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Article

Vitamin D Enhances Immune Effector Pathways of NK Cells Thus Providing a Mechanistic Explanation for the Increased Effectiveness of Therapeutic Monoclonal Antibodies

Department of Internal Medicine 1, Oncology, Hematology, Clinical Immunology and Rheumatology, Saarland University Medical Center, 66421 Homburg, Germany
*
Author to whom correspondence should be addressed.
Nutrients 2023, 15(16), 3498; https://doi.org/10.3390/nu15163498
Submission received: 16 June 2023 / Revised: 1 August 2023 / Accepted: 4 August 2023 / Published: 8 August 2023
(This article belongs to the Special Issue Regulatory Role of Vitamin D and Its Derivatives in the Immune System)

Abstract

:
Patients with diffuse large cell lymphoma who have an adequate vitamin D supply derive significantly more benefit from immuno-chemotherapy with rituximab than patients with vitamin D deficiency; this is especially true for female patients. We have already been able to show that vitamin D increases the antibody-dependent cytotoxicity (ADCC) of NK cells in a sex-dependent manner, but it is unclear how vitamin D makes NK cells more efficient. Methods: Healthy individuals with vitamin D deficiency were supplemented with vitamin D to sufficient levels. NK cells were isolated from blood samples before and after vitamin D saturation. For transcriptome analysis, we used the Affymetrix Gene-Chip 2.0™. Gene expression analysis as well as supervised and unsupervised pathway analysis were performed. Results: Among others the “NK cell-associated cytotoxicity pathway” increased after vitamin D substitution. Five IFN-α subtypes (2, 4, 6, 7 and 10) and IFN-κ were more highly expressed and are mainly responsible in these pathways. In contrast, the pathway “interferon-gamma response”, as well as other sets in cytokine production and chemotaxis showed a reduction. Toll-like receptor genes (TLR-8, TLR-7, TLR-2) were downregulated and, therefore, are responsible for the decline of these pathways. The same could be shown for the “ubiquitin-ligase” pathway. Conclusions: Increased expression of several IFN-α subtypes may explain the increased ADCC of NK cells in vitamin D-replenished and otherwise healthy subjects. Other regulators of interferon production and ADCC are compensatory upregulated in compensation, such as Toll-like receptors and those of the ubiquitin ligase, and normalize after vitamin D substitution.

1. Introduction

Vitamin D (cholecalciferol) is a secosteroid that plays a crucial role in maintaining bone health and regulating calcium levels in the body [1]. Cholecacliferol is activated by hydroxylation on position C25 by CYP27A1 in mitochondria or CYP2R1 in the endoplasmic reticulum and finally by CYP27B1 at position C1 alpha [2]. Alternative activation by CYP11A1 leads to a plethora of vitamin D variants which may cause many different modes of activation [2].
Therefore, it also has important immunomodulatory effects which are not yet fully understood [3]. Substitution of vitamin D affects the gene expression of several genes in human peripheral blood mononuclear cells [4]. Specifically, natural killer (NK) cells need vitamin D receptors for a proper development [5]. Therefore, it is no surprise that vitamin D is beneficial for NK cell function in the very elderly [6] or dialysis patients [7]. Furthermore, in patients with aggressive B cell lymphoma, vitamin D has helpful effects on the immune system antitumor response by activating macrophages [8]. In the context of modern cancer treatment, the question arises if the immunomodulatory effects of vitamin D can be harnessed to improve the antitumor effect of immunotherapy.
Therapeutic monoclonal antibodies (mAb) have had significant impact on lymphoma therapy in the last 25 years. The anti-CD20 mAb Rituximab is currently standard of care in all B cell malignancies [9], whereas the newer anti-CD20-mAb Obinutuzumab is used in indolent lymphoma and chronic lymphocytic leukemia [10,11].
Our group has been able to demonstrate, in a post-hoc analysis of a prospective phase III trial evaluating rituximab inpatients with diffuse large B-cell lymphoma in a randomized fashion, that patients who have adequate vitamin D serum levels derive significantly more benefit from the addition of rituximab to standard chemotherapy than patients with vitamin D deficiency [12], and, this is especially true for female patients. The main effector cell of therapeutic monoclonal antibodies are natural killer (NK) cells supported by macrophages, neutrophils and the complement system [13].
To further understand the above finding, healthy volunteers with vitamin D deficiency were supplemented with vitamin D up to predefined target serum levels. NK cells derived from these volunteers at various vitamin D serum levels were used to perform cytotoxicity assays against lymphoma cell lines in the presence of rituximab. We were able to show that vitamin D increases the antibody-dependent cytotoxicity (ADCC) of NK cells in a sex-dependent manner [14]. It still, however, remains unclear how vitamin D affects the NK cells to make them more efficient. In the present study, a transcriptome analysis from NK cells derived from those healthy volunteers was performed to identify the underlying mechanism behind increased NK-cell-mediated ADCC in the presence of rituximab after vitamin D supplementation.

2. Materials and Methods

2.1. Treatment

Eight volunteers who had insufficient vitamin D levels were substituted orally with 20,000 IE capsules of 25-OH-vitamin D3 (DekristolTM) (Mibe GmbH Arzneimittel, Brehna, Germany) to a mid-normal range. The dose was calculated using a modified version of the Van Groningen formula [15]. Depending on the body weight of the subject and the baseline 25-(OH)2-D3 vitamin status, volunteers took between 30 and 50 DekristolTM capsules (600,000–1,000,000 IE) over 6–10 days. Serum 25-OH Vitamin D was measured by chemoluminescence assay (LIASION®, DiaSorin, Saluggia, Italy) immediately prior to the NK cell assay, which was scheduled two weeks after supplementation.
The characteristics of the volunteers and the serum levels prior to and after supplementation are summarized in Table 1.
All participants gave written informed consent, and the Ethics Committee of the Saarland Medical Association (178/17) approved the study.

2.2. Isolation of NK Cells

PBMCs were isolated by density gradient centrifugation from the 50 mL EDTA blood donation of the respective donor. Thereafter, NK cells were isolated from PBMCs by magnetic depletion of all non-NK cells using the CD56+/CD16+ human NK-Cell Isolation Kit (Miltenyi Biotech GmbH, Bergisch Gladbach, Germany) according to the manufacturer`s instructions. NK cells were isolated immediately before the ADCC assay without additional activation (e.g., by IL-2). The viability of the NK cells after isolation averaged 99% and the share of the CD16+ fraction was between 90% and 98%, as assessed by flow cytometry using the corresponding antibodies (Miltenyi). The yield of CD16+ NK cells was between 3 × 106 cells and 1 × 107 cells. A FACSCalibur (BD Biosciences, Heidelberg, Germany) was used for flow cytometry analysis. Starting with a forward scatter (FSC) versus sideward scatter scan (SSC) to gate the lymphocyte population and followed by analysis of the corresponding fluorescence using CellQuest software (BD Biosciences), 5000 or 10,000 lymphocytes were examined per run.

2.3. RNA Extraction

The isolated NK cells had been previously used in an ADCC assay on lymphoma cells. The excess NK cells were frozen at −80 °C, containing 5 × 106 to 1 × 107 NK cells. After centrifugation for the culture medium removal, RNA isolation was performed using the miRNeasy Mini KitTM (Qiagen, Velno, The Netherlands). A volume of 700 µL QIAzol Lysis ReagentTM was added to the cells. Homogenization was performed with a QIAshredder Spin ColumnTM (Qiagen). Then, 700 µL of the lysate was added to spin columns and centrifuged. Next, 140 µL chloroform was added to the tubes, mixed for 15 s and left at 22 °C for 2 min. A second centrifugation step at 12,000× g at 4 °C for 15 min followed. The aqueous phase was transferred to fresh tubes and we added 525 µL of 100% ethanol. This mixture was added to the RNeasyTM Spin column in 2 mL tube and centrifugated at 8000× g for 15 s. This was repeated after addition of 500 µL RPE bufferTM. The spin column was then transferred to a new 2 mL collection tube and centrifuged at full speed for one minute to prevent carryover of flow-through into the RNA elution phase. Finally, the spin columns were placed into new 1.5 mL collection tubes, and 30 µL RNase-free water was pipetted onto each sample and then centrifuged at 8000× g for 1 min.

2.4. Expression Analysis per Microarray

RNA expression analysis was performed with the GeneChip™ Human Gene 2.1 ST Array Plate (Thermofisher, Waltham, MA, USA). Microarray hybridization and sample preparation was carried out at “KFB—Center of Excellence for Fluorescent Bioanalytics” (Regensburg, Germany) with the GeneChip WT PLUS Reagent Kit Thermofisher, Waltham, MA, USA) as described in the manual. We generated double-stranded cDNA from 200 ng of total RNA. Subsequently, 12 µg cRNA was synthesized, purified and reverse-transcribed into sense-strand (ss) cDNA, while incorporating unnatural dUTP residues. Fragmentation of purified ss cDNA was performed using a combination of uracil DNA glycosylase (UDG) and apurinic/apyrimidinic endonuclease 1 (APE 1). Finally, terminal labeling with biotin was performed, and 3.8 µg of each ss cDNA sample was used for hybridization. A GeneTitan system, controlled by the GeneChip Command Console software v4.2 (Thermofisher, Waltham, MA, USA), was used for hybridization, washing, staining and scanning. Signal strengths were normalized and log2-transformed. Quality controls according to the manufacturer’s instructions were performed prior to and after normalization without identification of any outliers.

2.5. Validation

Selected genes were validated using quantitative Real-Time Polymerase Chain Reaction (qRT-PCR). Genes with significant, divergent across samples expression and available primers were selected (allograft inflammatory factor 1, AIF1 and C-type lectin domain family 7, member A, CLEC7A). One housekeeping gene was chosen to act as a control: ubiquitin-conjugating enzyme E2D 2 (UBE2D2). Primers for the above mentioned genes were synthesized (Sigma-Aldrich, Darmstadt, Germany), followed by reverse transcription, cDNA synthesis and RT-PCR.

2.6. Statistical Analysis

At a first step, differentially expressed genes were analyzed. We initially performed a Kolmogorov–Smirnov test to confirm the normal distribution of the results using SPSS (IBM SPSS statistics, version 23). For the comparison of gene expression after vitamin D supplementation, the “R” software (R Foundation for Statistical Computing, version 3.6.3) with the “limma” package (Bioconductor, version 3.42.2) through the RobiNA graphical interface (version 1.24) was used with the default settings. Bonferroni correction for multiple correction was used and significance was defined at p < 0.01. For the subgroup analysis according to sex and vitamin D, we performed two-sided analysis of variance (ANOVA) with the Transcriptome Analysis Console (TAC) Software (Thermofisher Scientific, 4.0.2 Release) and its integrated “limma” package (Bioconductor, version 3.42.2).
We used the “Gene Set Enrichment Analysis” (GSEA) [16] software and the online tool GeneTrail to detect molecular pathways which were affected by vitamin D supplementation.
The default metric Signal2Noise (the difference in means scaled by the standard deviation) and the weighted (p = 2) enrichment statistic were selected for GSEA. The cut off for p was set at <0.05.
First, the “NK cell-associated cytotoxicity pathway” from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database was examined using only 7705 genes known to be involved in the NK-cell-mediated immune response, to increase the sensitivity. Second, the entire gene dataset was used in an supervised analysis in pathways involved in the immune system in general (n = 89 pathways), from databases such as “Biocarta”, “KEGG”, “Gene ontology (GO)”, and “Reactome” databases, all accessed through the Molecular Signature database. Both analyses were performed with the GSEA software, version 4.0.3, Broad Institute. When several pathways were enriched, we performed a leading-edge analysis to see which genes contributed the most to the observed pathway changes.
Finally, Gene Trail was used in an unsupervised manner to screen for pathways in the “Hallmark”, “BioCarta”, “Biological Process”, “Cellular Component”, “Molecular Function”, “KEGG”, “Reactome” and “WikiPathways” online databases, without any preselection of neither genes nor pathway category. Again, a weighted GSEA was used. For this unsupervised analysis, we decided to adjust the p-value for significance to <0.01 as this was a non-hypothesis driven test. Adjustment for multiple testing was conducted according to Benjamini–Hochberg.

3. Results

Supplementation was performed successfully, leading to an increase in vitamin D serum levels from 10.5 ng/mL to 65.6 ng/mL (Table 1).

3.1. Gene Expression Changes in Vitamin-D-Supplemented Volunteers

In total, 505 transcripts changed their expression levels after vitamin D administration with a cutoff p < 0.01. Only 256 had an annotation to a specific gene locus (coding and non-coding). Up- and downregulated transcripts are listed in Table 2. Among others, the IFNL3 gene, encoding IFN-λ3 (or IL28B) and IL17RE (interleukin 17 receptor E), as well as IL2RB (beta subunit of the interleukin receptor 2) were upregulated after administration of 25-(OH)2D3.
In the principal component analysis, in the samples we did not find specific gene clusters based on vitamin D levels (Figure 1) and the heatmap displays that vitamin D substitution had only a small effect on the NK cell transcriptome as such (Figure 2).
After Bonferroni correction for multiple testing, no gene was significantly differentially expressed in the analysis. Up- and downregulated transcripts are listed in Table 2. Table 3 shows differentially expressed genes associated with the immune system or vitamin D signal transduction according to the GO database.
Sex-specific differences in gene expression are shown in Table S1. In males, among others, the IFNL3 gene is significantly upregulated, while the IFNG gene is downregulated. In females, amidst other changes, the toll-like receptor genes TLR5 and TLR8 are downregulated.

3.2. Pathway Analysis

For GSEA pathway analysis, a ranked gene list was created according to the signal-to-noise metric. The 50 most up- or downregulated transcripts on the list are presented in a heatmap in Figure S1.
Importantly, the “NK cell-associated cytotoxicity pathway” from the KEGG database was shown to be upregulated. A gene set enrichment plot is shown in Figure 3 and the main genes responsible for the amplification of the pathway are shown in Table 4, and consist mostly of interferon alpha genes.
In the following part, the entire gene expression profiles by microarray were analyzed. With A cutoff p value < 0.05 we identified two pathways to be upregulated: the pathway “Regulation of type I interferon-mediated signaling” and the pathway “Response to type I interferon”. A leading-edge analysis identified the genes which contributed the most. As in the NK cytotoxicity pathway, five interferon alpha genes, IFN2,-4,-6,-7 and -10, had the highest expression change in these pathways.
We found eleven downregulated pathways related to immune functions (Table 5). Additionally, we performed a leading-edge analysis to see which genes contributed the most to this plethora of pathway changes. Toll-like receptors 2, 7 and 8 were involved in five out of the eleven altered pathways (Figure 4).
Additionally, in an unsupervised analysis, not restricted to the immune-related pathway, we found a significant downregulation of the E3 ubiquitin ligase pathway.

4. Discussion

Vitamin D substitution did not alter individual gene expression in NK cells after statistical correction for multiple testing. However, additional bioinformatical methods were able to demonstrate the transcriptomic background of the effect of vitamin D on NK cell activity.
GSEA determines whether the distribution of amplified genes are closely correlated with a particular pathway. The extent of correlation is measured by the enrichment score. GSEA can give additional insights in finely tuned regulation of gene expression changes by endogenous hormones or pro-hormones, such as vitamin D, which had a very long presence in the animal phylogeny to exert its effects rather subtly.
We could show that the “NK cell-associated cytotoxicity pathway” from the KEGG database among other Interferon-related pathways was significantly upregulated after vitamin D substitution. This is mainly caused by upregulated IFN-α genes (IFN2, -4, -6, -7, and -10) and this finding is in line with the expectation we had from our clinical [12] and translational [14] results of vitamin D activity and NK-cell-mediated ADCC. Also, IFNL3 was shown to be upregulated. We, therefore, propose that increased interferon production plays the most important role in vitamin D enhancement of NK cell activity. Other cytokines (IL-2 and IL-17) may also be involved, since their receptor genes were shown to be upregulated. IL-2 is a well-established NK cell activator [17], and IL-17 increases circulating NK cells, perforin and granzyme expression, and finally cytolytic function [18].
The downregulation of the TLRs (TLR2, TLR5, TLR7 and TLR8) in the vitamin-D-deficient state may seem at first paradox, as they are involved in the IFN-α response, due to exogenous signals such as pathogen-associated molecular patterns. This must, however, be considered in the context of the observed downregulation of the ubiquitin ligase pathway.
The ubiquitin ligase system is a known inhibitor of NK cell activity [19]. This is further supported by data demonstrating that inhibition of ubiquitin ligases in a mouse model [20] leads to remission of tumors by NK cell lysis. Furthermore, viral evasion of NK cell activity has been shown to be induced by the production of E3 ubiquitin ligases in human herpesvirus 8 [21]. Importantly, this ubiquitin ligase-dependent immune suppression is, at least in part, induced by regulation of the TLR/IFN axis. Ubiquitin ligases have been shown to inhibit TLR signaling through nuclear factor (NF)-kB [22] and their knockdown increases proinflammatory cytokines via the TLR/IL-1R/TRAF6 pathway [23]. Production of IFN-α upon TLR stimulation is known to depend on the formation of a complex consisting of MyD88, IRF7 and TRAF6, as well as TRAF6-dependent ubiquitination [24]. Most importantly in the context of our results, ubiquitin ligases inhibit type I IFN secretion (including IFN-α) through IRF3 and IRF7 ubiquitination [25].
Therefore, one coherent pattern emerges: the ubiquitin ligase downregulates type I interferon synthesis and is an inhibitor of NK cell activity, which is itself reduced by vitamin D, so substitution of vitamin D leads to increased IFN-α production. This would explain the increased NK cell activity observed after supplementation. TLR overexpression in the vitamin D deficiency state may be compensatory for the suppression of the type I interferon production pathway, which is triggered by ubiquitin ligase activity. We hypothesize a compensatory upregulation of TLRs, as in the vitamin-D-deficient state the NK cells are less likely to be successful in their task and, therefore, increase their activating receptors for exogenous stimuli.
The downregulation of IFNG is not necessarily inconsistent with the observed effect of increased NK cell ADCC against lymphoma. IFN-γ leads to increased expression of T-cell inhibitory ligands like PD-L1 in tumors [26]. Additionally, it causes overexpression of MHC-I molecules on target cells, which are inhibitory ligands for NK cells. However there is some experimental data that leukemia and lymphoma cell lines may be protected from NK cell activity in the presence of IFN-γ [26]. Increased NK cell activity, therefore, may be, in part, possible when IFN-γ is downregulated.
This study provides an explanation on the basis of gene expression data for the observation of increased NK-cell-mediated ADCC in the presence of the mAb rituximab. Newer therapeutic agents in lymphoma therapy such as antibody drug conjugates like polatuzumab–-vedotin [27] would not be predicted to be affected by vitamin D levels, as they do not rely solely on host immune mechanisms, but rather on the toxic component. Whether vitamin D influences the effect of bispecific antibodies like glofitamab and epcoritamab, which rely on T-cell-mediated direct cytotoxicity, remains to be discovered.
Considering the study design, our approach has the advantage of isolating the effect of vitamin D supplementation on NK cells through serial testing of the same individuals. Thus, confounders are limited to a minimum. The present study used NK cells from the same probands, for which our group has demonstrated increased NK-cell-associated ADCC in the presence of rituximab against lymphoma cells after vitamin D supplementation to target levels [14]. Thus, a mechanistic explanation for the increased NK cell activity is provided. However, the NK cells in this experiment have had no direct contact with target cells. It is possible that a transcriptome analysis of NK cells after they had interacted with lymphoma cells would demonstrate a different picture. Future experiments based on, for example, single cell RNA-Seq could answer this question.

5. Conclusions

We conclude that vitamin-D-deficient NK cells are less likely to produce IFN-α and are therefore less likely to kill a rituximab-coated target cell. Several pathways are upregulated to compensate but are not sufficient to restore NK cell activity. After the vitamin D is replenished, the NK cells restore IFN-α secretion and can more successfully exert ADCC. Increased expression of IL2 and IL17 receptors additionally supports this process. The analysis presented adds some information on how therapeutic monoclonal antibodies work in lymphoma treatment and gives additional hints how to improve and fully exploit the potential of monoclonal antibody therapy in malignancies.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/nu15163498/s1, Figure S1: Heat map of the 50 most significant transcripts for each phenotype as per GSEA ranked list; Table S1: Differentially expressed genes according to sex for a p value <0.01 and increase/decrease in fold change by at least 1.5.

Author Contributions

Conception and design: K.C., F.N. and J.T.B. Statistical analysis: K.C. Interpretation: All authors. Writing of the initial manuscript: J.T.B., K.C. and F.N. Final approval of manuscript: All authors. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of “Ärztekammer des Saarlandes” (protocol code 178/17).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data that support the findings of this study are deposited at the publicly available Gene Expression Omnibus database, reference number [GSE239308]. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE239308. Raw data can be downloaded from 26 July 2024.

Acknowledgments

We kindly thank Andreas Keller for his support in the bioinformatic analysis. Data analysis and interpretation, writing of the paper, and decision to submit were left to the authors’ discretion and were not influenced by third parties.

Conflicts of Interest

The authors declare to have no conflict of interests regarding this manuscript.

Abbreviations

ADCCAntibody-dependent cellular cytotoxicity
ANOVAAnalysis of variance
FcyIIIAlow affinity IgG receptor
FcεR1High affinity IgE receptor
GOGene ontology
GSEAGene set enrichment analysis
HER2Human epidermal growth factor receptor 2
IFNInterferon
KEGGKyoto encyclopedia of genes and genomes
mAbMonoclonal antibody
NKNatural killer
PBSphosphate-buffered saline
PBMCperipheral blood mononucleated cell
TLRtoll-like receptor
qRT-PCRquantitative real time polymerase chain reaction

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Figure 1. Principal component analysis—1 Red indicates samples before and blue after vitamin D supplementation. Cubes indicate males and spheres indicate females. The individual samples do not seem to form clusters based on vitamin D levels. Thus, no clearly changed expression pattern could be observed which clearly is attributable to supplementation. However, a clear spatial separation for sex is noted (males concentrate in the upper part of the diagram, females in the lower part).
Figure 1. Principal component analysis—1 Red indicates samples before and blue after vitamin D supplementation. Cubes indicate males and spheres indicate females. The individual samples do not seem to form clusters based on vitamin D levels. Thus, no clearly changed expression pattern could be observed which clearly is attributable to supplementation. However, a clear spatial separation for sex is noted (males concentrate in the upper part of the diagram, females in the lower part).
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Figure 2. Hierarchical clustering based on the expression profile of transcripts that are up- or downregulated based on p < 0.05 across the two different vitamin D levels. The color scale represents log2-transformed expression of the single gene samples. Brighter green represents lower expression and brighter red represents higher expression. No gene clusters which clearly separate the two states could be identified.
Figure 2. Hierarchical clustering based on the expression profile of transcripts that are up- or downregulated based on p < 0.05 across the two different vitamin D levels. The color scale represents log2-transformed expression of the single gene samples. Brighter green represents lower expression and brighter red represents higher expression. No gene clusters which clearly separate the two states could be identified.
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Figure 3. Enrichment Plot: “NK cell cytotoxicity” pathway. GSEA firstly creates a ranked list of differentially expressed genes across the dataset ranging from the highest change towards one phenotype (e.g., after substitution) to the highest change towards the other phenotype. Then, a predefined set of genes with a common biological function, e.g., components of one molecular “pathway” is compared against the ranked gene list. The GSEA then determines whether the genes belonging to this pathway are randomly distributed in the ranked list, or whether they are found at the top or bottom of the list. If a set of genes has a distribution associated with a particular phenotype (top or bottom of the list), the extent of the correlation is measured with an enrichment score (ES). The upper part of the figure shows the running ES for the gene set as the analysis walks down the ranked list. A positive ES (the green line is mostly above 0.0) means overexpression in the particular phenotype, in this case after vitamin D substitution. The middle part of the figure shows the placement of the individual genes of a certain set in the ranking of all genes. Each gene is represented by a line. The bottom part of the plot shows the value of the ranking metric, indicating the correlation of a gene to either one of the two phenotypes “substitution” or “deficiency”. It is positive (red) when the gene shows higher expression in the phenotype “substitution” and negative (blue) when expression is higher in the phenotype “deficiency”.
Figure 3. Enrichment Plot: “NK cell cytotoxicity” pathway. GSEA firstly creates a ranked list of differentially expressed genes across the dataset ranging from the highest change towards one phenotype (e.g., after substitution) to the highest change towards the other phenotype. Then, a predefined set of genes with a common biological function, e.g., components of one molecular “pathway” is compared against the ranked gene list. The GSEA then determines whether the genes belonging to this pathway are randomly distributed in the ranked list, or whether they are found at the top or bottom of the list. If a set of genes has a distribution associated with a particular phenotype (top or bottom of the list), the extent of the correlation is measured with an enrichment score (ES). The upper part of the figure shows the running ES for the gene set as the analysis walks down the ranked list. A positive ES (the green line is mostly above 0.0) means overexpression in the particular phenotype, in this case after vitamin D substitution. The middle part of the figure shows the placement of the individual genes of a certain set in the ranking of all genes. Each gene is represented by a line. The bottom part of the plot shows the value of the ranking metric, indicating the correlation of a gene to either one of the two phenotypes “substitution” or “deficiency”. It is positive (red) when the gene shows higher expression in the phenotype “substitution” and negative (blue) when expression is higher in the phenotype “deficiency”.
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Figure 4. Selected TLR gene expression before (blue columns) and after (red columns) vitamin D supplementation (A): across all subjects. (B): only in females. Standard deviation is shown as error bar. The unadjusted p values according to two-way ANOVA are given above each pair of columns. Across all subjects, of the five TLR genes, TLR5 and TLR8 were downregulated (p-value < 0.05) after vitamin D supplementation.
Figure 4. Selected TLR gene expression before (blue columns) and after (red columns) vitamin D supplementation (A): across all subjects. (B): only in females. Standard deviation is shown as error bar. The unadjusted p values according to two-way ANOVA are given above each pair of columns. Across all subjects, of the five TLR genes, TLR5 and TLR8 were downregulated (p-value < 0.05) after vitamin D supplementation.
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Table 1. Age distribution, pre- and post-supplementation vitamin D levels of the healthy participants.
Table 1. Age distribution, pre- and post-supplementation vitamin D levels of the healthy participants.
SexAge (Years)Vitamin D Levels in
Deficient Status (ng/mL)
Vitamin D Levels After
Supplementation (ng/mL)
male785.964.3
female712368.2
male5715.762.6
female784.668.8
male796.172.8
female799.768.5
male8610.358.2
female428.861.5
Mean71.310.565.6
Standard deviation14.66.14.8
Median789.2566.25
Range42–864.6–2358.2–72.8
Table 2. Up- and downregulated genes in NK cell isolates after vitamin D supplementation.
Table 2. Up- and downregulated genes in NK cell isolates after vitamin D supplementation.
Upregulated GenesDownregulated Genes
CDH19MAP4K1THOP1ADORA2BKIF15UGT2B17
CELSR3-AS1MED24TOX2AFF4KIF1BUPRT
CHST15MIR1244-1TP53I13ANAPC4KRTAP4-2VWC2L-IT1
COQ3MIR548TTRAV8-4ANP32BLRRC1ZEB2
CRELD2MIR570TRIM51HPANXA8L1LRRFIP2ZNF709
CRSP8PMT1BTTTY13ASIPMAFZNF839
CTDNEP1MTRNR2L5UCHL1ATP11CMBTD1
CUEDC2MYBL2UPK3BBBS5METTL14
CYP21A1PMYO7AUROSBLOC1S6MGC16025
DDX39ANAV2-AS5USH2AC15orf57MIEF1
DENND6BNRG4VAMP2C16orf46MIR1-1
DMRTA1OR52W1VIPCAMTA1MIR140
DRD2P4HA2WBSCR16CCDC81MIR3175
EEF1A2PCBP4YKT6CCR10MIR330
EIF5AL1PERPZNF324CCSAPMIR4496
ELFN1PI4KAZNF692CCT2MIR933
EPB41L1PI4KAP1ZNF728CDC14CNFATC4
EXOC6BPKD2L1ZNF733PCDH13NKAIN2
F9PLXDC1ZNF788CDKL5OPCML
FAM189BPOLR2L CEP57L1OR6C70
FAM217APPM1M CHORDC1PADI4
FAM219APPP1R15B CLIP4PGRMC2
FAM25CPRAMEF2 CMSS1PKN2
FAM53APRPF31 CTRB1PLA2G4A
FBLPRR23D2 CYTIPPLEKHH2
FBLN7PRRT3 DEFB124PRKCH
FBXL6PTPRCAP DENND5B-AS1PRR3
FN1PXMP2 DHX15PSMC6
GPR146RARRES3 DICER1PTGER4
HIST1H4FRASA4B DISP2RAD51AP2
HLA-CRASL10B DNAJA4RBM44
HPSE2RETN DOCK5RGPD1
HULCRHBDF1 DSCAM-IT1RHOB
IFITM5RNU5D-1 EFCAB10RIOK3
IFNL3RPH3AL EHHADHRPH3A
IL17RERPL21P28 EMC2RSPH10B
IL2RBRPL23AP87 EXOGSAYSD1
INO80ERPS27 FAM208BSCGB2B3P
ITGAMSEC11A FBXW9SCUBE1
ITGAXSH3GLB2 FEM1CSELK
IVLSLPI FETUBSENP2
KDM8SMARCB1 FGD5P1SENP5
KIF25SNAR-I GABPB1-AS1SERBP1
KRT17SNORA35 GACAT2SLMAP
KRTAP22-2SNORD116-14 GCOM1SMCHD1
KRTAP5-1SNORD20 GPATCH2LSMNDC1
LIN7ASNORD32B GS1-279B7.1SNORD114-3
LINC00619SOX13 HACD1SPINT1
LINC00881SPATA20 HOXD12SRPK1
LINC01144SPINK1 HYAL2TAOK1
LINC01456SSH3 IMMP1LTPI1P3
LINC01624STARD4-AS1 IP6K2TRBV5-4
LRRC42STARD9 IPO11TSFM
LRRC74ATAAR3 KCNE1TTC39B
Table 3. Genes associated with the immune function or vitamin D signaling according to the GO database. Decreased expression −, increased expression + fold change p < 0.01.
Table 3. Genes associated with the immune function or vitamin D signaling according to the GO database. Decreased expression −, increased expression + fold change p < 0.01.
Gene
Symbol
Gene NameFold Changep-Value
TRAV8–4T cell receptor alpha variable 8–4 −1.540.00761
MAP4K1mitogen-activated protein kinase kinase kinase kinase 1 −1.340.00388
IFNL3interferon, lambda 3 −1.300.00671
IL17REinterleukin 17 receptor E −1.240.00443
POLR2Lpolymerase (RNA) II (DNA directed) polypeptide L, 7.6kDa −1.230.00904
VIPvasoactive intestinal peptide −1.220.00146
C1orf147chromosome 1 open reading frame 147 −1.210.00525
IFITM5interferon induced transmembrane protein 5 −1.190.00350
SLPIsecretory leukocyte peptidase inhibitor −1.190.00999
DRD2dopamine receptor D2 −1.180.00661
ITGAMintegrin, alpha M (complement component 3 receptor 3 subunit) −1.160.00232
IL2RBinterleukin 2 receptor, beta −1.130.00802
VAMP2vesicle associated membrane protein 2 −1.100.00815
MED24mediator complex subunit 24 −1.090.00743
NRG4neuregulin 4 −1.090.00871
HLA-Cmajor histocompatibility complex, class I, C −1.080.00426
CCR10chemokine (C-C motif) receptor 10 1.120.00455
PSMC6proteasome 26S subunit, ATPase 6 1.130.00591
PTGER4prostaglandin E receptor 4 (subtype EP4) 1.160.00388
SRPK1SRSF protein kinase 1 1.180.00589
NFATC4nuclear factor of activated T-cells, cytoplasmic, calcineurin-dependent 4 1.190.00520
BLOC1S6biogenesis of lysosomal organelles complex-1, subunit 6, pallidin 1.220.00058
DEFB124defensin, beta 124 1.230.00058
TRBV5-4T cell receptor beta variable 5-4 1.300.00337
PADI4peptidyl arginine deiminase, type IV 1.410.00820
PLA2G4Aphospholipase A2, group IVA (cytosolic, calcium-dependent) 1.670.00099
Table 4. Genes contributing to the enrichment score of the “Natural Killer Cell mediated cytotoxicity” pathway.
Table 4. Genes contributing to the enrichment score of the “Natural Killer Cell mediated cytotoxicity” pathway.
Gene SymbolGene Name
IFNA10interferon, alpha 10
IFNA6interferon, alpha 6
IFNA4interferon, alpha 4
IFNA2interferon, alpha 2
PPP3R2protein phosphatase 3 (formerly 2B), regulatory subunit B, 19kDa, beta isoform (calcineurin B, type II)
RAC3ras-related C3 botulinum toxin substrate 3 (rho family, small GTP binding protein Rac3)
RAET1Lretinoic acid early transcript 1L
IFNA7interferon, alpha 7
NCR2natural cytotoxicity triggering receptor 2
IFNA13interferon, alpha 13
SHC2SHC (Src homology 2 domain containing) transforming protein 2
Abbreviation: ES: Enrichment Score.
Table 5. Downregulated pathways after vitamin D substitution.
Table 5. Downregulated pathways after vitamin D substitution.
Name of the PathwayESNESp-ValueFDR
q-Value
Biocarta—FCεRI0.901.88<0.001<0.001
GO—Activation of innate immune response0.711.87<0.001<0.001
Hallmark—Complement0.701.84<0.001<0.001
GO—Interferon Gamma response0.681.78<0.001<0.001
GO—Regulation of interferon beta production0.801.71<0.0010.002
GO—Positive regulation of interferon beta production0.861.70<0.0010.002
GO—Leukocyte chemotaxis0.671.64<0.0010.013
GO—Positive regulation of interferon alpha production0.891.590.0020.032
Hallmark—IL6/JAK/STAT3 signaling0.661.58<0.0010.032
GO—Activation of immune response0.571.57<0.0010.032
GO—Response to vitamin D0.771.570.0120.031
Abbreviations: ES: Enrichment Score, NES: Normalized Enrichment Score, FDR: false discovery rate, GO: Gene Ontology.
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Christofyllakis, K.; Neumann, F.; Bewarder, M.; Thurner, L.; Kaddu-Mulindwa, D.; Kos, I.A.; Lesan, V.; Bittenbring, J.T. Vitamin D Enhances Immune Effector Pathways of NK Cells Thus Providing a Mechanistic Explanation for the Increased Effectiveness of Therapeutic Monoclonal Antibodies. Nutrients 2023, 15, 3498. https://doi.org/10.3390/nu15163498

AMA Style

Christofyllakis K, Neumann F, Bewarder M, Thurner L, Kaddu-Mulindwa D, Kos IA, Lesan V, Bittenbring JT. Vitamin D Enhances Immune Effector Pathways of NK Cells Thus Providing a Mechanistic Explanation for the Increased Effectiveness of Therapeutic Monoclonal Antibodies. Nutrients. 2023; 15(16):3498. https://doi.org/10.3390/nu15163498

Chicago/Turabian Style

Christofyllakis, Konstantinos, Frank Neumann, Moritz Bewarder, Lorenz Thurner, Dominic Kaddu-Mulindwa, Igor Age Kos, Vadim Lesan, and Joerg Thomas Bittenbring. 2023. "Vitamin D Enhances Immune Effector Pathways of NK Cells Thus Providing a Mechanistic Explanation for the Increased Effectiveness of Therapeutic Monoclonal Antibodies" Nutrients 15, no. 16: 3498. https://doi.org/10.3390/nu15163498

APA Style

Christofyllakis, K., Neumann, F., Bewarder, M., Thurner, L., Kaddu-Mulindwa, D., Kos, I. A., Lesan, V., & Bittenbring, J. T. (2023). Vitamin D Enhances Immune Effector Pathways of NK Cells Thus Providing a Mechanistic Explanation for the Increased Effectiveness of Therapeutic Monoclonal Antibodies. Nutrients, 15(16), 3498. https://doi.org/10.3390/nu15163498

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