To the extent possible under law, AOP-Wiki has waived all copyright and related or neighboring rights to KER:2354

Relationship: 2354

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

Recruitment of inflammatory cells leads to Hyperinflammation

Upstream event
The causing Key Event (KE) in a Key Event Relationship (KER). More help
Downstream event
The responding Key Event (KE) in a Key Event Relationship (KER). 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

AOP Name Adjacency Weight of Evidence Quantitative Understanding Point of Contact Author Status OECD Status
Decreased fibrinolysis and activated bradykinin system leading to hyperinflammation adjacent Penny Nymark (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 Do not cite Under Development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 Laure-Alix Clerbaux (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 Do not cite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

Sex Applicability

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

Life Stage Applicability

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

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

The recruitment of proinflammatory cells occurs as a result of proinflammatory mediator signaling, recruiting the cells, such as monocytes which can differentiate into different macrophage types, to clear out invading toxic pathogens. However, when invading toxic pathogens are not properly cleared out and pro-inflammatory mediators are not controlled, the proinflammatory cells persist, causing a positive feedback loop leading to a dysregulated para-inflammation, which is responsible for chronic inflammation conditions (Medzhitov et al). This persistence causes over-activated proinflammatory macrophages, recruitment of neutrophils, and mass levels of proinflammatory cytokines (Medzhitov et al). Hyperinflammation properties include higher levels of inflammatory markers in blood (CRP, ferritin, and D- dimers), increased neutrophil to lymphocyte ratio, and increased proinflammatory cytokines. 

In COVID-19 patients, monocytes are derived into pro-inflammatory macrophages as a result of SARS-COV-2 infection (Merad et al).  Pro-inflammatory macrophages along with neutrophils and T-cells are recruited into the lung epithelium and exacerbate inflammation by establishing the proinflammatory feedback loop that persists and causes the hyperinflammatory state (Gustine et al).  Hyperinflammation in COVID-19 is also triggered by pyroptosis and tissue damage (reviewed in Tan et al. 2021 https://doi.org/10.3389/fimmu.2021.742941). SARS-COV-2 activates Gasdermin D (GSDMD), a key trigger of pyroptosis in pro-inflammatory macrophages. When pyroptosis causes cell death in these macrophages, it releases mass amounts of pro-inflammatory cytokines, ROS, and LDH, leading to hyperinflammation (Zhang et al). A number of so called alarmins have been associated with the evolution towards hyperinflammation. Alarmins are a family of immunomodulatory proteins that act as damage-associated molecular patterns (DAMPs) and recruit and activate various immune cells such as monocytes, macrophages, lymphoid cells and myeloid dendritic cells. Multiple proteins from this family, including especially IL33 and S100 family proteins (S100A4, S100A7, S100A9, S100A12, S100B, and S100P) have been identified to be associated with the later stages of inflammation culminating in hyperinflammation in the lungs (Desvaux et al. 2021 https://doi.org/10.1371/journal.pone.0254374). IL33 and the S100 family proteins can stimulate production of IL1B, IL6 and TNFA, some of the hallmark molecules associated with hyperinflammation (reviewed in Desvaux et al. 2021).

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

Evidence Supporting this KER

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

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
Modulating Factor (MF) MF Specification Effect(s) on the KER Reference(s)
       
       
SEX
  female sex (XX chromosomes)
Females produce higher amounts of the antiviral infection cytokine IFN- a than men [1].  Estrogens are critical regulators of gene expression and functions in innate immune cells, including monocytes, macrophages, and dendritic cells, as well as lymphocytes such as T-helper 1/2 (TH1/2) cells, regulatory T-cells (Tregs), and B cells. One of the major forms of estrogen, estradiol, has been shown to dampen the production of excessive innate inflammatory cytokines by monocytes and macrophages [2]. In the presence of progesterone, CD4+ T-helper cells skew from Th-1 to Th-2 in the production of anti-inflammatory cytokines, specifically IL-4 and IL-10 [3]. The cellular types involved in male and female immune responses to SARS-CoV-2 are distinct and immune response in females is enriched with activated T-cells [1]. In lactating women, higher SARS-CoV-2 reactive memory B-cells and antibody titers have been associated with the hormone prolactin [4]. Poor T-cell response to SARS-CoV-2 correlates with worse disease progression in female patients.

1.) DOI: 10.1038/s41586-020-2700-3

2.) DOI: 10.1038/s41577-020-0348-8

3.) DOI: 10.1016/S0140-6736(20)31561-0

4.) DOI: 10.1016/j.xcrm.2021.100468

male sex (XY chromosomes)

Males display a higher innate immune response to SARS-CoV-2 than females,which conditions their cytokine profile. Men have higher levels of the innate immune cytokines IL-8 and IL-18 in circulation  [1]. Moreover, elderly men in particular display autoantibodies against IFN-a more frequently [5]. The cellular types involved in male and female immune responses to SARS-CoV-2 are distinct. Men display higher circulating levels of non-classical monocytes [1]. Higher innate immune activation in men leads to higher plasma levels of the inflammatory cytokines IFN-a [6], IL-8 and IL-18 [1], driving hyperinflammation and more pronounced lymphopenia in males.

5.) DOI: 10.1126/science.abd4585

6.) DOI: 10.3389/fimmu.2021.739757

Age Young/old people During aging, a subclinical chronic inflammatory response develops leading to an immune senescent state, where pathogen protective immune responses are impaired, but the production of inflammatory cytokines, such as IL-6, is increased. This process is called inflammaging. The persistent IL-6 elevation can induce lung tissue inflammation and mortality. The rate of inflammaging is higher in men and accelerated inflammaging is believed to worsen COVID-19 outcomes [1]. The chronic inflammatory status is associated with a dramatic depletion of B lymphocyte-driven acquired immunity. Aging also attenuates the upregulation of co-stimulatory molecules critical for T-cell priming and reduces antiviral IFN production by alveolar macrophages and dendritic cells (DCs) in response to infection with the influenza virus [2].

1) 10.1016/j.cytogfr.2020.04.005

2) 10.1016/j.cger.2017.06.002

Vitamin D Vitamin D deficiency

Vitamin D deficiency was shown to promote intestinal mucosal barrier dysfunction with higher permeability in infection-induced or TNF-treated cells and in in vivo colitis models [1,2]. An association between increased markers of intestinal permeability and vitamin D deficiency has been observed in critically ill subjects from ICU [3].

[1] doi: 10.1093/infdis/jiu235

[2] doi: 10.1097/MIB.0000000000000526

[3] doi: 10.1136/jim-2019-001132

Genetic factors   The inflammatory response manifested by increased cytokine levels results in inhibition of heme oxygenase (HO-1), with a subsequent loss of cytoprotection. In the 50-non-coding regions of the HO-1 gene, there are two polymorphic sites, namely the (GT)n dinucleotide and T (-413) A sites, which regulate the transcriptional activity of HO-1. These polymorphisms have been shown to be associated with the occurrence and progression of numerous diseases, including COVID-19 [1]. The timing of the IFN response to SARS-CoV-2 infection can vary with viral load and genetic differences in host response. When the viral load is low, IFN responses are engaged and contribute to viral clearance, resulting in mild infection. When viral load is high and/or genetic factors slow antiviral responses, virus replication can delay the IFN response and cytokine storm can occur before adaptive responses clear the virus, resulting in severe disease including MIS-C [2].

[1] doi: 10.1016/j.freeradbiomed.2020.10.016

[2] doi: 10.1038/s41577-020-0367-5

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

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

References

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