Insights from deconvolution of cell subtype proportions enhance the interpretation of functional genomic data

Some lessons learned from genomic and epigenomic studies of the placenta

Recent attention to the "replicability crisis" in research has led to greater efforts to outline research practices that might improve the quality of research results. Steps to improve replicability through standardisation and control of covariates can be field specific. In this review, we highlight lessons learned through our own and other genome-wide studies of genetic, epigenetic and gene expression variation in the placenta. We share our understanding of the impact of placental genetics, possible confounders, sources of sampling variation, dataset stratification, and adjustment for covariates. Overall, we hope to raise awareness of these study considerations amongst placental researchers.

Discrepancies in readouts between Infinium MethylationEPIC v2.0 and v1.0 reflected in DNA methylation-based tools: implications and considerations for human population epigenetic studies

Background

The recently launched DNA methylation profiling platform, Illumina MethylationEPIC BeadChip Infinium microarray v2.0 (EPICv2), is highly correlated with measurements obtained from its predecessor MethylationEPIC BeadChip Infinium microarray v1.0 (EPICv1). However, the concordance between the two versions in the context of DNA methylation-based tools, including cell type deconvolution algorithms, epigenetic clocks, and inflammation and lifestyle biomarkers has not yet been investigated. To address this, we profiled DNA methylation on both EPIC versions using matched venous blood samples from individuals spanning early to late adulthood across four cohorts.

Findings

Within each cohort, samples primarily clustered by the EPIC version they were measured on. High concordance between EPIC versions at the array level, but variable concordance at the individual probe level was noted. Significant differences between versions in estimates from DNA methylation-based tools were observed, irrespective of the normalization method, with some nuanced differences across cohorts and tools. Adjusting for EPIC version or calculating estimates separately for each version largely mitigated these version-specific discordances.

Conclusions

Our work illustrates the importance of accounting for EPIC version differences in research scenarios, especially in meta-analyses and longitudinal studies, when samples profiled across different versions are harmonized. Alongside DNA methylation-based tools, our observations also have implications in interpretation of epigenome-wide association studies (EWAS) findings, when results obtained from one version are compared to another, particularly for probes that are poorly concordant between versions.

Blood transcriptomics analysis offers insights into variant-specific immune response to SARS-CoV-2

Bulk RNA sequencing (RNA-seq) of blood is typically used for gene expression analysis in biomedical research but is still rarely used in clinical practice. In this study, we propose that RNA-seq should be considered a diagnostic tool, as it offers not only insights into aberrant gene expression and splicing but also delivers additional readouts on immune cell type composition as well as B-cell and T-cell receptor (BCR/TCR) repertoires. We demonstrate that RNA-seq offers insights into a patient's immune status via integrative analysis of RNA-seq data from patients infected with various SARS-CoV-2 variants (in total 196 samples with up to 200 million reads sequencing depth). We compare the results of computational cell-type deconvolution methods (e.g., MCP-counter, xCell, EPIC, quanTIseq) to complete blood count data, the current gold standard in clinical practice. We observe varying levels of lymphocyte depletion and significant differences in neutrophil levels between SARS-CoV-2 variants. Additionally, we identify B and T cell receptor (BCR/TCR) sequences using the tools MiXCR and TRUST4 to show that-combined with sequence alignments and BLASTp-they could be used to classify a patient's disease. Finally, we investigated the sequencing depth required for such analyses and concluded that 10 million reads per sample is sufficient. In conclusion, our study reveals that computational cell-type deconvolution and BCR/TCR methods using bulk RNA-seq analyses can supplement missing CBC data and offer insights into immune responses, disease severity, and pathogen-specific immunity, all achievable with a sequencing depth of 10 million reads per sample.

Meta-analysis of single-cell RNA-sequencing data for depicting the transcriptomic landscape of chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a respiratory disease characterized by airflow limitation and chronic inflammation of the lungs that is a leading cause of death worldwide. Since the complete pathological mechanisms at the single-cell level are not fully understood yet, an integrative approach to characterizing the single-cell-resolution landscape of COPD is required. To identify the cell types and mechanisms associated with the development of COPD, we conducted a meta-analysis using three single-cell RNA-sequencing datasets of COPD. Among the 154,011 cells from 16 COPD patients and 18 healthy subjects, 17 distinct cell types were observed. Of the 17 cell types, monocytes, mast cells, and alveolar type 2 cells (AT2 cells) were found to be etiologically implicated in COPD based on genetic and transcriptomic features. The most transcriptomically diversified states of the three etiological cell types showed significant enrichment in immune/inflammatory responses (monocytes and mast cells) and/or mitochondrial dysfunction (monocytes and AT2 cells). We then identified three chemical candidates that may potentially induce COPD by modulating gene expression patterns in the three etiological cell types. Overall, our study suggests the single-cell level mechanisms underlying the pathogenesis of COPD and may provide information on toxic compounds that could be potential risk factors for COPD.

Blood transcriptomics analysis offers insights into variant-specific immune response to SARS-CoV-2

Bulk RNA sequencing (RNA-seq) of blood is typically used for gene expression analysis in biomedical research but is still rarely used in clinical practice. In this study, we argue that RNA-seq should be considered a routine diagnostic tool, as it offers not only insights into aberrant gene expression and splicing but also delivers additional readouts on immune cell type composition as well as B-cell and T-cell receptor (BCR/TCR) repertoires. We demonstrate that RNA-seq offers vital insights into a patient's immune status via integrative analysis of RNA-seq data from patients infected with various SARS-CoV-2 variants (in total 240 samples with up to 200 million reads sequencing depth). We compare the results of computational cell-type deconvolution methods (e.g., MCP-counter, xCell, EPIC, quanTIseq) to complete blood count data, the current gold standard in clinical practice. We observe varying levels of lymphocyte depletion and significant differences in neutrophil levels between SARS-CoV-2 variants. Additionally, we identify B and T cell receptor (BCR/TCR) sequences using the tools MiXCR and TRUST4 to show that - combined with sequence alignments and pBLAST - they could be used to classify a patient's disease. Finally, we investigated the sequencing depth required for such analyses and concluded that 10 million reads per sample is sufficient. In conclusion, our study reveals that computational cell-type deconvolution and BCR/TCR methods using bulk RNA-seq analyses can supplement missing CBC data and offer insights into immune responses, disease severity, and pathogen-specific immunity, all achievable with a sequencing depth of 10 million reads per sample.

Pre-pregnancy gene expression signatures are associated with subsequent improvement/worsening of rheumatoid arthritis during pregnancy

BACKGROUND

While many women with rheumatoid arthritis (RA) improve during pregnancy and others worsen, there are no biomarkers to predict this improvement or worsening. In our unique RA pregnancy cohort that includes a pre-pregnancy baseline, we have examined pre-pregnancy gene co-expression networks to identify differences between women with RA who subsequently improve during pregnancy and those who worsen.

METHODS

Blood samples were collected before pregnancy (T0) from 19 women with RA and 13 healthy women enrolled in our prospective pregnancy cohort. RA improvement/worsening between T0 and 3rd trimester was assessed by changes in the Clinical Disease Activity Index (CDAI). Pre-pregnancy expression profiles were examined by RNA sequencing and differential gene expression analysis. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules correlated with the improvement/worsening of RA during pregnancy and to assess their functional relevance.

RESULTS

Of the 19 women with RA, 14 improved during pregnancy (RAimproved) while 5 worsened (RAworsened). At the T0 baseline, however, the mean CDAI was similar between the two groups. WGCNA identified one co-expression module related to B cell function that was significantly correlated with the worsening of RA during pregnancy and was significantly enriched in genes differentially expressed between the RAimproved and RAworsened groups. A neutrophil-related expression signature was also identified in the RAimproved group at the T0 baseline.

CONCLUSION

The pre-pregnancy gene expression signatures identified represent potential biomarkers to predict the subsequent improvement/worsening of RA during pregnancy, which has important implications for the personalized treatment of RA during pregnancy.

Single-cell genomics meets human genetics

Single-cell genomic technologies are revealing the cellular composition, identities and states in tissues at unprecedented resolution. They have now scaled to the point that it is possible to query samples at the population level, across thousands of individuals. Combining single-cell information with genotype data at this scale provides opportunities to link genetic variation to the cellular processes underpinning key aspects of human biology and disease. This strategy has potential implications for disease diagnosis, risk prediction and development of therapeutic solutions. But, effectively integrating large-scale single-cell genomic data, genetic variation and additional phenotypic data will require advances in data generation and analysis methods. As single-cell genetics begins to emerge as a field in its own right, we review its current state and the challenges and opportunities ahead.

A statistical framework to identify cell types whose genetically regulated proportions are associated with complex diseases

Finding disease-relevant tissues and cell types can facilitate the identification and investigation of functional genes and variants. In particular, cell type proportions can serve as potential disease predictive biomarkers. In this manuscript, we introduce a novel statistical framework, cell-type Wide Association Study (cWAS), that integrates genetic data with transcriptomics data to identify cell types whose genetically regulated proportions (GRPs) are disease/trait-associated. On simulated and real GWAS data, cWAS showed good statistical power with newly identified significant GRP associations in disease-associated tissues. More specifically, GRPs of endothelial and myofibroblasts in lung tissue were associated with Idiopathic Pulmonary Fibrosis and Chronic Obstructive Pulmonary Disease, respectively. For breast cancer, the GRP of blood CD8+ T cells was negatively associated with breast cancer (BC) risk as well as survival. Overall, cWAS is a powerful tool to reveal cell types associated with complex diseases mediated by GRPs.

Pregnancy-associated systemic gene expression compared to a pre-pregnancy baseline, among healthy women with term pregnancies

Background

Pregnancy is known to induce extensive biological changes in the healthy mother. Little is known, however, about what these changes are at the molecular level. We have examined systemic expression changes in protein-coding genes and long non-coding (lnc) RNAs during and after pregnancy, compared to before pregnancy, among healthy women with term pregnancies.

Methods

Blood samples were collected from 14 healthy women enrolled in our prospective pregnancy cohort at 7 time-points (before, during and after pregnancy). Total RNA from frozen whole blood was used for RNA sequencing. Following raw read alignment and assembly, gene-level counts were obtained for protein-coding genes and long non-coding RNAs. At each time-point, cell type proportions were estimated using deconvolution. To examine associations between pregnancy status and gene expression over time, Generalized Estimating Equation (GEE) models were fitted, adjusting for age at conception, and with and without adjusting for changes in cell type proportions. Fold-changes in expression at each trimester were examined relative to the pre-pregnancy baseline.

Results

Numerous immune-related genes demonstrated pregnancy-associated expression, in a time-dependent manner. The genes that demonstrated the largest changes in expression included several that were neutrophil-related (over-expressed) and numerous immunoglobulin genes (under-expressed). Estimated cell proportions revealed a marked increase in neutrophils, and less so of activated CD4 memory T cells, during pregnancy, while most other cell type proportions decreased or remained unchanged. Adjusting for cell type proportions in our model revealed that although most of the expression changes were due to changes in cell type proportions in the bloodstream, transcriptional regulation was also involved, especially in down-regulating expression of type I interferon inducible genes.

Conclusion

Compared to a pre-pregnancy baseline, there were extensive systemic changes in cell type proportions, gene expression and biological pathways associated with different stages of pregnancy and postpartum among healthy women. Some were due to changes in cell type proportions and some due to gene regulation. In addition to providing insight into term pregnancy among healthy women, these findings also provide a "normal" reference for abnormal pregnancies and for autoimmune diseases that improve or worsen during pregnancy, to assess deviations from normal.

Using epigenomics to understand cellular responses to environmental influences in diseases

It is a generally accepted model that environmental influences can exert their effects, at least in part, by changing the molecular regulators of transcription that are described as epigenetic. As there is biochemical evidence that some epigenetic regulators of transcription can maintain their states long term and through cell division, an epigenetic model encompasses the idea of maintenance of the effect of an exposure long after it is no longer present. The evidence supporting this model is mostly from the observation of alterations of molecular regulators of transcription following exposures. With the understanding that the interpretation of these associations is more complex than originally recognised, this model may be oversimplistic; therefore, adopting novel perspectives and experimental approaches when examining how environmental exposures are linked to phenotypes may prove worthwhile. In this review, we have chosen to use the example of nonalcoholic fatty liver disease (NAFLD), a common, complex human disease with strong environmental and genetic influences. We describe how epigenomic approaches combined with emerging functional genetic and single-cell genomic techniques are poised to generate new insights into the pathogenesis of environmentally influenced human disease phenotypes exemplified by NAFLD.

Associations between circulating interferon and kynurenine/tryptophan pathway metabolites: support for a novel potential mechanism for cognitive dysfunction in SLE

OBJECTIVE

Quinolinic acid (QA), a kynurenine (KYN)/tryptophan (TRP) pathway metabolite, is an N-methyl-D-aspartate receptor agonist that can produce excitotoxic neuron damage. Type I and II interferons (IFNs) stimulate the KYN/TRP pathway, producing elevated QA/kynurenic acid (KA), a potential neurotoxic imbalance that may contribute to SLE-mediated cognitive dysfunction. We determined whether peripheral blood interferon-stimulated gene (ISG) expression associates with elevated serum KYN:TRP and QA:KA ratios in SLE.

METHODS

ISG expression (whole-blood RNA sequencing) and serum metabolite ratios (high-performance liquid chromatography) were measured in 72 subjects with SLE and 73 healthy controls (HCs). ISG were identified from published gene sets and individual IFN scores were derived to analyse associations with metabolite ratios, clinical parameters and neuropsychological assessments. SLE analyses were grouped by level of ISG expression ('IFN high', 'IFN low' and 'IFN similar to HC') and level of monocyte-associated gene expression (using CIBERSORTx).

RESULTS

Serum KYN:TRP and QA:KA ratios were higher in SLE than in HC (p<0.01). 933 genes were differentially expressed ≥2-fold in SLE versus HC (p<0.05). 70 of the top 100 most highly variant genes were ISG. Approximately half of overexpressed genes that correlated with KYN:TRP and QA:KA ratios (p<0.05) were ISG. In 36 IFN-high subjects with SLE, IFN scores correlated with KYN:TRP ratios (p<0.01), but not with QA:KA ratios. Of these 36 subjects, 23 had high monocyte-associated gene expression, and in this subgroup, the IFN scores correlated with both KY:NTRP and QA:KA ratios (p<0.05).

CONCLUSIONS

High ISG expression correlated with elevated KYN:TRP ratios in subjects with SLE, suggesting IFN-mediated KYN/TRP pathway activation, and with QA:KA ratios in a subset with high monocyte-associated gene expression, suggesting that KYN/TRP pathway activation may be particularly important in monocytes. These results need validation, which may aid in determining which patient subset may benefit from therapeutics directed at the IFN or KYN/TRP pathways to ameliorate a potentially neurotoxic QA/KA imbalance.

Vitamin D Deficiency During Development Permanently Alters Liver Cell Composition and Function

Vitamin D, a fat-soluble vitamin, plays a critical role in calcium homeostasis, the immune system, and normal development. Many epidemiological cohort studies globally have found high prevalence rates of vitamin D deficiency and insufficiency, recognized as an important health issue that needs to be solved. In particular, reproductive age and pregnant women low in vitamin D status may confer risks of diseases like obesity on their offspring. While observational studies have suggested associations between prenatal vitamin D deficiency and metabolic phenotypes in offspring, not yet determined is whether prenatal vitamin D deficiency permanently alters the development of the liver, a major metabolic organ. We tested the histopathology and the transcriptomic profiles of livers from male C57BL/6J mice exposed to prenatal vitamin D deficiency through a maternal dietary intervention model. We found that prenatal vitamin D deficiency increases the prevalence of histopathological changes in the liver, and alters its gene expression profile. Cell subtype proportion analysis showed that the liver of prenatal vitamin D deficiency alters non-parenchymal cells of the liver, specifically macrophages, a subset of endothelial cells, and dendritic cells. Our results indicate the long-term memory of prenatal vitamin D deficiency exposure in the adult liver, a potential contributor to offspring health risks.

The EWAS Catalog: a database of epigenome-wide association studies

Epigenome-wide association studies (EWAS) seek to quantify associations between traits/exposures and DNA methylation measured at thousands or millions of CpG sites across the genome. In recent years, the increase in availability of DNA methylation measures in population-based cohorts and case-control studies has resulted in a dramatic expansion of the number of EWAS being performed and published. To make this rich source of results more accessible, we have manually curated a database of CpG-trait associations (with p<1x10 ^-4) from published EWAS, each assaying over 100,000 CpGs in at least 100 individuals. From January 7, 2022, The EWAS Catalog contained 1,737,746 associations from 2,686 EWAS. This includes 1,345,398 associations from 342 peer-reviewed publications. In addition, it also contains summary statistics for 392,348 associations from 427 EWAS, performed on data from the Avon Longitudinal Study of Parents and Children (ALSPAC) and the Gene Expression Omnibus (GEO). The database is accompanied by a web-based tool and R package, giving researchers the opportunity to query EWAS associations quickly and easily, and gain insight into the molecular underpinnings of disease as well as the impact of traits and exposures on the DNA methylome. The EWAS Catalog data extraction team continue to update the database monthly and we encourage any EWAS authors to upload their summary statistics to our website. Details of how to upload data can be found here: http://www.ewascatalog.org/upload. The EWAS Catalog is available at http://www.ewascatalog.org.

The EWAS Catalog: a database of epigenome-wide association studies

Epigenome-wide association studies (EWAS) seek to quantify associations between traits/exposures and DNA methylation measured at thousands or millions of CpG sites across the genome. In recent years, the increase in availability of DNA methylation measures in population-based cohorts and case-control studies has resulted in a dramatic expansion of the number of EWAS being performed and published. To make this rich source of results more accessible, we have manually curated a database of CpG-trait associations (with p<1x10 ^-4) from published EWAS, each assaying over 100,000 CpGs in at least 100 individuals. From January 7, 2022, The EWAS Catalog contained 1,737,746 associations from 2,686 EWAS. This includes 1,345,398 associations from 342 peer-reviewed publications. In addition, it also contains summary statistics for 392,348 associations from 427 EWAS, performed on data from the Avon Longitudinal Study of Parents and Children (ALSPAC) and the Gene Expression Omnibus (GEO). The database is accompanied by a web-based tool and R package, giving researchers the opportunity to query EWAS associations quickly and easily, and gain insight into the molecular underpinnings of disease as well as the impact of traits and exposures on the DNA methylome. The EWAS Catalog data extraction team continue to update the database monthly and we encourage any EWAS authors to upload their summary statistics to our website. Details of how to upload data can be found here: http://www.ewascatalog.org/upload. The EWAS Catalog is available at http://www.ewascatalog.org.

PTGDR2 Expression in Peripheral Blood as a Potential Biomarker in Adult Patients with Asthma

Background: Precision medicine is a promising strategy to identify biomarkers, stratify asthmatic patients according to different endotypes, and match them with the appropriate therapy. This proof-of-concept study aimed to investigate whether gene expression in peripheral blood could provide a valuable noninvasive approach for the molecular phenotyping of asthma. Methods: We performed whole-transcriptome RNA sequencing on peripheral blood of 30 non-atopic non-asthmatic controls and 30 asthmatic patients. A quantitative PCR (qPCR) validation study of PTGDR2 that encodes for CRTH2 receptor, expressed in cells involved in T2 inflammation, was developed in a cohort of 361 independent subjects: 94 non-asthmatic non-atopic controls, 187 asthmatic patients [including 82 with chronic rhinosinusitis with nasal polyposis (CRSwNP) and 24 with aspirin-exacerbated respiratory disease (AERD)], 52 with allergic rhinitis, and 28 with CRSwNP without asthma. Results: PTGDR2 was one of the most differentially overexpressed genes in asthmatic patients’ peripheral blood (p-value 2.64 × 10⁶). These results were confirmed by qPCR in the validation study, where PTGDR2 transcripts were significantly upregulated in asthmatic patients (p < 0.001). This upregulation was mainly detected in some subgroups such as allergic asthma, asthma with CRSwNP, AERD, eosinophilic asthma, and severe persistent asthma. PTGDR2 expression was detected in different blood cell types, and its correlation with eosinophil counts showed differences in some groups of asthmatic patients. Conclusions: We found that PTGDR2 expression levels could identify asthma patients, introduce a minimally invasive biomarker for adult asthma molecular phenotyping, and add additional information to blood eosinophils. Although further studies are required, analyzing PTGDR2 expression levels in peripheral blood of asthmatics might assist in selecting patients for treatment with specific antagonists.

A computational method for direct imputation of cell type-specific expression profiles and cellular compositions from bulk-tissue RNA-Seq in brain disorders

The importance of cell type-specific gene expression in disease-relevant tissues is increasingly recognized in genetic studies of complex diseases. However, most gene expression studies are conducted on bulk tissues, without examining cell type-specific expression profiles. Several computational methods are available for cell type deconvolution (i.e. inference of cellular composition) from bulk RNA-Seq data, but few of them impute cell type-specific expression profiles. We hypothesize that with external prior information such as single cell RNA-seq and population-wide expression profiles, it can be computationally tractable to estimate both cellular composition and cell type-specific expression from bulk RNA-Seq data. Here we introduce CellR, which addresses cross-individual gene expression variations to adjust the weights of cell-specific gene markers. It then transforms the deconvolution problem into a linear programming model while taking into account inter/intra cellular correlations and uses a multi-variate stochastic search algorithm to estimate the cell type-specific expression profiles. Analyses on several complex diseases such as schizophrenia, Alzheimer’s disease, Huntington’s disease and type 2 diabetes validated the efficiency of CellR, while revealing how specific cell types contribute to different diseases. In summary, CellR compares favorably against competing approaches, enabling cell type-specific re-analysis of gene expression data on bulk tissues in complex diseases.

An Epigenetically Distinct Subset of Children With Autism Spectrum Disorder Resulting From Differences in Blood Cell Composition

Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that often involves impaired cognition, communication difficulties and restrictive, repetitive behaviors. ASD is extremely heterogeneous both clinically and etiologically, which represents one of the greatest challenges in studying the molecular underpinnings of ASD. While hundreds of ASD-associated genes have been identified that confer varying degrees of risk, no single gene variant accounts for >1% of ASD cases. Notably, a large number of ASD-risk genes function as epigenetic regulators, indicating potential epigenetic dysregulation in ASD. As such, we compared genome-wide DNA methylation (DNAm) in the blood of children with ASD (n = 265) to samples from age- and sex-matched, neurotypical controls (n = 122) using the Illumina Infinium HumanMethylation450 arrays. Results: While DNAm patterns did not distinctly separate ASD cases from controls, our analysis identified an epigenetically unique subset of ASD cases (n = 32); these individuals exhibited significant differential methylation from both controls than the remaining ASD cases. The CpG sites at which this subset was differentially methylated mapped to known ASD risk genes that encode proteins of the nervous and immune systems. Moreover, the observed DNAm differences were attributable to altered blood cell composition, i.e., lower granulocyte proportion and granulocyte-to-lymphocyte ratio in the ASD subset, as compared to the remaining ASD cases and controls. This ASD subset did not differ from the rest of the ASD cases in the frequency or type of high-risk genomic variants. Conclusion: Within our ASD cohort, we identified a subset of individuals that exhibit differential methylation from both controls and the remaining ASD group tightly associated with shifts in immune cell type proportions. This is an important feature that should be assessed in all epigenetic studies of blood cells in ASD. This finding also builds on past reports of changes in the immune systems of children with ASD, supporting the potential role of altered immunological mechanisms in the complex pathophysiology of ASD. The discovery of significant molecular and immunological features in subgroups of individuals with ASD may allow clinicians to better stratify patients, facilitating personalized interventions and improved outcomes.

Functional Genomics of the Pediatric Obese Asthma Phenotype Reveal Enrichment of Rho-GTPase Pathways

Rationale: Obesity-related asthma disproportionately affects minority children and is associated with nonatopic T-helper type 1 (Th1) cell polarized inflammation that correlates with pulmonary function deficits. Its underlying mechanisms are poorly understood.Objectives: To use functional genomics to identify cellular mechanisms associated with nonatopic inflammation in obese minority children with asthma.Methods: CD4+ (cluster of differentiation 4-positive) Th cells from 59 obese Hispanic and African American children with asthma and 61 normal-weight Hispanic and African American children with asthma underwent quantification of the transcriptome and DNA methylome and genotyping. Expression and methylation quantitative trait loci revealed the contribution of genetic variation to transcription and DNA methylation. Adjusting for Th-cell subtype proportions discriminated loci where transcription or methylation differences were driven by differences in subtype proportions from loci that were independently associated with obesity-related asthma.Measurements and Main Results: Obese children with asthma had more memory and fewer naive Th cells than normal-weight children with asthma. Differentially expressed and methylated genes and methylation quantitative trait loci in obese children with asthma, independent of Th-cell subtype proportions, were enriched in Rho-GTPase pathways. Inhibition of CDC42 (cell division cycle 42), one of the Rho-GTPases associated with Th-cell differentiation, was associated with downregulation of the IFNγ, but not the IL-4, gene. Differential expression of the RPS27L (40S ribosomal protein S27-like) gene, part of the p53/mammalian target of rapamycin pathway, was due to nonrandom distribution of expression quantitative trait loci variants between groups. Differentially expressed and/or methylated genes, including RPS27L, were associated with pulmonary function deficits in obese children with asthma.Conclusions: We found enrichment of Rho-GTPase pathways in obese asthmatic Th cells, identifying them as a novel therapeutic target for obesity-related asthma, a disease that is suboptimally responsive to current therapies.

DNA methylation in APOE: The relationship with Alzheimer's and with cardiovascular health

INTRODUCTION

Genetic variation in the apolipoprotein E (APOE) gene is associated with Alzheimer's disease (AD) and risk factors for cardiovascular disease (CVD). DNA methylationat APOE has been associated with altered cognition and AD. It is unclear if epigenetic marks could be used for predicting future disease.

METHODS

We assessed blood-based DNA methylation at 13 CpGs in the APOE gene in 5828 participants from the Generation Scotland (GS) cohort. Using linear mixed models regression, we examined the relationships among APOE methylation, cognition, cholesterol, the family history of AD and the risk for CVD.

RESULTS

DNA methylation at two CpGs was associated with the ratio of total cholesterol and HDL cholesterol, but not with cognition, family history of AD, or the risk of CVD.

DISCUSSION

APOE methylation is associated with the levels of blood cholesterol, but there is no evidence for the utility of APOE methylation as a biomarker for predicting AD or CVD.