To the extent possible under law, AOP-Wiki has waived all copyright and related or neighboring rights to KER:2311

Relationship: 2311


The title of the KER should clearly define the two KEs being considered and the sequential relationship between them (i.e., which is upstream and which is downstream). Consequently all KER titles take the form “upstream KE leads to downstream KE”.  More help

ACE2 binding to viral S-protein leads to ACE2 dysregulation

Upstream event
Upstream event in the Key Event Relationship. On the KER page, clicking on the Event name under Upstream Relationship will bring the user to that individual KE page. More help
Downstream event
Downstream event in the Key Event Relationship. On the KER page, clicking on the Event name under Upstream Relationship will bring the user to that individual KE page. More help

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

This table is automatically generated upon addition of a KER to an AOP. All of the AOPs that are linked to this KER will automatically be listed in this subsection. Clicking on the name of the AOP in the table will bring you to the individual page for that AOP. More help
AOP Name Adjacency Weight of Evidence Quantitative Understanding Point of Contact Author Status OECD Status
ACE2 dysregulation leading to microvascular disfunction adjacent Julija Filipovska (send email) Under development: Not open for comment. Do not cite

Taxonomic Applicability

Select one or more structured terms 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. Authors can indicate the relevant taxa for this KER in this subsection. The process is similar to what is described for KEs (see pages 30-31 and 37-38 of User Handbook) More help

Sex Applicability

Authors can indicate the relevant sex for this KER in this subsection. The process is similar to what is described for KEs (see pages 31-32 of the User Handbook). More help

Life Stage Applicability

Authors can indicate the relevant life stage for this KER in this subsection. The process is similar to what is described for KEs (see pages 31-32 of User Handbook). More help

Key Event Relationship Description

Provide a brief, descriptive summation of the KER. While the title itself is fairly descriptive, this section can provide details that aren’t inherent in the description of the KEs themselves (see page 39 of the User Handbook). This description section can be viewed as providing the increased specificity in the nature of upstream perturbation (KEupstream) that leads to a particular downstream perturbation (KEdownstream), while allowing the KE descriptions to remain generalised so they can be linked to different AOPs. The description is also intended to provide a concise overview for readers who may want a brief summation, without needing to read through the detailed support for the relationship (covered below). Careful attention should be taken to avoid reference to other KEs that are not part of this KER, other KERs or other AOPs. This will ensure that the KER is modular and can be used by other AOPs. More help

This KER summarises the evidence for dysregulation of ACE2 (KE 1854) as a result of binding of the viral spike (S) protein of SARS-COV and SARS-COV2 (KE1739). This is likely an important aspect of COVID19 pathogenesis as an initial step it is a potential target for intervention at (re)infection but also for treatment of the disease. In addition, it is important to summarise the exiting evidence for this KER as the majority of hypotheses on the pathogenesis of COVID19 and potential treatments consider S protein binding leads to down-regulation of ACE2.

Evidence Supporting this KER

Assembly and description of the scientific evidence supporting KERs in an AOP is an important step in the AOP development process that sets the stage for overall assessment of the AOP (see pages 49-56 of the User Handbook). To do this, biological plausibility, empirical support, and the current quantitative understanding of the KER are evaluated with regard to the predictive relationships/associations between defined pairs of KEs as a basis for considering WoE (page 55 of User Handbook). In addition, uncertainties and inconsistencies are considered. More help

The evidence include in this KER was identified in the period between October 2020 and March 2021 with a Pubmed search syntax……, and additional targeted searches for specific aspects to ACE2 regulation. The searches were aimed to identify the evidence for the effect of SARS-COV or SARS-COV2 on any aspect ofACE2 regulation i.e. effect on expression of ACE2 at mRNA or protein level, ACE2 shedding and enzymatic activity, all described in KE 1854.

The studies generally use SARS-COV or SARS-COV2 Virus like Particles (pseudovirus), recombinant or transiently expressed S protein from SARS-COV and SARS-COV2 (in Table 1 designated S protein (1) and S-protein (2), respectively) in various cell lines e.g. Vero E6 and Huh7-that express endogenous ACE2 or HEK293T transfected with ACE2 expressing vectors. Concurrent increase of the soluble form of ACE2 (sACE2) and/or enzymatic (peptidase) activity is monitored in some of the studies (see Table in empirical evidence).

The evidence mostly relates to evaluation of direct binding of S protein to ACE2, but some evidence is indirect and can potentially relate to dysregulation resulting from infection progression/viral replication and reinfection of new cells in the context of the parallel inflammatory processes [authors note: eventually this KER may be split to more, depending on the context of developing KEs and AOPs].

Overall, studies that focus on the expression level of the membrane bound form of ACE2 strongly suggest that interaction of, SARS-COV S protein with ACE2 result in the cleavage (shedding) of the N-terminal enzymatic domain of ACE2 and hence down-regulation of membrane full length ACE2 on the surface of the cells (Glowacka 2010, Haga 2008, Haga 2010, Kuba, 2005).

Evidence for ACE2 protein down-regulation with SARS-COV2 S-protein (2) appears more limited.  Patra et al., 2020 show endogenous ACE2 protein reduction in cell lysates treated by SARS-COV2 pseudovirus or recombinant S protein in vitro.

It should be noted that ACE2 shedding mediated by the SARS-COV S-proteins (1 and 2) is a mechanistically and consequentially different process from the endogenous TACE/ADAM17 mediated cleavage of ACE2 (Iwata et al., 2009, Heurich et all., 2014, Hoffmann et al., 2020, Senapati et al., 2021). However, it remains to be elucidated if the two types of shedding, reduction of membrane flACE2 and potential increase of circulating sACE2 are associated with significant physiological effect. Furthermore, it appears that binding of S-protein to ACE2 also affects the function of the endogenous ACE2 sheddase TACE/ADAM17 (Haga et al., 2008), and therefore potentially its endogenous function on ACE2 shedding or its role in inflammation as a Tumour necrosis factor-alpha converting enzyme (e.g. Reddy et al, 2000). [please include a good review if you have].

A recent preprint by Lei et al., 2020, using primary cell culture of human pulmonary vascular endothelial cells treated with recombinant S-protein (2) appears to also indicate down-regulation of the ACE2 cellular protein level mediated by the binding to the viral protein, but this study remains to be reviewed.

Reduction of ACE2 protein level has also been observed in vivo in mouse lung tissue following intraperitoneal treatment with SARS-COV S-protein (Kuba et al., 2005) and in the myocardial tissue of SARS-COV infected mouse and human patients following death and autopsy (Oudit et al., 2009).

Down-regulation of ACE2 mRNA expression has been predicted for SARS-COV2-S protein interaction, by a study based on Master Regulator Analysis (Guzzi, et al., 2020) of transcriptomics data from lung tissue of SARS/MERS patients compared to SARS-COV2 modelled interactome in a different study (Srinivasan 2020). [note to fellow reviewers: feel free to comment on any of the studies, but please comment on this one. I am also not sure about the implications of the modelled interactome in Srinivasan 2020 and experimentally informed one 10.3390/v12040360 where HEK923T cells were used to express the viral bite proteins.]

Cleavage of ACE2 (shedding) is critical for SARS-COV and SARS-COV2 cell entry mediated by the binding of the S protein (KE-XXX) and it is likely that ACE2 activity is down-regulated on the infected cell membrane by the shedding, or when endocytic pathway of entry is utilised by the virus and ACE2 is likely internalised together with the viral particles (Zhu et al., 2021). Internalisation and intracellular co-localisation of ACE2 and S-protein has been demonstrated with SARS-COV S-protein (Inoue et al., 2007) and with SARS-COV pseudovirus in vitro (Wang H. et al., 2008, Wnag S et al., 2008 [fellow authors: please include evidence for SAERS-COV2 S protein and ACE2 co-internalising, I could't find ]).

However, shedding does not appear to abrogate the enzymatic activity of the sACE2 as evident from the studies that monitor its activity in the cell supernatants after shedding mediated by the interaction with the S-protein (See Table). In addition, there is evidence that high affinity binding of S-protein from the SARS-COV2 can enhance ACE2 enzymatic activity against some model peptide substrates in vitro (Jinghua Lu 2020), indicating that S-mediated up-regulation of ACE2 activity in vivo with physiological substrates cannot be excluded. In this study ACE2 peptidase activity was increased ~3-10 fold against model peptide substrates, such as caspase-1 substrate and Bradykinin-analogue.

Interestingly, increased ACE2 activity has been reported in the plasma of SARS-COV2 positive patients median 36 days after positive PCR test, and remained increased up to median 114 days (last measured). The level of the increase of ACE2 activity in the plasma correlated with severity of the disease (although the number of severe patients was much lower compared to those with milder symptoms) (Patel et al., 2021). Information on medications and comorbid conditions were also collected and considered in this study, and effect of any treatments was excluded. Although this is study does not directly address binding of the S-protein to ACE2, it demonstrates that the level of sACE2 is dysregulated (potentially increased) overtime in the context of SARS-COV2 infection in vivo and that this dysregulation is correlated with the severity of disease, suggesting that ACE2 dysregulation may be one of the KE of COVID19 pathogenesis.

Studies focusing on mRNA measurement or analysis generally suggest up-regulation of ACE2 mRNA as a result of interaction with SARS-COV and SARS-COV2, but also MERS-COV that does not use ACE2 as a receptor.

However, considering the supporting evidence for ACE2 up-regulation by interferon and inflammation in humans (See KE 1854) it is possible that the ACE2 mRNA increase apparent in a number of transcriptomics studies (Lieberman 2020; Feng 2020; Garvin, 2020) and also quantitative RT-PCR (Zhuang et al., 2020) in BALF or Nasopahrungeal swabs from SARS-COV2 infected patients may reflect stimulation of ACE2 transcription by the interferon component of the inflammatory process after the initial infection at later stages of the COVID19 pathogenesis. Nevertheless, ACE2 mRNA up-regulation was also observed in vitro after infection of primary cultures of human bronchial epithelial cells with SARS-COV2 (Li et al., 2020,) as well as SARS-COV and MERS-COV (Smith, 2020; Zhuang et al., 2020).

Rockx et al., (2009) examined the time course of change of ACE2 mRNA levels in the lung tissue of mice exposed to strains of SARS-COV variants of different lethality.  They monitored mRNA levels by quantitative RT-PCR from infected and control mice (young and aged ) up to 72 hours and conclude that expression of ACE2 mRNA is down-regulated in lethal infection of aged compared to young mice. However, the time course of the fold change for ACE2 mRNA during individual viral strain infections reveals more complex dysregulation of the ACE2 mRNA synthesis. For example, in the mice infected with the least lethal strain Urbani, ACE2 mRNA shows increasing trend over time compared to the mock controls, in both young and aged mice. Furthermore, transcriptomic monitoring of mRNA levels in infected tissue in this study shows differential expression over time of a number of genes related to the inflammatory response, lymphocyte mediated immunity and apoptosis, with progressive differences in adult and young mice infected with SARS-COV (Rockx et all, 2009).

Overall the empitical evidence is high that ACE2 expression (both protein and mRNA) and activity is significantly dysregulated as a consequence of the interaction with SARS-COV S-proteins (1 and 2). In addition, evidence suggests that the S-protein mediated ACE2 dysregulation is a rather complex and would require careful consideration of the timing of the replication process together with other aspects of progression of the disease in order to be targeted with drug interventions effectively (e.g.  interaction with parallel KEs, KERs in other related AOPs, e.g. increased interferon synthesis, inflammation, cell transition differentiation, hypoxia, etc..).

Biological Plausibility
Define, in free text, the biological rationale for a connection between KEupstream and KEdownstream. What are the structural or functional relationships between the KEs? For example, there is a functional relationship between an enzyme’s activity and the product of a reaction it catalyses. Supporting references should be included. However, it is recognised that there may be cases where the biological relationship between two KEs is very well established, to the extent that it is widely accepted and consistently supported by so much literature that it is unnecessary and impractical to cite the relevant primary literature. Citation of review articles or other secondary sources, like text books, may be reasonable in such cases. The primary intent is to provide scientifically credible support for the structural and/or functional relationship between the pair of KEs if one is known. The description of biological plausibility 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 (see page 40 of the User Handbook for further information).   More help
Uncertainties and Inconsistencies
In addition to outlining the evidence supporting a particular linkage, it is also important to identify inconsistencies or uncertainties in the relationship. Additionally, while there are expected patterns of concordance that support a causal linkage between the KEs in the pair, it is also helpful to identify experimental details that may explain apparent deviations from the expected patterns of concordance. Identification of uncertainties and inconsistencies contribute to evaluation of the overall WoE supporting the AOPs that contain a given KER and to the identification of research gaps that warrant investigation (seep pages 41-42 of the User Handbook).Given that AOPs are intended to support regulatory applications, AOP developers should focus on those inconsistencies or gaps that would have a direct bearing or impact on the confidence in the KER and its use as a basis for inference or extrapolation in a regulatory setting. Uncertainties that may be of academic interest but would have little impact on regulatory application don’t need to be described. In general, this section details evidence that may raise questions regarding the overall validity and predictive utility of the KER (including consideration of both biological plausibility and empirical support). It also contributes along with several other elements to the overall evaluation of the WoE for the KER (see Section 4 of the User Handbook).  More help
Response-response Relationship
This subsection should be used to define sources of data that define the response-response relationships between the KEs. In particular, information regarding the general form of the relationship (e.g., linear, exponential, sigmoidal, threshold, etc.) should be captured if possible. If there are specific mathematical functions or computational models relevant to the KER in question that have been defined, those should also be cited and/or described where possible, along with information concerning the approximate range of certainty with which the state of the KEdownstream can be predicted based on the measured state of the KEupstream (i.e., can it be predicted within a factor of two, or within three orders of magnitude?). For example, a regression equation may reasonably describe the response-response relationship between the two KERs, but that relationship may have only been validated/tested in a single species under steady state exposure conditions. Those types of details would be useful to capture.  More help
This sub-section should be used to provide 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?). This can be useful information both in terms of modelling the KER, as well as for analyzing the critical or dominant paths through an AOP network (e.g., identification of an AO that could kill an organism in a matter of hours will generally be of higher priority than other potential AOs that take weeks or months to develop). Identification of time-scale can also aid the assessment of temporal concordance. For example, for a KER that operates on a time-scale of days, measurement of both KEs after just hours of exposure in a short-term experiment could lead to incorrect conclusions regarding dose-response or temporal concordance if the time-scale of the upstream to downstream transition was not considered. More help
Known modulating factors
This sub-section presents information regarding modulating factors/variables known to alter the shape of the response-response function that describes the quantitative relationship between the two KEs (for example, an iodine deficient diet causes a significant increase in the slope of the relationship; a particular genotype doubles the sensitivity of KEdownstream to changes in KEupstream). Information on these known modulating factors should be listed in this subsection, along with relevant information regarding the manner in which the modulating factor can be expected to alter the relationship (if known). Note, this section should focus on those modulating factors for which solid evidence supported by relevant data and literature is available. It should NOT list all possible/plausible modulating factors. In this regard, it is useful to bear in mind that many risk assessments conducted through conventional apical guideline testing-based approaches generally consider few if any modulating factors. More help
Known Feedforward/Feedback loops influencing this KER
This subsection should define whether there are known positive or negative feedback mechanisms involved and what is understood about their time-course and homeostatic limits? In some cases where feedback processes are measurable and causally linked to the outcome, they should be represented as KEs. However, in most cases these features are expected to predominantly influence the shape of the response-response, time-course, behaviours between selected KEs. For example, if a feedback loop acts as compensatory mechanism that aims to restore homeostasis following initial perturbation of a KE, the feedback loop will directly shape the response-response relationship between the KERs. Given interest in formally identifying these positive or negative feedback, it is recommended that a graphical annotation (page 44) indicating a positive or negative feedback loop is involved in a particular upstream to downstream KE transition (KER) be added to the graphical representation, and that details be provided in this subsection of the KER description (see pages 44-45 of the User Handbook).  More help

Domain of Applicability

As for the KEs, there is also a free-text section of the KER description that the developer can use to explain his/her rationale for the structured terms selected with regard to taxonomic, life stage, or sex applicability, or provide a more generalizable or nuanced description of the applicability domain than may be feasible using standardized terms. More help


List of the literature that was cited for this KER description using the appropriate format. Ideally, the list of references should conform, to the extent possible, with the OECD Style Guide (OECD, 2015). More help