The association of infant urinary adrenal steroids with the risk of childhood asthma development.

BACKGROUND: Adrenal steroids play important roles in early-life development. However, our understanding of the effects of perinatal adrenal steroids on the development of childhood asthma is incomplete. OBJECTIVE: To evaluate the associations between early-life adrenal steroid levels and childhood asthma. METHODS: Participants included the Infant Susceptibility to Pulmonary Infections and Asthma following Respiratory Syncytial Virus Exposure birth cohort children with untargeted urinary metabolomics data measured during early infancy (n = 264) and nasal immune mediator data measured concurrently at age 2 to 6 months (n = 76). A total of 11 adrenal steroid compounds were identified using untargeted metabolomics and 6 asthma-relevant nasal immune mediators from multiplex assays were a priori selected. Current asthma at ages 5 and 6 years was ascertained using validated questionnaires. Associations were tested using logistic and linear regression with confounders adjustment. RESULTS: Pregnenetriol disulfate (adjusted odds ratio [aOR] = 0.20, 95% CI = 0.06-0.68) and 3a,21-dihydroxy-5b-pregnane-11,20-dione-21-glucuronide (aOR = 0.34, 95% CI = 0.14-0.75) were inversely associated with childhood asthma at 5 and 6 years after multiple testing adjustment. There was a significant interaction effect of pregnanediol-3-glucuronide by biological sex assigned at birth (aOR = 0.11, 95% CI = 0.02-0.51, for those with female sex) on childhood asthma. Pregnenetriol disulfate was inversely associated with granulocyte-macrophage colony-stimulating factor (ß = -0.45, q-value = 0.05). 3a,21-dihydroxy-5b-pregnane-11,20-dione 21-glucuronide was inversely associated with interleukin [IL]-4 (ß = -0.29, q-value = 0.02), IL-5 (ß = -0.35, q-value = 0.006), IL-13 (ß = -0.26, q-value = 0.02), granulocyte-macrophage colony-stimulating factor (ß = -0.35, q-value = 0.006), and fibroblast growth factor-ß (ß = -0.24, q-value = 0.01) after multiple testing adjustment. CONCLUSION: The inverse association between adrenal steroids downstream of progesterone and 17-hydroxypregnenolone and the odds of childhood asthma and nasopharyngeal type 2 immune biomarkers suggest that increased early-life adrenal steroids may suppress type 2 inflammation and protect against the development of childhood asthma.

scMD facilitates cell type deconvolution using single-cell DNA methylation references.

The proliferation of single-cell RNA-sequencing data has led to the widespread use of cellular deconvolution, aiding the extraction of cell-type-specific information from extensive bulk data. However, those advances have been mostly limited to transcriptomic data. With recent developments in single-cell DNA methylation (scDNAm), there are emerging opportunities for deconvolving bulk DNAm data, particularly for solid tissues like brain that lack cell-type references. Due to technical limitations, current scDNAm sequences represent a small proportion of the whole genome for each single cell, and those detected regions differ across cells. This makes scDNAm data ultra-high dimensional and ultra-sparse. To deal with these challenges, we introduce scMD (single cell Methylation Deconvolution), a cellular deconvolution framework to reliably estimate cell type fractions from tissue-level DNAm data. To analyze large-scale complex scDNAm data, scMD employs a statistical approach to aggregate scDNAm data at the cell cluster level, identify cell-type marker DNAm sites, and create precise cell-type signature matrixes that surpass state-of-the-art sorted-cell or RNA-derived references. Through thorough benchmarking in several datasets, we demonstrate scMD's superior performance in estimating cellular fractions from bulk DNAm data. With scMD-estimated cellular fractions, we identify cell type fractions and cell type-specific differentially methylated cytosines associated with Alzheimer's disease.

scMD: cell type deconvolution using single-cell DNA methylation references.

The proliferation of single-cell RNA sequencing data has led to the widespread use of cellular deconvolution, aiding the extraction of cell type-specific information from extensive bulk data. However, those advances have been mostly limited to transcriptomic data. With recent development in single-cell DNA methylation (scDNAm), new avenues have been opened for deconvolving bulk DNAm data, particularly for solid tissues like the brain that lack cell-type references. Due to technical limitations, current scDNAm sequences represent a small proportion of the whole genome for each single cell, and those detected regions differ across cells. This makes scDNAm data ultra-high dimensional and ultra-sparse. To deal with these challenges, we introduce scMD (single cell Methylation Deconvolution), a cellular deconvolution framework to reliably estimate cell type fractions from tissue-level DNAm data. To analyze large-scale complex scDNAm data, scMD employs a statistical approach to aggregate scDNAm data at the cell cluster level, identify cell-type marker DNAm sites, and create a precise cell-type signature matrix that surpasses state-of-the-art sorted-cell or RNA-derived references. Through thorough benchmarking in several datasets, we demonstrate scMD's superior performance in estimating cellular fractions from bulk DNAm data. With scMD-estimated cellular fractions, we identify cell type fractions and cell type-specific differentially methylated cytosines associated with Alzheimer's disease.

Accurate estimation of rare cell type fractions from tissue omics data via hierarchical deconvolution.

Bulk transcriptomics in tissue samples reflects the average expression levels across different cell types and is highly influenced by cellular fractions. As such, it is critical to estimate cellular fractions to both deconfound differential expression analyses and infer cell type-specific differential expression. Since experimentally counting cells is infeasible in most tissues and studies, in silico cellular deconvolution methods have been developed as an alternative. However, existing methods are designed for tissues consisting of clearly distinguishable cell types and have difficulties estimating highly correlated or rare cell types. To address this challenge, we propose Hierarchical Deconvolution (HiDecon) that uses single-cell RNA sequencing references and a hierarchical cell type tree, which models the similarities among cell types and cell differentiation relationships, to estimate cellular fractions in bulk data. By coordinating cell fractions across layers of the hierarchical tree, cellular fraction information is passed up and down the tree, which helps correct estimation biases by pooling information across related cell types. The flexible hierarchical tree structure also enables estimating rare cell fractions by splitting the tree to higher resolutions. Through simulations and real data applications with the ground truth of measured cellular fractions, we demonstrate that HiDecon significantly outperforms existing methods and accurately estimates cellular fractions.

A functional genomics pipeline to identify high-value asthma and allergy CpGs in the human methylome.

BACKGROUND: DNA methylation of cytosines at cytosine-phosphate-guanine (CpG) dinucleotides (CpGs) is a widespread epigenetic mark, but genome-wide variation has been relatively unexplored due to the limited representation of variable CpGs on commercial high-throughput arrays. OBJECTIVES: To explore this hidden portion of the epigenome, this study combined whole-genome bisulfite sequencing with in silico evidence of gene regulatory regions to design a custom array of high-value CpGs. This study focused on airway epithelial cells from children with and without allergic asthma because these cells mediate the effects of inhaled microbes, pollution, and allergens on asthma and allergic disease risk. METHODS: This study identified differentially methylated regions from whole-genome bisulfite sequencing in nasal epithelial cell DNA from a total of 39 children with and without allergic asthma of both European and African ancestries. This study selected CpGs from differentially methylated regions, previous allergy or asthma epigenome-wide association studies (EWAS), or genome-wide association study loci, and overlapped them with functional annotations for inclusion on a custom Asthma&Allergy array. This study used both the custom and EPIC arrays to perform EWAS of allergic sensitization (AS) in nasal epithelial cell DNA from children in the URECA (Urban Environment and Childhood Asthma) birth cohort and using the custom array in the INSPIRE [Infant Susceptibility to Pulmonary Infections and Asthma Following RSV Exposure] birth cohort. Each CpG on the arrays was assigned to its nearest gene and its promotor capture Hi-C interacting gene and performed expression quantitative trait methylation (eQTM) studies for both sets of genes. RESULTS: Custom array CpGs were enriched for intermediate methylation levels compared to EPIC CpGs. Intermediate methylation CpGs were further enriched among those associated with AS and for eQTMs on both arrays. CONCLUSIONS: This study revealed signature features of high-value CpGs and evidence for epigenetic regulation of genes at AS EWAS loci that are robust to race/ethnicity, ascertainment, age, and geography.

Estimating and accounting for unobserved covariates in high-dimensional correlated data.

Many high dimensional and high-throughput biological datasets have complex sample correlation structures, which include longitudinal and multiple tissue data, as well as data with multiple treatment conditions or related individuals. These data, as well as nearly all high-throughput 'omic' data, are influenced by technical and biological factors unknown to the researcher, which, if unaccounted for, can severely obfuscate estimation of and inference on the effects of interest. We therefore developed CBCV and CorrConf: provably accurate and computationally efficient methods to choose the number of and estimate latent confounding factors present in high dimensional data with correlated or nonexchangeable residuals. We demonstrate each method's superior performance compared to other state of the art methods by analyzing simulated multi-tissue gene expression data and identifying sex-associated DNA methylation sites in a real, longitudinal twin study.

Robust and accurate estimation of cellular fraction from tissue omics data via ensemble deconvolution.

MOTIVATION: Tissue-level omics data such as transcriptomics and epigenomics are an average across diverse cell types. To extract cell-type-specific (CTS) signals, dozens of cellular deconvolution methods have been proposed to infer cell-type fractions from tissue-level data. However, these methods produce vastly different results under various real data settings. Simulation-based benchmarking studies showed no universally best deconvolution approaches. There have been attempts of ensemble methods, but they only aggregate multiple single-cell references or reference-free deconvolution methods. RESULTS: To achieve a robust estimation of cellular fractions, we proposed EnsDeconv (Ensemble Deconvolution), which adopts CTS robust regression to synthesize the results from 11 single deconvolution methods, 10 reference datasets, 5 marker gene selection procedures, 5 data normalizations and 2 transformations. Unlike most benchmarking studies based on simulations, we compiled four large real datasets of 4937 tissue samples in total with measured cellular fractions and bulk gene expression from different tissues. Comprehensive evaluations demonstrated that EnsDeconv yields more stable, robust and accurate fractions than existing methods. We illustrated that EnsDeconv estimated cellular fractions enable various CTS downstream analyses such as differential fractions associated with clinical variables. We further extended EnsDeconv to analyze bulk DNA methylation data. AVAILABILITY AND IMPLEMENTATION: EnsDeconv is freely available as an R-package from https://github.com/randel/EnsDeconv. The RNA microarray data from the TRAUMA study are available and can be accessed in GEO (GSE36809). The demographic and clinical phenotypes can be shared on reasonable request to the corresponding authors. The RNA-seq data from the EVAPR study cannot be shared publicly due to the privacy of individuals that participated in the clinical research in compliance with the IRB approval at the University of Pittsburgh. The RNA microarray data from the FHS study are available from dbGaP (phs000007.v32.p13). The RNA-seq data from ROS study is downloaded from AD Knowledge Portal. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genome-wide study of early and severe childhood asthma identifies interaction between CDHR3 and GSDMB.

BACKGROUND: Asthma with severe exacerbation is one of the most common causes of hospitalization among young children. Exacerbations are typically triggered by respiratory infections, but the host factors causing recurrent infections and exacerbations in some children are poorly understood. As a result, current treatment options and preventive measures are inadequate. OBJECTIVE: We sought to identify genetic interaction associated with the development of childhood asthma. METHODS: We performed an exhaustive search for pairwise interaction between genetic single nucleotide polymorphisms using 1204 cases of a specific phenotype of early childhood asthma with severe exacerbations in patients aged 2 to 6 years combined with 5328 nonasthmatic controls. Replication was attempted in 3 independent populations, and potential underlying immune mechanisms were investigated in the COPSAC2010 and COPSAC2000 birth cohorts. RESULTS: We found evidence of interaction, including replication in independent populations, between the known childhood asthma loci CDHR3 and GSDMB. The effect of CDHR3 was dependent on the GSDMB genotype, and this interaction was more pronounced for severe and early onset of disease. Blood immune analyses suggested a mechanism related to increased IL-17A production after viral stimulation. CONCLUSIONS: We found evidence of interaction between CDHR3 and GSDMB in development of early childhood asthma, possibly related to increased IL-17A response to viral infections. This study demonstrates the importance of focusing on specific disease subtypes for understanding the genetic mechanisms of asthma.

Longitudinal data reveal strong genetic and weak non-genetic components of ethnicity-dependent blood DNA methylation levels.

Epigenetic architecture is influenced by genetic and environmental factors, but little is known about their relative contributions or longitudinal dynamics. Here, we studied DNA methylation (DNAm) at over 750,000 CpG sites in mononuclear blood cells collected at birth and age 7 from 196 children of primarily self-reported Black and Hispanic ethnicities to study race-associated DNAm patterns. We developed a novel Bayesian method for high-dimensional longitudinal data and showed that race-associated DNAm patterns at birth and age 7 are nearly identical. Additionally, we estimated that up to 51% of all self-reported race-associated CpGs had race-dependent DNAm levels that were mediated through local genotype and, quite surprisingly, found that genetic factors explained an overwhelming majority of the variation in DNAm levels at other, previously identified, environmentally-associated CpGs. These results indicate that race-associated blood DNAm patterns in particular, and blood DNAm levels in general, are primarily driven by genetic factors, and are not as sensitive to environmental exposures as previously suggested, at least during the first 7 years of life.

Epigenetic landscape links upper airway microbiota in infancy with allergic rhinitis at 6 years of age.

BACKGROUND: The upper airways present a barrier to inhaled allergens and microbes, which alter immune responses and subsequent risk for diseases, such as allergic rhinitis (AR). OBJECTIVE: We tested the hypothesis that early-life microbial exposures leave a lasting signature in DNA methylation that ultimately influences the development of AR in children. METHODS: We studied upper airway microbiota at 1 week, 1 month, and 3 months of life, and measured DNA methylation and gene expression profiles in upper airway mucosal cells and assessed AR at age 6 years in children in the Copenhagen Prospective Studies on Asthma in Childhood birth cohort. RESULTS: We identified 956 AR-associated differentially methylated CpGs in upper airway mucosal cells at age 6 years, 792 of which formed 3 modules of correlated differentially methylated CpGs. The eigenvector of 1 module was correlated with the expression of genes enriched for lysosome and bacterial invasion of epithelial cell pathways. Early-life microbial diversity was lower at 1 week (richness P = .0079) in children with AR at age 6 years, and reduced diversity at 1 week was also correlated with the same module's eigenvector (ρ = -0.25; P = 3.3 × 10-5). We show that the effect of microbiota richness at 1 week on risk for AR at age 6 years was mediated in part by the epigenetic signature of this module. CONCLUSIONS: Our results suggest that upper airway microbial composition in infancy contributes to the development of AR during childhood, and this trajectory is mediated, at least in part, through altered DNA methylation patterns in upper airway mucosal cells.

ER-luminal [Ca2+] regulation of InsP3 receptor gating mediated by an ER-luminal peripheral Ca2+-binding protein.

Modulating cytoplasmic Ca2+ concentration ([Ca2+]i) by endoplasmic reticulum (ER)-localized inositol 1,4,5-trisphosphate receptor (InsP3R) Ca2+-release channels is a universal signaling pathway that regulates numerous cell-physiological processes. Whereas much is known regarding regulation of InsP3R activity by cytoplasmic ligands and processes, its regulation by ER-luminal Ca2+ concentration ([Ca2+]ER) is poorly understood and controversial. We discovered that the InsP3R is regulated by a peripheral membrane-associated ER-luminal protein that strongly inhibits the channel in the presence of high, physiological [Ca2+]ER. The widely-expressed Ca2+-binding protein annexin A1 (ANXA1) is present in the nuclear envelope lumen and, through interaction with a luminal region of the channel, can modify high-[Ca2+]ER inhibition of InsP3R activity. Genetic knockdown of ANXA1 expression enhanced global and local elementary InsP3-mediated Ca2+ signaling events. Thus, [Ca2+]ER is a major regulator of InsP3R channel activity and InsP3R-mediated [Ca2+]i signaling in cells by controlling an interaction of the channel with a peripheral membrane-associated Ca2+-binding protein, likely ANXA1.

Expression quantitative trait locus fine mapping of the 17q12-21 asthma locus in African American children: a genetic association and gene expression study.

BACKGROUND: African ancestry is associated with a higher prevalence and greater severity of asthma than European ancestries, yet genetic studies of the most common locus associated with childhood-onset asthma, 17q12-21, in African Americans have been inconclusive. The aim of this study was to leverage both the phenotyping of the Children's Respiratory and Environmental Workgroup (CREW) birth cohort consortium, and the reduced linkage disequilibrium in African Americans, to fine map the 17q12-21 locus. METHODS: We first did a genetic association study and meta-analysis using 17q12-21 tag single-nucleotide polymorphisms (SNPs) for childhood-onset asthma in 1613 European American and 870 African American children from the CREW consortium. Nine tag SNPs were selected based on linkage disequilibrium patterns at 17q12-21 and their association with asthma, considering the effect allele under an additive model (0, 1, or 2 effect alleles). Results were meta-analysed with publicly available summary data from the EVE consortium (on 4303 European American and 3034 African American individuals) for seven of the nine SNPs of interest. Subsequently, we tested for expression quantitative trait loci (eQTLs) among the SNPs associated with childhood-onset asthma and the expression of 17q12-21 genes in resting peripheral blood mononuclear cells (PBMCs) from 85 African American CREW children and in upper airway epithelial cells from 246 African American CREW children; and in lower airway epithelial cells from 44 European American and 72 African American adults from a case-control study of asthma genetic risk in Chicago (IL, USA). FINDINGS: 17q12-21 SNPs were broadly associated with asthma in European Americans. Only two SNPs (rs2305480 in gasdermin-B [GSDMB] and rs8076131 in ORMDL sphingolipid biosynthesis regulator 3 [ORMDL3]) were associated with asthma in African Americans, at a Bonferroni-corrected threshold of p<0·0055 (for rs2305480_G, odds ratio [OR] 1·36 [95% CI 1·12-1·65], p=0·0014; and for rs8076131_A, OR 1·37 [1·13-1·67], p=0·0010). In upper airway epithelial cells from African American children, genotype at rs2305480 was the most significant eQTL for GSDMB (eQTL effect size [ß] 1·35 [95% CI 1·25-1·46], p<0·0001), and to a lesser extent showed an eQTL effect for post-GPI attachment to proteins phospholipase 3 (ß 1·15 [1·08-1·22], p<0·0001). No SNPs were eQTLs for ORMDL3. By contrast, in PBMCs, the five core SNPs were associated only with expression of GSDMB and ORMDL3. Genotype at rs12936231 (in zona pellucida binding protein 2) showed the strongest associations across both genes (for GSDMB, eQTLß 1·24 [1·15-1·32], p<0·0001; and for ORMDL3 (ß 1·19 [1·12-1·24], p<0·0001). The eQTL effects of rs2305480 on GSDMB expression were replicated in lower airway cells from African American adults (ß 1·29 [1·15-1·44], p<0·0001). INTERPRETATION: Our study suggests that SNPs regulating GSDMB expression in airway epithelial cells have a major role in childhood-onset asthma, whereas SNPs regulating the expression levels of 17q12-21 genes in resting blood cells are not central to asthma risk. Our genetic and gene expression data in African Americans and European Americans indicated GSDMB to be the leading candidate gene at this important asthma locus. FUNDING: National Institutes of Health, Office of the Director.

ESTIMATION AND INFERENCE IN METABOLOMICS WITH NON-RANDOM MISSING DATA AND LATENT FACTORS.

High throughput metabolomics data are fraught with both non-ignorable missing observations and unobserved factors that influence a metabolite's measured concentration, and it is well known that ignoring either of these complications can compromise estimators. However, current methods to analyze these data can only account for the missing data or unobserved factors, but not both. We therefore developed MetabMiss, a statistically rigorous method to account for both non-random missing data and latent factors in high throughput metabolomics data. Our methodology does not require the practitioner specify a likelihood for the missing data, and makes investigating the relationship between the metabolome and tens, or even hundreds, of phenotypes computationally tractable. We demonstrate the fidelity of Metab-Miss's estimates using both simulated and real metabolomics data, and prove their asymptotic correctness when the sample size and number of metabolites grows to infinity.

Accounting for unobserved covariates with varying degrees of estimability in high-dimensional biological data.

An important phenomenon in high-throughput biological data is the presence of unobserved covariates that can have a significant impact on the measured response. When these covariates are also correlated with the covariate of interest, ignoring or improperly estimating them can lead to inaccurate estimates of and spurious inference on the corresponding coefficients of interest in a multivariate linear model. We first prove that existing methods to account for these unobserved covariates often inflate Type I error for the null hypothesis that a given coefficient of interest is zero. We then provide alternative estimators for the coefficients of interest that correct the inflation, and prove that our estimators are asymptotically equivalent to the ordinary least squares estimators obtained when every covariate is observed. Lastly, we use previously published DNA methylation data to show that our method can more accurately estimate the direct effect of asthma on DNA methylation levels compared to existing methods, the latter of which likely fail to recover and account for latent cell type heterogeneity.

Induction of the Immunoproteasome Subunit Lmp7 Links Proteostasis and Immunity in α-Synuclein Aggregation Disorders.

Accumulation of aggregated α-synuclein into Lewy bodies is thought to contribute to the onset and progression of dopaminergic neuron degeneration in Parkinson's disease (PD) and related disorders. Although protein aggregation is associated with perturbation of proteostasis, how α-synuclein aggregation affects the brain proteome and signaling remains uncertain. In a mouse model of α-synuclein aggregation, 6% of 6215 proteins and 1.6% of 8183 phosphopeptides changed in abundance, indicating conservation of proteostasis and phosphorylation signaling. The proteomic analysis confirmed changes in abundance of proteins that regulate dopamine synthesis and transport, synaptic activity and integrity, and unearthed changes in mRNA binding, processing and protein translation. Phosphorylation signaling changes centered on axonal and synaptic cytoskeletal organization and structural integrity. Proteostatic responses included a significant increase in the levels of Lmp7, a component of the immunoproteasome. Increased Lmp7 levels and activity were also quantified in postmortem human brains with PD and dementia with Lewy bodies. Functionally, the immunoproteasome degrades α-synuclein aggregates and generates potentially antigenic peptides. Expression and activity of the immunoproteasome may represent testable targets to induce adaptive responses that maintain proteome integrity and modulate immune responses in protein aggregation disorders.

Loss of PIKfyve in platelets causes a lysosomal disease leading to inflammation and thrombosis in mice.

PIKfyve is essential for the synthesis of phosphatidylinositol-3,5-bisphosphate [PtdIns(3,5)P2] and for the regulation of endolysosomal membrane dynamics in mammals. PtdIns(3,5)P2 deficiency causes neurodegeneration in mice and humans, but the role of PtdIns(3,5)P2 in non-neural tissues is poorly understood. Here we show that platelet-specific ablation of PIKfyve in mice leads to accelerated arterial thrombosis, and, unexpectedly, also to inappropriate inflammatory responses characterized by macrophage accumulation in multiple tissues. These multiorgan defects are attenuated by platelet depletion in vivo, confirming that they reflect a platelet-specific process. PIKfyve ablation in platelets induces defective maturation and excessive storage of lysosomal enzymes that are released upon platelet activation. Impairing lysosome secretion from PIKfyve-null platelets in vivo markedly attenuates the multiorgan defects, suggesting that platelet lysosome secretion contributes to pathogenesis. Our findings identify PIKfyve as an essential regulator for platelet lysosome homeostasis, and demonstrate the contributions of platelet lysosomes to inflammation, arterial thrombosis and macrophage biology.