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Relationship: 2892

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

Activation, Stellate cells leads to Increased extracellular matrix deposition

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
AhR activation leading to liver fibrosis adjacent High High 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 Do not cite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

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

Upon activation, hepatic stellate cells undergo a transition into a myofibroblast-like phenotype, leading to excessive extracellular matrix deposition, primarily consisting of collagen type I and III, fibronectin, and other fibrillar proteins (PMID: 37152902). This activation is triggered by oxidative stresspro-inflammatory cytokines (e.g., TGF-β, PDGF), and chronic tissue injury. Activated stellate cells express α-SMA (alpha-smooth muscle actin) and exhibit increased secretion of ECM components. This process is central to the development of fibrosis in the liver, pancreas, and retina, contributing to tissue stiffness and organ dysfunction.

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
    • The relationship between stellate cell activation and ECM accumulation is well-established. TGF-β1 is a key cytokine that directly stimulates the expression of ECM-related genes (PMID: 24265236).
    • Inhibition of TGF-β1 has been shown to prevent stellate cell activation and reduce collagen production (PMID: 37923895).
    • Transcriptomic analyses reveal a strong correlation between stellate cell activation markers (ACTA2, COL1A1, TIMP-1) and ECM deposition in fibrotic tissues (PMID: 39062514).
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
    • Suppressing stellate cell activation may not completely prevent ECM deposition; other cell types (e.g., portal fibroblasts, macrophages) may also contribute.
    • Species-specific differences in the regulation of stellate cell activation and ECM production may affect the translatability of animal model findings to humans.
    • Certain pro-inflammatory cytokines (IL-6, TNF-α) may have context-dependent effects that modulate stellate cell activation and fibrogenesis differently.

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
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
  • This KER is most relevant to hepatic tissues, where stellate cells play a crucial role in fibrosis development.
  • It applies primarily to chronic fibrotic diseases such as liver fibrosis, but also potentially to chronic pancreatitis, and retinal fibrosis.
  • This relationship has been observed across multiple mammalian species, although species-specific differences in regulatory pathways may influence the quantitative aspects of the response.

References

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

Zhao YQ, Deng XW, Xu GQ, Lin J, Lu HZ, Chen J. Mechanical homeostasis imbalance in hepatic stellate cells activation and hepatic fibrosis. Front Mol Biosci. 2023 Apr 20;10:1183808. doi: 10.3389/fmolb.2023.1183808. PMID: 37152902; PMCID: PMC10157180.

Kasahara N, Imi Y, Amano R, Shinohara M, Okada K, Hosokawa Y, Imamori M, Tomimoto C, Kunisawa J, Kishino S, Ogawa J, Ogawa W, Hosooka T. A gut microbial metabolite of linoleic acid ameliorates liver fibrosis by inhibiting TGF-β signaling in hepatic stellate cells. Sci Rep. 2023 Nov 3;13(1):18983. doi: 10.1038/s41598-023-46404-5. PMID: 37923895; PMCID: PMC10624680.

Puche JE, Saiman Y, Friedman SL. Hepatic stellate cells and liver fibrosis. Compr Physiol. 2013 Oct;3(4):1473-92. doi: 10.1002/cphy.c120035. PMID: 24265236.

Buakaew W, Krobthong S, Yingchutrakul Y, Potup P, Thongsri Y, Daowtak K, Ferrante A, Usuwanthim K. Investigating the Antifibrotic Effects of β-Citronellol on a TGF-β1-Stimulated LX-2 Hepatic Stellate Cell Model. Biomolecules. 2024 Jul 5;14(7):800. doi: 10.3390/biom14070800. PMID: 39062514; PMCID: PMC11274813.

Mu M, Zuo S, Wu RM, Deng KS, Lu S, Zhu JJ, Zou GL, Yang J, Cheng ML, Zhao XK. Ferulic acid attenuates liver fibrosis and hepatic stellate cell activation via inhibition of TGF-β/Smad signaling pathway. Drug Des Devel Ther. 2018 Dec 3;12:4107-4115. doi: 10.2147/DDDT.S186726. Erratum in: Drug Des Devel Ther. 2019 May 24;13:1819. doi: 10.2147/DDDT.S215949. PMID: 30584275; PMCID: PMC6284527.

Parola M, Pinzani M. Liver fibrosis in NAFLD/NASH: from pathophysiology towards diagnostic and therapeutic strategies. Mol Aspects Med. 2024 Feb;95:101231. doi: 10.1016/j.mam.2023.101231. Epub 2023 Dec 5. PMID: 38056058.

Hirabaru M, Mochizuki K, Takatsuki M, Soyama A, Kosaka T, Kuroki T, Shimokawa I, Eguchi S. Expression of alpha smooth muscle actin in living donor liver transplant recipients. World J Gastroenterol. 2014 Jun 14;20(22):7067-74. doi: 10.3748/wjg.v20.i22.7067. PMID: 24966580; PMCID: PMC4051953.