Genome-wide association study of individual differences of human lymphocyte profiles using large-scale cytometry data

Rare copy number variation in autoimmune Addison's disease

Autoimmune Addison's disease (AAD) is a rare but life-threatening endocrine disorder caused by an autoimmune destruction of the adrenal cortex. A previous genome-wide association study (GWAS) has shown that common variants near immune-related genes, which mostly encode proteins participating in the immune response, affect the risk of developing this condition. However, little is known about the contribution of copy number variations (CNVs) to AAD susceptibility. We used the genome-wide genotyping data from Norwegian and Swedish individuals (1,182 cases and 3,810 controls) to investigate the putative role of CNVs in the AAD aetiology. Although the frequency of rare CNVs was similar between cases and controls, we observed that larger deletions (>1,000 kb) were more common among patients (OR = 4.23, 95% CI 1.85-9.66, p = 0.0002). Despite this, none of the large case-deletions were conclusively pathogenic, and the clinical presentation and an AAD-polygenic risk score were similar between cases with and without the large CNVs. Among deletions exclusive to individuals with AAD, we highlight two ultra-rare deletions in the genes LRBA and BCL2L11, which we speculate might have contributed to the polygenic risk in these carriers. In conclusion, rare CNVs do not appear to be a major cause of AAD but further studies are needed to ascertain the potential contribution of rare deletions to the polygenic load of AAD susceptibility.

Proliferative activity of antigen-specific CD154+ T cells against bacterial and fungal respiratory pathogens in cystic fibrosis decreases after initiation of highly effective CFTR modulator therapy

Background: Together with impaired mucociliary clearance, lung disease in cystic fibrosis (CF) is driven by dysregulation of innate and adaptive immunity caused by dysfunctional CFTR (Cystic Fibrosis Transmembrane Conductance Regulator), leading to airway infection and hyperinflamma-tion. The highly effective CFTR modulator therapy (HEMT) elexacaftor/tezacaftor/ivacaftor (ETI) generates substantial improvements in clinical outcomes of people with CF (pwCF) by restoration of CFTR activity. Aberrant immune responses of lymphocytes due to CFTR dysfunction has been described in the past, but not the effects of CFTR restoration by HEMT on these cells. We aimed to examine the effect of ETI on the proliferative activity of antigen-specific CD154 (+) T cells against bacterial and fungal species relevant in CF and on total IgG and IgE as markers of B cell adaptive immunity. Methods: We performed ex vivo analyses of Ki-67 expression in antigen-specific CD154 (+) T cells against Pseudomonas aeruginosa, Staphylococcus aureus, Aspergillus fumigatus, Scedosporium apiospermum and Candida albicans from 21 pwCF by cytometric assay based on antigen-reactive T cell enrichment (ARTE), and analysis of total serum IgE and IgG before and after initiation of ETI. Results: Mean Ki-67 expression in antigen-specific CD154 (+) T cells against P. aeruginosa, A. fumigatus, S. apiospermum and C. albicans, but not S. aureus, mean total serum IgG and mean total serum IgE decreased significantly after initiation of ETI. No correlation was found to change in sputum microbiology of the examined pathogens. Mean BMI and FEV1 increased significantly. Conclusion: HEMT is associated with decreased antigen-specific CD154 (+) T cell proliferation activity in our cohort, independent of findings in sputum microbiology of the examined pathogens. Together with the observed clinical improvement and the decrease in total IgE and IgG, this indicates effects due to CFTR restoration on CD154 (+) T cells by ETI and a reduction of B cell activation with subsequent lower immunoglobulin synthesis under HEMT therapy. These results endorse earlier evidence of CFTR dysfunction in T and B cells leading directly to aberrant immune responses with hyperinflammation.

Novel genetic variants linked to prelabor rupture of membranes among Chinese pregnant women

INTRODUCTION

The etiology of prelabor rupture of membranes (PROM), either preterm or term PROM (PPROM or TPROM), remains largely unknown. This study aimed to investigate the association between maternal genetic variants (GVs) and PROM and further establish a GV-based prediction model for PROM.

METHODS

In this case-cohort study (n = 1166), Chinese pregnant women with PPROM (n = 51), TPROM (n = 283) and controls (n = 832) were enrolled. A weighted Cox model was applied to identify the GVs (single nucleotide polymorphisms [SNPs], insertions/deletions, and copy number variants) associated with either PPROM or TPROM. Gene set enrichment analysis (GSEA) was to explore the mechanisms. The suggestively significant GVs were applied to establish a random forest (RF) model.

RESULTS

PTPRT variants (rs117950601, P = 4.37 × 10^-9; rs147178603, P = 8.98 × 10^-9) and SNRNP40 variant (rs117573344, P = 2.13 × 10^-8) were associated with PPROM. STXBP5L variant (rs10511405, P = 4.66 × 10^-8) was associated with TPROM. GSEA results showed that genes associated with PPROM were enriched in cell adhesion, and TPROM in ascorbate and glucuronidation metabolism. The area under the receiver operating characteristic curve of SNP-based RF model for PPROM was 0.961, with a sensitivity of 100.0% and specificity of 83.3%.

DISCUSSION

Maternal GVs in PTPRT and SNRNP40 were associated with PPROM, and GV in STXBP5L was associated with TPROM. Cell adhesion participated in PPROM, while ascorbate and glucuronidation metabolism contributed in TPROM. The PPROM might be well predicted using the SNP-based RF model.

Data-driven identification and classification of nonlinear aging patterns reveals the landscape of associations between DNA methylation and aging

BACKGROUND

Aging affects the incidence of diseases such as cancer and dementia, so the development of biomarkers for aging is an important research topic in medical science. While such biomarkers have been mainly identified based on the assumption of a linear relationship between phenotypic parameters, including molecular markers, and chronological age, numerous nonlinear changes between markers and aging have been identified. However, the overall landscape of the patterns in nonlinear changes that exist in aging is unknown.

RESULT

We propose a novel computational method, Data-driven Identification and Classification of Nonlinear Aging Patterns (DICNAP), that is based on functional data analysis to identify biomarkers for aging and potential patterns of change during aging in a data-driven manner. We applied the proposed method to large-scale, public DNA methylation data to explore the potential patterns of age-related changes in methylation intensity. The results showed that not only linear, but also nonlinear changes in DNA methylation patterns exist. A monotonous demethylation pattern during aging, with its rate decreasing at around age 60, was identified as the candidate stable nonlinear pattern. We also analyzed the age-related changes in methylation variability. The results showed that the variability of methylation intensity tends to increase with age at age-associated sites. The representative variability pattern is a monotonically increasing pattern that accelerates after middle age.

CONCLUSION

DICNAP was able to identify the potential patterns of the changes in the landscape of DNA methylation during aging. It contributes to an improvement in our theoretical understanding of the aging process.

Mathematical model for the relationship between single-cell and bulk gene expression to clarify the interpretation of bulk gene expression data

BACKGROUND

Differential expression analysis is a standard approach in molecular biology. For example, genes whose expression levels differ between diseased and non-diseased samples are considered to be associated with that disease. On the other hand, differential variability analysis focuses on the differences of the variances of gene expression between sample groups. Although differential variability is also known to capture biological information, its interpretation remains unclear and controversial. Recent single-cell analyses have revealed that differences between sample groups can affect gene expression in a cellular subset-specific manner or by altering the proportion of a particular cellular subset. The aim of this study is to clarify the interpretation of mean and variance of bulk gene expression data.

METHOD

We developed a mathematical model in which the bulk gene expression value is proportional to the mean value of the single-cell gene expression profile. Based on this model, we performed theoretical, simulated and real single-cell RNA-seq data analyses.

RESULT AND CONCLUSION

We identified how differences in single-cell gene expression profiles affect the differences in the mean and the variance of bulk gene expression. It is shown that differential expression analysis of bulk expression data can overlook significant changes in gene expression at the single-cell level. Further, differential variability analysis capture the complex feature affected by different gene expression shifts for each subset, changes in the proportions of cellular subsets, and variation in single-cell distribution parameters among samples.

Data-driven comparison of multiple high-dimensional single-cell expression profiles

Comparing multiple single-cell expression datasets such as cytometry and scRNA-seq data between case and control donors provides information to elucidate the mechanisms of disease. We propose a completely data-driven computational biological method for this task. This overcomes the challenges of conventional cellular subset-based comparisons and facilitates further analyses such as machine learning and gene set analysis of single-cell expression datasets.

Cell population-based framework of genetic epidemiology in the single-cell omics era

Genetic epidemiology is a rapidly advancing field due to the recent availability of large amounts of omics data. In recent years, it has become possible to obtain omics information at the single-cell level, so genetic epidemiological models need to be updated to integrate with single-cell expression data. In this perspective paper, we propose a cell population-based framework for genetic epidemiology in the single-cell era. In this framework, genetic diversity influences phenotypic diversity through the diversity of cell population profiles, which are defined as high-dimensional probability distributions of the state spaces of biomolecules of each omics layer. We discuss how biomolecular experimental measurement data can capture the different properties of this distribution. In particular, single-cell data constitute a sample from this population distribution where only some coordinate values are observable. From a data analysis standpoint, we introduce methodology for feature extraction from cell population profiles. Finally, we discuss how this framework can be applied not only to genetic epidemiology but also to systems biology.

Application of Genetic Studies to Flow Cytometry Data and Its Impact on Therapeutic Intervention for Autoimmune Disease

In recent years, systematic genome-wide association studies of quantitative immune cell traits, represented by circulating levels of cell subtypes established by flow cytometry, have revealed numerous association signals, a large fraction of which overlap perfectly with genetic signals associated with autoimmune diseases. By identifying further overlaps with association signals influencing gene expression and cell surface protein levels, it has also been possible, in several cases, to identify causal genes and infer candidate proteins affecting immune cell traits linked to autoimmune disease risk. Overall, these results provide a more detailed picture of how genetic variation affects the human immune system and autoimmune disease risk. They also highlight druggable proteins in the pathogenesis of autoimmune diseases; predict the efficacy and side effects of existing therapies; provide new indications for use for some of them; and optimize the research and development of new, more effective and safer treatments for autoimmune diseases. Here we review the genetic-driven approach that couples systematic multi-parametric flow cytometry with high-resolution genetics and transcriptomics to identify endophenotypes of autoimmune diseases for the development of new therapies.