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Relationship: 2496

Title

A descriptive phrase which clearly defines the two KEs being considered and the sequential relationship between them (i.e., which is upstream, and which is downstream). More help

SARS-CoV-2 cell entry leads to IFN-I response, antagonized

Upstream event

The causing Key Event (KE) in a Key Event Relationship (KER). More help

SARS-CoV-2 cell entry

Downstream event

The responding Key Event (KE) in a Key Event Relationship (KER). More help

IFN-I response, antagonized

Key Event Relationship Overview

The utility of AOPs for regulatory application is defined, to a large extent, by the confidence and precision with which they facilitate extrapolation of data measured at low levels of biological organisation to predicted outcomes at higher levels of organisation and the extent to which they can link biological effect measurements to their specific causes.Within the AOP framework, the predictive relationships that facilitate extrapolation are represented by the KERs. Consequently, the overall WoE for an AOP is a reflection in part, of the level of confidence in the underlying series of KERs it encompasses. Therefore, describing the KERs in an AOP involves assembling and organising the types of information and evidence that defines the scientific basis for inferring the probable change in, or state of, a downstream KE from the known or measured state of an upstream KE. More help

AOPs Referencing Relationship

AOP Name	Adjacency	Weight of Evidence	Quantitative Understanding	Point of Contact	Author Status	OECD Status
Binding of SARS-CoV-2 to ACE2 leads to viral infection proliferation	adjacent	High	Not Specified	Arthur Author (send email)	Under development: Not open for comment. Do not cite	Under Development
Binding of SARS-CoV-2 to ACE2 in enterocytes leads to intestinal barrier disruption	adjacent	Low		Cataia Ives (send email)	Under development: Not open for comment. Do not cite	Under Development
Binding of SARS-CoV-2 to ACE2 receptor leading to acute respiratory distress associated mortality	adjacent	High		Evgeniia Kazymova (send email)	Open for comment. Do not cite	Under Development
Binding to ACE2 leading to thrombosis and disseminated intravascular coagulation	adjacent	Moderate	Moderate	Arthur Author (send email)	Under development: Not open for comment. Do not cite	Under Development
Binding of SARS-CoV-2 to ACE2 leads to hyperinflammation (via cell death)	adjacent	High	Moderate	Allie Always (send email)	Under development: Not open for comment. Do not cite

Taxonomic Applicability

Latin or common names of a species or broader taxonomic grouping (e.g., class, order, family) that help to define the biological applicability domain of the KER.In general, this will be dictated by the more restrictive of the two KEs being linked together by the KER. More help

Term	Scientific Term	Evidence	Link
mammals	mammals	High	NCBI

Sex Applicability

An indication of the the relevant sex for this KER. More help

Sex	Evidence
Unspecific	High

Life Stage Applicability

An indication of the the relevant life stage(s) for this KER. More help

Term	Evidence
All life stages	High

Key Event Relationship Description

Provides a concise overview of the information given below as well as addressing details that aren’t inherent in the description of the KEs themselves. More help

Upon entry of a virus into the host cell (KE1738), the virus is unpackaged from the structural nucleocapsid (N), envelope (E), and membrane (M) proteins. The viral RNA is detected by Pattern Recognition Receptor (PRR) proteins including RIG-I and MDA5 but the M proteins can interact with these PRRs directly, and block this initial host reaction (Fu et al., 2021). The viral genomic RNA can then be translated directly at the host ribosomes. The viral proteins are processed through cleavage by viral protease enzymes. This releases a repertoire of non-structural proteins (NSPs) and accessory open reading frame (ORF) proteins that has evolved, for example in the SARS-CoV-2 virus, to bind and block the proteins in the interferon I (IFN-I) antiviral cascade (KE1901). The normal function of the host’s IFN-I response to other viruses is the expression of IFN-I which in turn stimulates the expression of many interferon-stimulated gene (ISG) proteins with antiviral functions. The SARS-CoV-2 antagonism of the IFN-I pathway delays or curtails the expression of IFN-I and ISG proteins.

Evidence Collection Strategy

Include a description of the approach for identification and assembly of the evidence base for the KER. For evidence identification, include, for example, a description of the sources and dates of information consulted including expert knowledge, databases searched and associated search terms/strings. Include also a description of study screening criteria and methodology, study quality assessment considerations, the data extraction strategy and links to any repositories/databases of relevant references.Tabular summaries and links to relevant supporting documentation are encouraged, wherever possible. More help

The COVID-19 pandemic began in early 2020, after the cause of a respiratory illness first seen in Wuhan, China in December 2019 was identified as a severe acute respiratory syndrome-related coronavirus and the viral genome was sequenced in January 2020 (Zhu et al., February 2020; Gorbalenya et al., March 2020). Early literature characterizing the transcripts and proteins from SARS-CoV-2 started to become available soon after (e.g., Dongwan Kim et al., April 2020). Of two other papers published in April 2020, one indicated the angiotensin converting enzyme 2 (ACE2) cell entry receptor and the TMPRSS2 enzyme priming mechanism for the virus to enter the host cell (Hoffmann et al.), and another provided a map of protein-protein interactions of the host with the translated viral genome proteins after entry (Gordon et al.). These sources established a course that led to literature indicating the specific effects of protein-protein interactions between viral and host proteins. Other sources made comparisons with data from previous studies of the SARS and MERS coronaviruses (Channappanavar et al., 2019). Many of the host proteins engaged by the viral non-structural proteins (NSPs) and accessory open reading frame (ORF) proteins were part of the IFN-I antiviral response pathway (Xia et al., 2020; Xia and Shi, 2020). Therefore, IFN-I response antagonism was considered a key event and literature search strings targeted the specific NSPs and ORFs with IFN-I pathway proteins (e.g., SARS-CoV-2 NSP3 + interferon regulatory factor 3 [IRF3]). Literature searches utilized the large established digital libraries including NCBI, Google and Google Scholar. Database resources for 3D molecular structures and viral-host protein binding information include Aquaria-COVID Structural Models of COVID-19 Proteins at https://aquaria.ws/covid#Map, the Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank (PDB) at https://www.rcsb.org/ and Alphafold Protein Structure Database at https://alphafold.ebi.ac.uk/. General IFN and innate immune antiviral response literature was reviewed, and primary literature cited in review articles was also investigated.

Evidence Supporting this KER

Addresses the scientific evidence supporting KERs in an AOP setting the stage for overall assessment of the AOP. More help

Empirical evidence supporting this relationship is described below.

Biological Plausibility

Addresses the biological rationale for a connection between KEupstream and KEdownstream. This field can also incorporate additional mechanistic details that help inform the relationship between KEs, this is useful when it is not practical/pragmatic to represent these details as separate KEs due to the difficulty or relative infrequency with which it is likely to be measured. More help

This relationship is concerned with how entry of the virus into the host cell and subsequent release and transcription of viral proteins affects the downstream innate immune response. In particular, literature suggests the main pathway antagonized is the expression of type I interferons (IFN-I), consisting primarily of IFNα and IFNβ, and IFN-I stimulated genes (ISGs) (Banerjee et al., 2020; Blanco-Melo et al., 2020; Cheemarla et al., 2021; Xia et al., 2020; Sharif-Askari et al., 2022). Although there are few studies with evidence for cell entry leading directly to reduced IFN expression (Xia et al., 2020; Hatton et al. 2021), several studies demonstrate individual viral protein interactions with and blocking of host proteins in the IFN-I pathway or ISG proteins (Schubert et al. 2020; Thoms et al. 2020; Rui et al. 2021; Shin et al. 2020; Liu et al. 2021; Mostaqil et al., 2021; Xia et al. 2020; Quarleri and Delpino, 2021; Xia and Shi, 2020; Miorin et al. 2020; Kato et al. 2020; Fu et al. 2020; Chen et al. 2020; Han et al. 2020; Jiang et al. 2020; Wu et al. 2021; Gordon et al 2020; see below and also key event 1901). These studies provide the biological rationale that SARS-CoV-2 entry into the host cell causes interactions between viral proteins and known protein components of the host IFN-I antiviral response, resulting in inhibition of IFN-I and ISG expression.

Empirical Evidence

Provides specific (citable) evidence that supports the idea of a change in the upstream KE (KEupstream) leading to, or being associated with, a subsequent change in the downstream KE (KEdownstream), assuming the perturbation of KEupstream is sufficient. More help

Empirical evidence in support of temporal concordance comes from patient reports, showing that interferon expression is delayed by SARS-CoV-2 compared to other viruses like influenza, which is also described as an untuned or imbalanced response between interferons being initially low in moderate to severe cases (Banco-Melo et al. 2020; Galani et al., 2021; Hadjadj et al., 2020; Hatton et al., 2021; Rouchka et al., 2021). This indicates that SARS-CoV-2 stressors are suppressing the interferon response and highlights an important point regarding the difference between SARS-CoV-2 and other viruses in the stressors produced upon viral entry. Other viruses, as well as non-viral compounds used in research (e.g., polyinosinic:polycytidylic acid or poly[I:C]) enter the cell and stimulate the normal functional operation of the immune response, while SARS-CoV-2 blocks the response at multiple points, acting as a true prototypical stressor.

Hatton et al. (2021) used human nasal epithelium differentiated at the air-liquid interface (ALI) cultures (organoids) with several cell types. Secretory cells were the cell type with the highest expression of viral transcripts, with ciliated and deuterosomal cells also showing expression. The SARS-CoV-2-infected secretory and ciliated cells also had many downregulated ISGs. Compared to SARS-CoV-2, influenza A virus induced significantly higher levels of IFN-I (IFNβ) and IFN-III (IFNλ1) at 6 and 24 hours post infection, as well as ISGs Ubiquitin specific peptidase 18 (USP18), radical s-adenosyl methionine domain containing 2 (RSAD2), and ubiquitin-like protein ISG15 at 24 hours post infection (Hatton et al., 2021).

Individual stressors from the virus were investigated by Xia et al. (2020) using an IFN-β promoter luciferase assay. HEK293T cells were co-transfected with luciferase reporter plasmids, the specific viral protein expressing plasmid, and stimulator plasmid RIG-I (2CARD). Of the viral proteins tested (NSPs 1, 2, 4-16, S, N, E, M, and ORFs 3a, 3b, 6, 7a, 7b, 8, and 10), four proteins (NSPs 1, 6, and 13 and ORF6) significantly reduced INF-β induction compared to the control (empty vector). A similarly conducted ISRE-promoter luciferase assay showed significant inhibition of the IFN-I signaling pathway (normally resulting in induction of ISGs) by NSPs 1, 6, 7, 13 and 14, ORFs 3a, 6, 7a and 7b, and M protein (Xia et al., 2020). See Xia et al. (2020) and Xia and Shi (2020) for schematics depicting the actions of the SARS-CoV-2 proteins on the protein components of the IFN-I antiviral response pathway.

SARS-CoV-2 stressor proteins and the IFN-I pathway responses were investigated individually in the following studies:

Viral protein stressor	Host protein	Crystal Structure PDB	KER findings: Binding, Stressor/IFN-I or ISG expression
N (nucleocapsid)	RIG-I: Retinoic acid-inducible gene I	Not available (NA)	Significant reductions in IFNβ mRNA induction were seen when SARS-CoV-2 N protein was co-transfected into A549 cells with RIG-I, MAVS, or TBK1, and similar transfections resulted in IFNβ promoter activity reduction in poly(I:C)-stimulated HEK293T cells (Chen et al., 2020).
NSP3 Papain-like protease (Plpro)	MDA5: Melanoma differentiation-associated gene 5	NA	Sun et al. (2022) determined that SARS-CoV-2 and avian coronavirus infectious bronchitis virus (IBV) NSP3 PLpro N-terminal domain directly interacts with MDA5 to inhibit IFNβ expression when co-transfected in HEK293T cells.
M (membrane)	MAVS: Mitochondiral antiviral signaling protein	NA	Fu et al. (2020) found M interaction with MAVS (as determined by coimmunoprecipitation and in vitro pull-down assay) interferes with recruitment of downstream pathway proteins TRAF, TBK1, and IRF3, inhibiting IFNβ1 promoter, IFN-stimulated response element (ISRE), and NFκB promoter activity in a dose-dependent manner. The M protein inhibited the transcription of ISGs (ISG56, CXCL10, and TNF) based on mRNA levels, and inhibited IFNβ and TNFα secretion based on measures of these proteins in HEK293 cell culture.
NSP3 Papain-like protease (Plpro)	ISG15: Ubiquitin-like interferon stimulated gene 15	6YVA	Shin et al. (2020) generated a crystal structure and found that SARS-CoV-2 Plpro preferentially cleaves ISG15. ISG15 functions in antiviral immunity to directly inhibit viral replication (Perng and Lenschow, 2018).
ORF9b	TOMM70: Translocase of outer mitochondrial membrane	7KDT	Gordon et al. (2020) showed interaction between TOMM70 and ORF9b via affinity purification-mass spectrometry (AP-MS). TOMM70-ORF9b interaction is supported by several studies (Gao et al., 2021; Brandherm et al., 2021; Ayinde et al., 2022). Jiang et al. (2020) used a dual luciferase reporter assay to show human IFN-β promoter activity was significantly reduced in the presence SARS-CoV-2 Orf9b compared to controls.
ORF6	Nup98-RAE1: Nuclear pore complex 98-ribonucleic acid export 1	7VPG, 7VPH	Gordon et al. (2020) showed interaction between ORF6 and the host Nup98-RAE1 protein pair via AP-MS. The interaction was confirmed by Miorin et al., 2020 and Li et al., 2021 (see crystal structures). Miorin et al. (2020) also demonstrate that upon treatment with recombinant IFN-I in HEK293T cells, Nup98 binding to SARS-CoV-2 Orf6 blocks translocation of STAT1 into the nucleus, resulting in suppression of ISRE-dependent gene expression.
ORF6	KPNA2: Karyopherin subunit alpha 1	NA	Using co-immunoprecipitation, Xia et al. (2020) showed that ORF6 selectively bound with KPNA2. Expression of ORF6 blocked nuclear translocation of IRF3, suggesting that ORF6 inhibited IFN-β production by binding to KPNA2 to block IRF3 nuclear translocation.
N (nucleocapsid)	G3BP1/2: GTPase-activating protein SH3 domain–binding protein	7SUO	Biswal et al. (2022) solved the X-ray crystal structure of the G3BP1 N-terminal nuclear transport factor 2-like domain bound to the first intrinsically disordered region of SARS-CoV-2 N protein.
ORF9b	NEMO: Nuclear factor kappa-B (NF-κB) essential modulator	NA	The interaction of the N-terminus of ORF9b with NEMO upon viral infection interrupts its K63-linked polyubiquitination, thereby inhibiting viral-RNA-induced IFNβ1 activation in HEK293T cells in an ORF9b-dose-dependent manner (Wu et al., 2021)
NSP5 (3CLpro)	NEMO	7T2U	Hameedi et al. (2022) solved the X-ray crystal structure of 3CLpro bound to NEMO and characterized 3CLpro cleavage of NEMO.
NSP1	POLA1: DNA polymerase alpha 1, catalytic subunit 40S ribosomal subunit	7OPL 6ZOJ, 6ZOK, 6ZOL	Kilkenny et al., 2021 demonstrate that components of the host DNA polymerase α (Pol α)–primase complex or primosome directly bind with SARS-CoV-2 NSP1. They also provide a cryo-electron microscopy structure of NSP1 bound to the primosome. Schubert et al. (2020) provide cryo-EM structures of NSP1 bound to the 40S ribosome subunit, inhibiting translation of host proteins.
NSP6, NSP13	TBK1: TANK-binding kinase 1	NA	Sui et al. (2022) show that NSP13 recruits TBK1 to an aggregation of ubiquitinated proteins (p62) for autophagic degradation, resulting in inhibition of IFNβ production, and that NSP13 impaired IRF3 luciferase reporter activity induced by TBK1 in a dose-dependent manner. Xia et al. (2020) co-transfected HEK293T cells with plasmids containing TBK1 and either nsp6 or nsp13. Only NSP13 inhibited TBK1 phosphorylation, and did so in a dose-dependent manner, but both NSP6 and NSP13 suppressed IRF3 phosphorylation. Both NSP6 and NSP13 bind TBK1, as shown by co-immunoprecipitation. NSP6 binds to TBK1 without affecting TBK1 phosphorylation but this decreases IRF3 phosphorylation, while NSP13/TBK1 binding inhibits TBK1 phosphorylation. In both cases, IFN-β production is reduced (Xia et al., 2020).
NSP5 (3CLpro), ORF3a	STING: Stimulator of interferon genes	NA	Rui et al. (2021) SARS-CoV-2 ORF3a and 3CLpro inhibited IFNβ promoter activity through cyclic GMP-AMP synthase (cGAS)-STING pathways, specifically through interaction with STING, as indicated by co-immunoprecipitation. 3CLpro also bound to STING and specifically inhibited K63-ubiquitin-mediated modification of STING, which is required for signaling and downstream expression of IFN-I.
NSP3 Papain-like protease (Plpro)	IRF3: Interferon regulatory factor 3	NA	Mostaquil et al. (2020) showed with a fluorescent-based cleavage assay that NSP3 (Plpro) cleaves IRF-3, and thereby reduces IRF-3 available for induction of IFN-I expression.
N (nucleocapsid)	STAT1/STAT2: Signal transducer and activator of transcription	NA	Mu et al. (2020) used Sendai virus (SeV)-induced ISRE-promoter activation via the luciferase reporter assay to determine that SARS-CoV-2 N protein can inhibit the phosphorylation of STAT1 and STAT2 resulting in decrease in ISG production. They also showed through co-immunoprecipitation that N interacts with both STAT1 and STAT2, and that N inhibits STAT1/2 phosphorylation by blocking interactions with kinases including JAK1.

Uncertainties and Inconsistencies

Addresses inconsistencies or uncertainties in the relationship including the identification of experimental details that may explain apparent deviations from the expected patterns of concordance. More help

There are uncertainties based on differing disease outcomes, especially associated with timing of IFN increase or suppression under different cell culture circumstances and in different people infected with SARS-CoV-2. Effectiveness of IFN treatment is still uncertain due to some studies evaluating IFN along with other drugs (Sodeifian et al., 2021).

Interferon-induced transmembrane proteins (IFITMs 1, 2 and 3) are ISGs that have been implicated in SARS-CoV-2 entry as well as antiviral activity (Prelli Bozzo et al., 2021), in addition to the fact that the SARS-CoV-2 entry receptor ACE2 is an IFN-I stimulated gene (Ziegler et al., 2020). These are some of the paradoxes that confound transcriptomic studies that determine up- or downregulation of IFNs and ISGs in response to infection, and responses are highly dependent on the time points sampled. Efforts to address uncertainties around when and under what circumstances IFNs and ISGs either promote or supress the virus are ongoing.

Known modulating factors

This table captures specific information on the MF, its properties, how it affects the KER and respective references.1.) What is the modulating factor? Name the factor for which solid evidence exists that it influences this KER. Examples: age, sex, genotype, diet 2.) Details of this modulating factor. Specify which features of this MF are relevant for this KER. Examples: a specific age range or a specific biological age (defined by...); a specific gene mutation or variant, a specific nutrient (deficit or surplus); a sex-specific homone; a certain threshold value (e.g. serum levels of a chemical above...) 3.) Description of how this modulating factor affects this KER. Describe the provable modification of the KER (also quantitatively, if known). Examples: increase or decrease of the magnitude of effect (by a factor of...); change of the time-course of the effect (onset delay by...); alteration of the probability of the effect; increase or decrease of the sensitivity of the downstream effect (by a factor of...) 4.) Provision of supporting scientific evidence for an effect of this MF on this KER. Give a list of references. More help

Genetic mutations. Autoimmunity to IFNs has been found in some COVID-19 patients. These individuals produce autoantibodies that attack IFN (Bastard et al., 2021 and 2022), which may be associated with human leukocyte antigen (HLA) gene mutations (Ku et al., 2016; Chi et al., 2013). Zhang et al., 2020 note inborn errors (genetic mutations) in IFN-I immunity that result in severe COVID-19, but some are also genes for proteins involved in the initial response (TBK1, IRF3, NEMO, IFNAR1, IFNAR2, STAT1, and STAT2). Zhang et al. (2022) also found similar mutations (STAT2 and IFNAR1) in children with COVID-19 pneumonia.

Pollutant exposures. Most studies have been conducted with the endpoints to determine whether prior or concurrent exposure to chemical or air particulate pollutants exacerbates COVID-19 symptoms resulting in more severe disease or higher mortality rates. This would point to effects downstream of viral replication usually relating to antibody suppression, inflammation and organ/tissue damage. Fewer studies can be found that study pollutant effects on susceptibility to infection, which are relevant to this KER, specifically cell entry or interferon response antagonism.

Marques et al., (2022) reviews associations between COVID-19 and outdoor air pollutants including PM_2.5, PM₁₀, O₃, NO₂, SO₂ and CO, reporting that environmental air pollution increases both disease incidence and severity. Physiological mechanism is not investigated for most studies. One relevant study estimated significant odds ratios for increased risk of severe COVID-19 and gene transcriptional analysis showing downregulation of genes associated with the IFN-I pathway in patients with high short-term NO₂ exposure (Feng et al., 2023).

Per- and polyfluoroalkyl substances (PFAS) are a large group of contaminants of current concern, due to their potential for toxicity, ubiquitous presence in the environment and consumer products, as well as their resistance to degradation. Although most community exposure to PFAS is through diet and drinking water, airborne and dermal exposures may also occur, especially in the workplace (CDC/NIOSH 2022). Statistical links between high measured serum or urine concentrations of specific PFAS compounds or mixtures and higher rates of COVID-19-positive cases have been found. One study in Sweden calculated a sex- and age-Standardized Incidence Ratio (SIR) for the town of Ronneby that had highly PFAS-contaminated drinking water compared to a demographically matched town with background PFAS levels (Nielsen et al. 2021). Serum PFAS concentrations were previously measured in 2014-15 for 3507 participants (Xu et al. 2021), after the Ronneby drinking water contamination issue was identified in 2013. Ronneby residents had higher infection risk, with a SIR of 1.19 [95% CI: 1.12-1.27]. Ji et al. (2021) measured urine and serum in a smaller study in China with 160 subjects. They reported statistically significant odds ratios for infection of 1.94 [95% CI: 1.39–2.96] for perfluorooctane sulfonate (PFOS), 2.73 [1.71–4.55] for perfluorooctanoic acid (PFOA), and 2.82 [1.97–3.51] for Σ (12) PFASs, after controlling for age, sex, body mass index (BMI), comorbidities, and urine albumin-to-creatinine ratio (UACR). These odds of infection were clearly higher even though the PFAS-exposed subjects in China had serum concentrations lower than in the Ronneby study participants. Additionally, the risk of infection was similar for residents in a significantly more contaminated section of Ronneby compared with a less contaminated section, so there was no dose-response relationship (Nielsen et al. 2021). However, these associations warrant more study to determine causality. Ji et al. (2021) also found elevated PFAS to be associated with altered mitochondrial metabolism. A potential consideration is that inhibition of mitochondrial oxidative phosphorylation impairs MAVS-mediated induction of IFNs, indicating the coordination between antiviral response and mitochondrial metabolism (Yoshizumi et al., 2017). Another study proposes modulation of ACE2 and TMPRSS2 expression in the lungs of PFAS-exposed mice may play a role in PFAS-associated immune suppression (Yang et al. 2022). Houck et al., (2022) report testing 147 PFAS substances in screening platforms including the BioMAP® Diversity PLUS panel, which is used to model complex tissue adverse effects of pharmaceuticals and environmental chemicals. Toxicity Signatures within the BioMAP profile indicated the Skin Rash (MEK-Associated) Signature for PFOA, with IFNα/β as one of the target mechanisms. While not specific to COVID-19, one study found that exposure to aryl hydrocarbons and dioxins may block IFN production (Franchini and Lawrence, 2018).

Quantitative Understanding of the Linkage

Captures information that helps to define how much change in the upstream KE, and/or for how long, is needed to elicit a detectable and defined change in the downstream KE. More help

The current quantitative understanding of this relationship is described below.

Response-response Relationship

Provides sources of data that define the response-response relationships between the KEs. More help

A specific titer of virus can be used for infection, but as shown by Hatton et al. (2021), different cell types may express different levels of the actual stressors (viral protein transcripts). Because there are many stressors from each viral particle, which might be differentially expressed and also differentially inhibit each of their targets, a consistent whole viral entry dose leading to IFN-I or ISG response is difficult to measure. However, Chen et al. (2020), Xia et al. (2020), Fu et al. (2021), Wu et al. (2021) and Sui et al., (2022) all showed that individual protein stressor components of SARS-CoV-2 reduced IFN-I expression in a dose-dependent manner.

Time-scale

Information regarding the approximate time-scale of the changes in KEdownstream relative to changes in KEupstream (i.e., do effects on KEdownstream lag those on KEupstream by seconds, minutes, hours, or days?). More help

In humans, the viral entry MIE and early KEs coincide with the time from exposure to symptoms, within which are the latent period, or time from exposure to infectiousness, and the serial interval, or the time interval between the onset of symptoms in the primary (infector) and secondary case (infectee). Viral entry leading to antagonism of the IFN response occurs during the latent period of the disease prior to symptom onset. Latent period calculation is based on serial interval and median pre-symptomatic infectious period: Serial interval 5.2 days (Rai et al. 2021) – 2.5 days pre-symptom infectious period (Byrne et al. 2020) equals approximately 2.7 days. The latent period was longer in asymptomatic cases (4-9 days).

Known Feedforward/Feedback loops influencing this KER

Define whether there are known positive or negative feedback mechanisms involved and what is understood about their time-course and homeostatic limits. More help

SARS-CoV-2 uses the host ACE2-receptor for entry, upon which the host IFN response could upregulate ACE2 to enhance infection (Ziegler et al., 2020), a positive feedback loop for viral entry, while the IFN response also induces antiviral protein expression to help restore homeostasis as a positive feedback loop to KE 1901.

Domain of Applicability

A free-text section of the KER description that the developers can use to explain their rationale for the taxonomic, life stage, or sex applicability structured terms. More help

Sex and age applicability

It has been shown that in human populations males are more likely to suffer severe infections and deaths due to COVID-19 than females. However, in the viral entry and infection phase, one study found that women of working age had higher infection rates than men, but the suggested cause was higher contact rates among women (Doerre and Doblhammer, 2022). Contact rate increase is an important transmission factor but would not constitute a gender-based biological difference in viral entry or IFN-I pathway antagonism. A biological basis for females having higher levels of Type I IFN has been proposed concerning Toll-like receptor (TLR) 7. TLR7 is expressed in plasmacytoid dendritic cells (pDCs), an immune cell type that on infection with SARS-CoV-2 migrates from peripheral blood into the respiratory tract epithelium. TLR7 stimulates higher IFN-I production in pDCs in women than in men (Van der Sluis et al. 2022). It is proposed that this is due to the TLR7 gene being on the X chromosome, and that X inactivation in males is incomplete regarding the TLR7 gene, creating a double gene-dose effect in females (Spiering and de Vries, 2021). In a mouse SARS-CoV model, XY males had more adverse outcomes than XX females and XXY males (Gadi et al. 2020). Additionally, loss-of-function TLR7 mutations have been identified that are associated with increased COVID-19 severity (Szeto et al. 2021). However, these results focus on disease outcome as the endpoint, where factors beyond the initial antiviral response could be involved. Also note that the nasal and upper respiratory tract (URT) epithelial cells express ACE2 receptors for SARS-CoV-2 entry while the pDCs do not, relying on viral endocytosis (Van der Sluis et al. 2022). There is not a clear picture in the literature of the timing of pDC arrival in the epithelium after exposure, and the role of TLR7 in sex differences is currently hypothetical (Spiering and de Vries, 2021).

Taxonomic applicability

Generally, most mammals are likely susceptible to the SARS-CoV-2 virus based on reports of naturally and experimentally infected animals (See AO 1939). No infections have been reported in other classes of vertebrates. Other than bioinformatic studies on the ACE2 sequence across vertebrates however, there have been few studies on the mechanisms of susceptibility to infection of non-human hosts. Three studies were found on protein targets in the IFN-I innate immune response pathway that included other vertebrates. Rui et al. (2021) showed that SARS-CoV-2 3CLpro and ORF3a inhibit vertebrate (human, mouse, and chicken) STING ability to induce IFNβ promoter activity in a dose-dependent manner in HEK293T cells transfected with IFNβ-luciferase reporter plasmid vectors, together with tagged STING and cGAS vectors and increasing amounts of the SARS-CoV-2 3CLpro or ORF3a expression vectors. This study shows that the vulnerability of the host IFN-I pathway protein components to inhibition by SARS-CoV-2 protein stressors is not limited to humans, however Rui et al. (2021) did not determine the specific amino acids involved in the STING-ORF3a or STING-3CLpro interactions. Mostaquil et al. (2020) studied the cleavage site of IRF3 by PLpro (SARS-CoV-2 NSP3) and compared sequences across mammals. They determined that the IRF3 cleavage site in mammalian species in the taxonomic orders of primates, carnivora, artiodactyla, chiroptera (bats) and a few other mammals was conserved and would generally be susceptible to cleavage, and therefore IFN-I antagonism, but rodentia IRF3 would likely not be susceptible. Hameedi et al. (2022) compared molecular dynamic simulations of 3CLpro cleavage of NEMO in humans and mice showing a decrease in the average number of contacts between mNEMO and 3CLpro compared to hNEMO. Also, hNEMO may be more strongly bound to the catalytic site, and the mNEMO/3CLpro interaction appears more prone to destabilization (Hameedi et al., 2022).

References

List of the literature that was cited for this KER description. More help

Ayinde et al. 2022. Binding of SARS-CoV-2 protein ORF9b to mitochondrial translocase TOM70 prevents its interaction with chaperone HSP90. Biochimie 200, 99-106. https://doi.org/10.1016/j.biochi.2022.05.016

Banerjee et al., 2020 SARS-CoV-2 disrupts splicing, translation, and protein trafficking to supress host defenses. Cell 183, 1325–1339. https://doi.org/10.1016/j.cell.2020.10.004

Biswal et al. 2022. SARS-CoV-2 Nucleocapsid protein targets a conserved surface groove of the NTF2-like domain of G3BP1. J Mol Biol 434, 9, 167516. https://doi.org/10.1016/j.jmb.2022.167516

Blanco-Melo et al., 2020. Imbalanced host response to SARS-CoV-2 drives development of COVID-19. Cell 181, 1036–1045 https://doi.org/10.1016/j.cell.2020.04.026

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