multiple sclerosis


Zdroje

1. Schrödinger E. What is Life? The Physical Aspects of the Living Cell. Cambridge: Cambridge University Press; 1944.

2. Shannon CE. A Mathematical Theory of Communication. The Bell System Technical Journal 1948;27(3):379–423. 10.1002/j.1538-7305.1948.tb01338.x

3. Contu L, Arras M, Carcassi C, La Nasa G, Mulargia M. HLA structure of the Sardinian population: a haplotype study of 551 families. Tissue Antigens 1992;40:165–74. doi: 10.1111/j.1399-0039.1992.tb02041.x 1471143

4. Pembroke TP, Gallimore AM, Godkin A. Rapid innate control of antigen abrogates adaptive immunity. Immunology 2013;138:293–7. doi: 10.1111/imm.12048 23198899

5. Lanier LL. Up on the tightrope: natural killer cell activation and inhibition. Nat Immunol 2008;9:495–502. doi: 10.1038/ni1581 18425106

6. Bashirova AA, Martin MP, McVicar DW, Carrington M. The killer immunoglobulin-like receptor gene cluster: tuning the genome for defense. Annu Rev Genomics Hum Genet 2006;7:277–300. doi: 10.1146/annurev.genom.7.080505.115726 16824023

7. Poggi A and Zocchi MF. NK cell autoreactivity and autoimmune diseases. Frontiers in Immunology 2014;5:1–15. doi: 10.3389/fimmu.2014.00001

8. Rajagopalan S, Long EO. Understanding how combinations of HLA and KIR genes influence disease. J Exp Med 2005;201:1025–9. doi: 10.1084/jem.20050499 15809348

9. Littera R, Orrù N, Caocci G, Sanna M, Mulargia M, Piras E, et al. Interactions between killer immunoglobulin-like receptors and their human leucocyte antigen Class I ligands influence the outcome of unrelated haematopoietic stem cell transplantation for thalassaemia: a novel predictive algorithm. Br J Haematol 2012;156:118–28. doi: 10.1111/j.1365-2141.2011.08923.x 22077388

10. Hilton HG, Parham P. Missing or altered self: human NK cell receptors that recognize HLA-C. Immunogenetics 2017;69:567–79. doi: 10.1007/s00251-017-1001-y 28695291

11. Littera R, Chessa L, Onali Simona, Figorilli F, Lai S, Secci L, et al. Exploring the Role of Killer Cell Immunoglobulin-Like Receptors and Their HLA Class I Ligands in Autoimmune Hepatitis. PloS One 2016;11(1):e0146086. doi: 10.1371/journal.pone.0146086 eCollection 2016. 26744892

12. Compston A and Coles A. Multiple sclerosis. Lancet 2008;372(9648):1502–17. doi: 10.1016/S0140-6736(08)61620-7 18970977

13. Fusco C, Guerini FR, Nocera G, Ventrella G, Caputo D, Valentino MA, et al. KIRs and their HLA ligands in remitting-relapsing multiple sclerosis. J Neuroimmunol 2010;229:232–7. doi: 10.1016/j.jneuroim.2010.08.004 20826009

14. Hollenbach JA and Oksenberg JR. The Immunogenetics of Multiple Sclerosis: A Comprehensive Review. J Autoimmun 2015;64:13–25. doi: 10.1016/j.jaut.2015.06.010 26142251

15. Thompson AJ, Banwell BL, Barkhof F, Carroll WM, Coetzee T, Comi G, et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol 2018;17:162–73. doi: 10.1016/S1474-4422(17)30470-2 29275977

16. Lublin FD, Reingold SC. Defining the clinical course of multiple sclerosis: results of an international survey. National Multiple Sclerosis Society (USA) Advisory Committee on Clinical Trials of New Agents in Multiple Sclerosis. Neurology 1996;46:907–11. doi: 10.1212/wnl.46.4.907 8780061

17. Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology 1983;33:1444–52. doi: 10.1212/wnl.33.11.1444 6685237

18. Cendrowski WS. Progression index and disability status in multiple sclerosis: a resurvey of 207 patients in central Poland. Schweiz Arch Neurol Psychiatr (1985). 1986;137:5–13.

19. Uhrberg M, Valiante NM, Shum BP, Shilling HG, Lienert-Weidenbach K, Corliss B et al. Human diversity in killer cell inhibitory receptor genes. Immunity 1997;7:753–63. doi: 10.1016/s1074-7613(00)80394-5 9430221

20. Gagne K, Brizard G, Gueglio B, Milpied N, Herry P, Bonneville F, et al. Relevance of KIR gene polymorphisms in bone marrow transplantation outcome. Hum Immunol 2002;63:271–80. doi: 10.1016/s0198-8859(02)00373-7 12039408

21. Closa L, Vidal F, Herrero MJ, Caro JL. Design and Validation of a Multiplex KIR and HLA Class I Genotyping Method Using Next Generation Sequencing. Front Immunol 2018;9:2991. doi: 10.3389/fimmu.2018.02991 eCollection 2018. 30619344

22. Kirkwood BR and Sterne JAC. Essential Medical Statistics (2nd edition). Oxford UK: Blackwell Scientific Publications; 2003.

23. R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2018.

24. Bland M. An Introduction to Medical Statistics (fourth ed.). Oxford UK: Oxford University Press; 2015.

25. Watson JD and Crick FHC. A Structure for Deoxyribose Nucleic Acid. Nature 1953;171:737–38. doi: 10.1038/171737a0 13054692

26. Wilkins MHF, Stokes AR and Wilson HR. Molecular Structure of Deoxypentose Nucleic Acids. Nature 1953;171:738–40. doi: 10.1038/171738a0 13054693

27. Shannon CE. An algebra for theoretical genetics. Ph.D. Dissertation in Mathematics, Massachusetts Institute of Technology;1940.

28. Kaushansky N, Altmann DM, David CS, Lassmann H, Ben-Nun A. DQB1*0602 rather than DRB1*1501 confers susceptibility to multiple sclerosis-like disease induced by proteolipid protein (PLP). J Neuroinflammation 2012;9:29. doi: 10.1186/1742-2094-9-29 22316121

29. International Multiple Sclerosis Genetics Consortium. Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature 2011;476(7359):214–9. doi: 10.1038/nature10251 21833088

30. Marrosu MG, Murru R, Murru MR, Costa G, Zavattari P, Whalen M, et al. Dissection of the HLA association with multiple sclerosis in the founder isolated population of Sardinia. Hum Mol Genet 2001;10(25):2907–16. doi: 10.1093/hmg/10.25.2907 11741834

31. Pastorino R, Menni C, Barca M, Foco L, Saddi V, Gazzaniga G, et al. Association between protective and deleterious HLA alleles with multiple sclerosis in Central East Sardinia. PLoS One 2009;4(8):e6526. doi: 10.1371/journal.pone.0006526 19654877

32. Fusco C, Guerini FR, Nocera G, Ventrella G, Caputo D, Valentino MA, et al. KIRs and their HLA ligands in remitting-relapsing multiple sclerosis. J Neuroimmunol 2010;229:232–7. doi: 10.1016/j.jneuroim.2010.08.004 20826009

33. Lorentzen AR, Karlsen TH, Olsson M, Smestad C, Mero IL, Woldseth B, et al. Killer immunoglobulin-like receptor ligand HLA-Bw4 protects against multiple sclerosis. Ann Neurol 2009;65:658–66. doi: 10.1002/ana.21695 19630074

34. Shahsavar F, Mapar S, Ahmadi SA. Multiple sclerosis is accompanied by lack of KIR2DS1 gene: A meta-analysis. Genom Data 2016;10:75–8. eCollection 2016. doi: 10.1016/j.gdata.2016.09.009 27747156

35. Cree BA. Genetics of primary progressive multiple sclerosis. Handb Clin Neurol 2014;122:211–30. doi: 10.1016/B978-0-444-52001-2.00042-X 24507520

36. Ziemssen T., Rauer S., Stadelmann C., Henze T., Koehler J., Penner I.-K., et al. Evaluation of Study and Patient Characteristics of Clinical Studies in Primary Progressive Multiple Sclerosis: A Systematic Review. PLoS One. 2015;10:e0138243. doi: 10.1371/journal.pone.0138243 26393519

37. Sherwin WB. Entropy and information approaches to genetic diversity and its expression: genomic geography. Entropy 2010;12:1765–98.

38. Shaw CA, Seneff S, Kette SD, Tomljenovic L, Oller JW Jr, Davidson RM. Aluminum-induced entropy in biological systems: implications for neurological disease. J Toxicol 2014;2014:491316. doi: 10.1155/2014/491316 25349607

39. Andlauer TF, Buck D, Antony G, Bayas A, Bechmann L, Berthele A, et al. Novel multiple sclerosis susceptibility loci implicated in epigenetic regulation. Sci Adv 2016;17:2:e1501678. doi: 10.1126/sciadv.1501678

40. Steri M, Orrù V, Idda ML, Pitzalis M, Pala M, Zara I, et al. Overexpression of the Cytokine BAFF and Autoimmunity Risk. N Engl J Med 2017;376:1615–26. doi: 10.1056/NEJMoa1610528 28445677


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