SARS-CoV-2 Receptor ACE2 Is an Interferon-Stimulated Gene in Human Airway Epithelial Cells and Is Detected in Specific Cell Subsets across Tissues.

There is pressing urgency to understand the pathogenesis of the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV-2), which causes the disease COVID-19. SARS-CoV-2 spike (S) protein binds angiotensin-converting enzyme 2 (ACE2), and in concert with host proteases, principally transmembrane serine protease 2 (TMPRSS2), promotes cellular entry. The cell subsets targeted by SARS-CoV-2 in host tissues and the factors that regulate ACE2 expression remain unknown. Here, we leverage human, non-human primate, and mouse single-cell RNA-sequencing (scRNA-seq) datasets across health and disease to uncover putative targets of SARS-CoV-2 among tissue-resident cell subsets. We identify ACE2 and TMPRSS2 co-expressing cells within lung type II pneumocytes, ileal absorptive enterocytes, and nasal goblet secretory cells. Strikingly, we discovered that ACE2 is a human interferon-stimulated gene (ISG) in vitro using airway epithelial cells and extend our findings to in vivo viral infections. Our data suggest that SARS-CoV-2 could exploit species-specific interferon-driven upregulation of ACE2, a tissue-protective mediator during lung injury, to enhance infection

WGS-based telomere length analysis in Dutch family trios implicates stronger maternal inheritance and a role for RRM1 gene

Telomere length (TL) regulation is an important factor in ageing, reproduction and cancer development. Genetic, hereditary and environmental factors regulating TL are currently widely investigated, however, their relative contribution to TL variability is still understudied. We have used whole genome sequencing data of 250 family trios from the Genome of the Netherlands project to perform computational measurement of TL and a series of regression and genome-wide association analyses to reveal TL inheritance patterns and associated genetic factors. Our results confirm that TL is a largely heritable trait, primarily with mother’s, and, to a lesser extent, with father’s TL having the strongest influence on the offspring. In this cohort, mother’s, but not father’s age at conception was positively linked to offspring TL. Age-related TL attrition of 40 bp/year had relatively small influence on TL variability. Finally, we have identified TL-associated variations in ribonuclease reductase catalytic subunit M1 (RRM1 gene), which is known to regulate telomere maintenance in yeast. We also highlight the importance of multivariate approach and the limitations of existing tools for the analysis of TL as a polygenic heritable quantitative trait

Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders

Liability to alcohol dependence (AD) is heritable, but little is known about its complex polygenic architecture or its genetic relationship with other disorders. To discover loci associated with AD and characterize the relationship between AD and other psychiatric and behavioral outcomes, we carried out the largest genome-wide association study to date of DSM-IV-diagnosed AD. Genome-wide data on 14,904 individuals with AD and 37,944 controls from 28 case-control and family-based studies were meta-analyzed, stratified by genetic ancestry (European, n = 46,568; African, n = 6,280). Independent, genome-wide significant effects of different ADH1B variants were identified in European (rs1229984; P = 9.8 x 10(-13)) and African ancestries (rs2066702; P = 2.2 x 10(-9)). Significant genetic correlations were observed with 17 phenotypes, including schizophrenia, attention deficit-hyperactivity disorder, depression, and use of cigarettes and cannabis. The genetic underpinnings of AD only partially overlap with those for alcohol consumption, underscoring the genetic distinction between pathological and nonpathological drinking behaviors.Peer reviewe

Comparison of CHER with other methods.

<p>Pearson correlation coefficients between the prediction and the sensitivity data are calculated for each algorithm. The correlation coefficients from each algorithm (x-axis) are compared to those from CHER (y-axis). Each dot represents prediction performance for one drug sensitivity. Method abbreviation: EN, the elastic net, MIC, multiple inclusion criterion; BMKL: Bayesian multi-task multi-kernel regression; MTLASSO: multi-task lasso; EN-INT: EN with context-gene interactions. P-values show the significance of CHER’s prediction compared to other methods (one-tail t-test).</p

Example of predictive model for melanoma and glioma samples.

<p>A. CHER’s model for drug sensitivity to paclitaxel. Each vertical bar represents a data of a sample. All features are gene expression profiles except PTEN, which is a mutation profile feature (blue bars represents samples with mutations). AKT1 and WT1 are predictive for both melanoma and glioma. PTEN-mut, DUSP6 and USP6 are predictive features specific for melanoma whereas DUSP14 is specific for glioma. The greyed out heatmaps represents those features are not predictive for the samples. The predictions are obtained from leave-one-out procedure with the final selected features. B. Expression of WT1 is predictive of the cytotoxic drugs paclitaxel, irinotecan and topotecan, which likely due to IGF1-R activity.</p

Overview of CHER algorithm.

<p>A. Example of a model learned by CHER, where the drug sensitivity of melanoma samples can be predicted by mutation of M and gene expression of A and S, whereas in glioma, expression of gene S and B are the predictors. CHER takes advantage of pooling samples together to gain statistical power, identifying both shared (gene S) and context-specific features (A, B and M). In cases where the relevant context is unknown, the algorithm searches for the best “split”, if any, to separate samples into two groups. Yi represents drug sensitivity of the ith sample, xi are the corresponding features of the ith sample, zit = 1 presents the ith sample is melanoma, and I(.) is an indicator function. B. Iterative learning scheme of CHER. CHER initially learns models with uniform prior (meaning each genomic feature has the same probability of being included in the model). During each iteration, CHER trains the regression models with bootstrapping, which allows the algorithm to establish the frequency of each feature being selected. Then CHER adjusts the priors according to the distribution of frequency and the similarity between phenotypes.</p

Comparison of performance of CHER and elastic net on synthetic data.

<p>Bootstrapped elastic net (EN) is compared to bootstrapped CHER. A threshold of 0.3 and 0.5 are applied to the relevant frequency (<i>τ</i>) to determine robust features in CHER and elastic, respectively. The precision, recall, F-measure of each phenotype from EN (x-axis) is plotted against that from CHER (y-axis). The first row shows the results of CHER from the first iteration and the second row the results of CHER from the 10^th iteration. Each dot represents a phenotype, colored by the noise level added.</p

Comparison of features selected by CHER and elastic net (EN).

<p>A. Number of features selected by both and individual algorithms for each phenotype. For each phenotype (x-axis), numbers of features selected by CHER are represented on the positive y-axis whereas those selected by elastic net are represented on the negative y-axis. Features are separated into five groups, corresponding to features selected by both algorithms or by specific to individual algorithms. Phenotype 1–14 are from CCLE-SkinGlioma and the rest are from CCLE-Blood. B. Adjusted R² of CHER and elastic net models using the features selected by both algorithms (features of the first two categories in A). C. As B, but all features selected by each algorithm are used. Phenotypes in all three figures are sorted by the difference of R² between CHER and elastic net from C.</p

Conditional density-based analysis of T cell signaling in single-cell data

Cellular circuits sense the environment, process signals, and compute decisions using networks of interacting proteins. To model such a system, the abundance of each activated protein species can be described as a stochastic function of the abundance of other proteins. High-dimensional single-cell technologies, like mass cytometry, offer an opportunity to characterize signaling circuit-wide. However, the challenge of developing and applying computational approaches to interpret such complex data remains. Here, we developed computational methods, based on established statistical concepts, to characterize signaling network relationships by quantifying the strengths of network edges and deriving signaling response functions. In comparing signaling between naïve and antigen-exposed CD4+ T-lymphocytes, we find that although these two cell subtypes had similarly-wired networks, naïve cells transmitted more information along a key signaling cascade than did antigen-exposed cells. We validated our characterization on mice lacking the extracellular-regulated MAP kinase (ERK2), which showed stronger influence of pERK on pS6 (phosphorylated-ribosomal protein S6), in naïve cells compared to antigen-exposed cells, as predicted. We demonstrate that by using cell-to-cell variation inherent in single cell data, we can algorithmically derive response functions underlying molecular circuits and drive the understanding of how cells process signals