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Relationship: 1764

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

Increased pro-inflammatory mediators 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 adjacent High Brendan Ferreri-Hanberry (send email) Open for citation & comment WPHA/WNT Endorsed

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
rat Rattus norvegicus High NCBI

Sex Applicability

An indication of the the relevant sex for this KER. More help
Sex Evidence
Unspecific High

Life Stage Applicability

An indication of the the relevant life stage(s) for this KER.  More help
Term Evidence
All life stages High

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

HSC Initiation is associated with rapid gene induction resulting from paracrine stimulation by inflammatory cells and injured hepatocytes . Also Kupffer cell infiltration and activation play a prominent role in HSC activation.[Li et al., 2008]

Lymphocytes, especially CD4 T-helper (Th) lymphocytes, help orchestrate the host response via cytokine production and can differentiate into Th1 and Th2 subsets. In general, Th1 cells produce cytokines promoting cell-mediated immunity, including interferon (IFN)-γ, TNF, and interleukin (IL)-2. Th2 cells produce IL-4, IL-5, IL-6, and IL-13 and promote humoral immunity. Results from previous experimental models imply that Th2 lymphocytes favor fibrogenesis in liver injury over Th1 lymphocytes. [Shi et al., 1997] However, recent studies of Wynn [Wynn,2004]  suggest that more than two T-cell subsets underlying a highly complex, orchestrated response are involved, and they also provide us a more important paradigm for how these intersecting pathways may regulate fibrosis. In animal models, IL-13 has emerged as a key mediator because it increases TGF-β1 and MMP expression by macrophages, whereas IL-4 has a limited role. One study examined the activity of IL-13 in cultured HSCs and suggested that IL-4 and IL-13 directly affect HSCs by increasing collagen production and suppressing HSC proliferation. [Sugimoto et al., 2005] 

Leukocytes recruited to the liver during injury join with Kupffer cells in producing compounds that modulate HSC behavior

Transforming growth factor beta 1 (TGF-β1) is the most potent fibrogenic factor for hepatic stellate cells (HSCs). In response to TGF-β1, HSCs activate into myofibroblast-like cells, producing type I, III and IV collagen, proteoglycans like biglycan and decorin, glycoproteins like laminin, fibronectin, tenascin and glycosaminoglycan. [Kisseleva and Brenner, 2007]  In the further course of events activated HSCs themselves express TGF-β1. TGF-β1 induces its own mRNA to sustain high levels in local sites of liver injury. The effects of TGF-β1 are mediated by intracellular signalling via Smad proteins. Smads 2 and 3 are stimulatory whereas Smad 7 is inhibitory. Smad1/5/8, MAP kinase and PI3 kinase are further signalling pathways in different cell types for TGF-β1 effects. [Parsons et al., 2007] Concomitant with increased TGF-β production, HSC increase production of collagen. Connective tissue growth factor (CTGF) is a profibrogenic peptide induced by TGF-β, that stimulates the synthesis of collagen type I and fibronectin and may mediate some of the downstream effects of TGF-β. It is upregulated during activation of HSC, suggesting that its expression is another determinant of a fibrogenic response to TGF-β. [Williams et al.,2000] During fibrogenesis, tissue and blood levels of active TGF-β are elevated and overexpression of TGF-β1 in transgenic mice can induce fibrosis. Additionally, experimental fibrosis can be inhibited by anti-TGF-β treatments with neutralizing antibodies or soluble TbRs (TGF-β receptors). [Qi et al., 1999]    

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 good understanding and broad acceptance of this KER. [Kisseleva and Brenner, 2007; Williams et al., 2000; Qi et al., 1999; Gressner et al., 2002; Kolios et al., 2006; Bataller and Brenner,  2005; Guo and Friedman, 2007; Brenner, 2009; Kaimori et al., 2007; Kershenobich Stalnikowitz and Weissbrod, 2003; Li et al., 2008; Matsuoka and Tsukamoto, 1990; Kisseleva and Brenner, 2008; Poli, 2000; Parsons et al., 2007; Friedman, 2008; Liu et al., 2006]

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
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

Human [Kolios et al., 2006; Guo and Friedman, 2007]    

Rat [Dooley et al., 2000]   

References

List of the literature that was cited for this KER description. More help
  • Bataller, R. and D.A. Brenner (2005), Liver Fibrosis, J.Clin. Invest, vol. 115, no. 2, pp. 209-218.
  • Brenner, D.A. (2009), Molecular Pathogenesis of Liver Fibrosis, Trans Am Clin Climatol Assoc, vol. 120, pp. 361–368.
  • Czaja, M.J. et al. (1989), In vitro and in vivo association of transforming growth factor-beta 1 with hepatic fibrosis, J Cell Biol, vol. 108, no. 6, pp. 2477-2482.
  • De Minicis, S. et al. (2007), Gene expression profiles during hepatic stellate cell activation in culture and in vivo, Gastroenterology, vol. 132, no. 5, pp. 1937-1946.
  • Dooley, S. et al. (2000), Modulation of transforming growth factor b response and signaling during transdifferentiation of rat hepatic stellate cells to myofibroblasts,Hepatology, vol. 31, no. 5, pp. 1094-1106.
  • Friedman, S.L. (2008), Mechanisms of Hepatic Fibrogenesis, Gastroenterology, vol. 134, no. 6, pp. 1655–1669.
  • Gressner , A.M. et al. (2002), Roles of TGF-β in hepatic fibrosis. Front Biosci, vol. 7, pp. 793-807.
  • Guo, J. and S. L. Friedman (2007), Hepatic fibrogenesis, Semin Liver Dis, vol. 27, no. 4, pp. 413-426.
  • Jing-Ting Li, Zhang-Xiu Liao, Jie Ping, Dan Xu, and Hui Wang, Molecular mechanism of hepatic stellate cell activation and antifi brotic therapeutic strategies, J Gastroenterol 2008; 43:419–428
  • Kaimori, A. et al. (2007), Transforming growth factor-beta1 induces an epithelial-to-mesenchymal transition state in mouse hepatocytes in vitro, J Biol Chem, vol. 282, no. 30, pp. 22089-22101.
  • Kershenobich Stalnikowitz, D. and A.B. Weissbrod (2003), Liver Fibrosis and Inflammation. A Review, Annals of Hepatology, vol. 2, no. 4, pp.159-163.
  • Kisseleva T and Brenner DA, (2008), Mechanisms of Fibrogenesis, Exp Biol Med, vol. 233, no. 2, pp. 109-122.
  • Kisseleva, T. and Brenner, D.A. (2007), Role of hepatic stellate cells in fibrogenesis and the reversal of fibrosis, Journal of Gastroenterology and Hepatology, vol. 22, Suppl. 1; pp. S73–S78.
  • 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.
  • 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.
  • Liu, Xingjun et al. (2006), Therapeutic strategies against TGF-beta signaling pathway in hepatic fibrosis. Liver Int, vol.26, no.1, pp. 8-22.
  • Matsuoka, M. and H. Tsukamoto, (1990), Stimulation of hepatic lipocyte collagen production by Kupffer cell-derived transforming growth factor beta: implication for a pathogenetic role in alcoholic liver fibrogenesis, Hepatology, vol. 11, no. 4, pp. 599-605.
  • Parsons, C.J., M.Takashima and R.A. Rippe (2007), Molecular mechanisms of hepatic fibrogenesis. J Gastroenterol Hepatol, vol. 22, Suppl.1, pp. S79-S84.
  • Poli, G. (2000), Pathogenesis of liver fibrosis: role of oxidative stress, Mol Aspects Med, vol. 21, no. 3, pp. 49 – 98.
  • Qi Z et al. (1999), Blockade of type beta transforming growth factor signaling prevents liver fibrosis and dysfunction in the rat, Proc Natl Acad Sci USA, vol. 96, no. 5, pp. 2345-2349.
  • Shi Z, Wakil AE, Rockey DC. Strain-specifi c differences in mouse hepatic wound healing are mediated by divergent T helper cytokine responses. Proc Natl Acad Sci USA 1997;94:10663–8.
  • Sugimoto R, Enjoji M, Nakamuta M, Ohta S, Kohjima M, Fukushima M, et al. Effect of IL-4 and IL-13 on collagen production in cultured LI90 human hepatic stellate cells. Liver Int 2005;25:420–8.
  • Tan, A.B. et al. (2013), Cellular re- and de-programming by microenvironmental memory: why short TGF-β1 pulses can have long effects, Fibrogenesis Tissue Repair, vol. 6, no. 1, p. 12.
  • Williams, E.J. et al. (2000), Increased expression of connective tissue growth factor in fibrotic human liver and in activated hepatic stellate cells, J Hepatol, vol. 32, no. 5, pp. 754-761.
  • Wynn TA. Fibrotic disease and the T(H)1/T(H)2 paradigm. Nat Rev Immunol 2004;4:583–94.
  • Yin, C. et al. (2013), Hepatic stellate cells in liver development, regeneration, and cancer, J Clin Invest, vol. 123, no. 5, pp. 1902–1910.