Biology subjects

Kulminski, A.

Publications and source records attributed to Kulminski, A..

6 recordsLinked to original sources

bioRxiv2022

An Autoimmune Transcriptional Circuit Driving Foxp3+ Regulatory T cell Dysfunction

Autoimmune diseases, among the most common disorders of young adults, are mediated by genetic and environmental factors. While CD4+Foxp3+ regulatory T cells (Tregs) play a central role in preventing autoimmunity, the molecular mechanism underlying their dysfunction is unknown. Here, we performed comprehensive transcriptomic and epigenomic profiling of Tregs in the autoimmune disease multiple sclerosis (MS) to identify central transcriptional programs regulating human autoimmunity. We discovered that upregulation of a primate-specific short PRDM1 isoform (PRDM1-S) induces SGK1 independent from evolutionally conserved long PRDM1, leading to destabilization of Foxp3 and Treg dysfunction. This aberrant PRDM1-S/SGK1 axis is shared among other autoimmune diseases. Furthermore, by chromatin landscape profiling in MS Tregs we identified a PRDM1-S specific cis-regulatory element associated with enriched binding of AP-1/IRF transcription factors. Our study identifies evolutionally emerged PRDM1-S and epigenetic priming of AP-1/IRF as key drivers of pathogenic Treg programs leading to human autoimmune disease.

immunology↗

bioRxiv2022

A robust and fast two-sample test of equal correlations with an application to differential co-expression

A robust and fast two-sample test for equal Pearson correlation coefficients (PCCs) is important in solving many biological problems, including, e.g., analysis of differential co-expression. However, few existing methods for this test can achieve robustness against deviation from normal distributions, accuracy under small sample sizes, and computational efficiency simultaneously. Here, we propose such a method for testing DIfferential COrrelation using a Saddlepoint Approximation of the Residual bootstrap (DICOSAR). To achieve robustness, accuracy, and efficiency, DICOSAR combines the ideas underlying the pooled residual bootstrap, the signed root of a likelihood ratio statistic, and a multivariate saddlepoint approximation. Through a comprehensive simulation study and a real data analysis of gene co-expression, we demonstrate that DICOSAR is accurate and robust in controlling the type I error rate for detecting differential correlation and provides a faster alternative to the permutation method. We further show that it can also be used for testing differential correlation matrices. These results suggest that DICOSAR provides an analytical approach to facilitate rapid testing for the equality of PCCs in a large-scale analysis.

bioinformatics↗

bioRxiv2021

Allele-specific analysis reveals exon- and cell-type-specific regulatory effects of Alzheimer's disease-associated genetic variants

Elucidating regulatory effects of Alzheimers disease (AD)-associated genetic variants is critical for unraveling their causal pathways and understanding the pathology. However, their cell-type-specific regulatory mechanisms in the brain remain largely unclear. Here, we conducted an analysis of allele-specific expression quantitative trait loci (aseQTLs) for 33 AD-associated variants in four brain regions and seven cell types using ~3000 bulk RNA-seq samples and >0.25 million single nuclei. We develop a flexible framework using a hierarchical Poisson mixed model unifying samples in both allelic and genotype-level expression data. We identified 24 AD-associated variants (~73%) that are allele-specific eQTLs (aseQTLs) in at least one brain region. Multiple aseQTLs are region-dependent or exon-specific, such as rs2093760 with CR1, rs7982 with CLU, and rs3865444 with CD33. Notably, the APOE {varepsilon}4 variant reduces APOE expression across all regions, even in healthy controls. In pinpointing the cell types responsible for the observed region-level aseQTLs, we found rs2093760 as an aseQTL of CR1 in oligodendrocytes but not in microglia. Many AD-associated variants are aseQTLs in microglia or monocytes of immune-related genes, including HLA-DQB1, HLA-DQA2, CD33, FCER1G, MS4A6A, SPI1, and BIN1, highlighting the regulatory role of AD-associated variants in the immune response. These findings provide further insights into potential causal pathways and cell types mediating the effects of the AD-associated variants.

genetics↗

bioRxiv2020

NEBULA: a fast negative binomial mixed model for differential expression and co-expression analyses of large-scale multi subject single-cell data

The growing availability of large-scale single-cell data revolutionizes our understanding of biological mechanisms at a finer resolution. In differential expression and co-expression analyses of multi-subject single-cell data, it is important to take into account both subject-level and cell-level overdispersions through negative binomial mixed models (NBMMs). However, the application of NBMMs to large-scale single-cell data is computationally demanding. In this work, we propose an efficient NEgative Binomial mixed model Using a Large-sample Approximation (NEBULA)), which analytically solves the high-dimensional integral in the marginal likelihood instead of using the Laplace approximation. Our benchmarks show that NEBULA dramatically reduces the running time by orders of magnitude compared to existing tools. We showed that NEBULA controlled false positives in identifying marker genes, while a simple negative binomial model produced spurious associations. Leveraging NEBULA, we decomposed between-subject and within-subject overdispersions of an snRNA-seq data set in the frontal cortex comprising [~]80,000 cells from a cohort of 48 individuals for Alzheimers diseases (AD). We observed that subpopulations and known subject-level covariates contributed substantially to the overdispersions. We carried out cell-type-specific transcriptome-wide within-subject co-expression analysis of APOE. The results revealed that APOE was most co-expressed with multiple AD-related genes, including CLU and CST3 in astrocytes, TREM2 and C1q genes in microglia, and ITM2B, an inhibitor of the amyloid-beta peptide aggregation, in both cell types. We found that the co-expression patterns were different in APOE2+ and APOE4+ cells in microglia, which suggest an isoform-dependent regulatory role in the immune system through the complement system in microglia. NEBULA opens up a new avenue for the broad application of NBMMs in the analysis of large-scale multi-subject single-cell data.

bioinformatics↗

bioRxiv2020

Exome-wide age-of-onset analysis reveals exonic variants in ERN1, TACR3 and SPPL2C associated with Alzheimer's disease

Despite recent discovery in GWAS of genomic variants associated with Alzheimers disease (AD), its underlying biological mechanisms are still elusive. Discovery of novel AD-associated genetic variants, particularly in coding regions and from APOE {varepsilon}4 non-carriers, is critical for understanding the pathology of AD. In this study, we carried out an exome-wide association analysis of age-of-onset of AD with ~20,000 subjects and placed more emphasis on APOE {varepsilon}4 non-carriers. Using Cox mixed-effects models, we find that age-of-onset shows a stronger genetic signal than AD case-control status, capturing many known variants with stronger significance, and also revealing new variants. We identified two novel rare variants, rs56201815, a synonymous variant in ERN1, from the analysis of APOE {varepsilon}4 non-carriers, and a missense variant rs144292455 in TACR3. In addition, we detected rs12373123, a common missense variant in SPPL2C in the MAPT region in APOE {varepsilon}4 non-carriers. In an attempt to unravel their regulatory and biological functions, we found that the minor allele of rs56201815 was associated with lower average FDG uptake across five brain regions in ADNI. Our eQTL analyses based on 6198 gene expression samples from ROSMAP and GTEx revealed that the minor allele of rs56201815 was associated with elevated expression of ERN1, a key gene triggering unfolded protein response (UPR), in multiple brain regions, including posterior cingulate cortex and nucleus accumbens. Our cell-type-specific eQTL analysis of based on ~80,000 single nuclei in the prefrontal cortex revealed that the protective minor allele of rs12373123 significantly increased expression of GRN in microglia, and was associated with MAPT expression in astrocytes. These findings provide novel evidence supporting the hypothesis of the potential involvement of the UPR to ER stress in the pathological pathway of AD, and also give more insights into underlying regulatory mechanisms behind the pleiotropic effects of rs12373123 in multiple degenerative diseases including AD and Parkinsons disease.

neuroscience↗

bioRxiv2019

Genome-wide association analysis of age-at-onset traits using Cox mixed-effects models

Age-at-onset is one of the critical phenotypes in cohort studies of age-related diseases. Large-scale genome-wide association studies (GWAS) of age-at-onset can provide more insights into genetic effects on disease progression, and transitions between different stages. Moreover, proportional hazards or Cox regression generally achieves higher statistical power in a cohort study than a binary trait using logistic regression. Although mixed-effects models are widely used in GWAS to correct for population stratification and family structure, application of Cox mixed-effects models (CMEMs) to large-scale GWAS are so far hindered by intractable computational intensity. In this work, we propose COXMEG, an efficient R package for conducting GWAS of age-at-onset using CMEMs. COXMEG introduces fast estimation algorithms for general sparse relatedness matrices including but not limited to block-diagonal pedigree-based matrices. COXMEG also introduces a fast and powerful score test for fully dense relatedness matrices, accounting for both population stratification and family structure. In addition, COXMEG handles positive semidefinite relatedness matrices, which are common in twin and family studies. Our simulation studies suggest that COXMEG, depending on the structure of the relatedness matrix, is 100[~]100,000-fold computationally more efficient for GWAS than coxme for a sample consisting of 1000-10,000 individuals. We found that using sparse approximation of relatedness matrices yielded highly comparable performance in controlling false positives and statistical power for an ethnically homogeneous family-based sample. When applying COXMEG to a NIA-LOADFS sample with 3456 Caucasians, we identified the APOE4 variant with strong statistical power (p=1e-101), far more significant than previous studies using a transformed variable and a marginal Cox model. When investigating a multi-ethnic NIA-LOADFS sample including 3456 Caucasians and 287 African Americans, we identified a novel SNP rs36051450 (p=2e-9) near GRAMD1B, the minor allele of which significantly reduced the hazards of AD in both genders. Our results demonstrated that COXMEG greatly facilitates the application of CMEMs in GWAS of age-at-onset phenotypes.

bioinformatics↗