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3. Genes Associated with Differentially Methylated 228 GENOME-WIDE METHYLATION IS ASSOCIATED WITH HIV-1 INFECTION AND DISEASE PROGRESSION Sara Moron-Lopez1, Judith Dalmau1, Victor Urrea1, Miguel Lopez2, Maria C Puertas1, Beatriz Mothe1,3, Christian Brander1,4,5, Manel Esteller2, Maria Berdasco2, Javier Martinez-Picado1,4,5 1. AIDS Research Institute IrsiCaixa, Institut d'Investigació en Cièncias de la Salut Germans Trias i Pujol, Badalona, Spain; 2. Cancer Epigenetics Group, Cancer Epigenetics and Biology Program, Bellvitge Biomedical Biomedical Research Institute, Barcelona, Spain; 3. Fundació Lluita Contra la Sida, Hospital Universitari Germans Trias i Pujol, Badalona, Spain; 4. Universitat de Vic-Universitat Central de Catalunya, Vic, Spain; 5. Catalan Institution for Research and Advanced Studies, Barcelona, Spain. BACKGROUND RESULTS Human genetic variation –mostly in the HLA and CCR5 regions– explains 25% of the variability in CONCLUSIONS HIV-1 disease progression1. 1. HIV-1 infection affects host DNA methylation 3. Genes associated with differentially methylated regions (DMRs) are enriched in type I IFN and cytokine-mediated signaling pathways, and regulation of viral processes 2 However, it is also known that viral infections are able to modify cellular DNA methylation patterns . 450,000 normalized CGs ● HIV-1 infection affects host DNA methylation patterns in from Illumina Infinium 450K Human methylation array + Therefore, changes in the methylation of CpGs islands could modulate HIV-1 disease progression. Hypo- 0 Hyper- CD4 T cells, and cART partially recovers this changes. methylated methylated 470 DMRs (differentially methylated regions) Viremic between group comparisons HYPOTHESIS AND AIMS Uninfected ● We have determined 5 candidate genes which may play a May host DNA methylation be associated with HIV-1 disease progression phenotypes? 190 genes role in HIV pathogenesis. associated with these DMRs 29 genes ● Cellular DNA methylation is associated with HIV-1 DNA HIV disease with relevant differential methylation between groups HIV infection Biological process infection and disease progression, specifically modifying the methylation progression enrichment analysis -6 18 genes expression of genes with an immune- or viral regulation-related (p<10 ) with described biological function function. - type I IFN and cytokine-mediated 5 candidate genes DM in Viremic: DM in EC: signaling pathways with higher number of DMRs in - SPOCK2 - MIB2 - regulation of viral processes Viremic and EC - USP18 - NSD1 1. To compare genome-wide methylation patterns between different HIV-1-progression cohorts - AURKC ● The regulation of host epigenetics contributes to explain the variability in HIV-1 disease progression. - SPOCK2 → encodes a protein which binds with glycosaminoglycans to form part of the extracellular matrix. 2. To identify differentially methylated genes in multiple HIV-1-progression cohorts - USP18 (ubiquitin specific peptidase 18) → participates in type I IFN signaling pathway - MIB2 → mindbomb E2 ubiquitin proteinligase 2 - NSD1 → nuclear receptor binding set domain protein 1 METHODS - AURKC → serine/threonine protein kinase Study groups. A total of 85 samples were analyzed, obtained from: Figure 3. Analysis pipeline of the differentially methylated regions detected between group comparisons, biological process enrichment analysis and selected candidate genes. FUTURE WORK Figure 1. Heatmap resulting from the clustering of the 705 significant differentially methylated regions (DMRs) between viremic and uninfected group. 4. Differential methylation of the candidate genes clusters the HIV-1 disease progression 1. To validate differentially methylated regions by specific phenotypes methylation analysis EC Viremic cART Uninfected 2. cART partially recovers the changes in host DNA methylation HIV- VL<50 cp/ml Median VL VL<50 cp/ml donors no-cART 105 cp/ml median time on cART Hypo- 0 Hyper- methylated methylated 2. To confirm these results in individuals from independent 1.4 years Hypo- 0 Hyper- methylated methylated cohorts EC Viremic Viremic cART cART Uninfected 3. To perform functional analysis to determine the implication N=21 N=21 N=21 N=22 Uninfected of these genes in HIV-1 infection and pathogenesis CD4+ T cells ACKNOWLEDGEMENTS Genome-wide CpG methylation analysis QC + normalization This study was supported by IrsiCaixa and Idibell. (Infinium HumanMethylation 450k BeadChip Kit, Illumina) + statistical analysis + CG/gene selection S.M-L. was partially supported by Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement from Generalitat de Catalunya (2013FI_B 00275). Sample acquisition. CD4+ T lymphocytes were purified from blood samples by negative selection using magnetic beads. DNA was extracted from CD4+ T cells and then bisulfite converted using the EZ DNA Methylation kit (Zymo). All the patients of the study. DNA Methylation profiling. DNA methylation profiles were obtained using the Illumina Infinium 450K Human Methylation array. REFERENCES Quality control and normalization. The intensities of the images were extracted using GenomeStudio (2010.3) Methylation 1. McLaren PJ. et al. (2015) Polymorphisms of large effect explain the majority of the host genetic contribution to variation of HIV-1 virus load. PNAS module (1.8.5) software. The methylation score for each CpG was represented as a β value according to the fluores- cent intensity ratio. β 2. Ancey PB et al. (2015) Genomic responses to hepatitis B virus (HBV) infection in primary human hepatocytes. Oncotarget values may take any value between 0 (non-methylated) and 1 (completely methylated). Using GenomeStudio, outlayer CGs with a detection p-value >0.001, CGs associated with “rs” numbers (SNP), and X-chromosome CGs were excluded from the analysis, and methylation levels were normalized using different internal controls that are present on the HumanMethylation 450 BeadChip. Contact to: We compared DNA methylation patterns between the different groups (EC, Javier Martínez-Picado Statistical analysis and CG/gene selection. cg07147033 cg13897348 cg02909446 cg01861509 cg15331996 cg02595823 cg12623536 cg08641278 cg19568003 cg26332114 cg18644286 cg25432232 cg19603903 cg18121224 cg17493885 cg08369368 cg01963623 cg14293575 Viremic, cART and Uninfected) using Wilcoxon signed-rank test, and we selected as differentially methylated regions (DMRs) those with AIDS Research Institute IrsiCaixa Figure 2. Heatmap resulting from the clustering of the 705 differentially methylated regions (DMRs) MIB2 SPOCK2 AURKC NSD1 USP18 adjusted p<0.05 (FDR). We selected the DMRs with differential population mean rank >0.05 (>5% of differential methylation), constituting [email protected] part of CpG islands, and located in promoter regions; and listed the genes associated with these DMRs. between viremic and uninfected groups, adding the methylation levels these CGs in cART group. Figure 4. Heatmap resulting from the clustering analysis using the CGs of the selected candidate genes. .
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