Aop: 314


Each AOP should be given a descriptive title that takes the form “MIE leading to AO”. For example, “Aromatase inhibition [MIE] leading to reproductive dysfunction [AO]” or “Thyroperoxidase inhibition [MIE] leading to decreased cognitive function [AO]”. In cases where the MIE is unknown or undefined, the earliest known KE in the chain (i.e., furthest upstream) should be used in lieu of the MIE and it should be made clear that the stated event is a KE and not the MIE. More help

Binding to estrogen receptor (ER)-α in immune cells leading to exacerbation of systemic lupus erythematosus (SLE)

Short name
A short name should also be provided that succinctly summarises the information from the title. This name should not exceed 90 characters. More help
Binding to ER-α leading to exacerbation of SLE

Graphical Representation

A graphical summary of the AOP listing all the KEs in sequence, including the MIE (if known) and AO, and the pair-wise relationships (links or KERs) between those KEs should be provided. This is easily achieved using the standard box and arrow AOP diagram (see this page for example). The graphical summary is prepared and uploaded by the user (templates are available) and is often included as part of the proposal when AOP development projects are submitted to the OECD AOP Development Workplan. The graphical representation or AOP diagram provides a useful and concise overview of the KEs that are included in the AOP, and the sequence in which they are linked together. This can aid both the process of development, as well as review and use of the AOP (for more information please see page 19 of the Users' Handbook).If you already have a graphical representation of your AOP in electronic format, simple save it in a standard image format (e.g. jpeg, png) then click ‘Choose File’ under the “Graphical Representation” heading, which is part of the Summary of the AOP section, to select the file that you have just edited. Files must be in jpeg, jpg, gif, png, or bmp format. Click ‘Upload’ to upload the file. You should see the AOP page with the image displayed under the “Graphical Representation” heading. To remove a graphical representation file, click 'Remove' and then click 'OK.'  Your graphic should no longer be displayed on the AOP page. If you do not have a graphical representation of your AOP in electronic format, a template is available to assist you.  Under “Summary of the AOP”, under the “Graphical Representation” heading click on the link “Click to download template for graphical representation.” A Powerpoint template file should download via the default download mechanism for your browser. Click to open this file; it contains a Powerpoint template for an AOP diagram and instructions for editing and saving the diagram. Be sure to save the diagram as jpeg, jpg, gif, png, or bmp format. Once the diagram is edited to its final state, upload the image file as described above. More help


List the name and affiliation information of the individual(s)/organisation(s) that created/developed the AOP. In the context of the OECD AOP Development Workplan, this would typically be the individuals and organisation that submitted an AOP development proposal to the EAGMST. Significant contributors to the AOP should also be listed. A corresponding author with contact information may be provided here. This author does not need an account on the AOP-KB and can be distinct from the point of contact below. The list of authors will be included in any snapshot made from an AOP. More help

Yasuharu Otsubo (1) Takao Ashikaga (1) Tomoki Fukuyama (1) Ken Goto (1) Shinko Hata (1) Shigeru Hisada (1) Shiho Ito (1) Hiroyuki Komatsu (1) Sumie Konishi (1) Tadashi Kosaka (1) Kiyoshi Kushima (1) Shogo Matsumura (1) Takumi Ohishi (1) Junichiro Sugimoto (1) Yasuhiro Yoshida (1)

(1) AOP Working Group, Testing Methodology Committee, The Japanese Society of Immunotoxicology

Corresponding author: Yasuharu Otsubo (

Point of Contact

Indicate the point of contact for the AOP-KB entry itself. This person is responsible for managing the AOP entry in the AOP-KB and controls write access to the page by defining the contributors as described below. Clicking on the name will allow any wiki user to correspond with the point of contact via the email address associated with their user profile in the AOP-KB. This person can be the same as the corresponding author listed in the authors section but isn’t required to be. In cases where the individuals are different, the corresponding author would be the appropriate person to contact for scientific issues whereas the point of contact would be the appropriate person to contact about technical issues with the AOP-KB entry itself. Corresponding authors and the point of contact are encouraged to monitor comments on their AOPs and develop or coordinate responses as appropriate.  More help
Cataia Ives   (email point of contact)


List user names of all  authors contributing to or revising pages in the AOP-KB that are linked to the AOP description. This information is mainly used to control write access to the AOP page and is controlled by the Point of Contact.  More help
  • Takumi Ohishi
  • Yasuharu Otsubo
  • Cataia Ives


The status section is used to provide AOP-KB users with information concerning how actively the AOP page is being developed, what type of use or input the authors feel comfortable with given the current level of development, and whether it is part of the OECD AOP Development Workplan and has been reviewed and/or endorsed. “Author Status” is an author defined field that is designated by selecting one of several options from a drop-down menu (Table 3). The “Author Status” field should be changed by the point of contact, as appropriate, as AOP development proceeds. See page 22 of the User Handbook for definitions of selection options. More help
Author status OECD status OECD project SAAOP status
Under development: Not open for comment. Do not cite Under Development 1.73 Included in OECD Work Plan
This AOP was last modified on May 08, 2022 11:33
The date the AOP was last modified is automatically tracked by the AOP-KB. The date modified field can be used to evaluate how actively the page is under development and how recently the version within the AOP-Wiki has been updated compared to any snapshots that were generated. More help

Revision dates for related pages

Page Revision Date/Time
Binding to estrogen receptor (ER)-α in immune cells August 14, 2020 20:59
Induction of GATA3 expression August 14, 2020 21:16
Increase of Th2 cells producing IL-4 August 14, 2020 21:26
Increase of anti-DNA antibody from autoreactive B cell August 14, 2020 21:57
Exacerbation of systemic lupus erythematosus (SLE) August 14, 2020 22:11
Binding to estrogen receptor (ER)-α leads to Induction of GATA3 expression August 14, 2020 22:17
Induction of GATA3 expression leads to Increase of Th2 cells producing IL-4 August 14, 2020 22:22
Increase of Th2 cells producing IL-4 leads to Increase of autoantibody production August 14, 2020 22:29
Increase of autoantibody production leads to Exacerbation of SLE August 14, 2020 22:35
Bisphenol A December 29, 2019 18:38
17beta-Estradiol November 29, 2016 18:42


In the abstract section, authors should provide a concise and informative summation of the AOP under development that can stand-alone from the AOP page. Abstracts should typically be 200-400 words in length (similar to an abstract for a journal article). Suggested content for the abstract includes the following: The background/purpose for initiation of the AOP’s development (if there was a specific intent) A brief description of the MIE, AO, and/or major KEs that define the pathway A short summation of the overall WoE supporting the AOP and identification of major knowledge gaps (if any) If a brief statement about how the AOP may be applied (optional). The aim is to capture the highlights of the AOP and its potential scientific and regulatory relevance More help

This AOP describes the linkage between the binding to estrogen receptor (ER) α in immune cells with the exacerbation of the autoimmune disease systemic lupus erythematosus (SLE).

Estrogen receptors (ERs), ERα and ERβ, are a group of proteins that are activated by the steroid hormone estrogen and are widely expressed in most tissue types, including most immune cells.  ERα can be activated with exogenous and endogenous estrogens.  Also, there are numerous xenoestrogens that exist in the environment and imitate estrogen.  Bisphenol A (BPA) is an example of a xenoestrogen that is considered an endocrine disrupting (ED) compound.  SLE is an autoimmune disease characterized by overproduction of a variety of anti-cell nuclear and other pathogenic autoantibodies.  It is characterized by B-cell hyperactivity, polyclonal hypergammaglobulinemia, and immune complex deposition.

Binding to ERα in immune cells by a xenoestrogen or endogenous estrogen marks the molecular initiating event (MIE), which results in induction of GATA3 expression (KE1).  One theory of immune regulation involves homeostasis between T-helper 1 (Th1) and T-helper2 (Th2) activity, however GATA3 expression induce increase of Th2 cells producing cytokine interleukin-4 (IL-4) (KE2), which results in increase of anti-DNA antibody from autoreactive B cell (KE3).  This sequence of pathway means that the immune system skew from a Th1 to a Th2 profile, which results in the adverse outcome (AO) of exacerbated SLE.

We have identified a number of key events along this pathway and determined the key event relationships, based on which we have created an AOP for binding to ERα in immune cells leading to exacerbated SLE.

Background (optional)

This optional subsection should be used to provide background information for AOP reviewers and users that is considered helpful in understanding the biology underlying the AOP and the motivation for its development. The background should NOT provide an overview of the AOP, its KEs or KERs, which are captured in more detail below. Examples of potential uses of the optional background section are listed on pages 24-25 of the User Handbook. More help

It is well recognized that allergic diseases and autoimmune diseases are markedly increased the last several decades.  About the same time, increasing scientific and social attention had been paid to environmentally dispersed chemicals that can enter the body by ingestion or adsorption and that mimic the actions of estrogens.  These chemicals are termed endocrine disruptors (EDs) or environmental estrogens and are found in plastics (bisphenol-A, phthalates), pesticides (DDT, hexachlorobenzene, and dieldrin) and the like.  Some of these estrogenic chemicals have also been shown to influence the immune system.  Endocrine disruptors mimic hormones, block or alter hormone binding to receptors, or alter the metabolism of natural estrogens.  It has been widely noted that females have stronger immune capabilities than males, as evidenced by their better immune responses to a variety of self-antigens and non-self-antigens, or vaccination.  Paradoxically, the stronger immune response comes at a steep price, which is the high incidence of autoimmune diseases in females.  This phenomenon of gender-based immune capability is largely attributed to the effects of sex hormones.  Estrogens regulate the level of serum and uterine IgM, IgA, and IgG, and they augment antibody production to several nonself- antigens and self-antigens. It is possible that endocrine disruptors that mimic estrogenic activity may be involved in the increased incidence of autoimmune diseases such as SLE (Yurino H. 2004, Vaishali RM. 2018).

Summary of the AOP

This section is for information that describes the overall AOP. The information described in section 1 is entered on the upper portion of an AOP page within the AOP-Wiki. This is where some background information may be provided, the structure of the AOP is described, and the KEs and KERs are listed. More help


Molecular Initiating Events (MIE)
An MIE is a specialised KE that represents the beginning (point of interaction between a stressor and the biological system) of an AOP. More help
Key Events (KE)
This table summarises all of the KEs of the AOP. This table is populated in the AOP-Wiki as KEs are added to the AOP. Each table entry acts as a link to the individual KE description page.  More help
Adverse Outcomes (AO)
An AO is a specialised KE that represents the end (an adverse outcome of regulatory significance) of an AOP.  More help
Sequence Type Event ID Title Short name
MIE 1710 Binding to estrogen receptor (ER)-α in immune cells Binding to estrogen receptor (ER)-α
KE 1711 Induction of GATA3 expression Induction of GATA3 expression
KE 1712 Increase of Th2 cells producing IL-4 Increase of Th2 cells producing IL-4
KE 1713 Increase of anti-DNA antibody from autoreactive B cell Increase of autoantibody production

Relationships Between Two Key Events (Including MIEs and AOs)

This table summarises all of the KERs of the AOP and is populated in the AOP-Wiki as KERs are added to the AOP. Each table entry acts as a link to the individual KER description page.To add a key event relationship click on either Add relationship: events adjacent in sequence or Add relationship: events non-adjacent in sequence.For example, if the intended sequence of KEs for the AOP is [KE1 > KE2 > KE3 > KE4]; relationships between KE1 and KE2; KE2 and KE3; and KE3 and KE4 would be defined using the add relationship: events adjacent in sequence button.  Relationships between KE1 and KE3; KE2 and KE4; or KE1 and KE4, for example, should be created using the add relationship: events non-adjacent button. This helps to both organize the table with regard to which KERs define the main sequence of KEs and those that provide additional supporting evidence and aids computational analysis of AOP networks, where non-adjacent KERs can result in artifacts (see Villeneuve et al. 2018; DOI: 10.1002/etc.4124).After clicking either option, the user will be brought to a new page entitled ‘Add Relationship to AOP.’ To create a new relationship, select an upstream event and a downstream event from the drop down menus. The KER will automatically be designated as either adjacent or non-adjacent depending on the button selected. The fields “Evidence” and “Quantitative understanding” can be selected from the drop-down options at the time of creation of the relationship, or can be added later. See the Users Handbook, page 52 (Assess Evidence Supporting All KERs for guiding questions, etc.).  Click ‘Create [adjacent/non-adjacent] relationship.’  The new relationship should be listed on the AOP page under the heading “Relationships Between Two Key Events (Including MIEs and AOs)”. To edit a key event relationship, click ‘Edit’ next to the name of the relationship you wish to edit. The user will be directed to an Editing Relationship page where they can edit the Evidence, and Quantitative Understanding fields using the drop down menus. Once finished editing, click ‘Update [adjacent/non-adjacent] relationship’ to update these fields and return to the AOP page.To remove a key event relationship to an AOP page, under Summary of the AOP, next to “Relationships Between Two Key Events (Including MIEs and AOs)” click ‘Remove’ The relationship should no longer be listed on the AOP page under the heading “Relationships Between Two Key Events (Including MIEs and AOs)”. More help

Network View

The AOP-Wiki automatically generates a network view of the AOP. This network graphic is based on the information provided in the MIE, KEs, AO, KERs and WoE summary tables. The width of the edges representing the KERs is determined by its WoE confidence level, with thicker lines representing higher degrees of confidence. This network view also shows which KEs are shared with other AOPs. More help


The stressor field is a structured data field that can be used to annotate an AOP with standardised terms identifying stressors known to trigger the MIE/AOP. Most often these are chemical names selected from established chemical ontologies. However, depending on the information available, this could also refer to chemical categories (i.e., groups of chemicals with defined structural features known to trigger the MIE). It can also include non-chemical stressors such as genetic or environmental factors. Although AOPs themselves are not chemical or stressor-specific, linking to stressor terms known to be relevant to different AOPs can aid users in searching for AOPs that may be relevant to a given stressor. More help
Name Evidence Term
Bisphenol A Moderate
17beta-Estradiol High

Life Stage Applicability

Identify the life stage for which the KE is known to be applicable. More help
Life stage Evidence
All life stages Moderate

Taxonomic Applicability

Latin or common names of a species or broader taxonomic grouping (e.g., class, order, family) can be selected. In many cases, individual species identified in these structured fields will be those for which the strongest evidence used in constructing the AOP was available in relation to this KE. More help
Term Scientific Term Evidence Link
Homo sapiens Homo sapiens Moderate NCBI

Sex Applicability

The authors must select from one of the following: Male, female, mixed, asexual, third gender, hermaphrodite, or unspecific. More help
Sex Evidence
Mixed High

Overall Assessment of the AOP

This section addresses the relevant biological domain of applicability (i.e., in terms of taxa, sex, life stage, etc.) and WoE for the overall AOP as a basis to consider appropriate regulatory application (e.g., priority setting, testing strategies or risk assessment). The goal of the overall assessment is to provide a high level synthesis and overview of the relative confidence in the AOP and where the significant gaps or weaknesses are (if they exist). Users or readers can drill down into the finer details captured in the KE and KER descriptions, and/or associated summary tables, as appropriate to their needs.Assessment of the AOP is organised into a number of steps. Guidance on pages 59-62 of the User Handbook is available to facilitate assignment of categories of high, moderate, or low confidence for each consideration. While it is not necessary to repeat lengthy text that appears elsewhere in the AOP description (or related KE and KER descriptions), a brief explanation or rationale for the selection of high, moderate, or low confidence should be made. More help

Domain of Applicability

The relevant biological domain(s) of applicability in terms of sex, life-stage, taxa, and other aspects of biological context are defined in this section. Biological domain of applicability is informed by the “Description” and “Biological Domain of Applicability” sections of each KE and KER description (see sections 2G and 3E for details). In essence the taxa/life-stage/sex applicability is defined based on the groups of organisms for which the measurements represented by the KEs can feasibly be measured and the functional and regulatory relationships represented by the KERs are operative.The relevant biological domain of applicability of the AOP as a whole will nearly always be defined based on the most narrowly restricted of its KEs and KERs. For example, if most of the KEs apply to either sex, but one is relevant to females only, the biological domain of applicability of the AOP as a whole would be limited to females. While much of the detail defining the domain of applicability may be found in the individual KE and KER descriptions, the rationale for defining the relevant biological domain of applicability of the overall AOP should be briefly summarised on the AOP page. More help

It has long been appreciated that most autoimmune disorders are characterized by increased prevalence in females, suggesting a potential role for sex hormones (estrogen) in the etiology of autoimmunity.  Females generally exhibit a stronger response to a variety of antigens including ERα ligands than males, which is perhaps one reason that they are more prone to develop autoimmune and allergic diseases such as SLE in greater severity than males.  Therefore, this AOP is applicable to females and is dependent on the levels of estrogen, which means it varies with life stage, and age.

SLE frequently develop and progress in setting in which sympathoadrenomedullary and gonadal hormone levels are changing, e.g., during pregnancy, the postpartum period, or estrogen administration in menopause (Wilder RL. 1999).  Women using oral contraceptives that contain estrogen or undergoing hormone replacement therapy are susceptible to major flare ups and exacerbation of the disease (Whitelaw DA. 2007).

The mechanisms described in this AOP are applicable to rodents and humans, and then the findings of this AOP are not found in any other species.  However, Th2 dominant conditions induced by binding to ERα is considered likely to occur in a variety of mammalian species since ERα are expressed in all vertebrates (Eick GN. 2011).

Essentiality of the Key Events

An important aspect of assessing an AOP is evaluating the essentiality of its KEs. The essentiality of KEs can only be assessed relative to the impact of manipulation of a given KE (e.g., experimentally blocking or exacerbating the event) on the downstream sequence of KEs defined for the AOP. Consequently evidence supporting essentiality is assembled on the AOP page, rather than on the independent KE pages that are meant to stand-alone as modular units without reference to other KEs in the sequence.The nature of experimental evidence that is relevant to assessing essentiality relates to the impact on downstream KEs and the AO if upstream KEs are prevented or modified. This includes: Direct evidence: directly measured experimental support that blocking or preventing a KE prevents or impacts downstream KEs in the pathway in the expected fashion. Indirect evidence: evidence that modulation or attenuation in the magnitude of impact on a specific KE (increased effect or decreased effect) is associated with corresponding changes (increases or decreases) in the magnitude or frequency of one or more downstream KEs.When assembling the support for essentiality of the KEs, authors should organise relevant data in a tabular format. The objective is to summarise briefly the nature and numbers of investigations in which the essentiality of KEs has been experimentally explored either directly or indirectly. See pages 50-51 in the User Handbook for further definitions and clarifications.  More help

Stressor , MIE and later events:

The NZB/W F1 mouse is the oldest classical model of lupus generated by the F1 hybrid between the NZB and NZW strains.  The administration of the estrogen antagonist tamoxifen diminishes immune complex deposition in the kidneys and increases survival in NZB/W F1 strain.  Renal disease was evaluated by the development of albuminuria and histological changes in the kidney (Wu WM. 2000).  In females of the NZB/NZW F1 strain, disruption of ERα attenuated glomerulonephritis and increased survival and reduced anti-dsDNA antibodies (Bynote KK. 2008, Isenberg DA. 2007) and ovariectomy of NZB/W F1 mice not only delayed onset of the disease but also decreased autoantibody titer  Meanwhile, restoration of estradiol in ovariectomized NZB/W F1 mice reestablished high numbers of autoantibody-producing (DNA-specific) B cells, and thereby suggests a pathogenic role of estrogen in lupus (Daniel P. 2011). Both NZB and NZW display limited autoimmunity, while NZB/W F1 hybrids develop severe lupus-like phenotypes comparable to that of lupus patients.  In NZM female mice, ERα inactivation markedly prolonged life-span, lowered proteinuria, and ameliorated glomerulonephritis but resulted in higher serum anti-dsDNA antibody levels (Svenson JL. 2008).

KE1 and later events:

GATA3 mRNA expression has potential to induced IL-4 production in CD4+T cell (Lambert KC. 2005).  The differentiation of activated CD4+T cells into the T helper type 1 (Th1) or Th2 fate is regulated by cytokines and the transcription factors T-bet and GATA-3.  Early GATA-3 expression, required for Th2 differentiation, was induced by T cell factor 1 (TCF-1) and its cofactor β-catenin, mainly from the proximal Gata3 promoter upstream of exon 1b.  TCF-1 blocked Th1 fate by negatively regulating interferon-γ (IFN-γ) expression independently of β-catenin.  Thus, TCF-1 initiates Th2 differentiation of activated CD4+T cells by promoting GATA-3 expression and suppressing IFN-γ expression.  Higher GATA-3 expression promotes IL-4 production and initiates Th2 differentiation (Qing Y. 2009).  GATA-3 mRNA expression also increased in patients with SLE, compared with the healthy control groups (Zheng H. 2015, Sonia GR. 2012).

KE2 and later events:

Administration of mAb against IL-4 before the onset of lupus was effective in preventing the onset of lupus nephritis (Nakajima A. 1997).

KE3 and later events:

In a study to investigate a novel subpopulation of B-1 cells and its roles in murine lupus, anti-double-stranded DNA (anti-dsDNA) autoantibodies were preferentially secreted by a subpopulation of CD5+ B-1 cells that expressed programmed death ligand 2 (L2pB1 cells) (Xuemei Z. 2009).  A substantial proportion of hybridoma clones generated from L2pB1 cells reacted to dsDNA.  L2pB1 cells are potent antigen-presenting cells and a dramatic increase of circulating L2pB1 cells in lupus-prone BXSB mice correlates with elevated serum titers of anti-dsDNA antibodies (Xuemei Z. 2009).

Evidence Assessment

The biological plausibility, empirical support, and quantitative understanding from each KER in an AOP are assessed together.  Biological plausibility of each of the KERs in the AOP is the most influential consideration in assessing WoE or degree of confidence in an overall hypothesised AOP for potential regulatory application (Meek et al., 2014; 2014a). Empirical support entails consideration of experimental data in terms of the associations between KEs – namely dose-response concordance and temporal relationships between and across multiple KEs. It is examined most often in studies of dose-response/incidence and temporal relationships for stressors that impact the pathway. While less influential than biological plausibility of the KERs and essentiality of the KEs, empirical support can increase confidence in the relationships included in an AOP. For clarification on how to rate the given empirical support for a KER, as well as examples, see pages 53- 55 of the User Handbook.  More help

Biological Plausibility

KER KEup-KEdown Plausibility Rationales supported by literatures
KER 1 Binding, Estrogen receptor α in immune cells - Induction, GATA3 expression Weak In immune cells, this event is confirmed indirectly; using artificial STAT6-ER fusion protein.
KER 2 Induction, GATA3 expression - Increase, Th2 cells producing IL-4 Strong XXXX
KER 3 Increase, Th2 cells producing IL-4 - Increase, anti-DNA antibody production from autoreactive B cell Weak XXXX
KER 4 Increase, anti-DNA antibody production from autoreactive B cell - Strong XXXX

Empirical Support

KER Empirical support of KERs
MIE=>KE 1:Binding, Estrogen receptor α in immune cells leads to Induction, GATA3 expression

Empirical support of the MIE => KE1 is weak.



KE 1=> KE 2: Induction, GATA3 expression leads to Increase, Th2 cells producing IL-4

Empirical support of the KE 1=> KE 2 is strong.



KE 2=> KE 3: Increase, Th2 cells producing IL-4 leads to Increase, anti-DNA antibody production from autoreactive B cell

Empirical support of the KE 2=> KE 3 is weak.



KE 3=>AO:  Increase, antibody production from anti-DNA antibody production from autoreactive B cell leads to Exacerbation, systemic lupus erythematosus (SLE)

Empirical support of the KE 3 => AO is strong.




Quantitative Understanding

Some proof of concept examples to address the WoE considerations for AOPs quantitatively have recently been developed, based on the rank ordering of the relevant Bradford Hill considerations (i.e., biological plausibility, essentiality and empirical support) (Becker et al., 2017; Becker et al, 2015; Collier et al., 2016). Suggested quantitation of the various elements is expert derived, without collective consideration currently of appropriate reporting templates or formal expert engagement. Though not essential, developers may wish to assign comparative quantitative values to the extent of the supporting data based on the three critical Bradford Hill considerations for AOPs, as a basis to contribute to collective experience.Specific attention is also given to how precisely and accurately one can potentially predict an impact on KEdownstream based on some measurement of KEupstream. This is captured in the form of quantitative understanding calls for each KER. See pages 55-56 of the User Handbook for a review of quantitative understanding for KER's. More help


CD4+T cell expressed GATA3 mRNA cultured with 10-9 M (272.4 pg/mL) concentrations of 17β-estradiol for 12-16 hr (Lambert KC. 2005).  

BPA (0.1 mM) also indirectly induced GATA3 expression of Th cells, and this effect is mediated by dendritic cells exposed to BPA for 24 hr (Guo H. 2010).  Naïve Th cells increased GATA3 expression cultured with dendritic cells exposure of BPA (0.1 mM) for 7 days.


Pre-stimulation 16 hr of 17β-estradiol (the concentration 10-9 M = 272.4 pg/mL) increased IL-4 secretion from CD4+T cell (Lambert KC. 2005). 


PBMCs or B cells were cultured for 7 days with 17β-estradiol (10–8 mol/L)  and then, IgG and IgM production were increased up to about 150% (PBMC) and 200% (B cells) (Kanda N. 1999).



Considerations for Potential Applications of the AOP (optional)

At their discretion, the developer may include in this section discussion of the potential applications of an AOP to support regulatory decision-making. This may include, for example, possible utility for test guideline development or refinement, development of integrated testing and assessment approaches, development of (Q)SARs / or chemical profilers to facilitate the grouping of chemicals for subsequent read-across, screening level hazard assessments or even risk assessment. While it is challenging to foresee all potential regulatory application of AOPs and any application will ultimately lie within the purview of regulatory agencies, potential applications may be apparent as the AOP is being developed, particularly if it was initiated with a particular application in mind. This optional section is intended to provide the developer with an opportunity to suggest potential regulatory applications and describe his or her rationale.To edit the “Considerations for Potential Applications of the AOP” section, on an AOP page, in the upper right hand menu, click ‘Edit.’ This brings you to a page entitled, “Editing AOP.” Scroll down to the “Considerations for Potential Applications of the AOP” section, where a text entry box allows you to submit text. In the upper right hand menu, click ‘Update AOP’ to save your changes and return to the AOP page or 'Update and continue' to continue editing AOP text sections.  The new text should appear under the “Considerations for Potential Applications of the AOP” section on the AOP page. More help


List the bibliographic references to original papers, books or other documents used to support the AOP. More help
  1. Yurino, H., Ishikawa, S., Sato, T., Akadegawa, K., Ito, T., Ueha, S., Inadera, H. and Matsushima, K. (2004). Endocrine disruptors (environmental estrogens) enhance autoantibody production by B1 cells. Toxicological Sciences 81(1): 139-147.
  2. Vaishali RM. Sex Hormones in Acquired Immunity and Autoimmune Disease. Frontiers in Immunology 2018. 9: 2279; 1-21.
  3. Wilder RL, Elenkov IJ, Hormonal regulation of tumor necrosis factor-alpha, interleukin-12 and interleukin-10 production by activated macrophages. A disease-modifying mechanism in rheumatoid arthritis and systemic lupus erythematosus? Ann N Y Acad Sci. 1999. 22; 876:14-31.
  4. Whitelaw DA, Jessop SJ. Major flares in women with SLE on combined oral contraception. Clin Rheumatol. 2007; 26(12):2163-2165.
  5. Eick GN, Thornton JW. Evolution of steroid receptors from an estrogen-sensitive ancestral receptor. Molecular and cellular endocrinology. 2011; 334: 31-38.
  6. Wu WM, Lin BF, Su YC, et al. (2000). Tamoxifen decreases renal inflammation and alleviates disease severity in autoimmune NZB/W F1 mice. Scandinavian Journal of Immunology 52(4): 393-400.
  7. Bynote, KK, Hackenberg, JM., Korach, K.S., Lubahn, D. B., Lane, P. H. and Gould, K. A. (2008). Estrogen receptor-alpha deficiency attenuates autoimmune disease in (NZB xNZW) F1 mice. Genes and Immunity. 9: 137-152.
  8. Isenberg, DA., Manson, JJ., Ehrenstein, MR. and Rahman, A. (2007). Fifty years of anti-ds DNA antibodies: are we approaching journey’s end? Rheumatology 46:1052-6.
  9. Daniel, P., Allison, S., Yiming, Y., Ying-Yi, Z. and Laurence, M. Murine Models of Systemic Lupus erythematosus. Journal of Biomedicine and Biotechnology 2011: ArticleID 271694
  10. Svenson JL, EuDaly J, Ruiz P, Korach KS, Gilkeson GS. Impact of estrogen receptor deficiency on disease expression in the NZM2410 lupus prone mouse. Clin Immunol. 2008;128(2):259-68.
  11. Lambert KC, Curran EM, et al. Estrogen receptor alpha (ERalpha) deficiency in macrophages results in increased stimulation of CD4+ T cells while 17beta-estradiol acts through ERalpha to increase IL-4 and GATA-3 expression in CD4+ T cells independent of antigen presentation. J Immunol. 2005; 175(9): 5716-23.
  12. Qing Y., Archna S., Sun Y. O., Hyung-Geun M., M Zulfiquer H., Theresa M. S., Karen E. L., Hansen D., Beibei W., Marian L. W., Zhou Z. and Jyoti M. S., T cell factor 1 initiates the T helper type 2 fate by inducing the transcription factor GATA-3 and repressing interferon-γ. Nat Immunol. 2009; 10(9): 992–999.
  13. Zheng H, Guo X, Zhu Y, et al., Distinct role of Tim-3 in systemic lupus erythematosus and clear cell renal cell carcinoma. Int J Clin Exp Med 2015;8(5):7029-7038.
  14. Sonia GR, et al. Altered AKT1 and MAPK1 Gene Expression on Peripheral Blood Mononuclear Cells and Correlation with T-Helper-Transcription Factors in Systemic Lupus Erythematosus Patients. Mediators of Inflammation 2012, Article ID 495934
  15. Nakajima A, Hirose S, Yagita H and Okumura K, Roles of IL-4 and IL-12 in the development of lupus in NZB/W F1 mice. J Immunol 1997; 158 (3) 1466-1472.
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