Graphical abstract

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Highlights

     * •
       How NAD^+ boosting blunts inflammation is poorly understood
     * •
       Integrated transcriptomics and metabolomics identify T cell
       regulatory pathways
     * •
       NAD^+ increases arginine and fumarate biosynthesis
     * •
       These metabolites, via NRF2 signaling, blunt Th1 and Th17 immune
       responsiveness
     __________________________________________________________________

   Han et al. investigated NAD^+-boosting effects on inflammation and
   found that nicotinamide riboside supplementation blunts Th1 and Th17
   immune responsiveness in healthy volunteers and in psoriasis.
   Bioinformatics analysis uncovers that NAD^+ boosting, via arginine and
   fumarate biosynthesis, activates NRF2. This transactivates antioxidant
   regulatory programs that blunt adaptive immune cell inflammatory
   signaling.

Introduction

   Nicotinamide (NAM) adenine dinucleotide (NAD) is an essential co-enzyme
   and metabolic substrate for innumerable cellular processes to maintain
   homeostasis and energy metabolism.[51]^1 Cellular NAD^+ levels are
   determined by the balance between NAD consumption, de novo synthesis,
   and recycling through the NAD salvage pathway. Disruption in this
   balance results in reduced NAD levels in distinct organs, which is
   evident in aging,[52]^2^,[53]^3 obesity-induced diabetes,[54]^4 heart
   failure with preserved ejection fraction,[55]^5 and inflammatory
   conditions.[56]^6 Conversely, excessive NAD^+ may lead to cancer
   growth,[57]^1 disruption in stem cell function,[58]^7 augmentation of
   immune cell proliferative function,[59]^7 and enhancement of
   tumor-infiltrating lymphocyte (TIL) function.[60]^8^,[61]^9 Despite
   this potential divergent spectrum of effects and given the potential
   salutary effects of NAD augmentation against numerous metabolic and
   degenerative diseases, investigators have begun to explore the effects
   of NAD salvage pathway intermediates as supplements for targeted
   diseases and with aging. Intermediates in the NAD salvage pathway,
   including NAM, NAM mononucleotide (NMN), and NAM riboside (NR), have
   been employed to assess their role in biology and in disease
   pathophysiology. In mouse models, NAM has been shown to reduce
   autoimmune encephalitis[62]^10 and ameliorate genetic disrupted CD4^+
   T cell inflammation.[63]^11 Similarly, NMN has shown ameliorative
   effects against obesity, diabetes,[64]^4 neurodegeneration, cerebral
   and cardiac ischemia, and kidney damage. In parallel, NR
   supplementation augments CD8^+ TIL function[65]^9 and shows beneficial
   effects against murine models of type 2 diabetes.[66]^12 In humans,
   studies to date suggest that NMN may appear to have numerous beneficial
   effects, and a question has been posited as to whether NR may have
   tissue-specific rather than systemic effects.[67]^1

   Given that inflammation and/or perturbed immune responses are common
   underpinnings of diseases linked to NAD depletion, the role of these
   supplements in immune cell function are being actively pursued.[68]^13
   For example, NMN suppressed gene signatures of age-associated murine
   adipose tissue inflammation,[69]^14 and NR reduced neuroinflammation in
   a murine model of Alzheimer’s disease.[70]^15 The role of NR on the
   human immune system is further evident where NR depressed inflammatory
   cytokines in older men,[71]^16 reduced the inflammasome in human
   monocytes,[72]^17 blunted monocytic type I interferon (IFN)
   signaling,[73]^18 diminished inflammation in peripheral blood
   mononuclear cells (PBMCs) in heart failure subjects,[74]^19 and reduced
   systemic and neuro-inflammation in Parkinson disease.[75]^20 Moreover,
   coronavirus infection was recently shown to induce an IFN response that
   depressed cellular NAD^+ in a manner that was opposed by supporting NAD
   status.[76]^21 Given these findings, and the potential use of the NAD^+
   booster NR as a therapeutic supplement, further study into the
   regulatory mechanisms underpinning the NR effect on immune cells is
   warranted. Because prior studies have predominantly focused on innate
   immune effects, this study was directed at exploring adaptive immunity.
   To initiate this study, we evaluated ex vivo NR supplementation on
   primary CD4^+ T helper (Th) cell extracted from healthy volunteers.
   Here we uncovered that NAD^+ boosting blunted Th1 and Th17 immune
   responsiveness, with the more robust regulatory effects on Th17
   polarization. To link the role of NAD^+ boosting to a disease process
   linked to increased Th1 and Th17 activation, we then investigated NR
   effects on the inflammatory disease psoriasis. Psoriasis affects 2–3%
   of the US population. In addition to its skin and joint manifestations,
   psoriasis predisposes to premature cardiovascular co-morbidities in
   part through immune activation and systemic inflammation-induced
   atherosclerosis.[77]^22^,[78]^23 The skin-initiated immune activation
   through non-professional immune cells, including keratinocytes and
   fibroblasts, initiates and amplifies innate and adaptive immune
   activity of monocytes/macrophages, neutrophils and their subsets
   (low-density granulocytes), and effector T cells (predominantly Th1 and
   Th17 subsets),[79]^24^,[80]^25 which concurrently exacerbate local
   disease and initiate systemic sterile inflammation. The centrality of
   immune activation in psoriasis is reinforced by the therapeutic
   efficacy of anti-tumor necrosis factor alpha (anti-TNF-α) and
   anti-interleukin (IL)-17 therapy.[81]^26^,[82]^27^,[83]^28

   The major subsequent findings from this study show: (1) ex vivo NR
   supplementation blunted Th1 and Th17 immune-responsiveness in CD4^+
   T cells from healthy volunteers and from patients with mild-moderate
   psoriasis; (2) CD4^+ T cell RNA sequencing (RNA-seq) analysis from
   these study subjects identifies that commonly expressed differentially
   expressed (DE) genes in response to NR augment antioxidant defense
   pathways; (3) bioinformatics protein-protein interaction analysis of
   these DE genes implicated the sequestosome 1 (SQSTM1)-NRF regulatory
   pathway in coordinating these effects; (4) genetic, pharmacologic, and
   biochemical analysis confirmed that NR upregulated both sqstm1 and
   nuclear factor erythroid 2-related factor 2 (Nrf2), and that these
   pathways were instrumental in reducing cellular reactive oxygen species
   (ROS) and in blunting Th17 responsiveness; (5) metabolomics analysis
   identified that NR augmented the arginine biosynthesis pathway, and
   that the generation of arginine and fumarate, established inducers of
   Nrf2 activity,[84]^29^,[85]^30^,[86]^31 replicated the immunoregulatory
   effects of NR; and finally, (6) a pilot in vivo supplementation study
   in healthy volunteers showed that NR blunted Th17 gene regulation and
   immune responsiveness and induced antioxidant gene expression in
   parallel with increasing serum arginine levels.

Results

NR supplementation blunts CD4^+ Th cell responsiveness in volunteers with and
without psoriasis

   To initiate this study, we extracted primary human CD4^+ T cells from
   healthy volunteers to evaluate NAD^+ boosting on Th cell activation.
   Following ex vivo vehicle or NR supplementation and activation, flow
   cytometry showed that Th1 (Tbx21^+IFNγ^+) and Th17 (Rorc^+IL17^+)
   polarization were blunted by NR, whereas markers of the Th2 lineage
   (Gata3^+IL4^+) were unchanged ([87]Figure 1A). In parallel, at the
   transcript level in response to specific Th cell polarization, NR
   blunted canonical Th17 transcriptional activator rorc levels in Th17
   cells ([88]Figure S1A). In contrast, NR had no effect on the expression
   of the Th1 transcription factor tbx21 in Th1 and increased the Th2
   transcription factor gata3 in Th2 polarized cells, respectively
   ([89]Figure S1A). To then evaluate whether NR has an immunomodulatory
   effect on Th cell responsiveness in inflammation, we obtained primary
   CD4^+ T cells from age- and gender-matched control and psoriasis
   subjects ([90]Figure 1B) and subjected them to NR administration and
   T cell receptor (TCR) activation in vitro. Because the inflammatory
   skin disease psoriasis is linked to Th1 and Th17 activation, we
   measured the effect of NR supplementation on IFNγ and IL-17 secretion.
   In TCR-activated CD4^+ T cells and specific lineage polarized cells
   (Th1 and Th17), the levels of IFNγ and IL-17 were significantly blunted
   by NR in both groups ([91]Figures 1C and 1D). In alignment with the
   initial healthy volunteer subject transcription factor expression
   levels ([92]Figure S1A), the NR dampening effect was more robust for
   IL-17 rather than IFNγ when assessing relative changes in Th0, Th1, and
   Th17 cells ([93]Figures S1B and S1C). Interestingly, this NR-mediated
   reduction in cytokine levels was evident across the spectrum of disease
   severity measured by the psoriasis area and severity index (PASI)
   ([94]Figure S1D).

Figure 1.

   [95]Figure 1
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   NR supplementation blunts CD4^+ Th cell responsiveness in healthy
   volunteers and in psoriasis

   (A) Representative flow cytometry plots of activated CD4^+ T cells with
   vehicle and NR treatment. The dot-line plots show gated Th0 cell
   population (percentiles). Th1, Tbx21^+IFNγ^+; Th2, Gata3^+IL4^+; Th17,
   Rorc^+IL-17^+ (n = 8 individuals).

   (B) Characteristics of subjects enrolled in this study (male, n = 12
   per group). Data represent mean ± standard deviation (SD).

   (C) IFNγ and IL-17 release in activated CD4^+ T cells isolated from
   psoriatic and healthy subjects (male, n = 12/group). CD4^+ T cells
   activated with 0.5 mM NR and 10% autologous serum for 3 days by
   anti-CD3 and anti-CD28.

   (D) IFNγ and IL-17 release in differentiated CD4^+ T cells from
   psoriatic and healthy subjects (n = 12/group). Differentiation
   supplement: Th1 (STEMCELL Technologies) and Th17 (R&D Systems).

   (E) Schema describing exploration study undertaken to assess mechanisms
   of in vitro NR-mediated immunomodulation. Study included CD4^+ T cells
   isolated from healthy and psoriatic participants followed by NR
   treatment in vitro. Subsequent metabolomics and gene-expression
   profiling datasets were used to characterize gene-metabolite networks.

   (F) Venn diagram showing healthy vs. psoriasis datasets used to
   identify NR-modulated genes specifically impacted in activated CD4^+
   T cells for 3 h (n = 8 subjects/group). The 108 common DE genes
   regulated by NR were subjected to clusterProfiler pathway enrichment
   analysis.

   (G) Dot plot of clusterProfiler pathway from 39 upregulated genes out
   of a total of 108 common DE genes. The x axis represents negative
   log10-transformed q values, and the dot-plot color was scaled to
   transformed q values. The size of the dot was scaled to number of genes
   overlapping with the indicated pathway on the y axis.

   (H) Heatmap of Gene Ontology (GO) response to oxidative stress genes in
   108 common DE genes.

   (I–K) Quantitative RT-PCR analysis of the mRNA levels linked to ROS
   detoxification (I, nqo1), ROS elimination (J, txnrd1 and prdx1), and
   glutathione synthesis (K, gclc, gclm, and gsr) in activated CD4^+
   T cells from healthy and psoriatic subjects. NR was administrated to
   the CD4^+ T cells during TCR activation for 3 h (n = 12
   subjects/group). Data point of each subject shown as dots (c, d, i, j,
   and k). The p values for comparisons of two groups calculated using
   unpaired two-tailed Student’s t test (healthy vs. psoriasis) or paired
   two-tailed Student’s t test (vehicle vs. NR). ∗p < 0.05, ∗∗p < 0.01,
   ∗∗∗p < 0.001.

   BMI, body mass index; CRP, C-reactive protein; FRS, Framingham risk
   score; na, not applicable; NR, nicotinamide riboside; PASI, psoriasis
   area and severity index.

   To explore the regulatory mechanisms, we performed RNA-seq on CD4^+
   T cells isolated from these healthy and psoriatic subjects in the
   presence or absence of NR, and the complete database is available in
   NCBI’s Gene Expression Omnibus (GEO: [97]GSE237556). 549 and 1,160
   transcripts levels, respectively, were DE in TCR-activated CD4^+
   T cells from healthy control and psoriasis groups by NR compared with
   vehicle ([98]Figure S1E), and principal-component (PC) analysis (PCA)
   confirmed NR as the PC (PC1 axis) driving these DE genes
   ([99]Figure S1F; [100]Tables S1 and [101]S2). To uncover NR-regulated
   genes in control and psoriasis subjects, we examined intersecting DE
   genes comparing vehicle with NR-supplemented activated CD4^+ T cells by
   pathway analyses. This approach is schematized in [102]Figure 1E. The
   top 10 enriched pathways of activated cells in both groups show that NR
   regulated oxidative stress, T cell activation, and neutrophil
   activation, respectively ([103]Figures 1G–1J). A total of 108 DE genes
   were commonly impacted by NR in both groups and of these, 39 increased
   and 69 showed reduced transcript levels ([104]Figure 1F). Pathway
   analysis of these common upregulated and downregulated genes implicated
   NR in the response to oxidative stress, to protein catabolism, and to
   the negative regulatory effect on apoptosis and on RNA processing
   ([105]Figures 1G and [106]S1K). A heatmap of upregulated genes by NR in
   healthy and psoriasis subjects showed that NR robustly upregulated
   antioxidative response genes ([107]Figure 1H). To explore the
   transcripts induced by NR, we performed qRT-PCR analysis to measure the
   mRNA levels linked to ROS detoxification (nqo1), ROS elimination
   (txnrd1 and prdx1), and glutathione synthesis (glutamate-cysteine
   ligase catalytic subunit [gclc], glutamate-cysteine ligase modifier
   subunit [gclm], and gcr). These were similarly upregulated in response
   to NR in the control and psoriasis cohorts ([108]Figures 1I–1K).
   Transcript levels encoding genes linked to the oxidative degradation of
   heme groups (hmox1) and to xenobiotic metabolism (cyp1a1) were
   similarly induced by NR ([109]Figure S1L).

NR blunted ROS production and reduced lipid peroxidation products in CD4^+
T cells in a glutathione-dependent manner

   To evaluate the functional effect of NR on ROS production in CD4^+
   T cells, we measured mitochondrial superoxide levels and whole-cell ROS
   activity. Consistent with the RNA-seq data, in both cohorts NR blunted
   ROS levels and mitochondrial ROS activity in activated Th0 cells
   ([110]Figures 2A and 2B). In parallel, NR blunted lipid peroxidation
   products ([111]Figure 2C) and augmented the antioxidant capacity in
   healthy and psoriatic Th0 cells ([112]Figure 2D). Interestingly,
   vehicle treatment uncovers that antioxidant capacity is significantly
   lower in psoriasis than in controls ([113]Figure 2D). Furthermore,
   following CD4^+ T cell polarization into the Th17 cell lineage, NR
   again blunted ROS activity in the control and psoriatic groups
   ([114]Figures 2E and 2F). To explore the mechanisms orchestrating these
   immunomodulatory effects, we performed all subsequent experiments in
   either activated non-polarized CD4^+ T cells (Th0) or cells polarized
   to the Th17 lineage.

Figure 2.

   [115]Figure 2
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   NR blunts ROS production and reduces lipid peroxidation products in
   CD4^+ T cells in a glutathione-dependent manner

   (A and B) Cellular ROS level (A, dichlorodihydrofluorescein diacetate
   [DCFDA]) and mitochondrial ROS level (B, MitoSOX) in activated CD4^+
   T cells isolated from psoriatic and healthy subjects (DCFDA, n =
   6/group; MitoSOX, n = 5–8/group). CD4^+ T cells activated with 0.5 mM
   NR and 10% autologous serum for 3 days and anti-CD3 and anti-CD28.

   (C) 4-HNE (4-hydroxynonenal, lipid peroxidation) level in activated
   CD4^+ T cells from healthy and psoriatic subjects in the presence of
   vehicle or NR.

   (D) Trolox equivalent antioxidant capacity of NR was measured in
   activated CD4^+ T cells (n = 6 subjects/group).

   (E and F) Cellular ROS (E) and mitochondrial ROS level (F) in
   differentiated Th17 cells. CD4^+ T cells isolated from psoriatic and
   healthy subjects differentiated with specific differentiation media in
   10% autologous serum for 3 days with TCR activation
   [MATH: <mrow><mo linebreak="goodbreak"
   linebreakstyle="after">±</mo></mrow> :MATH]
   NR (DCFDA, n = 6/group; MitoSOX, n = 5–8/group). Th17 differentiation
   media, 20 ng/mL rIL-6, 2.5 ng/mL transforming growth factor β (TGF-β),
   10 μg/mL anti-IL-4, and 10 μg/mL anti-IFNγ.

   (G and H) The ratio of GSH/GSSH level in activated (G) and
   Th17-differentiated CD4^+ T cells (H) with NR for 3 days (n = 7).

   (I and L) IL-17 release following knockdown (KD) of
   glutathione-regulated genes (gclc, gclm, or gsr) in activated Th0 (I,
   n = 12) and Th17 cells (L, n = 3). CD4^+ T cells activated with 0.5 mM
   NR following siRNA delivery.

   (J, K, M, and N) Relative cellular ROS level (J and M) and
   mitochondrial ROS level (K and N) level in gclc, gclm, or gsr siRNA Th0
   (n = 6) and Th17 cells (n = 4). CD4^+ T cells were differentiated with
   Th17 differentiation media.

   Data point of each subject shown as dots in all figure panels. The p
   values for comparisons of two groups calculated using unpaired
   two-tailed Student’s t test (healthy vs. psoriasis) or paired
   two-tailed Student’s t test (vehicle vs. NR), or by ANOVA for multiple
   comparisons. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. FL, fluorescence.

   To further assess the NR effect, Kyoto Encyclopedia of Genes and
   Genomes (KEGG) analysis showed enrichment of upregulated DE genes in
   the glutathione metabolism ([117]Figures S2A and S2B). In parallel, the
   ratio of reduced glutathione (Gsh) to oxidized glutathione disulfide
   (Gssg) glutathione was induced by NR in Th0 and Th17 CD4^+ T cells
   ([118]Figures 2G and 2H). Furthermore, transcript levels encoding genes
   driving glutathione synthesis, including gclc and gclm, and in the
   reduction of Gssg to Gsh, i.e., glutathione-disulphide reductase (gsr),
   were all significantly increased in Th0 cells from healthy control and
   psoriasis subjects ([119]Figure 1K). Subsequent knockdown (KD) of these
   genes abrogated the blunting effect of NR on IL-17 secretion and on ROS
   levels assayed with 2′,7′-dichlorofluorescin diacetate (DCFDA) and
   MitoSOX fluorescence in Th0 and Th17 polarized cells
   ([120]Figures 2I–2N). KD efficiency of the respective genes is shown in
   [121]Figure S2C. As expected, KD of these enzymes abolished the NR
   effect on increasing cellular glutathione levels and antioxidant
   capacity ([122]Figures S2D and S2E).

The antioxidant and anti-inflammatory effects of NR are Nrf2 dependent

   To explore the putative mechanism orchestrating these effects, we
   analyzed the overlapping NR-responsive DE genes from healthy and
   psoriasis cohorts for curated protein-protein interactions (PPIs)
   ([123]Figure 3A). Notably, the pathway linked with oxidative stress
   pathways included changes in expression of sqstm1/p62 and Kelch-like
   ECH-associated protein 1 (keap1). An established mechanism whereby
   Sqstm1 modulates ROS regulatory transcript levels and ROS biology is
   via sequestration of Keap1 to enable Nrf2 transactivation of ROS
   regulatory genes.[124]^32 At the same time, sqstm1 is induced by Nrf2
   via binding to its antioxidant response element (ARE).[125]^33 qRT-PCR
   assessment of transcript levels encoding sqstm1, but not keap1, aligned
   with this RNA-seq bioinformatics analysis ([126]Figure 3B).
   Furthermore, NR increased the protein expression of Nrf2 and Sqstm1
   with a modest reduction in Keap1 levels ([127]Figure 3C).

Figure 3.

   [128]Figure 3
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   The antioxidant and anti-inflammatory effects of NR are dependent on
   NRF2

   (A) 108 common DE genes shown in [130]Figure 1F were used to build a
   protein-protein interactome. Each node depicts a gene and its potential
   interaction with other genes, represented by the edges (gray color
   lines). Gene-expression fold-change information is overlaid on the
   node, upregulated by NR (red border) and downregulated by NR (blue
   border). Healthy vs. psoriasis datasets were used to identify
   NR-modulated genes specifically impacted by 3 h of CD4^+ T cell
   activation (n = 8 subjects/group).

   (B) Quantitative RT-PCR analysis of sqstm1 and keap1 shown in PPI
   analysis in activated CD4^+ T cells from healthy and psoriatic subjects
   (n = 12 subjects/group).

   (C) Protein expression and quantification of Nrf2, Sqstm1, Keap1, and
   Nqo1 following NR treatment of activated cells (n = 8).

   (D and E) NRF2 activity in Th0 (D, n = 7) and Th17 following NR (E, n =
   4). Active Nrf2 is present in the nuclear extract binding to
   immobilized Nrf2 consensus binding oligonucleotide
   (5′-GTCACAGTGACTCAGCAGAATCTG 3′).

   (F) Protein expression of Sqstm1, Nqo1, and Keap1 in nrf2-KD CD4^+
   T cells following NR (n = 4–6).

   (G and J) IL-17 release in nrf2-KD CD4^+ T cells (G, n = 6) and Th17
   cells with NR (J, n = 4).

   (H, I, K, and L) Cellular ROS and mitochondrial ROS in nrf2 KD Th0 (H
   and I, n = 7–8) and Th17 cells with NR (K and L, n = 4).

   The p values for comparisons of two groups calculated using unpaired
   two-tailed Student’s t test (NC vs. nrf2 KD) or paired two-tailed
   Student’s t test (vehicle vs. NR), or by ANOVA for multiple
   comparisons. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. A.U., arbitrary
   units; N.C., negative control. Histograms represent mean ±SEM.

   To evaluate whether NRF2 is an important NR-responsive regulatory
   factor in immunomodulation, we assayed NRF2 activity in Th0 and Th17
   cells. In both lineages, NR increased NRF2 activity ([131]Figures 3D
   and 3E). Interestingly, psoriatic CD4^+ T cells had less NRF2 binding
   activity compared with healthy Th0 cells ([132]Figure S3A). To
   genetically interrogate this pathway, we depleted Nrf2 by small
   interfering RNA (siRNA) ([133]Figures 3F and [134]S3B). Nrf2 KD
   abolished the NR IL-17 blunting effect in Th0 and Th17 cells
   ([135]Figures 3G and 3J) and the effects on DCFDA and MitoSOX levels in
   Th17 cells ([136]Figures 3H, 3I, 3K, and 3L). The overexpression of
   Nrf2 had the opposite effect ([137]Figures S3C–S3F). In parallel, the
   Nrf2 activator dimethyl fumarate (DMF) and the Nrf2 inhibitor ML385 had
   opposing effects, with DMF paralleling the effects of NR in blunting
   IL-17 release and ML385 by increasing cytokine secretion
   ([138]Figures S3G–S3J). Nrf2 KD similarly blunted transcript levels of
   sqstm1 and nqo1 and the antioxidant defense genes nqo1 and gclm
   ([139]Figures 3F and [140]S3K). However, keap1 expression was not
   altered ([141]Figures 3F and [142]S3K).

   Because sqstm1 is a canonical NRF2 target, its levels were manipulated
   by siRNA and overexpression to assess these effects on Th17 biology.
   The interrelated regulation of these two proteins was confirmed where
   KD of Sqstm1 blunted Nrf2 and Nqo1 protein levels ([143]Figures 4A and
   [144]S4A). In parallel with this, KD of Sqstm1 abolished the blunting
   effects of NR on IL-17 release, DCFDA, and MitoSOX
   ([145]Figures 4B–4G). Conversely, overexpression of sqstm1 per se did
   not constitutively reduce the release of IL-17 or change the levels of
   DCFDA, although they did have a significant effect in reducing MitoSOX
   levels in Th0 cells ([146]Figures S4B–S4D).

Figure 4.

   [147]Figure 4
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   The antioxidant and anti-inflammatory effects of NR are dependent on
   NRF2-binding proteins

   (A) Protein expression and quantification of Nrf2 and Nqo1 following
   sqstm1 siRNA in Th0 cells (n = 6).

   (B and E). IL-17 release from sqstm1-KD CD4^+ T cells (B, n = 6) and
   Th17 cells following NR (E, n = 6).

   (C, D, F, and G). Cellular ROS and mitochondrial ROS in sqstm1 KD cells
   following NR (C and D, n = 6) and in Th17 differentiated cells (sqstm1
   siRNA delivery plus NR) (F and G, n = 4). The p values for comparisons
   of two groups were calculated using unpaired two-tailed Student’s t
   test (NC vs. sqstm1 KD) or paired two-tailed Student’s t test (vehicle
   vs. NR), or by ANOVA for multiple comparisons.

   All data are represented as mean ± SEM. ∗p < 0.05, ∗∗p < 0.01,
   ∗∗∗p < 0.001. Histograms represent mean ±SEM.

The CD4^+ T cell immunomodulatory effect of NR is dependent on cellular NAD^+
levels

   It is likely that NR boosts redox enzyme reactions and/or decreases
   inflammatory cytokines via modulation of NAD-consuming pathways. Prior
   to assessing this, we first evaluated whether these NR effects are
   NAD^+ dependent, increasing NAD levels in CD4^+ T cells via
   pharmacologic inhibition of NAD-consuming pathways using inhibitors of
   PARP enzymes and Cd38 and via genetic depletion of NAD-consuming
   proteins Cd38 and Sarm1.[149]^34^,[150]^35 Inhibition of PARPs with
   rucaparib and Cd38 activity by compound 78c both mirrored the effect of
   NR and rescued IL-17 release and ROS levels ([151]Figures 5A–5F).
   Similarly, siRNA KD of Cd38 and Sarm1 replicated the blunting effects
   on IL-17 levels, DCFDA, and MitoSOX in Th0 ([152]Figures 5G–5I) and
   Th17 cells ([153]Figures 5J–5L) and increased protein expression of
   NRF2 and SQSTM1 ([154]Figures 5M and 5N). Interestingly, and consistent
   with inflammation in psoriasis, the NAD/NADH ratio was reduced in
   psoriatic Th0 cells compared with the healthy group ([155]Figure S5A).
   In parallel, transcript levels of cd38, sarm1, parp4, and parp12, which
   promote NAD breakdown and consumption, were concordantly upregulated in
   the psoriatic subjects ([156]Figure S5B). Together, these data support
   that NAD consumption is probably increased in psoriasis, and that NAD^+
   boosting is both necessary and sufficient to blunt Th17 immune cell
   responsiveness.

Figure 5.

   [157]Figure 5
   [158]Open in a new tab

   The CD4^+ T cell immunomodulatory effect of NR is dependent on cellular
   NAD^+ levels

   (A) IL-17 release in response to Compound 78c (78c) to inhibit CD38 or
   rucaparib (Ruc) to inhibit PARPs (n = 8) in CD4^+ T cells.

   (B and C) Cellular ROS (B) and mitochondrial ROS (C) following 78c or
   Ruc (n = 4).

   (D) IL-17 release following 78c or Ruc in Th17 cells (n = 8).

   (E and F) Cellular ROS (E) and mitochondrial ROS (F) following 78c or
   Ruc in Th17 cells (n = 4).

   (G) IL-17 release in cd38- or sarm1-KD CD4^+ T cells (n = 8).

   (H and I) ROS (H) and mitochondrial ROS (I) in cd38- or sarm1-KD cells
   (n = 4).

   (J) IL-17 release in cd38- or sarm1-KD Th17 cells (n = 4–8).

   (K and L) Cellular ROS (K) and mitochondrial ROS (L) in cd38- or
   sarm1-KD Th17 cells (n = 4).

   (M and N) Protein expression of Nrf2 and Sqstm1 in cd38-KD CD4^+
   T cells (N, n = 8) and sarm1-KD cells (N, n = 4–6).

   The p values for comparisons of two groups calculated using paired
   two-tailed Student’s t test, or one-way ANOVA when comparing more than
   two groups (vehicle vs. treated or NC vs. CD38-KD/SARM1-KD). ∗p < 0.05,
   ∗∗p < 0.01, ∗∗∗p < 0.001. Histograms represent mean ±SEM.

Metabolomics analysis implicates CD4^+ T cell arginine and fumarate
biosynthesis as mediators of NR’s immunomodulatory effect

   Earlier in these studies, it was noted that the fumarate methyl ester
   DMF could replicate the effect of NR in blunting IL-17 release
   ([159]Figures S3F and S3G). A question that arose is whether NR
   modulated the fumarate synthesis in CD4^+ T cells. To pursue this
   concept, we performed steady-state metabolomics analysis in activated
   Th0 and Th17 cells. Orthogonal partial least squares discrimination
   analysis showed that the changes in metabolites were distinct in both
   different lineages in response to NR ([160]Figure 6A). Variables of
   importance in prediction (VIP) analysis with a score >1.5 were assessed
   in the Th0 and Th17 cells and uncovered that distinguishing metabolites
   between NR and vehicle was similar in both lineages ([161]Figures 6B,
   [162]S6A, and S6B; [163]Table S3). Interestingly, these included
   numerous NAD^+ intermediates, as well as amino acid intermediates, in
   the glutamine and arginine metabolic pathways. The top 10 pathways of
   these metabolites enriched in arginine biosynthesis and glutathione
   metabolism pathways. Additional pathways suggested that changes in
   other amino acid levels may also contribute to the effect of NR,
   including aspartate ([164]Figures 6C and [165]S6C). Furthermore, levels
   of arginine and fumarate were elevated in both Th0 and Th17 cells in
   response to NR supplementation ([166]Figure 6D). However, the
   metabolites (ornithine, citrulline, and argininosuccinic acid) upstream
   of arginine biosynthesis were not altered by NR ([167]Figure S6D). In
   parallel with the DMF effect in CD4^+ T cells, l-arginine
   supplementation similarly blunted IL-17 release and ROS levels
   ([168]Figures S6E–S6G). Arginine supplementation similarly increased
   Nrf2 activity and Nrf2 and Sqstm1 protein levels ([169]Figures S6H and
   S6I). Interesting, in the context of the urea cycle, the biosynthesis
   of fumarate requires aspartate and concurrently generates arginine,
   with argininosuccinate lyase (ASL) being the major enzyme for arginine
   and fumarate synthesis. To evaluate whether this metabolic pathway was
   involved, siRNA targeting ASL was performed. Following asl KD, the NR
   effect on blunting IL-17 and ROS levels was abrogated
   ([170]Figures 6E–6G and 6I–6K). In parallel, KD of asl blunted Nrf2
   activity and the transcript levels of genes encoding Nqo1 and Gclc
   ([171]Figures S6J and S6K). Together, these data support that
   NR-mediated biosynthesis of arginine and fumarate, with subsequent
   transactivation of Nrf2 and its canonical targets, blunted CD4^+ T cell
   IL-17 production.

Figure 6.

   [172]Figure 6
   [173]Open in a new tab

   Metabolomics analysis implicates CD4^+ T cell fumarate biosynthesis as
   a mediator of NR’s immunomodulatory effect

   (A) Orthogonal partial least squares discriminant analysis (OPLS-DA)
   scatterplot showing separation between vehicle and NR-supplemented
   sample clusters in Th0 and Th17 differentiated cells, with no
   significant outliers (n = 10 subjects/group).

   (B) The heatmap of relative levels of metabolites with VIP score >1.5
   in vehicle control and NR-supplemented groups in Th0 and Th17.

   (C) Top 10 pathways enrichment analysis (p < 0.05) showing
   significantly differentiated metabolites (VIP score > 1.0) in vehicle
   control and NR-supplemented groups in Th0 and Th17 cells.

   (D) Metabolic pathway schema showing involvement of arginine pathway
   metabolites in NRF2 activation. Relative abundance of arginine by light
   chromatography mass spectrometry (LC/MS) and fumarate assay (Sigma)
   regulated by NR in Th0 (n = 9) and Th17 (n = 7).

   (E) IL-17 release in Th0 following NR after ASL KD for 2 days (n = 4).

   (F and G) Cellular ROS (DCFDA, F) and mitochondrial ROS (MitoSOX, G)
   level after asl KD (n = 4–6).

   (H) Nrf2 activity in Th0 cells. Active Nrf2 present in the nuclear
   extract specifically binds to the immobilized NRF2 consensus binding
   oligonucleotide (n = 4).

   (I) IL-17 release in Th17 cells following NR after ASL KD for 2 days
   (n = 4).

   (J and K) Cellular ROS (J) and mitochondrial ROS (K) level after asl KD
   in Th17 cells (n = 4). The p values for comparisons of two groups were
   calculated using unpaired two-tailed Student’s t test (NC vs. asl-KD)
   or paired two-tailed Student’s t test (vehicle vs. NR) or by ANOVA for
   multiple comparisons. All data were represented as mean ± SEM.
   ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. Histograms represent mean ±SEM.

In vivo supplementation of NR in healthy volunteers recapitulated ex vivo NR
effects on CD4^+ T cell activation

   Finally, whether the supplementation of NR in vivo orchestrates the
   same effects remained unknown. To test this, we used primary CD4^+
   T cells from a prior placebo-controlled study where NR was found to
   blunt immune responsiveness in monocytes.[174]^18 As an exploratory
   endpoint in that study, we extracted CD4^+ T cells by negative
   selection from placebo and NR (500 mg twice daily) study subjects after
   7 days of supplementation ([175]Figure S7A). Datasets of
   gene-expression profiling and metabolomics were employed to assess
   whether the pathways and networks identified in the exploratory study
   ([176]Figure 1E) were operational in vivo. The schema of this
   validation study is shown in [177]Figure 7A. CD4^+ T cells were
   isolated from these subjects consuming placebo or NR, and RNA was
   extracted and prepared for RNA-seq analysis ([178]Figure S7B). The
   subject characteristics and the response to NR supplementation with
   respect to levels of NAD^+ metabolites were described
   previously.[179]^18 Flow cytometry from PBMCs following NR in vivo
   supplementation mirrored the ex vivo studies, showing that NAD^+
   boosting reduced Th1 and Th17 molecular signatures in CD4^+ gated
   T cells ([180]Figure 7B) with a greater effect on Th17
   ([181]Figure 7C). This finding was supported by the direct cytometric
   analysis of antibodies targeting IFNγ, IL-4, and IL-17 in CD25^+HLADR^+
   activated cells ([182]Figures S7C and S7D). In parallel, primary
   TCR-activated CD4^+ showed more pronounced dampening of IL-17 release
   in Th17 vs. Th0 cells ([183]Figure 7D), although NR blunted ROS/RNS
   (reactive nitrogen species) levels in both lineages ([184]Figure 7E).
   Furthermore, RNA-seq analysis of CD4^+ T cells isolated from subjects
   consuming placebo and NR showed that NR-responsive DE genes were
   different between the groups, and PCA plots of the DE genes identified
   NR supplementation as the PC driving gene expression changes
   ([185]Figures S7E and S7F; [186]Table S4). Gene set enrichment analysis
   (GSEA) was performed using the C7 immunologic signature database.
   Significant GSEA-C7 pathways identified Th1 and Th17 immune cell
   subsets ([187]Figure 7F). Consistent with this, NR blunted transcript
   levels of canonical master regulatory transcription factors for Th1 and
   Th17 polarization, namely, tbx21 and rorc, with no effect on the Th2
   regulatory factor gata3 ([188]Figure 7G). In parallel, activated Th0
   cells isolated from subjects taking NR supplementation had
   significantly higher transcript levels of sqstm1, nqo1, and hmox1 and
   of the genes encoding glutathione biosynthesis, including gclc and gclm
   ([189]Figures 7H–7J). Finally, the levels of circulating serum NAM and
   arginine were elevated in the subjects following in vivo NR
   administration compared with the placebo group ([190]Figure 7K). The
   differential genes and metabolites identified from the exploratory
   study were used to construct a network using the Ingenuity Pathway
   Analysis (IPA) knowledge base. The pathway of oxidative stress genes
   and arginine biosynthesis regulated by NR was connected to Nrf2
   (NFE2L2), and the network of gene-metabolite was shown in
   [191]Figure 7L.

Figure 7.

   [192]Figure 7
   [193]Open in a new tab

   In vivo supplementation of NR in healthy volunteers recapitulated the
   NR regulatory effects on CD4^+ T cell activation

   (A) Schema describing the validation study to assess whether NR mirrors
   this immunomodulation in vivo. This was a double-blinded study of
   healthy participants consuming NR or placebo. Subsequent metabolomics
   and gene-expression profiling datasets were employed to evaluate
   pathways identified in the exploratory study ([194]Figure 1E).

   (B) Representative flow cytometry of PBMCs from placebo- and
   NR-administrated subjects for 7 days. The dot-line plots show cell
   population (percentiles) gated to CD4^+ cells. Th1, Tbx21^+IFNγ^+; Th2,
   Gata3^+IL4^+; Th17, Rorc^+IL17^+ (n = 4/group).

   (C) Differential Th1 and Th17 levels in response to NR compared with
   placebo (n = 4).

   (D) IL-17 cytokine release in CD4^+ Th0- and Th17-differentiated cells
   from placebo- and NR-administrated subjects. CD4^+ T cells were
   isolated in the refed state, differentiated into Th17 cells, and
   exposed to TCR activation (placebo, n = 7; NR, n = 7). Unpaired
   two-tailed Student’s t test.

   (E) ROS/RNS (reactive nitrogen species) activity measured in Th0 and
   Th17 lysates by OxiSelect ROS/RNS Assay (placebo, n = 7; NR, n = 6).

   (F) GSEA-C7 pathway analysis of DE genes (2,380) from placebo vs. NR of
   naive CD4^+ T cells (placebo, n = 8; NR, n = 7).

   (G) RNA expression T cell regulatory transcriptional factors (tbx21 for
   Th1, gata3 for Th2, rorc for Th17) in activated Th0 cells from placebo
   and NR groups (n = 9/group). Data were normalized to ef1α and
   represented as mean ± SEM.

   (H–J) RNA expression level of sqstm1, keap1 (H), ROS
   detoxification-related genes (I, nqo1 and hmox1), and glutathione
   synthesis-related genes (J, gclc, gclm, and gsr) in activated Th0 cells
   from both groups (n = 9/group). Data were normalized to 18S rRNA and
   represented as mean ± SEM.

   (K) Relative abundance of nicotinamide and arginine by LC/MS in serum
   from placebo (n = 11) and NR (n = 13) groups. Unpaired two-tailed
   (placebo vs. NR) Student’s t test. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.

   (L) The differential genes and metabolites identified from the
   exploratory study arm were employed to construct a network by Ingenuity
   Pathway Analysis (IPA). Each colored node in the network is a
   gene/metabolite regulated by NR. The connection between the indicated
   nodes utilizes IPA network information to predict activation or
   inhibitory relationships. The biological process or groups of molecules
   are represented as white nodes. Histograms represent mean ±SEM.

Discussion

   This study demonstrated that the immunomodulatory action of NR extends
   to human CD4^+ T cells, where, in an NAD-level-dependent manner, NR
   blunts both Th1 and Th17 immune responsiveness in healthy volunteers
   in vivo and in healthy control and psoriasis CD4^+ T cells ex vivo. The
   dampening of IL-17 appears more robust than that of IFNγ, and in Th0
   and Th17 cells, NAD^+ boosting appears, via the action of ASL, to
   increase arginine and fumarate synthesis with subsequent NRF2-mediated
   transactivation of antioxidant response programs that blunt Th17
   polarization and IL-17 production.

   The challenge in exploring the mechanisms of action of NAD boosting is
   its diverse role in metabolic pathways that consume NAD and in the
   multitude of NAD^+-dependent redox enzymes in various cytosolic and
   mitochondrial metabolic pathways. Furthermore, a question arising is
   whether different NAD salvage pathway intermediates may have different
   effects on different extrahepatic tissues.[195]^1 An emerging
   additional role of NAD may be via the 5′-capping of RNA and a
   subsequent direct effect on RNA transcription in prokaryotes.[196]^36
   The practical ability to answer these questions comprehensively remains
   technically limited and present, and reductionist approaches are
   therefore employed to explore this biology. Specifically, a study with
   an exploratory and validation arm was designed to enable an unbiased
   and reductionist approach to explore the NR effect on CD4^+ T cells. In
   the exploratory study, NR effects on cells extracted from patients with
   psoriasis and matched controls ex vivo were assessed to identify
   gene-metabolic networks mediating NR effects. To further assess these
   findings, the validation study included a double-blinded clinical trial
   that recruited healthy participants that took either NR or placebo
   supplements. Using this approach, and by integrating gene-regulatory
   and metabolic pathway effects, we have identified an anti-inflammatory
   role of NR supplementation on Th17 immune responsiveness.

   Because prior data show that prolonged fasting blunts CD4^+ T cell
   responsiveness,[197]^37 we investigated whether boosting of NAD^+, a
   component of caloric restriction interventions,[198]^38 was sufficient
   to confer these immunomodulatory effects. To begin to explore whether
   NAD boosting by NR modulated CD4^+ T cell function, we compared healthy
   volunteers and matched psoriasis patient primary cells ex vivo. Initial
   screening showed that NR had a more robust blunting effect on Th1 and
   Th17 cytokines rather than on Th2 cytokines, and for the bulk of this
   study the underlying mechanisms were interrogated while focusing on Th0
   and Th17 polarized cells. Because NR has previously been shown to
   confer robust effects on gene-expression profiles in LPS-activated
   monocytes,[199]^18 the transcript levels in TCR-activated CD4^+ T cells
   were analyzed and showed substantial regulation in both healthy
   controls and psoriasis subjects. The initial bioinformatics
   interrogation of the data focused on the overlapping NR-linked DE genes
   in control and psoriasis subjects to uncover regulatory programs
   distinctly responsive to NR. Although numerous pathways were
   identified, the strongest signal was that NR modulated oxidative stress
   responses. Subsequent interrogation of validated PPIs based on changes
   in gene expression uncovered that the Nrf2-Sqstm1-Keap pathway may be
   operational in this regulation, and the subsequent biochemical and
   genetic studies supported this conclusion. At the same time a myriad of
   other pathways were identified that were distinct between the control
   and psoriasis groups and included changes in RNA metabolism, protein
   folding and metabolism, intracellular cytoskeletal organization, and
   signal transduction transcripts. As with the antioxidant control
   regulatory pathways, these additional regulatory programs will need to
   be investigated from a reductionist approach to explore their roles in
   CD4^+ T cell immunomodulation. These experiments should also expand our
   understanding of how NAD^+ boosting controls intracellular CD4^+ T cell
   homeostatic programs.

   We next exploited steady-state metabolomics analysis in both Th0 and
   Th17 CD4^+ T cells exposed to NR or placebo to evaluate whether
   NAD^+-boosting metabolic pathway effects may regulate this biology. As
   expected, NR increased NAD^+ metabolic intermediates. At the same time,
   metabolic pathway analysis identified that NAD^+ boosting modified
   multiple amino acid levels, implicating a role of NR in regulating
   amino acid metabolism. These metabolic effects probably contribute
   toward a potential myriad of intracellular immunomodulatory regulatory
   effects[200]^39 that require exploration. However, in the context of
   integrating the RNA-seq findings and these metabolic profiles and the
   established roles of the arginine biosynthetic pathway in
   immunomodulation,[201]^40 we focused on investigating the effect of the
   distinct changes in metabolite levels in this pathway. Here arginine
   and fumarate, both ASL products, are known to activate NRF2 via
   distinct mechanisms.[202]^29^,[203]^30^,[204]^31 The role of this
   pathway was confirmed by the abolition of the NR effect by the KD of
   asl. Interestingly, ASL, which breaks down argininosuccinate into
   arginine with fumarate as a byproduct, is also critical for tyrosine
   hydroxylase activity, an enzyme linked to catecholamine
   production.[205]^41 Interestingly, tyrosine hydroxylase activity
   modulates human T regulatory (Treg) cell function[206]^42 and CD4^+ T
   effector cell polarization.[207]^43^,[208]^44 Whether NAD^+ boosting
   modulates Th17 cell polarization and activation via the ASL tyrosine
   hydroxylase pathway additionally merits further investigation. Finally,
   although the role of Nrf2 and antioxidant signaling in this
   immunomodulation is well characterized, additional studies are also
   required to explore how the additional identified amino acid signaling
   pathways modulated by NAD boosting collectively contribute to the
   blunting of Th17 polarization and activation. Furthermore, the effect
   of increased arginine on nitric oxide biology may play a regulatory
   role, although metabolites downstream of arginine catabolism were not
   consistently changed in response to NR.

   Reactive oxygen signaling is operational in Th17 differentiation and
   inflammatory functioning,[209]^45^,[210]^46^,[211]^47 in part via the
   transcriptional induction of Rorc activity.[212]^45 At the same time,
   NR has been shown to confer antioxidant effects by augmenting
   mitochondrial superoxide dismutase activity in monocytes via promoting
   Sirt3 activation,[213]^17 and NR has been shown to blunt ROS production
   in PBMCs.[214]^48 In this study, the role of NR in modulating arginine
   biosynthesis to activate Nrf2 to control ROS levels in CD4^+ T cells
   has been uncovered and shows that NR additionally blunts transcript
   level of rorc after in vivo administration. The findings in this study
   support the prior observation that Nrf2 transactivation blunts Th17
   polarization and associated inflammation in the context of murine lupus
   nephritis.[215]^46

   At the same time, mitochondrial metabolic remodeling is a hallmark of
   CD4^+ T cell activation.[216]^49 Mechanisms uncovered that orchestrate
   this regulation include mitochondrial metabolism-derived acetyl-CoA in
   the epigenetic control of inflammatory gene expression[217]^50 and the
   rate of oxidative phosphorylation itself, which modulates the Th17/Treg
   cell balance by altering various regulatory control nodes in Th17
   differentiation.[218]^51 The RNA-seq data in this study support that NR
   modulated mitochondrial oxidative metabolism encoding gene expression,
   and studies show that both redox enzyme control and Sirtuin activation
   by NAD^+ boosting modulate mitochondrial oxidative phosphorylation.
   Here, also, further work will be required to explore how these effects
   of NR specifically attenuate Th17 activation. Also, whether NR
   modulates Treg differentiation to alter the Th17/Treg balance remains
   unresolved, although preliminary data of NR from our laboratory did not
   increase IL-10 secretion in anti-CD3/CD28 activated Th0 cells and
   in vivo NR supplementation did not induce transcript levels encoding
   IL-10 in PBMCs.[219]^48 Nevertheless, given the role of NAD^+ boosting
   on mitochondrial biology, cellular metabolism, and ROS biology, the
   contribution of these metabolic pathways in Treg homeostasis[220]^52
   suggests that a more detailed and directed analysis is required to
   evaluate the direct effect of NAD^+ boosting on Treg biology.

   The concept that inflammatory or autoimmune diseases, for example,
   obesity linked with diabetes and systemic lupus
   erythematosus,[221]^18^,[222]^53 are linked to excess NAD^+ consumption
   because of increased activity of NADase or NAD^+-consuming enzymes is
   being uncovered.[223]^18^,[224]^53 In this study, this idea is
   similarly implicated in psoriasis, given the increased transcript
   levels of cd38, sarm1, and parp enzymes. These data highlight the need
   to further understand the complex interaction of NAD^+ production and
   consumption pathways and interventions to modulate them in inflammatory
   and autoimmune diseases. In addition, the finding from this study that
   NR blunts Th1 and Th17 immune responsiveness in an ex vivo study
   supports that the utilization of NAD^+ boosting could potentially be
   employed as a supplemental therapy in this disease. The concept is now
   being explored in vivo, where mild-moderate psoriasis patients are
   being investigated in a placebo-controlled study using the same dose of
   NR employed for the healthy control population (500 mg BID) as
   described in this manuscript (ClinicalTrials.gov: [225]NCT04271735).

   In conclusion, this study has expanded our understanding of NAD^+
   boosting in the adaptive immune system with the focus on NR blunting
   Th17 immune activity, in part through arginine biosynthesis, Nrf2
   activation, and augmentation of cellular antioxidant defense programs.
   This biology was found to be operational following ex vivo
   supplementation in healthy control subjects, in psoriasis subjects, and
   in healthy volunteers following oral supplementation. The systems
   biological approaches employed in this study uncover numerous
   additional metabolic and gene-regulatory pathways to explore whereby
   NAD^+ boosting modulates immunometabolism and immune regulation.
   Furthermore, this study advances our understanding of the role of NAD^+
   boosting in immunoregulation and lays the foundation for future studies
   in humans with inflammatory diseases, including psoriasis.

Limitations of the study

   Although this study shows a clear role of NR supplementation in
   blunting Th17 activity, as discussed, multiple potential levels of
   regulation remain to be uncovered. In addition, NR appears to have a
   similar, albeit more modest, effect in blunting Th1 differentiation and
   activation, and this biology has not been explored. It should also be
   noted that despite inflammatory cell dampening effects of NAD^+
   boosting, murine and clinical studies suggest that these metabolic
   interventions may concurrently augment cytotoxic TIL
   function[226]^8^,[227]^9 and blunt cancer risk.[228]^54 Nevertheless,
   larger and longer-term studies will need to be performed to evaluate
   the putative cancer risk in the context of inflammatory or autoimmune
   diseases.

STAR★Methods

Key resources table

   REAGENT or RESOURCE SOURCE IDENTIFIER
   Antibodies
     __________________________________________________________________

   Rabbit monoclonal anti-NRF2 (E3J1V) Cell Signaling Cat# 33649; RRID:
   N/A
   Rabbit monoclonal anti-SQSTM1/p62 (D1Q5S) Cell Signaling Cat# 39749;
   RRID: [229]AB_2799160
   Rabbit monoclonal anti-KEAP1 (D6B12) Cell Signaling Cat# 8047; RRID:
   [230]AB_10860776
   Rabbit monoclonal anti-NQO1 (D6H3A) Cell Signaling Cat# 62262; RRID:
   [231]AB_2799623
   Rabbit polyclonal anti-CD38 Cell Signaling Cat# 92457; RRID: N/A
   Rabbit monoclonal anti-SARM1 (D2M5I) Cell Signaling Cat# 13022; RRID:
   [232]AB_2798090
   Mouse monoclonal anti-ACTIN (C4) Millipore Cat. MAB1501; RRID:
   AB_2223041
   IRDye800CW Goat anti-rabbit IgG Li-Cor Bioscience Cat. 926-32211; PRID:
   AB_621843
   IRDye680RD Donkey anti-mouse IgG Li-Cor Bioscience Cat. 926-68072;
   RRID: [233]AB_10954628
   IRDye680RD Goat anti-rabbit IgG Li-Cor Bioscience Cat. 926-68071; RRID:
   [234]AB_10956166
   Mouse monoclonal anti-human CD3 Biolegend Cat. 317326; RRID:
   AB_11150592
   Mouse monoclonal anti-human CD28 Biolegend Cat. 302934; RRID:
   AB_11148949
   Mouse monoclonal anti-human IFNγ eBioscience Cat. 16731885; RRID:
   AB_469251
   Mouse monoclonal anti-human IL-4 eBioscience Cat. 16704885; RRID:
   AB_469211
   Flow cytometry immunophenotyping This paper; See [235]Table S5 for
   details
     __________________________________________________________________

   Bacterial and virus strains
     __________________________________________________________________

   One Shot TOP10E. coli ThermoFisher Scientific Cat. C404003
   pLenti-c-Myc-DDK Origene Technologies Cat. PS100064
   pLenti-c-Myc-DDK-NRF2 Origene Technologies Cat. RC204140L2
   pLenti-c-Myc-DDK-SQSTM1 Origene Technologies Cat. RC203214L1
     __________________________________________________________________

   Biological samples
     __________________________________________________________________

   Human serum From subject cohort - NIH Clinical Center Blood Bank
   [236]ClinicalTrials.gov
   ID-[237]NCT01143454/[238]NCT01934660/[239]NCT02812238
     __________________________________________________________________

   Chemicals, peptides, and recombinant proteins
     __________________________________________________________________

   Nicotinamide Riboside ChromaDex Cat. ASB-00014315-005
   L-Arginine Sigma Cat. A5006
   Rucaparib camsylate Sigma Cat. PZ0036
   Compound 78c Selleckchem Cat. S8960
   ML385 Selleckchem Cat. S8790
   DMF Selleckchem Cat. S2586
   DCFDA (2',7'-Dichlorofluorescin Diacetate) Sigma Cat. 287810
   MitoSOX red Invitrogen Cat. [240]M36008
   DEAE-Dextran Sigma Cat. D9885
   LIVE/DEAD Fixable Yellow Dead Cell Stain Kit Invitrogen Cat.
   [241]L34959
   PMA Sigma Cat. P8139
   Cell stimulation cocktail plus protein transport inhibitors eBioscience
   Cat. 00-4975-93
   FoxP3 Transcription Factor Staining Buffer Set eBioscience Cat.
   00-5523-00
   Human IL-4 Recombinant protein Peprotech Cat. 200-04B
   Human IL-6 Recombinant protein Peprotech Cat. 200-06B
   Human IL-12 Recombinant protein Peprotech Cat. 200-12B
   Human IL-23 Recombinant protein Peprotech Cat. 200-23B
   Human TGF-β1 Recombinant protein Peprotech Cat. 100-21B
   Human IL-1β Recombinant protein Peprotech Cat. 200-01B
     __________________________________________________________________

   Critical commercial assays
     __________________________________________________________________

   CyQuant Cell Proliferation Assay Invitrogen Cat. C7026
   Pierce BCA Protein Assay Thermo Scientific Cat. 23227
   Lipid Peroxidation (4-HNE) Assay Kit Abcam Cat. ab238538
   OxiSelect in vitro ROS/RNS assay kit Cell Biolabs Cat. STA-347
   Antioxidant assay kit Sigma Cat. CS0790
   GSH/GSSG ratio detection assay Abcam
   BioVision Cat. ab138881
   Cat. K264
   GSH assay kit BioAssay Systems Cat. DIGT-250
   NRF2 Transcription Factor Assay Kit Abcam Cat. ab207223
   Nuclear extraction kit Abcam Cat. ab113474
   Fumarate Assay Kit Sigma Cat. MAK060
   Human IFNγ Duoset ELISA Kit R&D Systems Cat. DY285B
   Human IL-4 Duoset ELISA Kit R&D Systems Cat. DY204
   Human IL-5 Duoset ELISA Kit R&D Systems Cat. DY205
   Human IL-17 Duoset ELISA Kit R&D Systems Cat. DY317
     __________________________________________________________________

   Deposited data
     __________________________________________________________________

   RNAseq data This paper; See [242]Tables S1, [243]S2, and [244]S4 for
   details GEO database: [245]GSE237556
   Metabolomics data This paper; See [246]Table S3 for details Mendeley
   database: [247]https://data.mendeley.com/datasets/hskhyyss4c/1
     __________________________________________________________________

   Experimental models: Cell lines
     __________________________________________________________________

   HEK293T N/A
     __________________________________________________________________

   Experimental models: Organisms/strains
     __________________________________________________________________

   Human subjects NIH NIH Clinical Center Blood Bank
   [248]ClinicalTrials.gov
   ID-[249]NCT01143454/[250]NCT01934660/[251]NCT02812238
     __________________________________________________________________

   Oligonucleotides
     __________________________________________________________________

   Primers for RT-PCR This paper; See [252]Table S7 for details N/A
   Accell Non-targeting Control Dharmacon Cat. D-001910-01-20
   SMARTpool: Accell NFE2L2 Dharmacon Cat. E-003755-00-0010
   SMARTpool: Accell SQTM1 Dharmacon Cat. E-010230-00-0010
   SMARTpool: Accell GCLC Dharmacon Cat. E-009212-00-0010
   SMARTpool: Accell GCLM Dharmacon Cat. E-011670-00-0010
   SMARTpool: Accell GSR Dharmacon Cat. E-009647-00-0010
   SMARTpool: Accell ASL Dharmacon Cat. E-008840-00-0020
   SMARTpool: Accell CD38 Dharmacon Cat. E-009222-00-0010
   SMARTpool: Accell SARM1 Dharmacon Cat. E-008076-00-0010
     __________________________________________________________________

   Recombinant DNA
     __________________________________________________________________

   psPAX2 Addgene Cat. 12260
   pMD2.G Origene Technologies Cat. 12259
     __________________________________________________________________

   Software and algorithms
     __________________________________________________________________

   Thermo Xcalibur (version 2.2) Thermo Scientific Cat. OPTON-30965
   MetaboAnalyst (version 5.0) Pang et al.[253]^55
   [254]https://www.metaboanalyst.ca/
   Simca (version 17) Umetrics
   [255]https://www.sartorius.com/en/products/process-analytical-technolog
   y/data-analytics-software/mvda-software/simca
   OPLS-DA N/A
   [256]https://www.creative-proteomics.com/services/opls-da-service.htm
   FastQC N/A
   [257]http://www.bioinformatics.babraham.ac.uk/projects/fastqc
   HISAT2 N/A [258]https://daehwankimlab.github.io/hisat2/
   StringTie N/A [259]https://github.com/gpertea/stringtie
   Ballgown N/A
   [260]http://bioconductor.org/packages/release/bioc/html/ballgown.html
   KEGG database N/A [261]https://www.genome.jp/kegg/pathway.html
   MSigDB databases Broad institute
   [262]https://www.gsea-msigdb.org/gsea/msigdb
   ClusterProfiler N/A
   [263]https://bioconductor.org/packages/release/bioc/html/clusterProfile
   r.html
   Cytoscape 3.7.2 N/A [264]http://cytoscape.org/
   R and R Studio N/A [265]http://www.r-project.org/
   FlowJo 9.9.6 NIH FlowJo
   GraphPad Prism (version 9) NIH [266]https://www.graphpad.com/
   Image Studio Li-Cor Biosciences N/A
     __________________________________________________________________

   Other
     __________________________________________________________________

   Lymphocyte Separation Medium MP Biomedicals Cat. 0850494
   Human CD4^+ T Cell Isolation Kit Miltenyi Biotec Cat. 130-096-533
   NucleoSpin RNA Kit Macherey-Nagel Cat. 740955
   First-strand Synthesis SuperMix Invitrogen Cat. 11752250
   FastStart Essential DNA Green Master Mix Roche Life Science Cat.
   06924204001
   miRNeasy Micro Kit Qiagen Cat. 217084
   TruSeq Stranded Total RNA HT Kit Illumina Cat. 20020595
   Accell siRNA delivery media Horizon Discovery Cat. B-005000-100
   Polyjet In Vitro DNA Transfection Reagent Signagen Laboratories Cat.
   SL100688
   [267]Open in a new tab

Resource availability

Lead contact

   Further information and requests for resources or reagents should be
   directed to and will be fulfilled by the lead contact, Michael N. Sack
   ([268]sackm@nih.gov). Laboratory of Mitochondrial Biology and
   Metabolism, NHLBI, NIH, Bldg. 10-CRC, Room 5–3342, 10 Center Drive,
   Bethesda, MD 20892, USA.

Materials availability

   This study did not generate new unique reagents.

Experimental model and subject details

Study design and human subjects

   Blood from healthy volunteers for the initial vehicle versus NR
   supplementation studies and for initial flow cytometry and gene
   expression analysis and subsequent functional validation of pathways
   uncovered in the control vs. psoriasis cohorts (described below) were
   obtained from subjects that consented to enroll on a Disease Discovery
   Protocol ([269]NCT01143454). Psoriasis subjects with mild to moderate
   severity were then recruited at NIH Clinical Center and provided
   consent, on and the NHLBI IRB approved protocol
   ([270]ClinicalTrials.gov registration number [271]NCT01934660, n=12).
   As controls, male and age-matched healthy volunteers were recruited
   (n=12) through the healthy blood donor population from a Disease
   Discovery Protocol ([272]NCT01143454). The average age for both the
   patients with Psoriasis and healthy donors was 57 years old and they
   were all males. The characteristic of healthy and psoriatic subjects is
   described in [273]Figure 1B.

Clinical protocol of placebo/NR study

   The Placebo and NR pilot study was registered in
   [274]ClinicalTrials.gov with the registration number [275]NCT02812238,
   approved by NHLBI IRB, and performed at the National Institutes of
   Health (NIH) Clinical Center. We recruited 25 subjects with an average
   age of 24 and average body mass index (BMI) of 23. Healthy volunteers
   were screened prior to signing consent and subjects were then randomly
   assigned to a 7-day supplementation of either NR (500 mg bid) or a
   matching placebo. The study protocol has been described previously for
   the study of monocytes,[276]^18 and a schematic of the blood draw
   protocol and immune function studies is shown in [277]Figures S7A and
   S7B. The blood from healthy volunteers for functional study and
   metabolomic analysis were obtained from subjects that consented to
   enroll on the Disease Discovery Protocol ([278]NCT01143454) and from
   the NIH Clinical Center blood bank ([279]NCT00001846).

Method details

Flow cytometry immunophenotyping

   A 17-color antibody panel was designed to enable immunophenotyping of
   the predominant cell populations found in PBMCs and CD4^+ T cells
   ([280]Table S5). PBMCs were thawed in FACS buffer (PBS with 0.25 mM
   EDTA and 0.1% BSA) with nuclease (Sigma) and CD4^+ T cells were
   isolated from healthy volunteers. The cells were activated with Cell
   stimulation cocktail plus protein transport inhibitors (eBioscience)
   and PMA (500 ng/ml, Sigma) for 4 hrs. The antibodies were listed in
   [281]Table S5 followed by LIVE/DEAD Fixable Yellow stain (Invitrogen).
   Data were acquired with FACSymphony (BD) and post-acquisition analysis
   was performed using Flowjo 9.9.6 (Treestar Inc.). Analysis excluded
   debris and doublets using light scatter measurements, and dead cells by
   live/dead stain. Gating strategies used to identify immune cell subsets
   are provided in [282]Table S6 and [283]Figure S8. Briefly, the cells
   were first gated for singlets (FSC-H vs. FSC-A) and further analyzed
   for their uptake of the Live/Dead Yellow stain to determine live versus
   dead cells in CD4^+ T. Their expression of surface markers (CD25 and
   HLA-DR) and intracellular markers (Transcription factors and cytokines)
   is then determined within this gated population. Statistical
   significance between two groups was determined using a two-tailed
   Student’s t-test.

Human CD4^+ T cell Isolation and cytokine assay

   Primary peripheral blood mononuclear cells (PBMCs) were isolated from
   human blood by density centrifugation using Lymphocyte Separation
   Medium (MP Biomedicals). CD4^+ T cells were negatively selected from
   PBMCs using the CD4^+ T Cell Isolation Kit (Miltenyi Biotec) and
   cultured in RPMI 1640 media supplemented with 25 mM HEPES, 10%
   heat-inactivated FBS, and Penicillin/Streptomycin. Human CD4^+ T cells
   (3×10^5/well in 96-well plate) were activated with plate-coated αCD3
   (5 μg/ml, BioLegend) and αCD28 (10 μg/ml, BioLegend) for 3 days in the
   presence of 10% autologous serum from the subjects and 5% FBS. Also,
   CD4^+ T cells (2×10^5/well in 96-well plate) were differentiated into
   three T cell subtypes by incubation with the specific supplements for
   T[H]1 (20 ng/ml IL-12 and 10 μg/ml αIL-4), T[H]2 (10 ng/ml IL-4 and
   10 μg/ml αIFNγ) or T[H]17 (20 ng/ml IL-6, 2 ng/ml TGF-β1, 10 ng/ml
   IL-1β, 10 ng/ml IL-23, 10 μg/ml αIL-4, and 10 μg/ml αIFNγ),
   respectively. They were differentiated for 3 days on plate-coated αCD3
   and αCD28 in the presence of 10% autologous serum from the subjects and
   5% FBS. All recombinant proteins and antibodies for differentiation
   media were purchased from Peprotech and eBioscience. Supernatants were
   collected, centrifuged to remove cells and debris, and stored at −80°C.
   The levels of cytokines, including IFNγ, IL-5, and IL-17 were measured
   by ELISA (R&D Systems). Results were normalized to cell number using
   the CyQuant cell proliferation assay (Invitrogen) or BCA protein assay
   (Pierce).

RNA sequencing and bioinformatics analysis

   Total RNA from CD4^+ T cells was extracted with the miRNeasy Micro Kit
   (Qiagen) and RNA quality and integrity was assessed by Qubit 3.0
   fluorometer (Thermo Fisher Scientific) and Agilent Bioanalyzer (Agilent
   Technologies). Libraries were prepared using TruSeq stranded Total RNA
   HT kit (Illumina) and sequenced in a HISeq 3000 (Illumina) by the DNA
   Sequencing and Genomics Core at NHLBI. FastQC
   ([284]http://www.bioinformatics.babraham.ac.uk/projects/fastqc) was
   used to confirm quality of RNA seq fasta files. Adaptor trimming, RNA
   sequence alignment, gene expression quantification, and differential
   expression analysis was performed as previously described.[285]^37
   Genes with p value < 0.05 were considered differentially expressed (DE)
   genes. Pathway enrichment analysis was performed using the R package
   clusterProfiler[286]^56 on DE genes that were subset into upregulated
   and downregulated based on the fold change values. Additional pathway
   inferences were made by performing pathway enrichment using the KEGG
   database and one of the molecular signature databases (MSigDB)
   collection C7 immunologic signature database (Broad institute) in R
   ([287]https://www.R-project.org/). Previously published protein-protein
   interaction (PPI) networks between the gene of interest were extracted
   from the String protein database[288]^55 and the gene nodes in the PPI
   network were colored red and blue for up and downregulated genes
   open-source software Cytoscape.[289]^57 The pathway enrichment plots
   and heatmaps were generated using ggplot2 (Wickham H (2016). ggplot2:
   Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN
   978-3-319-24277-4) library in R.

Inhibitors, siRNAs, and lentiviruses

   NR (0.5 mM from ChromaDex) and L-Arginine (5 mM from Sigma) were
   incubated for 3 days during TCR activation was used prior to the
   subsequent assays. PARPs inhibitor Rucaparib camsylate (Sigma) and CD38
   inhibitor Compound-78c (Selleckchem) were used in cell culture at
   10 μM, 20 μM, or 10 μM for 12 hrs., respectively. NRF2 inhibitor (10 μM
   ML385 from Selleckchem) or activator (10 μM DMF from Selleckchem), was
   used for overnight incubation before harvesting the cells.

   For knockdown experiments, SMARTpool Accell siRNA was utilized
   targeting oxidative stress-related genes (NRF2, SQSTM1, GCLC, GCLM,
   GSR), and NAD-related genes (CD38, SARM1) (Horizon Discovery). Accell
   siRNA was transfected with Accell siRNA delivery media according to
   manufacturer’s instruction (Horizon Discovery). The cells (4✕10^6) with
   1.5 μM siRNA were activated with plate-coated αCD3 (BioLegend) and
   αCD28 (BioLegend) for 3 days before assay.

   To produce lentivirus, pLenti vector, pLenti-NRF2 with Myc-DDK tag, or
   pLenti-SQSTM1 with Myc-DDK (Origene Technologies) and packing plasmids
   (psPAX2 and pMD2.G from Addgene) were transfected into 293T cells using
   Polyjet reagent (Signagen Laboratories). Supernatants were collected 48
   hours after transfection, filtered through a 0.45 μm syringe filter and
   concentrated by ultracentrifugation (Beckman) at 25,000 rpm for 2
   hours. Virus was introduced into the CD4^+ T cell culture media in the
   presence of 8 μg/ml of DEAE-dextran (Sigma) and incubated in the
   presence of 50 ng/ml IL-2 for 24 hours before TCR activation.

RNA Isolation and Quantitative PCR analysis

   Total RNA was extracted using NucleoSpin RNA kit (Macherey-Nagel) and
   cDNA was synthesized with the SuperScript III First-Strand Synthesis
   System for RT-PCR (Thermo Fisher Scientific). Quantitative real-time
   PCR was performed using FastStart Universal SYBR Green Master (Roche)
   and run on LightCycler 96 Systems (Roche). The primers of canonical
   transcription factors (TFs) of CD4^+ T cell were made by Integrated DNA
   Technologies and the sequences of the primers are listed; TBX21 (F:
   CGTGACTGCCTACCAGAAT and R: ATCTCCCCCAAGGAATTGAC), GATA3 (F:
   GAACCGGCCCCTCATTAAG and R: ATTTTTCGGTTTCTGGTCTGGAT), RORC (F:
   GCATGTCCCGAGATGCTGTC and R: CTGGGAGCCCCAAGGTGTAG), and EF1α (F:
   GTTGATATGGTTCCTGGCAAGC and R: GCCAGCTCCAGCAGCCTTC). The transcript
   levels of other targets were measured using validated gene-specific
   QuantiTech primers (Qiagen, [290]Table S7). Relative gene expression
   was quantified by normalizing cycle threshold values with 18S rRNA or
   EF1α using the 2^−ΔΔCt cycle threshold method.

Western Blotting

   Human monocytes were lysed using RIPA buffer supplemented with protease
   inhibitor cocktail (Roche) and phosphatase inhibitors (Pierce). Lysates
   were separated by NuPAGE 4-12% Bis-Tris Protein Gels (Thermo Fisher
   Scientific) and transferred to nitrocellulose membranes (Trans-Blot
   Turbo Transfer System (Bio-Rad Laboratories). Membranes were blocked
   with Odyssey Blocking Buffer (Li-Cor) and incubated with appropriate
   antibodies overnight at 4°C. Antibodies were purchased from Cell
   Signaling Technologies (NRF2, SQSTM1/p62, KEAP1, NQO1), and Millipore
   (Actin). The secondary antibody conjugated with IRDye 800CW or IRDye
   680RD (Li-Cor) were then incubated for 1 hour at room temperature.
   Immunoblots were scanned using an Odyssey Clx imaging system (Li-Cor
   Biosciences). Protein band intensity was quantified using Image studio
   software (version 5.2) and normalized to Actin level.

Intracellular ROS and mitochondrial ROS assay

   ROS (Reactive oxygen species) generation in the form of superoxide was
   measured also by a FACS- and microplate reader-based assay using DCFDA
   (2',7'-Dichlorofluorescin Diacetate, Sigma) for intracellular ROS and
   MitoSOX red mitochondrial superoxide indicator (Invitrogen) for
   mitochondrial ROS. CD4^+ T cells (4-8×10^5 per well) were incubated
   with 10 μM DCFDA or 5 μM MitoSOX red in HBSS medium for 30 minutes at
   37°C. Cells were subsequently washed with HBSS, and the fluorescence
   intensities of each subset/cell were measured by flow cytometry and
   microplate reader (ROS: 488 nm excitation/535 nm emission and MitoSOX
   red: 510 nm excitation/580 nm emission excitation). Results were
   normalized using the BCA protein assay (Pierce) and CyQuant cell
   proliferation assay (Invitrogen).

   4-hydroxynonenal (4-HNE) is a product of lipid peroxidation and is
   employed as a stable marker for oxidative stress. 4-HNE adduct in
   2×10^5 CD4^+ T cells was quantified by Lipid Peroxidation (4-HNE) Assay
   Kit (Abcam).

   The total free radical presence of CD4^+ T cells (10-50 μg) was
   measured using OxiSelect in vitro ROS/RNS (reactive nitrogen species)
   assay kit (Cell Biolabs). The assay employs a proprietary quenched
   fluorogenic probe, dichlorodihydrofluorescin DiOxyQ (DCFH-DiOxyQ),
   which is a specific ROS/RNS probe that is based on similar chemistry to
   the popular DCFDA. ROS and RNS species can react with prepared DCFH
   probe, which is rapidly oxidized to the highly fluorescent DCF. Samples
   were measured fluorometrically against a DCF standard by microplate
   reader at 480 nm excitation/530 nm emission. Results were normalized to
   protein concentration using the BCA protein assay (Pierce).

Trolox equivalent antioxidant capacity assay

   Trolox equivalent antioxidant capacity of NR was carried out using the
   procedure described in the antioxidant assay kit (Sigma). The principle
   of the antioxidant assay is formation of a ferryl myoglobin radical
   from metmyoglobin and hydrogen peroxide, which oxidizes the ABTS
   (2,2¢-azino-bis(3-ethylbenzthiazoline-6-sulfonic acid) to produce a
   scavenge ABTS radicals, a soluble chromogen that is green in color and
   can be determined spectrophotometrically at 405 nm. The absorbance was
   plotted as a function of the antioxidant capacity relative to the
   Trolox standard according to the assay protocol. The sample from each
   subject was analyzed in duplicate and the results were normalized to
   protein concentration using the BCA protein assay (Pierce).

Glutathione (GSH) measurement

   GSH level was detected in 4×10^6 CD4^+ T cells using GSH/GSSG ratio
   detection assay kit (Abcam) and GSH assay kit (BioAssay Systems).
   Results were normalized to protein concentration using the BCA protein
   assay (Pierce).

NRF2 activation assay

   The nuclear extracts were prepared from CD4^+ T cells using a nuclear
   extraction kit (Abcam) and quantified using BCA protein assay (Pierce).
   The protein specifically bound to the specific double stranded DNA
   sequence containing the NRF2 consensus binding site within the core
   antioxidant response element (ARE) (5’ – GTCACAGTGACTCAGCAGAATCTG – 3’)
   provided in the NRF2 Transcription Factor Assay Kit (Abcam). NRF2 was
   detected by a primary antibody that recognizes an epitope of NRF2
   accessible only when the protein is activated and bound to its target
   DNA. NRF2 activation was measured as a colorimetric readout at OD
   450 nm.

Fumarate assay

   Fumarate level from CD4^+ T cells (1×10^6) was measured by Fumarate
   Assay Kit (Sigma) which results in a colorimetric (450 nm) product,
   proportional to the fumarate present.

Metabolomics and Data Analysis

   Human CD4^+ T cells isolated from healthy volunteers were supplemented
   with 0.5mM NR or vehicle control and activated with plate-coated αCD3
   and αCD28 for 3 days. Also, CD4^+ T cells were differentiated into Th17
   for 3 days on plate-coated αCD3 and αCD28. Males with an average age of
   24 years were recruited (n=10) through the healthy blood donor
   population of the Disease Discovery Protocol ([291]NCT01143454). Cells
   pellets were collected and snap frozen in liquid nitrogen and processed
   using LC-MS as previously described.[292]^18 Metabolite abundance was
   normalized to protein content. SIMCA software (version 15; Umetrics)
   was used to perform orthogonal partial least squares discriminant
   analysis (OPLS-DA) of metabolomic data from all 4 groups and identify
   variable importance in prediction (VIP) metabolites to discriminate NR
   vs. Vehicle. Pathway analysis of VIP metabolites was performed using
   MetaboAnalyst 5.0 (metaboanalyst.ca).

   The polar metabolites in serum samples in the in-vivo placebo vs. NR
   supplementation study were measured using targeted MS/MS as described
   here. Serum samples were extracted using a standard
   methanol-chloroform-water extraction and reconstituted in 10 mM
   ammonium acetate containing internal standards at a concentration of
   10 μM: proline, valine-D[8], leucine-D[10], lysine-U-^13C, glutamic
   acid-^13C, phenylalanine-D[5], succinic acid-D[3], and serotonin-D[4].
   Reverse-phase analysis was performed with an ACE Excel 2 C18 PFP (100A.
   150 x 2.1 mm 5μm) column conditioned at 30°C. The mobile phase
   consisted of: (A) 0.1% formic acid in water and (B) 0.1% formic acid in
   acetonitrile. All solvents were analytical grade or HPLC-MS quality and
   sourced from Sigma-Aldrich. Targeted MS/MS was performed with a Thermo
   Scientific Vanquish UHPLC+ coupled to a TSQ Quantiva mass spectrometer
   (Thermo Fisher Scientific). The electrospray ionization source was
   operated in positive ionization mode at 3500 V and negative ionization
   mode at 2500 V. 48 mTorr nitrogen at 420°C was used for solvent
   evaporation. Data acquisition and processing were performed using
   Thermo Xcalibur software (version 2.2; Thermo Scientific). Peak
   intensity was normalized to appropriate internal standards.

Statistical analysis

   Statistical analysis was performed using Prism 7 software (GraphPad)
   and results are represented as mean ± s.e.m. unless otherwise
   indicated. Comparisons of two groups were calculated using paired or
   unpaired two-tailed Student’s t-test. One-way ANOVA was used to measure
   statistical differences in more than 2 groups. Two-way ANOVA was used
   if there were two nominal variables in addition to a measurement
   variable. For all tests, p < 0.05 was considered significant. For
   in vivo and in vitro studies, n represents the number of biological
   replicates per group and is reported in Figure Legends.

Additional resources

   Clinical Study [293]NCT01934660:
   [294]https://classic.clinicaltrials.gov/ct2/show/NCT01934660, Clinical
   Study [295]NCT02812238:
   [296]https://classic.clinicaltrials.gov/ct2/show/NCT02812238, Clinical
   Study [297]NCT01143454:
   [298]https://classic.clinicaltrials.gov/ct2/show/NCT01143454, Clinical
   Study [299]NCT00001846:
   [300]https://classic.clinicaltrials.gov/ct2/show/NCT00001846.

Acknowledgments