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INFINIUM Inc custom infinium methylation array horvathmammalmethylchip40

( A ) Epigenome-wide association study of dpi in PBMCs based on the entire <t>methylation</t> array. The volcano plots display the −log 10 ( P values) and the directionality of association between CpG sites and infection stages (A, EC, and LC) compared with B: A versus B (left panel), EC versus B (center panel), and LC versus B (right panel). Each dot represents a specific DNAme site. Shown are significantly associated CpG sites ( q < 0.05) with hypomethylation (blue), hypermethylation (red), and nonsignificant (gray). The horizontal axis represents the mean methylation change (i.e., the difference between group means), and the vertical axis represents −log 10 ( P values). ( B ) Changes in EA during each infection stage (A, EC, and LC) relative to B. Biological age analysis was performed based on subsets of clock CpGs. EA at the 3 infection time points was compared with B using mixed-effects linear regression modeling of longitudinal EA changes in PBMCs based on 10 epigenetic clocks. The results are shown separately for young (right) and old (left) RMs. Epigenetic age changes in young (blue) and old (red) RMs are shown. Saturated colors indicate statistically significant changes ( P < 0.05); pale colors indicate nonsignificant changes ( P > 0.05). A statistically significant increase in EA was observed only in young RMs. B–H, Benjamini–Hochberg correction; DMP, differentially methylated positions; dpi, days after infection; RMs, rhesus macaques; B, baseline; A, acute; EC, early chronic; LC, late chronic; EA, epigenetic age.

Custom Infinium Methylation Array Horvathmammalmethylchip40, supplied by INFINIUM Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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custom infinium methylation array horvathmammalmethylchip40 - by Bioz Stars, 2026-09

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1) Product Images from "Pathogenic SIV infection is associated with acceleration of epigenetic age in rhesus macaques"

Article Title: Pathogenic SIV infection is associated with acceleration of epigenetic age in rhesus macaques

Journal: The Journal of Clinical Investigation

doi: 10.1172/JCI189574

( A ) Epigenome-wide association study of dpi in PBMCs based on the entire methylation array. The volcano plots display the −log 10 ( P values) and the directionality of association between CpG sites and infection stages (A, EC, and LC) compared with B: A versus B (left panel), EC versus B (center panel), and LC versus B (right panel). Each dot represents a specific DNAme site. Shown are significantly associated CpG sites ( q < 0.05) with hypomethylation (blue), hypermethylation (red), and nonsignificant (gray). The horizontal axis represents the mean methylation change (i.e., the difference between group means), and the vertical axis represents −log 10 ( P values). ( B ) Changes in EA during each infection stage (A, EC, and LC) relative to B. Biological age analysis was performed based on subsets of clock CpGs. EA at the 3 infection time points was compared with B using mixed-effects linear regression modeling of longitudinal EA changes in PBMCs based on 10 epigenetic clocks. The results are shown separately for young (right) and old (left) RMs. Epigenetic age changes in young (blue) and old (red) RMs are shown. Saturated colors indicate statistically significant changes ( P < 0.05); pale colors indicate nonsignificant changes ( P > 0.05). A statistically significant increase in EA was observed only in young RMs. B–H, Benjamini–Hochberg correction; DMP, differentially methylated positions; dpi, days after infection; RMs, rhesus macaques; B, baseline; A, acute; EC, early chronic; LC, late chronic; EA, epigenetic age.

Figure Legend Snippet: ( A ) Epigenome-wide association study of dpi in PBMCs based on the entire methylation array. The volcano plots display the −log 10 ( P values) and the directionality of association between CpG sites and infection stages (A, EC, and LC) compared with B: A versus B (left panel), EC versus B (center panel), and LC versus B (right panel). Each dot represents a specific DNAme site. Shown are significantly associated CpG sites ( q < 0.05) with hypomethylation (blue), hypermethylation (red), and nonsignificant (gray). The horizontal axis represents the mean methylation change (i.e., the difference between group means), and the vertical axis represents −log 10 ( P values). ( B ) Changes in EA during each infection stage (A, EC, and LC) relative to B. Biological age analysis was performed based on subsets of clock CpGs. EA at the 3 infection time points was compared with B using mixed-effects linear regression modeling of longitudinal EA changes in PBMCs based on 10 epigenetic clocks. The results are shown separately for young (right) and old (left) RMs. Epigenetic age changes in young (blue) and old (red) RMs are shown. Saturated colors indicate statistically significant changes ( P < 0.05); pale colors indicate nonsignificant changes ( P > 0.05). A statistically significant increase in EA was observed only in young RMs. B–H, Benjamini–Hochberg correction; DMP, differentially methylated positions; dpi, days after infection; RMs, rhesus macaques; B, baseline; A, acute; EC, early chronic; LC, late chronic; EA, epigenetic age.

Techniques Used: Methylation, Infection

Clinical Proteomics:

Article Title: Highly recurrent CBS epimutations in gastric cancer CpG island methylator phenotypes and inflammation.
Article Snippet: .. This is also supported by the fact that CBS-deficient mice display significant plasma homocysteinemia (where Fig. 3 CBS epimutation associates with CIMP in multiple tumors and in gastric intestinal metaplasia. a Box plot showing CBS gene expression levels in CIMP, non-CIMP, and normal categories per cancer type (CIMP vs non-CIMP; *P < 0.05, two-sided Wilcoxon rank sum test). b Box plot showing mean promoter methylation β-values of CBS gene in CIMP, non-CIMP, and normal categories per cancer type (CIMP vs nonCIMP; *P < 0.05, two-sided Wilcoxon rank sum test). c Breakdown of GIM CIMP [upper panel] and GC CIMP [lower panel] signatures in CpG contexts island, shore, shelf, and open sea; *P < 0.001 according to binomial test; “Annotated” refers to the distribution of CpG probes on the Infinium methylation array. d Three-way Venn overlap of hypermethylated or hypomethylated CpG sites in GIM CIMP and GC CIMP subtypes at CGI level. e Average promoter methylation β-values of CBS gene in normal [chronic gastritis] and antrum GIM lesions based on CIMP (high), and non-CIMP (intermediate) (*P < 0.05 in pair-wise twotailed Mann-Whitney test with mean β-value difference ≥ 0.2 compared to normal group). f Average promoter methylation β-values of CBS gene in GIM cases with or without regression at study-end point; cases in pink belong to CIMP subtype (*P < 0.05 in pair-wise two-tailed Mann-Whitney test) excess cellular homocysteine is exported), and tissue-specific metabolic defects in the methionine pathway [19]. ..

Gene Expression:

Methylation:

Article Title: Array-Based DNA Methylation Profiling for Breast Cancer Subtype Discrimination
Article Snippet: We compared quantitative methylation values studied by the Infinium methylation array with previously established methylation values by qMSP in 60 breast tumor samples . .. This was done for five genes ( APC , RASSF1A , TWIST , RARβ2 and DAPK ), which corresponded to 33 CpG loci on the Infinium methylation array. .. For 25 CpG loci, we observed significant positive correlations between methylation values by the Infinium methylation array and by qMSP ( ).

Article Title: The methylation of SDC2 and TFPI2 defined three methylator phenotypes of colorectal cancer
Article Snippet: .. Briefly, the distribution parameters of the methylation M-values were estimated by a Gaussian mixed model to determine the optimal M-value and the corresponding β-value. β-value and M-value are two metrics often used for the Infinium methylation array. β-value is more easily interpreted as an approximation of the overall methylation percentage at a given CpG site in a sample, while M-value is more statistically valid for the differential analysis of methylation levels. ..

Article Title: Remodeling of the Methylation Landscape in Breast Cancer Metastasis
Article Snippet: .. Copy number aberrations were identified from the Infinium methylation array using the sum of methylated and unmethylated signal intensities based on a strategy described by Strum et al using the circular binary segmentation algorithm developed by Venkatraman and Olshen and Integrative Genomics Viewer to calculate and visualize the mean log-ratio of windows contained in each segment . ..

Article Title: Ultra-fast deep-learned CNS tumour classification during surgery.
Article Snippet: .. Methylation calls are assigned to one of the 450K CpG sites present on the Infinium methylation array using windows centred on the CpG site targeted by each probe. ..

Article Title: Characterisation and reproducibility of the HumanMethylationEPIC v2.0 BeadChip for DNA methylation profiling
Article Snippet: As replicate probes are a new feature of the EPICv2 array, bioinformatic approaches developed for previous versions of the Infinium methylation array may require updates to accommodate them. .. For example, methods for identifying DMRs, such as R package DMRcate [ ], or for integrating methylation data from different Infinium methylation array versions, such as R package meffil [ ], would need to take account of multiple probes targeting the same location. ..

Article Title: Direction-aware functional class scoring enrichment analysis of infinium DNA methylation data
Article Snippet: .. This is made more complicated for Infinium methylation array data due to the presence of multiple probes per gene, meaning the data needs to be summarized from probes to genes and then to gene sets. .. Here, we outline eight potential approaches for FCS of Infinium methylation data implemented in R (v4.3.2), where methods 1–5 are type I and 6–8 are type II: Limma Average t-test (LAT).

Image Search Results

Journal: The Journal of Clinical Investigation

Article Title: Pathogenic SIV infection is associated with acceleration of epigenetic age in rhesus macaques

doi: 10.1172/JCI189574

Figure Lengend Snippet: ( A ) Epigenome-wide association study of dpi in PBMCs based on the entire methylation array. The volcano plots display the −log 10 ( P values) and the directionality of association between CpG sites and infection stages (A, EC, and LC) compared with B: A versus B (left panel), EC versus B (center panel), and LC versus B (right panel). Each dot represents a specific DNAme site. Shown are significantly associated CpG sites ( q < 0.05) with hypomethylation (blue), hypermethylation (red), and nonsignificant (gray). The horizontal axis represents the mean methylation change (i.e., the difference between group means), and the vertical axis represents −log 10 ( P values). ( B ) Changes in EA during each infection stage (A, EC, and LC) relative to B. Biological age analysis was performed based on subsets of clock CpGs. EA at the 3 infection time points was compared with B using mixed-effects linear regression modeling of longitudinal EA changes in PBMCs based on 10 epigenetic clocks. The results are shown separately for young (right) and old (left) RMs. Epigenetic age changes in young (blue) and old (red) RMs are shown. Saturated colors indicate statistically significant changes ( P < 0.05); pale colors indicate nonsignificant changes ( P > 0.05). A statistically significant increase in EA was observed only in young RMs. B–H, Benjamini–Hochberg correction; DMP, differentially methylated positions; dpi, days after infection; RMs, rhesus macaques; B, baseline; A, acute; EC, early chronic; LC, late chronic; EA, epigenetic age.

Article Snippet: DNAme profiles were generated using a custom Infinium methylation array (HorvathMammalMethylChip40) representing 37,492 CpG highly conserved sites in the mammals, with the NCBI’s Gene Expression Omnibus (GEO) accession number GPL28271 for microarray design ( ).

Techniques: Methylation, Infection

DNA methylation (DNAm) profiling of tissue samples of lung adenocarcinoma. A, B , Volcano plots of the differentially methylated positions (DMPs) from the combined GEO dataset (A) and the TCGA dataset (B). C, Venn diagram depicting DMPs overlapping between the combined GEO and TCGA datasets by methylation change. D, The distribution of DMPs by CpG density within the combined GEO dataset. E, Scatter plot of mean methylation difference versus log fold change of gene expression in the TCGA dataset (blue: hypomethylated and upregulated; red: hypermethylated and down-regulated).

Journal: Heliyon

Article Title: Blood-based biomarkers derived from tumor-informed DNA methylation analysis for lung adenocarcinoma

doi: 10.1016/j.heliyon.2025.e42581

Figure Lengend Snippet: DNA methylation (DNAm) profiling of tissue samples of lung adenocarcinoma. A, B , Volcano plots of the differentially methylated positions (DMPs) from the combined GEO dataset (A) and the TCGA dataset (B). C, Venn diagram depicting DMPs overlapping between the combined GEO and TCGA datasets by methylation change. D, The distribution of DMPs by CpG density within the combined GEO dataset. E, Scatter plot of mean methylation difference versus log fold change of gene expression in the TCGA dataset (blue: hypomethylated and upregulated; red: hypermethylated and down-regulated).

Article Snippet: This normalization procedure is well-suited for the incremental preprocessing of individual methylation arrays, especially when integrating data from multiple generations of Infinium methylation arrays [ ].

Techniques: DNA Methylation Assay, Methylation, Gene Expression

Two DMP signatures for tissue and blood.

Journal: Heliyon

Article Title: Blood-based biomarkers derived from tumor-informed DNA methylation analysis for lung adenocarcinoma

doi: 10.1016/j.heliyon.2025.e42581

Figure Lengend Snippet: Two DMP signatures for tissue and blood.

Techniques: Methylation

(A) Monocyte exhaustion experimental paradigm. BMMCs are cultured under control PBS or repetitive LPS stimulation for 5 days in the presence of M-CSF. Cells are then sorted into non-classical (purple), intermediate (yellow), or classical (red) pools and analyzed for changes in DNA methylation. (B) Heatmap of average DNA methylation at differentially methylated CpG probes (≥5% difference in percentage 5mC versus PBS control; false discovery rate [FDR] < 10%; n = 3 for each monocyte subtype, which was used as an analysis covariate). Rows represent individual probe CpG sites. (C) HOMER transcription factor (TF) binding motif analysis for hyper and hypo DMRs (±250 bp non-overlapping windows; bubbles colored by TF family). (D) Heatmaps of H3K27ac (GEO: GSE168190), H3K4me1, and H3K4me3 enrichment at DMRs. Metaplots above each heatmap indicated average signal at hyper (blue) and hypo (green) DMRs in each condition (±2.5 kb non-overlapping windows; normalized to IgG control). (E) Correlation plot of change in DNA methylation versus H3K27ac for DMRs with overlapping differential H3K27ac peaks. Simple linear regression was performed, with the line of best fit (blue) and 95% confidence interval (gray highlighted region) indicated. (F) UCSC browser track views of MiSeq-validated DMRs at the Plac8 promoter (chr5: 100,570,157–100,573,097), Erg promoter (chr16: 95,457,684–95,460,943), and Cebpa/g enhancer (chr7: 35,063,623–35,066,630). DNA methylation values represent the average of three biological replicates; values in classical monocytes were selected as representative of the LPS condition. ENCODE annotated promoters (red) and enhancers (orange) are depicted in the bottom track.

Journal: Cell reports

Article Title: Altered DNA methylation underlies monocyte dysregulation and immune exhaustion memory in sepsis

doi: 10.1016/j.celrep.2024.113894

Figure Lengend Snippet: (A) Monocyte exhaustion experimental paradigm. BMMCs are cultured under control PBS or repetitive LPS stimulation for 5 days in the presence of M-CSF. Cells are then sorted into non-classical (purple), intermediate (yellow), or classical (red) pools and analyzed for changes in DNA methylation. (B) Heatmap of average DNA methylation at differentially methylated CpG probes (≥5% difference in percentage 5mC versus PBS control; false discovery rate [FDR] < 10%; n = 3 for each monocyte subtype, which was used as an analysis covariate). Rows represent individual probe CpG sites. (C) HOMER transcription factor (TF) binding motif analysis for hyper and hypo DMRs (±250 bp non-overlapping windows; bubbles colored by TF family). (D) Heatmaps of H3K27ac (GEO: GSE168190), H3K4me1, and H3K4me3 enrichment at DMRs. Metaplots above each heatmap indicated average signal at hyper (blue) and hypo (green) DMRs in each condition (±2.5 kb non-overlapping windows; normalized to IgG control). (E) Correlation plot of change in DNA methylation versus H3K27ac for DMRs with overlapping differential H3K27ac peaks. Simple linear regression was performed, with the line of best fit (blue) and 95% confidence interval (gray highlighted region) indicated. (F) UCSC browser track views of MiSeq-validated DMRs at the Plac8 promoter (chr5: 100,570,157–100,573,097), Erg promoter (chr16: 95,457,684–95,460,943), and Cebpa/g enhancer (chr7: 35,063,623–35,066,630). DNA methylation values represent the average of three biological replicates; values in classical monocytes were selected as representative of the LPS condition. ENCODE annotated promoters (red) and enhancers (orange) are depicted in the bottom track.

Article Snippet: DNA methylation Infinium BeadChip array (LPS exhausted bone marrow monocytes and cecal slurry monocytes) , This paper , GEO: GSE242816.

Techniques: Cell Culture, Control, DNA Methylation Assay, Methylation, Binding Assay

(A) UMAP visualization of scRNA-seq profiles of WT and Ticam2 −/− BMMCs following PBS control or repetitive LPS stimulation (GEO: GSE182355). Cells are colored according to monocyte subtype, phenotype, and treatment. (B) Heatmap of intersectional gene expression for the top eight markers of each cell cluster. (C) DMRs for select immunologically significant genes with affiliate UMAP feature plots demonstrating expression levels across cell clusters. Illumina BeadChip probe IDs are indicated for each DMR. (D) Correlation plot of change in DNA methylation versus expression for the nearest matched gene between WT PBS control and Ly6C high LPS-treated BMMCs. Simple linear regression was performed, with the line of best fit (blue) and 95% confidence interval (gray highlighted region) indicated. (E) PCA of differentially methylated CpG probes. Ovals indicate the normal distribution for each treatment. (F) Correlation heatmap for average DNA methylation at DMR CpG probes in WT and Ticam2 −/− BMMCs (≥5% difference in percentage 5mC versus WT or Ticam2 −/− PBS control; FDR < 5%; n = 4 for each treatment). (G) Volcano plot for altered DNA methylation at DMRs in Ticam2 −/− versus WT LPS-treated BMMCs. Probes are colored based on observed differential methylation patterns in WT LPS-treated cells relative to WT PBS control (red, hypermethylated; blue, hypomethylated) or if differentially methylated only in Ticam2 −/− LPS cells (green). Dotted lines indicate the change in DNA methylation (±5%) and adjusted p value (<0.05) cutoffs for DMRs.

Journal: Cell reports

Article Title: Altered DNA methylation underlies monocyte dysregulation and immune exhaustion memory in sepsis

doi: 10.1016/j.celrep.2024.113894

Figure Lengend Snippet: (A) UMAP visualization of scRNA-seq profiles of WT and Ticam2 −/− BMMCs following PBS control or repetitive LPS stimulation (GEO: GSE182355). Cells are colored according to monocyte subtype, phenotype, and treatment. (B) Heatmap of intersectional gene expression for the top eight markers of each cell cluster. (C) DMRs for select immunologically significant genes with affiliate UMAP feature plots demonstrating expression levels across cell clusters. Illumina BeadChip probe IDs are indicated for each DMR. (D) Correlation plot of change in DNA methylation versus expression for the nearest matched gene between WT PBS control and Ly6C high LPS-treated BMMCs. Simple linear regression was performed, with the line of best fit (blue) and 95% confidence interval (gray highlighted region) indicated. (E) PCA of differentially methylated CpG probes. Ovals indicate the normal distribution for each treatment. (F) Correlation heatmap for average DNA methylation at DMR CpG probes in WT and Ticam2 −/− BMMCs (≥5% difference in percentage 5mC versus WT or Ticam2 −/− PBS control; FDR < 5%; n = 4 for each treatment). (G) Volcano plot for altered DNA methylation at DMRs in Ticam2 −/− versus WT LPS-treated BMMCs. Probes are colored based on observed differential methylation patterns in WT LPS-treated cells relative to WT PBS control (red, hypermethylated; blue, hypomethylated) or if differentially methylated only in Ticam2 −/− LPS cells (green). Dotted lines indicate the change in DNA methylation (±5%) and adjusted p value (<0.05) cutoffs for DMRs.

Article Snippet: DNA methylation Infinium BeadChip array (LPS exhausted bone marrow monocytes and cecal slurry monocytes) , This paper , GEO: GSE242816.

Techniques: Control, Gene Expression, Expressing, DNA Methylation Assay, Methylation

(A) Heatmap of average DNA methylation levels at MiSeq-validated DMRs in BMMCs following 5 days of PBS control or repetitive LPS stimulation in the presence of 250 nM 5-azacytidine (5-aza). (B) RT-qPCR for key exhaustion gene expression relative to PBS control. Expression levels were normalized to the geometric mean of Ube2l3 , Oaz1 , and Nktr (mean ± SD; n = 3–6; one-way ANOVA with Sidak’s multiple comparisons test; ****p-adj < 0.0001; ***p-adj < 0.001; **p-adj < 0.01; *p-adj < 0.05; ns, not significant). (C) Population statistics for non-classical (Ly6C low ), intermediate (Ly6C int ), and classical (Ly6C high ) monocyte subtypes in cultured BMMCs (n = 6–7; one-way ANOVA with Sidak’s multiple comparisons test). (D) Flow cytometry mean fluorescence intensity (MFI) for exhaustion markers relative to PBS control (n = 6–7; one-way ANOVA with Sidak’s multiple comparisons test).

Journal: Cell reports

Article Title: Altered DNA methylation underlies monocyte dysregulation and immune exhaustion memory in sepsis

doi: 10.1016/j.celrep.2024.113894

Figure Lengend Snippet: (A) Heatmap of average DNA methylation levels at MiSeq-validated DMRs in BMMCs following 5 days of PBS control or repetitive LPS stimulation in the presence of 250 nM 5-azacytidine (5-aza). (B) RT-qPCR for key exhaustion gene expression relative to PBS control. Expression levels were normalized to the geometric mean of Ube2l3 , Oaz1 , and Nktr (mean ± SD; n = 3–6; one-way ANOVA with Sidak’s multiple comparisons test; ****p-adj < 0.0001; ***p-adj < 0.001; **p-adj < 0.01; *p-adj < 0.05; ns, not significant). (C) Population statistics for non-classical (Ly6C low ), intermediate (Ly6C int ), and classical (Ly6C high ) monocyte subtypes in cultured BMMCs (n = 6–7; one-way ANOVA with Sidak’s multiple comparisons test). (D) Flow cytometry mean fluorescence intensity (MFI) for exhaustion markers relative to PBS control (n = 6–7; one-way ANOVA with Sidak’s multiple comparisons test).

Article Snippet: DNA methylation Infinium BeadChip array (LPS exhausted bone marrow monocytes and cecal slurry monocytes) , This paper , GEO: GSE242816.

Techniques: DNA Methylation Assay, Control, Quantitative RT-PCR, Gene Expression, Expressing, Cell Culture, Flow Cytometry, Fluorescence

(A) scRNA-seq UMAP feature plots for transcription factor TCF7L2 motif enrichment and gene expression. Numbers indicate different cell clusters, as outlined in . (B) RT-qPCR for key exhaustion genes in BMMCs under PBS control or repetitive LPS stimulation in the presence of different concentrations of Wnt agonist 1. Expression levels were normalized to the geometric mean of Ube2l3 , Oaz1 , and Nktr (mean expression ± SD; n = 3–6; one-way ANOVA with Sidak’s multiple comparisons test; ****p-adj < 0.0001; ***p-adj < 0.001; **p-adj < 0.01; *p-adj < 0.05; ns, not significant; differences are not significant unless otherwise specified; see for exact p values). (C) Flow cytometry mean fluorescence intensity (MFI) for exhaustion (CD38, MARCO, PD-L1, CXCR2) and macrophage (F4/80) markers relative to PBS control. Boxplots indicate median MFI values (n = 5–7; one-way ANOVA with Sidak’s multiple comparisons test). (D) Gating strategy (top) and population statistics (bottom) for non-classical (Ly6C low ), intermediate (Ly6C int ), and classical (Ly6C high ) monocyte subtypes in cultured BMMCs (n = 6–13; one-way ANOVA with Sidak’s multiple comparisons test). (E) NAD + levels in cultured BMMCs normalized to total protein levels (n = 3–6; one-way ANOVA with Sidak’s multiple comparisons test). (F) Correlation heatmap for average DNA methylation levels at key exhaustion loci in cultured BMMCs (n = 3–9 for each condition). (G) UCSC browser track views of average DNA methylation at the Plac8 promoter (chr5: 100,572,230–100,572,607), Cebpa/g distal enhancer (chr7: 35,064,805–35,065,304), and Tcf7l2 intron (chr19: 55,768,986–55,769,585). ENCODE annotated promoters (red) and enhancers (orange) are depicted in the bottom track.

Journal: Cell reports

Article Title: Altered DNA methylation underlies monocyte dysregulation and immune exhaustion memory in sepsis

doi: 10.1016/j.celrep.2024.113894

Figure Lengend Snippet: (A) scRNA-seq UMAP feature plots for transcription factor TCF7L2 motif enrichment and gene expression. Numbers indicate different cell clusters, as outlined in . (B) RT-qPCR for key exhaustion genes in BMMCs under PBS control or repetitive LPS stimulation in the presence of different concentrations of Wnt agonist 1. Expression levels were normalized to the geometric mean of Ube2l3 , Oaz1 , and Nktr (mean expression ± SD; n = 3–6; one-way ANOVA with Sidak’s multiple comparisons test; ****p-adj < 0.0001; ***p-adj < 0.001; **p-adj < 0.01; *p-adj < 0.05; ns, not significant; differences are not significant unless otherwise specified; see for exact p values). (C) Flow cytometry mean fluorescence intensity (MFI) for exhaustion (CD38, MARCO, PD-L1, CXCR2) and macrophage (F4/80) markers relative to PBS control. Boxplots indicate median MFI values (n = 5–7; one-way ANOVA with Sidak’s multiple comparisons test). (D) Gating strategy (top) and population statistics (bottom) for non-classical (Ly6C low ), intermediate (Ly6C int ), and classical (Ly6C high ) monocyte subtypes in cultured BMMCs (n = 6–13; one-way ANOVA with Sidak’s multiple comparisons test). (E) NAD + levels in cultured BMMCs normalized to total protein levels (n = 3–6; one-way ANOVA with Sidak’s multiple comparisons test). (F) Correlation heatmap for average DNA methylation levels at key exhaustion loci in cultured BMMCs (n = 3–9 for each condition). (G) UCSC browser track views of average DNA methylation at the Plac8 promoter (chr5: 100,572,230–100,572,607), Cebpa/g distal enhancer (chr7: 35,064,805–35,065,304), and Tcf7l2 intron (chr19: 55,768,986–55,769,585). ENCODE annotated promoters (red) and enhancers (orange) are depicted in the bottom track.

Article Snippet: DNA methylation Infinium BeadChip array (LPS exhausted bone marrow monocytes and cecal slurry monocytes) , This paper , GEO: GSE242816.

Techniques: Gene Expression, Quantitative RT-PCR, Control, Expressing, Flow Cytometry, Fluorescence, Cell Culture, DNA Methylation Assay

(A) Correlation heatmap for DNA methylation at differentially methylated CpG probes following 5 days of PBS control or repetitive LPS stimulation in the presence or absence of TDM (≥5% difference in percentage 5mC versus PBS control; FDR < 5%; n = 4 for each treatment). (B) Venn diagrams for differentially methylated CpG probes in cells treated with LPS alone (red) or LPS + TDM (green). (C) PCA of differentially methylated CpG probes. Ovals indicate the normal distribution for each treatment. (D) Volcano plot for altered DNA methylation at DMRs in LPS + TDM-treated BMMCs versus treatment with LPS alone. Probes are colored based on observed differential methylation patterns in LPS-treated cells relative to PBS control (red, hypermethylated; blue, hypomethylated) or if they exhibit differential methylation only in LPS + TDM cells (green). Dotted lines indicate the change in DNA methylation (±5%) and adjusted p value (<0.05) cutoffs for DMRs. (E) Representative DMRs linked to epigenetic modifiers ( Dot1l , Ankrd11 ), transcriptional regulators ( Pim1 , Foxp1 ), and immunologically significant genes ( Il12rb1 , Nfatc2 , Socs3 , CD83 ). Illumina BeadChip probe IDs are indicated for each DMR. (F) GO enrichment for cell signaling pathways among DMR-linked genes in LPS + TDM-treated BMMCs.

Journal: Cell reports

Article Title: Altered DNA methylation underlies monocyte dysregulation and immune exhaustion memory in sepsis

doi: 10.1016/j.celrep.2024.113894

Figure Lengend Snippet: (A) Correlation heatmap for DNA methylation at differentially methylated CpG probes following 5 days of PBS control or repetitive LPS stimulation in the presence or absence of TDM (≥5% difference in percentage 5mC versus PBS control; FDR < 5%; n = 4 for each treatment). (B) Venn diagrams for differentially methylated CpG probes in cells treated with LPS alone (red) or LPS + TDM (green). (C) PCA of differentially methylated CpG probes. Ovals indicate the normal distribution for each treatment. (D) Volcano plot for altered DNA methylation at DMRs in LPS + TDM-treated BMMCs versus treatment with LPS alone. Probes are colored based on observed differential methylation patterns in LPS-treated cells relative to PBS control (red, hypermethylated; blue, hypomethylated) or if they exhibit differential methylation only in LPS + TDM cells (green). Dotted lines indicate the change in DNA methylation (±5%) and adjusted p value (<0.05) cutoffs for DMRs. (E) Representative DMRs linked to epigenetic modifiers ( Dot1l , Ankrd11 ), transcriptional regulators ( Pim1 , Foxp1 ), and immunologically significant genes ( Il12rb1 , Nfatc2 , Socs3 , CD83 ). Illumina BeadChip probe IDs are indicated for each DMR. (F) GO enrichment for cell signaling pathways among DMR-linked genes in LPS + TDM-treated BMMCs.

Article Snippet: DNA methylation Infinium BeadChip array (LPS exhausted bone marrow monocytes and cecal slurry monocytes) , This paper , GEO: GSE242816.

Techniques: DNA Methylation Assay, Methylation, Control, Protein-Protein interactions

(A) Experimental paradigm and FlowSOM analysis of monocytic lineage populations in bone marrow collected from control (CTRL) or cecal slurry (CS)-injected mice at experimental day 7 (d7). Values to the right of each FlowSOM population indicate the percentage of cells for each condition clustering in that population. UMAP visualizations were prepared for the indicated markers (n = 8 CTRL and 12 CS day 7 mice). (B) Flow cytometry mean fluorescence intensity (MFI) for exhaustion markers in Pop1. Boxplots indicate median MFI values (n = 8–12; one-way ANOVA with Sidak’s multiple comparisons test; ****p-adj < 0.0001; ***p-adj < 0.001; *p-adj < 0.05). (C) RT-qPCR for key exhaustion gene expression in CTRL and CS day 6 or 7 monocytes. Expression levels were normalized to the geometric mean of Ube2l3 , Oaz1 , and Actb . Data points represent separate mice, with mean expression ± SD indicated (n = 3–12; one-way ANOVA with Sidak’s multiple comparisons test, excepting Il10 , Morrbid , and Foxp1 analyzed by Kruskal-Wallis with Dunn’s multiple comparisons test; differences are not significant unless otherwise specified; see for exact p values). (D) Correlation heatmap for DNA methylation at differentially methylated CpG probes in CTRL or CS bone marrow monocytes (≥5% difference in percentage 5mC versus CTRL; FDR < 10%; n = 7 CTRL, 4 CS day 6, 6 CS day 7, and 4 CS day 12 mice). (E) Venn diagrams for differentially methylated CpG probes in CS bone marrow monocytes at experimental days 6, 7, and 12. (F) Volcano plot for altered DNA methylation at DMRs in CS day 7 bone marrow monocytes versus CTRL. Probes are colored based on observed differential methylation state in CS day 7 monocytes (red, hypermethylated; blue, hypomethylated). Dotted lines indicate the change in DNA methylation (±5%) and adjusted p value (<0.1) cutoffs for DMRs. Nearest linked genes are indicated for select DMRs. (G) Select DMRs for immunologically significant genes in CTRL or CS monocytes. Illumina BeadChip probe IDs are indicated for each DMR.

Journal: Cell reports

Article Title: Altered DNA methylation underlies monocyte dysregulation and immune exhaustion memory in sepsis

doi: 10.1016/j.celrep.2024.113894

Figure Lengend Snippet: (A) Experimental paradigm and FlowSOM analysis of monocytic lineage populations in bone marrow collected from control (CTRL) or cecal slurry (CS)-injected mice at experimental day 7 (d7). Values to the right of each FlowSOM population indicate the percentage of cells for each condition clustering in that population. UMAP visualizations were prepared for the indicated markers (n = 8 CTRL and 12 CS day 7 mice). (B) Flow cytometry mean fluorescence intensity (MFI) for exhaustion markers in Pop1. Boxplots indicate median MFI values (n = 8–12; one-way ANOVA with Sidak’s multiple comparisons test; ****p-adj < 0.0001; ***p-adj < 0.001; *p-adj < 0.05). (C) RT-qPCR for key exhaustion gene expression in CTRL and CS day 6 or 7 monocytes. Expression levels were normalized to the geometric mean of Ube2l3 , Oaz1 , and Actb . Data points represent separate mice, with mean expression ± SD indicated (n = 3–12; one-way ANOVA with Sidak’s multiple comparisons test, excepting Il10 , Morrbid , and Foxp1 analyzed by Kruskal-Wallis with Dunn’s multiple comparisons test; differences are not significant unless otherwise specified; see for exact p values). (D) Correlation heatmap for DNA methylation at differentially methylated CpG probes in CTRL or CS bone marrow monocytes (≥5% difference in percentage 5mC versus CTRL; FDR < 10%; n = 7 CTRL, 4 CS day 6, 6 CS day 7, and 4 CS day 12 mice). (E) Venn diagrams for differentially methylated CpG probes in CS bone marrow monocytes at experimental days 6, 7, and 12. (F) Volcano plot for altered DNA methylation at DMRs in CS day 7 bone marrow monocytes versus CTRL. Probes are colored based on observed differential methylation state in CS day 7 monocytes (red, hypermethylated; blue, hypomethylated). Dotted lines indicate the change in DNA methylation (±5%) and adjusted p value (<0.1) cutoffs for DMRs. Nearest linked genes are indicated for select DMRs. (G) Select DMRs for immunologically significant genes in CTRL or CS monocytes. Illumina BeadChip probe IDs are indicated for each DMR.

Article Snippet: DNA methylation Infinium BeadChip array (LPS exhausted bone marrow monocytes and cecal slurry monocytes) , This paper , GEO: GSE242816.

Techniques: Control, Injection, Flow Cytometry, Fluorescence, Quantitative RT-PCR, Gene Expression, Expressing, DNA Methylation Assay, Methylation

Journal: Cell reports

Article Title: Altered DNA methylation underlies monocyte dysregulation and immune exhaustion memory in sepsis

doi: 10.1016/j.celrep.2024.113894

Figure Lengend Snippet: KEY RESOURCES TABLE

Article Snippet: DNA methylation Infinium BeadChip array (LPS exhausted bone marrow monocytes and cecal slurry monocytes) , This paper , GEO: GSE242816.

Techniques: Blocking Assay, Negative Control, Recombinant, Methylation, SYBR Green Assay, DNA Methylation Assay, Software, Isolation, Reverse Transcription, Multiplex Assay

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