@article {152, title = {Genetic and non-genetic factors affecting the expression of COVID-19-relevant genes in the large airway epithelium.}, journal = {Genome Med}, volume = {13}, year = {2021}, month = {2021 04 21}, pages = {66}, abstract = {

BACKGROUND: The large airway epithelial barrier provides one of the first lines of defense against respiratory viruses, including SARS-CoV-2 that causes COVID-19. Substantial inter-individual variability in individual disease courses is hypothesized to be partially mediated by the differential regulation of the genes that interact with the SARS-CoV-2 virus or are involved in the subsequent host response. Here, we comprehensively investigated non-genetic and genetic factors influencing COVID-19-relevant bronchial epithelial gene expression.

METHODS: We analyzed RNA-sequencing data from bronchial epithelial brushings obtained from uninfected individuals. We related ACE2 gene expression to host and environmental factors in the SPIROMICS cohort of smokers with and without chronic obstructive pulmonary disease (COPD) and replicated these associations in two asthma cohorts, SARP and MAST. To identify airway biology beyond ACE2 binding that may contribute to increased susceptibility, we used gene set enrichment analyses to determine if gene expression changes indicative of a suppressed airway immune response observed early in SARS-CoV-2 infection are also observed in association with host factors. To identify host genetic variants affecting COVID-19 susceptibility in SPIROMICS, we performed expression quantitative trait (eQTL) mapping and investigated the phenotypic associations of the eQTL variants.

RESULTS: We found that ACE2 expression was higher in relation to active smoking, obesity, and hypertension that are known risk factors of COVID-19 severity, while an association with interferon-related inflammation was driven by the truncated, non-binding ACE2 isoform. We discovered that expression patterns of a suppressed airway immune response to early SARS-CoV-2 infection, compared to other viruses, are similar to patterns associated with obesity, hypertension, and cardiovascular disease, which may thus contribute to a COVID-19-susceptible airway environment. eQTL mapping identified regulatory variants for genes implicated in COVID-19, some of which had pheWAS evidence for their potential role in respiratory infections.

CONCLUSIONS: These data provide evidence that clinically relevant variation in the expression of COVID-19-related genes is associated with host factors, environmental exposures, and likely host genetic variation.

}, keywords = {Adult, Aged, Aged, 80 and over, Angiotensin-Converting Enzyme 2, Asthma, Bronchi, Cardiovascular Diseases, COVID-19, Gene Expression, Genetic Variation, Humans, Middle Aged, Obesity, Pulmonary Disease, Chronic Obstructive, Quantitative Trait Loci, Respiratory Mucosa, Risk Factors, SARS-CoV-2, Smoking}, issn = {1756-994X}, doi = {10.1186/s13073-021-00866-2}, author = {Kasela, Silva and Ortega, Victor E and Martorella, Molly and Garudadri, Suresh and Nguyen, Jenna and Ampleford, Elizabeth and Pasanen, Anu and Nerella, Srilaxmi and Buschur, Kristina L and Barjaktarevic, Igor Z and Barr, R Graham and Bleecker, Eugene R and Bowler, Russell P and Comellas, Alejandro P and Cooper, Christopher B and Couper, David J and Criner, Gerard J and Curtis, Jeffrey L and Han, MeiLan K and Hansel, Nadia N and Hoffman, Eric A and Kaner, Robert J and Krishnan, Jerry A and Martinez, Fernando J and McDonald, Merry-Lynn N and Meyers, Deborah A and Paine, Robert and Peters, Stephen P and Castro, Mario and Denlinger, Loren C and Erzurum, Serpil C and Fahy, John V and Israel, Elliot and Jarjour, Nizar N and Levy, Bruce D and Li, Xingnan and Moore, Wendy C and Wenzel, Sally E and Zein, Joe and Langelier, Charles and Woodruff, Prescott G and Lappalainen, Tuuli and Christenson, Stephanie A} } @article {102, title = {The impact of sex on gene expression across human tissues.}, journal = {Science}, volume = {369}, year = {2020}, month = {2020 09 11}, abstract = {

Many complex human phenotypes exhibit sex-differentiated characteristics. However, the molecular mechanisms underlying these differences remain largely unknown. We generated a catalog of sex differences in gene expression and in the genetic regulation of gene expression across 44 human tissue sources surveyed by the Genotype-Tissue Expression project (GTEx, v8 release). We demonstrate that sex influences gene expression levels and cellular composition of tissue samples across the human body. A total of 37\% of all genes exhibit sex-biased expression in at least one tissue. We identify cis expression quantitative trait loci (eQTLs) with sex-differentiated effects and characterize their cellular origin. By integrating sex-biased eQTLs with genome-wide association study data, we identify 58 gene-trait associations that are driven by genetic regulation of gene expression in a single sex. These findings provide an extensive characterization of sex differences in the human transcriptome and its genetic regulation.

}, keywords = {Chromosomes, Human, X, Disease, Epigenesis, Genetic, Female, Gene Expression, Gene Expression Regulation, Genetic Variation, Genome-Wide Association Study, Humans, Male, Organ Specificity, Promoter Regions, Genetic, Quantitative Trait Loci, Sex Characteristics, Sex Factors}, issn = {1095-9203}, doi = {10.1126/science.aba3066}, author = {Oliva, Meritxell and Mu{\~n}oz-Aguirre, Manuel and Kim-Hellmuth, Sarah and Wucher, Valentin and Gewirtz, Ariel D H and Cotter, Daniel J and Parsana, Princy and Kasela, Silva and Balliu, Brunilda and Vi{\~n}uela, Ana and Castel, Stephane E and Mohammadi, Pejman and Aguet, Fran{\c c}ois and Zou, Yuxin and Khramtsova, Ekaterina A and Skol, Andrew D and Garrido-Mart{\'\i}n, Diego and Reverter, Ferran and Brown, Andrew and Evans, Patrick and Gamazon, Eric R and Payne, Anthony and Bonazzola, Rodrigo and Barbeira, Alvaro N and Hamel, Andrew R and Martinez-Perez, Angel and Soria, Jos{\'e} Manuel and Pierce, Brandon L and Stephens, Matthew and Eskin, Eleazar and Dermitzakis, Emmanouil T and Segr{\`e}, Ayellet V and Im, Hae Kyung and Engelhardt, Barbara E and Ardlie, Kristin G and Montgomery, Stephen B and Battle, Alexis J and Lappalainen, Tuuli and Guigo, Roderic and Stranger, Barbara E} } @article {25, title = {Genetic regulatory effects modified by immune activation contribute to autoimmune disease associations.}, journal = {Nat Commun}, volume = {8}, year = {2017}, month = {2017 Aug 16}, pages = {266}, abstract = {

The immune system plays a major role in human health and disease, and understanding genetic causes of interindividual variability of immune responses is vital. Here, we isolate monocytes from 134 genotyped individuals, stimulate these cells with three defined microbe-associated molecular patterns (LPS, MDP, and 5{\textquoteright}-ppp-dsRNA), and profile the transcriptomes at three time points. Mapping expression quantitative trait loci (eQTL), we identify 417 response eQTLs (reQTLs) with varying effects between conditions. We characterize the dynamics of genetic regulation on early and late immune response and observe an enrichment of reQTLs in distal cis-regulatory elements. In addition, reQTLs are enriched for recent positive selection with an evolutionary trend towards enhanced immune response. Finally, we uncover reQTL effects in multiple GWAS loci and show a stronger enrichment for response than constant eQTLs in GWAS signals of several autoimmune diseases. This demonstrates the importance of infectious stimuli in modifying genetic predisposition to disease.Insight into the genetic influence on the immune response is important for the understanding of interindividual variability in human pathologies. Here, the authors generate transcriptome data from human blood monocytes stimulated with various immune stimuli and provide a time-resolved response eQTL map.

}, keywords = {Acetylmuramyl-Alanyl-Isoglutamine, Adjuvants, Immunologic, Adolescent, Adult, Autoimmune Diseases, Gene Expression, Gene Expression Profiling, Gene Expression Regulation, Genetic Predisposition to Disease, Healthy Volunteers, Humans, Indicators and Reagents, Lipids, Lipopolysaccharides, Male, Monocytes, Quantitative Trait Loci, Regulatory Sequences, Nucleic Acid, RNA, Double-Stranded, RNA, Messenger, Young Adult}, issn = {2041-1723}, doi = {10.1038/s41467-017-00366-1}, author = {Kim-Hellmuth, Sarah and Bechheim, Matthias and P{\"u}tz, Benno and Mohammadi, Pejman and N{\'e}d{\'e}lec, Yohann and Giangreco, Nicholas and Becker, Jessica and Kaiser, Vera and Fricker, Nadine and Beier, Esther and Boor, Peter and Castel, Stephane E and N{\"o}then, Markus M and Barreiro, Luis B and Pickrell, Joseph K and M{\"u}ller-Myhsok, Bertram and Lappalainen, Tuuli and Schumacher, Johannes and Hornung, Veit} } @article {30, title = {Quantifying the regulatory effect size of -acting genetic variation using allelic fold change.}, journal = {Genome Res}, volume = {27}, year = {2017}, month = {2017 Nov}, pages = {1872-1884}, abstract = {

Mapping -acting expression quantitative trait loci (-eQTL) has become a popular approach for characterizing proximal genetic regulatory variants. In this paper, we describe and characterize log allelic fold change (aFC), the magnitude of expression change associated with a given genetic variant, as a biologically interpretable unit for quantifying the effect size of -eQTLs and a mathematically convenient approach for systematic modeling of -regulation. This measure is mathematically independent from expression level and allele frequency, additive, applicable to multiallelic variants, and generalizable to multiple independent variants. We provide efficient tools and guidelines for estimating aFC from both eQTL and allelic expression data sets and apply it to Genotype Tissue Expression (GTEx) data. We show that aFC estimates independently derived from eQTL and allelic expression data are highly consistent, and identify technical and biological correlates of eQTL effect size. We generalize aFC to analyze genes with two eQTLs in GTEx and show that in nearly all cases the two eQTLs act independently in regulating gene expression. In summary, aFC is a solid measure of -regulatory effect size that allows quantitative interpretation of cellular regulatory events from population data, and it is a valuable approach for investigating novel aspects of eQTL data sets.

}, keywords = {Alleles, Databases, Genetic, Gene Expression, Gene Expression Profiling, Gene Regulatory Networks, Genetic Variation, Humans, Models, Theoretical, Quantitative Trait Loci}, issn = {1549-5469}, doi = {10.1101/gr.216747.116}, author = {Mohammadi, Pejman and Castel, Stephane E and Brown, Andrew A and Lappalainen, Tuuli} }