Our new nhs study funded by innovate uk

COVID-19 Genetics

Investigating whether certain genes may protect individuals from developing severe complications following infection with COVID-19

Summary

Certain MHC genes are associated with impaired T cell lymphocyte recruitment following metal-on-metal hip implantation. T cell activation, while undesirable in the response to implants, is important in the response to pathogens and in the development of long-term immunity. Might regional differences in MHC gene distribution explain the variation in COVID-19 mortality rates across the globe?

Introduction

Superficially, it may appear that joint replacement surgery and virology have little in common. Yet on closer examination, there appear to be similarities which may have important clinical applications.

Over the last decade we investigated aggressive T4 lymphocyte driven responses to cobalt chrome metal-on-metal hip replacements. This pathological response, termed “aseptic lymphocyte dominated vasculitis association lesion” (ALVAL), often resulted in extensive soft tissue damage around the prostheses. In the most advanced cases, tertiary lymphoid organs developed in the periprosthetic tissues. Once ALVAL was established, patients could not tolerate reimplantation with CoCr. Essentially, they had become “immunised” to the metal.(1, 2)

The first patients we encountered at our centre were all females, though more male patients presented with ALVAL as the years went by. The predilection of ALVAL for female sex has been reported time and again(3), though the association with age is less well recognised.(4, 5)  

We initially suspected that ALVAL was a pathological response to implants wearing at excessive rates. To investigate this, we quantified the dose exposure of CoCr by analysing the removed components (“explants”) and calculating the volumetric wear. Although exposure to greater amounts of metal appeared to make ALVAL more likely, volumetric wear was a poor predictor of the intensity of lymphocyte infiltration, even after adjustment for age and sex.(6) Approximately 70% of the variation in the lymphocyte responses remained unexplained. We therefore set about investigating possible genetic links.

Which genes should be targeted in the investigation?

With respect to histological features, age and sex distribution, ALVAL shares a number of features with autoimmune and autoimmune like diseases. For this reason, we focused our investigation on genes which were sex linked(7-11) and known to directly affect the communication between macrophages and T lymphocytes in diseases such as coeliac, rheumatoid and SLE.(12) The obvious candidates were the human leukocyte antigen (HLA) or major histocompatibility complex (MHC) genes.(13)

So far, we have analysed the DNA samples of 85 patients who were highly satisfied with their metal-on-metal joint replacements (at a minimum of ten years follow up) and have compared them to samples taken from 135 patients who had experienced early joint failure with varying degrees of lymphocyte infiltration. Regression modelling indicated that specific MHC class II heterodimers were significantly associated with ALVAL.(5) As is the case with other MHC related disorders, genes which promote ALVAL appear to be different for males and female. Crucially, the presence of the class II haplotype DQA1*01:01/DQB1*05:01 (linked to DRB1*01) appeared to attenuate lymphocyte responses in both sexes but to a greater extent in female patients.

What are MHC complexes and why might they be important in the response to COVID-19?

MHC molecules are specialised glycoproteins found on the surface of cells throughout the body. The main function of the MHC is to bind antigens derived from pathogens (or foreign material, in the case of ALVAL) and display them on the cell surface for recognition by the appropriate T cells.

The peptide fragments bind to MHC molecules at a specific region termed the “the peptide-binding groove”(14) (PBG) which is formed between the alpha1 and beta1 glycoprotein chains.(15) The morphology of this structure is genetically determined and encoded by the HLA genes located on chromosome 6. The shape of the PBG influences which peptide chains will bind with greater or lesser stability and thus determines which antigens are presented to T cells.(14)

The process, with respect to MHC Class II molecules, is illustrated in the schematic images below. In the case of extracellular pathogens, they are ingested by antigen presenting cells (APCs) such as macrophages (figure 1, left tile). The resulting endosome progresses deeper into the cell where it is degraded in the lysosome(16) (figure 1, right tile). The acidic environment of the lysosome causes the pathogen to fragment into its constituent peptides. These peptide fragments compete to occupy the binding groove of an MHC class II molecule.(14)

Figure 1

If binding is sufficiently stable, the MHC molecule passes to the cell membrane and displays the bound peptide (figure 2, left tile). If the displayed peptide is recognised as an antigen, lymphocytes are activated (figure 2, right tile). The resulting peptide-MHC complex which is presented at the cell surface is therefore of critical importance in the recruitment of T lymphocytes.(14)

Figure 2. The structures of an individual’s peptide binding grooves are crucial to the resulting immune response.

Certain genetic combinations encode for PBGs which may have much greater or lesser probability to bind certain peptide fragments.  For example, in the schematic image (figure 3, left), a binding groove is formed in one individual which is particularly suited to the binding of one peptide structure (shown in purple). On the right, a different alpha/beta chain combination results in the creation of a binding groove with a different configuration, one more suited to bind alternative peptide structures (shown in green).  

Figure 3

Perhaps the best illustration of how an individual’s PBGs determine the risk of a disease is with coeliac disease. Coeliac patients have MHC DQ variants which encode PBG structures particularly suited to binding specific peptides derived from gluten.(17) As with ALVAL, simply having the “risk” genes does not mean an individual will inevitably become sensitised. But it does mean that there is a greater probability of sensitisation over the course of the lifetime of a patient (or prosthesis), with certain peptides being presented with greater frequency, thereby loading the odds of sensitisation. With respect to MoM hips, patients with ALVAL are more likely to possess PBGs suited to the binding of albumin derived peptides known to bind metal ions.(5)

MHC genes and the host response to viruses

While lymphocyte sensitisation may be detrimental to the success of joint replacement surgery, it is an important step in the fight against invading pathogens.(18) An effective, sustained immune response depends on recognition of viral proteins by T cells and their subsequent activation and proliferation. In patients with severe COVID-19 infection however, it has been demonstrated that there is progressive lymphopaenia.(19)

Although it is thought that presentation of viral antigens relies classically on MHC-I molecules, sufficient T-cell CD4+ engagement is an important predictor of outcome for several viral infectious diseases, including hepatitis A and B, and influenza. MHC-II is particularly important to sustain a long-term immune response during vaccination(20-22) and MHC-II has been associated with the outcome of many other viral infections such as EBV, dengue and West Nile Virus disease.(23-26) MHC-II genes have also been implicated in SARS and MERS though the evidence is equivocal.(27, 28).

Fundamentally, an individual’s MHCs determine which peptides are most likely to be presented on the surface of the individual’s cells. Certain MHCs will respond more rapidly and effectively to a specific novel virus than other MHCs.

MHC distributions vary in different populations across the world

Patterns of migration influence the distribution of HLA genes in a country’s population.(29) Might this provide an explanation for differing COVID mortality rates in different patient populations? Unfortunately, a lack of standardised testing protocols makes it difficult to directly compare COVID-19 mortality rates between countries. But it seems unlikely that the variation in testing protocols would account for the drastic differences in COVID deaths reported in East Asia compared to Western Europe. As an example, the total number of deaths in Shanghai, a densely populated city of 24 million inhabitants situated only 522 miles from the original source of the outbreak (Wuhan), has reported only six deaths to date. Such variation has largely been attributed to social and government policies. Could regional variation in MHC gene distributions provide a better explanation?

Peptide modelling, gene frequencies and mortality rates

The genome of COVID-19 was sequenced early during the outbreak.(30) The peptide sequences most likely to stimulate a lymphocyte response (“epitopes”) have been described.(31) In the COVID structure, there is a concentration of potential epitopes in sub-unit 2 of the spike region.

Validated peptide modelling software can now be used to determine the binding affinities of different MHC complexes to specific peptide chains of a pathogen.(32) These in silico techniques have been used in the past for studies on viral diseases such as West Nile Virus and MERS.(15)

Peptide modelling of MHC haplotypes found across the world indicates that the gene combination DQA1*05:01/DQB1*02:01 (a haplotype which is common in Europe and linked to DRB1*03) shows the lowest binding affinity to the epitope rich region of the COVID-19 spike protein (see table 1).

We reviewed the population gene data reported on the public database www.allelefrequency.net. and recorded the frequency of this gene in 36 different countries (see notes). We compared these frequencies to the corresponding country’s reported COVID mortality rates and found a highly significant correlation (figure 4).

Figure 4. Linear regression of the frequency of HLA-DRB1*03 in 36 different countries versus the log normalised COVID death rates per million citizens (R2 = 42%, p < 0.001).

This finding should be interpreted with caution due to the multiple diseases with which MHC genes are associated. These associations could, however, be disentangled using relevant public health data.

Gene frequencies and male to female mortality ratios

In order to circumvent the confounding issues of testing protocols and different thresholds for reporting a “COVID death”, we examined male to female mortality ratios (M:F MRs). M:F MRs are not reported by every country, though it is clear that they vary across the world, a phenomenon unexplained by social factors such as smoking.(26) Publicly available data demonstrate that this ratio varies from as high as 3.5:1 in Malaysia, to less than one in Portugal and Finland (see notes).

This time, we focused on DRB1*01, a gene that was associated with lymphocyte inhibition in females in the ALVAL studies. We hypothesized that impaired lymphocyte recruitment would lead to greater risk of COVID mortality in females and thus a reduction in M:F MR. We found a highly significant inverse correlation between the frequency of DRB1*01 in a country and the reported M:F MR (figure 5). Similar to DRB1*03, a higher DRB1*01 gene frequency  was also positively associated with overall mortality rates. Perhaps a chance finding, however one that would make sense if this gene is indeed linked to an impaired T lymphocyte response when a host is challenged with a new antigen.

Figure 5. The relationship between the frequency of HLA-DRB1*03 and the male:female mortality ratios reported in 36 countries (Spearman rank correlation = -0.643, p < 0.001).

Might this explain the variation in mortality rates reported across the globe? Obviously, these are simply associations. However, the idea that immunogenetic traits will play an important role in the response to viruses (as well as implants) is logical and evidence based.

Conclusions

It is readily apparent that this annotation reports associations rather than establishing causal links.  One must be aware that in any genetic studies, there are a host of linked genes and associated diseases, relationships which must be unpicked for definitive conclusions to be drawn. However, despite being recognised as a key factor in the response to pathogens, MHC variation seems to somewhat been neglected, with greater attention paid to social issues, government lockdown policies and vitamin deficiencies.

It is disappointing that many countries still do not provide disaggregated COVID data, particularly with regard to ethnicity, as it is likely that MHC gene variation may play a role in the increased rates of mortality observed in BAME groups. We are carrying out a dedicated MHC study of COVID-19 patients to investigate these issues further.

Perhaps we may be progressing to an era of implant selection according to patient genotype, but also the stratification of risk posed by emerging pathogens. The formulation of targeted vaccination and shielding strategies according to an individual’s genetic makeup might alleviate economic damage in this pandemic or the next. We must, however, be mindful of the complex ethical issues this may introduce.

Notes

Genetic data taken from www.allelefrequency.net. Countries were included in the analysis if there was genetic data of sufficient resolution and a large population sample was reported.

Mortality data taken from the Johns Hopkins Coronavirus Resource Center https://coronavirus.jhu.edu/map.html, data accurate as of 21st May 2020.

Male to female mortality data taken from Global Health 5050 “Sex, gender and COVID-19: overview and resources”www.globalhealth5050.org/covid19).

Data reported from: Albania, Armenia, Argentina, Australia, Austria, Bangladesh, Belgium, Bosnia, Czech Republic, England and Wales, Ecuador, Finland, Germany, Greece, Hungary, Iceland, Iran, Israel (Israel Jewish population), Italy, Japan, North Macedonia, Malaysia, The Netherlands, Northern Ireland, Norway, Poland, Portugal, Republic of Ireland, Romania, Serbia, South Korea, Spain, Sweden, Switzerland (Zurich), Thailand.

Strong Binders

Weak Binders

Epitope Rank EL

Epitope

Linkage

Haplotype

0

9

2.20

LQSLQTYVTQQLIRA

DRB1*01

DQA1*01:01/DQB1*05:01

0

9

4.20

NFGAISSVLNDILSR

DRB1*03

DQA1*05:01/DQB1*02:01

6

4

0.22

TQQLIRAAEIRASAN

DRB1*04

DQA1*03:01/DQB1*03:01

2

7

1.73

TQQLIRAAEIRASAN

DRB1*04

DQA1*03:01/DQB1*03:02

6

4

0.22

TQQLIRAAEIRASAN

DRB1*04

DQA1*03:03/DQB1*03:01

0

8

3.06

NFGAISSVLNDILSR

DRB1*07

DQA1*02:01/DQB1*02:02

3

8

0.57

TQQLIRAAEIRASAN

DRB1*08

DQA1*04:01/DQB1*04:02

2

7

1.73

TQQLIRAAEIRASAN

DRB1*09

DQA1*03:02/DQB1*03:02

8

5

0.06

TQQLIRAAEIRASAN

DRB1*09

DQA1*03:02/DQB1*03:03

0

9

2.20

LQSLQTYVTQQLIRA

DRB1*10

DQA1*01:05/DQB1*05:01

4

5

0.49

TQQLIRAAEIRASAN

DRB1*11

DQA1*05:05/DQB1*03:01

6

4

0.05

TQQLIRAAEIRASAN

DRB1*13

DQA1*01:03/DQB1*06:03

5

6

0.67

TQQLIRAAEIRASAN

DRB1*14

DQA1*01:04/DQB1*05:03

6

6

0

TQQLIRAAEIRASAN

DRB1*15

DQA1*01:02/DQB1*06:02

0

11

2.98

TQQLIRAAEIRASAN

DRB1*16

DQA1*01:02/DQB1*05:02

Table 1. Common haplotypes, DRB1 linkage and their binding affinities to the COVID-19 peptide chain under examination. Threshold for “strong binding” drawn at top 2.5% of binders, weak at 10%.

References

  1. Natu S, Sidaginamale RP, Gandhi J, Langton DJ, Nargol AV. Adverse reactions to metal debris: histopathological features of periprosthetic soft tissue reactions seen in association with failed metal on metal hip arthroplasties. Journal of clinical pathology. 2012;65(5):409-18.
  2. Jameson SS, Langton DJ, Natu S, Nargol TV. The influence of age and sex on early clinical results after hip resurfacing: an independent center analysis. J Arthroplasty. 2008;23(6 Suppl 1):50-5.
  3. Pandit H, Glyn-Jones S, McLardy-Smith P, Gundle R, Whitwell D, Gibbons CL, et al. Pseudotumours associated with metal-on-metal hip resurfacings. J Bone Joint Surg Br. 2008;90(7):847-51.
  4. Kolatat K, Perino G, Wilner G, Kaplowitz E, Ricciardi BF, Boettner F, et al. Adverse local tissue reaction (ALTR) associated with corrosion products in metal-on-metal and dual modular neck total hip replacements is associated with upregulation of interferon gamma-mediated chemokine signaling. J Orthop Res. 2015;33(10):1487-97.
  5. Langton D, Sidaginamale R, Wells S, Wainwright B, Holland J, Deehan D, et al. IS THERE A GENETIC PREDISPOSITION TO ALVAL? Orthopaedic Proceedings. 2019;101-B(SUPP_6):25-.
  6. Langton D, Joyce T, Jameson S, Lord J, Van Orsouw M, Holland J, et al. Adverse reaction to metal debris following hip resurfacing: the influence of component type, orientation and volumetric wear. The Journal of bone and joint surgery British volume. 2011;93(2):164-71.
  7. Taneja V. Sex Hormones Determine Immune Response. Frontiers in immunology. 2018;9:1931.
  8. Behrens M, Trejo T, Luthra H, Griffiths M, David CS, Taneja V. Mechanism by which HLA-DR4 regulates sex-bias of arthritis in humanized mice. Journal of autoimmunity. 2010;35(1):1-9.
  9. Grimaldi CM, Cleary J, Dagtas AS, Moussai D, Diamond B. Estrogen alters thresholds for B cell apoptosis and activation. The Journal of clinical investigation. 2002;109(12):1625-33.
  10. Mangalam AK, Taneja V, David CS. HLA class II molecules influence susceptibility versus protection in inflammatory diseases by determining the cytokine profile. Journal of immunology (Baltimore, Md : 1950). 2013;190(2):513-8.
  11. Daien CI, Morel J. Predictive factors of response to biological disease modifying antirheumatic drugs: towards personalized medicine. Mediators of inflammation. 2014;2014:386148.
  12. Cutolo M, Sulli A, Capellino S, Villaggio B, Montagna P, Seriolo B, et al. Sex hormones influence on the immune system: basic and clinical aspects in autoimmunity. Lupus. 2004;13(9):635-8.
  13. Kilb BKJ, Kurmis AP, Parry M, Sherwood K, Keown P, Masri BA, et al. Frank Stinchfield Award: Identification of the At-risk Genotype for Development of Pseudotumors Around Metal-on-metal THAs. Clinical orthopaedics and related research. 2018;476(2):230-41.
  14. Afridi S, Hoessli DC, Hameed MW. Mechanistic understanding and significance of small peptides interaction with MHC class II molecules for therapeutic applications. Immunological reviews. 2016;272(1):151-68.
  15. Sarri CA, Papadopoulos GE, Papa A, Tsakris A, Pervanidou D, Baka A, et al. Amino acid signatures in the HLA class II peptide-binding region associated with protection/susceptibility to the severe West Nile Virus disease. PloS one. 2018;13(10):e0205557.
  16. Ciechanover A. Intracellular protein degradation: From a vague idea thru the lysosome and the ubiquitin-proteasome system and onto human diseases and drug targeting. Best practice & research Clinical haematology. 2017;30(4):341-55.
  17. Sollid LM. The roles of MHC class II genes and post-translational modification in celiac disease. Immunogenetics. 2017;69(8-9):605-16.
  18. Karakus U, Thamamongood T, Ciminski K, Ran W, Gunther SC, Pohl MO, et al. MHC class II proteins mediate cross-species entry of bat influenza viruses. Nature. 2019;567(7746):109-12.
  19. Zhao Q, Meng M, Kumar R, Wu Y, Huang J, Deng Y, et al. Lymphopenia is associated with severe coronavirus disease 2019 (COVID-19) infections: A systemic review and meta-analysis. International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases. 2020;96:131-5.
  20. Gu J, Gong E, Zhang B, Zheng J, Gao Z, Zhong Y, et al. Multiple organ infection and the pathogenesis of SARS. The Journal of experimental medicine. 2005;202(3):415-24.
  21. Murray PJ, Wynn TA. Protective and pathogenic functions of macrophage subsets. Nature reviews Immunology. 2011;11(11):723-37.
  22. Zhou J, Chu H, Li C, Wong BH-Y, Cheng Z-S, Poon VK-M, et al. Active Replication of Middle East Respiratory Syndrome Coronavirus and Aberrant Induction of Inflammatory Cytokines and Chemokines in Human Macrophages: Implications for Pathogenesis. The Journal of Infectious Diseases. 2013;209(9):1331-42.
  23. El-Bendary M, Neamatallah M, Elalfy H, Besheer T, Kamel E, Mousa H, et al. HLA Class II-DRB1 Alleles with Hepatitis C Virus Infection Outcome in Egypt: A Multicentre Family-based Study. Annals of hepatology. 2019;18(1):68-77.
  24. Ranasinghe S, Cutler S, Davis I, Lu R, Soghoian DZ, Qi Y, et al. Association of HLA-DRB1-restricted CD4(+) T cell responses with HIV immune control. Nature medicine. 2013;19(7):930-3.
  25. Rubicz R, Yolken R, Drigalenko E, Carless MA, Dyer TD, Bauman L, et al. A genome-wide integrative genomic study localizes genetic factors influencing antibodies against Epstein-Barr virus nuclear antigen 1 (EBNA-1). PLoS genetics. 2013;9(1):e1003147.
  26. Nguyen TP, Kikuchi M, Vu TQ, Do QH, Tran TT, Vo DT, et al. Protective and enhancing HLA alleles, HLA-DRB1*0901 and HLA-A*24, for severe forms of dengue virus infection, dengue hemorrhagic fever and dengue shock syndrome. PLoS neglected tropical diseases. 2008;2(10):e304.
  27. Ng MH, Lau KM, Li L, Cheng SH, Chan WY, Hui PK, et al. Association of human-leukocyte-antigen class I (B*0703) and class II (DRB1*0301) genotypes with susceptibility and resistance to the development of severe acute respiratory syndrome. J Infect Dis. 2004;190(3):515-8.
  28. Hajeer AH, Balkhy H, Johani S, Yousef MZ, Arabi Y. Association of human leukocyte antigen class II alleles with severe Middle East respiratory syndrome-coronavirus infection. Annals of thoracic medicine. 2016;11(3):211-3.
  29. Fernandez Vina MA, Hollenbach JA, Lyke KE, Sztein MB, Maiers M, Klitz W, et al. Tracking human migrations by the analysis of the distribution of HLA alleles, lineages and haplotypes in closed and open populations. Philos Trans R Soc Lond B Biol Sci. 2012;367(1590):820-9.
  30. Liu J, Zheng X, Tong Q, Li W, Wang B, Sutter K, et al. Overlapping and discrete aspects of the pathology and pathogenesis of the emerging human pathogenic coronaviruses SARS-CoV, MERS-CoV, and 2019-nCoV. Journal of medical virology. 2020;92(5):491-4.
  31. Bhattacharya M, Sharma AR, Patra P, Ghosh P, Sharma G, Patra BC, et al. Development of epitope-based peptide vaccine against novel coronavirus 2019 (SARS-COV-2): Immunoinformatics approach. Journal of medical virology. 2020.
  32. Jensen KK, Andreatta M, Marcatili P, Buus S, Greenbaum JA, Yan Z, et al. Improved methods for predicting peptide binding affinity to MHC class II molecules. Immunology. 2018;154(3):394-406.
Contact Us

Make an enquiry

Please fill in the form and we will get back to you.

Thank you! Your message has been received!
Oops! Something went wrong while submitting the form.