ORIGINAL RESEARCH article

Front. Aging, 04 February 2025

Sec. Aging and the Immune System

Volume 6 - 2025 | https://doi.org/10.3389/fragi.2025.1471202

Immunogenetics of longevity and its association with human endogenous retrovirus K

  • 1. Department of Veterans Affairs Health Care System, The HLA Research Group, Brain Sciences Center, Minneapolis, MN, United States

  • 2. Department of Neuroscience, University of Minnesota Medical School, Minneapolis, MN, United States

  • 3. Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, United States

  • 4. Institute for Health Informatics, University of Minnesota Medical School, Minneapolis, MN, United States

Abstract

Introduction:

The human immune system is equipped to neutralize and eliminate viruses and other foreign antigens via binding of human leukocyte antigen (HLA) molecules with foreign antigen epitopes and presenting them to T cells. HLA is highly polymorphic, resulting in subtle differences in the binding groove that influence foreign antigen binding and elimination. Here we tested the hypothesis that certain HLA alleles may promote longevity by enhanced ability to counter virus antigens that may otherwise contribute to morbidity and mortality.

Methods:

We utilized high-resolution genotyping to characterize HLA and apolipoprotein E in a large sample (N = 986) of participants (469 men, 517 women) ranging in age from 24 to 90+ years old (mean age: 58.10 years) and identified 244 HLA alleles that occurred in the sample. Since each individual carries 12 classical HLA alleles (6 alleles of each Class I and Class II), we determined in silico the median predicted binding affinity for each individual (across the 12 HLA alleles) and each of 13 common viruses (Human Herpes Virus 1 [HHV1], HHV2, HHV3, HHV4, HHV5, HHV6A, HHV6B, HHV7, HHV8, human papilloma virus [HPV], human polyoma virus [JCV], human endogenous retrovirus K [HERVK], and HERVW). Next, we performed a stepwise multiple linear regression where the age of the participant was the dependent variable and the 13 median predicted HLA-virus binding affinities were the independent variables.

Results:

The analyses yielded only one statistically significant effect–namely, a positive association between age and HERVK (P = 0.005). Furthermore, we identified 13 HLA alleles (9 HLA-I and 4 HLA-II) that occurred at greater frequency in very old individuals (age ≥90 years) as compared to younger individuals. Remarkably, for those 13 alleles, the predicted binding affinities were significantly higher for HERVK than for the other viruses (P < 0.001). ApoE genotypes did not differ significantly between older and younger groups.

Discussion:

Taken together, the results showed that HLA-HERVK binding affinity is a robust predictor of longevity and that HLA alleles that bind with high affinity to HERVK were enriched in very old individuals. The findings of the present study highlight the influence of interactions between host immunogenetics and virus exposure on longevity and suggest that specific HLA alleles may promote longevity via enhanced immune response to specific common viruses, notably HERVK.

1 Introduction

Exposure to viruses and other foreign antigens is associated with numerous and wide-ranging health conditions and sequelae (; Mui et al., 2017; Krump and You, 2018; Houen and Trier, 2021; Hussein and Rahal, 2019; Lotz et al., 2021; ; ). Under optimal conditions–that is, immunocompetence–the human immune system is equipped to neutralize and eliminate foreign antigens that may otherwise contribute to morbidity and mortality. Specifically, the Human Leukocyte Antigen (HLA) region of chromosome 6 codes for cell-surface glycoproteins that play a critical role in the host immune response to foreign antigens including viruses, bacteria, and cancer neoantigens. Each individual possesses 12 classical HLA alleles, inherited in a Mendelian fashion, including six each from Class I (HLA-A, B, C) and Class II (HLA-DPB1, DQB1, DRB1). The steps involved in antigen processing and presentation by HLA-I and HLA-II alleles are reviewed elsewhere (Pishesha et al., 2022). Briefly, HLA-I molecules, which are expressed on all nucleated cells, help clear foreign antigens via binding and transporting cytosolic foreign antigen epitopes to the cell surface for presentation to cytotoxic CD8+ T cells to signal destruction of an infected cell. HLA-II molecules, which are expressed on lymphocytes and professional antigen presenting cells, bind and present endocytosed exogenous antigen epitopes to CD4+ T cells to stimulate antibody production and adaptive immunity. The HLA region is the most highly polymorphic of the human genome (Trowsdale and Knight, 2013). Even single amino acid differences can alter HLA-antigen binding (Hov et al., 2011), thereby influencing foreign antigen elimination and disease susceptibility (Dendrou et al., 2018). A recent genome-wide association meta-analysis documented that the HLA-DRB1 region is also associated with longevity as is apolipoprotein E (apoE) (Joshi et al., 2017). Here we tested the hypothesis that certain HLA alleles may promote longevity by enhanced ability to eliminate virus antigens that may otherwise contribute to morbidity and mortality. Specifically, we utilized high-resolution genotyping to 1) characterize the composition of HLA in a large sample of participants, 2) evaluate in silico the binding affinity of their HLA alleles with common viruses, and 3) determine the association between HLA-virus antigen binding affinity and age. We also evaluated the influence of apoE on longevity.

2 Materials and methods

2.1 Participants

A total of 986 participants (469 men, 517 women), the majority (87%) of whom were United States (US) veterans, were included in the present analyses. Participants were excluded from participation if they had been diagnosed with medical or psychiatric conditions that could impair ability to provide informed consent or participate in the study (e.g., Alzheimer’s dementia, schizophrenia). All participants provided written informed consent and were compensated for their participation. The study was approved by the Minneapolis Veterans Affairs Healthcare System Institutional Review Board and all research was performed in accordance with relevant guidelines and regulations.

2.2 HLA genotyping

DNA isolation was carried out from whole blood or saliva samples using commercially available kits (blood: ArchivePure cat. 2300730 from 5Prime distributed by Fisher Scientific or VWR; saliva: Oragene-Discover cat. OGR-500 coupled with prepIT purifier reagent cat. PT-L2P/DNA Genotek Inc. Ottawa, ON, Canada). The purified DNA samples were sent to HistoGenetics (http://www.histogenetics.com/) for high-resolution HLA Sequence-based Typing (SBT; details are given in https://bioinformatics.bethematchclinical.org/HLA-Resources/HLA-Typing/High-Resolution-Typing-Procedures/ and https://bioinformatics.bethematchclinical.org/WorkArea/DownloadAsset.aspx?id=6482). Their sequencing DNA templates are produced by locus- and group-specific amplifications that include exon 2 and 3 for Class I (A, B, C) and exon 2 for Class II (DRB1, DRB3/4/5, DQB1, and DPB1) and reported as Antigen Recognition Site (ARS) alleles as per ASHI recommendation ().

2.3 ApoE genotyping

DNA samples were genotyped using PCR amplification followed by restriction enzyme digestion (Reymer et al., 1995). Each amplification reaction contained PCR buffer with 15 mmol/L MgCl2 ng amounts of genomic DNA, 20 pmol apoE forward (5N TAA GCT TGG CAC GGC TGT CCA AGG A 3N) and reverse (5N ATA AAT ATA AAA TAT AAA TAA CAG AAT TCG CCC CGG CCT GGT ACA C 3N) primers, 1.25 mmol/L of each deoxynucleotide triphosphate, 10% dimethylsulfoxide, and 0.25 μL AmpliTaq DNA polymerase. Reaction conditions in a thermocycler included an initial denaturing period of 3 min at 95 C, 1 min at 60 C, and 2 min at 72 C; followed by 32 cycles of 1 min at 95 C, 1 min at 60 C, and 2 min at 72 C; and a final extension of 1 min at 95 C, 1 min at 60 C, and 3 min at 72 C. PCR products were digested with HhaI and separated on a 4% Agarose gel which was stained with ethidium bromide. Known apoE isoform standards were included in the analysis.

2.4 Viruses

Thirteen viruses implicated in various diseases in humans were investigated, including 9 human herpes viruses (HHV1, HHV2, HHV3, HHV4, HHV5, HHV6A, HHV6B, HHV7, HHV8), human polyoma JC virus (JCV), human papilloma virus 16 (HPV), human endogenous retrovirus K (HERVK), and human endogenous retrovirus W (HERVW). Details of the viral proteins used are given in Table 1.

TABLE 1

IndexVirusProtein descriptionUniprotKB ID
1HHV1Envelope glycoprotein DQ69091
2HHV2Envelope glycoprotein DP03172
3HHV3Envelope glycoprotein EQ9J3M8
4HHV4Envelope glycoprotein BP03188
5HHV5Envelope glycoprotein BP06473
6HHV6AEnvelope Glycoprotein Q2P0DOE0
7HHV6BEnvelope Glycoprotein Q1Q9QJ11
8HHV7Envelope glycoprotein HP52353
9HHV8Envelope glycoprotein HF5HAK9
10JCVMajor capsid protein VP1P03089
11HPVMajor capsid protein L1Q81007
12HERVK10 Pol proteinP10266
13HERVWEnvelope proteinQ9UQF0

Viral proteins used. HHV, human herpes virus; JCV, human polyomavirus JC; HPV, human papillomavirus; HERVK, human endogenous retrovirus K; HERVW, human endogenous retrovirus W.

2.5 In silico determination of predicted binding affinity of HLA-I and HLA-II alleles

Predicted binding affinities were obtained for viral protein epitopes using the Immune Epitope Database (IEDB) NetMHCpan (ver. 4.1) tool (Reynisson et al., 2020; IEDB, 2020). More specifically, we used the sliding window approach (; ; ) to test exhaustively all possible linear 9-mer (for HLA-I predictions) and 15-mer (for HLA-II predictions) AA residue epitopes of the 13 viral proteins analyzed (Table 1; Supplementary Table S1). The method is illustrated in Figure 1 for the HERVK virus protein. For each epitope-HLA molecule tested, this tool gives, as an output, the percentile rank of binding affinity of the HLA molecule and the epitope, among predicted binding affinities of the same HLA molecule to a large number of different peptides of the same AA length; the smaller the percentile rank, the better the binding affinity. Now, given a protein of N amino acid length and an epitope length of k AA, there are N-k binding affinity predictions, i.e., N-k percentile ranks. Of these predictions, for each viral protein and HLA molecule tested, we retained the lowest percentile rank (LPR) as the best binding affinity of the protein-HLA molecule pair. Finally, we took the inverse of LPR, so that higher values mean better binding affinities for more intuitive interpretation Equation 1:

FIGURE 1

2.6 Statistical analyses

Standard statistical methods were used to analyze the data using the IBM-SPSS statistical package (version 29). For multidimensional scaling (MDS), the ALSCAL procedure of the IBM-SPSS package was used (Level, ordinal; Condition, Matrix; Model, Euclid; Dimension [2,2]; Criteria: S-stress convergence = 0.001, minimum s-stress = 0.005, number of iterations = 30), and the K-means clustering procedure of the same package for identifying clusters in the MDS plot (Number of clusters = 3, Method: Iterate and Classify).

3 Results

3.1 Age

The frequency distribution of age is shown in Figure 2; mean = 58.10 y, SD = 13.77 y, median = 56.67 y, minimum = 24.08 y, maximum = 90 + y.

FIGURE 2

3.2 HLA alleles

There were 244 distinct HLA alleles (142 of Class I and 93 of Class II) comprising the classical genes of Class I (A, B, C) and Class II (DPB1, DQB1, DRB1). The alleles and their frequencies (percentages) in our sample (N = 986) are given in Tables 2, 3.

TABLE 2

HLA-I gene AHLA-I gene BHLA-I gene C
IndexAlleleNFrequency (%)IndexAlleleNFrequency (%)IndexAlleleNFrequency (%)
1A*01:0129029.4121B*07:0229229.6151C*01:02747.505
2A*02:0154154.8682B*07:0580.8112C*02:0110.101
3A*02:0240.4063B*07:0410.1013C*02:0211511.663
4A*02:05181.8264B*08:0122122.4144C*02:1010.101
5A*02:0640.4065B*13:02464.6655C*03:0280.811
6A*02:1710.1016B*14:01232.3336C*03:039910.041
7A*02:3010.1017B*14:02616.1877C*03:0419119.371
8A*02:3510.1018B*14:0310.1018C*04:0119219.473
9A*02:6310.1019B*15:0114014.1999C*04:0910.101
10A*02:7720.20310B*15:0330.30410C*05:0114014.199
11A*03:0130831.23711B*15:0710.10111C*05:0710.101
12A*03:0220.20312B*15:0920.20312C*06:0215916.126
13A*03:8110.10113B*15:1010.10113C*07:0130130.527
14A*11:0111311.4614B*15:1620.20314C*07:0231131.542
15A*11:0210.10115B*15:1730.30415C*07:04303.043
16A*23:01404.05716B*15:1820.20316C*07:1910.101
17A*24:0216917.1417B*15:2420.20317C*08:0120.203
18A*24:0340.40618B*15:3510.10118C*08:02848.519
19A*25:01454.56419B*18:01787.91119C*08:0310.101
20A*26:01464.66520B*18:0910.10120C*12:02121.217
21A*26:0840.40621B*27:02131.31821C*12:03848.519
22A*26:1210.10122B*27:0410.10122C*14:02232.333
23A*29:0160.60923B*27:05899.02623C*15:02414.158
24A*29:02494.9724B*27:0720.20324C*15:0410.101
25A*30:01292.94125B*27:0810.10125C*15:0580.811
26A*30:02131.31826B*27:1010.10126C*15:0610.101
27A*30:0420.20327B*35:01979.83827C*15:0910.101
28A*31:01545.47728B*35:02111.11628C*16:01707.099
29A*32:01747.50529B*35:03303.04329C*16:0230.304
30A*33:01151.52130B*35:08101.01430C*16:0420.203
31A*33:03111.11631B*35:1710.10131C*16:5010.101
32A*34:0110.10132B*37:01242.43432C*17:01111.116
33A*34:0220.20333B*38:01292.94133C*18:0120.203
34A*36:0130.30434B*39:01303.043
35A*66:0150.50735B*39:0210.101
36A*68:01818.21536B*39:0520.203
37A*68:02202.02837B*39:06131.318
38A*68:3710.10138B*39:2410.101
39A*74:0130.30439B*40:0112312.475
40A*74:0310.10140B*40:02313.144
41A*80:0150.50741B*41:0140.406
42B*41:0260.609
43B*42:0210.101
44B*44:0215215.416
45B*44:03798.012
46B*44:0430.304
47B*44:0570.71
48B*44:0710.101
49B*44:2740.406
50B*45:01222.231
51B*47:0180.811
52B*48:0120.203
53B*48:0710.101
54B*49:01232.333
55B*50:01171.724
56B*50:0210.101
57B*51:01969.736
58B*51:0210.101
59B*51:0710.101
60B*51:0910.101
61B*52:01161.623
62B*53:01151.521
63B*54:0110.101
64B*55:01222.231
65B*56:01202.028
66B*57:01474.767
67B*57:0220.203
68B*57:0320.203
69B*58:01141.42
70B*58:0220.203
71B*59:0110.101
72B*81:0110.101

Alleles of HLA Class I, Genes A, B, C found in our sample (N = 986 participants).

TABLE 3

HLA-II gene DPB1HLA-II gene DQB1HLA-II gene DRB1
AlleleN%AlleleN%AlleleN%
1DPB1*01:0110010.1421DQB1*02:0127728.0931DRB1*01:0116116.329
2DPB1*02:0124424.7462DQB1*02:0213713.8952DRB1*01:02171.724
3DPB1*02:0270.713DQB1*02:1020.2033DRB1*01:03121.217
4DPB1*03:0121722.0084DQB1*03:0135836.3084DRB1*03:0123924.239
5DPB1*04:0181182.2525DQB1*03:0219820.0815DRB1*03:0220.203
6DPB1*04:0224524.8486DQB1*03:03747.5056DRB1*04:0116516.734
7DPB1*05:01484.8687DQB1*03:0430.3047DRB1*04:0260.609
8DPB1*06:01252.5358DQB1*03:0510.1018DRB1*04:03131.318
9DPB1*09:01151.5219DQB1*03:1210.1019DRB1*04:04787.911
10DPB1*105:0180.81110DQB1*03:19101.01410DRB1*04:05121.217
11DPB1*10:01323.24511DQB1*04:02575.78111DRB1*04:0630.304
12DPB1*11:01363.65112DQB1*05:0121421.70412DRB1*04:07262.637
13DPB1*124:0130.30413DQB1*05:02474.76713DRB1*04:08111.116
14DPB1*126:0110.10114DQB1*05:03474.76714DRB1*04:1120.203
15DPB1*131:0010.10115DQB1*06:01131.31815DRB1*07:0122923.225
16DPB1*131:0120.20316DQB1*06:0227928.29616DRB1*08:01464.665
17DPB1*13:01303.04317DQB1*06:0316516.73417DRB1*08:0230.304
18DPB1*14:01262.63718DQB1*06:04616.18718DRB1*08:0340.406
19DPB1*15:01121.21719DQB1*06:09272.73819DRB1*08:0460.609
20DPB1*16:01151.52120DQB1*06:8410.10120DRB1*08:1120.203
21DPB1*17:01323.24521DRB1*09:01202.028
22DPB1*18:0140.40622DRB1*10:01191.927
23DPB1*19:01222.23123DRB1*11:0112512.677
24DPB1*20:01141.4224DRB1*11:0280.811
25DPB1*23:01131.31825DRB1*11:03161.623
26DPB1*29:0110.10126DRB1*11:04474.767
27DPB1*350:0110.10127DRB1*12:01313.144
28DPB1*35:0110.10128DRB1*13:0116016.227
29DPB1*40:0110.10129DRB1*13:02929.331
30DPB1*46:0110.10130DRB1*13:03212.13
31DPB1*50:0110.10131DRB1*13:0410.101
32DPB1*57:0110.10132DRB1*13:0520.203
33DPB1*59:0110.10133DRB1*13:0610.101
34DPB1*92:0110.10134DRB1*13:1210.101
35DRB1*14:0160.609
36DRB1*14:0220.203
37DRB1*14:0410.101
38DRB1*14:0610.101
39DRB1*14:54414.158
40DRB1*15:0127427.789
41DRB1*15:02111.116
42DRB1*15:0390.913
43DRB1*16:01363.651
44DRB1*16:02101.014

Alleles of HLA Class II, Genes DPB1, DQB1, DRB1 found in our sample (N = 986 participants).

We used the age of 90 years as a conservative cut-off point to separate participants in 2 groups: Group1 (age <90 years, N = 964) and Group 2 (age ≥90 years, N = 22, “very old”). We hypothesized that surviving to very old age could/would be associated with the presence of specific HLA alleles. For that purpose, we searched for alleles with higher frequencies in Group 2 (as compared to Group 1) and found 13 alleles with significantly higher proportions in Group 2 than in Group 1 (Very Old Alleles, VOA); since we were testing the hypothesis of only higher allele frequencies in Group 2, we used a one-tailed test of proportions to obtain the statistical significance of the difference between the two proportions. These alleles with detailed statistics are shown in Table 4; they comprise 9 alleles of HLA Class I (3 of each A, B, and C genes) and 4 of Class II (1 of DPB1 gene, 1 of DQB1 gene, and 2 of DRB1 gene). The ratios of Group 2/Group 1 proportions ranged from 1.62 (allele A*03:01) to 6.43 (alleles C*03:02 and DRB1*11:02).

TABLE 4

Group 1 (N = 964)Group 2 (N = 22)Comparison of proportions
<90 years≥90 years
IndexAlleleCountsProportion group 1CountsProportion group 2G2-G1P (1-tailed; uncorrected)
1A*02:05160.01720.0910.0740.005
2A*03:012970.308110.5000.1920.027
3A*29:02460.04830.1360.0890.029
4B*27:05810.08480.3640.2803.01E-06
5B*39:01270.02830.1360.1080.002
6B*56:01180.01920.0910.0720.009
7C*01:02700.07340.1820.1090.027
8C*02:021090.11360.2730.1600.011
9C*03:0270.00710.0450.0380.024
10DQB1*05:012040.212100.4550.2430.003
11DRB1*01:011530.15980.3640.2050.005
12DRB1*08:01430.04530.1360.0920.022
13DRB1*11:0270.00710.0450.0380.024

HLA alleles occurring more frequently in the very old (Group 2).

In a different analysis, we computed proportions of occurrence of the 13 VOAs across age starting at 51 years and moving forward every year–i.e., those younger than 51 vs those 51 years and older, those younger than 52 vs those 52 years and older, and so forth. The time course of the difference of proportions between the older and younger groups is shown in Figure 3. It can be seen that a systematic, monotonic increase in proportions (“enrichment”) of the 13 VOAs starts at age 74, marked by a thin vertical line in Figure 3.

FIGURE 3

3.3 Association of virus PBA with longevity

Here we tested the hypothesis that longevity could be associated with higher PBA for viruses that impede it, given that higher PBA entails a higher chance in eliminating a harmful virus and, hence, a higher chance of living longer. We obtained PBA estimates for 235/244 (96.3%) alleles, since 9 alleles could not be analyzed by the binding affinity tool (These alleles occurred in 10 participants, hence the number of participants carrying, as a pool, 235 alleles was 976.) We tested the hypothesis above as follows: (i) Given that there are 12 HLA alleles per participant (6 of HLA-I and 6 of HLA-II classical genes, namely, A, B, C for HLA-I and DPB1, DQB1, DRB1 for HLA-II) and 13 virus PBAs per allele, we first obtained the median PBA for each participant and virus, from the set of these12 alleles. This yielded a matrix of 976 participants x 13 median virus PBAs. (ii) Next, we performed a stepwise multiple linear regression where the age of the participant was the dependent variable and the 13 median virus PBAs were the independent variables. This analysis yielded only one statistically significant effect, namely, a positive association between age and HERVK (P = 0.005). (iii) This effect was further quantified and visualized using a simple linear regression between the annual averages of age and HERVK median PBA (across all participants). The result is plotted in Figure 4, illustrating the finding that HERVK PBA increases significantly with age (r = 0.357, P = 0.002) (It can be seen in Figure 4 that there is a high PBA value; if that is removed from the analysis, the positive association between and HERVK PBA remains strong and highly significant [r = 0.326, P = 0.005], attesting to the robustness of this effect).

FIGURE 4

3.4 HLA alleles in the very old and their association with virus PBA

We then analyzed the PBAs of the 13 viruses in 11/13 VOA alleles (alleles C*02:02 and C*03:02 could not be modeled by the IEDB NetMHCpan affinity tool) using a repeated-measures analysis of variance (ANOVA), where virus PBAs were the dependent variables. We found that the PBA for HERVK was significantly higher than those of all the other viruses (P < 0.001, Bonferroni corrected for multiple comparisons) (Figure 5). The relative contribution of specific viral PBAs of the 11 VOAs, considered as an aggregate, is given in Table 5 (as percent of the sum of all 13 PBAs) and illustrated in Figure 6. It can be seen that HERVK PBA contributed the most (16.2%), followed by HHV7 (11.11%) and HHV5 (10.61%); collectively, the PBAs of these 3 viruses accounted for 37.92% of the total PBA of the 11 VOA alleles. Finally, the grouping of viral PBAs was evaluated using MDS and is shown in Figure 7. It can be seen that HERVK is farthest away from all other viruses, and that HHV7 and HHV5 are well separated from the remaining viruses. The apparent 3 cluster formation in the MDS plot of Figure 7 was documented by the result of a K-means clustering analysis of the 13 X-Y viral MDS coordinates, which assigned each viral PBA to one of the 3 distinct clusters, as demarcated in Figure 7. The cluster separation was further evaluated by performing a multivariate analysis of variance (MANOVA), where the MDS X and Y coordinates were the dependent variables and the cluster assignment was a fixed factor. This analysis yielded a highly statistically significant cluster separation (Hoteling’s Trace test = 6.019, P = 0.0001).

FIGURE 5

TABLE 5

RankVirus% PBACumulative %
1HERVK16.2016.20
2HHV711.1127.31
3HHV510.6137.92
4HHV18.1546.07
5HHV37.7153.78
6HHV27.3761.15
7HHV6B6.7567.90
8HHV46.6974.59
9HPV6.5781.16
10HERVW6.1487.30
11HHV86.0693.36
12JCV3.6296.98
13HHV6A3.02100.00
Total100.00

Relative (%) contributions of the 13 viral PBAs to the VOAs. See text for details.

FIGURE 6

FIGURE 7

3.5 ApoE

ApoE genotype is shown in Table 6. ApoE data were missing for three of the individuals in the younger group. The ApoE genotype distribution did not differ between the two groups (P = 0.898, chi-square test). There was also no difference in the proportion of the E2 allele (E2/E2, E2/E3) and E4 allele (E3/E4, E4/E4) between the two groups (E2: P = 0.978; E4: P = 0.379; Wilson test of two proportions).

TABLE 6

Age group
ApoE<90 years≥90 yearsTotal
22606
0.60%0.00%0.60%
231273130
13.20%13.60%13.20%
2423124
2.40%4.50%2.40%
3355014564
57.20%63.60%57.40%
342264230
23.50%18.20%23.40%
4429029
3.00%0.00%3.00%
Total96122983
100.00%100.00%100.00%

ApoE composition of very old and younger groups.

4 Discussion

Here we evaluated longevity with respect to HLA and tested the hypothesis that longevity may be related to the enhanced ability to counter common viruses that have been implicated in morbidity and mortality. We first identified 244 HLA alleles in this sample and documented a highly significant positive association between the predicted binding affinity of those alleles to HERVK (but not any of the other viruses) and age. We then identified 13 HLA alleles (out of 244) that were more common among very old individuals than their younger counterparts, suggesting that those alleles may promote longevity. We documented that the set of very old alleles was associated with significantly higher binding affinity to HERVK, followed by HHV5 and HHV7, than to other common viruses. Taken together, the present findings suggest that specific HLA alleles may promote longevity via enhanced ability to mount immune responses to HERVK, an endogenous retrovirus that may otherwise contribute to morbidity and mortality.

4.1 Methodological considerations

With respect to HLA genotyping, we analyzed alleles at 2-field resolution, where the first field defines the allele group that corresponds to the serologically defined specificity of the HLA protein and the second field indicates differences in the DNA sequence that lead to a difference in the amino acid sequence of the resulting protein. The third field indicates synonymous DNA substitutions in the coding region, whereas the fourth field refers to differences in the non-coding regions. Since differences in the third and fourth fields do not have any influence on the resulting protein, which was the focus of this study, we did not use them in this analysis. However, ambiguities stemming from match/mismatches in the coding (third field) and/or non-coding (fourth field) DNA sequences (Voorter et al., 2014) are likely to be important with regard to successful transplantation (Mayor et al., 2021).

Finally, with respect to epitope length, although various lengths are possible, it is recommended that 9-mers for HLA-I (Lafuente and Reche, 2009) and 15-mers for HLA-II) (Rudensky et al., 1991) are most suitable. More specifically, for HLA-I, “… most MHCI-peptide ligands have nine residues (they are 9-mers), making models for the prediction of 9-mer binders preferable” (23, page 3211), as exemplified by the choice to use 9-mers in a detailed biophysical study of antigen presentation to HLA-I molecules (Garstka et al., 2015).

4.2 Viruses and disease associations

HERVK is part of a broad class of human endogenous retroviruses that are integrated into the human genome. Among the endogenous retroviruses, HERVK (particularly the HML-2 subgroup evaluated here) is the most transcriptionally active, possessing open reading frames allowing for coding of proteins (Garcia-Montojo et al., 2018) that are found in several tissues (Flockerzi et al., 2008). HERVK is minimally expressed in healthy adult cells but is upregulated and expressed, producing virus-like particles, in various conditions (Shin et al., 2023) and has been associated with cancers (Rivas et al., 2022; Curty et al., 2020), neurological conditions, diabetes, and autoimmune disorders (Garcia-Montojo et al., 2018; Xue et al., 2020). HERVK has also been associated with cellular senescence and tissue aging (Liu et al., 2023). Here, the predicted binding affinity of HERVK was highly significantly and positively associated with age. In addition, the 13 alleles identified in the very old group were shown to confer enhanced protection against HERVK, suggesting reduced HERVK-associated risks in the very old group. Notably, the 13 very old alleles were also significantly associated with the predicted binding affinities of HHV7 and HHV5. Several studies, reviewed elsewhere () have documented that infection with herpes viruses, among others, can induce HERV transactivation, contributing to virus-associated diseases and tumors. Thus, possessing the very old alleles identified here may promote longevity by facilitating not only an immune response to HERVK but also to HHV7 and HHV5 thereby reducing herpes virus-related HERV transactivation and long-term impacts on HERVK-associated morbidity and mortality. It should be noted that these 13 alleles were not absent in the younger group but were significantly less frequent; thus, members of the younger cohort who possess these 13 alleles would presumably be afforded greater longevity.

4.3 HLA and longevity

Previous research has documented an association between the HLA-DRB1 region and longevity (Joshi et al., 2017). Similarly, we found that 3 of the 13 alleles which were more frequent in the very old group belong to the Class II DRB1 gene. Here, however, several other HLA genes including Class I HLA-A (3 alleles), HLA-B (3 alleles), HLA-C (3 alleles), and Class II DQB1 (1 allele) were also more frequent in the very old group. Thus, the findings of our study which was based on high-resolution genotyping (vs imputation), confirm those of previous GWAS studies and document the relevance of additional HLA genes to longevity. Indeed, several of the very old alleles have been associated with disease protection. For instance, HLA-A*02:05 and HLA-A*03:01 are protective against severe clinical manifestations of SARS-CoV-2 (Littera et al., 2020; Shkurnikov et al., 2021), DRB1*01:01 is protective against MS (Mamedov et al., 2020), and DRB1*11:02 protects against rheumatoid arthritis (). Perhaps the most well-investigated is HLA-B*27:05 which has been associated with risk for ankylosing spondylitis (Khan, 2023), yet superior immune control of HIV (International et al., 2010) and hepatitis C virus (Neumann-Haefelin et al., 2010). Thus, this group of alleles that are enhanced in very old individuals have been shown to protect against certain viruses and diseases, presumably promoting longevity.

4.4 Host-virus interactions

When considering protective or susceptibility effects related to HLA, it is crucial to keep in mind the role of HLA in host protection via signaling the immune system to eliminate foreign antigens. That is, protective or susceptibility effects are not conferred by HLA alone but by interactions between an individual’s HLA composition and exposure to viruses and other foreign antigens. Consequently, a given allele may preferentially bind and mount an immune response to certain pathogens over others. Indeed, this is what was documented for the 13 very old alleles here with regard to the 13 viruses we investigated. That is, as a group of alleles, those that were more common in the very old group had enhanced ability to eliminate HERVK relative to others.

4.5 ApoE and longevity

Notably, apolipoprotein E composition did not differ between the very old cohort and the younger cohort in the present study. Some previous research has found that the E2 allele is associated with increased longevity and the E4 allele with decreased longevity (Sebastiani et al., 2019), although the overall findings with regard to apoE and longevity are mixed (). ApoE is most prominently implicated in dementia (Serrano-Pozo et al., 2021); individuals who had been diagnosed with dementia were not included in the present study.

4.6 Conclusions and limitations

In summary, the findings of the present study suggest that longevity is associated with enhanced immune response to specific common viruses–particularly, HERVK–conferred by specific HLA alleles. These novel findings, which highlight the effect of interaction between host immunogenetics and virus exposure on longevity, need to be considered in light of several qualifications. First, the findings here are based on in silico analyses; future studies evaluating HLA and HERVK antigen-specific CD8 T cell receptor repertoire and in long-living individuals are warranted to further substantiate the conclusions herein. Second, we focused exclusively on the 244 alleles that were documented in our sample and particularly on 13 alleles that were enriched in the very old group of participants. That does not preclude the possibility of other alleles also mounting robust immune responses to HERVK. Indeed, in light of the role of HLA in host protection against viruses, it is likely that additional alleles that were equally present (or absent) in both groups bind with high affinity to the common viruses investigated here. In addition, it is worth noting that the current findings are specific to longevity and may not equate with overall health as only health conditions that would interfere with comprehension or participation were exclusionary. Finally, although the viruses and HLA alleles are relatively common globally, the frequencies of both are known to vary geographically (Looker et al., 2017; ; Prugnolle et al., 2005; Singh et al., 2007). Although additional studies are warranted to determine whether the longevity alleles identified here generalize to other populations, all of the 13 alleles that were enriched in the very old group of participants here are common worldwide (Hurley et al., 2020), suggesting these alleles with high affinity to HERVK may promote global longevity.

Statements

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by Institutional Review Board of the Minneapolis VA Health Care System. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

LJ: Data curation, Investigation, Project administration, Writing–original draft, Writing–review and editing. AG: Conceptualization, Formal Analysis, Methodology, Visualization, Writing–original draft, Writing–review and editing.

Funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. Partial funding for this study was provided by the University of Minnesota (the Anita Kunin Chair in Women’s Healthy Brain Aging, the Brain and Genomics Fund, the McKnight Presidential Chair of Cognitive Neuroscience, and the American Legion Brain Sciences Chair) and the U.S. Department of Veterans Affairs. The sponsors had no role in the current study design, analysis or interpretation, or in the writing of this paper. The contents do not represent the views of the U.S. Department of Veterans Affairs or the United States Government.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fragi.2025.1471202/full#supplementary-material

References

  • 1

    AbondioP.SazziniM.GaragnaniP.BoattiniA.MontiD.FranceschiC.et al (2019). The genetic variability of APOE in different human populations and its implications for longevity. Genes10, 222. 10.3390/genes10030222

  • 2

    AdaneT.GetawaS. (2021). Cytomegalovirus seroprevalence among blood donors: a systematic review and meta-analysis. J. Int. Med. Res.49, 03000605211034656. 10.1177/03000605211034656

  • 3

    Al-HaddadB. J. S.JacobssonB.ChabraS.ModzelewskaD.OlsonE. M.BernierR.et al (2019). Long-term risk of neuropsychiatric disease after exposure to infection in utero. JAMA Psychiat.76, 594602. 10.1001/jamapsychiatry.2019.0029

  • 4

    BurgdorfK. S.TrabjergB. B.PedersenM. G.NissenJ.BanasikK.PedersenO. B.et al (2019). Large-scale study of Toxoplasma and Cytomegalovirus shows an association between infection and serious psychiatric disorders. Brain Behav. Immun.79, 152158. 10.1016/j.bbi.2019.01.026

  • 5

    CanoP.KlitzW.MackS. J.MaiersM.MarshS. G. E.NoreenH.et al (2007). Common and well-documented HLA alleles: report of the ad-hoc committee of the American society for histocompatiblity and immunogenetics. Hum. Immunol.68, 392417. 10.1016/j.humimm.2007.01.014

  • 6

    ChangY.MooreP. S.WeissR. A. (2017). Human oncogenic viruses: nature and discovery. Philos. Trans. R. Soc. Lond B Biol. Sci.372, 20160264. 10.1098/rstb.2016.0264

  • 7

    CharonisS.JamesL. M.GeorgopoulosA. P. (2020a). In silico assessment of binding affinities of three dementia-protective Human Leukocyte Antigen (HLA) alleles to nine human herpes virus antigens. Curr. Res. Transl. Med.68, 211216. 10.1016/j.retram.2020.06.002

  • 8

    CharonisS.TsilibaryE. P.GeorgopoulosA. P. (2020b). SARS-CoV-2 virus and Human Leukocyte Antigen (HLA) Class II: investigation in silico of binding affinities for COVID-19 protection and vaccine development. J. Immunol. Sci.4, 1223. 10.29245/2578-3009/2020/4.1198

  • 9

    CharonisS. A.TsilibaryE. P.GeorgopoulosA. P. (2021). In silico investigation of binding affinities between human leukocyte antigen class I molecules and SARS-CoV-2 virus spike and ORF1ab proteins. Explor. Immunol.1, 1626. 10.37349/ei.2021.00003

  • 10

    ChenJ.ForoozeshM.QinZ. (2019). Transactivation of human endogenous retroviruses by tumor viruses and their functions in virus-associated malignancies. Oncogenesis8, 6. 10.1038/s41389-018-0114-y

  • 11

    Cruz-TapiasP.CastiblancoJ.AnayaJ. M. (2013). “HLA association with autoimmune diseases,” in Autoimmunity: from bench to bedside. Editors AnayaJ. M.ShoenfeldY.Rojas-VillarragaA.LevyR. A.CerveraR. (Bogota, Colombia: El Rosario University Press), 271284.

  • 12

    CurtyG.MarstonJ. L.de Mulder RougvieM.LealF. E.NixonD. F.SoaresM. A.et al (2020). Human Endogenous Retrovirus K in cancer: a potential biomarker and immunotherapeutic target. Viruses12, 726. 10.3390/v12070726

  • 13

    DendrouC. A.PetersenJ.RossjohnJ.FuggerL. (2018). HLA variation and disease. Nat. Rev. Immunol.18, 325339. 10.1038/nri.2017.143

  • 14

    FlockerziA.RuggieriA.FrankO.SauterM.MaldenerE.KopperB.et al (2008). Expression patterns of transcribed human endogenous retrovirus HERV-K(HML-2) loci in human tissues and the need for a HERV Transcriptome Project. BMC Genomics9, 354. 10.1186/1471-2164-9-354

  • 15

    Garcia-MontojoM.Doucet-O’HareT.HendersonL.NathA. (2018). Human endogenous retrovirus-K (HML-2): a comprehensive review. Crit. Rev. Microbiol.44, 715738. 10.1080/1040841X.2018.1501345

  • 16

    GarstkaM. A.FishA.CelieP. H.JoostenR. P.JanssenG. M.BerlinI.et al (2015). The first step of peptide selection in antigen presentation by MHC class I molecules. Proc. Natl. Acad. Sci. U. S. A.112, 15051510. 10.1073/pnas.1416543112

  • 17

    HouenG.TrierN. H. (2021). Epstein-Barr virus and systemic autoimmune diseases. Front. Immunol.11, 587380. 10.3389/fimmu.2020.587380

  • 18

    HovJ. R.KosmoliaptsisV.TraherneJ. A.OlssonM.BobergK. M.BergquistA.et al (2011). Electrostatic modifications of the human leukocyte antigen‐DR P9 peptide‐binding pocket and susceptibility to primary sclerosing cholangitis. Hepatology53, 19671976. 10.1002/hep.24299

  • 19

    HurleyC. K.KempenichJ.WadsworthK.SauterJ.HofmannJ. A.SchefzykD.et al (2020). Common, intermediate and well-documented HLA alleles in world populations: CIWD version 3.0.0. HLA95, 516531. 10.1111/tan.13811

  • 20

    HusseinH. M.RahalE. A. (2019). The role of viral infections in the development of autoimmune diseases. Crit. Rev. Microbiol.45, 394412. 10.1080/1040841X.2019.1614904

  • 21

    IEDB (2020). Analysis resource. Available at: http://tools.iedb.org/mhci/result/(Accessed April 2, 2024).

  • 22

    InternationalH. I. V.PereyraF.JiaX.McLarenP. J.TelentiA.de BakkerP. I. W.et al (2010). The major genetic determinants of HIV-1 control affect HLA class I peptide presentation. Science330, 15511557. 10.1126/science.1195271

  • 23

    JoshiP. K.PiratsuN.KenitsouK. A.FischerK.HoferE.SchrautK. E.et al (2017). Genome-wide meta-analysis associates HLA-DQA1/DRB1 and LPA and lifestyle factors with human longevity. Nat. Commun.8, 910. 10.1038/s41467-017-00934-5

  • 24

    KhanM. A. (2023). HLA-B*27 and ankylosing spondylitis: 50 years of insights and discoveries. Curr. Rheumatol. Rep.25, 327340. 10.1007/s11926-023-01118-5

  • 25

    KrumpN. A.YouJ. (2018). Molecular mechanisms of viral oncogenesis in humans. Nat. Rev. Microbiol.16, 684698. 10.1038/s41579-018-0064-6

  • 26

    LafuenteE. M.RecheP. A. (2009). Prediction of MHC-peptide binding: a systematic and comprehensive overview. Curr. Pharm. Des.15 (28), 32093220. 10.2174/138161209789105162

  • 27

    LitteraR.CampagnaM.DeiddaS.AngioniG.CipriS.MelisM.et al (2020). Human leukocyte antigen complex and other immunogenetic and clinical factors influence susceptibility or protection to SARS-CoV-2 infection and severity of the disease course. The Sardinian experience. Front. Immunol.11, 605688. 10.3389/fimmu.2020.605688

  • 28

    LiuX.LiuZ.WuZ.RenJ.FanY.SunL.et al (2023). Resurrection of endogenous retroviruses during aging reinforces senescence. Cell186, 287304.e26. 10.1016/j.cell.2022.12.017

  • 29

    LookerK. J.MagaretA. S.MayM. T.TurnerK. M. E.VickermanP.NewmanL. M.et al (2017). First estimates of the global and regional incidence of neonatal herpes infection. Lancet Glob. Health5, e300e309. 10.1016/S2214-109X(16)30362-x

  • 30

    LotzS. K.BlackhurstB. M.ReaginK. L.FunkK. E. (2021). Microbial infections are a risk factor for neurodegenerative diseases. Front. Cell Neurosci.15, 691136. 10.3389/fncel.2021.691136

  • 31

    MamedovA.VorobyevaN.FilimonovaI.ZakharovaM.KiselevI.BashinskayaV.et al (2020). Protective allele for multiple sclerosis HLA-DRB1*01:01 provides kinetic discrimination of myelin and exogenous antigenic peptides. Front. Immunol.10, 3088. 10.3389/fimmu.2019.03088

  • 32

    MayorN. P.WangT.LeeS. J.KuxhausenM.Vierra-GreenC.BarkerD. J.et al (2021). Impact of previously unrecognized HLA mismatches using ultrahigh resolution typing in unrelated donor hematopoietic cell transplantation. J. Clin. Oncol.39, 23972409. 10.1200/JCO.20.03643

  • 33

    MuiU. N.HaleyC. T.TyringS. K. (2017). Viral oncology: molecular biology and pathogenesis. J. Clin. Med.6, 111. 10.3390/jcm6120111

  • 34

    Neumann-HaefelinC.TimmJ.SchmidtJ.KerstingN.FitzmauriceK.Oniangue-NdzaC.et al (2010). Protective effect of human leukocyte antigen B27 in hepatitis C virus infection requires the presence of a genotype-specific immunodominant CD8+ T-cell epitope. Hepatology51, 5462. 10.1002/hep.23275

  • 35

    PisheshaN.HarmandT. J.PloeghH. L. (2022). A guide to antigen processing and presentation. Nat. Rev. Immunol.22, 751764. 10.1038/s41577-022-00707-2

  • 36

    PrugnolleF.MnicaA.CharpentierM.GuéganJ. F.GuernierV.BallouxF. (2005). Pathogen-driven selection and worldwide HLA class I diversity. Curr. Biol.15, 10221027. 10.1016/j.cub.2005.04.050

  • 37

    ReymerP. W.GroenemeyerB. E.Van de BurgR.KasteleinJ. J. (1995). Apolipoprotein E genotyping on agarose gels. Clin. Chem.41, 10461047. 10.1093/clinchem/41.7.1046

  • 38

    ReynissonB.AlvarezB.PaulS.PetersB.NielsenM. (2020). NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic Acids Res.48 (W1), W449W454. 10.1093/nar/gkaa379

  • 39

    RivasS. R.ValdezM. J. M.GovindarajanV.SeetharamD.Doucet-O'HareT. T.HeissJ. D.et al (2022). The role of HERV-K in cancer stemness. Viruses14, 2019. 10.3390/v14092019

  • 40

    RudenskyY.Preston-HurlburtP.HongS. C.BarlowA.JanewayC. A.Jr (1991). Sequence analysis of peptides bound to MHC class II molecules. Nature353, 622627. 10.1038/353622a0

  • 41

    SebastianiP.GurinovichA.NygaardM.SasakiT.SweigartB.BaeH.et al (2019). APOE alleles and extreme human longevity. J. Gerontol. A Biol. Sci. Med. Sci.74, 4451. 10.1093/gerona/gly174

  • 42

    Serrano-PozoA.DasS.HymanB. T. (2021). APOE and Alzheimer's disease: advances in genetics, pathophysiology, and therapeutic approaches. Lancet Neurol.20, 6880. 10.1016/S1474-4422(20)30412-9

  • 43

    ShinW.MunS.HanK. (2023). Human endogenous retrovirus-K (HML-2)-Related genetic variation: human genome diversity and disease. Genes (Basel)14 (12), 2150. 10.3390/genes14122150

  • 44

    ShkurnikovM.NersisyanS.JankevicT.GalatenkoA.GordeevI.VechorkoV.et al (2021). Association of HLA class I genotypes with severity of coronavirus disease-19. Front. Immunol.12, 641900. 10.3389/fimmu.2021.641900

  • 45

    SinghR.KaulR.KaulA.KhanK. (2007). A comparative review of HLA associations with hepatitis B and C viral infections across global populations. World J. Gastron.13, 17701787. 10.3748/wjg.v13.i12.1770

  • 46

    TrowsdaleJ.KnightJ. C. (2013). Major histocompatibility complex genomics and human disease. Ann. Rev. Genom Hum. Genet.14, 301323. 10.1146/annurev-genom-091212-153455

  • 47

    VoorterC. E.PalusciF.TilanusM. G. (2014). Sequence-based typing of HLA: an improved group-specific full-length gene sequencing approach. Methods Mol. Biol.1109, 101114. 10.1007/978-1-4614-9437-9_7

  • 48

    XueB.SechiL. A.KelvinD. J. (2020). Human endogenous retrovirus K (HML-2) in health and disease. Front. Microbiol.11, 1690. 10.3389/fmicb.2020.01690

Summary

Keywords

longevity, human leukocyte antigen (HLA), viruses, human endogenous retrovirus K (HERV-K), apolipoprotein E

Citation

James LM and Georgopoulos AP (2025) Immunogenetics of longevity and its association with human endogenous retrovirus K. Front. Aging 6:1471202. doi: 10.3389/fragi.2025.1471202

Received

26 July 2024

Accepted

20 January 2025

Published

04 February 2025

Volume

6 - 2025

Edited by

Calogero Caruso, University of Palermo, Italy

Reviewed by

Elena Ciaglia, University of Salerno, Italy

Milena Ivanova Ivanova-Shivarova, Aleksandrovska University Hospital, Bulgaria

Updates

Copyright

*Correspondence: Lisa M. James,

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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