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Event: 1868

Key Event Title

The KE title should describe a discrete biological change that can be measured. It should generally define the biological object or process being measured and whether it is increased, decreased, or otherwise definably altered relative to a control state. For example “enzyme activity, decreased”, “hormone concentration, increased”, or “growth rate, decreased”, where the specific enzyme or hormone being measured is defined. More help


Short name
The KE short name should be a reasonable abbreviation of the KE title and is used in labelling this object throughout the AOP-Wiki. The short name should be less than 80 characters in length. More help

Biological Context

Structured terms, selected from a drop-down menu, are used to identify the level of biological organization for each KE. Note, KEs should be defined within a particular level of biological organization. Only KERs should be used to transition from one level of organization to another. Selection of the level of biological organization defines which structured terms will be available to select when defining the Event Components (below). More help
Level of Biological Organization

Organ term

Further information on Event Components and Biological Context may be viewed on the attached pdf.The biological context describes the location/biological environment in which the event takes place.  For molecular/cellular events this would include the cellular context (if known), organ context, and species/life stage/sex for which the event is relevant. For tissue/organ events cellular context is not applicable.  For individual/population events, the organ context is not applicable. More help

Key Event Components

Further information on Event Components and Biological Context may be viewed on the attached pdf.Because one of the aims of the AOP-KB is to facilitate de facto construction of AOP networks through the use of shared KE and KER elements, authors are also asked to define their KEs using a set of structured ontology terms (Event Components). In the absence of structured terms, the same KE can readily be defined using a number of synonymous titles (read by a computer as character strings). In order to make these synonymous KEs more machine-readable, KEs should also be defined by one or more “event components” consisting of a biological process, object, and action with each term originating from one of 22 biological ontologies (Ives, et al., 2017; See List). Biological process describes dynamics of the underlying biological system (e.g., receptor signalling). The biological object is the subject of the perturbation (e.g., a specific biological receptor that is activated or inhibited). Action represents the direction of perturbation of this system (generally increased or decreased; e.g., ‘decreased’ in the case of a receptor that is inhibited to indicate a decrease in the signalling by that receptor).Note that when editing Event Components, clicking an existing Event Component from the Suggestions menu will autopopulate these fields, along with their source ID and description. To clear any fields before submitting the event component, use the 'Clear process,' 'Clear object,' or 'Clear action' buttons. If a desired term does not exist, a new term request may be made via Term Requests. Event components may not be edited; to edit an event component, remove the existing event component and create a new one using the terms that you wish to add. More help
Process Object Action
chronic inflammatory response increased
regulation of chronic inflammatory response increased
interleukin-6 production interleukin-6 increased
interleukin-1 beta production interleukin-1 beta increased
tumor necrosis factor secretion tumor necrosis factor alpha increased
Increased inflammatory response increased
Increased serum ferritin Ferritin increased
lactate dehydrogenase activity increased
Elevated C-reactive protein level C-reactive protein increased
Lymphopenia lymphocyte decreased

Key Event Overview

AOPs Including This Key Event

All of the AOPs that are linked to this KE will automatically be listed in this subsection. This table can be particularly useful for derivation of AOP networks including the KE. Clicking on the name of the AOP will bring you to the individual page for that AOP. More help
AOP Name Role of event in AOP Point of Contact Author Status OECD Status
Dysregulated fibrinolysis/bradykinin leading to hyperinflammation AdverseOutcome Penny Nymark (send email) Under development: Not open for comment. Do not cite Under Development
SARS-CoV2 to hyperinflammation AdverseOutcome Hasmik Yepiskoposyan (send email) Under development: Not open for comment. Do not cite
TLR9 activation leading to Multi Organ Failure and ARDS KeyEvent Gillina Bezemer (send email) Under development: Not open for comment. Do not cite


This is a structured field used to identify specific agents (generally chemicals) that can trigger the KE. Stressors identified in this field will be linked to the KE in a machine-readable manner, such that, for example, a stressor search would identify this as an event the stressor can trigger. NOTE: intermediate or downstream KEs in one AOP may function as MIEs in other AOPs, meaning that stressor information may be added to the KE description, even if it is a downstream KE in the pathway currently under development.Information concerning the stressors that may trigger an MIE can be defined using a combination of structured and unstructured (free-text) fields. For example, structured fields may be used to indicate specific chemicals for which there is evidence of an interaction relevant to this MIE. By linking the KE description to a structured chemical name, it will be increasingly possible to link the MIE to other sources of chemical data and information, enhancing searchability and inter-operability among different data-sources and knowledgebases. The free-text section “Evidence for perturbation of this MIE by stressor” can be used both to identify the supporting evidence for specific stressors triggering the MIE as well as to define broad chemical categories or other properties that classify the stressors able to trigger the MIE for which specific structured terms may not exist. More help

Taxonomic Applicability

Latin or common names of a species or broader taxonomic grouping (e.g., class, order, family) can be selected from an ontology. In many cases, individual species identified in these structured fields will be those for which the strongest evidence used in constructing the AOP was available in relation to this KE. More help
Term Scientific Term Evidence Link
humans Homo sapiens High NCBI
mouse Mus musculus Moderate NCBI

Life Stages

The structured ontology terms for life-stage are more comprehensive than those for taxa, but may still require further description/development and explanation in the free text section. More help

Sex Applicability

The authors must select from one of the following: Male, female, mixed, asexual, third gender, hermaphrodite, or unspecific. More help
Term Evidence

Key Event Description

A description of the biological state being observed or measured, the biological compartment in which it is measured, and its general role in the biology should be provided. For example, the biological state being measured could be the activity of an enzyme, the expression of a gene or abundance of an mRNA transcript, the concentration of a hormone or protein, neuronal activity, heart rate, etc. The biological compartment may be a particular cell type, tissue, organ, fluid (e.g., plasma, cerebrospinal fluid), etc. The role in the biology could describe the reaction that an enzyme catalyses and the role of that reaction within a given metabolic pathway; the protein that a gene or mRNA transcript codes for and the function of that protein; the function of a hormone in a given target tissue, physiological function of an organ, etc. Careful attention should be taken to avoid reference to other KEs, KERs or AOPs. Only describe this KE as a single isolated measurable event/state. This will ensure that the KE is modular and can be used by other AOPs, thereby facilitating construction of AOP networks. More help

Hyperinflammation can be defined as an uncontrolled and self-perpetuating inflammatory process that results in tissue damage. The state of hyperinflammation is also observed in cytokine storm syndrome, cytokine release syndrome, haemophagocytic lymphohistiocytosis, macrophage activation syndrome and in conditions of sepsis; however, it is not a frequent observation. For example, in COVID-19 infection, hyperinflammation plays a critical role in driving the disease severity. Although high viral titre initiates the cascade, the disease severity itself is dependent on the severity of the inflammatory state.

Clinically, the hallmarks of hyperinflammation state include excessive serum levels of pro-inflammatory mediator C-reactive protein (CRP), reduced or absence of lymphocytes (lymphopenia), high levels of ferritin and D-dimer, and increased lactate dehydrogenase. Higher neutrophil to lymphocyte ratio is another clinical marker. Some research studies have also associated high serum levels of IL6 protein and accumulation of neutrophils to be causal and indicative of hyperinflammation. Other molecular markers associated with hyperinflammation include IL1ꞵ and TNFɑ and have together with IL6 and a multitude of other cytokines, chemokines and other proinflammatory factors been identified as potential therapeutic targets (Desvaux et al. 2021). While the total serum levels of these markers is important, more critically, how fast the levels increase in serum is taken into consideration in judging the severity (Bergamaschi et al. 2021). The number of studies that have reported on the various markers of hyperinflammation is listed in Table-1.

Although the initiation and promotion of inflammation involves several cell types including epithelial cells, alveolar macrophages, type I and II pneumocytes and dendritic cells, the cell types that play role on inducing hyperinflammatory state may include macrophages, dendritic cells and neutrophils. Lack of neutrophil plays an important role in slowing the viral clearance and thus perpetuating the condition. Hyperferritinaemia is associated with high macrophage activation.

Weight of evidence

KE Hyperinflammation














Lactate dehydrogenase

Impaired IFN 1 type response


Lazear H.M et al., Immunity. 2019;50:907–923. 

Increased protein levels, NFkB pathway activation

Reduced IFN stimulated genes


Zhang B, Zhou X, Qiu Y, et al. Clinical characteristics of 82 death cases with COVID19. medRxiv. 2020.



Present, also thrombocytopenia

IncreasedIncreased D-dimer



J Clin.  Invest. 2020;130(5):2620-2629.




Increased D-dimer



Hadjadj et al., Science doi: 10.1126/science.abc6027




Del Valle DM et al., Medrxiv : the Preprint Server for Health Sciences. 2020 May. 

Increased protein



Chen G ei al., J Clin Invest. 2020;130(5):2620-2629

Increased IL-6


Increased ferritin and D-dimer


Marginal reduction


Cheng L et al.,

Journal of Clinical Laboratory Analysis Volume34, Issue10

October 2020 e23618

IL-6 increased

Increased ferritin levels

Review – meta analysis of 52 studies that have data for ferritin levels. Showing severity can be predicted by ferritin levels. Connections with inflammation state.


Manson JJ et al., The Lancet Rheumatology

Volume 2, Issue 10, October 2020, Pages e594-e602


Increased Ferritin levels

Logitudinal cohort study showing association of hyperinflammation with prognosis. Only CRP and Ferritin levels considered.


Caricchio R, et al.,  Ann Rheum Dis doi:10.1136/ annrheumdis-2020-218323




Recommended criteria for assessing hyperinflammation


Mojtabavi, H., et al., Eur Cytokine Netw 31, 44–49 (2020).


Review- meta-analysis of available data. 11 studies included.


Henry B et al.,  Acta Biomed. 2020;91(3):e2020008. doi:10.23750/abm.v91i3.10217

Lymphopenia and neutrophilia

Meta-analysis study –

22 studies included. Correlation between lymphopenia and neutrophilia at admission with severity of disease.


Jin J-M et al., Front. Public Health, 29 April 2020 |

Gender differences

Ex vivo, human lung tissue

Chu H, et al., Clin Infect Dis. 2020;71(6):1400-1409.

Increased IL-6

Impaired IFN I, II, III signalling


Channappanavar et al.,  Cell Host Microbe 19 (2) (2016) 181–193,

Reduced IFN I response


How It Is Measured or Detected

One of the primary considerations in evaluating AOPs is the relevance and reliability of the methods with which the KEs can be measured. The aim of this section of the KE description is not to provide detailed protocols, but rather to capture, in a sentence or two, per method, the type(s) of measurements that can be employed to evaluate the KE and the relative level of scientific confidence in those measurements. Methods that can be used to detect or measure the biological state represented in the KE should be briefly described and/or cited. These can range from citation of specific validated test guidelines, citation of specific methods published in the peer reviewed literature, or outlines of a general protocol or approach (e.g., a protein may be measured by ELISA).Key considerations regarding scientific confidence in the measurement approach include whether the assay is fit for purpose, whether it provides a direct or indirect measure of the biological state in question, whether it is repeatable and reproducible, and the extent to which it is accepted in the scientific and/or regulatory community. Information can be obtained from the OECD Test Guidelines website and the EURL ECVAM Database Service on Alternative Methods to Animal Experimentation (DB-ALM). ?

Domain of Applicability

This free text section should be used to elaborate on the scientific basis for the indicated domains of applicability and the WoE calls (if provided). While structured terms may be selected to define the taxonomic, life stage and sex applicability (see structured applicability terms, above) of the KE, the structured terms may not adequately reflect or capture the overall biological applicability domain (particularly with regard to taxa). Likewise, the structured terms do not provide an explanation or rationale for the selection. The free-text section on evidence for taxonomic, life stage, and sex applicability can be used to elaborate on why the specific structured terms were selected, and provide supporting references and background information.  More help

Hyperinflammation is observed in all age groups with high rates of infection and mortality observed in aged population. In children, although the rate of infection is low, hyperinflammatory syndrome is observed leading to long term disabilities. However, mortality rate in young children and adults below 40 years of age is less pronounced. Data in other developmental stages is lacking.

Prevalence of hyperinflammation is same in men and women; however, studies have found that men develop more severe symptoms than women.

Evidence for Perturbation by Stressor

Regulatory Significance of the Adverse Outcome

An AO is a specialised KE that represents the end (an adverse outcome of regulatory significance) of an AOP. For KEs that are designated as an AO, one additional field of information (regulatory significance of the AO) should be completed, to the extent feasible. If the KE is being described is not an AO, simply indicate “not an AO” in this section.A key criterion for defining an AO is its relevance for regulatory decision-making (i.e., it corresponds to an accepted protection goal or common apical endpoint in an established regulatory guideline study). For example, in humans this may constitute increased risk of disease-related pathology in a particular organ or organ system in an individual or in either the entire or a specified subset of the population. In wildlife, this will most often be an outcome of demographic significance that has meaning in terms of estimates of population sustainability. Given this consideration, in addition to describing the biological state associated with the AO, how it can be measured, and its taxonomic, life stage, and sex applicability, it is useful to describe regulatory examples using this AO. More help


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

1. Caricchio R, et al.,  Ann Rheum Dis doi:10.1136/ annrheumdis-2020-218323

2. Channappanavar et al.,  Cell Host Microbe 19 (2) (2016) 181–193,

3. Chen G ei al., J Clin Invest. 2020;130(5):2620-2629

4. Cheng L et al.,Journal of Clinical Laboratory Analysis Volume34, Issue10, October 2020 e23618,

5. Chu H, et al., Clin Infect Dis. 2020;71(6):1400-1409.

6. Del Valle DM et al., Medrxiv : the Preprint Server for Health Sciences. 2020 May.

7. J Clin.  Invest. 2020;130(5):2620-2629.

8. Jin J-M et al., Front. Public Health, 29 April 2020 |

9. Hadjadj et al., Science doi: 10.1126/science.abc6027

10. Henry B et al.,  Acta Biomed. 2020;91(3):e2020008. doi:10.23750/abm.v91i3.10217

11. Lazear H.M et al., Immunity. 2019;50:907–923.

12. Manson JJ et al., The Lancet Rheumatology Volume 2, Issue 10, October 2020, Pages e594-e602

13. Mojtabavi, H., et al., Eur Cytokine Netw 31, 44–49 (2020).

14. Zhang B, Zhou X, Qiu Y, et al. Clinical characteristics of 82 death cases with COVID‐19. medRxiv. 2020.