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Relationship: 295


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

Activation, Stellate cells leads to Accumulation, Collagen

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
Protein Alkylation leading to Liver Fibrosis adjacent High Brendan Ferreri-Hanberry (send email) Open for citation & comment TFHA/WNT Endorsed
Endocytic lysosomal uptake leading to liver fibrosis adjacent High Allie Always (send email) Under development: Not open for comment. Do not cite EAGMST Under Review

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
Term Scientific Term Evidence Link
human Homo sapiens High NCBI
Rattus norvegicus Rattus norvegicus High NCBI

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
Sex Evidence

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
Term Evidence
All life stages

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

Up-regulation of collagen synthesis following hepatic stellate cell (HSC) activation is among the most striking molecular responses of HSCs to injury and is mediated by both transcriptional and post-transcriptional mechanisms. Activated HSCs do not only proliferate and increase cell number, but also increase collagen production per cell. Synthesis of type I collagen is initiated by expression of the col1a1 and col1a2 genes, giving rise to α 1(I) and α 2(I) procollagen mRNAs in a 2:1 ratio. Upon activation of HSCs and other myofibroblast precursors, there is a > 50-fold increase in α 1(I) procollagen mRNA levels. The half-life of collagen α1(I) mRNA increases 20-fold in activated HSCs compared with quiescent HSCs. Monocytes and macrophages are involved in inflammatory actions by producing large amounts of Nitric oxide (NO) and inflammatory cytokines such as TNF-α which have a direct stimulatory effect on HSC collagen synthesis. Synthesis of TGF-α and TGF-β promotes activation of neighbouring quiescent HSCs, whereas the release of HGF (hepatocyte growth factor) stimulates regeneration of adjacent hepatocytes.

The basement membrane-like matrix is normally comprised of collagens IV and VI, which is progressively replaced by collagens I and III and cellular fibronectin during fibrogenesis. Although multiple extracellular matrix (ECM) components are up-regulated, type I collagen is the most abundant protein. These changes in ECM composition initiate several positive feedback pathways that further amplify collagen production. Increasing matrix stiffness is a stimulus for HSC activation and matrix-provoked signals link to other growth factor receptors through integrin-linked kinase and transduce via membrane-bound guanosine triphosphate binding proteins, in particular Rho67 and Rac, signals to the actin cytoskeleton that promote migration and contraction.

The overall amount of collagen deposited by fibroblasts is a regulated balance between collagen synthesis and collagen catabolism. Down-regulated expression of degrading Matrix metalloproteinases (MMPs) and up-regulation of tissue inhibitors of metalloproteinases (TIMPs), MMP- inhibitors, lead to a net decrease in protease activity, and therefore, matrix accumulation. Chronic inflammation, hypoxia and oxidative stress reactivate epithelial-mesenchymal transition (EMT) developmental programmes that converge in the activation of NF-kB. Cells that may transdifferentiate into fibrogenic myofibroblasts are hepatocytes and cholangiocytes. Additional sources of ECM include bone marrow (which probably gives rise to circulating fibrocytes) and portal fibroblasts (Benyon and Arthur; 2001; Milani et al., 1994; Safadi and Friedman, 2002; Kolios et al.,2006; Bataller and Brenner, 2005; Lee und Friedman 2011; Guo and Friedman, 2007; Li, Jing-Ting et al., 2008;  Kershenobich Stalnikowitz and Weisssbrod , 2003; López-Novoa and Nieto, 2009; Friedman, 2010; 2008; Dalton et al., 2009; Leung, et al., 2008; Nan et al., 2013;  Hamdy and El-Demerdash, 2012;Li, Li et al., 2012; Natajaran et al., 2006; Luckey and Petersen, 2001;  Chen and Raghunath, 2009;Thompson et al., 2011; Henderson and Iredale, 2007).

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
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

There is general acceptance that HSCs are collagen producing cells and key actors in fibrogenesis. The functional relationship between these KEs is consistent with biological knowledge (Benyon and Arthur; 2001; Milani et al., 1994; Safadi and Friedman, 2002; Kolios et al.,2006; Bataller and Brenner, 2005; Lee und Friedman 2011; Guo and Friedman, 2007; Li, Jing-Ting et al., 2008;  Kershenobich Stalnikowitz and Weisssbrod , 2003; López-Novoa and Nieto, 2009).

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

no inconsistencies

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

Human: Safadi and Friedman, 2002; Bataller and Brenner, 2005; Lee und Friedman 2011.

Rat: Li, Li et al., 2012; Luckey and Petersen, 2001;  Rockey et al., 1992


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
  • Benyon, R.C. and M.J. Arthur (2001), Extracellular matrix degradation and the role of stellate cells, Semin Liver Dis, vol. 21, no. 3, pp. 373-384.
  • Milani, S. et al. (1994), Differential expression of matrix-metalloproteinase-1 and -2 genes in normal and fibrotic human liver, Am J Pathol, vol. 144, no. 3, pp. 528-537.
  • ↑Safadi, R. and S.L. Friedman (2002), Hepatic fibrosis--role of hepatic stellate cell activation, MedGenMed, vol 4, no. 3, p. 27.
  • 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.
  • Bataller, R. and D.A. Brenner (2005), Liver Fibrosis, J.Clin. Invest, vol. 115, no. 2, pp. 209-218.
  • Lee, U.E. and S.L. Friedman (2011), Mechanisms of Hepatic Fibrogenesis, Best Pract Res Clin Gastroenterol, vol. 25, no. 2, pp. 195-206.
  • Guo, J. and S. L. Friedman (2007), Hepatic fibrogenesis, Semin Liver Dis, vol. 27, no. 4, pp. 413-426.
  • Brenner, D.A. (2009), Molecular Pathogenesis of Liver Fibrosis, Trans Am Clin Climatol Assoc, vol. 120, pp. 361–368.
  • 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.
  • Kershenobich Stalnikowitz, D. and A.B. Weisssbrod (2003), Liver Fibrosis and Inflammation. A Review, Annals of Hepatology, vol. 2, no. 4, pp.159-163.
  • López-Novoa, J.M. and M.A. Nieto (2009), Inflammation and EMT: an alliance towards organ fibrosis and cancer progression, EMBO Mol Med, vol. 1. no. 6-7, pp. 303–314.
  • Friedman, S.L (2010), Evolving challenges in hepatic fibrosis, Nat. Rev. Gastroenterol. Hepatol, vol. 7, no. 8, pp. 425–436.
  • Friedman, S.L. (2008), Mechanisms of Hepatic Fibrogenesis, Gastroenterology, vol. 134, no. 6, pp. 1655–1669.
  • Dalton, S.R. et al. (2009), Carbon tetrachloride-induced liver damage in asialoglycoprotein receptor-deficient mice, Biochem Pharmacol, vol. 77, no. 7, pp. 1283-1290.
  • Leung, T.M. et al. (2008), Endothelial nitric oxide synthase is a critical factor in experimental liver fibrosis, Int J Exp Pathol, vol. 89, no. 4, pp. 241-250.
  • Nan, Y.M. et al. (2013), Activation of peroxisome proliferator activated receptor alpha ameliorates ethanol mediated liver fibrosis in mice, Lipids in Health and Disease, vol. 12, p. 11.
  • Hamdy, N. and E. El-Demerdash. (2012), New therapeutic aspect for carvedilol: antifibrotic effects of carvedilol in chronic carbon tetrachloride-induced liver damage, Toxicol Appl Pharmacol, vol. 261, no. 3, pp. 292-299.
  • Li, Li et al. (2012), Establishment of a standardized liver fibrosis model with different pathological stages in rats, Gastroenterol Res Pract; vol. 2012, Article ID 560345.
  • Natajaran, S.K. et al. (2006), Oxidative stress in the development of liver cirrhosis: a comparison of two different experimental models, J Gastroenterol Hepatol, vol. 21, no. 6, pp. 947-957.
  • Luckey, S.W., and D.R. Petersen (2001), Activation of Kupffer cells during the course of carbon tetrachloride-induced liver injury and fibrosis in rats, Exp Mol Pathol, vol. 71, no. 3, pp. 226-240.
  • Chen, C. and M. Raghunath (2009), Focus on collagen: in vitro systems to study fibrogenesis and antifibrosis state of the art, Fibrogenesis Tissue Repair, vol. 15, no. 2, p. 7.
  • Thompson, K.J., I.H. McKillop and L.W. Schrum (2011), Targeting collagen expression in alcoholic liver disease, World J Gastroenterol, vol. 17, no. 20, pp. 2473-2481.
  • Henderson, N.C. and J.P. Iredale (2007), Liver fibrosis: cellular mechanisms of progression and resolution, Clin Sci (Lond), vol. 112, no. 5, pp. 265-280.
  • Rockey, D.C. et al. (1992), Rat hepatic lipocytes express smooth muscle actin upon activation in vivo and in culture, J Submicrosc Cytol Pathol, vol. 24, no. 2, pp. 193-203.