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Relationship: 1778

Title

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

Leukocyte recruitment/activation leads to Activation, Stellate cells

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
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 Do not cite EAGMST Under Review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 NCBI
mouse Mus musculus NCBI
rat Rattus norvegicus 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
Unspecific

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

During hepatic injury quiescent hepatic stellate cells (HSCs) undergo activation which is associated with proliferation, increased contractile activity, fibrogenesis, changes in matrix protease activity, loss of intracellular retinoid storage, production of cytokines, and phenotypic transformation (Friedmann, 2000).

Different inflammatory cells activate HSCs to secrete collagen (Casini et al., 1997). Factors that promote activation of HSC include the cytokines, TNF-α and TGF-β (Bachem et al., 1993), endothelin-l (ET-1) (Rockey and Chung, 1996) and oxidative stress (Lee et al., 1995).

TGF-β is considered the most powerful mediator of HSC activation in vitro and in vivo (Friedmann, 2000; Bataller and Brenner, 2005). TGF-β triggers phenotypical HSC activation by paracrine and autocrine action, and induces collagen I expression and α-smooth muscle actin (α-SMA) stress fiber organization (Gressner et al., 2002; Dooley et al., 2003). TGF-β binds to heteromeric transmembrane receptors, including TβRI and TβRII (Heldin et al., 1997). Binding to TβRII triggers heteromerization with and transphosphorylation of TβRI. The signal is then propagated through phosphorylation of receptor associated Smad2 and Smad3 and their oligomerization with the common mediator Smad4. Complexes of phosphorylated Smad2 or 3 and Smad4 translocate into the nucleus, where they modulate the transcription of target genes, including those encoding extracellular matrix components (ECM) components (Piek et al., 1999; Miyazono et al., 2000; Moustakas et al., 2001). IFN-γ suppresses TGF-β and PDGF-dependent signalling pathways (Fujita et al., 2006).

IL-33 is released by stressed hepatocytes and attracts type 2 innate lymphoid cells (ILC2), which trigger the profibrogenic activation of HSCs via mediators such as IL-13 (McHedlidze et al., 2013; Heymann and Tacke, 2016). The binding of IL-33 to ST2 receptor activates NF-kB and mitogen activated protein kinases (MAPKs) and drives the production of pro-inflammatory and Th2 associated cytokines (Schmitz et al., 2005), which resulted in the stimulation of α-SMA and collagen expression in HSCs (Tan et al., 2018).

IL-13 indirectly activates TGF-β by upregulating the expression of matrix metalloproteinases (MMPs) that cleave the LAP–TGF-β1 complex (Lee et al., 2001; Lanone et al., 2002). Also, IL-13 has been reported to directly induce production of TGF-β1 in HSCs during liver fibrosis (Shimamura et al., 2008). When treating HSCs with IL-13 and performing a time-course analysis, a time-dependent activation of Smad proteins was observed (Liu et al., 2011).

TNF-α stimulated both p38 MAPK and JNK activity in a time-dependent manner, same as ET-1. However, TGF-β had no significant stimulatory effect on either of these MAPKs. The addition of p38 MAPK inhibitor pyridinyl imidazole derivative SB202190 resulted in reduction of α-SMA, indicator of activated HSC (Reeves et al., 2000)

Oxidative stress enhanced activation of HSCs and collagen synthesis in them, whereas antioxidants stopped the stimulatory effect of free radicals (Lee et al., 1995; Svegliati-Baroni et al., 2001). Oxidative stress molecules, such as superoxide, hydrogen peroxide, hydroxyl radicals, may be derived from hepatocytes, activated KCs, other inflammatory cells and HSCs (Natarajan et al., 2006; Kisseleva and Brenner, 2007; Lee and Friedman, 2011).

Liver-infiltrating CD14+ CD16+ monocytes secrete high levels of chemokines (such as CCL1, CCL2, CCL3, CCL5), cytokines (IL-1α, IL-1β, IL-6, IL-13, IL-16, TNF-α and macrophage migration inhibitory factor), growth factors (granulocyte colony-stimulating factor and granulocyte-macrophage colony-stimulating factor) and can efficiently activate primary HSC in vitro (Liaskou et al., 2013; Zimmermann et al., 2010).

Neutrophils may activate HSCs through matrix degradation, secreting compounds like elastase, which then degrades laminin, an extracellular matrix protein in normal liver that is critical for keeping stellate cells in a quiescent state (Friedman et al., 1989). Neutrophil-derived reactive oxygen species (ROS) significantly stimulated procollagen type I accumulation in the HSC culture medium, while the addition of vitamin E or SOD impaired the ROS stimulated stimulation of procollagen I (Casini et al., 1997).

Macrophages are primary source of TGF-β1 in the fibrotic liver (Bataller and Brenner, 2009). Macrophages release several pro-inflammatory cytokines like TNF-α or IL-1 (Tacke and Zimmermann, 2014), which activate the transcription factor NF-kB in HSC and promote the survival of activated HSC (Pradere et al., 2013).

Kupffer cells, liver resident macrophages, after their activation, activate HSC via mechanisms that involve the potent profibrotic cytokines like TGF-β and platelet derived growth factor (PDGF), and ROS (Karlmark et al., 2008; Bataller and Brenner, 2009; Bataller and Lemon, 2012). Apart from directly stimulating matrix-secreting HSC, hepatic macrophages may aggravate scarring by promoting HSC survival via IL-1 and TNF-α induced NF-kB activation (Pradere et al., 2013).  Beside resident macrophages, infiltration of macrophages from blood is essential for liver fibrogenesis (Duffield et al., 2005; Imamura et al., 2005).

Th2 cell– derived cytokines, IL-4, IL-5, IL-13, can enhance fibrosis progression by stimulating TGF-β production in macrophages and by direct effects on HSCs (Wynn, 2004).

DCs have a minor contribution to NF-kB activation (Pradere et al., 2013).

NKT cells were also found to promote liver fibrogenesis in vivo, likely by releasing pro-inflammatory cytokines and activating HSCs (Wehr et al., 2013; Syn et al., 2012). However, there are also studies demonstrating that NKT cells may exert antifibrotic actions, because they can, under certain conditions, also kill HSC and produce IFN-γ, like NK cells (Gao et al., 2013; Park et al., 2009).

Signalling pathways for HSC activation include, for example, NF-kB that is involved in HSC activation upon lipopolysaccharide (LPS) or TLR4 stimulation or ATP induced cytosolic Ca2+ influx via purinergic signalling receptors (Dranoff et al., 2004). The activation of TLR4 receptor in HSC downregulates TGF-β pseudoreceptor BAMBI and sensitizes these cells for TGF-β, resulting in increased ECM production by HSCs and fibrosis (Seki et al., 2007).

Activated HSCs secrete inflammatory chemokines, express cell adhesion molecules, and modulate the activation of lymphocytes (Vinas et al., 2003). Therefore, a vicious circle in which inflammatory and fibrogenic cells stimulate each other is likely to occur (Maher, 2001).

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

The recruitment of immune cells from the circulation into the injured tissue is the key mechanism during fibrogenesis in the liver (Heymann and Tacke, 2016).

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
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
Time-scale
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 (Zimmermann et al., 2010; Liaskou et al., 2013)

Mouse (Seki et al., 2007; Gäbele et al., 2009; Pradere et al., 2013; McHedlidze et al., 2014)

Rat (Reeves et al., 2000;  Duffield et al., 2005; Imamura et al., 2005)

References

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

Bachem MG, Sell KM, Melchior R, Kropf J, Eller T, Gressner AM. Tumor necrosis factor alpha (TNFa) and transforming growth factor pl (TGF/31) stimulate fibronectin synthesis and the transdifferentiation of fat-storing cells in the rat liver into myofibroblasts. Virchows Archiv B Cell Path (1993) 63: 123-30.

Bataller R, Brenner DA. Liver fibrosis. J Clin Invest (2005) 115:209-218.

Bataller R, Lemon SM. Fueling fibrosis in chronic hepatitis C. Proc Natl Acad Sci U S A. (2012) 109:14293–4.

Casini A, Ceni E, Salzano R, Biondi P, Parola M, Galli A, Foschi M, Caligiuri A, Pinzani M, Surrenti C. Neutrophil-derived superoxide anion induces lipid peroxidation and stimulates collagen synthesis in human hepatic stellate cells: role of nitric oxide. Hepatology. (1997) 25:361–367.

Dooley S, Hamzavi J, Breitkopf K, Wiercinska E, Said HM, Lorenzen J, Ten Dijke P, Gressner AM. Smad7 prevents activation of hepatic stellate cells and liver fibrosis in rats. Gastroenterology (2003) 125: 178–191.

Dranoff JA, Ogawa M, Kruglov EA, Gaça MD, Sévigny J, Robson SC, Wells RG. Expression of P2Y nucleotide receptors and ectonucleotidases in quiescent and activated rat hepatic stellate cells. Am J Physiol Gastrointest Liver Physiol (2004) 287:G417-24.

Duffield JS, Forbes SJ, Constandinou CM, Clay S, Partolina M, Vuthoori S, Wu S, Lang R, Iredale JP. Selective depletion of macrophages reveals distinct, opposing roles during liver injury and repair. J Clin Invest (2005) 115:56-65.

Friedman SL, Roll FJ, Boyles J, Arenson DM, Bissell D. Maintenance of differentiated phenotype of cultured rat hepatic lipocytes by basement membrane matrix. J Biol Chem (1989) 264:10756-10762.

Friedman SL. Molecular regulation of hepatic fibrosis, an integrated cellular response to tissue injury. J Biol Chem (2000) 275:2247-2250.

Fujita T, Maesawa C, Oikawa K, Nitta H, Wakabayashi G, Masuda T. Interferon-gamma down regulates expression of tumor necrosis factor-alpha converting enzyme/a disintegrin and metalloproteinase 17 in activated hepatic stellate cells of rats. Int J Mol Med (2006) 17:605-16.

Gao B, Radaeva S. Natural killer and natural killer T cells in liver fibrosis. Biochim Biophys Acta (2013) 1832:1061-9.

Gäbele E, Froh M, Arteel GE, Uesugi T, Hellerbrand C, Schölmerich J, Brenner DA, Thurman RG, Rippeb RA. TNFalpha is required for cholestasis-induced liver fibrosis in the mouse. Biochem Biophys Res Commun. (2008) 378(3):348-53.

Gressner AM, Weiskirchen R, Breitkopf K, Dooley S. Roles of TGF-β in hepatic fibrosis. Front. Biosci. (2002) 7: d793–807.

Heldin CH, Miyazono K, ten Dijke P. TGF-β signalling from cell membrane to nucleus through SMAD proteins. Nature (1997) 390:465-471.

Heymann F, Tacke F. Immunology in the liver–from homeostasis to disease. Nat Rev Gastroenterol Hepatol (2016) 13: 88–110.

Imamura M, Ogawa T, Sasaguri Y, Chayama K, Ueno H. Suppression of macrophage infiltration inhibits activation of hepatic stellate cells and liver fibrogenesis in rats. Gastroenterology (2005) 128:138-146.

Karlmark KR, Wasmuth HE, Trautwein C, Tacke F. Chemokine-directed immune cell infiltration in acute and chronic liver disease. Expert Rev Gastroenterol Hepatol (2008) 2:233-242.

Karlmark KR, Weiskirchen R, Zimmermann HW, Gassler N, Ginhoux F, Weber C, Merad M, Luedde T, Trautwein C, Tacke F. Hepatic recruitment of the inflammatory Gr1+ monocyte subset upon liver injury promotes hepatic fibrosis. Hepatology (2009) 50:261-74.

Kisseleva T, Brenner DA. Role of hepatic stellate cells in fibrogenesis and the reversal of fibrosis. J Gastroenterol Hepatol (2007) 22(Suppl 1):S73–S78.

Lanone S, Zheng T, Zhu Z, Liu W, Lee CG, Ma B, Chen Q, Homer RJ, Wang J, Rabach LA, Rabach ME, Shipley JM, Shapiro SD, Senior RM, Elias JA. Overlapping and enzyme-specific contributions of matrix metalloproteinases-9 and-12 in IL-13-induced inflammation and remodeling. J. Clin. Invest (2002) 110:463–474.

Lee KS, Buck M, Houglum K, Chojkier M. Activation of hepatic stellate cells by TGF alpha and collagen type I is mediated by oxidative stress through c-myb expression. J Clin Invest (1995) 96: 2461-8.

Lee CG, Homer RJ, Zhu Z, Lanone S, Wang X, KOteliansky V, Shipley M, Gotwals P, Noble P, Chen Q, Senior RM, Eliasa JA. Interleukin-13 induces tissue fibrosis by selectively stimulating and activating transforming growth factor beta(1). J Exp Med. (2001) 194(6):809-21.

Lee UE, Friedman SL. Mechanisms of hepatic fibrogenesis. Best Pract Res Clin Gastroenterol (2011) 25:195–206.

Liaskou E, Zimmermann HW, Li KK, Oo YH, Suresh S, Stamataki Z, Qureshi O, Lalor PF, Shaw J, Syn WK, Curbishley SM, Adams DH. Monocyte subsets in human liver disease show distinct phenotypic and functional characteristics. Hepatology (2013) 57:385-98.

Liu Y, Meyer C, Müller A, Herweck F, Li Q, Müllenbach R, Mertens PR, Dooley S, Weng HL. IL-13 induces connective tissue growth factor in rat hepatic stellate cells via TGF-β-independent Smad signaling. J Immunol. (2011) 187(5): 2814–2823.

Maher JJ. Leukocytes as modulators of stellate cell activation.  Alcohol Clin Exp Res. (1999) 23(5): 917–921.

Maher JJ. Interactions between hepatic stellate cells and the immune system. Semin. Liver Dis. (2001) 21:417–426.

Marra F, DeFranco R, Grappone C, Milani S, Pastacaldi S, Pinzani M, Romanelli RG, Laffi G, Gentilini P. Increased expression of monocyte chemotactic protein-1 during active hepatic fibrogenesis: correlation with monocyte infiltration.  Am J Pathol. (1998) 152(2): 423–430.

McHedlidze T, Waldner M, Zopf S, Walker J, Rankin AL, Schuchmann M, Voehringer D, McKenzie AN, Neurath MF, Pflanz S, Wirtz S. Interleukin-33-dependent innate lymphoid cells mediate hepatic fibrosis. Immunity  (2013) 39: 357–371.

Miyazono K, ten Dijke P,  Heldin CH. TGF-β signalling by Smad proteins. Adv Immunol (2000) 75:115-157.

Montosi G, Garuti C, Iannone A, Pietrangelo A. Spatial and temporal dynamics of hepatic stellate cell activation during oxidant-stress-induced fibrogenesis. Am J Pathol (1998) 152: 1319-1326.

Moustakas A, Souchelnytskyi S, Heldin CH. Smad regulation in TGF-beta signal transduction. J. Cell Sci. (2001) 114: 4359–4369.

Natarajan SK, Thomas S, Ramamoorthy P, Basivireddy J, Pulimood AB, Ramachandran A, Balasubramanian KA. Oxidative stress in the development of liver cirrhosis: a comparison of two different experimental models.  J Gastroenterol Hepatol.  (2006) 21(6): 947–957.

Park O, Jeong WI, Wang L, Wang H, Lian ZX, Gershwin ME, Gao B. Diverse roles of invariant natural killer T cells in liver injury and fibrosis induced by carbon tetrachloride. Hepatology (2009) 49:1683 94.

Piek E, Heldin CH,  ten Dijke P. Specificity, diversity, and regulation in TGF-beta superfamily signaling. FASEB J (1999) 13:2105-2124.

Pradere JP, Kluwe J, De Minicis S, Jiao JJ, Gwak GY, Dapito DH, Jang MK, Guenther ND, Mederacke I, Friedman R, Dragomir AC, Aloman C, Schwabe RF. Hepatic macrophages but not dendritic cells contribute to liver fibrosis by promoting the survival of activated hepatic stellate cells in mice. Hepatology (2013) 58:1461–1473.

Reeves HL, Dack CL, Peak M, Burt AD, Day CP. Stress-activated protein kinases in the activation of rat hepatic stellate cells in culture. J Hepatol.  (2000), 32: 465-472.

Rockey DC, Chung JJ. Endothelin antagonism in experimental hepatic fibrosis: implications for endothelin in the pathogenesis of wound healing. J Clin Invest (1996) 98: 1381-8.

Schmitz J, Owyang A, Oldham E, Song Y, Murphy E, McClanahan TK, Zurawski G, Moshrefi M, Qin J, Li X, Gorman DM, Bazan JF, Kastelein RA. IL-33, an interleukin-1-like cytokine that signals via the IL-1 receptor-related protein ST2 and induces T helper type 2-associated cytokines. Immunity (2005) 23: 479–490.

Seki E, De Minicis S, Osterreicher CH, Kluwe J, Osawa Y, Brenner DA, Schwabe RF. TLR4 enhances TGF-beta signaling and hepatic fibrosis. Nat Med. (2007) 13:1324–32.

Shimamura T, Fujisawa T, Husain SR, Kioi M, Nakajima A, Puri RK. Novel role of IL-13 in fibrosis induced by nonalcoholic steatohepatitis and its amelioration by IL-13R-directed cytotoxin in a rat model. J. Immunol. (2008) 181: 4656–4665.

Stewart RK, Dangi A, Huang C, Murase N, Kimura S, Stolz DB, Wilson GC, Lentsch AB, Gandhi CR. A novel mouse model of depletion of stellate cells clarifies their role in ischemia/reperfusion- and endotoxin-induced acute liver injury. J Hepatol.  (2014) 60:298–305.

Svegliati-Baroni G, Saccomanno S, van Goor H, Jansen P, Benedetti A, Moshage H. Involvement of reactive oxygen species and nitric oxide radicals in activation and proliferation of rat hepatic stellate cells. Liver (2001) 21, 1-12.

Syn WK, Agboola KM, Swiderska M, Michelotti GA, Liaskou E, Pang H, Xie G, Philips G, Chan IS, Karaca GF, Pereira Tde A, Chen Y, Mi Z, Kuo PC, Choi SS, Guy CD, Abdelmalek MF, Diehl AM. NKT associated hedgehog and osteopontin drive fibrogenesis in non-alcoholic fatty liver disease. Gut (2012) 61:1323-9.

Tacke F, Zimmermann HW. Macrophage heterogeneity in liver injury and fibrosis. J Hepatol (2014) 60:1090-6.

Tan Z, Liu Q, Jiang R, Lv L, Shoto SS, Maillet I,  Quesniaux V, Tang J, Zhang W, Sun B, Ryffel B. Interleukin-33 drives hepatic fibrosis through activation of hepatic stellate cells. Cell Mol Immunol (2018) 15(4): 388–398.

Vinas O, Bataller R, Sancho-Bru P, Ginès P, Berenguer C, Enrich C, Nicolás JM, Ercilla G, Gallart T, Vives J, Arroyo V, Rodés J. Human hepatic stellate cells show features of antigen-presenting cells and stimulate lymphocyte proliferation. Hepatology. (2003) 38:919–929.

Wehr A, Baeck C, Heymann F, Niemietz PM, Hammerich L, Martin C, Zimmermann HW, Pack O, Gassler N, Hittatiya K, Ludwig A, Luedde T, Trautwein C, Tacke F. Chemokine receptor CXCR6-dependent hepatic NK T Cell accumulation promotes inflammation and liver fibrosis. J Immunol (2013) 190:5226-36.

Wynn TA. Fibrotic disease and the T(H)1/T(H)2 paradigm. Nat Rev Immunol (2004) 4:583-594.

Zimmermann HW, Seidler S, Nattermann J, Gassler N, Hellerbrand C, Zernecke A, Tischendorf JJ, Luedde T, Weiskirchen R, Trautwein C, Tacke F. Functional contribution of elevated circulating and hepatic non-classical CD14CD16 monocytes to inflammation and human liver fibrosis. PLoS One (2010) 5:e11049.