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ArticleBCG Vaccinations Upregulate Myc, a Central
Switch for Improved Glucose Metabolism inDiabetes
Willem M.
Kühtreiber,
Hiroyuki
Takahashi, Ryan C.
Keefe, ..., Sarah M.
Sinton, Jessica C.
Graham, Denise L.
Faustmanfaustman@helix.mgh.harvard.
edu

HIGHLIGHTS
T1D has insufficientaerobic glycolysis; thiscauses insufficient sugar
utilization
BCG vaccine lowers blood
sugar levels in T1D byaugmenting aerobic
glycolysis
BCG-induced shift toaerobic glycolysis isassociated with Myc
activation
Host-microbe BCG
interactions through Myc
activate sugar-regulating
genes in T1DKühtreiber et al., iScience 23,101085
May 22, 2020 ª 2020 The
Authors.
https://doi.org/10.1016/
j.isci.2020.101085

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ArticleBCG Vaccinations Upregulate
Myc, a Central Switch for Improved
Glucose Metabolism in Diabetes
Willem M. Kühtreiber,1 Hiroyuki Takahashi,1 Ryan C. Keefe,1 Yaerin Song,1 Lisa Tran,1 Trevor G. Luck,1Gabriella Shpilsky,1 Louisa Moore,1 Sarah M. Sinton,1 Jessica C. Graham,1 and Denise L. Faustman1,2,*
SUMMARY

Myc has emerged as a pivotal transcription factor for four metabolic pathways:
aerobic glycolysis, glutaminolysis, polyamine synthesis, and HIF-1a/mTOR. Eachof these pathways accelerates the utilization of sugar. The BCG vaccine, a derivative of Mycobacteria-bovis, has been shown to trigger a long-term correction ofblood sugar levels to near normal in type 1 diabetics (T1D). Here we reveal theunderlying mechanisms behind this beneficial microbe-host interaction. We
show that baseline glucose transport is deficient in T1D monocytes but isimproved by BCG in vitro and in vivo. We then show, using RNAseq in monocytes
and CD4 T cells, that BCG treatment over 56 weeks in humans is associated withupregulation of Myc and activation of nearly two dozen Myc-target genes underlying the four metabolic pathways. This is the first documentation of BCG induction of Myc and its association with systemic blood sugar control in a chronic
disease like diabetes.
INTRODUCTION
Several metabolic pathways are central to aerobic glycolysis, the process of accelerated utilization of extracellular glucose instead of the less glucose-dependent process of oxidative phosphorylation (OXPHOS)
and the Krebs cycle. Myc, also known as c-Myc, is a family of regulator genes that code for transcription
factors that play a major intracellular role in glucose homeostasis (Lewis et al., 1997). Myc’s pivotal rolein the control of sugar metabolism first emerged as central target genes were identified in sugar transportand glycolysis, such as involvement of lactate dehydrogenase (LDH) (Ramanathan et al., 2005; Shim et al.,1997). Myc-regulated glycolysis utilizes lactate dehydrogenase A (LDHA), which converts pyruvate tolactate as part of the glycolytic pathway. This research evolved into the subsequent identification of 20 putative Myc target genes in the glycolysis pathway (Dang et al., 2009). Myc upregulates glucose transporter
GLUT1, hexokinase 2 (HK2), phosphofructokinase (PFKM), and enolase 1 (ENO1) (Osthus et al., 2000).
Through upregulation of these genes, Myc was effectively contributing heavily to the entire direct multistep
process of aerobic glycolysis, a high cellular glucose utilization state. It is now appreciated that Myc is socentral to aerobic glycolysis that nearly all genes encoding glycolytic enzymes are direct Myc targets andare under Myc control (Dang et al., 2006; Guo et al., 2000; Kim and Dang, 2006).
The metabolic pathways under Myc control include glycolysis, glutaminolysis, and polyamine synthesis (Leet al., 2012). Ornithine decarboxylase, which promotes polyamine synthesis, is a Myc trigger (Nilsson et al.,2005). Glutamine metabolism is a complementary pathway to glycolysis and coordinates increased aminoacids, lipids, and nucleotide biosynthesis while also providing energy to support these processes. Myc regulates glutaminolysis for cell metabolism at multiple levels of sequential gene induction. It is important tonote that Myc regulates both glycolysis and glutaminolysis. Polyamine synthesis is also under Myc control.
It has recently become appreciated that host-microbe interactions result in dramatic changes in cellular
metabolism often toward accelerated aerobic glycolysis. Some microorganisms utilize host toll-like receptor 4 (TLR4) or lipopolysaccharide (LPS) external stimuli to alter target cell metabolism toward glycolysis
with or without suppression of OXPHOS (Palsson-McDermott et al., 2015; Rodrı́guez-Prados et al., 2010;
Ruiz-Garcı́a et al., 2011; Tan et al., 2015). Other microorganisms appear to drive whole organisms toboth accelerated glycolysis and OXPHOS in human monocytes through TLR2 receptors, such as through1Immunobiology

Laboratories, Harvard
Medical School &Massachusetts General
Hospital, Building 149, 13thStreet, Rm 3602, Boston, MA
02116, USA
2Lead Contact*Correspondence:
faustman@helix.mgh.
harvard.edu
https://doi.org/10.1016/j.isci.
2020.101085

iScience 23, 101085, May 22, 2020 ª 2020 The Authors.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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P3C cell wall component exposures (Lachmandas et al., 2016). And, lastly, b-glucan, a fungal derivative,
uses the Dectin-1 receptor for host-induced glycolysis, increasing both glutamine and cholesterol synthesis. In general, many microorganisms or their cell wall components can promote a shift in host metabolism
to reliance on glucose as an energy source, a switch to aerobic glycolysis. As more data have accumulated it
has become apparent that not all microorganisms stimulate this switch to aerobic glycolysis through thesame host intracellular pathways (Everts et al., 2014; Kelly and O’Neill, 2015; Krawczyk et al., 2010; Rodrı́guez-Prados et al., 2010; Stienstra et al., 2017). It is important in the case of type 1 diabetes (T1D), where
improved sugar utilization is desired to understand the monocyte and T cell pathways that might beaugmented by beneficial reintroduction of microbes such as by vaccination with bacillus calmette guerin(BCG).
The introduction of BCG, a >100-year-old vaccine and a derivative of Mycobacterium bovis, given in two
doses to T1D adults resulted in the recovery of tight and long-lasting blood sugar control during an 8year long human study (Kühtreiber et al., 2018). The mechanism underlying this benefit was not only by increase in more beneficial immune cells such as suppressive regulatory T cells (Tregs) through changes in
BCG-controlled methylation patterns but also by a systemic restoration of proper glucose metabolism
through aerobic glycolysis (Kühtreiber et al., 2018). These findings were based on a double-blind randomized controlled clinical trial using two repeat BCG vaccines and tracking of patients for several years before
long-lasting changes in metabolism were sufficient to control blood sugars (Faustman et al., 2012). BCG
was found to shift cellular metabolism to optimize sugar energy sources, to use the pentose phosphateshunt, and to turn off OXPHOS (Kühtreiber et al., 2018). In tuberculosis, the pathologic version of the
BCG vaccine, these effects were observed in the lung tuberculosis granuloma in a murine model (Shiet al., 2015). With repeat BCG vaccinations via intradermal injection in humans a systemic shift occurs;
this shift can be monitored by sampling lymphocytes obtained from the peripheral blood for direct sugar
uptake rates. Global data suggest that many microorganisms that infect humans optimize energy metabolism by shifting the host to sugar metabolism from low sugar utilization energy states such as OXPHOS
to high glucose utilization states. High sugar utilization states appear to contribute to a meaningful contribution to glucose homeostasis.
Why do TID patients have underlying defects in glucose transport? They may be due to insufficient hostmicrobe interactions, a concept supported by the hygiene hypothesis (Kühtreiber and Faustman, 2019).
The hygiene hypothesis asserts that all forms of autoimmunity and allergies are more prevalent in culturesand societies with minimal bacterial interactions due to cleanliness, lack of well water as a source of mycobacteria, and movement from an agrarian culture to a ‘‘clean’’ suburban location (Bach, 2002; Strachan,1989). Indeed it has been noted that high tuberculosis mycobacteria burden in populations protects
against T1D and other autoimmune diseases (Airaghi and Tedeschi, 2006; Andersen et al., 1981). Also
dramatic shifts in autoimmune disease incidence can occur with changes in the environmental microbe
exposure, as evidenced by a 6-fold gradient in T1D burden between eastern Finland and nearby Russia
from recently separated populations (Kondrashova et al., 2005).
Recent in vitro data have begun to show how microorganisms reset host metabolism. This reset can occur at
the gene level through epigenetic imprinting, at the mRNA level, and ultimately by monitoring gradualalteration of metabolites in the serum. Terms such as ‘‘trained immunity’’ and ‘‘old friends’’ apply to epigenetic host-microbe changes that benefit both host and microbe (Bloomfield et al., 2016; Netea et al., 2016).
Ultimately the major cellular readout for confirmed physiological relevance is increased glucose uptake by
T cells or monocytes regardless of the gene, mRNA, phosphorylation, or protein modifications conferredby the beneficial re-introduction of these ancient organisms that co-evolved with humans until recently(Brites and Gagneux, 2015; Kodaman et al., 2014). It should be noted that BCG effects, as observed in thesereported results, are not confined to only innate cells such as monocytes but also extend to T cells as part of
the adaptive immune response as well, thus broading the impact of BCG to both limbs of the immune
system.
Here we present a basic science study of diabetic humans receiving repeat BCG vaccines. We explore therole of Myc control of glycolysis, glutaminolysis, polyamine synthesis, and the hypoxia-inducible factor(HIF)-1a/mTOR pathway. Human subjects are monitored in vivo and in vitro at serial time points spanningat least one year during receipt of three BCG vaccinations. This study represents one step closer to understanding beneficial host-microbe interactions that involve metabolic corrections affecting sugar utilization.

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Figure 1. T1Ds Have a Baseline Defect in Aerobic Glycolysis That Is Corrected by BCG Exposure(A)Timeline of in vivo BCG treatments (three vaccinations) and corresponding five serial blood samples during the 56week-long monitoring study. The patients were treated with BCG at week 0, at week 4, and at 1 year. Blood samples werecollected at serial visit times (V1–V5): V1 (0 weeks), V2 (10 weeks), V3 (26 weeks), V4 (52 weeks), and V5 (56 weeks). Total
number of in vivo collected clinical trial blood samples (n) = 15.
(B)In vitro glucose uptake by untreated monocytes of T1D and nondiabetic controls (NDC) at baseline (left) comparedwith treatment with BCG (right). Untreated monocytes from T1Ds have poorer glucose utilization than NDC, as measured
by uptake via the fluorescent glucose derivative 2-NBDG. After overnight culture, BCG increased 2-NBDG uptake inmonocytes of both T1Ds (n = 21) and NDCs (n = 6) unpaired; 2-tailed t test, p = 0.02 (left), p = 0.96 (right).
(C)In vitro glucose uptake in monocytes of NDC (left) and T1D (right) with and without BCG exposure. Monocytes wereisolated from T1D and NDC patients, cultured overnight with or without BCG, and analyzed for glucose uptake via thefluorescent glucose derivative 2-NBDG. BCG increased 2-NBDG uptake in monocytes of both T1D (n = 21) and NDC (n =6); unpaired, 2-tailed t test, p = 0.04 (left), p < 0.0001 (right).
(D) Longitudinal in vivo analysis of glucose uptake by isolated monocytes harvested from a single T1D subject receiving
serial BCG vaccinations (samples at V1, 2, 3, and 6). For this analysis additional samples were collected at week 78 (V6).
In vitro glucose uptake was quantified using 2-NBDG uptake assays and showed a gradual increase over time.

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Figure 1. Continued(E)Representative oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) assays using T1D
monocytes and the Seahorse XFp analyzer. Isolated monocytes from a T1D patient were cultured for 24 h with (red) orwithout (blue) BCG, after which the OCR and ECAR were determined using a Seahorse XFp analyzer. At the arrow,rotenone and antimycin A (Rot/AA) were added. These reagents inhibit the electron transfer chain and as a consequence
reduce OXPHOS and thus oxygen consumption as shown. BCG treatment reduces the overall oxygen consumption,
indicating a switch from OXPHOS to aerobic glycolysis.
(F) Baseline OCR, a marker of OXPHOS, in monocytes of untreated T1D compared with NDC using the Seahorse XFp
analyzer. The graph shows basal OCR results. Average OCR is abnormally high in T1D (n = 61) versus NDC (n = 16);
unpaired t test, 2-tailed, p = 0.002.
(G) BCG increases ECAR) in cultured primary monocytes from T1D and control subjects. T1D or control monocytes were
cultured with and without BCG for 7 days and analyzed using a Seahorse XFp analyzer. The increase in acidification
indicates increased aerobic glycolysis after BCG treatment (total number of T1D subjects n = 42; paired t test, p = 0.0004).
Level of significance in figures is indicated by asterisks: *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001.

RESULTSBCG Corrects an Underlying Defect in Glucose Utilization in T1D Monocytes
Isolated monocytes and T cells were obtained from T1D patients treated with repeat BCG vaccinations (Figure 1A). Five serial blood collections were obtained from baseline to 56 weeks after two BCG vaccines spaced
four weeks apart. We also studied isolated fresh monocytes of T1D or control subjects after in vitro BCG exposure. Multiple methods to assess glucose utilization were employed to document the underlying defects in theutilization of glucose as the main energy source in T1D monocytes. The induction of glycolysis results in both
regulated cellular glucose transport across the cell membrane and increased glucose utilization, which are ofpossible benefit to high glucose disease states such as diabetes. Increased glucose utilization is a result of accelerated or restored aerobic glycolysis; underactive glucose utilization can be from overactive OXPHOS (including
Krebs cycle). In vitro cellular sugar utilization can be measured directly using the 2-NBDG assay, which measuresthe uptake of fluorescent deoxyglucose from the media to inside of the cell and then measured with a flow cytometer. Aerobic glycolysis is measured through the extracellular acidification rate (ECAR), whereas OXPHOS
can be assessed via reduction in oxygen consumption rate (OCR), both on the Seahorse Analyzer platform.
Comparison of T1D monocytes with non-diabetic control (NDC) monocytes at baseline showed underlying
defects in the uptake of 2-NBDG from the culture media, a marker of underactive aerobic glycolysis (Figure 1B, left). After a short 24-h exposure to BCG in culture, both T1D and NDC monocytes increase sugar
utilization (Figure 1B, right). T1D monocytes managed to almost fully restore sugar transport as evidencedby 2-NBDG uptake that equals uptake by NDC monocytes. This suggests that T1D monocytes have theability to augment their use of sugar and restore uptake to normal levels after BCG treatment.
We next compared baseline 2-NBDG uptake in monocytes from NDC with and without BCG treatment(Figure 1C, left). We also compared baseline 2-NBDG uptake in monocytes from TIDs with and without
BCG treatment (Figure 1C, right). NDC monocytes exposed to BCG have significant increases in sugar uptake. T1D monocytes exhibit lower glucose uptake as compared with NDC monocytes at baseline but
augment upward after BCG treatment.
BCG administered in vivo to T1D is believed to similarly utilize the lymphoid compartment to augment
sugar utilization and thus lower blood sugar. Figure 1D shows the longitudinal study of one T1D patientstudied for 78 weeks during three BCG vaccinations. The isolated monocytes at baseline show low glucose
uptake using the 2-NBDG assay and from visit 2 to visit 6 (up to 78 weeks) show improvements in sugar utilization. Thus, BCG in vitro or in vivo can increase glucose uptake by monocytes. At baseline T1D subjects
have underlying defects in the use of sugar for energy production.

BCG Treatment of Cultured T1D Monocytes Decreases Oxygen Consumption and Increases
Glycolysis
We then determined the effect of BCG on cultured T1D monocytes from a single patient using the Seahorse platform. The cells were isolated from blood and analyzed in triplicate using a Seahorse Glycolytic
Rate Assay (GRA assay). The basal OCR level is represented by the first three data points (the Seahorse integrates data for 3 min per data point). OXPHOS is inhibited by the addition of the electron chain inhibitors
rotenone and antimycin A (Rot/AA; arrow in Figure 1E). As a result, the OCR drops and oxygen consumption is limited to nonmitochondrial processes. The convergence of the blue and red curve after Rot/AA

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indicates that the differences in basal OCR are due to differences in mitochondrial oxygen consumption
and not a result of differences in cell number. The OCR baseline (the first three data points) in BCG-treatedmonocytes is reduced compared with untreated monocytes, indicating that BCG reduces OXPHOS monitored in this assay. In a reciprocal fashion we determined the effects of BCG on cultured T1D monocytes
using the ECAR assay, a measure of extracellular acidification, an indicator of augmented aerobic glycolysis. The data show the BCG induced responsiveness of these monocytes to acidify the media with BCG
treatment.

T1D Monocytes Display Abnormally High Baseline Oxygen Consumption versus Nondiabetic
Controls
We isolated monocytes from T1D and NDCs to compare their basal OCR after overnight culture using a
Seahorse XFp external flux analyzer. Oxygen consumption is a measure of the rate of OXPHOS. Figure 1F
summarizes the measurement of basal OCR by monocytes of 61 T1Ds and 16 NDCs. Average OCR is significantly higher in the T1D cohort (60.8 G 3.7 pMoles/min) as compared with the NDC cohort (42.0 G 4.5)
(unpaired two-tailed t test, p = 0.002). Increased oxygen consumption indicates that T1D monocytes
have increased OXPHOS as compared with NDC, suggesting that the TCA cycle in T1D is increased.

BCG Increases the Extracellular Acidification Rate in T1D and Control Monocytes
We also measured the ECAR as a result of BCG in vitro treatment of monocytes in T1D subjects. ECAR measures
the production of acids such as lactate acid by cells, thereby serving as a measure of the amount of aerobic
glycolysis. When the TCA cycle is inhibited using Rot/AA, the cells respond to the resulting deficit in ATP byincreasing aerobic glycolysis. This increases the production of lactate, which increases the ECAR (Figure 1G).
In monocytes cultured without BCG, there was a small reduction in ECAR after adding Rot/AA (0.47 G 0.60
mPH/min), whereas there was an increase in ECAR for cells cultured in the presence of BCG for 7 days
(4.30 G 0.91 mPH/min with BCG). The difference with and without BCG was significant (Figure 1G). These
data indicate that BCG treatment of T1D or NDC monocytes causes an increase in aerobic glycolysis.

BCG Is Associated with Greater Myc Transcription
Myc is a "master regulator" that controls diverse cellular pathways related to glucose metabolism such as
glycolysis and glutaminolysis. Figure 2A is a schematic of the central role that Myc plays in normal glucose
and glutamine metabolism. According to this Myc ‘‘master regulator’’ model, Glut1, HK2, LDHA, and
SLC1A5 are upregulated by Myc (Figure 2A; Ref 6). Glucose transporter 1 (GLUT1) is one of the transportersthat facilitates the transport of glucose across the plasma membrane. HK2 phosphorylates glucose to
glucose-6-phosphate. Lactate dehydrogenase (LDHA) is the key enzyme that catalyzes the conversion of
pyruvate to lactate. Solute carrier family 1 member 5 (SLC1A5) is a neutral amino acid transporter thatamong other functions can transport glutamine across the plasma membrane.
Two other genes are under negative control by Myc. Pyruvate dehydrogenase (PDH) is the primary link between glycolysis and the TCA cycle. It catalyzes the conversion of pyruvate into acetyl CoA. Myc stimulates
pyruvate dehydrogenase kinase (PDK), part of the PDH multi-enzyme complex, which in turn inhibits PDH.
Thus, Myc inhibits PDH via PDK and as a result inhibits the TCA cycle. Myc also is thought to upregulate
GLS, which catalyzes the hydrolysis of glutamine to glutamate. The consequence is more glutamate
entering the TCA cycle, providing an alternative fuel to glucose.
We first analyzed BCG-mediated changes in transcription of Myc from in vivo samples of T1D treated atbaseline (visit 1) and at serial BCG vaccinations (visits 2, 5). Monocytes and CD4 T cells were purified
from blood of three T1D patients receiving BCG vaccinations at serial time points and Myc mRNA isolated
and then analyzed using RNAseq. Figure 2B shows a comparison of RNAseq normalized reads for Myc at
Visits 1, 2, and 5 (baseline, 10 weeks, 56 weeks, respectively). The Log 2 normalized reads are also presented(Figure S1). Transcription of Myc was increased in the monocytes and CD4 T cells of all T1D patients as BCG
treatment progresses and was followed over the greater than one year time period.

BCG Is Associated with Increased Transcription of Myc-Controlled Genes Affecting Glucose
and Glutamine Metabolism
We compared the transcription of the Myc-controlled genes in T1Ds at V1 (baseline) versus V5 (56 weeks)
after the first BCG vaccination in both isolated T cells and monocytes from vaccinated human subjects.

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Figure 2. Induction of Myc after Repeat in vivo BCG Vaccinations in T1D; Restored Glycolysis and Augmented
Glycolysis(A)Schematic overview of central role of Myc and Myc-controlled pathways. The schematic shows the genes that are
central to glucose and glutamine metabolism and that are under control of Myc. Gray rectangle indicates cell membrane.
Red arrows indicate upregulation of gene expression by Myc. Blue dotted lines indicate downregulation by Myc. Fullnames of genes and their function are listed in Table S1.
(B) Myc expression, measured by mRNA, gradually increases over time in isolated monocytes from BCG-vaccinatedsubjects. The graph shows longitudinal data on Myc for three patients treated with BCG at baseline (V1), at 10 weeks (V2),and at 56 weeks (V5). The increase in Myc from V1 to V5 is statistically significant (1 tail, paired; p = 0.04, n = 3).
(C)Visualization of mRNA expression of Myc-controlled glycolysis and glutaminolysis genes at V1 (baseline) and V5(56 weeks) in freshly isolated T cells and monocytes. The table shows the average normalized RNAseq data in BCGtreated T1D patients at two monitoring times. The higher numbers for each data pair are highlighted in red. For the
directly controlled Myc genes (glycolysis and glutaminolysis) in both monocytes and CD4 T cells a comparison of Visit 1(baseline prior to BCG administrations) to Visit 5 (52 weeks after two BCG vaccinations) demonstrated statisticalsignificance of p = 0.05. Two genes during this monitoring interval did not respond in monocytes—GLUT1 and LDHA; one
gene did not respond in CD4 T cells—GLUT1.

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The results are shown in Figure 2C (higher reads are shaded in red). In monocytes, with the exception of
Glut1 and LDHA, transcription of Myc-controlled genes at V5 (56 weeks) was statistically upregulated forthese Myc target genes. In CD4 T cells, only Glut1 transcription was reduced while all other Myc target
genes were upregulated. Therefore, overall upregulation for this Myc-controlled glycolysis pathway forboth CD4 T cells and monocytes was statistically significant (p = 0.05).

Myc Upregulates Transcription of Genes Involved in Aerobic Glycolysis
As shown above, Myc regulates several key genes of glycolysis, and Myc is upregulated after BCG treatment of T1D monocytes and T cells. We therefore analyzed the key genes that are involved in glycolysis
to determine whether these genes are also upregulated after BCG treatment. Figure 3A is a schematic
overview of aerobic glycolysis and its connection to the TCA cycle as well as the alternative pathway tolactate, which is used during anaerobic conditions. The connection of glycolysis to the pentose phosphateshunt (PPP) is also shown.
The analyzed genes involved in glucose metabolism are listed in Table S1. All genes were analyzed by RNAseq in monocytes and in CD4 T cells isolated from T1Ds receiving serial BCG vaccinations as outlined in
Figure 1A. Percent changes in transcription versus baseline (V1) were calculated at V2, V4, and V5 andthe results color-coded for visual comparisons. Pink and red backgrounds indicate progressive upregulation of transcription. In monocytes, changes are noticeable in V4 but become more pronounced at V5. In
CD4 T cells, upregulation is already largely at a maximum at V4. The results for CD4 T cells are also shown asa bar graph in Figure 3C. At V2 many of the glucose metabolism-related genes are downregulatedcompared with baseline (V1), but by V4 and V5 versus baseline the great majority of these genes are upregulated. This suggests that both CD4 T cells and monocytes respond to BCG treatment in vivo but the timecourse of induction of genes may be slightly different. But both cell types increase glucose utilization
through aerobic glycolysis pathways controlled by Myc.

BCG Is Associated with Increased Expression of Myc-Controlled Glutaminolysis andPolyamine Synthesis Genes
We next analyzed the effect of BCG on glutaminolysis and polyamine synthesis, two additional pathways
controlled by Myc. An overview of these pathways is shown in Figure 4A. A description of the genes associated with these pathways is found in Table S1. For glutaminolysis, glutamine enters cells via transporter
SLC1A5. Glutamine is then hydrolyzed to glutamate by GLS and converted to a-ketoglutarate by GLUD1,
OAT1, GOT1, and GOT2. a-Ketoglutarate then enters the TCA cycle.
For polyamine synthesis, ornithine is usually produced from arginine in the urea cycle but can also be
produced from glutamine. In this alternative pathway, ALDH18A1 converts glutamate into pyrroline-5carboxylate (P-5-C), which then is turned into ornithine by OAT. Conversion of ornithine to polyamines isthen performed by ODC, SRM, and SMS.
We determined the percent change in expression of these genes as compared with baseline (V1) in T1Ds
treated with serial BCG vaccinations to later time points over a year later (Figure 4B). Increased expression
is shown in pink and red. Decreased expression is shown in blue. Overall results show that BCG exposure isassociated with a progressive and statistically significant increase in transcription of the polyamine pathway
at trial start to time points of V4 (52 weeks) (p = 0.006) and V5 (56 weeks) (p = 0.002) (Figure 4B). For glutaminolysis pathway only early steps of this pathway were statistically significant such as GLS (p = 0.02) at bothV4 (52 weeks) and V5 (56 weeks) compared with baseline but later steps in the pathway were not statisticallysignificant.

BCG’s Induction of Myc Is Associated with the HIF-1a/mTOR Pathway but Not the Akt
Pathway to Promote Increased Sugar Transport
Myc and mTOR work together to increase glycolysis (Pourdehnad et al., 2013). The actions of Myc on HIF1a- and mTOR-linked pathways are usually not related to decreased DNA methylation with increasedmRNA (Figure 5A). Instead Myc regulation of this pathway can be at the level of phosphorylation or stabilization of the HIF-1a protein (Cheng et al., 2014; Doe et al., 2012). We first examined the mRNA levels of
HIF-1a, mTOR, AMPK, and AKT after in vivo BCG treatment. As expected, we did not see a change in mRNA
regulation. In past published data using PBLs and monocytes, BCG in vitro easily upregulated HIF-1a, incontrol and T1D samples (Kühtreiber, W.M. 2018). This difference is thus not due to some inherent

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Figure 3. BCG Treatment in T1D Gradually Increases Aerobic Glycolysis and Pentose Phosphate Pathway (PPP)(A)Schematic of glucose uptake, glycolysis and Pentose Phosphate Shunt. The data for the genes listed are shown in
Figure B and C Gray rectangle indicates cell membrane. Full names of genes and their function are listed in Table S1.
(B)BCG increases the expression of many of the genes involved in glucose uptake and glycolysis in both humanmonocytes and CD4 T cells. The tables show the percent change of V2 (week 10), V4 (week 52), and V5 (week 56) ascompared with V1 (baseline). Increases are shown in shades of red. The results show a progressive overall increase in gene
expression as related to glycolysis and pentose phosphate shunt. CD4 T cells: t test V2 versus V5, p = 0.001, paired, 1tailed, n = 3.
(C) Glycolysis- and pentose phosphate shunt-related gene expression data for CD4 T cells (n = 3, % change in genetranscription from baseline). Statistical significance values were p = 0.002, 0.80, and 0.002 for comparison of V2 versus V4;V4 versus V5; and V2 versus V5, respectively.

resistance of T1D cells to use this pathway but likely represents a timing issue with sampling extending to
one year. Alternatively, in vitro exposures could have stronger effects.
We explored this pathway further by adding inhibitors to cultured monocytes treated for 24 h with BCG andquantifying sugar uptake into the cells using 2-NBDG flow cytometry. First, the addition of rapamycin

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Figure 4. BCG Treatment Increases Glutaminolysis and Polyamine Synthesis in T1D(A) Schematic of glutaminolysis and polyamine synthesis pathways. Expression data for the genes listed are shown in
Figure 4B. Gray rectangle indicates cell membrane. Full names of genes and their function are listed in Table S1.
(B) Glutaminolysis and polyamine synthesis in CD4 T cells as compared with basal levels after in vivo BCG vaccinations.
Shown are the up- or downregulation of transcription at visits 2 (week 10), 4 (week 52), and 5 (week 56) as compared withbaseline (V1) expressed as % change. Trends are visualized by color-coding. Upregulation is highlighted in shades of red,white for no change, and blue for downregulation. The majority of genes are upregulated at V4 and V5 as compared withbaseline. For polyamine synthesis there was statistically significant upregulation of all gene members occurred at baselinecompared with V4 (52 weeks) (p = 0.01) and persisting through V5 (56 weeks) (p < 0.00). For glutaminolysis only the earlysignaling pathway was activated but later steps after glutamate were not statistically significant at this time point of 1 +years after two BCG vaccinations.

inhibited augmented glycolysis in BCG-treated monocytes (Figure 5B, left). This confirmed that BCG could
augment glycolysis via mTOR. Second, inhibition of HIF-1a with ascorbate also inhibited BCG-augmented
glycolysis (Figure 5B, middle). This indicated that BCG-augmented glycolysis was dependent on HIF-1a.
Finally, the addition of the PI3K/AKT inhibitor, Wortmannin, to BCG-treated cells had no or minimal effecton sugar transport (Figure 5B, right). The data from these inhibition experiments support our hypothesisthat BCG effects are independent of the Dectin-1 pathway and the Akt pathway. These pathways arecommonly used by other microorganisms such as Candida albicans and work through the Dectin-1 receptor with glucan to change host cellular metabolism (Figure 5A). For BCG in vivo systemic effects, RNAseq
analysis of CLEC7A (Dectin-1) showed that BCG is associated with gradual downregulation of this genefrom in vivo samples (Figure S2). This also suggests that BCG works on monocytes at a different level
than b-glucan to Dectin-1 (CLEC7A) receptor.
Metformin was also studied in the in vitro BCG-trained model of monocytes in culture (Figure S3). Metformin is a drug that activates AMPK (50 adenosine monophosphate-activated protein kinase), an inhibitor ofmTOR in vitro and in vivo. Metformin also has other targets as well. Utilizing our in vitro cellular sugar utilization assay (the 2-NBDG assay), T1D and control monocytes were cultured for 24 h without treatment,with BCG, with metformin, or with both. As the data in Figure S3 show in both T1D and NDC, metformin
at a dose of 1 mM decreased sugar utilization; whereas, BCG, as expected, increased sugar transport. Metformin was dominant, in that the addition of metformin to BCG-treated monocytes abrogated BCG’siScience 23, 101085, May 22, 20209ll

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Figure 5. Confirmation of Myc Control of mTOR/HIF-1a after BCG Vaccinations, a Role in Increasing Glucose
Utilization in T1D(A) mTOR upregulates the transcription of HIF-1a, whereas Myc post-transcriptionally stabilizes HIF 1a. This allows HIF-1a to
upregulate glucose uptake and aerobic glycolysis. The application of inhibitors to these pathways allowed testing of BCG’s effects
on this pathway. Rapamycin inhibits mTOR, which then would decrease HIF 1a levels and thus glycolysis. Ascorbate inhibits HIF
1a, and if BCG used this pathway would inhibit glycolysis. Finally if BCG’s mechanism of action was similar to Candida albicans
working through the Dectin-1 receptor then Wortmannin with BCG would inhibit Akt and decrease glycolysis.
(B) In vitro treatment with BCG for 24 h on monocytes with the various inhibitors helped to define the BCG dependent
pathways. With the addition of rapamycin to monocytes with augmented glycolysis the accelerated glycolysis is inhibited,confirming BCG for glycolysis utilizes mTOR. Ascorbate, an inhibitor of HIF-1a, also inhibited BCG-augmented glycolysis
in monocytes; BCG-augmented glycolysis was dependent on HIF-1a. Finally the addition of Wortmannin to BCGaugmented monocytes with accelerated glycolysis had no or minimal effect on sugar transport; this supports the data that
BCG effects are independent of the Dectin-1 pathway and the Akt pathway.
(C) Using siRNA silencing in the CRL-9855 monocyte cell line, BCG-induced increases in glucose update are inhibited by
Myc siRNA silencing treatment.

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acceleration of glucose transport. Therefore, at a functional level regardless of the T1D or NDC state, Myc
was exerting a level of control on this BCG-activated pathway. This was confirmed using metformin, rapamycin, and ascorbate. All three inhibited BCG-induced and Myc-accelerated sugar transport. We conclude
that, at least in T1D monocytes, HIF-1a and mTOR pathways are under Myc control. These in turn assist inaccelerated glycolysis as shown by glucose transport measurements.
siRNA methods were also studied to support the data in this paper on the central role of Myc in BCGinduced glucose transport in primary monocytes from T1D and NDC individuals. The siRNA method requires a cell line, because primary monocytes survive poorly with this methodology. The screening of
monocyte cell lines confirmed the CRL-9855 human monocyte cell line as showing a modest increase inglucose transport when exposed in vitro to BCG for 3 days (Figure 5C, left). Using this cell line, we wereable to show that Myc siRNA silencing inhibited augmented glucose transport (Figure 5C left). Silencing
of Myc by siRNA was confirmed by comparing Myc levels in cells that were transfected with Myc siRNA
versus cells that were transfected with negative control siRNA. Flow cytometry showed that Myc was substantially downregulated in the cells treated with Myc siRNA as compared with the negative controls (Figure 5C, right). This thus confirmed the functional association between Myc control and BCG-accelerated
glucose transport in monocytes.

DISCUSSION
Metabolic pathways are altered after immune cells become activated or stimulated with live microorganisms, a process called immunometabolism (Pearce and Pearce, 2013). As the fine details of immunometabolism become worked out in various test host microbe systems, often in vitro, the metabolic changes can becorrelated to the type of external cellular stimulus (Kelly and O’Neill, 2015; Everts et al., 2014, Krawczyd
et al, 2010, Rodrı́guez-Prados et al., 2010). Commonly the end result is increased glycolytic rates and induction of pathways for enhanced sugar utilization with minimized reliance on the Krebs cycle. Indeed this iswhat we find in the present year-long human study of in vivo BCG vaccination in the setting of T1D.
We have shown here that T1D lymphoid cells display an underlying baseline defect in aerobic glycolysis,
perhaps due to inadequate microbial exposures. This explanation is consistent with the hygiene hypothesis(Strachan, 1989). After either in vitro or in vivo BCG exposure, we observe the gradual correction andimprovement of aerobic glycolysis toward normal levels. We profiled the in vivo immune cell metabolism
in T1D subjects and focused on the metabolic pathways that require Myc involvement. Here we demonstrated the gradual year-long elevation in Myc mRNA after BCG vaccinations that appears to affect all
known pathways regulated by Myc. The data consistently show the gradual in vivo induction by Myc of
the glycolysis pathways, in both T cells and monocytes in vaccinated humans with T1D. Furthermore, we
found that BCG stimulates glutamine utilization while in parallel stimulating the mRNA proteins of glutaminolysis. Polyamine synthesis is also gradually induced in BCG-treated cohorts. Finally, Myc control of
HIF-1a/mTOR augments glycolysis, whereas inhibition with metformin, rapamycin, and ascorbate confirm
a close connection to three Myc pathways for lowering blood sugars. The data pull together many of thesugar regulation pathways under Myc’s regulatory control: glycolysis, glutaminolysis, polyamine synthesis,
and the HIF-1a/mTOR pathway (Goetzman and Prochownik, 2018; Rathmell, 2011; Wang et al., 2011).
Innate immunity from host microbe interactions is recognized to change immune system metabolism.
Glycolytic reprogramming has been described related to the TLR ligands, and mycobacteria use these receptors (Krawczyk et al., 2010). The levels of host-microbe control for mycobacteria, including the attenuated Mycobacterium bovis of the BCG vaccine include changes in DNA methylation, changes in mRNA
splice forms, changes in the phosphorylation patterns of proteins, changes in mRNA levels, and even protein to protein stability. Multiple confirmatory studies show that microbes including BCG can at the DNA
level alter gene methylation patterns, a process now often termed ‘‘trained immunity’’ (Netea et al., 2016).
BCG as well as its pathogenic version, tuberculosis, are known to also exert host control by changing protein phosphorylation patterns, often with increasing cAMP levels from a multitude of mycobacterial genes
devoted to this process inflicted on the host (Bai et al., 2011). Infection with Mycobacterium tuberculosis
induces aerobic glycolysis in human alveolar macrophages (Gleeson and Sheedy, 2016). BCG bothin vitro and in vitro induces a strong increase in glycolysis and also glutamine metabolism (Arts et al.,2016). Not all infections may use the same host metabolic pathways with viruses and bacteria pursuing varying course as it relates to glucose. Glucose utilization was required for survival in animal models of viral
inflammation but for bacterial infections ketogenesis was required (Wang et al., 2016). For the B-glucan

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component of Candida albicans, the host interaction is through the surface Dectin-1 receptor and the intracellular Akt pathway (Cheng S.C. et al., 2014). The presented data show the BCG organism does not use the
Akt pathway to augment host aerobic glycolysis.
Myc itself, in the present study, is shown to exert its effects at the mRNA transcription level, but ultimately its
effect on the HIF-1a/mTOR pathway has previously been demonstrated to be at the phosphorylation-dependent and protein stability level. This powerful host-microbe level of control is exceptional because there areat least 15 distinct adenyl cyclases, the protein that generates cAMP in the genomes of Mycobacterium tuberculosis and also similar cAMP genes in Mycobacterium bovis for the BCG vaccine. Myc also regulates glycolytic
genes by preferentially switching the expression of PKM1 to PKM2 by alternative splicing of the transcript (Dangand Semenza, 1999; David et al., 2010). With so many molecular levels for microbes to control host responses, inthis study we reverted to direct sugar transport assays as an endpoint for the host microbe outcome, such as toverify the Myc signaling control on HIF-1a/mTOR pathway.
There is still much unknown about the effects of BCG on in vivo cellular metabolism. The present study of humans
with T1D extended over a year of 5–6 serial blood sample collections and three BCG vaccinations for over oneyear time period. Future studies will be aimed at following patients for 5 years. Two such studies are underway. A
human study by Matsumiya and colleagues showed BCG by 7–14 days as having effects on glycolysis pathways,
although the study did not identify specific genes or pathway steps (Matsumiya et al., 2015). A three-month studyof normal humans vaccinated with one BCG dose saw control of epigenetics with effects on histone markers(H3K4me3 and H3K9me3) (Arts R, 2016). Earlier in vitro studies found epigenetic modifications at the chromatinlevel after BCG exposure (Kleinnijenhuis et al., 2012). It is recognized that diverse environmental challenges frommicrobes can alter epigenetic patterns especially those involving interactions between histones and effector
proteins (Busslinger and Tarakhovsky, 2014). Published studies show that the epigenetic effects of BCG thatare related to the immune system lead to the desired in vivo expansion and activation of beneficial immune cells
such as Tregs, formerly known as T suppressor cells. BCG vaccinations in T1D subjects demonstrated bothmRNA and corresponding statistically significant epigenetic changes to many CG sites in six Treg signature
genes—Foxp3, TNFRSF18, IL2RA, IKZF2, IKZF4, and CTLA4. These epigenetic changes could be observed as
early as eight weeks after the first BCG vaccine (Kühtreiber et al., 2018). We are now studying gene methylation
patterns as a mechanism for how an infection by BCG can have such long lasting metabolic effects on glucose
regulation. In addition, we will look for effects at the level of phosphorylation and protein activation or othermodes of interactions of host and microbes, such as the role of Myc in stabilizing the HIF-1a protein for prolonged function (Doe et al., 2012).
It is known that many of the clinical effects of BCG vaccinations in humans are delayed by months and years
after vaccine administration but effects can persist for decades. Indeed, in the present study we chart a timecourse of at least a year during which there is a gradual upregulation of Myc at the mRNA level, and this isaccompanied by fresh human T1D monocyte samples gradually obtaining restored sugar transport. In our
Phase 1 clinical trial in adults with >15 years of disease it took up to 3 years after BCG administration toreach almost complete correction of blood sugars as measured by HbA1c. Children with T1D may respondfaster but this has not yet been formally tested in double-blinded clinical trials. Also, a multiple sclerosis
clinical trial using BCG vaccines found a similar 2-year delay prior to observing clinical improvement (Ristori
et al., 1999). This stresses that the basic science of host-microbe interactions need to be studied in longstudies with longitudinal sampling. This is the first long-term longitudinal data on this host microbe interaction at the molecular level for Myc gene regulation in the diabetic host. It has been speculated that thereason for the delay in significant clinical effects is because the vaccine is administered intradermally. In
animal models, if BCG is administered IV, thus resulting in bone marrow chimerism and re-programmingof bone marrow stem cells, the effects are dramatically expedited (Das et al., 2013; Kaufmann et al.,2018). BCG resetting of the bone marrow may also account for why the durability of this host-microbe interaction is so long lasting, not only in BCG-treated T1D but also in phase II clinical trials of BCG in multiple
sclerosis. Similar to T1D, multiple sclerosis patients have a baseline impairment in glycolysis, and somestudies suggest that the earliest CNS brain lesions first show aerobic glycolysis defects (La Rocca et al.,2017). Therefore, defective aerobic glycolysis may be a common feature of the lymphoid dysfunction in human autoimmune subjects, as well as these same defects perhaps also expressing in target tissue.
T1D has a propensity for bacterial infections, thus resulting in skin infections, styes, gangrene of the legs,
urinary tract infections, and more (Abu-Ashour et al., 2018). Metabolic requirements including ambient

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glucose during infection vary with the etiology of the infections. For instance, bacterial infections often
improve with normal glucose levels and bacterial infections progress with high glucose levels in feedingstates (Wang et al., 2016). Therefore, BCG vaccinations might ultimately decrease bacterial infections in
T1D. The current study is too small and too short of duration (just over a year) but further longer studies
with BCG vaccinations in T1D up to 5 years have been designed to qualify and tract infectious diseaseconsequences after BCG vaccinations.
Does the improved blood glucose control seen in type 1 diabetes have any applicability to type 2 diabetes?
Although some animal data support this premise, there is also human data. A 60-year follow-up study of thelong-term efficacy of the BCG vaccine in American Indians and Alaska Natives has shown not only protection from tuberculosis and protection from lung cancer but also a statistically significant lowering of type 2
diabetes incidence (Aronson et al., 2019; Usher et al., 2019; van Dam et al., 2016; Inafuku et al., 2015).
The goal in immune metabolism is to attain the right balance—not too much of a good thing. Indeed BCG
almost restores glucose metabolism to near normal as measured by mRNA expression, at least in monocytes and T cells, and also improves monocyte glucose transport to near normal in typical early onset
type 1 diabetic subjects. It is not the overcorrection of glucose metabolism to pathologic states (such as
the Warburg effect) that is desired. BCG’s correction of a glucose utilization defect by Myc cellular mechanisms is a tightly controlled process preventing hypoglycemia, unlike delivery of a protein such as insulinthat lowers blood sugars in unregulated ways. In sum, we have shown here that BCG induces Myc transcription and heightens transcription of most Myc glucose-regulating target genes in the four pathways
depicted here. Taken together, the data suggest a beneficial host-microbe interaction that delivers a systemic correction of a lymphoid sugar utilization defect in T1D.

Limitations of the Study
The limitations of this study include the use of BCG in type 1 diabetic subjects with long-standing type 1
diabetes; pediatric trials have not yet been conducted to understand the kinetics of blood sugar corrections and the central signaling pathways in younger subject with BCG vaccination treatment. The speedof glucose utilization recovery may also be different in younger subjects.
In adult humans we can easily measure glucose utilization and the role of Myc in T cells and monocytes
derived from peripheral blood. Although the lymphoid system is the second largest cellular organ in thehuman, we do not yet know if BCG could also be working at the level of glucose utilization in other organs
such as the liver, muscle, or the kidneys. For human studies we cannot sample human liver, brain, etc. to see
if those changes are also occurring in response to BCG. An ongoing 5-year-long study with FDG-PET scan
imaging of organ system for utilization of sugar after BCG with FDG-PET scan imaging should help toanswer these questions in the future.
The cellular volume of the lymphoid system is frequently underestimated so its’ capacity to use sugar andregulated sugar transport is underappreciated. Using cell numbers alone, the lymphoid system is thelargest human organ. Remember the human lymphoid system is composed of blood (RBC, WBC, platelets,
neutrophils, monocytes, eosinophils, etc.), lymph nodes, spleen, bone marrow, lymph system, thymus etc.
For an average human body it is composed of 1.12 3 10^13 lymphoid cells. The total body cell number is
3.7 3 10^13 cells. This compares to liver with 2.31 3 10^11 cells, compares to muscle with 2.50 3 10^8cells, brain 1 3 10^11 cells (Bianconi et al., 2013; Harrison, 1962). Using cell numbers the lymphoid systemdwarfs the liver, brain, and muscle combined in total cellular numbers that could utilize sugar more efficiently with restored normal levels of aerobic glycolysis. It is feasible the lymphoid system alone corrects
this defect but human data on other organ systems will gradually accumulate. These subjects are not off
insulin but use reduced insulin and their HbA1c drop to the near normal range without hypoglycemia.
Lastly very rare inborn errors of metabolism in aerobic glycolysis in lymphocytes are associated in somecases with ketosis-prone insulin-dependent diabetes suggesting but not proving the novel role of thelymphoid system contributing to regulated sugar levels (Afadhel and Babiker, 2018).

METHODS
All methods can be found in the accompanying Transparent Methods supplemental file.

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SUPPLEMENTAL INFORMATION
Supplemental Information can be found online at https://doi.org/10.1016/j.isci.2020.101085.

ACKNOWLEDGMENTS
We thank Dr Miriam Davis for a critical read of the manuscript and The Iacocca Foundation for their financial
support.

AUTHOR CONTRIBUTIONS
Conducting experiments: WK, RK, YS, LT, TL, SS, LM, JG, GS, HT. Conceptual input, design, statistical analysis, and supervision: WK, DF.

DECLARATION OF INTERESTS
The authors declare no competing interests.

Received: January 26, 2020
Revised: April 3, 2020
Accepted: April 15, 2020
Published: May 22, 2020
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iScience 23, 101085, May 22, 2020

15

iScience, Volume 23

Supplemental Information

BCG Vaccinations Upregulate
Myc, a Central Switch for Improved
Glucose Metabolism in Diabetes
Willem M. Kühtreiber, Hiroyuki Takahashi, Ryan C. Keefe, Yaerin Song, Lisa Tran, Trevor
G. Luck, Gabriella Shpilsky, Louisa Moore, Sarah M. Sinton, Jessica C.
Graham, and Denise L. Faustman

Supplemental Table 1. Metabolism genes discussed in this paper
SymbolName

Description (See Ref 13)

Glucose Metabolism
HK2
GPI
PFKL
PFKFB3
ALDOA

Hexokinase 2
Glucose-6-Phosphate
Isomerase
Phosphofructokinase,
Liver Type
6-Phosphofructo-2-Kinase/
Fructose-2,6-Biphosphatase 3
Aldolase, FructoseBisphosphate A

TPI

Triose-Phosphate Isomerase

PGK1

Phosphoglycerate Kinase 1

PGAM1

Phosphoglycerate Mutase 1

ENO1

Enolase 1, α-Enolase

PKM

Pyruvate Kinase M1/2

LDHA

Lactate Dehydrogenase A

G6PD

Glucose-6-Phosphate
Dehydrogenase

TKT

Transketolase

MYC(cMYC) Cellular
Myelocytomatosis Proto
Oncogene

mTOR

Mechanistic (Mammalian)
Target of Rapamycin

Phosphorylates Glucose to Glucose-6P and is known to be a ratecontrolling step in glucose metabolism
Interconverts glucose-6-phosphate and fructose-6-phosphate
Catalyzes the conversion of D-fructose 6-phosphate to D-fructose
1,6-biphosphate
Involved in both synthesis and degradation of fructose-2,6bisphosphate
Catalyzes the reversible conversion of fructose-1,6-bisphosphate
to glyceraldehyde 3-phosphate and dihydroxyacetone phosphate
Catalyzes the isomerization of glyceraldehydes 3-phosphate anddihydroxy-acetone phosphate
Catalyzes the conversion of 1,3-diphosphoglycerate to 3phosphoglycerate
Catalyzes the reversible reaction of 3-phosphoglycerate (3-PGA) to
2-phosphoglycerate (2-PGA) in the glycolytic pathway
Catalyzes the conversion of 2-phosphoglycerate tophosphoenolpyruvate
Catalyzes the transfer of a phosphoryl group from
phosphoenolpyruvate to ADP, generating ATP and pyruvate
Catalyzes the conversion of L-lactate and NAD to pyruvate and
NADH
Converts D-glucose 6-phosphate into glucono-1,5-lactone
Plays a role in the channeling of excess sugar phosphates toglycolysis in the pentose phosphate pathway
Plays a pivotal role in metabolic reprogramming of cells byenhancing glycolysis, lactate production, glutaminolysis andoxidative phosphorylation.
Protein kinase that directly or indirectly regulates thephosphorylation of many proteins

Glutaminolysis
SLC1A5
GLS2
GLUD1
OAT
GOT1,
GOT2

Solute Carrier Family 1
Member 5
Glutaminase 2
Glutamate Dehydrogenase 1
Ornithine Aminotransferase
Glutamic- Oxaloacetic
Transaminase 1, 2

Amino acid transporter that transports glutamine into cells
Catalyzes the hydrolysis of glutamine to glutamate and ammonia
Converts glutamate into 𝞪-ketoglutarate
Converts glutamate into 𝞪-ketoglutarate
Converts glutamate into 𝞪-ketoglutarate

Polyamine Synthesis
ALDH18A1
OAT
ODC
SRM
SMS

Aldehyde dehydrogenase 18
family member A1Ornithine Aminotransferase
Ornithine Decarboxylase
Spermine Synthase
Spermidine Synthase

Converts glutamate into pyrroline-5-carboxylate (P-5-C)
Converts P-5-C into ornithine
Converts ornithine to polyamines
Converts ornithine to polyamines
Converts ornithine to polyamines

Supplemental Table 1. Description of glucose metabolism, glutaminolysis and polyaminesynthesis genes by gene symbol, metabolic pathway and description of function. Related to
Figures 2, 3, 4 and 5

Supplemental Figure 1

Gene
GLUT1
HK2
LDHA
PDHA1
SLC1A5GLS

Log 2 Avg Normalized Reads
Monocytes
V1
V5
7.37
7.24
13.02
13.04
13.70
13.67
10.90
11.01
11.77
11.81
11.16
11.31

Gene
GLUT1
HK2
LDHA
PDHA1
SLC1A5GLS

CD4 Tcells
V1
10.54
9.67
13.02
10.67
8.65
12.32

V5
10.53
9.98
13.24
10.68
9.43
12.81

Supplemental Figure 1: Presentation of Figure 2 data as Log 2 normalized reads.
Related to Figure 2

Supplemental
Figure 2

22000

Normalized Reads

mRNA

20000
18000
16000
14000
12000
10000

1

2
4
Visit

5

Supplemental Figure 2: BCG vaccination reduces Dectin-1 transcription. Related to Figure 5A.
Longitudinal RNAseq analysis shows that CLEC7A (Dectin-1) transcription is reduced over timein monocytes isolated from BCG-vaccinated T1Ds (n=3). This indicates that BCG does not work
via Dectin-1 as is the case for altered metabolism from 𝝱-Glucan .

Supplemental Figure 3

T1D

NDC
10000

*
*

8000

*

6000

8000

*
****

**

6000

ea

et

nt
r

M

U

te
d
fo
rm
B
in
C
G
+M BC
G
et
fo
rm
in

ea

et

M

nt
r

te
d
fo
rm
B
in
C
G
+M BC
G
et
fo
rm
in

4000

4000

U

****
2-NBDG (MFI)

2-NBDG (MFI)

10000

Supplemental Figure 3: Metformin provides additional confirmation of the utilization of HIF1a and mTOR for augmented MYC dependent glycolysis in T1D. Related to Figure 5A.

In both NDC and T1D, metformin treatment reduces glucose transport as measured by 2-NBDG. Sugar
transport, an indication of augmented aerobic glycolysis, is reduced. This reduction is partially correctedby BCG treatment concurrent with metformin. This demonstrates that BCG utilizes the AMPK, mTOR and
HIF-1𝞪 pathway for its signaling pathway. (NDC n=5; T1D n=16. Level of significance in figures isindicated by asterisk(s): * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001)

TRANSPARENT METHODSSTAR*METHODSKEY RESOURCES TABLE
Reagent or Resource
BCG

Japan BCG Laboratory, Tokyo, Japan

2-NBDG
ImmunoCult-SF

Thermo Fisher, Grand Island, NY
Stemcell Technologies, Cambridge, MA
Sigma Aldrich, St. Louis, MO

Metformin
APC-anti CD14 antibody
EasySep Direct Human Monocyte
Isolation Kit
EasySep Direct Human CD4+ Tcell
Isolation Kit
PBS

BD Biosciences, San Jose, CAFreeze dried glutamate BCG vaccine (Japan) for
intradermal use
Cat# N13195
Cat# 10961
Cat# 317240
Cat# 555399

Stemcell Technologies, Cambridge, MA

Cat# 19669

Stemcell Technologies, Cambridge, MA

Cat# 19662

Gibco/Thermo Fisher, Grand Island, NY
Sigma Aldrich, St. Louis, MO

Cat# 20012-027

Seahorse XF RPMI medium, pH 7.4

Agilent, Santa Clara, CA

Cat# 103576-100

Seahorse XFp Glycolytic Rate Assay Kit

Agilent, Santa Clara, CA

Cat# 103346-100

Seahorse XFp FluxPak

Agilent, Santa Clara, CA

Cat# 103022-100

Seahorse XFp Analyzer

Agilent, Santa Clara, CA

Cat# S7802A

EDTA

CRL-9855 Cell Line
ATCC, Manassas, VA
cMYC siRNA (SilencerTMSelect)
Thermo Fisher Scientific,
Origene, Rockwille, MD
ViromerR○Green
c-MYC antibody
Bio-Rad, Hercules, CA
Cellular permeabilization buffe
BioLegend, San Diego, CA
Transcription Profiling analysis (RNAseq)
RNeasy Plus Mini Kit
QIAGEN Sciences, Germantown, MD
2100 Bioanalyzer Instrument
Agilent, Santa Clara, CA
NEBNext® Poly(A) mRNA Magnetic
New England BioLabs, Ipswich, MA
Isolation Module
NEBNext® Ultra Directional RNA
New England BioLabs, Ipswich, MA
Library Prep Kit for Illumina
KAPABiosystems Illumina Library
KAPABiosystems, Wilmington, MA
Quantification kit

Cat# E-7889

Cell Line: CRL-9855
Cat# AM16708
Cat# TT100301
Cat# Clone 9E10
Cat# 425401
Cat# 74104
Cat# G2939BA
Cat# E7490L
Cat# E7420L
Cat# KR0405
Cat# SY-415-1002

NextSeq 500 sequencer

Illumina Inc., San Diego, CA

TopHatJohns Hopkins University, Center for
Computational Biology

Cufflinks

GitHub

DESeq

Part of Bioconductor

Ingenuity Pathway Analysis

Qiagen BioInformatics, Redwood City,
CA

Open Source. Hosted athttps://github.com/cole-trapnell-lab/cufflinks
Open Source. Hosted athttps://www.bioconductor.org
https://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis/

https://data.mendeley.com

http://dx.doi.org/10.17632/6frc2b83rv.1

Deposited Data
RNASeq data

Open Source. Hosted at
http://ccb.jhu.edu/software/tophat/index.shtml

CONTACT FOR REAGENT AND RESOUCE SHARING
Further information and requests for resources and reagents should be directed to and will be fulfilledby the Lead Contact, Denise L. Faustman (faustman@helix.mgh.harvard.edu).

DEPOSITED DATAThe normalized raw RNAseq data for the genes in this paper is deposited
at http://data.mendeley.com and can be accessed using http://dx.doi.org/10.17632/x9kh4m22gy.1

EXPERIMENTAL MODEL AND SUBJECT DETAILSClinical trial and research study participants
All human studies had full institutional approvals through Massachusetts General Hospital and Partners
Health Care (Study# 2007P001347, 2012P002243 and 2013P002633). The BCG interventional studies
were also formally approved by the FDA (IND#2007P001347 and IND#2013P16434). All other blood
donors both T1D and non-diabetic control (NDC) 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 BCG vaccinations and serial blood sampling times for visits 1 through visit 5 aredepicted in Figure 1A. This study spanned 56 total weeks and includes type 1 diabetic patients with > 15
years of disease. All cohorts studied participated in open label studies conducted with serial BCG
administrations and were not part of double blinded placebo controlled clinical trials.

Clinical chemistries
All human HbA1c, and glucose levels were determined directly from fresh blood by certified diagnostic
laboratories approved by The Massachusetts General Hospital and the FDA. Human serum samples wereassayed for C-peptide using regular (Cat# 10-1136-01) or ultrasensitive (Cat# 10- 1141-01) C-peptide
ELISA kits from Mercodia AB (Uppsala, Sweden) using serum frozen at -80°C as previously described(Faustman et al, 2012).

Isolation of monocytes and of CD4 T cells
Monocytes and CD4 cells were isolated from blood that was collected in purple tops (K2-EDTA
anticoagulant) using magnetic EasySep Direct Human Monocyte Isolation Kit or Easysep Direct Human
CD4+ T Cell Isolation Kits from Stemcell Technologies (Vancouver, BC, Canada), following the instructionsof the manufacturer. Briefly, 1200 μL of Isolation Cocktail and 1200 μL of RapidSpheres were mixed with
24 mL of whole blood in a 50 mL centrifuge tube and incubated for 5 min at room temperature. Twentyfour milliliter of Ca- and Mg-free PBS (for CD4 T cells) or PBS + 1mM EDTA (for monocytes) was thenadded and the tube placed into a “Easy 50” magnet (Stemcell Technologies). This immobilized theunwanted cells at the side of the tube. After 10 min, the monocyte- or CD4- enriched cell suspension
was then transferred into a new tube and the magnetic separation process repeated for 5 min with fresh
RapidSpheres. The resulting highly monocyte or CD4 enriched cell suspension was transferred into a
new tube and purified for a third time using the magnet. The resulting final monocyte or CD4 T cell
preparations had purities of >95%.

2-NBDG uptake in monocytes
Monocytes were isolated from T1D patients and cultured for 24 hours on Nunc UpCell 24-Well plates(Thermo Scientific, Cat# 174899) at a concentration of 1x106 cells/mL in 1mL of Immunocult-SF Media(Stemcell Technologies), with or without 1x105 CFU BCG added to the media (Multiplicity of Infection,
MOI 0.1). Some cultures also included 1 mM metformin. UpCell plates have a coating that exhibits atemperature dependent transition from hydrophobic to hydrophilic. Thus, the coating allows cellattachment at 37°C whereas the cells will detach at room temperature or lower. In our experience this is
the gentlest way to detach primary monocytes with minimal damage. Cells were recovered, washed,
and incubated for 30 minutes at 37°C with 100uM 2-NBDG ((2-(N-(7-Nitrobenz-2-oxa-1,3-diazol-4yl)Amino)-2-Deoxyglucose); ThermoFisher Cat# N13195) in Hepes buffered RPMI without glucose (XFp

medium, pH 7.4; Agilent Technologies, Wilmington, DE, USA. Cat# 103576-100). The cells were thenwashed with XFp medium and analyzed on a BD FacsCanto II flow cytometer. Data was processed using
FlowJo software to determine 2-NBDG median fluorescence intensity (MFI).

Glycolytic Rate Assay
Changes in Oxygen Consumption Rate (OCR) and Extracellular Acidification Rate (ECAR) were measured
using a Seahorse XFp Analyzer (Agilent). Isolated monocytes were cultured (2 x 105 per well) for sevendays with or without 1 x 106 CFU of BCG (MOI=5) in Immunocult-SF Media (Stemcell Technologies). The
cells were then washed with Seahorse XFp medium and analyzed using the Seahorse XFp Analyzer. Afterobtaining three baseline data points, Rotenone and AntiMycin A (final concentration, 0.5 uM) wereadded to the media to inhibit oxygen usage due to oxidative phosphorylation.

Transcription profiling analysis (RNAseq)
Total RNA was isolated from purified monocytes and CD4 T cells using the RNeasy Plus Mini Kit of
QIAGEN (QIAGEN Sciences, MD, Cat# 74104) and processed by the CCCB (Center for Cancer
Computational Biology) at Dana Farber Cancer Institute (Boston, Mass). RNA quality was determinedusing an Agilent 2100 bioanalyzer. RNA with a RIN value greater than 6 and less than 10% DNAcontamination was used for library preparation. Using an input of 100 ng of total RNA, poly-A selection
was performed using a NEBNext® Poly(A) mRNA Magnetic Isolation Module. The resulting mRNA wasused for library preparation using the NEBNext® Ultra Directional RNA Library Prep Kit for Illumina®. The
RNAseq libraries were run on a high sensitivity DNA chip on the Agilent 2100 Bioanalyzer, and thefunctional concentration of the library was determined through qPCR using the KAPA Biosystems
Illumina Library Quantification kit. Libraries to be sequenced were pooled at a concentration of 2 nM,
and were denatured and diluted to a final concentration of 2pM and loaded onto the Illumina NextSeq
500. Alignment of reads against reference genome HG19 was performed using TopHat and analyzed
using Cufflinks. The resulting data was then normalized using DESeq, part of the R— Bioconductor

package. Normalized data were analyzed using the Ingenuity Pathway Analysis software from QIAGEN,
the R Statistical Programming language.

cMyc silencing and analysis
CRL-9855 (ATCC, Manassas, VA), a cell line established from human monocytes, was seeded at a densityof 1×106 cells in normal 24 well culture plate with or without 1×105 CFU of BCG (JAPAN BCG
LABORATORY, Tokyo, Japan) in total 1mL normal medium; IMDM (Thermo Fisher Scientific, Waltham,MA) including 10% FBS (Merck Millipore, Darmstadt, Germany), 1% Penicillin Streptomycin (CORNING,
Corning, NY), 1% HT supplement (Thermo Fisher Scientific) and 0.1% 2-mercaptoethanol (Thermo Fisher
Scientific). After 3 days culture at 37℃, the cells were divided into double the number of wells with themedium. At day 5, the cells were collected and seeded at a density of 2×105 cells in total 500uL medium
without antibiotics. Half of the wells with expanded cells were transfected by final dosage 1nM cMyc
siRNA (SilencerTM Select, Thermo Fisher Scientific) with ViromerTM Green (ORIGENE, Rockville, MD)following the manufacture’s protocol. The sequences of used siRNAs are following: 1) Sense (5’->3’),
AGACCUUCAUCAAAAACAUtt; Antisense (5’->3’), AUGUUUUUGAUGAAGGUCUcg: 2) Sense (5’->3’),
ACAGCCCACUGGUCCUCAAtt; Antisense (5’->3’), UUGAGGACCAGUGGGCUGUga. The other wells wereused as control cells, which were transfected with negative control siRNA (SilencerTM Select, Thermo
Fisher Scientific). Two days after the transfection, the cells were harvested, and used in the 2-NBDG
assay or flow cytometry.

The silencing of cMyc was confirmed via flow cytometry (FACS cantoⅡ, BD Bioscience, Franklin Lakes,
NJ). Prior to cMyc staining, 1×106 cells were fixed and permeabilized with FoxP3 Fix/Perm Buffer(BioLegend, San Diego, CA) following the manufacture’s protocol. The permeabilized cells were stainedwith Alexa Fluor 647 labeled-c-Myc antibody (Bio-Rad, Hercules, CA), analyzed in the flow cytometry andthe fluorescent intensity (MFI) was evaluated via FlowJo software (BD Bioscience).

Statistical methods
Statistical significance was determined using the unpaired or paired two-tailed Student’s t-test.
Computations were performed using SAS version 9.4 (SAS Institute, Inc, Cary, NC), the R StatisticalComputing Language, or in Microsoft Excel. Confidence levels were set to 0.05. Level of significance inFigures is indicated by asterisk(s): * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001