SCIENCE ADVANCES | RESEARCH ARTICLEIMMUNOLOGYBCG vaccinations drive epigenetic changes
to the human T cell receptor: Restored expression
in type 1 diabetes
Hiroyuki Takahashi1, Willem M. Kühtreiber1, Ryan C. Keefe1, Amanda H. Lee1,
Anna Aristarkhova1, Hans F. Dias1, Nathan Ng1, Kacie J. Nelson1,
Stephanie Bien2, Danielle Scheffey2, Denise L. Faustman1*
The BCG (Bacille Calmette-Guérin) vaccine, introduced 100 years ago for tuberculosis prevention, has emerging
therapeutic off-target benefits for autoimmunity. In randomized controlled trials, BCG vaccinations were shownto gradually improve two autoimmune conditions, type 1 diabetes (T1D) and multiple sclerosis. Here, we investigate the mechanisms behind the autoimmune benefits and test the hypothesis that this microbe synergy could
be due to an impact on the host T cell receptor (TCR) and TCR signal strength. We show a quantitative TCR defectin T1D subjects consisting of a marked reduction in receptor density on T cells due to hypermethylation of TCR-­
related genes. BCG corrects this defect gradually over 3 years by demethylating hypermethylated sites on members of the TCR gene family. The TCR sequence is not modified through recombination, ruling out a qualitative
defect. These findings support an underlying density defect in the TCR affecting TCR signal strength in T1D.
INTRODUCTION

1Immunobiology Department, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02129, USA. 2Adaptive Biotechnologies, Seattle, WA 98109, USA.
*Corresponding author. Email: dfaustman@mgh.harvard.edu

Takahashi et al., Sci. Adv. 8, eabq7240 (2022)

16 November 2022

(methylation, acetylation, etc.) the histones in the promoters andenhancers of the genes.
This led to the term “innate immune training” because, before
this time, it was not known that innate immune responses had memory (18, 19). BCG is known to influence gene expression in monocytes
at the level of histone acetyltransferases and chromatin remodelingby increasing the methylation of histone H3 (20).
Less evidence exists on beneficial off-target effects of BCG vaccinations on the adaptive human immune system and on gene pathways for immune responses and metabolism that correspond to theclinical improvements in autoimmune vaccinated subjects. Adaptive
immunity involves B and T cells with antigen receptors that undergo somatic gene recombination, a feature unique to adaptive immunity. Published data show that BCG vaccinations induce hostregulatory T (Treg) cells by direct gene demethylation of signature
genes for potent Treg generation; this occurs over a 3-year time period (12). BCG vaccinations also in humans induce T cells (andmonocytes) to augment aerobic glycolysis pathways and diminish
energy metabolism through oxidative phosphorylation; this also
occurs with increasing magnitude over a 3-year time frame (12, 13).
These BCG-induced changes again occur directly on the gene signaling pathways and gradually unfold over a time period that corresponds to clinical improvement, i.e., 2- to 3-year time course.
BCG vaccinations also alter the off-target effects through T helper 1(TH1)/TH17 response (21). An association between BCG stimulation of dendritic cell that then influences T cell populations has
been reported (22).
The T cell receptor (TCR) is a membrane-bound heterodimer of
two polypeptide chains,  or . The TCR is the central protein
complex that drives the immune response (23, 24). Its diversity andantigen specificity are modulated in the extracellular domains consisting of constant (C), joining (J), and variable (V) segments. Hypervariable complementarity determining regions (CDRs) interact
with various peptide antigen and major histocompatibility molecules(24, 25). Recognition of ligands via the TCR chain is transmitted
into the cell through the CD3 complex (26). TCR/CD3 signaling
attenuation contributes to the immaturity and malfunction in T cells
1 of 14

Downloaded from https://www.science.org on November 16, 2022

Ten years of clinical trial data show that BCG (Bacille CalmetteGuérin) vaccinations confer health benefits to humans unrelated toprotection from tuberculosis, the original goal of the vaccine. These
off-target or heterologous health benefits span diverse human immune-­mediated diseases. For example, BCG vaccinations can protect humans from a range of viral, bacterial, and parasitic infections and thus
affect newborn mortality risks (1–6).
Adult BCG vaccinations can prevent and inhibit two different
autoimmune diseases, multiple sclerosis and type 1 diabetes (T1D)(7–13). In autoimmune disease, the BCG vaccinations in adults takeabout 3 years for the full beneficial effects, suggesting a gradual stepwise change in the immune system. For multiple sclerosis, clinical
BCG protection includes less disease relapses and also resolution of
detectable brain pathologies (8, 9). In diabetes, the BCG protection
improves blood sugar levels and reduces insulin usage for up to
8 years (11). BCG vaccinations protect humans from bladder cancer
and are associated with decreased risk of lung cancer (14, 15). How
can a >100-year-old vaccine composed of live Mycobacterium bovis,
the bovine version of Mycobacterium tuberculosis (TB), lead to somany off-target and beneficial benefits in humans?
Research on the beneficial human host BCG microbe interactions
frequently centers on the innate immune system. The innate immune
system is composed of both monocytes and macrophages, and BCG
vaccinations stimulate innate immunity days to weeks after the vaccinations are administered. Many forms of microbial and microbial
cell wall components can stimulate innate immunity. Often, themonitoring of this response centers on altered cytokine patterns after microbe exposures (16). An older literature first noted host microbe interaction in plants and invertebrates. These studies observeda recall immune response for the innate immunity, and thus, there
was some sort of “memory” (17). This was eventually tracked to theability of certain infections in an acute fashion to chemically modify

Copyright © 2022
The Authors, somerights reserved;
exclusive licensee
American Association
for the Advancementof Science. No claim to
original U.S. Government
Works. Distributed
under a Creative
Commons Attribution
NonCommercial
License 4.0 (CC BY-NC).

SCIENCE ADVANCES | RESEARCH ARTICLERESULTS

Demographics
This study includes in vivo and in vitro experiments with a total of
117 T1D and nondiabetic control (NDC) participants. Details on
the number of subjects in each experimental group are provided inTable 1, as well as in the figure legends.
Human hypermethylated genes in autoimmune subjects are
TCR-related
Epigenetic mechanisms modulate the T cell phenotype and function(42). We therefore first compared the DNA methylation patterns of
Takahashi et al., Sci. Adv. 8, eabq7240 (2022)

16 November 2022

CD4+ T cells from autoimmune diabetic subjects to those of controls (NDCs) before BCG vaccinations (baseline) using the Illumina
methylation chips. Here, we calculated the overall methylation stateof a gene at a given time point by first calculating the difference in value of each CpG versus baseline and then calculating the average.
Here, we refer to a gene as being hypermethylated if this overall
 value difference is positive. For a hypomethylated or demethylated
gene, this overall difference is negative.
In a genome-wide comparison of the average  values of T1D versus NDC, we found that from a total of 35,184 studied genes, 48 of
the top 200 most hypermethylated genes were TCR-related (Fig. 1A,
blue bars). In contrast, only 9 genes related to TCR were among the
top 200 most hypomethylated genes (Fig. 1A, red bars). Thus, methylation levels of the TCR-related genes were heavily skewed toward
hypermethylation in T1D subjects. A list of all top 200 hyper- andhypomethylated genes is provided (table S1), and all demographics
are listed in Table 1.
Because many of the top 200 hyper- or hypomethylated genes
in autoimmune diabetes were TCR-related at baseline (Fig. 1A), we
studied the differences in methylation patterns for all TCR-related
genes including the ones that are not in the top 200. Most individual
TCR genes showed hypermethylation patterns. That was true especially in TCR and TCR genes and, to a lesser extent, in TCR andTCR genes (Fig. 1B and fig. S1). Statistical significance in T1D ascompared to NDC, especially in the J and V segments, was achieved.
The bars for these are shown in blue (Fig. 1B). The results indicatedthat there are substantial differences in fundamental epigenetic levels in TCR of CD4+ T cells of autoimmune diabetes as compared to
NDC. These differences were even more pronounced when overall
average differences between T1D and NDC were calculated on a per
TCR segment basis. Although full statistical significance was notreached, the P values for the segments that came close are indicatedin Fig. 1C. A heatmap of T1D versus NDC at the CpG level is shown(fig. S5A, left lane). The overall blue color indicates substantial reduced demethylation (increased methylation) in TCR-related genes
from autoimmune diabetes subjects.
To test whether the methylation changes were associated withchanges in protein expression, we next evaluated TCR expressionin CD4+ T cells at the protein level by flow cytometry. The population of TCR+ cells was significantly reduced in T1D as compared
to NDC (97.2 ± 1.6% versus 93.3 ± 4.6%, P = 0.005; Fig. 1D and fig.
S6A). The mean fluorescent intensity (MFI) density of TCR antibody in T1D was also significantly decreased compared to NDC(545.8 ± 101.4 versus 423.9 ± 74.5, P = 0.01; Fig. 1E and fig. S6A).
We also attempted to quantify this difference using a TCR enzyme-­
linked immunosorbent assay (ELISA) assay, a less sensitive assay,on cell lysates. We were able to show the same trends, although thedifference stopped short of statistical significance (fig. S7A). These
results indicate that TCR expression on T1D CD4+ T cells is statistically decreased in autoimmune diabetes and associated withdemethylation patterns that are correlated with lower levels of surface protein expression as measured by flow cytometry. This suggests a quantitative defect in TCR expression in T1D subjects.
BCG vaccinations demethylate human TCR genes in vivo
to restore protein expression
We sought evidence that BCG might influence the epigenetics inT cells, an adaptive immune response, at the level of DNA methylation. We evaluated the methylation patterns of CD4+ T cells from
2 of 14

Downloaded from https://www.science.org on November 16, 2022

(27, 28). Thus, efficient structural organization and signaling of TCR/
CD3 underlie the development and function of T cells.
Genetic defects in the TCR and CD3 loci can lead to faulty T cell
development that culminates in severe life-threatening immune
disease. For example, quantitative impairments of TCR constant(TRAC), CD3, and CD3 are involved in human immunodeficiency
disorders characterized by a lack of TCR+ T cells (29–31). Monogenetic TCR and CD3 defects are rare in human immune diseases
but show the central role of the TCRs in human disease. Overall T cellmaturation defects have long been associated with diverse forms of
autoimmunity, suggesting an underlying defect in TCR selection(32–35). Intensive research has looked for T cell clones with specificmutant receptors or altered recombination errors that might explain
autoimmunity, but such clones have so far never been identified.
Diverse bacteria under select circumstances epigenetically modify
host DNA (36). The interaction of bacteria with their host is beneficial
for long-lived bacteria or bacterial persistence (37). Bacteria-driven
epigenetic changes to host cells mean that the host phenotype of cellschanges without a corresponding change in the DNA sequences of
the host. While the actual DNA nucleotide sequence is not affected,
there can be changes in DNA methylation, in histone proteins associated with DNA or even bacterial modifications of the transcribed
RNA, or more recently on the genes themselves in critical immune
and metabolism pathways (12, 13, 38). The BCG organism has beenreported to change host histone proteins such as H3K27 and thus cancontrol DNA usage (39, 40).
This study explores the hypothesis that microorganisms containedin the BCG vaccine can mold the human immune response at the levelof the TCR and associated CD3 co-signaling receptor genes, not just at
the level of histones. These two events appear to have very different timecourses and also either restricted involvement of only the innate immune
response or a broad response on the entire immune response. Because
human autoimmunity is known to show discordance among identicaltwins, it is suspected that epigenetics could play a role in altered immunity. The rise in autoimmunity and allergies from deficient host-microbe
interactions is an old epidemiology concept historically recognized as
the hygiene hypothesis (41). We sought evidence of a microbe-driven
epigenetic immune training process at the level of host TCR and CD3genes. First, we compared autoimmune T cells from subjects with autoimmune diabetes to controls and then explore the in vivo impact of BCG
vaccinations on direct methylation patterns on all 159 genes of TCR
complex including 504 CpG sites. We also investigated at baseline andafter BCG therapy all four genes of the CD3 co-­receptor and 92 CpG
methylation sites at baseline and after BCG therapy. We seek evidenceof baseline autoimmune TCR defects driven by quantitative defects
and their possible corrections with repeat BCG administrations.

SCIENCE ADVANCES | RESEARCH ARTICLE
Table 1. Demographics for participants in in vivo or in vitro studies. N/A, not available.
Baseline characteristics of type 1 diabetic and control subjects
T1D subjects,
DNA
methylation(n = 12)

T1D subjects,
immunoSEQ(n = 27)

T1D subjects,
RNA-seq(n = 12)NDC subjects,
DNA
methylation(n = 8)NDC subjects,
RNA-seq(n = 5)

T1D subjects forvarious in vitro
experiments(n = 40)

NDC subjects
for various
in vitro
experiments(n = 29)

Age at blood
donation, years

46.6 ± 3.0

45.5 ± 2.1

37.1 ± 3.2

26.3 ± 3.2

23.1 ± 0.3

39.2 ± 2.8

44.2 ± 3.0

Age of type 1
diabetes onset,
years

24.7 ± 2.4

29 ± 2.0

19.4 ± 3.0

N/A

N/A

22.2 ± 2.2

N/A

Diabetes duration,
years

21.9 ± 2.7

16.6 ± 1.1

17.7 ± 1.0

N/A

N/A

17.0 ± 1.7

N/A% Female

7.7

40.7

30.8

62.5

60

42.5

44.4

DR3 and/or DR4
positive, %

61.6

59.3

N/A

N/A

N/A

N/A

N/A

Additional
autoimmune
disease—self, % *

23.1

40.7

15.4

N/A

N/A

30.0

N/A

Autoimmune
disease in
immediatefamily, %*

76.9

81.5

16.5

N/A

N/A

67.5

N/A

Islet
autoantibodies
at diagnosis, %

100

100

100

N/A

N/A

100

N/A

*Autoimmune diseases reported for self or for immediate family include ulcerative colitis, Crohn’s disease, psoriasis, rheumatoid arthritis, lupus, celiac disease,fibromyalgia, Hashimoto’s disease, hypothyroidism, ankylosing spondylitis, Graves’ disease, and Raynaud’s disease.

autoimmune diabetic patients at yearly intervals receiving BCG andobserved a large number of genes that showed altered methylation
patterns after BCG vaccination.
At year 1 after BCG vaccination, 90 of the top 200 most hypomethylated genes were TCR-related (Fig. 2A, red bars), while 3 CD3-related
genes were also hypomethylated (Fig. 2A, red bars). At this same earlytime point, 12 TCR-related genes were hypermethylated and no CD3gene was hypermethylated (Fig. 2A, blue bars). At year 2 after BCG
vaccinations, there were 74 TCR-related and 3 CD3-related genes
among the top 200 hypomethylated genes and 9 TCR-related genes
among the top 200 hypermethylated genes. This trend persisted into
year 3 as well. These results demonstrate that the suppression of various immunological functions including TCR/CD3 signaling may
be associated with changes at the epigenetic level in the T1D CD4+
T cells as compared to NDC (Fig. 1A), and BCG vaccination might
restore these defects, especially in TCR/CD3-related genes (Fig. 2A).
We also evaluated the distribution of  value differences for all
TCR-related genes at years 1 to 3 after BCG vaccinations versus baseline (Fig. 2B and table S2). Over time, the progressive shift toward
negative  values (years 1 to 3, baseline) indicates that most of thechanges corresponded to demethylation events of fairly  value–
neutral genes from the middle of the histograms.
Next, we asked whether in vitro BCG treatment (i.e., cell culture in
the presence of BCG) could result in the up-regulation of the TCR
expression at the protein level on CD4+ T cells obtained from T1D
patients. After 1 week of culture, the percentage of TCR+ cells among
CD4+ T cells was significantly increased in the BCG-­treated group
Takahashi et al., Sci. Adv. 8, eabq7240 (2022)

16 November 2022

in both NDC and T1D (NDC, 98.2 ± 1.0% versus 99.2 ± 0.5%,
P = 0.007; T1D, 98.3 ± 1.6% versus 99.5 ± 0.4%, P = 0.02; Fig. 2C andfig. S6B). The MFI, a measure of density of TCR+ cells, was alsosignificantly up-regulated in the BCG-treated group compared tothose of the nontreated group in both NDC and T1D (NDC, 446.9 ±58.0 versus 571.2 ± 118.4, P = 0.01; T1D, 469.9 ± 121.8 versus 555.4 ±133.9, P = 0.01; Fig. 2D). We also performed TCR antibody ELISA oncell lysates, and these showed the same trend (fig. S7B). These results illustrated that BCG treatment can directly up-regulate TCR
expressions on the CD4+ T cells and correct the reduced expression
in T1D, at least in vitro. The mechanism of restored TCR and CD3
expression from in vivo treatment with BCG vaccinations appears to
be controlled, at least in part, by demethylation of the autoimmune
diabetes overmethylation TCR genes.
At baseline, untreated naïve T1D subjects had many of the TCR
genes hypermethylated as compared to NDCs (Figs. 1C and 3C).
We therefore wanted to see whether this hypermethylation would
normalize fully or partially after BCG treatment. To do this, we identified the genes that were significantly hypermethylated for T1D baseline versus NDC and then asked the question what happened 3 years
later (fig. S9). We identified 28 TCR genes that were hypermethylated
in baseline T1D versus NDC (fig. S9). By year 3 after BCG, 20 of
28 genes showed corrected methylation, and these were no longer statistically significant as compared to NDC (red significance lines). In
5 of 28 genes, there was desired directional correction but also someovercorrection (pink significance lines). In 3 of 28 genes, there was nocorrection, and these were still overmethylated (blue significance lines).
3 of 14

Downloaded from https://www.science.org on November 16, 2022

VariableSCIENCE ADVANCES | RESEARCH ARTICLE

0.10

0.05

0.00
0.00

0.00
0.00

-0.05

Hypomethylated TCR-related genes (90)

-0.05

-0.10

Unrelated genes (107)

Hypomethylated TCR-related genes (9)

-0.10

-0.15

TCR-unrelated genes (191)
0.15

-0.15

Top 200 most hyper-/hypomethylated genes

P, q < 0.05

0.15

ns

0.10
0.05

-0.10

TRBJ

TRAJ
TRAV

TRAC

-0.15
-0.20

TRBV

D

P = 0.08
0.00

0.05

0.10

TCRαβ+ MFI

P = 0.09

100
90
80

β Value (T1D – NDC

T1D

NDC

T1D

Fig. 1. TCR-related genes are hypermethylated in CD4 T cells from T1D patients
as compared to NDCs. (A)  Values of all CpGs per gene were averaged, and thegenes were ordered on the basis of the difference in average  value between T1D(n = 12) and NDC (n = 8). The figure shows the top 200 hypermethylated and top
200 hypomethylated genes among 35,184 genes in total. Forty-eight of the
top 200 hypermethylated genes (24%) were TCR-related (blue bars). Among the
200 most hypomethylated genes, only 9 genes (4.5%) were TCR-related (red bars).
TCR-unrelated genes are shown in black. (B) Differences in average  values between
T1D at baseline (n = 12) and NDC (n = 8) for all TCR-related genes (average ± SD;
blue bars indicate significant hypermethylation; P < 0.05). See fig. S1 for more details. ns, not significant. (C) Overall methylation in TCR segments at baseline;  valuesof the local region genes in C, J, V, and D segments were averaged. The differencesin  values between NDC (n = 8) and T1D (n = 12) at baseline are shown for each
TCR segment (average). (D) Analysis of the expression of TCR on CD4+ T cells by
flow cytometry. The gating strategy for these experiments is shown in fig. S6A. The
ratio of TCR+ cells among CD4+ T cells in NDC (n = 11) and T1D (n = 10) is shown(average ± SD). (E) Mean fluorescent intensity (MFI) of allophycocyanin (APC) in
TCR+ cells in NDC (n = 11) and T1D (n = 10). All statistics were Mann-Whitney U tests.
*P < 0.05; **P < 0.01.

This indicates that BCG had restored methylation to NDC levels for
25 of 28 TCR genes.
BCG demethylation of the host TCR occurs across most generegions, TCR, TCR, and TCRIn addition to analyzing TCR and CD3 presence in the top 200
hypo- and hypermethylated genes, we also evaluated the chronological changes for all TCR-related genes after BCG vaccination in
16 November 2022

100
90

NDC NDC T1D T1D
In vitro BCG ++Hypermethylated TCR-related genes (8)Unrelated genes (192)

-0.10

+

Takahashi et al., Sci. Adv. 8, eabq7240 (2022)

0.10

TCRαβ+

80

Year 3 vs. baseline

-0.05

0
NDC

Hypomethylated CD3-related genes (3)

0.00

200

Year 3 vs. baseline

1500Hypomethylated TCR-related genes (74)

0.00

400

0
200

C

0.00

0.05

600

50

D

800
600
400
200

Hypomethylated TCR-related genes (40)
Unrelated genes (160)-0.15

Top 200 most hyper-/hypomethylated genes

0

NDC NDC T1D T1D
In vitro BCG ++Fig. 2. Demethylation of TCR/CD3-related genes in CD4+ T cells from T1D patients
after vaccination with BCG. (A) Top 200 most hyper- and hypomethylated genes
in CD4+ T cells from T1D treated with BCG at baseline as compared to years 1 to 3.
TCR-related genes are shown in blue (hypermethylated) or red (hypomethylated),and CD3-related genes are shown in white (hypomethylated). Unrelated genes areshown in black. Each gene and the difference in  value are shown in fig. S1A (datalisted in table S1). (B) Distribution of  values as compared to baseline for CpGs of
TCR-related genes at year 1 (top), year 2 (middle), and year 3 (bottom). A progressive shift of  values toward the left indicates progressive demethylation over time.
(C) Changes in TCR expression after 7-day culture of CD4+ T cells from NDC andT1D subjects in the presence of BCG. CD4+ T cells were isolated from NDC and T1D
patients and cultured for 7 days in RPMI medium with or without added BCG. The
cells were then analyzed by flow cytometry. The gating strategy for these experiments is shown in fig. S6B. The percentage of TCR+ cells for CD4+ T cells in NDC(n = 9) and T1D (n = 8) is shown (average ± SD). (D) MFI of TCR+ cells in NDC(n = 9) and T1D (n = 8) cultured with and without BCG. All statistics were performed
with two-tailed Wilcoxon testing. *P < 0.05; **P < 0.01.

more detail. Figure 3A shows the TCR-related genes individually,
whereas Fig. 3B summarizes these genes by region. To assess false
discovery rate (FDR), we calculated corresponding q values for all
P values. The red bars in Fig. 3A indicate genes with both P < 0.05 andq < 0.05 (see table S3 for individual statistics for each gene). A moredetailed view is shown in figs. S2 to S4. As was the case for the top
200 hypomethylated genes, most genes were significantly demethylated as compared to baseline at years 1 to 3 after BCG vaccination. A heatmap of these results shows the hypermethylation of the
4 of 14

Downloaded from https://www.science.org on November 16, 2022

P = 0.08

CD4+ T cell (%)

P = 0.052

100

Change in β value

0.00

0.15

TCRαβ+

Year 2 vs. baseline

150

0.05

TCRγ TCRδ

800

0

200

50

Hypermethylated TCR-related genes (9)Unrelated genes (123)E

TCRαβ+

50

100

Year 2 vs. baseline

-0.15

TCRβ

100

Unrelated genes (191)

-0.10

TRBC

TRDD
TRDV
TRDJ
TRDC
TRGV
TRGJ
TRGC
TRBV
TRBJ
TRBC
TRAV
TRAJ
TRAC
-0.05

150

-0.05

0.00
-0.05

TCR segment-0.10

Change in β value

TCR-related genes

0.20

TCRα

C

0.10

TRAC
TRAJ1
TRAJ2
TRAJ3
TRAJ4
TRAJ5
TRAJ6
TRAJ7
TRAJ8
TRAJ9
TRAJ10
TRAJ11
TRAJ12
TRAJ13
TRAJ14
TRAJ16
TRAJ17
TRAJ18TRAJ19TRAJ20
TRAJ21
TRAJ22
TRAJ23
TRAJ24
TRAJ25
TRAJ26
TRAJ27
TRAJ28
TRAJ29TRAJ30TRAJ31
TRAJ32
TRAJ33
TRAJ34
TRAJ35
TRAJ36
TRAJ37
TRAJ38
TRAJ39TRAJ40TRAJ41
TRAJ42
TRAJ43
TRAJ44
TRAJ45
TRAJ46
TRAJ47
TRAJ48
TRAJ49TRAJ50TRAJ52TRAJ53
TRAJ54
TRAJ56
TRAJ57
TRAJ58TRAJ59TRAJ61
TRAV1-1
TRAV1-2
TRAV2
TRAV3
TRAV4
TRAV5
TRAV6
TRAV7
TRAV8-1
TRAV8-2
TRAV8-3
TRAV8-4
TRAV8-6
TRAV8-7
TRAV9-1
TRAV9-2
TRAV10
TRAV12-1
TRAV12-2
TRAV12-3
TRAV13-1
TRAV13-2
TRAV14DV4
TRAV16
TRAV17
TRAV18
TRAV19
TRAV20
TRAV21
TRAV22
TRAV23DV6
TRAV24
TRAV25
TRAV26-1
TRAV26-2
TRAV27
TRAV29DV5
TRAV30
TRAV34
TRAV35
TRAV36DV7
TRAV38-1
TRAV38-2DV8
TRAV39
TRAV40
TRAV41TRBC2
TRBJ2-1
TRBJ2-2
TRBJ2-2P
TRBJ2-3
TRBJ2-4
TRBJ2-5
TRBJ2-6
TRBJ2-7
TRBV2
TRBV3-1
TRBV4-1
TRBV4-2
TRBV5-1
TRBV6-1
TRBV7-1
TRBV19
TRBV20-1
TRBV21OR9-2
TRBV23-1
TRBV23OR9-2
TRBV24-1
TRBV25-1
TRBV25OR9-2
TRBV27
TRBV28
TRBV29-1
TRBV30
TRGC1
TRGC2
TRGJ1
TRGJ2
TRGJP
TRGJP1TRGJP2
TRGV1
TRGV2
TRGV3
TRGV4
TRGV5
TRGV8
TRGV9
TRGV10
TRGV11
TRDC
TRDJ1
TRDJ2
TRDJ3
TRDJ4
TRDV1
TRDV2
TRDV3
TRDD1
TRDD2
TRDD3

β Value (T1D – NDC

B

Hypomethylated CD3-related genes (3)Year 1 vs. baseline

200

Hypermethylated TCR-related genes (12)Unrelated genes (188)

0.05

B

0
5
0
5
0
5
0
5
0
5
0
5
0
5
0
5
0

β Value (T1D – NDC

0.10

Year 1 vs. baseline-0.200-0.175-0.150-0.125-0.100-0.075-0.050-0.025
0.000
0.025
0.050
0.075
0.100
0.125
0.150
0.175
0.200

0.15

Number of TCR-related CpG

AHypermethylated TCR-related genes (48)
TCR-unrelated genes (152)

CD4+ T cell (%)

0.15

TCRαβ+ MFIASCIENCE ADVANCES | RESEARCH ARTICLE
A

CYear 1 vs. baseline

0.20

TRAV

TRAJ

0.15
0.10

T1D vs.
NDC

T1D with BCG
Year 1 Year 2 Year 3

TRBJ

TRAC

TRBC TRBV

TRAC

0.05
0.00

g

-0.05

TRAJ

-0.10

Change in β value-0.20

Year 2 vs. baseline

0.20

P, q < 0.05

0.15

ns

0.10

TCRα

-0.15

0.05
0.00

-0.05

TRAV

-0.10
-0.15
-0.20

Year 3 vs. baseline

0.20

TRBC

0.15

TRBJ

TCRβ

0.10
0.05
0.00

TRBV

g

-0.05

TCRα

B

Year 1 vs. baselineYear 2 vs. baseline

**

***
***

*

***

*

-0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10

**

**

*
***

***
**

TRDC
TRDJ
TRDV
TRDD

Year 3 vs. baseline

*

*

**
***
*

TCRγ TCRδ

**

**

*

-0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10

Demethylation(difference in β value)

TRDD
TRDV
TRDJ
TRDC
TRGV
TRGJ
TRGC
TRBV
TRBJ
TRBC
TRAV
TRAJ
TRAC

TCRβ

TRGV

***
*
*
**

-0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10

Change in β value

Fig. 3. The abnormally methylated TCR genes observed in T1D undergoes gradual demethylation after BCG vaccine treatment. (A) Chronological changes in values of TCR-related genes from CD4+ T cells of T1D (n = 12) after BCG vaccination as compared to baseline (year 1, top; year 2, middle; year 3, bottom) (average ± SD;
red bars mean significant hypomethylation; P < 0.05 and q < 0.05, one-tailed Wilcoxon test). The graphs show progressive demethylation of the TCR after BCG vaccination
over time. Figures S2 to S4 show these data in more detail. (B) Chronological change of average change in  values as compared to baseline in T1D (n = 12) at the TCR
segment level after BCG vaccination (*P < 0.05 and q < 0.05; **P < 0.01 and q < 0.05; ***P < 0.001 and q < 0.05; one-tailed Wilcoxon test). All data are shown (table S2).
(C) Heatmap of change in  values for all TCR-related genes. The left lane shows difference in  value between T1D (n = 12) and NDC (n = 8), and the right three lanes show
average methylation as compared to baseline (before vaccination) at years 1 to 3 (n = 12 vaccinated T1D). Decreased methylation is shown in shades of red, and increased
methylation is shown in shades of blue.

TCR-related genes in T1D as compared to NDC as indicated by theoverall blue color (Fig. 3C, left lane), whereas subsequent demethylation as compared to baseline after BCG treatment is indicated by
the mostly red color (Fig. 3C, right three lanes). Methylation patterns
in TCR and TCR at year 2 were very similar to those at year 1,
suggesting that BCG vaccinations resulted in a stable epigenetic
change in TCR-related genes that persist into year 2. Furthermore,
Takahashi et al., Sci. Adv. 8, eabq7240 (2022)

16 November 2022

this demethylation pattern in TCR genes was mostly retained atyear 3. On the other hand, the demethylation in TCR CpGs was attenuated by year 3. Last, the opposite color patterns of TCR as compared to TCR and TCR in the heatmap (Fig. 3C) are indicative ofrelative hypermethylation in TCR.
The amount and intensity of red color in TCR and TCR as compared to TCR in the right three columns of the heatmap (Fig. 3C)
5 of 14

Downloaded from https://www.science.org on November 16, 2022

TRAC
TRAJ1
TRAJ2
TRAJ3
TRAJ4
TRAJ5
TRAJ6
TRAJ7
TRAJ8
TRAJ9
TRAJ10
TRAJ11
TRAJ12
TRAJ13
TRAJ14
TRAJ16
TRAJ17
TRAJ18TRAJ19TRAJ20
TRAJ21
TRAJ22
TRAJ23
TRAJ24
TRAJ25
TRAJ26
TRAJ27
TRAJ28
TRAJ29TRAJ30TRAJ31
TRAJ32
TRAJ33
TRAJ34
TRAJ35
TRAJ36
TRAJ37
TRAJ38
TRAJ39TRAJ40TRAJ41
TRAJ42
TRAJ43
TRAJ44
TRAJ45
TRAJ46
TRAJ47
TRAJ48
TRAJ49TRAJ50TRAJ52TRAJ53
TRAJ54
TRAJ56
TRAJ57
TRAJ58TRAJ59TRAJ61
TRAV1-1
TRAV1-2
TRAV2
TRAV3
TRAV4
TRAV5
TRAV6
TRAV7
TRAV8-1
TRAV8-2
TRAV8-3
TRAV8-4
TRAV8-6
TRAV8-7
TRAV9-1
TRAV9-2
TRAV10
TRAV12-1
TRAV12-2
TRAV12-3
TRAV13-1
TRAV13-2
TRAV14DV4
TRAV16
TRAV17
TRAV18
TRAV19
TRAV20
TRAV21
TRAV22
TRAV23DV6
TRAV24
TRAV25
TRAV26-1
TRAV26-2
TRAV27
TRAV29DV5
TRAV30
TRAV34
TRAV35
TRAV36DV7
TRAV38-1
TRAV38-2DV8
TRAV39
TRAV40
TRAV41TRBC2
TRBJ2-1
TRBJ2-2
TRBJ2-2P
TRBJ2-3
TRBJ2-4
TRBJ2-5
TRBJ2-6
TRBJ2-7
TRBV2
TRBV3-1
TRBV4-1
TRBV4-2
TRBV5-1
TRBV6-1
TRBV7-1
TRBV19
TRBV20-1
TRBV21OR9-2
TRBV23-1
TRBV23OR9-2
TRBV24-1
TRBV25-1
TRBV25OR9-2
TRBV27
TRBV28
TRBV29-1
TRBV30
TRGC1
TRGC2
TRGJ1
TRGJ2
TRGJP
TRGJP1TRGJP2
TRGV1
TRGV2
TRGV3
TRGV4
TRGV5
TRGV8
TRGV9
TRGV10
TRGV11
TRDC
TRDJ1
TRDJ2
TRDJ3
TRDJ4
TRDV1
TRDV2
TRDV3
TRDD1
TRDD2
TRDD3

-0.15
-0.20

TRGC
TRGJ

TCRδ TCRγ-0.10

SCIENCE ADVANCES | RESEARCH ARTICLE
indicate that the methylation of TCR changes in an opposite manner. A heatmap of the TCR-related genes at the CpG level is shownin fig. S5A (right three lanes). Together, these results implied that
BCG vaccination affects the epigenetics of TCR-related genes, particularly of TCR, and that this change is maintained for at least
3 years. Thus, BCG vaccination drives DNA demethylation in the human TCR, TCR, and TCR genes after the treatment, and methylation is relatively stable for at least 3 years.

BCG vaccinations influence phenotypic expressions in TCR
but do not affect clonotype compositionof the TCR repertoire
We have shown that BCG vaccination can up-regulate the expression (density) of TCR- and CD3-related genes in T1D CD4+ T cellsby means of epigenetic modifications that are associated with increases in both mRNA and protein levels. Furthermore, we haveshown that this is associated with an attenuation in the quantitative
TCR defects observed in autoimmunity across TRAJ, TRAV, and
TRBV genes. These BCG-mediated effects were observed as early as
1 year after the vaccination and continued to be mostly present 3 years
after the vaccination. All the BCG vaccination effects in the TCR
receptor described above were quantitative changes to phenotypic
expression.
We next explored whether BCG also induced changes to clonotypes present in the TCR repertoire by performing TCR sequencingof genomic DNA (gDNA). We explored changes in TCR diversity,
gene usage, and clonal expansion by sequencing the CDR3 regionsof human TCR and TCR chains using the immunoSEQ Assays(Adaptive Biotechnologies, Seattle, WA). The clinical traits of thesubjects in this study are available (Table 1). At year 3, we observeda trend for increasing TCR clonality, suggesting a focusing of therepertoire (Fig. 5), but this was not accompanied by a decrease insample richness (Fig. 5). There was no significant increase in clonal
Takahashi et al., Sci. Adv. 8, eabq7240 (2022)

16 November 2022

The CD3 chains associated with the TCR are
hypermethylated at baseline in autoimmunity
Having demonstrated consistent quantitative defects in DNA methylation patterns and protein levels in TCR in CD4+ T cells from T1D
patients, we also analyzed CD3 genes in the same manner for otherdefects, possibly affecting TCR activation and receptor expression.
For proper TCR signaling, the multiple chains of CD3 associate with
the TCR to generate the activation signal in T cells. All CD3 genes
except for CD3 exhibited hypermethylated patterns in T1D at baseline, although none reached significance (Fig. 6A). A heatmap of CD3
methylation at the CpG level is shown (fig. S5B, left lane). As expectedin the case of hypermethylation, RNA sequencing (RNA-seq) analysis showed the same trend and showed that the mRNA expressionsof CD3, CD3, CD3, and CD3 were all reduced in autoimmune
diabetes at baseline (Fig. 6B and table S4; note that the data did notreach significance). We also quantified the protein expression levels
using flow cytometry and saw that the percentage CD3+ T cells in
the CD4+ T cell population was reduced in T1D as compared to NDC
at baseline (96.7 ± 1.8% versus 92.9 ± 6.2%, P = 0.04; Fig. 6C). The
MFI of CD3+ T cells in T1D was significantly decreased as comparedto those in NDC (1393.2 ± 107.2 versus 1237.9 ± 135.6, P = 0.02;
Fig. 6D). Protein levels as measured by ELISA to evaluate CD3 subunit levels supported the underlying quantitative defects in T1Dof lowered expression, which could be controlled by hypermethylation (fig. S7C).
BCG vaccinations demethylate the CD3 gene subunits
and restore mRNA expression
Having shown that CD3 is hypermethylated and that CD3 mRNA
expression is reduced in CD4+ T cells from T1D subjects, we next
asked whether or not BCG vaccination could correct the CD3 gene
methylation pattern and restore protein levels.
All subunits of the CD3 gene were demethylated at 1 year after
BCG vaccination as compared to baseline (Fig. 7, A and B). Thisdemethylation pattern persisted in the subsequent 2 years except forCD3 that became slightly hypermethylated at year 3. This tendency
is supported by the distribution of  value differences for all CD3
CpGs at years 1 to 3 versus baseline (Fig. 7E and table S2). The CD3mRNA expression over time was also consistent with the methylation
patterns. After the BCG vaccinations, the expression of all CD3 genes
was all increased during all 3 years (Fig. 7, C and D). Figure S5B showsa heatmap of these results at the CpG level (right three lanes).
We also wanted to see whether these same in vivo BCG effects
could be observed in vitro. After in vitro culture with BCG for 7 days,
the percentage of CD3+ cells among CD4+ T cells was significantly
6 of 14

Downloaded from https://www.science.org on November 16, 2022

Quantitative defects in T1D TCR mRNA expression
and restoration by BCG vaccination
Using samples from diabetic humans and NDC treated with BCG
vaccination and followed for 2 years, we saw a reduction of TCR
mRNA expression in most TCR genes of T1D CD4+ T cells at baseline as compared to those of NDC (Fig. 4A). These differences wereeven more pronounced when overall average differences between
T1D and NDC were calculated on a per TCR segment basis (Fig. 4B).
The expression of TCR-related genes was down-regulated, except forthat of TRBJ and TRBD. Tracking the change in mRNA expression
after BCG vaccination in vivo over time shows that, at year 1, most
TCR-related genes were up-regulated as compared to those at baseline and that the up-regulation became more pronounced at year 2(Fig. 4C). These changes were also more pronounced on a per TCR
segment basis (Fig. 4D). These results mostly tracked the methylation
patterns, especially in TCR and TCR genes before and after BCG
vaccination (Figs. 1, 3, and 4). A heatmap of these results shows the
reduced mRNA expression of the TCR-related genes in T1D as compared to NDC as indicated by the overall blue color (Fig. 4E, left lane),with the notable exception of TRBJ that shows mostly increasedmRNA expression (red color). Subsequent up-regulation as compared to baseline after BCG treatment is indicated by the mostly red
color (Fig. 4E, right three lanes). Together, these results suggested
that mRNA expression of TCR-related genes in T1D CD4+ T cells
was reduced at baseline and that this defect was corrected.

expansion (Fig. 5D). A few significant changes in gene usage at year 3
relative to baseline were found and were limited to TCR (Fig. 5A).
No significant changes were seen for TCR (fig. S8). These results
argue against BCG mechanism of clinical improvement being centered on BCG, altering a specific islet autoreactive T cell clone thatsolely causes T1D. Rather, these results suggest that BCG vaccinations
alter phenotypic expressions of density for improved T cell selection
and signaling. At least with this method of TCR repertoire profiling,
BCG is not playing a major role in altering the frequency of particular clonotypes present in the immune repertoire of TCR T cells.
Our results suggest that, while BCG vaccinations alter phenotypic
expressions, they do not alter the frequency of particular clonotypes
present in the immune repertoire of TCR T cells.

SCIENCE ADVANCES | RESEARCH ARTICLE% Difference in mRNA

A

TRAJ

TRBC

2000
1500
1000
500

P, q < 0.05

TRBV

B

TRBJ

TRAC
TRAJ1
TRAJ2
TRAJ3
TRAJ4
TRAJ5
TRAJ6
TRAJ7
TRAJ8
TRAJ9
TRAJ10
TRAJ11
TRAJ12
TRAJ13
TRAJ14
TRAJ16
TRAJ17
TRAJ18TRAJ19TRAJ20
TRAJ21
TRAJ22
TRAJ23
TRAJ24
TRAJ25
TRAJ26
TRAJ27
TRAJ28
TRAJ29TRAJ30TRAJ31
TRAJ32
TRAJ33
TRAJ34
TRAJ35
TRAJ36
TRAJ37
TRAJ38
TRAJ39TRAJ40TRAJ41
TRAJ42
TRAJ43
TRAJ44
TRAJ45
TRAJ46
TRAJ47
TRAJ48
TRAJ49TRAJ50TRAJ51TRAJ52TRAJ53
TRAJ54
TRAJ55TRAJ56
TRAJ57
TRAJ58TRAJ59TRAJ60TRAJ61
TRAV1-1
TRAV1-2
TRAV2
TRAV3
TRAV4
TRAV5
TRAV6
TRAV7
TRAV8-1
TRAV8-2
TRAV8-3
TRAV8-4
TRAV8-5
TRAV8-6
TRAV8-7
TRAV9-1
TRAV9-2
TRAV10
TRAV11
TRAV12-1
TRAV12-2
TRAV12-3
TRAV13-1
TRAV13-2
TRAV14DV4
TRAV15
TRAV16
TRAV17
TRAV18
TRAV19
TRAV20
TRAV21
TRAV22
TRAV23DV6
TRAV24
TRAV25
TRAV26-1
TRAV26-2
TRAV27
TRAV28
TRAV29DV5
TRAV30
TRAV31
TRAV32
TRAV33
TRAV34
TRAV35
TRAV36DV7
TRAV37
TRAV38-1
TRAV38-2DV8
TRAV39
TRAV40
TRAV41TRBC1
TRBC2TRBJ1-1
TRBJ1-2
TRBJ1-3
TRBJ1-4
TRBJ1-5
TRBJ1-6
TRBJ2-1
TRBJ2-2
TRBJ2-2P
TRBJ2-3
TRBJ2-4
TRBJ2-5
TRBJ2-6
TRBJ2-7
TRBV1
TRBV2
TRBV3-1
TRBV4-1
TRBV4-2
TRBV5-1
TRBV5-2
TRBV5-3
TRBV5-4
TRBV5-5
TRBV5-6
TRBV5-7
TRBV6-1
TRBV6-2
TRBV6-4
TRBV6-5
TRBV6-6
TRBV6-7
TRBV6-8
TRBV7-1
TRBV7-2
TRBV7-3
TRBV7-4
TRBV7-5
TRBV7-6
TRBV7-7
TRBV7-9
TRBV8-1
TRBV8-2
TRBV9
TRBV10-1
TRBV10-2
TRBV10-3
TRBV11-1
TRBV11-2
TRBV11-3
TRBV12-1
TRBV12-2
TRBV12-3
TRBV12-4
TRBV12-5
TRBV13
TRBV14
TRBV15
TRBV16
TRBV17
TRBV18
TRBV19
TRBV20-1
TRBV20OR9-2
TRBV21-1
TRBV21OR9-2
TRBV22-1
TRBV22OR9-2
TRBV23-1
TRBV23OR9-2
TRBV24-1
TRBV24OR9-2
TRBV25-1
TRBV25OR9-2
TRBV26
TRBV26OR9-2
TRBV27
TRBV28
TRBV29-1
TRBV29OR9-2
TRBV30
TRBD1
TRGC1
TRGC2
TRGJ1
TRGJ2
TRGJP
TRGJP1TRGJP2
TRGV1
TRGV2
TRGV3
TRGV4
TRGV5
TRGV5P
TRGV6
TRGV7
TRGV8
TRGV9
TRGV10
TRGV11
TRDC
TRDJ1
TRDJ2
TRDJ3
TRDJ4
TRDV1
TRDV2
TRDV3
TRDD1
TRDD2
TRDD3

150
100
50
0
-50
-100

C

Year 1 vs. baseline% Change in mRNAg

*
*
**
**
**

-150 -100 -50

E

**

*
T1D vs.
NDC

050

100 150

T1D with BCG
Year 1 Year 2

TRAC

TCRα

TRAJ

Year 2 vs. baseline

TRAV

TRBC

TRDD
TRDV
TRDJ
TRDC
TRGV
TRGJ
TRGC
TRBD
TRBV
TRBJ
TRBC
TRAV
TRAJ
TRAC

*

0

100 200

10
00
15
00

-200 -100

*

*
**
**
*
**
**

-200 -100**0

100 200

TCRβYear 2 vs. baselineYear 1 vs. baseline
TRDD
TRDV
TRDJ
TRDC
TRGV
TRGJ
TRGC
TRBD
TRBV
TRBJ
TRBC
TRAV
TRAJ
TRAC

TRBV

TRBD
TRGC
TRGJ
TRGV
TRDC
TRDJ
TRDV
TRDD
mRNA hyperexpression

10
00
15
00

D

TCRδ TCRγ

TRAC
TRAJ1
TRAJ2
TRAJ3
TRAJ4
TRAJ5
TRAJ6
TRAJ7
TRAJ8
TRAJ9
TRAJ10
TRAJ11
TRAJ12
TRAJ13
TRAJ14
TRAJ16
TRAJ17
TRAJ18TRAJ19TRAJ20
TRAJ21
TRAJ22
TRAJ23
TRAJ24
TRAJ25
TRAJ26
TRAJ27
TRAJ28
TRAJ29TRAJ30TRAJ31
TRAJ32
TRAJ33
TRAJ34
TRAJ35
TRAJ36
TRAJ37
TRAJ38
TRAJ39TRAJ40TRAJ41
TRAJ42
TRAJ43
TRAJ44
TRAJ45
TRAJ46
TRAJ47
TRAJ48
TRAJ49TRAJ50TRAJ51TRAJ52TRAJ53
TRAJ54
TRAJ55TRAJ56
TRAJ57
TRAJ58TRAJ59TRAJ60TRAJ61
TRAV1-1
TRAV1-2
TRAV2
TRAV3
TRAV4
TRAV5
TRAV6
TRAV7
TRAV8-1
TRAV8-2
TRAV8-3
TRAV8-4
TRAV8-5
TRAV8-6
TRAV8-7
TRAV9-1
TRAV9-2
TRAV10
TRAV11
TRAV12-1
TRAV12-2
TRAV12-3
TRAV13-1
TRAV13-2
TRAV14DV4
TRAV15
TRAV16
TRAV17
TRAV18
TRAV19
TRAV20
TRAV21
TRAV22
TRAV23DV6
TRAV24
TRAV25
TRAV26-1
TRAV26-2
TRAV27
TRAV28
TRAV29DV5
TRAV30
TRAV31
TRAV32
TRAV33
TRAV34
TRAV35
TRAV36DV7
TRAV37
TRAV38-1
TRAV38-2DV8
TRAV39
TRAV40
TRAV41TRBC1
TRBC2TRBJ1-1
TRBJ1-2
TRBJ1-3
TRBJ1-4
TRBJ1-5
TRBJ1-6
TRBJ2-1
TRBJ2-2
TRBJ2-2P
TRBJ2-3
TRBJ2-4
TRBJ2-5
TRBJ2-6
TRBJ2-7
TRBV1
TRBV2
TRBV3-1
TRBV4-1
TRBV4-2
TRBV5-1
TRBV5-2
TRBV5-3
TRBV5-4
TRBV5-5
TRBV5-6
TRBV5-7
TRBV6-1
TRBV6-2
TRBV6-4
TRBV6-5
TRBV6-6
TRBV6-7
TRBV6-8
TRBV7-1
TRBV7-2
TRBV7-3
TRBV7-4
TRBV7-5
TRBV7-6
TRBV7-7
TRBV7-9
TRBV8-1
TRBV8-2
TRBV9
TRBV10-1
TRBV10-2
TRBV10-3
TRBV11-1
TRBV11-2
TRBV11-3
TRBV12-1
TRBV12-2
TRBV12-3
TRBV12-4
TRBV12-5
TRBV13
TRBV14
TRBV15
TRBV16
TRBV17
TRBV18
TRBV19
TRBV20-1
TRBV20OR9-2
TRBV21-1
TRBV21OR9-2
TRBV22-1
TRBV22OR9-2
TRBV23-1
TRBV23OR9-2
TRBV24-1
TRBV24OR9-2
TRBV25-1
TRBV25OR9-2
TRBV26
TRBV26OR9-2
TRBV27
TRBV28
TRBV29-1
TRBV29OR9-2
TRBV30
TRBD1
TRGC1
TRGC2
TRGJ1
TRGJ2
TRGJP
TRGJP1TRGJP2
TRGV1
TRGV2
TRGV3
TRGV4
TRGV5
TRGV5P
TRGV6
TRGV7
TRGV8
TRGV9
TRGV10
TRGV11
TRDC
TRDJ1
TRDJ2
TRDJ3
TRDJ4
TRDV1
TRDV2
TRDV3
TRDD1
TRDD2
TRDD3

TRBJ

% Change in mRNA

Fig. 4. Comparison of mRNA expression from CD4+ T cells of T1D and NDC, and change of all TCR gene expression after BCG vaccination using RNA-seq.
(A) Relative difference (%) in mRNA expressions in NDC (n = 4) and T1D (n = 10) CD4+ T cells at baseline for all TCR-related genes (average ± SD; blue bars indicate significantly decreased expressions with P < 0.05 and q < 0.05; one-tailed Mann-Whitney U test). (B) Overall relative difference in mRNA expression of TCR segments at baseline;
mRNA expression of the local region genes in C, J, V, and D segments was averaged. The percent differences in mRNA expression between NDC (n = 5) and T1D (n = 10) atbaseline (T1D/NDC) are shown at each TCR segment (average, *P < 0.05 and q < 0.05; **P < 0.01 and q < 0.05; one-tailed Mann-Whitney U test). (C) Chronological changes
in mRNA expression of TCR genes from CD4+ T cells of T1D (n = 9) after BCG vaccination as compared to baseline (year 1, top; year 2, bottom) (average ± SD; red bars meansignificantly increased expressions; P < 0.05 and q < 0.05; one-tailed Wilcoxon test). (D) Chronological change of average change in mRNA expression as compared tobaseline in T1D (n = 9) at the TCR segment level after BCG vaccination (*P < 0.05; **P < 0.01; one-tailed Wilcoxon test). (E) Heatmap of change in mRNA expression for all
TCR-related genes. The left lane shows difference in mRNA expression between T1D (n = 4) and NDC (n = 4), and the right three lanes show the average mRNA expression
as compared to baseline (before vaccination) at years 1 and 2 (n = 9 vaccinated T1D). Shifts to increased expression are displayed in shades of red, and shifts to reduced
expression are displayed in shades of blue.

increased in the BCG-treated group as compared to the nontreatedcontrol group in both NDC and T1D (NDC, 99.3 ± 0.4% versus
99.6 ± 0.3%, P = 0.03; T1D, 99.3 ± 0.4% versus 99.8 ± 0.2%, P = 0.01;
Fig. 7E). The density of protein measured through the MFI of CD3+
on T cells was also significantly higher in the BCG-treated group as
Takahashi et al., Sci. Adv. 8, eabq7240 (2022)

16 November 2022

compared to the nontreated group in both NDC and T1D (NDC,
1279.1 ± 182.7 versus 1560.8 ± 219.9, P = 0.005; T1D, 1312.9 ± 246.1
versus 1509.1 ± 220.1, P = 0.02; Fig. 7F). ELISA results also showed
the same trend of up-regulation of protein levels by BCG treatment inculture, although the flow cytometry measurements of CD3 density
7 of 14

Downloaded from https://www.science.org on November 16, 2022

2000
1500
1000
500
200
150
100
50
0
-50
-100
-150

P, q < 0.05
ns

2000
1500
1000
500
200
150
100
50
0
-50
-100
-150

T1D vs. NDC
TRDD
TRDV
TRDJ
TRDC
TRGV
TRGJ
TRGC
TRBD
TRBV
TRBJ
TRBC
TRAV
TRAJ
TRAC

ns

TRAV

SCIENCE ADVANCES | RESEARCH ARTICLE
A

B

C

TCR

D

TCR

TCR

Total baseline
expanded clones

20

10

r2
Ye
ar
3

Ye
a

el
B

as

r3

in

e

r3

0

Ye
a

r2
Ye
a

B
as

el

in

e

0.00

6200

Ye
a

0.02

6600

r2

0.04

30

7000

Ye
a

Richness (downsampled
unique rearrangements)

0.06

Fig. 5. BCG vaccinations are not associated with clonotype expansion, changes in gene usage, or diversity of TCRB in the T1D CD4+ T cell repertoire. (A) Foldchange of TRBJ and TRBV gene usages in peripheral CD4+ T cells from patients with T1D (n = 27). Each point represents a change in gene usage from baseline to year 3 fora given patient. Light and dark gray coloring of points corresponds to increasing and decreasing usage, respectively. Significant changes in usage shown; Benjamini-­
Hochberg adjusted P from one-sample Wilcoxon with  = 0, *P < 0.05; **P < 0.005; ***P < 0.0005. (B to D) Simpson clonalities, downsampled richness, and count of baseline
expanded rearrangements shown for TCR; P from Wilcoxon signed-rank test. Lines connect longitudinal samples from the same subject.

were more statistically significant (fig. S7D). Together, these results
demonstrate that epigenetic and protein expression defects in CD3from epigenetics to protein expression in T1D are correlated with BCG
treatment as studied at the gene, mRNA, and protein levels. Theseobservations of BCG-mediated systemic effects in CD3 on autoimmune diabetes mirror the effects described above for the TCR.
Up-regulated TCR/CD3 expression by BCG results in restored
signaling through multiple kinases
For evaluating the change of kinase activity in TCR/CD3 signaling
pathway by BCG vaccination, we performed a protein array with CD4+
T cell lysate. The results show that not only well-known TCR signal
pathway–related kinases such as extracellular signal–regulated kinase
1/2 (ERK1/2), p38 mitogen-activated protein kinase (MAPK), and signaltransducer and activator of transcription (STAT) but also c-Jun, glycogensynthase kinase–3/ (GSK-­3/), c-Jun N-terminal kinase (JNK), andTakahashi et al., Sci. Adv. 8, eabq7240 (2022)

16 November 2022

WNK1 were phosphorylated by BCG (Fig. 7, F and G). Thus, BCG vaccination increases phosphorylation of multiple kinases in TCR signaling pathway, confirming the functional significance of these changes.
DISCUSSION

In this study, we describe the effects of BCG vaccinations on TCR/CD3
expression in the context of autoimmunity. We identify that significant
quantitative defects in TCR and CD3 proteins in CD4+ T cells in untreated T1D subjects both in the TCR complex genes and the associatedCD3 genes were overmethylated; this correlated with down-regulated
cell surface expression or density. With multidose BCG vaccine therapy,
we observed that more than 23,000 genes were demethylated at theyear 1 time point, and 90 of the top 200 most hypomethylated genes
were TCR-related. This process was over a 3-year-long time periodthat correlated with past reported improvements in the disease (12).
8 of 14

Downloaded from https://www.science.org on November 16, 2022

Simpson clonality

0.08

SCIENCE ADVANCES | RESEARCH ARTICLE
0.06
0.04
0.02
0.00

-0.02

% Difference in mRNA
(T1D/NDC)

B

-40
-60
-80

CD3

CD3+

D
1600

CD3+ MFI

100

CD4+ T cell (%)

CD3

CD3

CD3

CD3

CD3

CD3+

CD3

-100

-0.04

C

0

-20

CD3

β Value (T1D – NDC

A

90

80
NDC

T1D

1400
1200
1000
100
50
0
NDC

T1D

+

Demethylation driven by BCG vaccinations is demonstrated to
be stable based on over 3 years of monitoring, suggesting a durable
epigenetic effect that correlated with clinical improvement, whichlasts at least 8 years (12). We show that the BCG induced demethylation of most of the TCR-related genes and all four subunits of theCD3 complex at the level of individual gene methylation patterns by
the BCG microbe and that this 3-year time period correlated with
reported clinical improvements. The change in methylation patterns
was associated with up-regulation of the mRNA of the same genes
due to signaling being restored and a restoration of the protein levelsof CD3/TCR complex. This is a human example of microbe-driven
direct gene changes by a microbe, M. bovis, at the DNA level, affecting expression and function of the human TCR/CD3 complex. Restored function was shown in parallel kinase assays.
A goal for a significant time period in the field of immune diseases has been to identify the specific T cell clones reactive to organ-­
specific antigens, a process that might drive immune-mediated disease
and allow specific therapy design. TCR control can also be influenced
by rare point mutations associated with disease from random recombination, poor or augmented cognitive interactions with humanleukocyte antigen (HLA) class I/II polymorphisms, and improper
recombination events (43). The data here suggest an alternative defect, not necessarily exclusive, as a driver of autoimmunity and thus
altered T cell selection. We suggest that this defect can be resolved
by quantitative corrections to restore immune signaling. This paper
Takahashi et al., Sci. Adv. 8, eabq7240 (2022)

16 November 2022

9 of 14

Downloaded from https://www.science.org on November 16, 2022

Fig. 6. Comparison of CD3 levels in CD4 T cells of T1D and NDC patients using
DNA methylation, RNA-seq, and flow cytometry. (A) Differences in  values
between NDC (n = 8) and T1D (n = 12) at baseline (T1D-NDC) for each CD3 gene(average ± SD; none of the differences were significant, one-tailed Mann-Whitney
U test). The positive bars indicate hypermethylation in T1D, whereas the negative
bars indicate hypomethylation. (B) Relative difference (%) in mRNA expressions of
NDC (n = 5) and T1D (n = 10) CD4+ T cells at baseline (average ± SD; none of thedifferences were significant, one-tailed Wilcoxon test). (C) Percentage of CD3+ cells
among CD4+ T cells in NDC (n = 11) and T1D (n = 10) (average ± SD; *P < 0.05, onetailed Mann-Whitney U test). (D) MFI of APC in CD3+ cells in NDC (n = 11) and T1D(n = 10) (average ± SD; two-tailed Mann-Whitney U test).

identifies a major quantitative, not qualitative, defect in the TCR/CD3
protein complex and the nearly 127 genes of this complex resulting
in decreased expression in T1D. This underlying defect, i.e., the surface density of the TCR, can be treated at least partly with repeat BCG
vaccinations. From a basic science viewpoint, it is recognized that
TCR engagement and signaling are affected by the intermoleculardistance between TCR proteins and activation diminishes when proximity is increased between TCR surface proteins (44). Collectively,
our results and the basic science data suggest that T cells are triggered by a mechanism of overall receptor proximity for intracellularsignaling, now also confirmed in these studies. Spatial thresholds for
TCR triggering are now identified as an underlying defect in autoimmunity, corrected by BCG as it increases TCR density after therapy
to near-normal levels (45).
Graded knockout murine models with mutations in the TCR signaling pathway can drive different murine autoimmune disease byaltering thymic T cell selections as well as regulatory T cell function(46–48). The expression density of receptors on the surface of T cells
affects the type and intensity of T cell signaling responses (49–53).
The data here support the concept of the regulation of T cell signaling in T1D by quantitative changes in TCR expression and not by
TCR defects, resulting in a pathogenic clone. The data here did not
identify a single impaired TCR that influenced disease outcome. For
all autoimmune diseases, it has long been appreciated from humanidentical twin studies that discordance in autoimmune twins is thenorm, and thus, a strong environment influence exists for autoimmune disease expression (54). Epigenetic relationship of genes controlled by microbes could be a candidate for this well-establisheddisease discordance. This discordance in human identical twins with
autoimmunity is more compatible with quantitative gene expression
defects, not qualitative gene effects, which would not necessary be
concordant between twins because their DNA should be identical.
Epigenetics is known be a strong influencer of proper T cell selection
among TCR affinity, TCR density, and T cell activity (55). Experimental murine data suggest that the TCR ligand potency and density
are both contributory to the routes to T cell responses and peripheral
tolerance in the induction of Foxp3 cells as well (56). The hypermethylation patterns were not limited to specific segments of the TCR or
CD3 genes, but nearly all segments in TCR and CD3 genes were affected. Upon BCG vaccinations, regions of the TCR/CD3 complex
displayed demethylation, resulting in restoration of near-normal protein levels. Because the density of TCR reflects strength of the signal(57), decreased MFI of TCR/CD3 on CD4+ T cells argues that reduced
density of the complex and abnormally short TCR length might imply
the immaturity of the cells in autoimmunity. For over 20 years, it has
been noted that the peripheral T cells of many autoimmune disease
have a surface phenotype of immaturity, such as low levels of mature
T cells expressing CD45RO (formerly known as the 4B4 protein)(32, 33). This developmentally immature phenotype is a symptom ofT cell immaturity linked most commonly to inadequate TCR/CD3
engagement (33, 34). In addition, many human autoimmune diseases
have defects in Treg action, another cell population influenced by TCR
density (58). BCG organisms can also modulate host genes such as theTreg genes to restore Treg potency from a baseline state of a deficiency(59). In many autoimmune diseases, the HLA class I complex, thereciprocal engagement complex for the TCR/CD3 complex on CD8+
T cell selection, is also known in human and murine autoimmune
diseases to be associated with lowered density that would potentiate
poor T cell selection (60–63).

SCIENCE ADVANCES | RESEARCH ARTICLE
Year 2

Year 10.05

CD3

Demethylation(difference in β value)

CD3

CD3

CD3

CD3

**

CD3ζ

*

** *

E

100

30

F

RPMI

Kinase activity

CD3+

**
CD3

**
CD3

CD3

***

CD3ε

CD3

**

*

CD3

***

-0.10

BCG

Year 1 vs. baseline

20

WNK1

p38

STAT6

+

STAT3(Y705)-

0.0

STAT3(S727)+

Change in β value

0.5

STAT2

-

RPMI
BCG

1.0

10

NDC NDC T1D T1DKinase activity

1.5

20

0

G

STAT5a/b

Year 3 vs. baseline

STAT1

In vitro BCG

30

-0.10
-0.09
-0.08
-0.07
-0.06
-0.05
-0.04
-0.03
-0.02
-0.01
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.10

1000
100
50
0

0

JNK1/2/3

1500

10

Fig. 7. Increased CD3 expression after BCG treatment in vivo and in vitro. (A) Change in average  values for CD3 genes in T1D (n = 12) as compared to baseline at
1 to 3 years after BCG vaccination (average ± SD; *P < 0.05; **P < 0.01; ***P < 0.001, one-tailed Wilcoxon test). (B) Heatmap indicating methylation patterns in all CD3
genes. The left lane shows difference in  value between T1D (n = 12) and NDC (n = 8), and the right three lanes show the difference in  value for T1D (n = 12) at baseline
versus at 1 to 3 years after BCG vaccination. Hypomethylation is shown in shades of red, whereas hypermethylation is shown in shades of blue. (C) CD4+ T cells were isolated
from NDC (n = 9) and T1D (n = 8) and cultured with or without BCG for 7 days. The cells were then washed, stained for CD3, and analyzed by flow cytometry. The percentage
of cells that are positive for CD3 is shown (average ± SD; *P < 0.05; two-tailed Wilcoxon test). (D) MFI CD3 flow cytometry. The MFI for NDC (n = 9) and T1D (n = 8) cultured
with or without BCG is shown (average ± SD; *P < 0.05; **P < 0.01; two-tailed Wilcoxon test). (E) Histograms showing the distribution of the  values as compared to baseline
for CpGs of CD3-related genes at year 1 (top), year 2 (middle), and year 3 (bottom). A progressive shift of  values toward the left indicates progressive demethylation overtime. (F) A protein array was performed to evaluate the change in some kinase in TCR signaling pathway. Upper and lower images indicate the nontreated and BCG-treated
CD4+ T cells, respectively. (G) The density of kinase in the protein array was qualified by densitometry. White and black bars indicate the values in the nontreated (n = 1)
and BCG-treated CD4+ T cells (n = 1), respectively.

This study extends predominantly histone modification and chromatin remodeling during bacterial infections or epigenetics of a single gene from a microbe exposure to an entire family of 163 TCR/
CD3 genes involved in the immune response. This is an example ofdirect gene regulation, not just regulation at the level of chromatin andhistones that occurs but at very early time points after the vaccines(days or weeks). Recently, the topic of epigenetics and microbe exposures through vaccinations to infectious disease has been reviewed
and covered diverse human exposures beyond BCG and tuberculosis
but extended to human papilloma virus, Epstein-Barr virus (EBV),
hepatitis C, Helicobacter pylori, and more that influence single gene
expression (36). In addition, the emerging role of epigenetics in humanautoimmune disorders frequently centers on defects in microRNAs
or histone posttranslational modifications, including H3K27me3
Takahashi et al., Sci. Adv. 8, eabq7240 (2022)

16 November 2022

(64). Vaccinations and direct microbe exposures can modify the human epigenome and modulate the immune response, and histones
are often modified and single genes are involved. These fundamental
and BCG-driven BCG modifications of the central TCR change in
the TCR are remarkable.
Limitations of the study
There are study limitations to the basic science study of these human data. We do not know whether the major changes in methylationof the TCR/CD3 receptor complex are specific to the BCG microorganisms or whether other less ancient organism also epigenetically
can modify the host TCR complex.
In the human, we cannot distinguish between functionally important CpG sites in the human and CpG sites with more minor
10 of 14

Downloaded from https://www.science.org on November 16, 2022

CD3+ MFI

2000

20

c-Jun

CD3+

Year 2 vs. baseline

ERK1/2NDC NDC T1D T1D
In vitro BCG +
+

0

30

GSK-3a/b

98

10

Relative expression

99

Number of CD3-related CpG

CD4+ T cell (%)

Year 1 Year 2 Year 3

CD3δ

CD3

-0.05

T1D with BCG

T1D vs
NDC

CD3γ

C

D

B

Year 30.00

CD3Change in β value

ASCIENCE ADVANCES | RESEARCH ARTICLE

MATERIALS AND METHODS

Human clinical trial study and the data
All studies with human samples had full institutional approvals in
Massachusetts General Hospital and Partners Health Care (study nos.
2007P001347, 2012P002243, and 2013P002633), and the interventional studies using past BCG-treated subjects and open label–treatedsubjects who were also formally approved by the U.S. Food and Drug
Administration (IND#2007P001347 and IND#2013P16434). All blood
donors, both T1D and NDC subjects, were informed and consented
following the principles set out in the World Medical Association
Declaration of Helsinki and the Department of Health and Human
Services Belmont Report, and study no. 2001P001379. Demographics
for the different subject groups are listed in Table 1.
Isolation of human CD4+ T cells and phenotyping via
flow cytometry
Human primary CD4 + T cells were isolated using the EasySep
Direct Human CD4+ T Cell Isolation Kit (STEMCELL Technologies,
Cambridge, MA) following the manufacturer’s protocol. Briefly,
1000 l of Isolation Cocktail and 1000 l of RapidSpheres were mixed
with 20 ml of whole blood in a 50-ml centrifuge tube and incubated
Takahashi et al., Sci. Adv. 8, eabq7240 (2022)

16 November 2022

for 5 min at room temperature. Thirty milliliters of phosphate-bufferedsaline (PBS) was added, and the tube was placed into a “Easy 50” magnet (STEMCELL Technologies). This immobilized the unwanted
non-CD4+ T cells at the side of the tube. After 10 min, the CD4 +T cell–enriched suspension was transferred into a new tube, fresh
RapidSpheres were added and incubated for 5 min, and the magnetic
separation was repeated. The resulting highly enriched CD4+ T cellsuspension was transferred into a new tube again and purified for athird time using the magnet. The final CD4+ T cell preparations had
purities of >95%.
For phenotyping, the isolated CD4+ T cells were immediately
stained with mouse allophycocyanin (APC)–labeled anti-humanCD3 antibody (BioLegend, San Diego, CA) or TCR antibody(BioLegend). The cells were analyzed using BD FACSCanto II (BD
Biosciences, Franklin Lakes, NJ). For culture experiments with BCG,
the leftover CD4+ T cells were seeded in 24-well tissue culture plates
at 5 × 105 cells in 1 ml of RPMI 1640 medium (Thermo Fisher Scientific, Waltham, MA) supplemented with 10% fetal bovine serum (Thermo Fisher Scientific), penicillin (100 U/ml), streptomycin(100 mg/ml; Thermo Fisher Scientific), human interleukin-2 (IL-2;
100 U/ml; MilliporeSigma, Burlington, MA), and concanavalin A(2.5 g/ml; Thermo Fisher Scientific). BCG [1 × 105 colony-forming
units (CFU); strain BCG Japan, BCG Laboratory, Tokyo, Japan] wasadded to half of the wells, and the other wells remained as controls.
Culture was at 37°C in 5% CO2 and 95% air. At days 3 and 5, the cells
were split with the same dosage of any reagents. At day 7, the cells were
harvested and washed with PBS (Thermo Fisher Scientific), then incubated with the same antibodies as above, and analyzed by flow cytometry. MFI was determined using FlowJo software (BD Biosciences).
ELISA for detecting CD3 and TCR protein
CD3 and TCR protein levels were evaluated by ELISA. Freshly isolated primary CD4+ T cells were lysed by repeated freeze-thaw cycles
in lysis buffer [PBS supplemented with a protein inhibitor cocktail tablet (cOmplete ULTRA Tablets, Mini, EDTA-free, EASYpack;
Sigma-Aldrich, St. Louis, MO)] at 1 × 106 cells/100 l. For in vitro
culture evaluation of BCG responses, some of the isolated CD4+
T cells were seeded in 24-well tissue culture plates at 1 × 106 cells in
1 ml of RPMI culture medium supplemented with 2.5% ImmunoCult
Human CD3/CD28 T Cell Activator (STEMCELL Technologies) andhuman IL-2 (100 U/ml). BCG (1 × 105 CFU; strain BCG Japan) was
added to half of the wells, and the other wells remained as controls.
Culture was at 37°C in 5% CO2 and 95% air. After 3 days of incubation, the cells were harvested and lysed by repeated freeze-thaw cycles
in lysis buffer at 1 × 106 cells/100 l. The lysate was then centrifuged
at 8000g for 30 min, and the supernatant was tested for the presenceof CD3 using Human CD3 gamma, CD3 delta, or CD3 epsilon ELISA
Kits (Novus Biologicals, Centennial, CO). The levels of CD3 (CD247)
and TCR were also measured using CD247 ELISA kits (Aviva Systems
Biology, San Diego, CA) or QuickDetect TCR (Human) ELISA Kit(BioVision Inc., Milpitas, CA), respectively. Whole protein levels were
measured with a BCA protein assay (Thermo Fisher Scientific), and thevalues were expressed as the TCR or CD3 levels (nanograms per milliliter) normalized by whole protein levels (micrograms per milliliter).
Methylation data analysis
DNA was prepared from CD4+ T cells using DNeasy Blood & Tissue
Kits (QIAGEN, Hilden, Germany) and stored at −80°C. For eachsample, 1 g of DNA was bisulfite-converted using the EZ-96 DNA
11 of 14

Downloaded from https://www.science.org on November 16, 2022

functional roles. Therefore, these results should be revisited in the
future as newer, more comprehensive methylation chips become available and when more information becomes available in the human inwhich CpG sites encode the pivotal functions in TCR/CD3 protein
complex expression.
Our clinical trials do not allow for the treatment of NDC with
BCG. Therefore, a comparison of T1D and NDC with BCG vaccinations is not possible. In addition, there is a substantial differencein average age between T1D and NDC groups, and we cannot exclude
that there may be an age-associated difference in gene methylation
between young and old T1D.
Here, we describe associations and correlations between treatment with BCG and longitudinal changes in gene methylation. We
would like to hypothesize that there is a causative relation between
the treatment and our longitudinal methylation observations andpast published data on the corresponding beneficial clinical effects
over the same time course. However, causation in complex biological systems is very hard to prove. The same also holds for our observations that show correlations between gene methylation changes
and changes at the mRNA or protein expression level.
These freshly isolated CD4+ T cells were confirmed >85% pureby flow cytometry (fig. S6E). Even with excellent cell sorting, someantigen-presenting cells including dendritic cells or macrophages
might have been present. This would not affect the TCR only present
on T cells but could bridge BCG recognition by CD4+ T cell in vitro
culture experiments. In addition, this study used these bulk CD4+
T cells and subcomponents such as autoreactive cells and naïve, memory, and regulatory T cells were not distinguished. This was due tolimited amount of blood sampling from one trial patient. We obtained approximately 10 million to 20 million CD4+ T cells from one
blood collection (20 ml), and at least 5 million cells are required for
DNA, RNA extraction, and ELISA, respectively. Therefore, analyses
in detailed populations and fractions from CD4+ T cells were notrealistic in this study. Future studies will also need to focus on CD8+
T cells from subjects treated with BCG vaccinations for the impact
on this T cell subpopulation as well.

SCIENCE ADVANCES | RESEARCH ARTICLE

Protein array
A protein array was performed to evaluate multiple kinase activities with the Human Phospho-Kinase Array Kit (R&D Systems,
Minneapolis, MN) following the manufacturer’s protocol. Samples were established by the attached lysis buffer supplemented withaprotinin, leupeptin, and pepstatin (10 g/ml; R&D Systems). The images were developed with HYBLOT CL-Film (Thomas Scientific). The
signaling strength was quantified by densitometry using ImageJ software (National Institutes of Health), and the values are expressed
as relative mean pixel density against the average of those of reference spots.
mRNA sequencing data analysis
Total RNA was extracted from isolated CD4+ T cells using the
RNeasy Plus Mini Kit (QIAGEN). RNA-seq was performed at the
Takahashi et al., Sci. Adv. 8, eabq7240 (2022)

16 November 2022

Center for Cancer Computational Biology (CCCB) of the Dana-Farber
Cancer Institute (DFCI) as well as at the BioMicro Center (BMC) of
the Massachusetts Institute of Technology. For mRNA isolationfrom total RNA, NEBNext Poly(A) mRNA Magnetic Isolation Modules were used, and for library preparation, the NEBNext Ultra or
NEBNext Ultra II Directional RNA Library Prep Kits for Illumina
were used. Sequencing was performed with Illumina NextSeq 500 at
the CCCB or with NovaSeq 6000 with S4 Flowcell at the BMC. Reads
were aligned to the GRCh38.95 reference genome using the STAR
Aligner application. The quality control software included FastQC,
RSeQC, and MultiQC, and the reads were normalized using DESeq2.
Before the analyses, all raw data were normalized by average value
in the mRNA expressions of multiple housekeeping genes including
ATCB, GAPDH, PGK1, YWHAZ, SDHA, TFRC, GUSB, HMBS, and
TUBB for the calibration among sequences. Then, the normalized
mRNA value was compared and displayed as the percent change between NDC and T1D at baseline or as T1D at baseline and 1 to 3 years
after BCG vaccinations. For the TCR segmental data analysis (i.e.,
mRNA expressions in the CVJ regions), the average mRNA expression at each segmental gene was used for the comparisons.
Quantitative analysis in TCR and TCRgDNA was extracted from CD4+ T cells with QIAamp DNA mini kits
(QIAGEN), and 128 to 430 ng of them were submitted for immunosequencing of the CDR3 regions of human TCR/ chains using
the immunoSEQ Assay (Adaptive Biotechnologies, Seattle, WA).
Extracted gDNA was amplified in a bias-controlled multiplex PCR,
followed by high-throughput sequencing. Sequences were collapsed
and filtered to identify and quantitate the absolute abundance of eachunique TCR/ CDR3 regions for further analysis as previously described (65–69).
Statistical analyses of TCR sequencing results
All data were statistically assessed by two-way analysis of variance,
followed by Wilcoxon testing to compare two groups. The data of clonality and usage in TCR and TCR were analyzed by paired Wilcoxon
test. P values less than 0.05 were considered statistically significant.
Two quantitative components of diversity were compared acrosssamples in this study._
Simpson clonality was calculated on productive√R

rearrangements by ​​ ​ ∑​​ ​​  p​2i​  ​ ​​, where R is the total number of rearrangei=1

ments and pi is the productive frequency of rearrangement i. Valuesof Simpson clonality range from 0 to 1 and measure how evenly
receptor sequences (rearrangements) are distributed among a set of
T cells. Clonality values approaching 0 indicate a very even distribution of frequencies, whereas values approaching 1 indicate anincreasingly asymmetric distribution in which a few clones arepresent at higher frequencies.
Sample richness was calculated as the number of unique productive rearrangements in a sample after computationally downsampling
to a common number of T cells to control for variation in sample
depth. Repertoires were randomly sampled without replacement
five times, and richness is reported as the mean number of uniquerearrangements (downsampled to 53,679 and 7,340 templates in
TCR/, respectively).
To calculate the number of expanded clones in each patient, rearrangement frequencies from baseline and postvaccination samples (year 2 or year 3) were compared using a binomial distribution
framework as previously described (69). In brief, for each clone, we
12 of 14

Downloaded from https://www.science.org on November 16, 2022

Methylation Kit (Shallow Well Format; Zymo Research Corporation, Irvine, CA) following the manufacturer’s protocol. Standard
polymerase chain reaction (PCR) of the highly methylated P19 gene
was performed to confirm the quality of bisulfite conversion using
primers that target known sites of methylation. The primers for the
methylated targets are as follows: Bisulfite_QC.F, CTAAAACCCCA­
ACTACCTAAA; Bisulfite_QC.R, TAGGTTTTTTAGGAAGGAGAG. PCR cycling conditions were 95°C for 15 min, 5 cycles of (95°C
for 30 s, 60°C for 2 min, and 72°C for 1 min), 30 cycles of (95°C for
30 s, 65°C for 1 min, and 72°C for 1 min), and 72°C for 5 min. PCR
products were run on a 2% agarose gel, and a single band at 286 basepairs indicated successful conversion.
The methylation assay was performed with the Human
MethylationEPIC BeadChip (Illumina, San Diego, CA) using 200
to 400 ng of bisulfite-converted DNA product for each sample according to the Infinium HD Methylation Assay protocol (Illumina).
This assay targets over 850,000 known and potential methylationsites across the genome via 50-mer probes attached to the Infinium
BeadChips. The bisulfite-converted DNA was hybridized to the probes
on the BeadChip followed by a single base extension to incorporate aCy3- or Cy5-labeled nucleotide at the target position to differentiate
methylation status. The BeadChips were then imaged on the Illumina
iScan reader, and the methylation level of each CpG locus was calculated in the GenomeStudio Methylation module as  value [intensity of the methylated allele (M)/(intensity of the unmethylated
allele (U) + intensity of the methylated allele (M) + 100)]. The  values of all represented CpG sites on the chip were averaged for eachgene, and the methylation patterns were then compared and displayed
as the difference between NDC and T1D at baseline or as the difference of T1D at baseline versus 1 to 3 years after BCG vaccination. For
TCR regional data analysis (i.e., methylation patterns in the C, V, J,
and D regions), the average  values for CpGs across each segmental
gene were calculated. For picking up the most hyper/hypomethylated
genes, the average difference or change in  value for each CpG siteof the genes was calculated, and the genes were sorted in order excluding noncoding DNA or RNA genes.
As described above, we calculated the overall methylation stateof a gene at a given time point by first calculating the difference in 
value of each CpG at that time point versus baseline and then by calculating an overall difference by averaging. Here, we refer to a geneas being hypermethylated if this overall  value difference is positive. For a hypomethylated or demethylated gene, this overall difference is negative.

SCIENCE ADVANCES | RESEARCH ARTICLE
performed a two-sided test that the baseline and postvaccination frequencies at year 2 or year 3 were the same. The Benjamini-Hochberg
procedure was used to control FDR at 0.01 (70).
Diversity metrics were compared between years using Wilcoxon
signed-rank tests. All statistical analyses were performed in R
version 3.6.1.
Statistical analysis for methylation and RNA-seq data
Some of the methylation and RNA-seq data were not normally distributed and thus not suitable for Student’s t testing. For consist­ency, we therefore calculated nonparametric statistics for these data,
whether it was normally distributed or not. For unpaired data, we used
Mann-Whitney U testing, whereas for paired data we used Wilcoxon
testing. All testing was one-tailed because we had clear expectations
in which direction the data would change based on previous results.
FDR corrections were determined by calculating q values for all
P values using the R Bioconductor qvalue package version 2.22.0.
Results were only considered significant if both P < 0.05 and q < 0.05.
There are hundreds of genes listed here, so to prevent clutter we have
only highlighted some of the most important statistics in the text. In
the figures, we indicated significant results with asterisks: *P < 0.05
and q < 0.05; **P < 0.01 and q < 0.05; ***P < 0.001 and q < 0.05. A
comprehensive list of all P and q values is provided in tables S1 to
S4. Statistical analysis was performed in GraphPad Prism version 9.2.0
and in R version 4.0.3.
SUPPLEMENTARY MATERIALS

View/request a protocol for this paper from Bio-protocol.

REFERENCES AND NOTES

1. P. Aaby, A. Roth, H. Ravn, B. M. Napirna, A. Rodrigues, I. M. Lisse, L. Stensballe, B. R. Diness,
K. R. Lausch, N. Lund, S. Biering-Sørensen, H. Whittle, C. S. Benn, Randomized trial of BCG
vaccination at birth to low-birth-weight children: Beneficial nonspecific effects
in the neonatal period? J. Infect. Dis. 204, 245–252 (2011).
2. E. Elguero, K. B. Simondon, J. Vaugelade, A. Marra, F. Simondon, Non-specific effectsof vaccination on child survival? A prospective study in Senegal. Trop. Med. Int. Health 10,
956–960 (2005).
3. I. Kristensen, P. Aaby, H. Jensen, Routine vaccinations and child survival: Follow up study
in Guinea-Bissau, West Africa. BMJ 321, 1435–1438 (2000).
4. V. Nankabirwa, J. K. Tumwine, P. M. Mugaba, T. Tylleskar, H. Sommerfelt; PROMISE- EBF
Study Group, Child survival and BCG vaccination: A community based prospective cohort
study in Uganda. BMC Public Health 15, 175 (2015).
5. M.-G. Hollm-Delgado, E. A. Stuart, R. E. Black, Acute lower respiratory infection among
Bacille Calmette-Guérin (BCG)-vaccinated children. Pediatrics 133, e73–e81 (2014).
6. M. J. de Castro, J. Pardo-Seco, F. Martinon-Tórres, Nonspecific (Heterologous) protectionof neonatal BCG vaccination against hospitalization due to respiratory infection
and sepsis. Clin. Infect. Dis. 60, 1611–1619 (2015).
7. G. Ristori, M. G. Buzzi, U. Sabatini, E. Giugni, S. Bastianello, F. Viselli, C. Buttinelli,
S. Ruggieri, C. Colonnese, C. Pozzilli, M. Salvetti, Use of bacille calmette-guerin (BCG)
in multiple sclerosis. Neurology 53, 1588–1589 (1999).
8. A. Paolillo, M. G. Buzzi, E. Giugni, U. Sabatini, S. Bastianello, C. Pozzilli, M. Salvetti,
G. Ristori, The effect of Bacille Calmette-Guerin on the evolution of new enhancing lesions
to hypointense T1 lesions in relapsing remitting MS. J. Neurol. 250, 247–248 (2003).
9. G. Ristori, S. Romano, S. Cannoni, A. Visconti, E. Tinelli, L. Mendozzi, P. Cecconi, R. Lanzillo,
M. Quarantelli, C. Buttinelli, C. Gasperini, M. Frontoni, G. Coarelli, D. Caputo,
V. Bresciamorra, N. Vanacore, C. Pozzilli, M. Salvetti, Effects of Bacille Calmette-Guerin
after the first demyelinating event in the CNS. Neurology 82, 41–48 (2014).
10. D. L. Faustman, L. Wang, Y. Okubo, D. Burger, L. Ban, G. Man, H. Zheng, D. Schoenfeld,
R. Pompei, J. Avruch, D. M. Nathan, Proof-of-concept, randomized, controlled clinical trialof Bacillus-Calmette-Guerin for treatment of long-term type 1 diabetes. PLOS ONE 7,
e41756 (2012).
11. W. M. Kuhtreiber, D. L. Faustman, BCG therapy for type 1 diabetes: Restorationof balanced immunity and metabolism. Trends Endocrinol. Metab. 30, 80–92 (2019).

Takahashi et al., Sci. Adv. 8, eabq7240 (2022)

16 November 2022

13 of 14

Downloaded from https://www.science.org on November 16, 2022

Supplementary material for this article is available at https://science.org/doi/10.1126/
sciadv.abq7240

12. W. M. Kühtreiber, L. Tran, T. Kim, M. Dybala, B. Nguyen, S. Plager, D. Huang, S. Janes,
A. Defusco, D. Baum, H. Zheng, D. L. Faustman, Long-term reduction in hyperglycemia
in advanced type 1 diabetes: The value of induced aerobic glycolysis with BCG
vaccinations. NPJ Vaccines 3, 23 (2018).
13. W. M. Kühtreiber, H. Takahashi, R. C. Keefe, Y. Song, L. Tran, T. G. Luck, G. Shpilsky,
L. Moore, S. M. Sinton, J. C. Graham, D. L. Faustman, BCG Vaccinations upregulate myc,a central switch for improved glucose metabolism in diabetes. iScience 23, 101085 (2020).
14. S. Guallar-Garrido, E. Julián, Bacillus Calmette-Guérin (BCG) therapy for bladder cancer:
An update. Immunotargets Ther. 9, 1–11 (2020).
15. N. T. Usher, S. Chang, R. S. Howard, A. Martinez, L. H. Harrison, M. Santosham,N. E. Aronson, Association of BCG vaccination in childhood with subsequent cancer
diagnoses: A 60-year follow-up of a clinical trial. JAMA Netw. Open 2, e1912014 (2019).
16. M. K. Lalor, S. G. Smith, S. Floyd, P. Gorak-Stolinska, R. E. Weir, R. Blitz, K. Branson, P. E. Fine,
H. M. Dockrell, Complex cytokine profiles induced by BCG vaccination in UK infants.
Vaccine 28, 1635–1641 (2010).
17. J. Sharrock, J. C. Sun, Innate immunological memory: From plants to animals. Curr. Opin.
Immunol. 62, 69–78 (2020).
18. D. M. E. Bowdish, M. S. Loffredo, S. Mukhopadhyay, A. Mantovani, S. Gordon, Macrophage
receptors implicated in the “adaptive” form of innate immunity. Microbes Infect. 9,
1680–1687 (2007).
19. M. G. Netea, L. A. Joosten, E. Latz, K. H. Mills, G. Natoli, H. G. Stunnenberg, L. A. O'Neill,
R. J. Xavier, Trained immunity: A program of innate immune memory in healthand disease. Science 352, aaf1098 (2016).
20. V. R. Ramirez-Carrozzi, D. Braas, D. M. Bhatt, C. S. Cheng, C. Hong, K. R. Doty, J. C. Black,A. Hoffmann, M. Carey, S. T. Smale, A unifying model for the selective regulationof inducible transcription by CpG islands and nucleosome remodeling. Cell 138, 114–128(2009).
21. J. Kleinnijenhuis, J. Quintin, F. Preijers, C. S. Benn, L. A. Joosten, C. Jacobs, J. van Loenhout,
R. J. Xavier, P. Aaby, J. W. van der Meer, R. van Crevel, M. G. Netea, Long-lasting effects
of BCG vaccination on both heterologous Th1/Th17 responses and innate trained
immunity. J. Innate Immun. 6, 152–158 (2014).
22. J. Madura Larsen, C. S. Benn, Y. Fillie, D. van der Kleij, P. Aaby, M. Yazdanbakhsh, BCG
stimulated dendritic cells induce an interleukin-10 producing T-cell population with noT helper 1 or T helper 2 bias in vitro. Immunology 121, 276–282 (2007).
23. M. E. Call, K. W. Wucherpfennig, The T cell receptor: Critical role of the membrane
environment in receptor assembly and function. Annu. Rev. Immunol. 23, 101–125(2005).
24. E. Hodges, M. T. Krishna, C. Pickard, J. L. Smith, Diagnostic role of tests for T cell receptor(TCR) genes. J. Clin. Pathol. 56, 1–11 (2003).
25. F. W. Alt, E. M. Oltz, F. Young, J. Gorman, G. Taccioli, J. Chen, VDJ recombination. Immunol.
Today 13, 306–314 (1992).
26. B. Alarcon, D. Gil, P. Delgado, W. W. Schamel, Initiation of TCR signaling: Regulation
within CD3 dimers. Immunol. Rev. 191, 38–46 (2003).
27. L. D. Notarangelo, Immunodeficiency and immune dysregulation associatedwith proximal defects of T cell receptor signaling. Curr. Opin. Immunol. 31, 97–101(2014).
28. A. G. Schrum, L. A. Turka, E. Palmer, Surface T-cell antigen receptor expression
and availability for long-term antigenic signaling. Immunol. Rev. 196, 7–24 (2003).
29. H. K. Dadi, A. J. Simon, C. M. Roifman, Effect of CD3 deficiency on maturation of /and / T-cell lineages in severe combined immunodeficiency. N. Engl. J. Med. 349,
1821–1828 (2003).
30. G. de Saint Basile, F. Geissmann, E. Flori, B. Uring-Lambert, C. Soudais, M. Cavazzana-Calvo,
A. Durandy, N. Jabado, A. Fischer, F. Le Deist, Severe combined immunodeficiency
caused by deficiency in either the delta or the epsilon subunit of CD3. J. Clin. Invest. 114,
1512–1517 (2004).
31. N. V. Morgan, S. Goddard, T. S. Cardno, D. McDonald, F. Rahman, D. Barge, A. Ciupek,
A. Straatman-Iwanowska, S. Pasha, M. Guckian, G. Anderson, A. Huissoon, A. Cant,
W. P. Tate, S. Hambleton, E. R. Maher, Mutation in the TCRalpha subunit constant gene
(TRAC) leads to a human immunodeficiency disorder characterized by a lackof TCRalphabeta+ T cells. J. Clin. Invest. 121, 695–702 (2011).
32. D. Faustman, D. Schoenfeld, R. Ziegler, T-lymphocyte changes linked to autoantibodies:
Association of insulin autoantibodies with CD4+CD45R+ lymphocyte subpopulationin prediabetic subjects. Diabetes 40, 590–597 (1991).
33. D. Faustman, G. Eisenbarth, J. Daley, J. Breitmeyer, Abnormal T lymphocyte subsets
in type I diabetes mellitus: Analysis with anti-2H4 and anti-4B4 antibodies. Diabetes 38,
1462–1468 (1989).
34. D. L. Faustman, Occult CD45 T cell developmental defect in type I diabetes. Diabetes
Metab. 19, 446–457 (1993).
35. D. A. Mendes-da-Cruz, J. P. Lemos, G. A. Passos, W. Savino, Abnormal T-cell developmentin the thymus of non-obese diabetic mice: Possible relationship with the pathogenesisof type 1 autoimmune diabetes. Front. Endocrinol. (Lausanne) 9, 381 (2018).

SCIENCE ADVANCES | RESEARCH ARTICLE

Takahashi et al., Sci. Adv. 8, eabq7240 (2022)

16 November 2022

56. R. A. Gottschalk, E. Corse, J. P. Allison, TCR ligand density and affinity determine
peripheral induction of Foxp3 in vivo. J. Exp. Med. 207, 1701–1711 (2010).
57. C. Joseph, J. Klibi, L. Amable, L. Comba, A. Cascioferro, M. Delord, V. Parietti, C. Lenoir,
S. Latour, B. Lucas, C. Viret, A. Toubert, K. Benlagha, TCR density in early iNKT cell
precursors regulates agonist selection and subset differentiation in mice. Eur. J. Immunol.
49, 894–910 (2019).
58. Y. Okubo, H. Torrey, J. Butterworth, H. Zheng, D. L. Faustman, Treg activation defectin type 1 diabetes: Correction with TNFR2 agonism. Clin. Transl. Immunol. 5, e56 (2016).
59. R. C. Keefe, H. Takahashi, L. Tran, K. Nelson, N. Ng, W. M. Kuhtreiber, D. L. Faustman, BCG
therapy is associated with long-term, durable induction of Treg signature genes by
epigenetic modulation. Sci. Rep. 11, 14933 (2021).
60. R. Huang, J. Guo, X. Li, D. L. Faustman, Elimination of self-peptide major
histocompatibility complex class I reactivity in NOD and ß2-microglobulin-negative mice.
Diabetes 44, 1114–1120 (1995).
61. F. Li, J. Guo, Y. Fu, G. Yan, D. Faustman, Abnormal class I assembly and peptide
presentation in the diabetic NOD mouse. Proc. Natl. Acad. Sci. U.S.A. 91, 11128–11132(1994).
62. D. Faustman, X. Li, H. Y. Lin, Y. Fu, G. Eisenbarth, J. Avruch, J. Guo, Linkage of faulty major
histocompatibility complex class I to autoimmune diabetes. Science 254, 1756–1761(1991).
63. Y. Fu, D. M. Nathan, F. Li, X. Li, D. L. Faustman, Defective major histocompatibility
complex class I expression on lymphoid cells in autoimmunity. J. Clin. Invest. 91,
2301–2307 (1993).
64. R. Mazzone, C. Zwergel, M. Artico, S. Taurone, M. Ralli, A. Greco, A. Mai, The emerging roleof epigenetics in human autoimmune disorders. Clin. Epigenetics 11, 34 (2019).
65. H. S. Robins, P. V. Campregher, S. K. Srivastava, A. Wacher, C. J. Turtle, O. Kahsai,
S. R. Riddell, E. H. Warren, C. S. Carlson, Comprehensive assessment of T-cell receptor
beta-chain diversity in alphabeta T cells. Blood 114, 4099–4107 (2009).
66. C. S. Carlson, R. O. Emerson, A. M. Sherwood, C. Desmarais, M. W. Chung, J. M. Parsons,
M. S. Steen, M. A. LaMadrid-Herrmannsfeldt, D. W. Williamson, R. J. Livingston, D. Wu,
B. L. Wood, M. J. Rieder, H. Robins, Using synthetic templates to design an unbiased
multiplex PCR assay. Nat. Commun. 4, 2680 (2013).
67. N. Ritz, B. Dutta, S. Donath, D. Casalaz, T. G. Connell, M. Tebruegge, R. Robins-Browne,
W. A. Hanekom, W. J. Britton, N. Curtis, The influence of bacille Calmette-Guerin vaccine
strain on the immune response against tuberculosis: A randomized trial. Am. J. Respir. Crit.
Care Med. 185, 213–222 (2012).
68. H. Robins, C. Desmarais, J. Matthis, R. Livingston, J. Andriesen, H. Reijonen, C. Carlson,
G. Nepom, C. Yee, K. Cerosaletti, Ultra-sensitive detection of rare T cell clones. J. Immunol.
Methods 375, 14–19 (2012).
69. W. S. DeWitt, R. O. Emerson, P. Lindau, M. Vignali, T. M. Snyder, C. Desmarais, C. Sanders,
H. Utsugi, E. H. Warren, J. McElrath, K. W. Makar, A. Wald, H. S. Robins, Dynamics of thecytotoxic T cell response to a model of acute viral infection. J. Virol. 89, 4517–4526 (2015).
70. Y. Benjamini, J. Hochberg, Controlling the false discovery rate: A practical and powerfulapproach to multiple testing. J. R. Stat. Soc. Ser. B Methodol. 57, 289–300 (1995).
Acknowledgments: We thank L. Murphy and S. A. Williams for administrative support. We
thank M. Davis for detailed proofreading of the paper. We also thank M. Vignali for editing of
the final paper. Funding: This work was funded by all nonprofit foundation gifts to the laboratory.
Author contributions: Conceptualization: R.C.K., H.T., W.M.K., and D.L.F. Methodology:
H.T., W.M.K., R.C.K., H.F.D., D.L.F., S.B., and D.S. Investigation: H.T., W.M.K., R.C.K., A.H.L., S.B.,
D.S., A.A., N.N., and K.J.N. Writing: H.T., W.M.K., and D.L.F. Supervision: W.M.K. and D.L.F.
Competing interests: S.B. has a financial interest in Adaptive Biotechnologies. The
authors declare no other competing interests. Data and materials availability: All dataneeded to evaluate the conclusions in the paper are present in the paper and/or the
Supplementary Materials.
Submitted 26 April 2022
Accepted 27 September 2022
Published 16 November 2022
10.1126/sciadv.abq7240

14 of 14

Downloaded from https://www.science.org on November 16, 2022

36. S. Bannister, N. L. Messina, B. Novakovic, N. Curtis, The emerging role of epigenetics
in the immune response to vaccination and infection: A systematic review. Epigenetics 15,
555–593 (2020).
37. L. Riber, L. H. Hansen, Epigenetic memories: The hidden drivers of bacterial persistence?
Trends Microbiol. 29, 190–194 (2021).
38. S. B. Rothbart, B. D. Strahl, Interpreting the language of histone and DNA modifications.
Biochim. Biophys. Acta 1839, 627–643 (2014).
39. J. Kleinnijenhuis, J. Quintin, F. Preijers, L. A. Joosten, D. C. Ifrim, S. Saeed, C. Jacobs,
J. van Loenhout, D. de Jong, H. G. Stunnenberg, R. J. Xavier, J. W. van der Meer,
R. van Crevel, M. G. Netea, Bacille Calmette-Guerin induces NOD2-dependent nonspecificprotection from reinfection via epigenetic reprogramming of monocytes. Proc. Natl.
Acad. Sci. U.S.A. 109, 17537–17542 (2012).
40. R. J. W. Arts, S. Moorlag, B. Novakovic, Y. Li, S. Y. Wang, M. Oosting, V. Kumar, R. J. Xavier,C. Wijmenga, L. A. B. Joosten, C. Reusken, C. S. Benn, P. Aaby, M. P. Koopmans,
H. G. Stunnenberg, R. van Crevel, M. G. Netea, BCG vaccination protects against
experimental viral infection in humans through the induction of cytokines associatedwith trained immunity. Cell Host Microbe 23, 89–100.e5 (2018).
41. D. P. Strachan, Hay fever, hygiene, and household size. Br. Med. J. 299, 1259–1260 (1989).
42. K. Hirahara, G. Vahedi, K. Ghoreschi, X. P. Yang, S. Nakayamada, Y. Kanno, J. J. O'Shea,
A. Laurence, Helper T-cell differentiation and plasticity: Insights from epigenetics.
Immunology 134, 235–245 (2011).
43. J. H. Rowe, O. M. Delmonte, S. Keles, B. D. Stadinski, A. K. Dobbs, L. A. Henderson,
Y. Yamazaki, L. M. Allende, F. A. Bonilla, L. I. Gonzalez-Granado, S. Celikbilek Celik,
S. N. Guner, H. Kapakli, C. Yee, S. Y. Pai, E. S. Huseby, I. Reisli, J. R. Regueiro,
L. D. Notarangelo, Patients with CD3G mutations reveal a role for human CD3 in Treg
diversity and suppressive function. Blood 131, 2335–2344 (2018).
44. J. R. Cochran, T. O. Cameron, J. D. Stone, J. B. Lubetsky, L. J. Stern, Receptor proximity, notintermolecular orientation, is critical for triggering T-cell activation. J. Biol. Chem. 276,
28068–28074 (2001).
45. H. Cai, J. Muller, D. Depoil, V. Mayya, M. P. Sheetz, M. L. Dustin, S. J. Wind, Full controlof ligand positioning reveals spatial thresholds for T cell receptor triggering. Nat.
Nanotechnol. 13, 610–617 (2018).
46. N. R. Gascoigne, V. Rybakin, O. Acuto, J. Brzostek, TCR signal strength and T cell
development. Annu. Rev. Cell Dev. Biol. 32, 327–348 (2016).
47. G. Gaud, R. Lesourne, P. E. Love, Regulatory mechanisms in T cell receptor signalling.
Nat. Rev. Immunol. 18, 485–497 (2018).
48. S. Tanaka, S. Maeda, M. Hashimoto, C. Fujimori, Y. Ito, S. Teradaira, K. Hirota, H. Yoshitomi,
T. Katakai, A. Shimizu, T. Nomura, N. Sakaguchi, S. Sakaguchi, Graded attenuation of TCR
signaling elicits distinct autoimmune diseases by altering thymic T cell selection
and regulatory T cell function. J. Immunol. 185, 2295–2305 (2010).
49. L. Conti, M. Cardone, B. Varano, P. Puddu, F. Belardelli, S. Gessani, Role of the cytokine
environment and cytokine receptor expression on the generation of functionally distinct
dendritic cells from human monocytes. Eur. J. Immunol. 38, 750–762 (2008).
50. S. Booy, C. H. van Eijck, F. Dogan, P. M. van Koetsveld, L. J. Hofland, Influence of type-I
Interferon receptor expression level on the response to type-I interferons in humanpancreatic cancer cells. J. Cell. Mol. Med. 18, 492–502 (2014).
51. M. B. Lutz, M. Schnare, M. Menges, S. Rossner, M. Rollinghoff, G. Schuler, A. Gessner,
Differential functions of IL-4 receptor types I and II for dendritic cell maturation and IL-12
production and their dependency on GM-CSF. J. Immunol. 169, 3574–3580 (2002).
52. W. Jiang, V. M. Estes, X. S. Wang, Z. Shao, B. J. Lee, X. Lin, J. L. Crowe, S. Zha,
Phosphorylation at S2053 in murine (S2056 in Human) DNA-PKcs is dispensable
for lymphocyte development and class switch recombination. J. Immunol. 203, 178–187(2019).
53. B. Alarcon, M. Swamy, H. M. van Santen, W. W. Schamel, T-cell antigen-receptor
stoichiometry: Pre-clustering for sensitivity. EMBO Rep. 7, 490–495 (2006).
54. S. T. Jerram, M. N. Dang, R. D. Leslie, The role of epigenetics in type 1 diabetes. Curr. Diab.
Rep. 17, 89 (2017).
55. B. A. Schodin, T. J. Tsomides, D. M. Kranz, Correlation between the number of T cell
receptors required for T cell activation and TCR-ligand affinity. Immunity 5, 137–146(1996).

BCG vaccinations drive epigenetic changes to the human T cell receptor: Restored
expression in type 1 diabetes
Hiroyuki TakahashiWillem M. KühtreiberRyan C. KeefeAmanda H. LeeAnna AristarkhovaHans F. DiasNathan NgKacie J.
NelsonStephanie BienDanielle ScheffeyDenise L. Faustman

Sci. Adv., 8 (46), eabq7240. • DOI: 10.1126/sciadv.abq7240

View the article online
https://www.science.org/doi/10.1126/sciadv.abq7240
Permissions
https://www.science.org/help/reprints-and-permissions

Downloaded from https://www.science.org on November 16, 2022

Use of this article is subject to the Terms of service
Science Advances (ISSN ) is published by the American Association for the Advancement of Science. 1200 New York Avenue NW,
Washington, DC 20005. The title Science Advances is a registered trademark of AAAS.
Copyright © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim
to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).