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OPEN

BCG therapy is associatedwith long‑term, durable
induction of Treg signature genes
by epigenetic modulation
Ryan C. Keefe1,4, Hiroyuki Takahashi1,2,4, Lisa Tran3, Kacie Nelson1, Nathan Ng1,
Willem M. Kühtreiber1,2 & Denise L. Faustman1,2*
Induction of immunosuppressive T-regulatory cells (Tregs) is a desirable goal in autoimmunity, andperhaps other immune diseases of activation. One promising avenue is with the bacille-calmetteguérin (BCG) vaccine in autoimmune type 1 diabetes (T1D). Its administration is associated with
gradual clinical improvements in human autoimmunity over a 2–3 year post-vaccination period. We
hypothesize that those improvements, and their unusually long time course to fully materialize,
are partially attributable to BCG’s induction of Tregs. Here we report on a 3 year-long longitudinalcohort of T1Ds and examine the mechanism by which Treg induction occurs. Using the Human
Infinium Methylation EPIC Bead Chip, we show that BCG vaccination is associated with gradualdemethylation of most of 11 signature genes expressed in highly potent Tregs: Foxp3, TNFRSF18,
CD25, IKZF2, IKZF4, CTLA4, TNFR2, CD62L, Fas, CD45 and IL2; nine of these 11 genes, by year 3,
became demethylated at the majority of CpG sites. The Foxp3 gene was studied in depth. At baseline
Foxp3 was over-methylated compared to non-diabetic controls; 3 years after introduction of BCG, 17of the Foxp3 gene’s 22 CpG sites became significantly demethylated including the critical TSDR region.
Corresponding mRNA, Treg expansion and clinical improvement supported the significance of theepigenetic DNA changes. Taken together, the findings suggest that BCG has systemic impact on the Tcells of the adaptive immune system, and restores immune balance through Treg induction.
Abbreviations
Tregs	T-regulatory cells
GVHD	Graft versus host disease
BCG	Bacille–Calmette–Guérin
MS	Multiple sclerosis
TB	Tuberculosis
T1D	Type 1 diabetes
NDC	Non-diabetic control
AOO	Age of onset
MFI	Mean fluorescent intensity
TSDR	T-regulatory cell specific de-methylated region
Exposure to chronic infectious diseases can result in increases in host Treg ­cells1,2. The Treg and infectious
disease link is thought to result from the co-evolutionary existence of pathogens developing mechanisms forlong-term survival within host cells without host-immune recognition. In tuberculosis, the granuloma is surrounded by host-induced Treg cells. Infection with tuberculosis (Mycobacterium tuberculosis), as well as leprosy(Mycobacterium leprae), is accompanied by an increase in circulating Treg ­cells3,4 Mice experimentally infectedwith tuberculosis show expansion of T
­ regs5.

1

Immunobiology Laboratories, Massachusetts General Hospital, Bldg 149, 13th Street, Boston, MA 02116,
USA. 2Harvard Medical School, Boston, MA 02116, USA. 3Alles Research Institute, Massachusetts General Hospital,
Boston, MA 02116, USA. 4These authors contributed equally: Ryan C. Keefe and Hiroyuki Takahashi. *email:faustman@helix.mgh.harvard.edu

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Induction of Treg cells, in numbers or activation state, is a one type of therapeutic approach in human inflammatory diseases. One possible way to increase the numbers or function of Tregs is to use microorganisms thattypically with infection already uses this mechanism for host immune escape. Strategic introduction of long-lived
organisms that are not harmful to hosts, such as the attenuated Mycobacterium bovis of the BCG vaccine, might
be beneficial for host Treg induction. Increasing Tregs numbers or function maybe one solution to inflammation.
Underlying defects in Treg function, sometimes related to Foxp3 expression or function, are common in severe
human immune diseases. The first disorder associated with dysfunction of the Foxp3 gene impacting Tregs, is
IPEX syndrome (immunodysregulation polyendocrinopathy enteropathy X-linked). This syndrome of autoimmune driven diarrhea, eczema and often type 1 diabetes, is caused by a multitude of now identified germline
mutation in the Foxp3 gene with over 70 m
­ utations6. Subsequent studies support the Foxp3 gene as the defining
protein of the Treg cellular lineage and identify potent Tregs with suppressive f­ unction7. The Foxp3 gene works
in concert with other signature genes of potent Tregs. In other immune diseases, specific genetic mutations insingle Treg-associated genes have not been identified, suggesting other genetic mechanisms for the poor Treg
function in inflammatory diseases. This paves the way for alternative therapeutic approaches with quantitative
defects to overcome the lackluster performance of Tregs in autoimmunity.
The BCG vaccine is being increasingly used in autoimmune clinical trials as an anti-inflammatory d­ rug8–13.
14–16
BCG vaccines are also being used for combatting childhood asthma and a­ llergies. Evidence exists especially inat least two human autoimmune diseases, multiple sclerosis (MS) and type 1 diabetes (T1D), of BCG’s therapeutic
effects manifesting gradually over a post-infection period of 3 or so ­years10,12. In autoimmune diabetic subjects,
8 weeks after two BCG vaccinations, CpG sites in Treg signature genes have shown statistically significant but
small early demethylation ­changes12. This, along with corresponding increases in mRNA, supports increased
Treg function to control inflammation. Although this time course is short, similarly short time courses with
BCG therapy also suggest, at the protein level, that inflammatory cytokines expressed for 2 or 3 months are alsochanging which could be a consequence of BCG impact on cytokines produced by ­monocytes16. Long-term andmulti-year effects of BCG vaccines for durable boosting of the Treg response have not been studied. A vaccinewith an avirulent version of the microorganism might show benefit by Treg induction if these benefits could besustained on a multi-year basis. BCG’s apparent control over Treg signature genes, including Foxp3, appears to
be a path to understanding its clinical success in multiple human inflammatory diseases.
Traditionally the BCG vaccine has been thought to have influence over the innate immune response, meaning lasting changes predominantly in host monocytes. The innate immune response of BCG on the host is certainly controlled in part by the histone activity of key genes involved in cytokine s­ ecretion18–20. The methylation
changes in monocytes have been coined “trained innate immunity”. BCG’s impact on the host immune response
may also involve the adaptive immune system. Indeed the impact of BCG on glycolysis can be monitored bothwithin T cells and monocytes suggesting adaptive immune changes from BCG vaccine t­ herapy12,13,17. The MYC
pathway for glycolysis after BCG vaccinations is equally induced in T cells as well as monocytes by methylationand downstream reciprocal mRNA c­ hanges20. Data in mice supports the concept that long-lasting BCG immune
effects can be driven by infection of host bone marrow stem cells resulting in presumed multi-lymphoid lineage­effects21. These effects support the concept of BCG’s therapeutic reach extending beyond innate immunity. Since
T cells are part of the adaptive immune response, a long-term examination of the CD4 Treg function after BCG
vaccination could support the broader utilization of short-term and long-term immune alterations.
It is appreciated that epigenetic mechanisms regulate T cell responses during T cell d­ evelopment22–24. Traditionally Treg numbers have been studied at the level of surface markers with flow cytometry methods, but thesemethods are cumbersome and are best studied in freshly harvested cells. Although CD4 + T cells expressing
intracellular Foxp3 are almost always studied, other studies have focused on the surface expression of CD45
(naïve RA and memory RO) cells and CD25 surface protein on Tregs as additional markers of potency. Potent
Treg cells also express high densities of TNFR2. In T1D there are defects in both the abunbance of potent Treg
cells with TNFR2 and CD45RO e­ xpression25–29. It has been proposed that poor numbers and function of Tregs
in T1D can be corrected with antibody agonism of the TNFR2 r­ eceptor29. DNA methylation patterns in T1D
may mediate genetic risk to d
­ isease26.
DNA methylation patterns may mediate the plasticity of human Tregs during development, but also BCG
vaccine therapy may also have impact on the Treg signature genes, also through methylation changes. Unlike
cytokine therapy, a durable and long lasting BCG effect on the Tregs through the Treg Foxp3 gene or the CD25
receptor (IL2 receptor) has the potential to permanently change methylation thereby resulting in the desired
stability and durability of this microorganism and host approach. This longitudinal human cohort study of
3 years duration, BCG therapy is associated with induction of most of 11 signature genes of potent Tregs: Foxp3,
TNFRSF18, CD25, IKZF2, IKZF4, CTLA4, TNFR2, CD62L, Fas, CD45 and IL2. A central question underlying
the use of BCG as a method to reset the immune system by Treg induction is whether its benefits are durable
and can it work in a disease setting of autoimmunity. The safety of the BCG vaccine has long been confirmed forover 125 years of continuous use around the globe. This is the first long-term study of methylation patterns ofTreg signature genes that might be influenced by host microbe interactions of the BCG organism.

Results

Human studies of repeat BCG vaccinations in subjects with type 1 diabetes investigate theinduction of Treg cells. In this cohort study we ask the following question: can BCG vaccination induce

long-term epigenetic changes in Treg signature genes? Studying the epigenetics of Treg biology over a 3-yeartime period was compared to mRNA changes to see if BCG changed methylation in a meaningful way to impact
gene expression. Throughout these studies, in most cases the Human Infinium Methylation assay is used to study
these Treg signature genes: FOXP3, TNFRSF18, CD25, IKZF2, IKZF4, CTLA4, TNFR2, CD62L, FAS, CD45 andScientific Reports |Vol:.(1234567890)(2021) 11:14933 |https://doi.org/10.1038/s41598-021-94529-2

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IL2. For the Treg specific demethylation region (TSDR) of the Foxp3 gene an alternative method was used since
the Infinium assay did not contain this DNA region.

BCG vaccine therapy over 3 years gradually demethylates most Treg signature genes. We
analyzed the methylation state of the CG Probe IDs (CpGs) for the eleven signature genes of Treg cells (Fig. 1a).
The data are presented as the overall demethylation per gene (Fig. 1a) and the heat map data of all individual
CpG sites of each gene over the same 3-year time period compared to baseline (pre-vaccination) (Fig. 1b). We
present calculated average beta values across all CpGs for each gene separately at yearly timepoints and calculatechanges compared to baseline (Supplementary Tables). Individual CpG sites per gene are also presented as aheatmap of color coding, with blue representing greatest demethylation.
As compared to baseline, most of the signature genes demethylate after BCG therapy. For these genes—FoxP3,
IKZF4, CTLA4, TNFR2, CD62L and CD45, IL2—the demethylation patterns are already significant at year 1 (redbars) but then become even more pronounced by year 3. For the CD25 and IKZF2 genes, demethylation also
occurs but was not observed until year 3. TNFRSF18, FAS do not show significant differences. These methylation
studies support BCG as a possible in vivo method to decrease methylation of Treg signature genes.
Similar to the overall demethylation patterns of entire genes, it is also apparent that individual CpGs for the
11 signature genes are also demethylating (more blue) over the yearly intervals and demethylation continued
at least up to year 3 (Fig. 1b). These data suggest BCG vaccine-induced modulations of the Treg cell are at the
DNA level (by changing demethylation) and are a slow and gradual process that mirrors the time course of clinical improvement, i.e. lowering of the HbA1c for diabetes and halt of CNS pathology in multiple s­ clerosis10,12.
Simultaneously, we accessed the overall methylation pattern of CpGs in CD4 + T cells. In total 697315 CpG
sites were evaluate. Approximately half of CpG sites were hyper- or hypomethylated in T1D at baseline compared to NDC (Supplementary Fig. S2). On the other hand, after BCG vaccination, the epigenetics tended tobe hypomethylated at year 1 and 2, and the trend became pronounced at year 3 (Supplementary Fig. S2). Thus,
BCG microbe seems to demethylate the many of the immune related CpG sites in T1D CD4 + T cells and thispatterning was not associated with over-methylation DNA trends with 3 years of monitoring.
T1Ds have excessive methylation of the Foxp3 gene; this over methylation defect is correctedby multi‑dose BCG therapy at most of the gene’s CpG sites. Foxp3 is arguably the most important

marker gene for Treg cells. It is well-known from flow cytometry studies that Treg cells have a CD4 +, ­FOXP3high,­CD25high phenotype, and these cells might show improvements in overall cell numbers or potency early after
BCG ­vaccinations12. Here we change the method of analysis to see if BCG might be mechanistically working
at the level of host DNA and changing the methylation patterns of CpG sites of the Foxp3 gene that controls
Treg potency and function. At baseline (before BCG introduction) we show that Foxp3 gene methylation isover-methylated in T1D DNA samples (n = 13) compared to non-diabetic control DNA samples (n = 8); this isconsistent with Tregs not having fully potent function (Fig. 2a). We also show that at year 3, the diabetic has lessoverall Foxp3 methylation than earlier, a pattern indicating a correction towards normal because the statisticallysignificant difference is eliminated (Fig. 2a).
We next examined the 22 CpG sites of the human Foxp3 gene and studied the patterns of change in type 1
diabetics compared to self at baseline prior to a BCG vaccination over the 3 year observation period (Fig. 2b).
Using the Infinium Methylation EPIC Bead Chip Kit, which includes 22 CGs for Foxp3, we isolated genomic
DNA (gDNA) from CD4 + T cells (n = 13 patients) and determined beta values for all Foxp3 CGs at baseline(before BCG treatment) and at yearly intervals after BCG therapy. The average difference in beta values for each
CpG are shown in the heatmap. Each CpG was color-coded separately. The heatmap shows that demethylationof several CG starts in year 2, and the majority of CGs are demethylated by year 3 (17 out of 22 CGs are coloredblue) (Fig. 2c).
In the graphs, the individual CpGs were averaged across the 13 T1D patients at each timepoint and thendisplayed as the average change in beta value as compared to baseline. The graphs show the progression of demethylation of the Foxp3 CGs over time. At year 1 only one CG was significantly different from baseline, whereas
the number of significant CGs increased to 5 by year 2 and to 17 by year 3; a total of 22 Foxp3 CpG sites werestudied. The great majority of CpGs show progressive demethylation. Demethylation of a gene usually results inincreased transcription of the gene and thus usually results in increased protein.

Type 1 diabetic subjects with early (< 20 years) versus later (> 20 years) age of onset have dif‑ferent trends in the rate of Foxp3 gene demethylation over the 3 year time course. Age of

onset of diabetes is often considered an indication of the aggressiveness of the autoimmune disease. Indeed thepancreatic insulin cell rate of decline from the autoimmune attack is faster if the age of onset is early; if type 1
diabetes occurs at later times in life the destruction of the pancreas occurs at a much slower rate, often measured by the decline in C-peptide30. Here we define early onset diabetes as childhood diabetes with an age of
onset < 20 years (n = 5, Fig. 3a); older onset is defined as disease onset > 20 years of age (n = 8, Fig. 3b). Although
the sample sizes of these groups were small, trends were observed when the two groups were directly compared(Fig. 3c). At year 1, the demethylation patterns were statistically different between early and late onset with early
onset diabetic subjects showing faster demethylation (p = 0.009). At year 2 the demethylation patters were again
statistically different with the early onset diabetic subjects showing faster Foxp3 gene demethylation (p = 0.0005)
but by year 3 both clinical subjects with type 1 diabetics but varying is the disease onset had achieved equal
demethylation of the Foxp3 gene (p = 0.64).

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Figure 1.  Most Treg signature genes gradually demethylate over a three year time course after in vivo BCG vaccinations. Methylation
changes in eleven well-known Treg signature genes were quantified yearly after multi-dose BCG vaccine therapy (n = 13 type 1
diabetic subjects). (a) The methylation values are current methylation values at year 1, year 2 or year 3 compared to baseline expressed
as average change in beta values. Positive beta values indicate increased methylation and thus decreased gene expression; negative
methylation values indicate demethylation and increased gene expression. Significant decreases in methylation after BCG were seenin eight of 11 genes: FOXP3, CD25, IKZF2, IKZF4, CTLA4, TNFR2, CD62L, CD45 and IL2 (p value < 0.05: *; p value < 0.01: **; pvalue < 0.001: ***). Red bars represent statistically significant change in methylation. (b) The left heatmap shows average difference inbeta values for individual Illumina CG Identifiers (CGs) of the Treg signature genes at yearly intervals. Increased blue coloration in theheat map means increased demethylation of the CpG sites within a Treg signature gene. The right heatmap indicates the p-values in thecorresponding CpG sites; Blank and red indicate without or with significance, respectively, and degree of red color reflects the strengthof significance. In the both heatmaps each individual CpG gene site is represented as a single bar.

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CD25, a central gene in Treg suppression shows 3 years of gradual CpG demethylation acrossthe majority of its CpG sites.. In addition to ­Foxp3high , Tregs also feature a ­CD25high phenotype with highsurface expression in potent Tregs. A high phenotype means by flow cytometric studies the density and thus theoverall expression of the CD25 protein is high. CD25 is also known as IL2R (IL2 receptor). We therefore analyzed changes in methylation of the CpGs of the CD25 gene (Fig. 4). As shown for the Foxp3 gene, we also foundprogressive demethylation of CD25 CpGs over time, with the greatest demethylation differences vs baseline
occurring by year 3 (29 out of 39 CpG demethylated at year 3, out of which 22 reached statistical significance.
This is also reflected in the heatmap (Fig. 4b). The data shows that the greatest number of demethylated CpGs of
CD25 are present at year 3, i.e., most CpG sites at year 3 have a dark blue color.

CTLA4 gene after BCG treatment shows early demethylation and de‑methylation of a CpG
site associated with diabetes risk. We also looked in detail at the methylation states of CTLA4 CpGs

(Fig. 5). In contrast to Foxp3 and CD25, for which demethylation happened gradually, peaking at year 3, thedemethylation of CTLA4 CpGs is mostly complete by year 1. CTLA4-cg22572158 is of particular interest
because it is known to be associated with diabetes r­ isk31 and it is worth noting that this particular CpG was overmethylated at baseline as compared to Non-Diabetic Controls (Fig. 5c) and then gradually demethylated during
BCG therapy, with the largest demethylation in year 3 (Fig. 5d). This might suggest this at risk marker of type 1
diabetes is controlled by over-methylation and corrected here in vivo by BCG therapy.

BCG treatment activates Tregs in vivo, following CD45 Treg activation, CD45RA to CD45RO
conversion. We next asked whether the Tregs in vivo were activated from a naïve state after BCG treatment.

Prior published data have shown both by phenotyping for both Foxp3 and for CD25 surface markers and byfunctional in vitro studies, Tregs are more activated after in vivo BCG therapy and data suggests this frequently
uses the TNFR2 receptor (TNFRSF2)29. It is known that in CD4 + T cells the CD45 surface marker flips from
CD45RA in naïve CD4 + T cells to CD45RO in mature CD4 + T cells (Fig. 6a). In Type 1 diabetes a baseline defectin the CD4 + T cells, including the poorly potent Tregs is too many immature CD45RA c­ ells27,28. The CD45 geneis significantly demethylated very early after BCG therapy (year 1) and continues to be demethylation throughyear 3 (Fig. 1). The demethylation of the CD45 gene is broadly apparent at many CpG sites (Fig. 6b,c).
Another method to confirm that the demethylation of CD45 gene was associated with increased protein
expression is to perform flow cytometric studies. In this case, CD45RA naïve Treg cells convert to CD45RO
mature cells; we therefore performed flow cytometry to observe the possible disappearance of the naïve T cellmarker with the time course of BCG therapy over a 3 year observation period (Fig. 6d). A reduction in CD45RA
is equivalent to an increase in CD45RO. Similar to the demethylation data, the flow data showed the continuous loss of CD45RA marker, measured at the mean florescent intensity (MFI), over the 3 years of monitoring of
BCG-treated compared to untreated placebo T1Ds (Fig. 6d). Student’s T-test over the whole time period showsthat the overall difference is statistically significant (unpaired, 2-tailed p = 0.003). For this marker and with thisanalysis at the protein level, as early as 8 weeks after the first BCG vaccination, protein changes for this marker
could be observed (Visit 2) (p = 0.05).

Other Treg genes to consider.

Other Treg genes in a statistically significant manner de-methylated within vivo BCG vaccine therapy in type 1 diabetic subjects. This data is shown in heat map format for TNFR2,IKZF4, IKZF2; these genes were part of the original Treg signature genes studied in this paper (Fig. 1). Some
Treg studies also investigate additional genes such as ICOS, CCR7, CD28, CD127 and SLAMF1 as important for
Treg biology; these genes also de-methylated over the 3 year time course (Supplementary Figs. S3–S5). Three
Treg signature genes studied in detail in this paper did not de-methylate (TNFRSF18, CD62L, Fas) over the
3 year observation period and their heat maps of individual CpG regions are shown (Supplementary Fig. S6).
Also some Treg biology focus on CCR5, CCR6 and CXCR3 and these genes also overall did not show de-methylation patterns after in vivo BCG vaccinations (Supplementary Figs. S3, S6).

BCG vaccination impacts mRNA transcripts of Treg genes, a pattern consistent with thede‑methylation trends of the same Treg genes. Next, we accessed if BCG vaccinations also affected

the mRNA transcripts in the Treg genes that were being demethylated by the introduction of the BCG microbe.
While most genes except for IL-2 showed subtle changes at year 1, their transcriptions were clearly upregulated
at year 2 (Fig. 7a,b and Supplementary Table). FOXP3, CD25, IZF4, TNFR2 and CD45, all demethylated at year
2 and/or 3, and all exhibited increased mRNA expressions at similar serial time points. The TNFSFR18, CD62L
and FAS genes did not show significant demethylation by BCG and did not show mRNA changes. Other Treg
genes such as ICOS, CCR6, CD28, and SLAMF1 all promoted their transcript levels at year 2 and/or 3 (Fig. 7a,b
and Supplementary Fig. S7). Thus, the majority of Treg genes showed increased mRNA expression patterns after
BCG, corresponding to the demethylation by the microbe.
We also investigated the change of methylation in the TSDR and subsequent change in Treg population by
BCG vaccinations. Stable Foxp3 expression is associated with epigenetic modification of this TSDR associatedwith more sable Foxp3 gene expression and more long lasting Treg stability. This DNA region was not part of the
Human Infinium Methylation EPIC Bead Chip of DNA sites and therefore required separate study. The TSDR siteat baseline in T1D patients showed 83.3 ± 5.5%. After BCG vaccinations, the average methylations were reduced
to 80.8 ± 3.6% at 8 weeks and 65.5 ± 14.4% at 58 weeks (Fig. 7c). This increasing demethylation pattern is clear
when compared between the percentage at baseline and those at 58 weeks (p = 0.052). Furthermore, we evaluatedthe Treg population in these vaccinated patients by flow cytometry. Figure 7d shows the representative gating
strategies. We found that average Treg population were significantly increasing in number at 58 weeks compared
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www.nature.com/scientificreports/◂Figure 2.  Baseline over methylation of the Foxp3 gene of Treg cells compared to controls; BCG therapy

gradually restores towards normal methylation patterns over three years. (a) At baseline, prior to BCG
treatment, type 1 diabetic subjects (n = 13) have over methylation of the Foxp3 Treg gene compared to nondiabetic control subjects (n = 8) suggesting inadequate Treg mediated suppression, a trait of autoimmunity(p = 0.03). At three year follow-up after multi-dosing with BCG therapy, the type 1 diabetic subjects’ overmethylation defect in the Foxp3 gene corrects towards normal and is no longer statistically different from thedegree of methylation in non-diabetic control cells (p = 0.39). Data is expressed as beta values of the difference
between diabetic and control methylation. The data for each CpG is color coded separately (p value < 0.05: *;p value < 0.01: **; p value < 0.001: ***). Red bars represent statistically significant change in methylation. (b)Methylation changes for the individual CpG sites of Foxp3 gene after BCG were quantified compared to self atbaseline. Average yearly changes in Beta Values of Foxp3 CpG sites in CD4 + cells of patients given BCG (n = 13).
Beta value differences were calculated for each patient relative to baseline. (c) The left heatmap shows average
difference in beta values for individual Foxp3 CpGs sites at yearly intervals. Increased blue coloration in theheatmap means increased demethylation of the CpG sites within the Foxp3 gene. The right heatmap indicatesthe p-values in the corresponding CpG sites; Blank and red indicate without or with significance, respectively,
and degree of red color reflects the strength of significance.

to those at baseline (4.4 ± 1.1 vs. 6.3 ± 1.4%, p = 0.01, Fig. 7e). Taken together, these results suggested that BCG
vaccination can demethylate many Treg-related CpG sites including the controlling TSDR for long-term andlong term Treg induction in T1D patients.

Discussion

Treg cells are of central important to immune balance. Restoration or promotion of Treg activity could havemeaningful clinical applications for combating autoimmune diseases or inflammatory diseases. One of thechallenges of Treg induction or treatment is the chronicity of some diseases such as autoimmune diseases, sotherapies need durability and safety. In this study we analyze the possible long-term induction of Tregs in humans
with autoimmune diabetes treated with multi-dose BCG therapy. We mechanistically tested the assumption that
BCG, like tuberculosis itself and many other chronic infections, promotes Treg expansion on a chronic basis.
Furthermore we tested the mechanism of action of BCG on Treg induction at the level of demethylation of key
Treg signature genes. We show a gradual and a 3-year time course of BCG vaccine therapy demethylating thesignature Treg genes known to be associated with Treg potency. The specific genes studied in this study were
Foxp3, TNFSF18, CD25, IKZF2, IKZF4, CTLA4, TNFR2, CD62L, FAS, CD45 and IL2. Foxp3, one of the mostessential Treg genes was central to the BCG effect. The Foxp3 gene was over methylated at baseline in type 1
diabetes and over 3 years became demethylated to a level close to non-diabetic controls. The epigenetic effectsof the BCG vaccination reported here at the level of DNA of key genes were supported as functionally importantsince the directional changes were reflected with increased overall mRNA and increased numbers of Treg cells.
Clinically and as previously reported, type 1 diabetic subjects respond to repeat BCG vaccinations by a clinically meaningful drop in HbA1c v­ alues12 (Supplementary Fig. S8). This multi-level data in total supports a roleof Treg induction as a possible treatment for the previously observed beneficial effects of this vaccine in clinical
trials in autoimmunity.
Although BCG was originally developed as a vaccine for tuberculosis prevention, current clinical trials areusing BCG therapy across a broad array of human disease. Not only are there advanced clinical trials with BCG
in autoimmune diabetes, but there are also Phase III clinical trials in multiple sclerosis and early stage trial in­allergies6–15. Epidemiology data also suggests that the unwanted inflammation of Alzheimer’s disease may be
responsive to BCG vaccination in adulthood; clinical trials have just b­ egun32. Trials using BCG vaccines tolessen the impact of COVID-19 on mortality and morbidity are also underway (clinicaltrial.gov). These ongoing
infectious disease trials are based on nearly 10 years of clinical trial data showing BCG not only boosts immune
resistance to tuberculosis but also boosts immunity to unrelated infections such as viral respiratory infections,parasites, yellow fever and m­ alaria33–38. It is known that BCG vaccines certainly have an impact of immediatecytokine responses and have a strong influence on innate immunity and biological pathways in monocytes aspart of the acute innate immune response. For protection against infectious diseases, the time course has beensuggested to be short. The innate immune response triggered by BCG, which includes cytokine regulation andinnate immunity in monocytes, would be expected to be rapid and to confer broad protection against infections.
It is of interest mechanistically that the effects of BCG on autoimmunity take years to manifest clinically, takeyears to manifest in the T cell compartment, and are durable and gradual at the DNA epigenetic level. Our new
data suggests the time course of an adaptive T cell role contrasts with the acute innate role for infection protection from BCG administration.
The data here substantiates a growing view that the impact of BCG vaccines extends to the adaptive immune
response. T cells are part of the adaptive immune response. The effects on Treg biology are barely visible formany genes at early evaluation time points (one year) in contrast to genes involved in innate immune responses.
Murine and even human data suggest that years after BCG vaccines the bacterium can impact bone marrow, sitesof mesenchymal stem c­ ells21,39. The reset of stem cells in the primary lymphoid organs suggests adaptive immunity maybe a more central immune mechanism. The theory of a systemic reset of the immune system certainly
correlates with autoimmune disease treated with BCG therapy; consistent clinical data shows it takes years formeaningful clinical e­ ffects10,12. This is not the time course for a sole innate immune response from peripheral
exposures. Many would argue that the global rise in autoimmune disease is from too little microbial exposureswith our clean modern societies, the well-known Hygiene H­ ypothesis40.

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Figure 3.  Younger Age of onset of diabetic subjects appear to show a different rates of demethylation at the
CpG sites of Foxp3 gene. Type 1 diabetic subjects with a young age of onset (AOO) are considered to have amore serious and aggressive disease with rapid destruction of the pancreatic insulin secreting cells often definedas AOO < 20 years (a, left column, n = 5). Type 1 diabetic subjects with an older age of onset (AOO > 20) years
are considered to have a more indolent disease with onset at older ages (b, right column, n = 8). This in theclinic correlates with a faster lowering of blood sugars after BCG t­ herapy10. Both groups of type 1 diabetics withvarying ages of onset had a BCG response as related to Foxp3 demethylation, but early AOO type diabetics(AOO < 20) had an apparent faster responding trend over the 3 year monitoring time period. c. Averagechange in beta value of Foxp3-related CpG sites are shown at AOO < 20 and 20 <. At year 1, the demethylation
patterns were statistically different between early and late onset with early onset diabetic subjects showing faster
demethylation (p = 0.009). At year 2 the demethylation patters were again statistically different with the early
onset diabetic subjects showing faster Foxp3 gene demethylation (p = 0.0005) but by year 3 both clinical subjects
with type 1 diabetics but varying is the disease onset had achieved equal demethylation of the Foxp3 gene(p = 0.64). Right column indicates the difference in beta value between AOO < 20 and 20 <. Red bars represent
statistically significant changes in methylation vs baseline for individual CpGs.
Aggressive type 1 diabetes with rapid pancreatic islet destruction occurs in children with early age of onset;
a more indolent form of diabetes occurs with age of onset in adulthood, still with insulin dependence and­autoantibodies30. If the synergy with microorganisms could change this time course, this study shows that early
onset diabetics has time-wise a much more rapid response to BCG in terms of Treg demethylation than the
more indolent form of autoimmune diabetes in older adults. This is the trend observed in this study. Previously
published data suggests the age of onset of diabetes may be influenced by the timing and the number of child
BCG ­vaccinations41. The role of epigenetics in T1D is recognized as a reason why identical twins can have lessthan 50% concordance of disease and select genes have been associated with hypermethylation in the affected
twin ­pair42,43. The presence of de-methylation with BCG and the time course of Treg demethylation in this study
is consistent with these observations and mechanisms. Most autoimmune disease show discordance in twinpairs suggestive of a supportive role of epigenetics in human autoimmune d­ iseases44. Also the value of microbe
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Figure 4.  The Treg associated CD25 gene (IL2 receptor) of potent Treg cells after in vivo BCG treatment shows
gradual, significant, and a three year decreases in methylation. (a) Methylation changes for the individual
CpG sites (n = 39) of the CD25 gene after BCG vaccine therapy were quantified compared to baseline. Average
yearly changes in Beta values of CD25 CpG sites is shown at Year 1, Year 2 and Year 3 for multi-dosed type1 diabetic subjects (n = 13). Beta value differences were calculated for each patient relative to baseline. The
data for each CpG is color coded separately (p value < 0.05: *; p value < 0.01: **; p value < 0.001: ***). Red bars
represent statistically significant change in methylation. (b) The heat map shows average difference in Beta
values for individual CD25 CpGs sites at yearly intervals. Increased blue coloration in the heatmap meansincreased demethylation of the CpG sites within the CD25 gene. The right heatmap indicates the p-values in thecorresponding CpG sites; Blank and red indicate without or with significance, respectively, and degree of redcolor reflects the strength of significance.
exposures through vaccinations might be controlled in part by epigenetics and these studies extend beyond the
BCG microbe effect to other various vaccines and infection controlling the host immune ­system45.
Both innate and adaptive immunity can involve the impact of microorganism on protection through epigenetic imprinting, likely with very different gene sets for the acquired resistance and also the immunologic
memory of both processed. The innate impact on memory was first demonstrated in insects and plants; systemicacquired resistance translated to protection with reinfection and was at the level of e­ pigenetics46,47. It is now
well-established for the acute effects of the BCG vaccinations in mice and humans. Often studies using LPS(lipopolysaccharide) for gram negative bacteria show immunity can occur for days and weeks and involves
epigenetics. This also related to innate immunity working through NOD1 medicated s­ ignaling48. Short term
3 months effects of BCG are also associated with epigenetic reprogramming on innate immunity (monocytes) at
the level of histones such as H3K4 methylation p­ atterns49. The time course we see here on Treg biology is gradualand sustained, but gradually increasing over the passage of time with a time frame of years.
With the recognition that Treg cells can be potent suppressor cells of the immune response, diverse approaches
have been tried in the clinic to achieve enhanced Treg effects in vivo therapeutic efficacy. No specific Treg
therapy has been approved by a regulatory authority. Human autoimmune diseases could benefit. Studies haveinvestigated the feasibility of using Treg expansion in vitro from autologous cells followed by re-infusion of
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www.nature.com/scientificreports/◂Figure 5.  The Treg associated CTLA4 gene of potent Treg cells after in vivo BCG treatment shows gradual,

significant and multi-year decrease in methylation after multi-dose BCG therapy. (a) Methylation changes
for the individual CpG sites of the CTLA4 gene after BCG vaccine therapy were quantified compared tobaseline. Average yearly changes in Beta values of CTLA4 CpG sites is shown at Year 1, Year 2 and Year 3 formulti-dosed type 1 diabetic subjects (n = 13). Beta value differences were calculated for each patient relative tobaseline. (b) The heat map shows average difference in Beta values for individual CTLA4 CpGs sites at yearlyintervals. Increased blue coloration in the heatmap means increased demethylation of the CpG sites within the
CTLA4 gene. The data for each CpG is color coded separately. The right heatmap indicates the p-values in thecorresponding CpG sites; Blank and red indicate without or with significance, respectively, and degree of redcolor reflects the strength of significance. (c) Comparison of Beta values for CD4 + T cells of T1D at baseline
versus NDC for the CTLA4 CpG site cg22572158 (known to be associated with diabetes risk) shows that the
T1D is over-methylated as compared to control (p = 0.005; unpaired, 2-tailed; T1D n = 13 and NDC n = 8). (d)Comparison of Beta values for cg22572158 in T1D CD4 + T cells (n = 13) after treatment with BCG over 3 years
time. Asterisks indicate level of significance: p value < 0.05: *; p value < 0.01: **; p value < 0.001: ***. Red bars
represent statistically significant change in methylation.

the expanded ­Tregs50. These adoptive Treg therapy experiments establish the high desirability of disease statesneeding more Tregs but also present the challenges of expansion with enough autologous Tregs for a treatment,i.e., durability and potency of cell populations after transfer are also a factor. Direct in vivo approaches include
the IL2 ligand that works through the CD25 receptor or TNF that works through the TNFR1 and TNFR2 receptors for Treg e­ xpansion51,52. The challenge with IL2 or TNF ligand therapy are the short in vivo half-life (­ T1/2) ofthese small proteins and these ligands require careful dosing to prevent t­ oxicity49,50. In mouse studies, TNFRp75
deficiency improves mouse survival after tuberculosis exposures and is associated with increased control ofbacterial growth during acute and chronic stages of ­infection53. Lack of the TNFR2 receptor would prevent theorganism from inducing the most potent human Tregs. It is known TNFR2 expressing Tregs are highly suppressive, consistent with BCG vaccines in vivo also inducing this ­protein54. At baseline, the Treg activation defect,
which is found in type 1 diabetes, can be corrected in vitro with TNFR2 a­ gonism29. In this current study, by year
3, the TNFR2 receptor is also demethylated with BCG vaccine introduction. Future approaches to inducing Tregs
in vivo could be by agonistic antibodies to the TNFR2 r­ eceptor54,55.

Conclusion

We show multi-dose BCG vaccinations result in systemic long-term induction of potent Tregs in humans withan underlying autoimmune disease. The clinical time course of BCG-related improvements (2–3 years postvaccination) correlates with the slow Treg induction through epigenetic modifications. This was manifest here bya general pattern of demethylation of key signature genes of an immunosuppressive Treg cell. In autoimmunity
this represents a restoration of Treg biology to near normal expression levels thus demonstrating microorganism
synergy and a relatively simple albeit slow and steady approach to the restoration of Treg function in humans. Thisdata supports a role for BCG host microbe interactions on adaptive immunity using methylation mechanisms
and beneficial and sustained microbe modifications of host Treg cells.

Methods

Research study participants. All human studies had full institutional approvals through Human ResearchCommittee/Institutional Review Board at Partners Healthcare which reviews all protocols for the Massachusetts
General Hospital (Study# 2007P001347, 2012P002243 and 2013P002633)12,20. The BCG interventional studies
were also formally approved by the FDA (IND#2007P001347 and IND#2013P16434). All blood donors, both
T1D and non-diabetic control subjects consented through Study #2001P001379. Informed consent was obtained
from all subjects and the experiments conformed to the principles set out in the WMA Declaration of Helsinki
and the Department of Health and Human Services Belmont Report. The timing of the serial blood samplingtimes for visits were screening (pre-BCG vaccine), year 1, year 2 and year 3. This study included 13 open-label
T1D subjects with 20 years of disease duration. All BCG vaccine treated subjects participated in open-label
studies or were unblinded by their participation in earlier Phase I clinical trials. Patients were given two initialintradermal BCG vaccinations at baseline and a second vaccine four weeks later. Non-diabetic control subjects(n = 8) were also studied and compare against T1D patients. The control subjects had a mean age of 26 years. The
T1D have a mean age of 45 years and age of disease onset of 24 years.

Isolation of CD4 + T cells. Blood from BCG treated T1D patients was collected in purple top tubes­(K2EDTA anti-coagulant) and CD4 + T cells were isolated using magnetic EasySep Direct Human CD4 + T Cell
isolation kits from Stem Cell Technologies (Vancouver, BC, Canada, Cat# 19662), following the instructionsof the manufacturer as previously ­described12,20,25. Briefly, 1200 μL of Isolation Cocktail and 1200 μL of Rapid
Spheres were mixed with 24 mL of whole blood in a 50 mL centrifuge tube and incubated for 5 min at room
temperature. PBS (24 mL) was then added and the tube mixed and placed into an “Easy 50” magnet (Stem Cell
Technologies, Cat# 18001). This immobilized the unwanted non-CD4 cells at the side of the tube. After 10 min,the CD4-enriched cell suspension was then transferred into a new tube and the magnetic separation processrepeated for 5 min with 1200 uL of fresh Rapid Spheres. The cell suspension was then transferred into a newtube and purified for a third time using the magnet. The resulting final CD4 + T cell preparations had puritiesof > 90% (Supplementary Fig. S1).

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www.nature.com/scientificreports/◂Figure 6.  The Treg-associated CD45 gene of activated Treg cells gradually shows decreased methylation after

multi-dose BCG vaccine therapy. (a) The CD45 protein in naive T cells is the CD45RA splice variant and the
CD45RO protein is the splice variant of mature Tregs. (b) Methylation changes for the individual CpG sites(n = 34) of the CD45 gene after BCG vaccine therapy are shown as difference in beta values over the threeyear time course after multi-dose BCG therapy. Data at baseline shows the beginning state of methylation andthen the methylation patterns at year 1, year 2 and year 3. The data for each CpG is color coded separately(p value < 0.05: *; p value < 0.01: **; p value < 0.001: ***). Red bars represent statistically significant change inmethylation. (c) The left heatmap shows average difference in Beta values for individual CD25 CpGs sites atyearly intervals. Increased blue coloration in the heatmap means increased demethylation of the CpG sites
within the CD45 gene. The right heatmap indicates the p-values in the corresponding CpG sites; Blank and red
indicate without or with significance, respectively, and degree of red color reflects the strength of significance.
(d) It is common practice to monitor Treg maturation using flow cytometric methods and with antibodies
specific to only CD45RA or CD45RO proteins. A longitudinal flow cytometer phenotyping of the cell surface
density of naïve CD45RA proteins on CD4 + T cells with APC-CD45RA antibody was performed. The datashows that with BCG therapy (red line) the naïve CD4 + Tregs (CD45RA) cells gradually decreased while theuntreated diabetic CD4 T cells placebo had CD45RA naive cells (blue line) that did not decrease over the 3 year
monitoring period. At the end of year 3 of monitoring, BCG treated patients have consistently lower CD45RA
mean fluoresce intensity (MFI) as compared to placebo which means that the naive cells are decreasing with
CD45 cellular maturation. Student’s T-testing over the whole time period shows that the overall difference isstatistically significant (unpaired, 2-tailed p = 0.003).

Phenotyping using flow cytometry. Isolated CD4 + T cells were washed in PBS and resuspended in
PBS with 2% FBS at a density of 3 × ­105 cells/100 μL. The cells were then stained with fluorescent antibodies
against surface markers (CD25, CD45RA), washed and fixed and permeabilized for intracellular staining of
FoxP3 (BioLegend FoxP3 Fix/Perm Buffer Set, Cat# 421403)25. The cells were then analyzed on a FacsCalibur
Flow Cytometer (BD Biosciences, San Jose, CA). Antibodies used were PE anti-human CD25 (Biolegend, Clone
BC96, Cat# 302606), Alexa Fluor 488 anti-human FoxP3 (BioLegend, Clone 259D, Cat# 320212) and APC antihuman CD45RA (BD Pharmingen, Clone HI100, Cat# 550855).
Methylation studies. BCG-treated T1D subjects were studied at several time points before and after BCG
administrations for changes in methylation of their peripheral blood CD4 + T cells as described ­previously20.
These T1D subjects had blood drawn for CD4 + T cells and DNA prepared and stored at − 80 °C. Methylation
analysis was performed by the Partners Personalized Medicine Translational Genomics Core. For each sample, 1 μg of DNA was bisulfite-converted using the EZ-96 DNA Methylation Kit (Shallow Well Format) (Zymo
Research Corporation, Irvine, CA) following the manufacturer’s protocol with modifications in incubation conditions as recommended for the Infinium Methylation Assay. To verify the quality of bisulfite conversion assay,
standard PCR of the highly methylated P19 gene was performed using primers targeting known methylationsites. These primers will anneal to these sites only if the bisulfite conversion is successful. The primers for the
methylated targets are: Bisulfite_QC.F (CTA​AAA​CCC​CAA​CTA​CCT​AAA) and Bisulfite_QC.R (TAG​GTT​TTT​TAG​GAA​GGA​GAG). PCR cycling conditions are 95 °C for 15 min, 5 cycles of 95 °C for 30 s, 60 °C for 2 min,
72 °C for 1 min, 30 cycles (of 95 °C for 30 s, 65 °C for 1 min, 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 bp indicated successful conversion.
The methylation assay was then performed on the Human Infinium Methylation EPIC Bead Chip Kit (Illumina, San Diego, CA) using 200–400 ng of bisulfite- converted DNA product for each sample according to theInfinium HD Methylation Assay protocol (Illumina, San Diego, CA). This assay targets over 850,000 known
and potential methylation sites across the genome via 50-mer probes attached to the Infinium Bead Chips. The
bisulfite converted DNA was hybridized to the probes on the Bead Chip followed by a single base extension toincorporate a fluorescent-labeled nucleotide (Cy3 or Cy5) at the target position to differentiate methylation status.
The Bead Chips were then imaged on the Illumina iScan reader and the methylation level of each CG locus wascalculated in the Genome Studio Methylation module as methylation beta value (beta value = intensity of themethylated allele (M)/(intensity of the unmethylated allele (U) + intensity of the methylated allele (M) + 100).
The Illumina CG Database Identifiers (CGs) and corresponding beta values for various genes were then filteredout using fgrep and analyzed using Excel and R.
Prior to settling on the Human Infinium Methylation EPIC Bead Chip, pilot studies were performed comparing various DNA based methods to quantify Treg cells through methylation methods. Those methods included
MS-PCR (methylation specific PCR), MS-HRM (methylation specific high resolution melting), pyrosequencing(pyro) and targeted next generation sequencing. All DNA methods of quantifying methylation were comparedto the traditional methods of studying Tregs using flow cytometry. All the above methods in our studies wereinferior (less quantitative) to flow or to the Human Infinium Methylation EPIC Chip for most DNA sites, so themajority of the data presented in this study only used the Illumina technology. Since the Human Infinium Methylation Epic Bead Chip did not contain the DNA for the TSDR region of the Foxp3 gene, both pyrosequencing
and NGBS methods were used to assess methylation of this region (EpigenDx, Hopkinton, MA). With these site
specific methylation studies of the TSDR region we used human Foxp3 intron 1 TXDR region (CNS2), throughtranscript ID; ENST000000376207 with these primers i.e. − 2330 to − 2263 from ATG and + 3997 to + 4064 from
TSS, ChrX:49260835–49260768.

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www.nature.com/scientificreports/◂Figure 7.  The change in mRNA expression and Treg population by BCG vaccination; direct data on the TSDR

region of the Foxp3 gene and quantification of Foxp3 Treg cells. (a) The change in transcripts in CD4 + T cellby BCG was evaluated with mRNA seq. While no prominent changes were shown at year 1, most of Treg genes
including FOXP3, TNFRSF18, CD25, IKZF4, CTLA4, FAS and CD45 were significantly upregulated at year 2
and year 3. Meanwhile, IKZF2, CD62L and IL2 showed upregulated mRNA expressions without significant.
(b) The left heatmap indicate overall change in mRNA expression after BCG vaccination in each Treg gene.
Red and green indicate up- and down-regulation in mRNA, respectively. (c) The average change in methylation(%) in Treg specific demethylated region (TSDR) was shown at baseline, 8 and 58 weeks after BCG vaccination(T1D, n = 4). While the ratio was 83.3 ± 5.5% at baseline, 80.8 ± 3.4% at 8 weeks and 65.5 ± 14.4% at 58 weeks(at 58 weeks, vs. baseline, p = 0.052; vs. 8 weeks, p = 0.08; one-tailed, paired t-test). (d) The representative gating
images in flow cytometry are shown. Images of unstained, from one patient at baseline and 58 weeks after BCG
vaccination are displayed in right, middle and right, respectively. (e) The percentage of Treg population was
analyzed by flowcytometry. The individual data points for Treg frequency pre- and post-therapy were shown(n = 4). While the average ratio was 4.4 ± 1.1% at baseline, it was 6.3 ± 1.4% at 58 weeks (p = 0.01, two-tailed,paired t-test).

Although many genes contribute to the potent Treg phenotype with these cells having maximal suppression, inthe human we chose these 11 signature genes to study in this paper. These genes were chosen from the publishedliterature as representing “universal” signature genes of human T­ regs56,57.
To obtain a core set of Treg signature genes, genes from four sets of data were combined to represent characteristic Treg genes. Those four information sets were: Marodon, G. The meta-Treg signature generated in silico
is centered on IL2, members of the TNF superfamily and the endogenous opioid pathway bioRxiv (2019) https://​doi.​org/​10.​1101/​638072, Pesenacker et al. A regulatory T cell gene signature is a specific and sensitive biomarker
to identify children with new onset type 1 diabetes, Diabetes (2019), IPA (Ingenuity Pathway Analysis—Qiagen
Digital Analysis database of T cell receptor signaling pathway for Tregs, Okubo, Y et al. Homogeneous expansionof human Treg cells via TNFR2, Scientific Reports (2013). The overlapping data set was Foxp3, TNFRSF18, CD25,
IKZF2, IKZF4, CTLA4, TNFR2, CD62L, FAS, CD45 and IL2. Although not part of this complete overlapping
signature gene set, some additional Treg associated genes were also present as supplemental information: ICOS,
CCR7, CD28, CD127, CXCR3, CCR6, CCR5, CD62L since these genes were listed in > 2 references.

Data analysis in methylation. Beta values were used to directly quantify methylation levels at each CG

site as previously ­described20. Changes in methylation were calculated for each BCG treated patient using beta
values on an individual CG level relative to baseline. These individual patient differences in methylation werethen averaged at yearly time point after initial BCG doses. These yearly average changes at individual CpG sites
were then averaged for each Treg associated gene to quantify the overall yearly methylation changes after BCG.
This analysis was also repeated after separating the patients according to diabetes age of onset (AOO) in which
patients with an AOO below 20 years old (n = 5) and patients with an AOO above 20 years old (n = 8) wereplaced in separated groups. Foxp3 was studied in two ways. Type 1 diabetics compared to non-diabetic controls
at baseline prior to any treatment and then like other critical Treg genes as a comparison to self over the 3 year
observation period. The individual CpG analysis was also conducted by separated the diabetic patients according to AOO as mentioned.

Data analysis in RNA sequencing. As previously described, Total RNA was extracted from fleshly iso-lated CD4 + T cells using the RNeasy Plus Mini Kit (QIAGEN), and RNA sequencing was performed at the
BioMicro Center (BMC) of the Massachusetts Institute of Technology and the Center for Cancer Computational Biology (CCCB) of the Dana-Farber Cancer Institute (DFCI)12,20. The quality of the extracted mRNA wasdetermined with an Agilent 2100 bioanalyzer. The poly A selection was performed with a NEBNext Poly(A)mRNA Magnetic Isolation Module and the extracted mRNA was used for library preparation with the NEBNext
UltraTM Directional RNA Library Prep Kit for Illumina. The RNAseq libraries were run on a high sensitivity
DNA chip on the Agilent 2100 Bioanalyzer and the final concentration of 2 pM of libraries were loaded onto theIllumina NextSeq 500. TopHat was used for alignment of reads against reference genome HG19. Cufflinks wasused for the analyses and the data then was normalized with DESeq (part of the R, Bioconductor package). The
resulting raw data were also 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 prior to the analyses. Then the normalized value was compared and exhibited as the percent
change between NDC and T1D at baseline or as T1D at baseline and 1–3 year after BCG vaccinations.

Received: 10 January 2021; Accepted: 13 July 2021

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Acknowledgements

This Treg study was supported in large part by The Alles Institute; The Iacocca Foundation supported the clinical trial.

Author contributions

W.K. and D.L.F. conceived and designed the study. L.T. and R.K. performed experiments from blood isolations extending to cell sorting, with phenotypical assays and DNA preparation methods. W.K. spearheaded thepreliminary studies on the choice of analysis methods. W.K. H.T. and R.K. analyzed the epigenetic data. L.T.
analyzed the flow data. K.N., and N.N. recruited clinical trial subjects, performed human studies documentationand monitoring and performed patient visits for blood collections. H.T. did advanced data analysis, statistics andmanuscript redrafting. All reviewed and contributed to the assembly of the manuscript.

Competing interests

The authors declare no competing interests.

Additional information

Supplementary Information The online version contains supplementary material available at https://​doi.​org/​
10.​1038/​s41598-​021-​94529-2.
Correspondence and requests for materials should be addressed to D.L.F.
Reprints and permissions information is available at www.nature.com/reprints.
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