Differential DNA Methylation in Purified Human Blood Cells: Implications for Cell Lineage and Studies on Disease Susceptibility

WOS:000306806600056Peer reviewe

Amino Acid Signatures to Evaluate the Beneficial Effects of Weight Loss

Aims. We investigated the relationship between circulating amino acid levels and obesity; to what extent weight loss followed by weight maintenance can correct amino acid abnormalities; and whether amino acids are related to weight loss. Methods:. Amino acids associated with waist circumference (WC) and BMI were studied in 804 participants from the Malmö Diet and Cancer Cardiovascular Cohort (MDC-CC). Changes in amino acid levels were analyzed after weight loss and weight maintenance in 12 obese subjects and evaluated in a replication cohort (n = 83). Results:. Out of the eight identified BMI-associated amino acids from the MDC-CC, alanine, isoleucine, tyrosine, phenylalanine, and glutamate decreased after weight loss, while asparagine increased after weight maintenance. These changes were validated in the replication cohort. Scores that were constructed based on obesity-associated amino acids and known risk factors decreased in the ≥10% weight loss group with an associated change in BMI (R2 = 0.16–0.22, p < 0.002), whereas the scores increased in the <10% weight loss group (p < 0.0004). Conclusions:. Weight loss followed by weight maintenance leads to differential changes in amino acid levels associated with obesity. Treatment modifiable scores based on epidemiological and interventional data may be used to evaluate the potential metabolic benefit of weight loss

Age-associated DNA methylation changes in immune genes, histone modifiers and chromatin remodeling factors within 5 years after birth in human blood leukocytes

Abstract Background Age-related changes in DNA methylation occurring in blood leukocytes during early childhood may reflect epigenetic maturation. We hypothesized that some of these changes involve gene networks of critical relevance in leukocyte biology and conducted a prospective study to elucidate the dynamics of DNA methylation. Serial blood samples were collected at 3, 6, 12, 24, 36, 48 and 60 months after birth in ten healthy girls born in Finland and participating in the Type 1 Diabetes Prediction and Prevention Study. DNA methylation was measured using the HumanMethylation450 BeadChip. Results After filtering for the presence of polymorphisms and cell-lineage-specific signatures, 794 CpG sites showed significant DNA methylation differences as a function of age in all children (41.6% age-methylated and 58.4% age-demethylated, Bonferroni-corrected P value <0.01). Age-methylated CpGs were more frequently located in gene bodies and within +5 to +50 kilobases (kb) of transcription start sites (TSS) and enriched in developmental, neuronal and plasma membrane genes. Age-demethylated CpGs were associated to promoters and DNAse-I hypersensitivity sites, located within −5 to +5 kb of the nearest TSS and enriched in genes related to immunity, antigen presentation, the polycomb-group protein complex and cytoplasm. Conclusions This study reveals that susceptibility loci for complex inflammatory diseases (for example, IRF5, NOD2, and PTGER4) and genes encoding histone modifiers and chromatin remodeling factors (for example, HDAC4, KDM2A, KDM2B, JARID2, ARID3A, and SMARCD3) undergo DNA methylation changes in leukocytes during early childhood. These results open new perspectives to understand leukocyte maturation and provide a catalogue of CpG sites that may need to be corrected for age effects when performing DNA methylation studies in children

DNA Methylation in the Neuropeptide S Receptor 1 (NPSR1) Promoter in Relation to Asthma and Environmental Factors

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Differentially methylated probes for each cell population in comparison to whole blood.

<p>PBMC-Peripheral blood mononuclear cells. Differentially methylated probes were defined by a linear model using the M-values. M-value is the log2 ratio of the intensities of methylated probe versus unmethylated probe, a measurement of how much more a probe is methylated compared to unmethylated <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0041361#pone.0041361-Du2" target="_blank">[46]</a>. *To extract the probes with largest difference in methylation, a gamma fit model was applied to M-values in order to define the three calls: “unmethylated”, “marginal” and “methylated”. Significant probes sharing the same call in the two compared populations were removed. **Variation is based on the estimate of the log2-fold-change corresponding to the effect obtained from the linear model, absolute M-values are presented. The percentages are based on the call distribution.</p

Schematic presentation of the isolation protocol and purity of the cell populations as measured by flow cytometry.

<p>Peripheral blood mononuclear cells (PBMC) and granulocytes were obtained by density gradient centrifugation and seven cell populations were purified by magnetic sorting. Upper panel shows forward and side scatter which confirmed cell morphology and granularity. The lower panel shows the overlay of cell surface markers for all the six donors. The purities of the cell populations were highly similar among all the six donors. Th cells  =  CD4⁺ T cells, Tc cells  =  CD8⁺ T cells, NK cells  =  CD56⁺ NK cells, B cells  =  CD19⁺ B cells, Monocytes  =  CD14⁺ monocytes. Data analyses are based on the comparison of all cell populations to whole blood.</p

Differentially methylated CpG sites in candidate genes related to inflammatory diseases.

<p>A) Heatmap of 1865 probes representing 293 candidate genes for selected inflammatory diseases showing differential methylation in blood cell populations. Candidate genes for the diseases asthma, atopy, atopic dermatitis, inflammatory bowel disease, rheumatoid arthritis, systemic lupus erythematosus, Type 1 and Type 2 diabetes were selected from the Genome wide association study atlas (<a href="http://www.genome.gov/gwastudies/" target="_blank">http://www.genome.gov/gwastudies/</a>) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0041361#pone.0041361-Hindorff1" target="_blank">[30]</a>. The heatmap is based on median M-values. The M-value is calculated as the log2 ratio of the intensities of methylated probe versus unmethylated probe <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0041361#pone.0041361-Du2" target="_blank">[46]</a>. Blue color indicates low DNA methylation while yellow represents high DNA methylation. WB  =  whole blood, CD4T  =  CD4⁺ T cells, CD8T  =  CD8⁺ T cells, CD56NK  =  CD56⁺ NK cells, CD19B  =  CD19⁺ B cells, CD14Mono  =  CD14⁺ monocytes. B) The genomic distribution of the differentially methylated probes associated with inflammatory complex diseases according to the UCSC RefGene group (included in the Illumina annotation data). Intergenic  =  site not annotated in a gene, TSS  =  transcription start site at 200–1500 bp, 5′ region  = 5′UTR and 1st exon, Intragenic  =  gene body including introns and exons and, 3′ region  = 3′UTR. UTR – untranslated region.</p

Gene ontology enrichment for isolated cell populations.

<p>Gene ontology was performed using DAVID (<a href="http://david.abcc.ncifcrf.gov" target="_blank">http://david.abcc.ncifcrf.gov</a>) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0041361#pone.0041361-Huangda1" target="_blank">[47]</a>. The human genome was used as background and the level of significance was set to p<0.05. The top ten enriched pathways are described for genes showing significantly differentially methylated probes in comparison to whole blood where the cell population shows unmethylated state and whole blood shows methylated state according to the gamma fit model. Red color indicates peripheral blood mononuclear cells (PBMC) and green color indicates granulocytes.</p