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. 2022 Feb 21;14(1):27.
doi: 10.1186/s13148-022-01247-1.

A core of differentially methylated CpG loci in gMDSCs isolated from neonatal and adult sources

Isabella Berglund-Brown  1 , Emily Nissen  2 , Devin C Koestler  2 , Rondi A Butler  3 , Melissa N Eliot  3 , James F Padbury  1 , Lucas A Salas  4 , Annette M Molinaro  5 , Brock C Christensen  4   6 , John K Wiencke  5 , Karl T Kelsey  7
Affiliations

A core of differentially methylated CpG loci in gMDSCs isolated from neonatal and adult sources

Isabella Berglund-Brown et al. Clin Epigenetics. .

Abstract

Background: Myeloid-derived suppressor cells (MDSCs), which include monocytic (mMDSCs) and granulocytic (gMDSCs) cells, are an immunosuppressive, heterogeneous population of cells upregulated in cancer and other pathologic conditions, in addition to normal conditions of stress. The origin of MDSCs is debated, and the regulatory pattern responsible for gMDSC differentiation remains unknown. Since DNA methylation (DNAm) contributes to lineage differentiation, we have investigated whether it contributes to the acquisition of the gMDSC phenotype.

Results: Using the Illumina EPIC array to measure DNAm of gMDSCs and neutrophils from diverse neonatal and adult blood sources, we found 189 differentially methylated CpGs between gMDSCs and neutrophils with a core of ten differentially methylated CpGs that were consistent across both sources of cells. Genes associated with these loci that are involved in immune responses include VCL, FATS, YAP1, KREMEN2, UBTF, MCC-1, and EFCC1. In two cancer patient groups that reflected those used to develop the methylation markers (head and neck squamous cell carcinoma (HNSCC) and glioma), all of the CpG loci were differentially methylated, reaching statistical significance in glioma cases and controls, while one was significantly different in the smaller HNSCC group.

Conclusions: Our findings indicate that gMDSCs have a core of distinct DNAm alterations, informing future research on gMDSC differentiation and function.

Keywords: Cancer; DNA methylation; Immunomethylomics; MDSCs; gMDSCs.

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Conflict of interest statement

JKW and KTK are co-founders of Cellintec, which had no role in this research. The other authors declare no potential conflicts of interest.

Figures

Fig. 1
Fig. 1
Study design and resulting overlaps of DMRs identified. A Diagram of the study design. Briefly, we (1) obtained diverse sources of gMDSCs and neutrophils (2) Measured DNAm with the Illumina EPIC array (3) Pre-processed the datasets (4) Identified DMRs using statistical tests (5) Defined a core set of DMRs and then (6) Tested the core DMRs in DNAm from cancer studies. B Venn diagram of putative DMRs identified and the overlaps between the sets. This includes any loci that met the thresholds set and the subset of the top 100 DMRs that were identified by smallest q-value. The numbers in the rings are the number of CpG loci in that set
Fig. 2
Fig. 2
gMDSC sample deconvolution. The relative prevalence of each cell type in the low-density neutrophil fraction. Isolated gMDSCs were arrayed and the resulting data combined and assessed by deconvolution to generate relative proportions for each cell type. The means for each cell type from all donors are shown
Fig. 3
Fig. 3
Identifying putative neutrophil/gMDSC DMRs from adult blood samples and cord blood samples. A Volcano plot of − log 10(q-value) against delta-beta, which represents the difference in mean methylation beta-value between neutrophils and gMDSCs, adjusted for sex, in adult blood samples. The horizontal black bar represents the threshold of significance (q-value (FDR) = 0.05) and the two vertical black bars represent the threshold for delta-beta (delta-beta =  ± 0.2). B Unsupervised clustering heatmap of the top 100 gMDSC DMRs identified in adult blood samples, defined as the 100 CpG loci with the smallest adjusted p-value and absolute adjusted delta-beta greater than 0.2. Each column represents a sample, and each row represents a CpG locus. Above the heatmap, color indicates cell-type (blue = gMDSC and pink = neutrophil). Within the heatmap, color indicates methylation beta-value (blue = β of 1, or methylated, and yellow = β of 0, or unmethylated). C Volcano plot of − log10(q-value) against delta-beta, which represents the difference in mean methylation beta-value between neutrophils and gMDSCs in cord blood samples. The horizontal black bar represents the threshold of significance (q-value (FDR) = 0.05) and the two vertical black bars represent the threshold for delta-beta (delta-beta =  ± 0.2). D Unsupervised clustering heatmap of the top 100 gMDSC DMRs identified in cord blood samples, defined as the 100 CpG loci with the smallest adjusted p-value and absolute delta-beta greater than 0.2. Each column represents a sample, and each row represents a CpG locus. Above the heatmap, color indicates cell-type (blue = gMDSC and pink = neutrophil). Within the heatmap, color indicates methylation beta-value (blue = β of 1, or methylated, and yellow = β of 0, or unmethylated)
Fig. 4
Fig. 4
Core of differentially methylated CpG loci between gMDSCs and neutrophils from diverse adult and neonatal sources. Unsupervised clustering heatmap of the core ten gMDSC loci. Each column represents a sample, and each row represents a CpG locus. Above the heatmap, colors indicate sample type (green = adult blood, purple = cord blood) and cell-type (blue = gMDSC and pink = neutrophil). Within the heatmap, color indicates methylation beta-value (blue = β of 1, or methylated, and yellow = β of 0, or unmethylated)
Fig. 5
Fig. 5
Assessing uniform change of whole blood DNAm in cancer studies for the 189 consistent loci and the core 10 loci. A Upset plot showing the overlap of the 189 DMRs with consistent gMDSC-Neu delta-betas in adult and cord blood samples and the respective CpG loci in the HNSCC and glioma studies that had case–control delta-betas in the opposite direction (termed “opposite direction CpGs”). B Histogram of the distribution under the null hypothesis of the number of loci with case–control delta-betas that are in the opposite direction of the respective gMDSC-Neu delta-beta in both the HNSCC and glioma study. Distribution was created by randomly drawing 189 CpG loci from the set of all loci with consistent gMDSC-Neu delta-betas and the counting the number of loci that have case–control delta-betas in the opposite direction of gMDSC-Neu delta-betas in both the glioma and HNSCC studies. The horizontal red line is at our observed value of 122 loci, far outside the range of the distribution. C Upset plot of CpGs that were significantly different between cases and cancer-free controls from testing the association between whole blood methylation beta-value and cancer status for each of the gMDSC core loci in two cancer studies. D Heatmap of the whole blood methylation delta-beta for cancer versus control in the glioma study population and the HNSCC study population. Each row represents one of the core ten loci. The color in the heatmap indicates the delta-beta value
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