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Relationship: 68


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

Cell injury/death leads to Activation, Stellate cells

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
Protein Alkylation leading to Liver Fibrosis non-adjacent High Brigitte Landesmann (send email) Open for citation & comment WPHA/WNT Endorsed
AhR activation leading to liver fibrosis adjacent Moderate Moderate Xavier COUMOUL (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 Do not cite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
human Homo sapiens High NCBI
Rattus norvegicus Rattus norvegicus High NCBI
Mus musculus Mus musculus High NCBI

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

Damaged hepatocytes can lead to activation of hepatic stellate cells (HSCs) through the release of ROS, cytokines and chemokines. Engulfment of apoptotic bodies from hepatocytes results in HSC activation and induces NOX (NADPH oxidases) expression in HSCs. DNA from apoptotic hepatocytes induces toll-like receptor 9 (TLR9)-dependent changes of HSCs that are consistent with late stages of HSC differentiation (activation), with up-regulation of collagen production and inhibition of platelet derived growth factor (PDGF)-mediated chemotaxis to retain HSCs at sites of cellular apoptosis. The release of latent TGF-beta complex into the micro-environment by damaged hepatocytes is likely to be one of the first signals for adjacent HSCs leading to their activation.

[1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

Damaged hepatocytes also influence liver sinusoidal endothelial cell (LSECs), which make an integral part of the hepatic reticulo-endothelial system and have a role in HSC activation. LSECs are morphologically identified by their fenestrations, which are transcytoplasmic canals arranged in sieve plates. In healthy liver, hepatocytes and HSCs maintain this phenotype of LSECs through release of vascular endothelial growth factor (VEGF). Differentiated (i.e. fenestrated) LSECs prevent HSC activation and promote reversal of activated HSC to quiescence, but LSEC lose this effect when they are de-differentiated due to liver injury. Preclinical studies have demonstrated that LSECs undergo defenestration as an early event that not only precedes liver fibrosis, but may also be permissive for it. Changes in LSEC differentiation might be an integral part of the development of fibrosis. Furthermore, in fibrosis LSECs become highly pro-inflammatory and secrete an array of cytokines and chemokines [11] [12] [13] [14] [15]

This relationship is classified as indirect as HSCs activation is partly mediated by TGF-β1 and LSECs.

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

There is a functional relationship between KE 1 and KE 4 consistent with established biological knowledge. [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

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 no inconsistencies

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

Human: [7][18] Rat: [12] Mouse: [4]


List of the literature that was cited for this KER description. More help
  1. 1.0 1.1 Roth, S., K. Michel and A.M. Gressner (1998), (Latent) transforming growth factor beta in liver parenchymal cells, its injury-dependent release, and paracrine effects on rat HSCs, Hepatology, vol. 27, no. 4, pp. 1003-1012.
  2. 2.0 2.1 Gressner , A.M. et al. (2002), Roles of TGF-β in hepatic fibrosis. Front Biosci, vol. 7, pp. 793-807.
  3. 3.0 3.1 Malhi, H. et al. (2010), Hepatocyte death: a clear and present danger, Physiol Rev, vol. 90, no. 3, pp. 1165-1194.
  4. 4.0 4.1 4.2 Canbay, A., S.L. Friedman and G.J. Gores (2004), Apoptosis: the nexus of liver injury and fibrosis, Hepatology, vol. 39, no. 2, pp. 273-278.
  5. 5.0 5.1 Orrenius, S., P. Nicotera and B. Zhivotovsky (2011), Cell death mechanisms and their implications in toxicology, Toxicol. Sci, vol. 119, no. 1, pp. 3-19.
  6. 6.0 6.1 Kolios, G., V. Valatas and E. Kouroumalis (2006), Role of Kupffer cells in the pathogenesis of liver disease, World J.Gastroenterol, vol. 12, no. 46, pp. 7413-7420.
  7. 7.0 7.1 7.2 Kisseleva T and Brenner DA, (2008), Mechanisms of Fibrogenesis, Exp Biol Med, vol. 233, no. 2, pp. 109-122.
  8. 8.0 8.1 Li, Jing-Ting et al. (2008), Molecular mechanism of hepatic stellate cell activation and antifibrotic therapeutic strategies, J Gastroenterol, vol. 43, no. 6, pp. 419–428.
  9. 9.0 9.1 Friedman, S.L. (2008), Mechanisms of Hepatic Fibrogenesis, Gastroenterology, vol. 134, no. 6, pp. 1655–1669.
  10. 10.0 10.1 Lee, U.E. and S.L. Friedman (2011), Mechanisms of Hepatic Fibrogenesis, Best Pract Res Clin Gastroenterol, vol. 25, no. 2, pp. 195-206.
  11. DeLeve, L.D. (2013), Liver sinusoidal endothelial cells and liver regeneration, J Clin Invest, vol. 123, no. 5, pp. 1861–1866.
  12. 12.0 12.1 Xie, G. et al. (2012), Role of differentiation of liver sinusoidal endothelial cells in progression and regression of hepatic fibrosis in rats, Gastroenterology, vol. 142, no. 4, pp. 918–927.
  13. Xie, G. et al. (2013), Hedgehog signalling regulates liver sinusoidal endothelial cell capillarisation, Gut, vol. 62, no. 2, pp. 299–309.
  14. Ding, B.S. et al. (2014), Divergent angiocrine signals from vascular niche balance liver regeneration and fibrosis, Nature, vol. 505, no. 7481, pp. 97–102.
  15. Connolly, M.K. et al. (2010), In hepatic fibrosis, liver sinusoidal endothelial cells acquire enhanced immunogenicity, J Immunol, vol. 185, no. 4, pp. 2200-2208.
  16. Canbay, A. et al. (2002), Fas enhances fibrogenesis in the bile duct ligated mouse: a link between apoptosis and fibrosis, Gastroenterology, vol. 123, no. 4, pp. 1323-1330.
  17. Canbay, A. et al. (2004), The caspase inhibitor IDN-6556 attenuates hepatic injury and fibrosis in the bile duct ligated mouse, J Pharmacol Exp Ther, vol. 308, no. 3, pp. 1191-1196.
  18. 18.0 18.1 Coulouarn, C. et al. (2012), Hepatocyte-stellate cell cross-talk in the liver engenders a permissive inflammatory micro-environment that drives progression in hepatocellular carcinoma, Cancer Res, vol. 72, no. 10, pp. 2533–2542.