Sex bias in MHC I-associated shaping of the adaptiveimmune systemTilman Schneider-Hohendorfa, Dennis Görlichb, Paula Savolac, Tiina Kelkkac, Satu Mustjokic, Catharina C. Grossa,Geoffrey C. Owensd, Luisa Klotza, Klaus Dornmaire, Heinz Wiendla, and Nicholas Schwaba,1

aDepartment of Neurology, University of Muenster, 48149 Muenster, Germany; bInstitute of Biostatistics and Clinical Research, University of Muenster,48149 Muenster, Germany; cHematology Research Unit Helsinki, Department of Clinical Chemistry and Hematology, University of Helsinki and HelsinkiUniversity Hospital Comprehensive Cancer Center, 00029 Helsinki, Finland; dDepartment of Neurosurgery, David Geffen School of Medicine at the Universityof California Los Angeles, Los Angeles, CA 90095; and eInstitute of Clinical Neuroimmunology, University Hospital and Biomedical Center, Ludwig-Maximilians University Munich, 80539 Munich, Germany

Edited by K. Christopher Garcia, Stanford University, Stanford, CA, and approved January 12, 2018 (received for review September 14, 2017)

HLA associations, T cell receptor (TCR) repertoire bias, and sex biashave independently been shown for many diseases. While someimmunological differences between the sexes have been de-scribed, they do not fully explain bias in men toward manyinfections/cancers, and toward women in autoimmunity. Next-generation TCR variable beta chain (TCRBV) immunosequencing of824 individuals was evaluated in a multiparametric analysisincluding HLA-A -B/MHC class I background, TCRBV usage, sex,age, ethnicity, and TCRBV selection/expansion dynamics. We foundthat HLA-associated shaping of TCRBV usage differed between thesexes. Furthermore, certain TCRBVs were selected and expanded inunison. Correlations between these TCRBV relationships and bio-chemical similarities in HLA-binding positions were different in CD8T cells of patients with autoimmune diseases (multiple sclerosis andrheumatoid arthritis) compared with healthy controls. Withinpatients, men showed higher TCRBV relationship Spearman’s rhosin relation to HLA-binding position similarities compared withwomen. In line with this, CD8 T cells of men with autoimmunediseases also showed higher degrees of TCRBV perturbation com-pared with women. Concerted selection and expansion of CD8T cells in patients with autoimmune diseases, but especially inmen, appears to be less dependent on high HLA-binding similaritythan in CD4 T cells. These findings are consistent with studiesattributing autoimmunity to processes of epitope spreading andexpansion of low-avidity T cell clones and may have further impli-cations for the interpretation of pathogenic mechanisms of infec-tious and autoimmune diseases with known HLA associations.Reanalysis of some HLA association studies, separating the databy sex, could be informative.

T cell receptor repertoire | immunological sex bias | autoimmunity |multiple sclerosis | rheumatoid arthritis

The T cell receptor (TCR) repertoire is highly diverse (1) andcan generate up to 1015 theoretical TCR sequences (2, 3)

with the effective size of the CD8 TCR repertoire in vivo beingmade up of 105 to 108 sequences (2, 4–6). The repertoire startsdeveloping prenatally (7) and is subject to continuous adaptationduring the course of a lifetime due to antigen exposure and otherenvironmental influences (8, 9). While its complementary de-termining region 3 (CDR3) is hypervariable and binds to theMHC-presented antigen (10), the binding sites of the TCR to theMHC are germline encoded within the CDR1 and CDR2 ofthe TCR variable chains (11, 12). Many studies over the last de-cades have shown that evolutionarily conserved anchor amino acidsin these CDRs determine how single TCR constructs bind to theirrespective MHC (13). Concerning the TCR variable beta chains(TCRBVs), 31 individual genes are known to encode a functional,nonpseudogenic, nonsegregated TCR, each with an individualCDR1 and CDR2 (14, 15). The HLA background is geneticallypredetermined and invariable (16), leading to the accepted notionthat the HLA shapes the TCR repertoire during their continuousinteraction (17–20). Very recently, it was shown that mutations in

TCR binding amino acids of MHC molecules specifically influ-ence the binding to and usage of TCR variable alpha (TCRAV)and TCRBV chains (19), and that the presence of specific TCRrearrangements is an indication of the HLA background of thehost and also of previous exposure to pathogens (20). Dysregu-lation in the TCR–antigen–MHC complex can lead to autoim-munity (21) in the case of uncontrolled immune cell expansion orcross-reactivity between foreign and autoantigens. Hypoexpansions orlow TCR avidity might lead to susceptibility to infections. Addition-ally, to combat tumor-associated immune system suppression, cancerpatients can now be treated with T cell-relevant immune checkpointinhibitors such as blockade of CTLA-4 (22) or PD-1 (23).Most immune system dysfunctions show strong sex bias: men

are more susceptible to many infectious diseases and cancers ofnonreproductive organs, whereas autoimmune diseases are muchmore common in women (24, 25). Even in psychological disor-ders such as major depression (26) and in neurodegenerativediseases such as Alzheimer’s, recent studies have shown immunesystem involvement (27) and sex bias (28). Many studies havebeen conducted concerning either sex (25), HLA (29, 30), orTCR (31) associations with diseases. However, even thoughsome sex differences in immune regulation have been reported(25, 32), it is still not completely clear why this bias exists.Multiparametric analysis of the influence of sex on HLA-mediated shaping of the TCR repertoire has been hindered by

Significance

Our analysis shows that sex can be associated with the degreeto which HLA molecules propagate selection and expansion ofT cells as characterized by their T cell receptor variable betachain (TCRBV). Furthermore, CD8 T cells, especially in men withautoimmune diseases such as multiple sclerosis or rheumatoidarthritis, are capable of expanding in unison with other CD8T cells, even without expressing TCRBVs with biochemicalsimilarity in pivotal HLA-binding regions. Our findings add tothe understanding of sex bias in diseases with immune systeminvolvement: autoimmunity, infection, and cancer. These re-sults also reveal pathology-associated TCRBVs of interest forfuture studies and support the argument for sex-separatedanalysis of HLA disease associations in general.

Author contributions: T.S.-H., H.W., and N.S. designed research; T.S.-H. and N.S. per-formed research; D.G. and N.S. contributed new reagents/analytic tools; T.S.-H., D.G.,and N.S. analyzed data; and T.S.-H., D.G., P.S., T.K., S.M., C.C.G., G.C.O., L.K., K.D., H.W.,and N.S. wrote the paper.

The authors declare no conflict of interest.

This article is a PNAS Direct Submission.

Published under the PNAS license.1To whom correspondence should be addressed. Email: nicholas.schwab@ukmuenster.de.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1716146115/-/DCSupplemental.

Published online February 12, 2018.

2168–2173 | PNAS | February 27, 2018 | vol. 115 | no. 9 www.pnas.org/cgi/doi/10.1073/pnas.1716146115

Dow

nloa

ded

by g

uest

on

Mar

ch 1

5, 2

020

low subject numbers in most studies. The advent of next-generationand high-throughput TCR immunosequencing in large cohorts ofHLA-typed subjects now allows for a combined analysis of the TCRrepertoire dependence on sex and HLA alleles (20).

ResultsInfluence of HLA-A and -B on TCRBV Usage Depends on the BiologicalSex. To assess whether the HLA background influences thefrequency of T cells expressing certain TCRBVs in a sex-associated manner, TCRBV immunosequencing data from673 individuals were analyzed (for details see Datasets S1 andS2). The resulting dataset, therefore, contained the followingparameters: sex (2; male, female), HLA-A and HLA-B genes(19; 8 HLA-A, 7 HLA-B, 4 most common haplotypes in theCaucasian population), and 31 TCRBV genes represented aspercentage of the productive TCR repertoire in a sample. OnlyHLA with at least 40 individuals in the dataset or a balancedfemale-to-male ratio (percentage of female donors between 45%and 55%) and only functional TCRBV genes according to theInternational ImMunoGeneTics (IMGT) nomenclature (14, 15)were included. Nonfunctional TCRBV genes, pseudogenes, seg-regating, or open-reading frame genes (with potential mutationsin the leader sequence) were not analyzed. A multiparametricanalysis using multivariate linear models assessing the interactionof HLA and sex showed that the manner in which HLA genesinfluence TCRBV usage can differ between men and women (Fig.1). Controlled for the known immunosequencing covariates, wefound sex-associated differences in the propagation of TCRBVselection and expansion by the HLA [raw data in Dataset S2, Pvalues in Dataset S3, and effect sizes (partial eta squared) inDataset S4]. With the number of individuals in our study, 42 HLA/TCRBV combinations (P < 0.05) were found in this dataset, ofwhich three remained significant after correcting for multiplecomparisons among those 589 assessed combinations, whereTCRBV usage by a specific HLA in one sex was elevated com-pared with the other sex, independent of a potential influence ofHLA or sex alone (Fig. S1 A–C). The Bonferroni-corrected sex-specific TCRBV expressions were challenged in a sensitivity anal-ysis, where only healthy donors or only donors with determined (notinferred) HLA were assessed. The sex-specific differences couldalso be observed, when age or ethnicity (which was not available forall donors) or other relevant hidden covariates [as determined by

probabilistic estimation of expression residuals (PEER) analysis](33, 34) were included in the model (Fig. S1 D and E).

CD8 T Cells in Men with Autoimmune Diseases Are Less Dependent onBiochemical CDR1/2 Similarity for Concerted Selection/Expansion.Having shown that the HLA background can alter TCRBV us-age in a sex-dependent manner and given that MHC moleculesinteract with the TCR mainly via the CDR1 and -2, we hy-pothesized that there could be a functional relationship betweenhow similar TCRBV chains are to one another (in these regions)and how they are selected in the thymus or expanded in theperiphery via their HLA interaction. To assess this, the germline-encoded CDR1 and -2 sequences of the 31 functional TCRBVtranscripts were compared via a Blocks Substitution Matrix(BLOSUM62) to estimate biochemical amino acid similarities(35). Resulting alignment matrices are used to score singleamino acids at a specific position in two different sequences withrespect to their biophysical and biochemical similarities. Thescores of each pairing are then computed and indicate howsimilar two sequences are to each other. The current nomen-clature groups TCRBV chains according to the nucleotide sim-ilarity of their complete DNA sequence. As our question did notrelate to evolutionary homology per se, but rather to biologicalfunction with regard to TCR–MHC binding, we only assessed theknown binding amino acids (position 28, and 29 in CDR1 as wellas 46, 48, and 54 in CDR2) with the BLOSUM62 matrix (13)(Fig. S2A). The resulting 465 individual similarity scores be-tween −9 and 29 were color coded (values listed in Dataset S5)and clustered using the Ward (36) method. This resulted in fouroptimal clusters [calculated via gap statistic (37)] (Fig. S2B),grouping TCRBV gene segments into the known homology-based subgroups or “families” (14) (i.e., subgroups 4, 5, 7, and10, but not TCRBV06). However, the analysis also suggestedrelationships, which were not expected from the nomenclature(i.e., TCRBV05–08 and the TCRBV07 subgroup). To evaluatethe in vivo relevance of these relationships in health and disease,the TCRBV usage by CD4 and CD8 T cells from age-matchedmale and female healthy donors (HD) or patients suffering fromautoimmune diseases [multiple sclerosis (MS) and rheumatoidarthritis (RA)] was analyzed for potential correlations in allpossible TCRBV chain combinations (raw data in Dataset S6)resulting in 465 TCRBV relationship Spearman’s rank correlationcoefficients (TCRBV rhos, Dataset S5). To allow for a categorical

Fig. 1. Sex bias in HLA-associated shaping of the TCRBV usage. Shown are uncorrected P values from a multivariate linear regression model (covariates inDataset S2), assessing the interaction of HLA and sex on TCRBV usage. HLA/TCRBV combinations highlighted in red indicate P < 0.05 after Bonferroni cor-rection for multiple comparisons; yellow color suggests additional, potentially sex-specific HLA/TCRBV combinations, which did not pass Bonferroni correction.Left columns indicate the total number (N) as well as the male (M) and female (F) subject numbers for each HLA (raw data in Dataset S3), and green colorindicates that this inclusion criterion was passed (one criterion was enough for inclusion).

Schneider-Hohendorf et al. PNAS | February 27, 2018 | vol. 115 | no. 9 | 2169

IMMUNOLO

GYAND

INFLAMMATION

Dow

nloa

ded

by g

uest

on

Mar

ch 1

5, 2

020

assessment, BLOSUM values were divided at the median into232 low- and 233 high-similarity scores. Subsequently the TCRBVrhos were used as the dependent variable in linear models for CD4(Fig. 2 A and B) and CD8 T cells (Fig. 2 C and D) with the maineffects “disease state (HD/MS/RA),” “sex (male/female),” and“BLOSUM score (high/low).” As hypothesized, TCRBV rhos ofboth T cell subsets were influenced by BLOSUM scores. However,only CD8 T cells were additionally influenced by disease state, aswell as sex: (i) CD8 T cells from patients with both autoimmunediseases (MS or RA) differed from CD8 T cells from healthydonors. (ii) With regard to sex, men with autoimmune diseasesshowed significantly higher rhos in TCRBV combinations withlow BLOSUM scores than women, which in the case of RAcould also be observed in TCRBV combinations with highBLOSUM scores. To further explore the hypothesis that anMHC–TCR stimulus of the same affinity could lead to higherCD8 expansions of similar TCRBV chains in men with auto-immune diseases than in women, TCRBV chain perturbationwas assessed (4, 38). In an age-matched, paired analysis, malesubjects with autoimmune diseases (MS or RA) presented withhigher “productive clonality” (a measurement of TCRBVperturbation), than female subjects in the CD8 T cell com-partment (Fig. 2E).

In Vivo Relationships Between TCRBV Transcripts Are Cause and NotResult of TCRBV Perturbations. Twenty-five pairs of naïve andmemory CD4 and CD8 T cells from healthy donors (100 samplesin total, raw data in Dataset S7) were analyzed to address thefollowing questions: (i) Is the higher TCRBV perturbation inCD8 T cells in men cause or effect of the relationships seen inFig. 2? (ii) Can a high degree of TCRBV perturbation, whichwould be expected in memory T cells, influence the relationshipsbetween TCRBV gene transcripts? As expected, memory T cellspresented with higher TCRBV perturbation compared with naïveT cells (Fig. 3A). However, this neither influenced the relationshipbetween BLOSUM scores and TCRBV rhos in CD4 T cells norCD8 T cells (Fig. 3 B and C). Additionally, the relationships betweenTCRBV transcripts found in naïve cells could also be found inmemory cells (Fig. S3 A and B, TCRBV rhos listed in Dataset S5).

DiscussionThe most surprising finding of our study was that the biologicalsex of an individual can influence the HLA association withT cell selection and expansion. Most autoimmune diseases morefrequently affect women (25), whereas men are more susceptibleto many infectious diseases, e.g., leishmaniasis or hepatitis A(39). HLA associations with cancer and the fact that men de-velop cancer in nonreproductive organs much more frequentlythan women has also been known for a long time (24). As cancercan also be viewed as a failure of the immune system to controlneoplastic tissue, low expression in certain TCRBV families

-0.1

0.0

0.1

0.2

0.3

0.4

Spea

rman

R

CD4MaleFemale

-0.1

0.0

0.1

0.2

0.3

0.4

Spea

rman

R

CD8

MaleFemale

HD MSlow high

HD MS RA

low high

low high low

A

C

B

CD4 CD8

MSHD RAMSHD

Ep val. 0.129 0.374 0.700 0.011 0.005n 9 21 11 17 11

0.000.050.100.150.200.250.300.350.400.450.500.550.600.65

TCR

BV p

ertu

rbat

ion

MaleFemale

Model parameters1. Disease state (HD / MS)2. Sex (male / female)3. BLOSUM score (low / high)4. Disease state * BLOSUM * Sex

Type III SS0.0700.1291.6350.369

df1114

Mean Squ.0.0700.1291.6350.092

F score0.5160.953

12.0920.682

p value0.4730.329

5.14 E-040.604

Ƞp20.0000.0010.0060.001

CD

4

1. Disease state (HD / MS / RA)2. Sex (male / female)3. Disease state * Sex MS: male versus female RA: male versus female

9.1112.1372.194

212

4.5562.1371.097

58.68727.53214.131

3.42 E-251.79 E-078.41 E-07

0.04421.24 E-10

0.0780.0190.020

Model parameters1. Disease state (HD / MS / RA) HD versus MS HD versus RA2. Sex (male / female)3. BLOSUM score (low / high)4. Disease state * BLOSUM * Sex

Type III SS18.332

4.7830.8836.299

df2

117

Mean Squ.9.166

4.7830.8830.900

F score115.695

60.37211.15111.359

p value<1 E-35

1.50 E-09<1 E-35

1.10 E-148.51 E-042.83 E-14

Ƞp20.077

0.0210.0040.028

CD

8B

LOS

UM

-low

BLO

SU

M-h

igh 1. Disease state (HD / MS / RA)

2. Sex (male / female)3. Disease state * Sex MS: male versus female RA: male versus female

9.2432.6614.069

212

4.6222.6612.034

57.18732.92125.175

1.35 E-241.18 E-081.82 E-11

1<1 E-35

0.0760.0230.035

D

aa

aa

a

a Bonferroni-corrected p values (Student‘s t-test)

ahigh low highBLOSUM

BLOSUM

Fig. 2. CD8 T cells in men with autoimmune diseases are less dependent on biochemical CDR1/2 similarity for concerted selection/expansion. (A) TCRBVrelationships Spearman’s correlation coefficients (TCRBV rhos) of CD4 T cells of healthy donors (HD) and MS patients grouped according to BLOSUM-low andBLOSUM-high scores and divided by sex [male (red) versus female (blue)]. Shown are mean and 95% confidence interval. (B) Parameters of the linear modelfor CD4 T cells. Shown are the type III sum of squares (type III SS), degrees of freedom (df), mean square (mean squ.), F score, significance (P value), andpartial eta squared (ɳp2) as effect size. (C ) TCRBV rhos of CD8 T cells of healthy donors (HD), and MS and RA patients grouped according to A. (D) Pa-rameters of the linear model for CD8 T cells according to B. Two nested models are shown (BLOSUM-low and BLOSUM-high) within CD8 T cells. Pairwisecomparison (superscript a) of the mean is shown for groups of interest (Bonferroni-corrected Student’s t test). (E ) TCRBV perturbation, measured asproductive clonality. Shown are paired comparisons (Wilcoxon matched pairs signed rank test) between age-matched male and female individuals(CD4 and CD8 T cells; HD, MS, or RA).

CD4 CD8naive memory naive memory

A B

C

0.0

0.1

0.2

0.3

0.4

0.5

0.6

TCR

BV p

ertu

rbat

ion

-0.1

0.0

0.1

0.2

0.3

0.4

Spea

rman

R

NaiveMemory

CD4low high

CD8low high

Model parameters1. Cell type (CD4 / CD8)2. Maturation (naive / memory)3. BLOSUM score (low / high)4. Cell type * Maturation * BLOSUM

Type III SS0.0150.1323.7090.423

df1114

Mean Squ.0.0150.1323.7090.106

F score0.0970.843

23.6700.675

p value0.755780.35861

1.241E-060.60931

Ƞp2

0.000050.000460.012630.00146

p val. <0.0001 <0.0001 n 25 25

BLOSUM

Fig. 3. In vivo relationships between TCRBV chains are similar in naïve andmemory T cell subsets. (A) TCRBV perturbation quantified by the se-quencing parameter productive clonality of naïve and memory CD4 andCD8 T cell subsets from 25 healthy subjects. (B) TCRBV rhos of naïve (red)and memory (blue) CD4/CD8 T cells grouped according to low and highBLOSUM scores. Shown is mean and 95% confidence interval (TCRBV rhosin Dataset S5 and raw data in Dataset S7). (C ) Parameters of the linearmodel with TCRBV rhos as dependent and cell type, maturation, BLOSUMscores as main effects with the added interaction of those three maineffects.

2170 | www.pnas.org/cgi/doi/10.1073/pnas.1716146115 Schneider-Hohendorf et al.

Dow

nloa

ded

by g

uest

on

Mar

ch 1

5, 2

020

could be an indication of this defect as well. Apart from sus-ceptibilities and disease incidences, general sex differences inparameters of the adaptive immune system have been foundbefore. Women have higher amounts of circulating IgMs (40)and more CD4 T cells (41). These differences are thought to bemainly mediated by steroid hormones, with estrogens oftenpromoting and androgens suppressing immune responses duringinfections, after vaccination, or in case of autoimmunity (reviewedin refs. 42–44). Recent studies also suggest an influence of glu-cocorticoid receptors in the suppression of autoimmunity inpregnant MS patients (32). Therefore, the observed sex differ-ences in HLA-mediated shaping of the TCR repertoire could inpart be caused or facilitated by the steroid hormone milieu,influencing lymphocyte activation during immune responses (32,45), but also their maturation during thymic selection (46). Ourdata support these studies by providing evidence that the ob-served relationships between TCRBV transcripts play a similarrole in thymic selection (reflected in naïve cells), as well as duringexpansion in the periphery due to antigenic stimuli (reflected inmemory cells).It has previously been shown that the HLA background shapes

the TCR repertoire by favoring or neglecting certain T cellsubpopulations represented by TCRBV usage (19). CertainHLAs are better at presenting auto- or foreign antigens toTCRBV-specific subgroups of T cells. Over time this can lead toan individually diverse “immunological fingerprint” shaped bygenetics and exposure to antigens. This better or worse HLA–

TCR interaction will have consequences for infectious diseases(susceptibility, progression, adverse effects), and also for anti-tumor responses, autoimmunity, or adverse medical reactions.Unexpectedly, we found differences between men and womenwith respect to the influence of HLA genes on TCRBV tran-scripts. In light of these findings, it makes sense that the TCRrepertoire at birth is similar between cohorts with vastly differentHLA backgrounds (47), but is then shaped in the course of lifedue to exposure to and HLA presentation of different antigensas shown in twin studies (17, 48). Consistent with this idea, it hasrecently been shown that T cell expansions show high degrees ofhomology on an amino acid level, but not on a DNA sequencelevel in individuals with similar HLA backgrounds (49). Onepossible explanation might have been a different expression levelof the HLA molecules on the cells of men and women, but arecently published large study showed that at least the solubleform of the β-2 microglobulin in serum of men and women iscomparable (50), which would fit the above-mentioned hypoth-esis that the observed differences could be a hormone-mediateddownstream effect taking place after the MHC–TCR binding.The second finding that CD8 T cell subpopulations with low

biochemical similarities in their TCRBV chains correlate morestrongly in patients with autoimmune diseases (MS and RA) ingeneral, but particularly in men, is consonant with the overallhigher CD8 T cell TCRBV perturbation in men compared withwomen. We hypothesize that a defined antigen stimulus wouldthereby lead to more clonal expansions in CD8 T cells in men, asthe presentation by a given HLA molecule could also triggerT cell expansions with low binding affinity between TCR andMHC. Additionally, it is known that CD4 and CD8 in theirfunction as coreceptors bind to the MHC molecules with dif-ferent binding affinities (reviewed in ref. 51): CD4 has a weakbinding affinity to MHC class II (52), whereas CD8 has a highbinding affinity to MHC class I (53). These studies have alsosuggested that MHC class II ligands modulate TCR signalingdirectly via the TCR, whereas MHC class I ligands do not in-fluence TCR signaling via the TCR, but via CD8 itself (54), andCD8 T cells are less dependent on continuous antigen pre-sentation (55). Therefore, our data showing that low CDR sim-ilarity can still lead to parallel expansions in unrelated TCRBVsfit the hypothesis that CD8 T cells are less dependent on a strong

MHC–TCR binding. Furthermore, antigen-triggered expansionscould also occur in TCRs despite suboptimal binding to MHC aslong as the CD8 binding affinity is strong enough (56). Manyhypotheses concerning autoimmunity involve an immune re-action against self-antigens via antigen spreading, molecularmimicry, or because low-avidity clones become activated, eventhough the threshold for activation should not have beenreached, especially in cases of dual TCR expression by CD8T cells (ref. 57 and reviewed in ref. 58). Our finding that CD8T cells of MS patients in general, but particularly in men with MSexpand in concert in regions of low CDR1/2 similarity is con-sistent with the finding that men present with higher diseaseburden, if they suffer from MS (59). We found similar correla-tions in CD8 T cells of patients with RA, but in contrast to MS,the clinical picture in RA is not as clear concerning sex differ-ences and disease severity, potentially due to the fact that themeasurement of disease activity in RA involves secondary out-come parameters with inherent sex bias (60). Our study providesevidence that biological sex modifies associations between HLAmolecules and T cell TCRBV subpopulations and might, there-fore, have implications regarding prior studies of HLA associa-tions. These studies could possibly be revisited and male andfemale participants analyzed separately to ensure that somesignificant results are not lost due to statistical adjustments.

Materials and MethodsStudy Cohorts. This study included samples from five cohorts: (cohort 1)healthy subjects (20); (cohort 2) subjects suffering from epilepsy, multiplesclerosis, or a common cold, as well as corresponding healthy subjects (ref. 61and raw data in Datasets S2 and S6); (cohort 3) subjects suffering fromrheumatoid arthritis and corresponding healthy subjects (62); (cohort 4)healthy subjects (63); and (cohort 5) healthy subjects (64). Details of thesecohorts and their contribution to the analyses are listed in Dataset S1.

Study Approval. Studies on human samples were approved by the local ethicscommittee (University of Muenster: Ethik-Kommission der ÄrztekammerWestfalen-Lippe und der Medizinischen Fakultät der Westfälischen Wilhelms-Universität, registration number: 2010–245-f-S). Written informed consent wasgiven by all participants in the study and all experiments were performedaccording to the Declaration of Helsinki.

Sequencing. TCR immunosequencing was done according to the immunoSEQassay (Adaptive Biotechnologies) as previously described (38, 61, 65); fordetails please see SI Materials and Methods.

Statistics. In Fig. 1, the interaction of sex and HLA on TCRBV usage wasanalyzed with multivariate linear regression models with adjustment forcovariates influencing TCRBV usage. Eight covariates (see below and DatasetS2) were used for all comparisons. The linear regression model included 31TCRBV transcripts as dependents, the interaction of sex and one HLA as fixedfactors, together with covariates (TCRBV = HLA + sex + HLA × sex + covariates;one model per HLA, resulting in 31 models with 589 HLA × sex interactions).Significance was corrected for multiple comparisons according to the Bonferronimethod (66) using R (function p.adjust). Significances not passing Bonferronicorrection, but exhibiting an uncorrected P value ≤0.05, are considered ex-ploratory. Covariates are as follows: fraction productive, the fraction of in-frame templates; productive rearrangements, count of unique and functionalrearrangements; productive clonality, normalized TCRBV repertoire diversitymeasure using log (base 2) of the total number of unique productive rear-rangements to normalize the entropy and subtracting the result from thevalue 1; max productive frequency, the highest frequency of a rearrangement;productive entropy, diversity measure for the TCRBV repertoire according toShannon’s entropy (67), calculated over all productive rearrangements; celltype, isolated cell subset (PBMC, CD4, or CD8 T cells); sequencing source ma-terial (cgDNA), genomic DNA (gDNA), or cDNA (cDNA); and batch, sequencingbatch as assessed by time of sequencing (24 batches). Covariates “age” (ofdonor at time point of sampling) and “ethnicity” (self-reported ethnicity of adonor, Caucasian, or non-Caucasian) were included in a sensitivity analysis (Fig.S1E), as these variables were not available for all donors. The sensitivity as-sessment also included one analysis performed only with “healthy” individualsand one only with individuals, where the HLA background was determined, not“inferred.” HLA-A/B background was fully available for cohorts 1 and 3 and

Schneider-Hohendorf et al. PNAS | February 27, 2018 | vol. 115 | no. 9 | 2171

IMMUNOLO

GYAND

INFLAMMATION

Dow

nloa

ded

by g

uest

on

Mar

ch 1

5, 2

020

was inferred for part of samples of cohort 2, based on HLA-A/B-specificCDR3 sequences according to Emerson (20). To determine possible hiddenfactors in addition to known covariates, we used the PEER package (33, 34).From 25 hidden factors provided by the algorithm, 9 were included inthe sensitivity analysis, as these were in the αC range of the known covariates(αC < 100) (Fig. S1D). The SPSS syntax used for Fig. 1 is given in Dataset S2.

In Fig. 2, all combinations of 31 TCRBVs resulted in 465 scores rangingfrom −9 to 26, each of them the sum of the CDR1 and -2 binding sites’BLOSUM62 scores (Fig. S2). BLOSUM62 scores were calculated using thefunction “pairwiseAlignment” from the Biostrings package within Bio-conductor (68, 69) and are presented as a heatmap clustered according toWard (36). Optimal number of clusters was determined with gap statistic(37) by using the functions “clusGap” and “fviz_gap_stat” from the fac-toextra package. The 465 scores were divided at the median value of 3 intotwo groups of low (BLOSUM-low, score −9–2, n = 232) and high (BLOSUM-high, score 3–26, n = 233) biochemical similarity. Multivariate linear modelswere applied to CD4 and CD8 datasets with TCRBV rhos as dependent andthe main effects “sex,” “disease state,” and “BLOSUM score,” as well as theirinteraction. Subgroup analyses (nested models) were applied to BLOSUM-low and BLOSUM-high with the main effects of sex and disease state, as wellas their interaction.

In Fig. 3, a multivariate model was applied to CD4 and CD8 naïve andmemory T cell datasets with the main effects “cell type,” “maturation,” andBLOSUM score as well as their interaction.

The following statistical software was used: IBM SPSS Statistics V24,GraphPad Prism V6 and R V3.4.1 using the packages PEER, Bioconductor,Biostrings, made4, dendextend, factoextra, cluster, pheatmap, corrplot, stats,and their respective dependencies.

ACKNOWLEDGMENTS. This study was funded by DFG Grant SFB/CRC128Project B1 (to N.S.), A5 (to K.D.), and Z2 (to H.W.); single Grant GR3946_3/1(to C.C.G.); the Kompetenznetz Multiple Sklerose (Competence Network forMultiple Sclerosis) funded by the Federal Ministry of Education and Research(Bundesministerium für Bildung und Forschung) Grants FKZ 01GI1308B01GI0907 and GER-TYS-12-10-401 (Immuntherapieregister zur Verbesserungder Arzneimittelsicherheit in der Multiple Sklerose Therapie Innerhalb desKrankheitsbezogenen Kompetenznetzes MS) (to H.W.); the InterdisziplinäreZentrum für Klinische Forschung Münster (Wie3/009/16) (to N.S. and H.W.);the European Research Council (Novel etiology of autoimmune disorders:The role of acquired somatic mutations in lymphoid cells, M-IMM project);Academy of Finland; Finnish special governmental subsidy for health sci-ences, research and training; the Sigrid Juselius Foundation; and the FinnishCancer Institute (S.M.).

1. Kappler J, et al. (1983) The major histocompatibility complex-restricted antigen re-ceptor on T cells in mouse and man: Identification of constant and variable peptides.Cell 35:295–302.

2. Arstila TP, et al. (1999) A direct estimate of the human alphabeta T cell receptor di-versity. Science 286:958–961.

3. Lythe G, Callard RE, Hoare RL, Molina-París C (2016) How many TCR clonotypes does abody maintain? J Theor Biol 389:214–224.

4. Robins HS, et al. (2010) Overlap and effective size of the human CD8+ T cell receptorrepertoire. Sci Transl Med 2:47ra64.

5. Qi Q, et al. (2014) Diversity and clonal selection in the human T-cell repertoire. ProcNatl Acad Sci USA 111:13139–13144.

6. Li HM, et al. (2016) TCRβ repertoire of CD4+ and CD8+ T cells is distinct in richness,distribution, and CDR3 amino acid composition. J Leukoc Biol 99:505–513.

7. Haynes BF, Heinly CS (1995) Early human T cell development: Analysis of the humanthymus at the time of initial entry of hematopoietic stem cells into the fetal thymicmicroenvironment. J Exp Med 181:1445–1458.

8. Batliwalla F, Monteiro J, Serrano D, Gregersen PK (1996) Oligoclonality of CD8+ T cellsin health and disease: Aging, infection, or immune regulation? Hum Immunol 48:68–76.

9. Britanova OV, et al. (2014) Age-related decrease in TCR repertoire diversity measuredwith deep and normalized sequence profiling. J Immunol 192:2689–2698.

10. Dai S, et al. (2008) Crossreactive T cells spotlight the germline rules for alphabetaT cell-receptor interactions with MHC molecules. Immunity 28:324–334.

11. Moss PAH, Bell JI (1995) Sequence analysis of the human alpha beta T-cell receptorCDR3 region. Immunogenetics 42:10–18.

12. Yin L, et al. (2011) A single T cell receptor bound to major histocompatibility complexclass I and class II glycoproteins reveals switchable TCR conformers. Immunity 35:23–33.

13. Marrack P, Scott-Browne JP, Dai S, Gapin L, Kappler JW (2008) Evolutionarily con-served amino acids that control TCR-MHC interaction. Annu Rev Immunol 26:171–203.

14. Lefranc MP, et al. (1998) IMGT, the International ImMunoGeneTics database. NucleicAcids Res 26:297–303.

15. Dean J, et al. (2015) Annotation of pseudogenic gene segments by massively parallelsequencing of rearranged lymphocyte receptor loci. Genome Med 7:123.

16. van Leeuwen A, Termijtelen A, Shaw S, van Rood JJ (1982) Recognition of a poly-morphic monocyte antigen in HLA. Nature 298:565–567.

17. Hoover ML, et al. (1986) Insulin-dependent diabetes mellitus is associated withpolymorphic forms of the T-cell receptor beta chain gene. Hum Immunol 17:176(abstr).

18. Akolkar PN, Gulwani-Akolkar B, McKinley M, Fisher SE, Silver J (1995) Comparisons ofT cell receptor (TCR) V β repertoires of lamina propria and peripheral blood lym-phocytes with respect to frequency and oligoclonality. Clin Immunol Immunopathol76:155–163.

19. Sharon E, et al. (2016) Genetic variation in MHC proteins is associated with T cellreceptor expression biases. Nat Genet 48:995–1002.

20. Emerson RO, et al. (2017) Immunosequencing identifies signatures of cytomegalovirusexposure history and HLA-mediated effects on the T cell repertoire. Nat Genet 49:659–665.

21. Garcia KC, et al. (1998) Structural basis of plasticity in T cell receptor recognition of aself peptide-MHC antigen. Science 279:1166–1172.

22. Ramagopal UA, et al. (2017) Structural basis for cancer immunotherapy by the first-in-class checkpoint inhibitor ipilimumab. Proc Natl Acad Sci USA 114:E4223–E4232.

23. Le DT, et al. (2017) Mismatch repair deficiency predicts response of solid tumors to PD-1blockade. Science 357:409–413.

24. Clocchiatti A, Cora E, Zhang Y, Dotto GP (2016) Sexual dimorphism in cancer. Nat RevCancer 16:330–339.

25. Whitacre CC, Reingold SC, O’Looney PA (1999) A gender gap in autoimmunity.Science 283:1277–1278.

26. Labonté B, et al. (2017) Sex-specific transcriptional signatures in human depression.Nat Med 23:1102–1111.

27. Hong S, et al. (2016) Complement andmicroglia mediate early synapse loss in Alzheimermouse models. Science 352:712–716.

28. Mosconi L, et al. (2017) Sex differences in Alzheimer risk: Brain imaging of endocrinevs chronologic aging. Neurology 89:1382–1390.

29. Gough SC, Simmonds MJ (2007) The HLA region and autoimmune disease: Associa-tions and mechanisms of action. Curr Genomics 8:453–465.

30. Blackwell JM, Jamieson SE, Burgner D (2009) HLA and infectious diseases. ClinMicrobiol Rev 22:370–385.

31. Kirsch I, Vignali M, Robins H (2015) T-cell receptor profiling in cancer. Mol Oncol 9:2063–2070.

32. Engler JB, et al. (2017) Glucocorticoid receptor in T cells mediates protection fromautoimmunity in pregnancy. Proc Natl Acad Sci USA 114:E181–E190.

33. Stegle O, Parts L, Piipari M, Winn J, Durbin R (2012) Using probabilistic estimation ofexpression residuals (PEER) to obtain increased power and interpretability of geneexpression analyses. Nat Protoc 7:500–507.

34. Stegle O, Parts L, Durbin R, Winn J (2010) A Bayesian framework to account forcomplex non-genetic factors in gene expression levels greatly increases power ineQTL studies. PLoS Comput Biol 6:e1000770.

35. Henikoff S, Henikoff JG (1992) Amino acid substitution matrices from protein blocks.Proc Natl Acad Sci USA 89:10915–10919.

36. Ward JH (1963) Hierarchical grouping to optimize an objective function. J Am StatAssoc 58:236–244.

37. Tibshirani R, Walther G, Hastie T (2001) Estimating the number of clusters in a data setvia the gap statistic. J R Stat Soc Series B Stat Methodol 63:411–423.

38. Robins HS, et al. (2009) Comprehensive assessment of T-cell receptor beta-chain di-versity in alphabeta T cells. Blood 114:4099–4107.

39. Guerra-Silveira F, Abad-Franch F (2013) Sex bias in infectious disease epidemiology:Patterns and processes. PLoS One 8:e62390.

40. Grundbacher FJ (1972) Human X chromosome carries quantitative genes for immu-noglobulin M. Science 176:311–312.

41. Amadori A, et al. (1995) Genetic control of the CD4/CD8 T-cell ratio in humans. NatMed 1:1279–1283.

42. Giefing-Kröll C, Berger P, Lepperdinger G, Grubeck-Loebenstein B (2015) How sex andage affect immune responses, susceptibility to infections, and response to vaccina-tion. Aging Cell 14:309–321.

43. Fish EN (2008) The X-files in immunity: Sex-based differences predispose immuneresponses. Nat Rev Immunol 8:737–744.

44. Klein SL (2012) Sex influences immune responses to viruses, and efficacy of pro-phylaxis and treatments for viral diseases. BioEssays 34:1050–1059.

45. Grimaldi CM, Cleary J, Dagtas AS, Moussai D, Diamond B (2002) Estrogen altersthresholds for B cell apoptosis and activation. J Clin Invest 109:1625–1633.

46. Zhu M-L, et al. (2016) Sex bias in CNS autoimmune disease mediated by androgencontrol of autoimmune regulator. Nat Commun 7:11350.

47. Ramakrishnan NS, Grunewald J, Janson CH, Wigzell H (1992) Nearly identical T-cellreceptor V-gene usage at birth in two cohorts of distinctly different ethnic origin: In-fluence of environment in the final maturation in the adult. Scand J Immunol 36:71–78.

48. Zvyagin IV, et al. (2014) Distinctive properties of identical twins’ TCR repertoires re-vealed by high-throughput sequencing. Proc Natl Acad Sci USA 111:5980–5985.

49. Ruggiero E, et al. (2015) High-resolution analysis of the human T-cell receptor rep-ertoire. Nat Commun 6:8081.

50. Juraschek SP, et al. (2013) Comparison of serum concentrations of β-trace protein, β2-microglobulin, cystatin C, and creatinine in the US population. Clin J Am Soc Nephrol8:584–592.

51. Stone JD, Chervin AS, Kranz DM (2009) T-cell receptor binding affinities and kinetics:Impact on T-cell activity and specificity. Immunology 126:165–176.

52. Krummel MF, Sjaastad MD, Wülfing C, Davis MM (2000) Differential clustering ofCD4 and CD3zeta during T cell recognition. Science 289:1349–1352.

2172 | www.pnas.org/cgi/doi/10.1073/pnas.1716146115 Schneider-Hohendorf et al.

Dow

nloa

ded

by g

uest

on

Mar

ch 1

5, 2

020

53. Garcia KC, et al. (1996) CD8 enhances formation of stable T-cell receptor/MHC class I

molecule complexes. Nature 384:577–581.54. Yachi PP, Ampudia J, Gascoigne NRJ, Zal T (2005) Nonstimulatory peptides contribute

to antigen-induced CD8-T cell receptor interaction at the immunological synapse. Nat

Immunol 6:785–792.55. Rabenstein H, et al. (2014) Differential kinetics of antigen dependency of CD4+ and

CD8+ T cells. J Immunol 192:3507–3517.56. Tscharke DC, Croft NP, Doherty PC, La Gruta NL (2015) Sizing up the key determinants

of the CD8(+) T cell response. Nat Rev Immunol 15:705–716.57. Ji Q, Perchellet A, Goverman JM (2010) Viral infection triggers central nervous system au-

toimmunity via activation of CD8+ T cells expressing dual TCRs. Nat Immunol 11:628–634.58. Cusick MF, Libbey JE, Fujinami RS (2012) Molecular mimicry as a mechanism of au-

toimmune disease. Clin Rev Allergy Immunol 42:102–111.59. Leray E, et al. (2010) Evidence for a two-stage disability progression in multiple

sclerosis. Brain 133:1900–1913.60. Sokka T, et al.; QUEST-RA Group (2009) Women, men, and rheumatoid arthritis:

Analyses of disease activity, disease characteristics, and treatments in the QUEST-RA

study. Arthritis Res Ther 11:R7.

61. Schneider-Hohendorf T, et al. (2016) CD8(+) T-cell pathogenicity in Rasmussen en-cephalitis elucidated by large-scale T-cell receptor sequencing. Nat Commun 7:11153.

62. Savola P, et al. (2017) Somatic mutations in clonally expanded cytotoxic T lymphocytesin patients with newly diagnosed rheumatoid arthritis. Nat Commun 8:15869.

63. Emerson R, et al. (2013) Estimating the ratio of CD4+ to CD8+ T cells using high-throughput sequence data. J Immunol Methods 391:14–21.

64. Kanakry CG, et al. (2016) Origin and evolution of the T cell repertoire after post-transplantation cyclophosphamide. JCI Insight 1:e86252.

65. Dandekar S, et al. (2016) Shared HLA class I and II alleles and clonally restricted publicand private brain-infiltrating αβ T cells in a cohort of Rasmussen encephalitis surgerypatients. Front Immunol 7:608.

66. Salkind N (2010) Teoria Statistica Delle Classi e Calcolo Delle Probabilità (SAGE Publications,Thousand Oaks, CA).

67. Shannon CE (2013) A mathematical theory of communication. Bell Syst Tech J 27:379–423.

68. Gentleman RC, et al. (2004) Bioconductor: Open software development for compu-tational biology and bioinformatics. Genome Biol 5:R80.

69. HuberW, et al. (2015) Orchestrating high-throughput genomic analysis with Bioconductor.Nat Methods 12:115–121.

Schneider-Hohendorf et al. PNAS | February 27, 2018 | vol. 115 | no. 9 | 2173

IMMUNOLO

GYAND

INFLAMMATION

Dow

nloa

ded

by g

uest

on

Mar

ch 1

5, 2

020