Search bioRxiv⌕ Search

bioRxiv · 10.1101/2021.06.07.447351

Intronic regulation of SARS-CoV-2 receptor (ACE2) expression mediated by immune signaling and oxidative stress pathways

Abstract

The angiotensin-converting enzyme 2 (ACE2) protein has been highly studied as a key catalytic regulator of the renin-angiotensin system (RAS), involved in fluid homeostasis and blood pressure modulation. In addition to its important physiological role as a broadly-expressed membrane-bound protein, ACE2 serves as a cell-surface receptor for some viruses - most notably, coronaviruses such as SARS-CoV and SARS-CoV-2. Differing levels of ACE2 expression may impact viral susceptibility and subsequent changes to expression may be a pathogenic mechanism of disease risk and manifestation. Therefore, an improved understanding of how ACE2 expression is regulated at the genomic and transcriptional level may help us understand not only how the effects of pre-existing conditions (e.g., chronic obstructive pulmonary disease) may manifest with increased COVID-19 incidence, but also the mechanisms that regulate ACE2 levels following viral infection. Here, we initially perform bioinformatic analyses of several datasets to generate hypotheses about ACE2 gene-regulatory mechanisms in the context of immune signaling and chronic oxidative stress. We then identify putative non-coding regulatory elements within ACE2 intronic regions as potential determinants of ACE2 expression activity. We perform functional validation of our computational predictions in vitro via targeted CRISPR-Cas9 deletions of the identified ACE2 cis-regulatory elements in the context of both immunological stimulation and oxidative stress conditions. We demonstrate that intronic ACE2 regulatory elements are responsive to both immune signaling and oxidative-stress pathways, and this contributes to our understanding of how expression of this gene may be modulated at both baseline and during immune challenge. Our work supports the further pursuit of these putative mechanisms in our understanding, prevention, and treatment of infection and disease caused by ACE2-utilizing viruses such as SARS-CoV, SARS-CoV-2, and future emerging SARS-related viruses. Author SummaryThe recent emergence of the virus SARS-CoV-2 which has caused the COVID-19 pandemic has prompted scientists to intensively study how the virus enters human host cells. This work has revealed a key protein, ACE2, that acts as a receptor permitting the virus to infect cells. Much research has focused on how the virus physically interacts with ACE2, yet little is known on how ACE2 is turned on or off in human cells at the level of the DNA molecule. Understanding this level of regulation may offer additional ways to prevent or lower viral entry into human hosts. Here, we have examined the control of the ACE2 gene, the DNA sequence that instructs ACE2 protein receptor formation, and we have done so in the context of immune stimulation. We have indeed identified a number of DNA on/off switches for ACE2 that appear responsive to immuno-logical and oxidative stress. These switches may fine-tune how ACE2 is turned on or off before, during, and/or after infection by SARS-CoV-2 or other related coronaviruses. Our studies help pave the way for additional functional studies on these switches, and their potential therapeutic targeting in the future.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Richard, D., Muthuirulan, P., Aguiar, J., Doxey, A., Banerjee, A., Mossman, K., Hirota, J., Capellini, T. D.. 2021-06-07. Intronic regulation of SARS-CoV-2 receptor (ACE2) expression mediated by immune signaling and oxidative stress pathways. https://doi.org/10.1101/2021.06.07.447351

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

SARDINE unlocks EWAS summary statistics for large-scale epigenetic biomarker analysis

Epigenetic clocks are increasingly used to study biological aging, yet their interpretation and utility remain uncertain. Meanwhile, the growing wealth of epigenome-wide association study (EWAS) summary statistics remains largely untapped for evaluating these biomarkers. Here, we introduce SARDINE, a statistical framework that estimates epigenetic clock-phenotype associations using EWAS summary statistics, clock weights, and reference methylation covariance, without individual-level data. SARDINE accurately reproduced individual-level association estimates across 38 epigenetic biomarkers and four phenotypes in 15,677 individuals. Applying SARDINE to diverse EWAS datasets revealed substantial heterogeneity in biomarker associations with aging, diseases, and lifestyle factors. By transforming CpG-level EWAS results into interpretable biomarker-level associations, SARDINE unlocks a largely underutilized data resource and establishes a scalable paradigm for systematic biomarker evaluation and discovery in aging research.

genetics↗

Deep transcriptomic analysis of molecular mediators of complex traits in heterogeneous stock (HS) rats

Rats are a powerful model organism for genome-wide association studies (GWAS) of physiological and behavioral traits. Heterogeneous Stock (HS) rats, which were created by intercrossing 8 inbred rat strains, are heavily used for this purpose because of their genetic diversity and accumulated recombinations due to over 100 generations of outbreeding, which improves mapping precision. However, functional genomics resources for post-GWAS interpretation of genomic findings have been limited. We processed 3,160 RNA-seq samples, 1,932 of which were previously unpublished, from 13 tissues as part of RatGTEx. We used Pantry to quantify six modalities of transcriptome regulation (expression, isoform ratio, intron excision ratio, alternative transcription start site (TSS), alternative poly(A), and RNA stability) in each sample, which allowed us to identify 144,876 independent quantitative trait loci (QTL) for 22,599 genes. We performed a transcriptome-wide association study (TWAS) with 19,796 unique genes across tissues and transcriptional modalities and 308 traits collected from 14 studies using FUSION, which identified 7,473 significant gene-trait associations. This work presents a significant expansion of the RatGTEx resource in terms of samples, modes of transcriptome regulation, and genetic analyses. Our findings provide molecular evidence for the involvement of specific genes in mediating GWAS signals through a range of transcriptome regulatory mechanisms. These gene-to-phenotype relationships can be easily applied to genetic analyses in humans and other species. Further experiments can use this evidence to confirm these molecular mediators and target them for the treatment of behavioral and physiological traits and disorders. To facilitate use of these results, we have updated results on RatGTEx.org and provided a new interactive TWAS web portal (twas.ratgtex.org).

genetics↗

Genomic and Epigenomic Relationships Capture Different Patterns of Genotype-by-Environment Interaction in Grapevine Clones

Genotype-by-environment interaction (GEI) challenges selection in clonally propagated perennial crops by reducing the consistency of clone performance across years. In perennial crops, both genetic and epigenetic variation are likely to contribute to complex trait variation among clones. However, the extent to which each molecular source contribute to GEI remains poorly understood. Here, we used UMR-seq, an assay that recovers SNPs and unmethylated-region (UMR; DNA methylation) profiles from a single library, to generate paired genomic and epigenomic profiles for 206 Riesling (Vitis vinifera L.) clones phenotyped for yield and juice quality traits across nine growing seasons. We then partitioned the contribution of each omic layer to clonal variation and GEI, and examined the genomic distribution of their effects. In models without explicit GEI, environmental and residual effects were the largest source of phenotypic variance and the clonal main effect was modest. Genomic relationships accounted for most of the clonal variance in four of five traits, and adding an epigenomic relationship matrix (ERM) to the genomic relationship matrix (GRM) had little effect. In contrast, when GEI effects were modelled using factor-analytic covariance structures, the epigenomic layer captured a substantial proportion of GEI variance and exceeded the genomic contribution in four of five traits. The two omic layers differed not only in the amount of GEI captured, but also in the pattern of clone re-ranking across years. When back-solved effects were aggregated into genomic windows, the largest genomic and epigenomic effects tended to fall in different regions, with overlap no greater than expected by chance. Together, these results show that genomic and epigenomic relationships capture complementary components of year-to-year variation in clone performance, with the epigenomic contribution expressed primarily through GEI. These findings provide a basis for investigating the molecular drivers of GEI and for testing whether genomic and epigenomic information can inform selection for stable clonal performance across variable environments.

genetics↗