@article {119, title = {Identification of Required Host Factors for SARS-CoV-2 Infection in Human Cells.}, journal = {Cell}, volume = {184}, year = {2021}, month = {2021 01 07}, pages = {92-105.e16}, abstract = {

To better understand host-virus genetic dependencies and find potential therapeutic targets for COVID-19, we performed a genome-scale CRISPR loss-of-function screen to identify host factors required for SARS-CoV-2 viral infection of human alveolar epithelial cells. Top-ranked genes cluster into distinct pathways, including the vacuolar ATPase proton pump, Retromer, and Commander complexes. We validate these gene targets using several orthogonal methods such as CRISPR knockout, RNA interference knockdown, and small-molecule inhibitors. Using single-cell RNA-sequencing, we identify shared transcriptional changes in cholesterol biosynthesis upon loss of top-ranked genes. In addition, given the key role of the ACE2 receptor in the early stages of viral entry, we show that loss of RAB7A reduces viral entry by sequestering the ACE2 receptor inside cells. Overall, this work provides a genome-scale, quantitative resource of the impact of the loss of each host gene on fitness/response to viral infection.

}, keywords = {A549 Cells, Alveolar Epithelial Cells, Angiotensin-Converting Enzyme 2, Biosynthetic Pathways, Cholesterol, Clustered Regularly Interspaced Short Palindromic Repeats, COVID-19, Endosomes, Gene Expression Profiling, Gene Knockdown Techniques, Gene Knockout Techniques, Genome-Wide Association Study, Host-Pathogen Interactions, Humans, rab GTP-Binding Proteins, rab7 GTP-Binding Proteins, RNA Interference, SARS-CoV-2, Single-Cell Analysis, Viral Load}, issn = {1097-4172}, doi = {10.1016/j.cell.2020.10.030}, author = {Daniloski, Zharko and Jordan, Tristan X and Wessels, Hans-Hermann and Hoagland, Daisy A and Kasela, Silva and Legut, Mateusz and Maniatis, Silas and Mimitou, Eleni P and Lu, Lu and Geller, Evan and Danziger, Oded and Rosenberg, Brad R and Phatnani, Hemali and Smibert, Peter and Lappalainen, Tuuli and tenOever, Benjamin R and Sanjana, Neville E} } @article {123, title = {Type 2 and interferon inflammation regulate SARS-CoV-2 entry factor expression in the airway epithelium.}, journal = {Nat Commun}, volume = {11}, year = {2020}, month = {2020 10 12}, pages = {5139}, abstract = {

Coronavirus disease 2019 (COVID-19) is caused by SARS-CoV-2, an emerging virus that utilizes host proteins ACE2 and TMPRSS2 as entry factors. Understanding the factors affecting the pattern and levels of expression of these genes is important for deeper understanding of SARS-CoV-2 tropism and pathogenesis. Here we explore the role of genetics and co-expression networks in regulating these genes in the airway, through the analysis of nasal airway transcriptome data from 695 children. We identify expression quantitative trait loci for both ACE2 and TMPRSS2, that vary in frequency across world populations. We find TMPRSS2 is part of a mucus secretory network, highly upregulated by type 2 (T2) inflammation through the action of interleukin-13, and that the interferon response to respiratory viruses highly upregulates ACE2 expression. IL-13 and virus infection mediated effects on ACE2 expression were also observed at the protein level in the airway epithelium. Finally, we define airway responses to common coronavirus infections in children, finding that these infections generate host responses similar to other viral species, including upregulation of IL6 and ACE2. Our results reveal possible mechanisms influencing SARS-CoV-2 infectivity and COVID-19 clinical outcomes.

}, keywords = {Angiotensin-Converting Enzyme 2, Betacoronavirus, Child, Coronavirus Infections, COVID-19, Epithelial Cells, Gene Expression Profiling, Gene Expression Regulation, Genetic Variation, Host-Pathogen Interactions, Humans, Inflammation, Interferons, Interleukin-13, Middle Aged, Nasal Mucosa, Pandemics, Peptidyl-Dipeptidase A, Pneumonia, Viral, SARS-CoV-2, Serine Endopeptidases, Virus Internalization}, issn = {2041-1723}, doi = {10.1038/s41467-020-18781-2}, author = {Sajuthi, Satria P and DeFord, Peter and Li, Yingchun and Jackson, Nathan D and Montgomery, Michael T and Everman, Jamie L and Rios, Cydney L and Pruesse, Elmar and Nolin, James D and Plender, Elizabeth G and Wechsler, Michael E and Mak, Angel C Y and Eng, Celeste and Salazar, Sandra and Medina, Vivian and Wohlford, Eric M and Huntsman, Scott and Nickerson, Deborah A and Germer, Soren and Zody, Michael C and Abecasis, Gon{\c c}alo and Kang, Hyun Min and Rice, Kenneth M and Kumar, Rajesh and Oh, Sam and Rodriguez-Santana, Jose and Burchard, Esteban G and Seibold, Max A} } @article {32, title = {Genetic effects on gene expression across human tissues.}, journal = {Nature}, volume = {550}, year = {2017}, month = {2017 Oct 11}, pages = {204-213}, abstract = {

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.

}, keywords = {Alleles, Chromosomes, Human, Disease, Female, Gene Expression Profiling, Gene Expression Regulation, Genetic Variation, Genome, Human, Genotype, Humans, Male, Organ Specificity, Quantitative Trait Loci}, issn = {1476-4687}, doi = {10.1038/nature24277}, author = {Battle, Alexis and Brown, Christopher D and Engelhardt, Barbara E and Montgomery, Stephen B} } @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} } @article {26, title = {Simultaneous epitope and transcriptome measurement in single cells.}, journal = {Nat Methods}, volume = {14}, year = {2017}, month = {2017 Sep}, pages = {865-868}, abstract = {

High-throughput single-cell RNA sequencing has transformed our understanding of complex cell populations, but it does not provide phenotypic information such as cell-surface protein levels. Here, we describe cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq), a method in which oligonucleotide-labeled antibodies are used to integrate cellular protein and transcriptome measurements into an efficient, single-cell readout. CITE-seq is compatible with existing single-cell sequencing approaches and scales readily with throughput increases.

}, keywords = {Epitope Mapping, Epitopes, Gene Expression Profiling, High-Throughput Nucleotide Sequencing, Sequence Analysis, RNA, Tissue Array Analysis, Transcriptome}, issn = {1548-7105}, doi = {10.1038/nmeth.4380}, author = {Stoeckius, Marlon and Hafemeister, Christoph and Stephenson, William and Houck-Loomis, Brian and Chattopadhyay, Pratip K and Swerdlow, Harold and Satija, Rahul and Smibert, Peter} }