Female-male differences in COVID vaccine adverse events have precedence in seasonal flu shots: a potential link to sex-associated baseline gene expression patterns

Nearly 150 million doses of FDA-authorized COVID vaccines have been administered in the United States. Sex-based differences of adverse events remain poorly understood, mandating the need for real-world investigation from Electronic Health Records (EHRs) and broader epidemiological data sets. Based on an augmented curation of EHR clinical notes of 31,064 COVID-vaccinated individuals (19,321 females and 11,743 males) in the Mayo Clinic, we find that nausea and vomiting were documented significantly more frequently in females than males after both vaccine doses (nausea: RRDose 1 = 1.67, pDose 1 <0.001, RRDose 2 = 2.2, pDose 1 < 0.001; vomiting: RRDose 1 = 1.58, pDose 1 < 0.001, RRDose 2 = 1.88, pDose 1 = 3.4x10-2). Conversely, fever, fatigue, and lymphadenopathy were more common in males after the first dose vaccination (fever RR = 0.62; p = 8.65x10-3; fatigue RR = 0.86, p = 2.89x10-2; lymphadenopathy RR = 0.61, p = 3.45x10-3). Analysis of the Vaccine Adverse Events Reporting System (VAERS) database further confirms that nausea comprises a larger fraction of total reports among females than males (RR: 1.58; p<0.001), while fever comprises a larger fraction of total reports among males than females (RR: 0.84; p<0.001). Importantly, increased reporting of nausea and fever among females and males, respectively, is also observed for prior influenza vaccines in the VAERS database, establishing that these differences are not unique to the recently developed COVID-19 vaccines. Investigating the mechanistic basis underlying these clinical findings, an analysis of bulk RNA-sequencing data from 12,158 human blood samples (8626 female, 3532 male) reveals 85 genes that are not only significantly different in their gene expression between females and males at baseline, but also have established literature-based associations to COVID-19 as well as the vaccine-related adverse events of clinical consequence. The NLRP3 inflammasome and the NR3C1 glucocorticoid receptor emerge as particularly promising baseline links to sex-associated vaccine adverse events, warranting targeted investigation of these signaling pathways and associated cell types. From a public health standpoint, our clinical findings shall aid in educating patients on the sex-associated risks they should expect for COVID-19 vaccines and also promote better clinical management of vaccine-associated adverse events.

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Identifying cell types from single-cell data based on similarities and dissimilarities between cells

BMC Bioinformatics ◽

10.1186/s12859-020-03873-z ◽

2021 ◽

Vol 22 (S3) ◽

Author(s):

Yuanyuan Li ◽

Ping Luo ◽

Yi Lu ◽

Fang-Xiang Wu

Keyword(s):

Gene Expression ◽

Single Cell ◽

Spectral Clustering ◽

Incidence Matrix ◽

Expression Patterns ◽

Cell Types ◽

Clustering Method ◽

Different Types ◽

Cell Data ◽

Spectral Clustering Method

Abstract Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data. Results Inspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets. Conclusions In summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.

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Histone modifications form a cell-type-specific chromosomal bar code that persists through the cell cycle

Scientific Reports ◽

10.1038/s41598-021-82539-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

John A. Halsall ◽

Simon Andrews ◽

Felix Krueger ◽

Charlotte E. Rutledge ◽

Gabriella Ficz ◽

...

Keyword(s):

Gene Expression ◽

Cell Cycle ◽

Histone Modifications ◽

Expression Patterns ◽

Cell Types ◽

Cell Type ◽

Bar Code ◽

Genes Encoding ◽

Cell Type Specific ◽

Rolling Windows

AbstractChromatin configuration influences gene expression in eukaryotes at multiple levels, from individual nucleosomes to chromatin domains several Mb long. Post-translational modifications (PTM) of core histones seem to be involved in chromatin structural transitions, but how remains unclear. To explore this, we used ChIP-seq and two cell types, HeLa and lymphoblastoid (LCL), to define how changes in chromatin packaging through the cell cycle influence the distributions of three transcription-associated histone modifications, H3K9ac, H3K4me3 and H3K27me3. We show that chromosome regions (bands) of 10–50 Mb, detectable by immunofluorescence microscopy of metaphase (M) chromosomes, are also present in G1 and G2. They comprise 1–5 Mb sub-bands that differ between HeLa and LCL but remain consistent through the cell cycle. The same sub-bands are defined by H3K9ac and H3K4me3, while H3K27me3 spreads more widely. We found little change between cell cycle phases, whether compared by 5 Kb rolling windows or when analysis was restricted to functional elements such as transcription start sites and topologically associating domains. Only a small number of genes showed cell-cycle related changes: at genes encoding proteins involved in mitosis, H3K9 became highly acetylated in G2M, possibly because of ongoing transcription. In conclusion, modified histone isoforms H3K9ac, H3K4me3 and H3K27me3 exhibit a characteristic genomic distribution at resolutions of 1 Mb and below that differs between HeLa and lymphoblastoid cells but remains remarkably consistent through the cell cycle. We suggest that this cell-type-specific chromosomal bar-code is part of a homeostatic mechanism by which cells retain their characteristic gene expression patterns, and hence their identity, through multiple mitoses.

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FC 011KIDNEYNETWORK: USING KIDNEY DERIVED GENE EXPRESSION DATA TO PREDICT AND PRIORITIZE NOVEL GENES INVOLVED IN KIDNEY DISEASE

Nephrology Dialysis Transplantation ◽

10.1093/ndt/gfab131.001 ◽

2021 ◽

Vol 36 (Supplement_1) ◽

Author(s):

Floranne Boulogne ◽

Laura Claus ◽

Henry Wiersma ◽

Roy Oelen ◽

Floor Schukking ◽

...

Keyword(s):

Gene Expression ◽

Kidney Disease ◽

Candidate Gene ◽

Exome Sequencing ◽

Rna Sequencing ◽

Expression Patterns ◽

Genetic Diagnosis ◽

Specific Gene ◽

Sequencing Data ◽

Exome Sequencing Data

Abstract Background and Aims Genetic testing in patients with suspected hereditary kidney disease does not always reveal the genetic cause for the patient's disorder. Potentially pathogenic variants can reside in genes that are not known to be involved in kidney disease, which makes it difficult to prioritize and interpret the relevance of these variants. As such, there is a clear need for methods that predict the phenotypic consequences of gene expression in a way that is as unbiased as possible. To help identify candidate genes we have developed KidneyNetwork, in which tissue-specific expression is utilized to predict kidney-specific gene functions. Method We combined gene co-expression in 878 publicly available kidney RNA-sequencing samples with the co-expression of a multi-tissue RNA-sequencing dataset of 31,499 samples to build KidneyNetwork. The expression patterns were used to predict which genes have a kidney-related function, and which (disease) phenotypes might be caused when these genes are mutated. By integrating the information from the HPO database, in which known phenotypic consequences of disease genes are annotated, with the gene co-expression network we obtained prediction scores for each gene per HPO term. As proof of principle, we applied KidneyNetwork to prioritize variants in exome-sequencing data from 13 kidney disease patients without a genetic diagnosis. Results We assessed the prediction performance of KidneyNetwork by comparing it to GeneNetwork, a multi-tissue co-expression network we previously developed. In KidneyNetwork, we observe a significantly improved prediction accuracy of kidney-related HPO-terms, as well as an increase in the total number of significantly predicted kidney-related HPO-terms (figure 1). To examine its clinical utility, we applied KidneyNetwork to 13 patients with a suspected hereditary kidney disease without a genetic diagnosis. Based on the HPO terms “Renal cyst” and “Hepatic cysts”, combined with a list of potentially damaging variants in one of the undiagnosed patients with mild ADPKD/PCLD, we identified ALG6 as a new candidate gene. ALG6 bears a high resemblance to other genes implicated in this phenotype in recent years. Through the 100,000 Genomes Project and collaborators we identified three additional patients with kidney and/or liver cysts carrying a suspected deleterious variant in ALG6. Conclusion We present KidneyNetwork, a kidney specific co-expression network that accurately predicts what genes have kidney-specific functions and may result in kidney disease. Gene-phenotype associations of genes unknown for kidney-related phenotypes can be predicted by KidneyNetwork. We show the added value of KidneyNetwork by applying it to exome sequencing data of kidney disease patients without a molecular diagnosis and consequently we propose ALG6 as a promising candidate gene. KidneyNetwork can be applied to clinically unsolved kidney disease cases, but it can also be used by researchers to gain insight into individual genes to better understand kidney physiology and pathophysiology. Acknowledgments This research was made possible through access to the data and findings generated by the 100,000 Genomes Project; http://www.genomicsengland.co.uk.

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Cell-type Specific Expression Quantitative Trait Loci Associated with Alzheimer Disease in Blood and Brain Tissue

10.1101/2020.11.23.20237008 ◽

2020 ◽

Author(s):

Devanshi Patel ◽

Xiaoling Zhang ◽

John J. Farrell ◽

Jaeyoon Chung ◽

Thor D. Stein ◽

...

Keyword(s):

Gene Expression ◽

Quantitative Trait ◽

Expression Patterns ◽

Regulation Of Gene Expression ◽

Cell Types ◽

Eqtl Analysis ◽

Cell Type ◽

Specific Expression ◽

Cell Type Specific Expression ◽

Cell Type Specific

ABSTRACTBecause regulation of gene expression is heritable and context-dependent, we investigated AD-related gene expression patterns in cell-types in blood and brain. Cis-expression quantitative trait locus (eQTL) mapping was performed genome-wide in blood from 5,257 Framingham Heart Study (FHS) participants and in brain donated by 475 Religious Orders Study/Memory & Aging Project (ROSMAP) participants. The association of gene expression with genotypes for all cis SNPs within 1Mb of genes was evaluated using linear regression models for unrelated subjects and linear mixed models for related subjects. Cell type-specific eQTL (ct-eQTL) models included an interaction term for expression of “proxy” genes that discriminate particular cell type. Ct-eQTL analysis identified 11,649 and 2,533 additional significant gene-SNP eQTL pairs in brain and blood, respectively, that were not detected in generic eQTL analysis. Of note, 386 unique target eGenes of significant eQTLs shared between blood and brain were enriched in apoptosis and Wnt signaling pathways. Five of these shared genes are established AD loci. The potential importance and relevance to AD of significant results in myeloid cell-types is supported by the observation that a large portion of GWS ct-eQTLs map within 1Mb of established AD loci and 58% (23/40) of the most significant eGenes in these eQTLs have previously been implicated in AD. This study identified cell-type specific expression patterns for established and potentially novel AD genes, found additional evidence for the role of myeloid cells in AD risk, and discovered potential novel blood and brain AD biomarkers that highlight the importance of cell-type specific analysis.

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Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms

10.1101/2021.07.05.450786 ◽

2021 ◽

Author(s):

Milton Pividori ◽

Sumei Lu ◽

Binglan Li ◽

Chun Su ◽

Matthew E. Johnson ◽

...

Keyword(s):

Gene Expression ◽

Association Studies ◽

Expression Patterns ◽

Cell Types ◽

Joint Analysis ◽

Genetic Associations ◽

Disease Etiology ◽

Mediating Role ◽

Gene Modules ◽

Trait Associations

Understanding how dysregulated transcriptional processes result in tissue-specific pathology requires a mechanistic interpretation of expression regulation across different cell types. It has been shown that this insight is key for the development of new therapies. These mechanisms can be identified with transcriptome-wide association studies (TWAS), which have represented an important step forward to test the mediating role of gene expression in GWAS associations. However, due to pervasive eQTL sharing across tissues, TWAS has not been successful in identifying causal tissues, and other methods generally do not take advantage of the large amounts of RNA-seq data publicly available. Here we introduce a polygenic approach that leverages gene modules (genes with similar co-expression patterns) to project both gene-trait associations and pharmacological perturbation data into a common latent representation for a joint analysis. We observed that diseases were significantly associated with gene modules expressed in relevant cell types, such as hypothyroidism with T cells and thyroid, hypertension and lipids with adipose tissue, and coronary artery disease with cardiomyocytes. Our approach was more accurate in predicting known drug-disease pairs and revealed stable trait clusters, including a complex branch involving lipids with cardiovascular, autoimmune, and neuropsychiatric disorders. Furthermore, using a CRISPR-screen, we show that genes involved in lipid regulation exhibit more consistent trait associations through gene modules than individual genes. Our results suggest that a gene module perspective can contextualize genetic associations and prioritize alternative treatment targets when GWAS hits are not druggable.

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GSDMA drives the most replicated association with asthma in naïve CD4+ T cells

10.1101/774760 ◽

2019 ◽

Cited By ~ 1

Author(s):

Anne-Marie Madore ◽

Lucile Pain ◽

Anne-Marie Boucher-Lafleur ◽

Jolyane Meloche ◽

Andréanne Morin ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

T Cells ◽

Genetic Variants ◽

Immune Cell ◽

Cell Types ◽

Sequencing Data ◽

Opposite Pattern ◽

Genetic Mechanisms ◽

New Gene

AbstractBackgroundThe 17q12-21 locus is the most replicated association with asthma. However, no study had described the genetic mechanisms underlying this association considering all genes of the locus in immune cell samples isolated from asthmatic and non-asthmatic individuals.ObjectiveThis study takes benefit of samples from naïve CD4+ T cells and eosinophils isolated from the same 200 individuals to describe specific interactions between genetic variants, gene expression and DNA methylation levels for the 17q12-21 asthma locus.Methods and ResultsAfter isolation of naïve CD4+ T cells and eosinophils from blood samples, next generation sequencing was used to measure DNA methylation levels and gene expression counts. Genetic interactions were then evaluated considering genetic variants from imputed genotype data. In naïve CD4+ T cells but not eosinophils, 20 SNPs in the fourth and fifth haplotype blocks modulated both GSDMA expression and methylation levels, showing an opposite pattern of allele frequencies and expression counts in asthmatics compared to controls. Moreover, negative correlations have been measured between methylation levels of CpG sites located within the 1.5 kb region from the transcription start site of GSDMA and its expression counts.ConclusionAvailability of sequencing data from two key cell types isolated from asthmatic and non-asthmatic individuals allowed identifying a new gene in naïve CD4+ T cells that drives the association with the 17q12-21 locus, leading to a better understanding of the genetic mechanisms taking place in it.

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Identification of Split-GAL4 Drivers and Enhancers That Allow Regional Cell Type Manipulations of the Drosophila melanogaster Intestine

Genetics ◽

10.1534/genetics.120.303625 ◽

2020 ◽

Vol 216 (4) ◽

pp. 891-903

Author(s):

Ishara S. Ariyapala ◽

Jessica M. Holsopple ◽

Ellen M. Popodi ◽

Dalton G. Hartwick ◽

Lily Kahsai ◽

...

Keyword(s):

Gene Expression ◽

Progenitor Cells ◽

Expression Patterns ◽

Cell Types ◽

Enteroendocrine Cells ◽

Epithelial Tissue ◽

Cell Type ◽

Cell Functions ◽

Distinct Subset ◽

Targeted Gene Expression

The Drosophila adult midgut is a model epithelial tissue composed of a few major cell types with distinct regional identities. One of the limitations to its analysis is the lack of tools to manipulate gene expression based on these regional identities. To overcome this obstacle, we applied the intersectional split-GAL4 system to the adult midgut and report 653 driver combinations that label cells by region and cell type. We first identified 424 split-GAL4 drivers with midgut expression from ∼7300 drivers screened, and then evaluated the expression patterns of each of these 424 when paired with three reference drivers that report activity specifically in progenitor cells, enteroendocrine cells, or enterocytes. We also evaluated a subset of the drivers expressed in progenitor cells for expression in enteroblasts using another reference driver. We show that driver combinations can define novel cell populations by identifying a driver that marks a distinct subset of enteroendocrine cells expressing genes usually associated with progenitor cells. The regional cell type patterns associated with the entire set of driver combinations are documented in a freely available website, providing information for the design of thousands of additional driver combinations to experimentally manipulate small subsets of intestinal cells. In addition, we show that intestinal enhancers identified with the split-GAL4 system can confer equivalent expression patterns on other transgenic reporters. Altogether, the resource reported here will enable more precisely targeted gene expression for studying intestinal processes, epithelial cell functions, and diseases affecting self-renewing tissues.

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1450-1545 Young Investigator Awards & Presentations Basic/Translational Exploring cellular subpopulations in glioblastoma and matched organoids using single-cell RNA-seq 52

Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques ◽

10.1017/cjn.2018.297 ◽

2018 ◽

Vol 45 (S3) ◽

pp. S13-S14

Author(s):

VG LeBlanc ◽

D Trinh ◽

M Hughes ◽

I Luthra ◽

D Livingstone ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Tissue Sample ◽

Expression Patterns ◽

Model Systems ◽

Sequencing Data ◽

Tissue Samples ◽

Original Tumour ◽

Current Therapies ◽

Intratumoural Heterogeneity

Glioblastomas (GBMs) account for nearly half of all primary malignant brain tumours, and current therapies are often only marginally effective. Our understanding of the underlying biology of these tumours and the development of new therapies have been complicated in part by widespread inter- and intratumoural heterogeneity. To characterize this heterogeneity, we performed regional subsampling of primary glioblastomas and derived organoids from these tissue samples. We then performed single-cell RNA-sequencing (scRNA-seq) on these primary regional subsamples and 1-3 matched organoids per sample. We have profiled samples from six tumour sets to date and have obtained sequencing data for 21,234 primary tissue cells and 14,742 organoid cells. While the most apparent differences in gene expression appear to be between individual tumours, we were also able to identify similar cellular subpopulations across tissue samples and across organoids. Importantly, organoids derived from the same tissue sample appeared to be composed of similar cellular subpopulations and were highly comparable to each other, indicating that replicate organoids faithfully represent the original tumour tissue. Overall, our scRNA-seq approach will help evaluate the utility of tumour-derived organoids as model systems for GBM and will aid in identifying cellular subpopulations defined by gene expression patterns, both in primary GBM regional subsamples and their associated organoids. These analyses will allow for the characterization of clonal or subclonal populations that are likely to respond to different therapeutic approaches and may also uncover novel therapeutic targets previously unrevealed through bulk analyses.

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Impact of AHR Ligand TCDD on Human Embryonic Stem Cells and Early Differentiation

International Journal of Molecular Sciences ◽

10.3390/ijms21239052 ◽

2020 ◽

Vol 21 (23) ◽

pp. 9052

Author(s):

Indrek Teino ◽

Antti Matvere ◽

Martin Pook ◽

Inge Varik ◽

Laura Pajusaar ◽

...

Keyword(s):

Gene Expression ◽

Stem Cells ◽

Embryonic Stem Cells ◽

Human Embryonic Stem Cells ◽

Target Genes ◽

Expression Patterns ◽

Embryonic Stem ◽

Stem Cell Differentiation ◽

Cell Types ◽

Early Differentiation

Aryl hydrocarbon receptor (AHR) is a ligand-activated transcription factor, which mediates the effects of a variety of environmental stimuli in multiple tissues. Recent advances in AHR biology have underlined its importance in cells with high developmental potency, including pluripotent stem cells. Nonetheless, there is little data on AHR expression and its role during the initial stages of stem cell differentiation. The purpose of this study was to investigate the temporal pattern of AHR expression during directed differentiation of human embryonic stem cells (hESC) into neural progenitor, early mesoderm and definitive endoderm cells. Additionally, we investigated the effect of the AHR agonist 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) on the gene expression profile in hESCs and differentiated cells by RNA-seq, accompanied by identification of AHR binding sites by ChIP-seq and epigenetic landscape analysis by ATAC-seq. We showed that AHR is differentially regulated in distinct lineages. We provided evidence that TCDD alters gene expression patterns in hESCs and during early differentiation. Additionally, we identified novel potential AHR target genes, which expand our understanding on the role of this protein in different cell types.

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Global Analysis of Cell Type-Specific Gene Expression

Comparative and Functional Genomics ◽

10.1002/cfg.281 ◽

2003 ◽

Vol 4 (2) ◽

pp. 208-215 ◽

Cited By ~ 16

Author(s):

David W. Galbraith

Keyword(s):

Gene Expression ◽

Fluorescent Protein ◽

Global Analysis ◽

Expression Patterns ◽

Three Dimensional ◽

Global Gene Expression ◽

Cell Types ◽

Reasonable Assumption ◽

Specific Gene ◽

Different Cell Types

The tissues and organs of multicellular eukaryotes are frequently observed to comprise complex three-dimensional interspersions of different cell types. It is a reasonable assumption that different global patterns of gene expression are found within these different cell types. This review outlines general experimental strategies designed to characterize these global gene expression patterns, based on a combination of methods of transgenic fluorescent protein (FP) expression and targeting, of flow cytometry and sorting and of high-throughput gene expression analysis.

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