Sex-Biased Aging Effects on Ig Somatic Hypermutation Targeting

Age and sex are important sources of variation in immune responses. Older individuals tend to be more vulnerable to bacterial and viral infections, more prone to developing cancer and autoimmune diseases, and less likely to respond to vaccinations (1–3). The effects of aging on the immune system, however, are not uniform between sexes (4). Females tend to display a more robust immunologic response to infections and vaccinations, yet females are also at a higher risk of developing autoimmune diseases (5–8). Although the age and sex biases have long been observed, the molecular mechanisms causing these differences remain unclear. Gaining an understanding of the molecular mechanisms is crucial for designing more targeted therapies and vaccinations for different age and sex groups.

Among many immune cells that experience immunosenescence, B cells are of great interest because of their central role in humoral immunity. B cells produce Abs that defend us from a wide range of extracellular pathogens or bind to cells to elicit Ab-dependent cytotoxicity. Moreover, most vaccines for viral and bacterial infections to date target B cells and use their memory response to prevent future infections. Previous studies have shown that although the quantity of Abs does not alter significantly between young and aged individuals, older individuals display a decrease in the affinity, specificity, and diversity of the Abs they produce (9–12). Such observations prompted us to examine the mechanisms governing the Ab diversification process.

To generate high-affinity BCRs, which are the membrane-bound form of Abs, the Ig loci in B cells undergo a number of diversification steps, including V(D)J recombination and somatic hypermutation (SHM) (13). SHM, a process that occurs in germinal centers (GCs) in secondary lymphoid organs, diversifies BCRs by introducing point mutations into the Ig genes at a high rate (14). B cells with mutations leading to a high affinity to Ags survive and clonally expand in a selection process. SHM is initiated when the enzyme activation-induced cytidine deaminase (AID) converts cytosine (C) to uracil (U) in the Ig loci in B cells. Following this, three scenarios may occur. In Phase Ia, a simple replication leads to a C-to-thymine (T) or a guanine (G)-to-adenine (A) transition on the complement strand. In Phase Ib, the activation of UNG-induced short-patch base-excision repair (BER) pathway introduces mutation at the targeted C or G site (hereafter written as C/G site). Phase II results in the activation of long-patch BER and mismatch repair (MMR) pathways, leading to mutations on neighboring bases that can be on any nucleotide, including the A and T site (hereafter written as A/T site). Although stochastic, SHM preferentially targets certain DNA motifs (hot spots) while avoiding others (cold spots) (15). A previous study shows a decrease in AID expression level in older individuals, suggesting that a lower degree of SHM initiation may contribute to the compromised immunologic responses in these individuals (16). However, there has been a lack of investigation on sex biases or the impact of aging on SHM targeting, which dictates the types of mutations resulting from SHM.

With the advancement of high-throughput technologies, we can now profile immune repertoires using adaptive immune receptor repertoire sequencing (AIRR-seq) at an unprecedented depth (17, 18). Such technologies involve sequencing the Ig loci in DNA or RNA, enabling us to examine mutation patterns in BCRs in great detail. For example, it has been observed that aging is associated with longer CDR3 regions (the most diverse region in B cell Ig genes), an accumulation of more highly mutated IgM and IgG Ig genes, larger persistent clones in B cells in peripheral blood, and diminished intralineage diversification (19–21). Previously, we used such datasets to study SHM-targeting patterns across individuals and species at a high resolution (22, 23). We hypothesized that such approaches may also provide insights into the effects of age and sex in the SHM-targeting process.

In this study, we aimed to compare and contrast the SHM-targeting patterns between different age and sex groups. We analyzed high-throughput B cell Ig sequences from peripheral blood of 27 healthy individuals from two age groups: younger individuals (20–31 y old) and older individuals (61–89 y old) (19). As a validation, we examined mutations in Ig genes in whole-transcriptome RNA-sequencing (RNA-seq) data from 147 individuals aged 20–70 in the Genotype-Tissue Expression (GTEx) project (24). We examined mutations involved in Phase I versus Phase II SHM and found a significant decrease in Phase II–induced mutations in older males. Such observations lead to the hypothesis that older individuals, particularly males, experience alterations in SHM activities, which may contribute to their lower level of immunologic responses.

The high-throughput Ig repertoire AIRR-seq dataset was published in (19) by Wang et al. Raw high-throughput sequencing reads were quality controlled, assembled, and filtered using the REpertoire Sequencing TOolkit (pRESTO) (25). The IMGT/High-VQUEST tool was used to assign germline V(D)J segments and determine functionality (26). The Change-O command line tool was used to partition sequences into clonal groups (27). V(D)J sequences were assigned into clones based on identical IGHV gene, IGHJ gene, and junction length, with a weighted intraclonal distance threshold of 10 using the substitution probabilities previously described (28). Mutations were defined as nucleotides that were different from the germline sequence. A sliding-window approach was used to filter out sequences with highly clustered mutations, defined by six or more nucleotide differences in 10 consecutive nucleotides. Sequences with <5% mutation frequency were filtered out to reduce the effect of sequencing errors in the analysis.

The 1-mer substitution model was generated using the shazam R package version 0.1.1 in the Change-O suite (22, 27). In brief, the substitution matrix was defined by calculating the frequency of substitutions for each nucleotide. The 5-mer targeting model used for the construction of synonymous 5-mer models was previously described (22).

The raw whole-transcriptome RNA-seq data were collected by the GTEx project (v6) (24). The IgH locus was isolated and mapped to the germline Ig genes published by IMGT via IgBLAST (29). Reads with more than 65 nt (out of 75 nt) aligned were used for mutation analyses. To maximize the number of mutations we could obtain per individual, mutations from different tissues were aggregated for each individual. The A/T mutation frequency was normalized by the total number of A and T nucleotides in the germline sequences. Individuals with >0.8 or <0.2 A/T mutation frequency were considered outliers and excluded from the analysis.

Gene expression data of healthy individuals of different age and sex groups were obtained from Gene Expression Omnibus accession number GSE65442 (30). Subject metadata were accessed from ImmPort SDY404. The baseline data of B cells prior to vaccination from the 2011–2012 season, which has at least four individuals in each age and sex group, were selected for the analysis. The gene expression profiles were quantile normalized using the R package limma. The genes involved in SHM repair activities, including AICDA, EXO1, MSH2, MSH6, PMS2, POLH, POLI, POLK, POLL, POLQ, REV1, UNG, and UNG2, were considered in the initial analysis (13). Probes with low detection signals (i.e., those with <90% of samples having a gene expression detection p value <0.01) were eliminated from downstream analyses.

The operational definition of frailty was based on the following criteria: 1) low grip strength, 2) low energy, 3) slowed walking speed, 4) low physical activity, and 5) unintentional weight loss. Individuals with three to five symptoms were defined as frail.

To assess the effect of aging on SHM targeting, we analyzed a publicly available B cell Ig repertoire–sequencing (AIRR-seq) data from 27 healthy individuals (hereafter referred to as the “AIRR-seq” dataset) (19). The study includes 10 young adults (20–31 y old), five of whom are females, and 17 older adults (61–89 y old), 12 of whom are females. Within each age group, male and female subjects were of comparable ages (young: p = 0.65, older: p = 0.83; Welch t test).

High-throughput next-generation sequencing was used to profile the Ig sequences. For each individual, three replicates were obtained from different sequencing methods or platforms: genomic data sequenced at Stanford Genomic Center (DNA-GC), genomic data sequenced on the Roche 454 platform (DNA-Roche), and cDNA data from RNAs. We analyzed each replicate separately to prevent potential biases that arise from sequencing methods. A total of 123,415 unique sequences passed quality control (2Materials and Methods) and were used for the downstream analyses (Table I). IMGT was used to determine the functionality of the Ig sequences (26). Nonfunctional sequences were defined as sequences containing out-of-frame junction region and were presumably not having participated in selection. To identify independent SHM events, we partitioned the sequences into groups that were likely to be clonally-related (2Materials and Methods), and identified a set of distinct mutations from each clone. We collected a total of 985,069 SHMs, where mutations were defined as the nucleotides that were different from the inferred germline sequences in the V region. From the RNA replicate in which the isotype information is available, we found that the majority of the mutations were obtained from sequences of the IgG, IgA, and IgM isotypes. To study intrinsic SHM-targeting patterns in the absence of selection pressure, we analyzed two types of mutations from each replicate: 1) silent mutations (i.e., mutations that do not alter the amino acid sequence) from functional sequences and 2) both silent and replacement mutations from nonfunctional sequences (i.e., sequences that do not encode proteins).

To assess whether aging influences the intrinsic biases of SHM targeting (i.e., hot/cold spots), we built targeting models based on microsequence motifs. To capture the classic SHM hot spot (WRC/GYW, where W = [A, T], R = [G, A], Y = [C, T]) on both the forward and reverse strands, the model was based on 5-mer motifs including two bases up- and two bases downstream of the mutated base (2Materials and Methods) (22). The mutability of each 5-mer was defined as the probability of the central base being targeted by SHM relative to all other motifs. Because 5-mer mutability hierarchies are highly similar across individuals (22), we built a single targeting model for each age and sex group. We then used “hedgehog” plots to visualize the targeting biases (i.e., hot/cold-spot motifs) (Fig. 1A). In all age and sex groups, we observed high mutability in classic hot spots and low mutability in classic cold spots, consistent with our expectations. We compared the targeting models between age and sex groups and found that the mutability hierarchies were highly consistent between different ages and sexes (Spearman ρ > 0.92 in silent mutations in functional sequences, and ρ > 0.79 in both silent and replacement mutations in nonfunctional sequences in the RNA replicate) (Fig. 1B, 1C). The results suggest that age and sex have little effect on the hierarchy of SHM targeting on microsequence motifs.

In SHM, C/G nucleotides are targeted in both Phase I and Phase II, whereas A/T nucleotides are targeted in Phase II only. To detect whether there is a shift in SHM phases between age and sex groups, we examined the balance between mutations at C/G nucleotides versus mutations at A/T nucleotides. For each individual, we computed the proportion of mutations that occur on A/T bases, normalized by the number of each base in the germline sequences (hereafter written as A/T mutability). Across all technical replicates, we observed a significant decrease in A/T mutability in older males in nonfunctional sequences, with at least four of the five older males having lower mutations on A/T than all of the younger males (Fig. 2). We also observed a decrease in A/T mutability in older males in silent mutations from functional sequences, and the difference was statistically significant in the RNA replicate. In the male group, there may be a continuous decrease in A/T mutability with age (r < −0.70, p < 0.03, Pearson product-moment correlation in the RNA replicate) (Supplemental Fig. 1), although the level of continuity was unclear in the 31- to 60-y-old age group because of a lack of subjects. In females, the older individuals had comparable mutation patterns as the younger individuals. The older males also exhibited lower A/T mutabilities compared with older females across all replicates.

We used an RNA-seq dataset published by the GTEx project to validate the results (hereafter referred to as the “GTEx RNA-seq” dataset) (24). This dataset contains RNA sequences from the entire transcriptome across many cell types, and therefore includes a limited number of mutated B cell Ig transcripts. We extracted the IgH locus and mapped the cDNA sequences to germline Ig genes using IgBLAST (2Materials and Methods). The resulting data contained 11,312 mutations from 147 individuals. To maximize the number of mutations we can obtain per individual, sequences from different tissue sites were aggregated. We, again, calculated the A/T mutability by normalizing the proportion of mutations on A/T bases by the number of A/T nucleotides in the germline genes. Although the data contained a large amount of variation because of a small number of mutations that can be extracted from such types of data, we observed a similar trend as before: both sex groups displayed a decrease in A/T mutability with age, but the males had a much more prominent decrease (Pearson r = −0.24, p = 0.023 in males versus Pearson r = −0.13, p = 0.363 in females) (Supplemental Fig. 2). By applying linear models, we observed that A/T mutabilities in males decreased at a slightly faster rate (a mutability value of 0.5826 at age 0, decreasing by 0.0018 per year) compared with their female counterparts (a mutability value of 0.5291 at age 0, decreasing by 0.0011 per year). The decrease in A/T (versus C/G) mutability in older males suggests that these individuals have fewer mutations involved in Phase II SHM, which are induced by the activation of the long-patch BER and MSH2/MSH6 DNA MMR pathway that drives mutation spreading from the original AID-targeted C-to-U lesion to neighboring bases.

To determine whether there is also a shift between Phase Ia and Ib SHM repair pathways (i.e., simple replication versus UNG-induced short-patch BER at the AID-targeted C/G bases), we examined the frequency of C-to-T and G-to-A mutations (Phase Ia mutations) with respect to all mutations on C or G bases (Phase I mutations) in the AIRR-seq dataset. To observe this, we computed the frequency of each substitution (Supplemental Fig. 3A) and normalized them by the total number of substitutions from that base (Supplemental Fig. 3B). Although there appeared to be an insignificant elevation of Phase Ia mutations before normalization, this elevation disappeared after normalization. We tested the hypothesis on the GTEx RNA-seq dataset, and again did not see any correlation between age and Phase Ia mutation frequency. Thus, our analysis does not support a shift between Phase Ia and Phase Ib with age in these datasets.

The observation of a decreased mutation frequency occurring on A and T bases may suggest that the DNA repair mechanisms involved in SHM are altered in some older individuals. To assess whether DNA repair molecules involved in SHM have a different gene expression level, we analyzed the gene expression profiles of peripheral blood B cells collected from another cohort of healthy individuals (see 2Materials and Methods; hereafter referred to as the “gene expression” dataset). This cohort includes 16 younger (including seven male) and 14 older (including four male) subjects. B cells were sorted from blood samples and the gene expression levels of 47,260 probes were measured (30).

We examined the genes involved in SHM that were measured with significant detection level on the microarray (detection p < 0.01 for >90% of the samples), including MSH6, PMS2, REV1, and UNG. We compared the expression levels between the younger and older individuals in each sex group and found that these genes consistently displayed lower expression values in older males compared with younger males (Fig. 3). In particular, REV1 exhibited the largest gap between age groups (p = 0.01, Welch t test). In females, the expressions were comparable between age groups, although the older individuals had slightly lower expressions in most of the genes. Next, we examined whether the decrease in SHM repair activities was linked to health state. We divided the older individuals into “healthy” and “frail” categories based on the criteria described in 2Materials and Methods. There were a total of 11 healthy and three frail older individuals. Interestingly, the frail male showed lowest expression in multiple genes among the male group, although such a trend was not observed in the female group. These findings of gene expression changes in SHM molecules further demonstrate that older males may experience alterations in SHM-targeting activities.

We hypothesized that an alteration in SHM targeting at the nucleotide level would affect the resulting Ig phenotypes and, consequently, contribute to a different immunological response. To detect the impact of a shift in A/T mutability on Ig amino acid composition, we examined the characteristics of amino acid substitutions by computationally translating the Ig nucleotide sequences into amino acids. We hypothesized that a decrease in A/T mutability would result in a decrease in the amino acid substitutions that occur because of mutations at A/T bases. Therefore, we examined a list of amino acid substitutions that would occur from a single nucleotide change unambiguously occurring at an A/T site (Table II). For example, the change from phenylalanine (Phe) to serine (Ser) was included in the list because the nucleotide T must be converted to C to induce this amino acid exchange in a single mutational event. In contrast, the change from serine to threonine was not included because it could be induced by mutations on T or G. There are 57 aa substitutions that match the criteria, with the most common ones being tyrosine (Tyr) to Phe or histidine (His), lysine (Lys) to arginine (Arg), Val to Ala, and Ser to Gly.

For each individual, we calculated the proportion of such amino acid substitutions on functional sequences (i.e., sequences that encode BCRs) and compared it to the A/T mutability in nonfunctional sequences, which reflects the level of SHM-targeting activities not influenced by selection pressure. We found that a lower A/T mutability was significantly correlated with a lower frequency of amino acid substitutions that result from mutations at A/T sites for all replicates in the AIRR-seq dataset (RNA replicate: Spearman ρ = 0.82, p = 2 × 10⁻⁶) (Fig. 4, Supplemental Fig. 4). We observed that younger individuals clustered at the top right corner, which represents a higher A/T mutability and a higher frequency of amino acid substitutions due to such mutations at the nucleotide level. Older males and some older females occupied the bottom left corner, indicating a decrease in the proportion in amino acid substitutions involving A/T mutations. These results indicate that a shift in Phase I versus Phase II mutations affects the resulting Ig phenotype even if they are under selection pressure. As different amino acids have distinct properties (i.e., sizes, charges, and hydrophobicity), alterations in Ig amino acid composition would potentially impact immunologic responses, such as causing changes in Ig-binding affinity.

Elucidating age and sex discrepancies in immune responses at the molecular level can provide mechanistic insights to the differences in immune responses observed in clinical settings. Although it is well established that males and females of different age groups have different immune responses, the mechanisms responsible for the lack of Ab diversity and potency for certain groups are yet to be clarified. In this study, we analyzed high-throughput sequencing and gene expression datasets to study an Ab diversification process in different age and sex groups. We analyzed a large number of mutations gathered from high-throughput Ig-sequencing data, and used 1-mer and 5-mer microsequence-targeting frameworks to show a shift in A/T versus C/G targeting in older males. We used RNA-seq data from 147 individuals to validate the patterns observed. We also used gene expression profiles of an independent study to show that males have decreased levels of expression in several genes crucial to SHM-targeting activities. Finally, we showed that a shift in the balance between Phase I and Phase II SHM at the nucleotide level measured in the absence of selection pressure influences the amino acid compositions of functional Igs, potentially altering immunologic responses.

It has been observed that the Abs generated by older individuals have a lower level of affinity, specificity, and diversity. However, the molecular processes responsible for such declines have not been elucidated. Previous repertoire analyses have shown alterations in repertoire properties in older individuals, such as CDR3 lengths and clonal sizes. In this study, we investigated DNA repair pathways involved in SHM, processes upstream of these observations, and found that the mutations involved in different phases of SHM repair pathways are altered in some older individuals. Using one of the cohorts analyzed in this study (19), we previously found a decrease in WA hot spot targeting with age (31). However, unlike the sex-specific differences observed in this study in the targeting of A/T more generally, these hot spot differences were similar in males and females. Our finding that males exhibit much more prominent alterations in these pathways with age is consistent with the clinical finding that older men have a weaker response to vaccination and are more likely to develop infections or cancer. One hypothesis based on this study would be that the Phase II SHM process is less efficient in older males, resulting in a less optimal Ab repertoire in response to Ags.

Aside from aging, other factors may also affect SHM targeting, such as genetic factors and health state. Despite the fact that many factors can impact one’s immunity, we found significant correlations between A/T mutability and age in males. It is interesting that the frail male showed the largest decrease in the major SHM repair molecules, which might suggest that a lack of SHM repair activities is associated with weakened immune response. Future studies should examine the role of sex-specific molecules in the SHM process. For example, a study revealed the immunosuppressive function of testosterone in response to influenza vaccination, indicating that sex hormones could play a role (32).

It is interesting to note that many DNA repair enzymes and polymerases are involved in both Ab diversification and DNA damage repair in other parts of the genome. A previous study observed a correlation between microsatellite stability, particularly PMS2 expression, and types of mutations in B cell repertoires (33). In this study, we observed a slight reduction of PMS2 expression in older males, which may reflect their conclusion on the influence of MMR activity on the types of mutations generated through SHM. Future studies should examine the pleiotropic effects of DNA repair molecules. For example, individuals with deficiency in DNA repair genes may not only have an impaired Ab repertoire, but also a higher predisposition to diseases caused by DNA damages, such as cancer.

A limitation of the gene expression dataset is that the B cells were collected from peripheral blood instead of the GC where SHM takes place. Because of this, we found that some genes encoding important molecules in SHM, such as AID and pol-η, were not highly expressed, limiting our ability to study all genes involved in SHM. Additionally, other machineries, aside from gene expression of SHM-related molecules, may also influence SHM targeting. This prompts future studies to examine B cells in GCs to see what other DNA repair pathways contribute to the aging effects that we observed. A limitation of the GTEx RNA-seq dataset is the small number of B cell Ig genes one can extract from transcriptome-wide RNA-seq data on all cell types in a tissue. The AIRR-seq dataset has many more mutations per individual, although it has fewer subjects. However, the fact that both AIRR-seq and GTEx RNA-seq analyses yielded consistent findings improves our confidence in the sex-biased aging effects in SHM targeting. In the future, larger cohorts of individuals should be recruited to characterize the extent to which age and sex can influence SHM-targeting activities and which genes experience the most alteration in expression levels with age. Nevertheless, this study showed that age and sex could influence the balance between the two phases of SHM targeting, which is crucial to Ab diversification.

In conclusion, our analyses on BCR sequencing and B cell gene expression data showed alterations in SHM mutation patterns and gene expressions between age and sex groups. These results suggest an uneven rate of alteration in SHM DNA repair machineries between males and females across lifetime, providing insights into designing more targeted treatment and vaccination strategies for different age and sex groups.

We thank the Kleinstein laboratory members for feedback and technical assistance and Dr. Scott Boyd for sharing data. We thank the Yale University Biomedical High Performance Computing Center and the Broad Institute Computing Center for use of the computing resources.

S.H.K. receives consulting fees from Northrop Grumman.