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Relationship: 2273

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

Altered, Transcription of genes by AR leads to Reduced granulosa cell proliferation

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

Altered, Transcription of genes by AR

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

Reduced granulosa cell proliferation

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
Androgen receptor (AR) antagonism leading to decreased fertility in females	adjacent	Moderate	Low	Terje Svingen (send email)	Under development: Not open for comment. Do not cite

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	Low	NCBI
mouse	Mus musculus	High	NCBI
rat	Rattus norvegicus	High	NCBI
Pig	Pig	High	NCBI
cow	Bos taurus	Moderate	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
Female	Not Specified

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

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

Decreased transcription of genes that are downstream to androgen receptor activation leads to reduced granulosa cell proliferation of the gonadotropin-independent ovarian follicles. Therefore, the follicle growth to the antral stage is attenuated.

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

AR is a ligand-activated nuclear transcription factor expressed in the ovaries across mammalian species, including humans (Gervásio, Bernuci, Silva-de-Sá, & Rosa-e-Silva, 2014). In humans, both mRNA and protein of AR are present in the oocyte, stroma cells, theca cells, but most prominently in granulosa cells of preantral follicles (Gervásio et al., 2014).

In the rodent ovary, AR mRNA and protein are present in the oocyte, theca and granulosa cells (Gill, Jamnongjit, & Hammes, 2004; Hirai, Hirata, Osada, Hagihara, & Kato, 1994; Szoltys & Slomczynska, 2000; Tetsuka & Hillier, 1996; Tetsuka et al., 1995). In the bovine and ovine ovary, AR mRNA is present in granulosa and theca cells, and most prominently in granulosa of preantral and antral follicles (Hampton, Manikkam, Lubahn, Smith, & Garverick, 2004; Juengel, Heath, Quirke, & McNatty, 2006). In the porcine ovary, AR mRNA is mainly expressed in the granulosa cells until the antral stage (Cárdenas & Pope, 2002; Slomczynska, Duda, & Sl zak, 2001). In the non-human primate ovary, AR mRNA and protein is present in theca, but mainly granulosa cells of preantral and antral follicles (Hillier, Tetsuka, & Fraser, 1997; S. J. Weil et al., 1998).

In human follicles, the expression of the AR transcript is observed after the primordial stage and is most pronounced during the preantral stage (Rice, Ojha, Whitehead, & Mason, 2007). Throughout early folliculogenesis, AR expression controls transcription of genes involved in promoting growth and differentiation of granulosa cells and formation of antrum (Gervásio et al., 2014). Genes under the control of AR that are involved in these processes include Kit ligand (kitl), Bone morphogenetic protein 15 (bmp15), and Hepatocyte growth factor (hgf) (Astapova, Minor, & Hammes, 2019; Prizant, Gleicher, & Sen, 2014).

In the monkey ovary, high levels of AR mRNA correlates with high levels of granulosa cell proliferation (K. A. Vendola, Zhou, Adesanya, Weil, & Bondy, 1998; S. J. Weil et al., 1998) Increased AR activation is associated with increased follicle growth and increased granulosa cell proliferation in preantral rat follicles (Lim, Han, Lee, & Tsang, 2017), supporting the important role for AR during this developmental stage.

AR may mediate preantral follicle growth through FSHR, supported by studies correlating mRNA levels of AR and FSHR in granulosa cells of small antral follicles (Nielsen et al., 2011), (S. Weil, Vendola, Zhou, & Bondy, 1999). In mice, FSH-mediated in vitro follicle growth is increased by androgens (Sen et al., 2014), suggesting that androgens through AR may act synergistically with FSHR, which in turn increases preantral follicle growth.

AR activation has been associated with IGF-1 and IGFR-1 and other key factors of the IGF signaling pathway, which is essential for granulosa growth and differentiation (Baumgarten et al., 2014), (K. Vendola et al., 1999). In human granulosa cells of primordial and primary follicles, AR and IGF-related factors are highly enriched at the transcriptional level (Steffensen, Ernst, Amoushahi, Ernst, & Lykke-Hartmann, 2018).

AR activation affects the level of connexins, proteins that form gap junctions between granulosa cells and the oocyte and hence regulate intracellular communication; a prerequisite for folliculogenesis (Kamal, Ibrahim, & Mokhtar, 2020).

Empirical Evidence

In this section authors are encouraged to cite specific evidence that supports the idea that a change in the upstream KE (KEupstream) will lead to, or is associated with, a subsequent change in the downstream KE (KEdownstream), assuming the perturbation of KEupstream is sufficient. Given the likelihood that new empirical support will be developed over time, particularly as various AOPs are tested and applied, it is most practical to provide empirical support in the form of bulleted lists or tables that include a short description of the nature of the empirical support along with the corresponding reference(s).In particular, it is useful to cite evidence showing that stressors that perturb KEupstream also perturb KEdownstream. Because this section of the KER description cites evidence from specific studies, it is also helpful to provide as much detail about the toxicological and biological context in which the measurements were made, as is feasible, including the stressor(s) tested, the effective doses at each KE, etc. While the KER itself is not intended to be stressor-specific, those details can aid the overall assessment of the individual AOPs that include that KER. These details also help inform the question of consistency of supporting data, consistency across different biological contexts for which the KER is relevant, and the applicability domain of the KER. However, authors are cautioned that this evidence should focus on data that only relate KEupstream to KEdownstream, and should avoid reference to other KEs, KERs and AOPs as much as possible in order to maintain modularity of the KER. Please see pages 40-41 of the User Handbook for more information. More help

Androgen Receptor Knock Out (ARKO) mouse model

GC-specific ARKO model	Relevant observations	Reference
GCARKO^Ex2	Premature ovarian failure, subfertility, longer estrous cycles, slower in vitro follicle growth compared to wild type	(Sen & Hammes, 2010)
GCARKO^Ex3	Subfertility, longer estrous cycles, decreased number of preantral and antral follicles compared to wild type, trend of lower ovarian expression of kitl, ifgr1 and fshr compared to wild type (not statistically significant)	(Walters et al., 2012)

In vitro/Ex vivo

Study type	Species	Compound	EffectDose	Duration	Method	Result	Reference
Isolated preantral follicles in culture	Mouse	Hydroxyflutamide, Bicalutamide	50μΜ	8d, 12d	Follicle diameter measured	Reduced follicular growth	(Lenie & Smitz, 2009)
Isolated preantral follicles in culture	Mouse	Bicalutamide	10μΜ, 32μΜ	6d	Follicle diameter measured	Reduced follicular growth (dose-depended)	(Murray, Gosden, Allison, & Spears, 1998)
Isolated preantral follicles in culture	Mouse	Hydroxyflutamide	1μΜ, 10μΜ	4d	Follicle diameter measured	Reduced androgen-induced follicular growth	(Wang et al., 2001)
Isolated preantral follicles in culture	Mouse	Flutamide	20μΜ	2d, 3d	Follicle diameter and area measured	Reduced androgen –induced follicular growth	(Laird et al., 2017)
Isolated secondary follicles in culture	Mouse	Enzalutamide	1μΜ	2d, 4d, 6d	Follicle diameter measured and number of antral follicles counted	Reduced follicular growth and antrum formation	(Lebbe et al., 2017)
Fetal ovaries in culture	Mouse	Vinclozolin	10μΜ, 50μΜ, 100μΜ, 200μΜ	7d of 17d	Follicle diameter measured	Reduced follicular growth	(González-Sanz, Barreñada, Rial, Brieño-Enriquez, & del Mazo, 2020)
Isolated preantral follicles in culture	Rat	Flutamide	10μΜ	2d, 4d	Follicle diameter measured and expressed as follicular volume	Reduced GDF9 and INSL3 induced follicular growth	(Xue, Kim, Liu, & Tsang, 2014)
Isolated preantral follicles in culture	Rat	Flutamide	10μΜ	2d, 4d	Follicle diameter measured and expressed as follicular volume	Reduced NR4A1-induced follicular growth	(Xue, Liu, Murphy, & Tsang, 2012)
Isolated preantral follicles in culture	Rat	Flutamide	10μΜ	4d	Follicle diameter measured and expressed as follicular volume	Reduced GDF9-induced follicle growth	(Orisaka, Jiang, Orisaka, Kotsuji, & Tsang, 2009)
Ovarian cortex pieces in culture	Cow	Flutamide	0.1μΜ, 1μΜ, 10μΜ	10d	Follicle counting and classification (histology)	Reduced number of secondary follicles compared to testosterone group (dose-dependent)	(Yang & Fortune, 2006)
Isolated granulosa cells from antral follicles	Pig	Hydroxyflutamide	5μΜ	1d	Incorporation of [³H]-thymidine	Reduced granulosa cell proliferation	(T. E. Hickey, Marrocco, Gilchrist, Norman, & Armstrong, 2004)
Isolated mural granulosa cells from small antral follicles	Pig	Hydroxyflutamide	0.1μΜ, 1μΜ	1h of 24h	Incorporation of [³H]-thymidine	Reduced granulosa cell proliferation	(T. E. Hickey et al., 2005)
Ovarian cortex pieces in culture	Pig	Cyproterone acetate	0.001 μΜ, 0.0001 μΜ	7d	Follicle counting and classification (histology)	Reduced primordial follicle activation	(Magamage, Zengyo, Moniruzzaman, & Miyano, 2011)

In vivo

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

Genomic and non-genomic effects are not distinguished in the studies included in the KER analysis. Hence, it cannot be concluded that all observations are solely due to perturbed transcription. However, since AR transcribes genes necessary for early folliculogenesis (kitlg, bmp15, hgf), it is reasonable to assume that genomic mechanisms are involved.

Other uncertainties to consider: different anti-androgenic compounds have different effects on the AR (e.g. different IC₅₀, C_max); compounds that are anti-androgenic may affect also other mechanisms/modalities; downstream effects of perturbed AR transcriptional function might depend on the duration of exposure as well as the developmental stage of the follicles. In humans, effects can be inconclusive since a part of the population can have androgen-related conditions such as polycystic ovary syndrome (PCOS)(Gleicher, Weghofer, & Barad, 2011).

Quantitative Understanding of the Linkage

The quantitative understanding section of the KER description is intended to capture information that helps to define how much change in the upstream KE, and/or for how long, is needed to elicit a detectable and defined change in the downstream KE. Empirical support addresses whether data between the two KEs are consistent with the patterns that are expected if the upstream event is causing the downstream event to occur, while the quantitative understanding section helps to define the precision with which the state of the downstream KE can be predicted from knowledge of the state of the upstream KE. These quantitative relationships may be defined in terms of correlations, response-response relationships, dose-dependent transitions or points of departure (i.e., a threshold of change in KEupstream needed to elicit a change in KEdownstream), etc. They may take the form of simple mathematical equations or sophisticated biologically-based computational models that consider other modulating factors such as compensatory responses, or interactions with other biological or environmental variables. Regardless of form, the idea is to briefly describe what is known regarding the quantitative relationship between the KEs and cite appropriate literature that defines those relationships and/or provides support for them.Data that confer quantitative understanding of a KER are not necessarily mutually exclusive from those addressing other weight of evidence considerations. In that respect, the quantitative understanding section of the KER description is not intended to be redundant with the other WoE sections. Rather, it is intended to aid application of the AOP by allowing a reader to rapidly identify the relationships that would support a quantitative prediction of the probability or magnitude of change in KEdownstream based on a known state of KEupstream. For transparency, the toxicological and biological context in which the quantitative relationships were defined should be indicated within the description. However, the ultimate goal is to identify quantitative relationships that generalize across the entire applicability domain of the two KEs being linked via the KER. 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

The nature of the response-response relationship between decreased AR activation and reduced granulosa cell proliferation is not clear. Some of the aforementioned studies claim the effects were dose-dependent; however, the limited number of concentrations tested prevents a solid conclusion (Murray et al., 1998; Wang et al., 2001; Yang & Fortune, 2006). Therefore, at present, the quantitative understanding of this KER is rated as low.

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

In vitro studies included in establishing this KER in the present report exhibit observed changes at 24h, and in vivo studies after 48h (T. E. Hickey et al., 2005, 2004; Kumari et al., 1978). The conclusion that can be drawn at present is that the approximate timescale of the changes in KEdownstream relative to changes in KEupstream is less than 48h.

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

The E3 ubiquitin ligase protein Ring Finger Protein 6 (RNF6) regulates AR levels in granulosa cells through polyubiquitination and AR transcriptional activity for kitlg expression in preantral follicles (Lim, Han, et al., 2017; Lim, Lima, Salehi, Lee, & Tsang, 2017). Epidermal growth factor receptor (EGFR) may mediate the androgen-induced granulosa cell proliferation (Franks & Hardy, 2018). Sixteen different mutations of the AR gene (Xq11.2-q12) that cause androgen insensitivity syndrome have been identified (Jiang et al., 2020). The number of CAG repeats on the N terminal domain of the AR gene has been associated with effects on fertility and ovarian reserve (T. Hickey, Chandy, & Norman, 2002)(Lledo et al., 2014).

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

Regulations in the endocrine system are characterized by many positive and negative feedback loops. Activated AR can transcriptionally regulate its own expression through a negative feedback loop (Gelmann, 2002). However, in granulosa cells of monkey ovaries, AR was shown to have the opposite effect, thus creating an autocrine positive feedback (S. J. Weil et al., 1998). More studies are needed to understand when AR regulation of its own expression is positive and negative.

During the early stages of folliculogenesis, mainly from the preantral to the small antral stage, activated AR can induce FSH activities in granulosa cells and promote granulosa cell differentiation and follicle maturation, even though follicles are still not gonadotropin-dependent (Gleicher et al., 2011). These activities include changes in androgen metabolism due to altered expression of steroidogenic enzymes. Therefore, it has been suggested that androgens can bind to the AR to establish a loop between activated AR and FSH action (Gleicher et al., 2011; Lenie & Smitz, 2009).

There is also evidence of an intra-follicular feedback loop that regulates steroidogenesis during the secondary follicle stage, causing downregulation of androgen synthesis upon exogenous androgen exposure and upregulation upon androgen receptor antagonism (Lebbe et al., 2017).

As mentioned, genes involved in early folliculogenesis like kitlg and hgf are under the control of AR. Those two genes have been shown to create a positive feedback loop in mice, by increasing the expression levels of each other (Guglielmo et al., 2011).

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

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

Astapova, O., Minor, B. M. N., & Hammes, S. R. (2019). Physiological and Pathological Androgen Actions in the Ovary. Endocrinology, Vol. 160, pp. 1166–1174. https://doi.org/10.1210/en.2019-00101

Baumgarten, S. C., Convissar, S. M., Fierro, M. A., Winston, N. J., Scoccia, B., & Stocco, C. (2014). IGF1R signaling is necessary for FSH-induced activation of AKT and differentiation of human cumulus granulosa cells. Journal of Clinical Endocrinology and Metabolism, 99(8), 2995–3004. https://doi.org/10.1210/jc.2014-1139

Cárdenas, H., & Pope, W. F. (2002). Androgen receptor and follicle-stimulating hormone receptor in the pig ovary during the follicular phase of the estrous cycle*. Molecular Reproduction and Development, 62(1), 92–98. https://doi.org/10.1002/mrd.10060

Franks, S., & Hardy, K. (2018, August 10). Androgen action in the ovary. Frontiers in Endocrinology, Vol. 9, p. 452. https://doi.org/10.3389/fendo.2018.00452

Gelmann, E. P. (2002, July 1). Molecular biology of the androgen receptor. Journal of Clinical Oncology, Vol. 20, pp. 3001–3015. https://doi.org/10.1200/JCO.2002.10.018

Gervásio, C. G., Bernuci, M. P., Silva-de-Sá, M. F., & Rosa-e-Silva, A. C. J. de S. (2014). The Role of Androgen Hormones in Early Follicular Development. ISRN Obstetrics and Gynecology, 2014, 1–11. https://doi.org/10.1155/2014/818010

Gill, A., Jamnongjit, M., & Hammes, S. R. (2004). Androgens promote maturation and signaling in mouse oocytes independent of transcription: a release of inhibition model for mammalian oocyte meiosis. Molecular Endocrinology, 18(1), 97–104. Retrieved from http://ovidsp.ovid.com/ovidweb.cgiT=JS&CSC=Y&NEWS=N&PAGE=fulltext&D=me Ad5&AN=14576339

Gleicher, N., Weghofer, A., & Barad, D. H. (2011). The role of androgens in follicle maturation and ovulation induction: friend or foe of infertility treatment? Reproductive Biology & Endocrinology, 9, 116. https://doi.org/https://dx.doi.org/10.1186/1477-7827-9-116

González-Sanz, S., Barreñada, O., Rial, E., Brieño-Enriquez, M. A., & del Mazo, J. (2020). The antiandrogenic vinclozolin induces differentiation delay of germ cells and changes in energy metabolism in 3D cultures of fetal ovaries. Scientific Reports, 10(1), 1–13. https://doi.org/10.1038/s41598-020-75116-3

Guglielmo, M. C., Ricci, G., Catizone, A., Barberi, M., Galdieri, M., Stefanini, M., & Canipari, R. (2011). The effect of hepatocyte growth factor on the initial stages of mouse follicle development. Journal of Cellular Physiology, 226(2), 520–529. https://doi.org/10.1002/jcp.22361

Hampton, J. H., Manikkam, M., Lubahn, D. B., Smith, M. F., & Garverick, H. A. (2004). Androgen receptor mRNA expression in the bovine ovary. Domestic Animal Endocrinology, 27(1), 81–88. Retrieved from http://ovidsp.ovid.com/ovidweb.cgi?T=JS&CSC=Y&NEWS=N&PAGE=fulltext&D=me d5&AN=15158536

Hickey, T., Chandy, A., & Norman, R. J. (2002). The androgen receptor CAG repeat polymorphism and X-chromosome inactivation in Australian Caucasian women with infertility related to polycystic ovary syndrome. Journal of Clinical Endocrinology and Metabolism, 87(1), 161–165. https://doi.org/10.1210/jcem.87.1.8137

Hickey, T. E., Marrocco, D. L., Amato, F., Ritter, L. J., Norman, R. J., Gilchrist, R. B., & Armstrong, D. T. (2005). Androgens augment the mitogenic effects of oocyte secreted factors and growth differentiation factor 9 on porcine granulosa cells. Biology of Reproduction, 73(4), 825–832. Retrieved from http://ovidsp.ovid.com/ovidweb.cgi?T=JS&CSC=Y&NEWS=N&PAGE=fulltext&D=me d6&AN=15972887

Hickey, T. E., Marrocco, D. L., Gilchrist, R. B., Norman, R. J., & Armstrong, D. T. (2004). Interactions between androgen and growth factors in granulosa cell subtypes of porcine antral follicles. Biology of Reproduction, 71(1), 45–52. Retrieved from http://ovidsp.ovid.com/ovidweb.cgi?T=JS&CSC=Y&NEWS=N&PAGE=fulltext&D=me d5&AN=14973257

Hillier, S. G., Tetsuka, M., & Fraser, H. M. (1997). Location and developmental regulation of androgen receptor in primate ovary. Human Reproduction, 12(1), 107–111. Retrieved from http://ovidsp.ovid.com/ovidweb.cgi?T=JS&CSC=Y&NEWS=N&PAGE=fulltext&D=me d4&AN=9043913

Hirai, M., Hirata, S., Osada, T., Hagihara, K., & Kato, J. (1994). Androgen receptor mRNA in the rat ovary and uterus. Journal of Steroid Biochemistry & Molecular Biology, 49(1), 1–7. Retrieved from http://ovidsp.ovid.com/ovidweb.cgi?T=JS&CSC=Y&NEWS=N&PAGE=fulltext&D=me d3&AN=8003434

Jiang, X., Teng, Y., Chen, X., Liang, N., Li, Z., Liang, D., & Wu, L. (2020). Six novel Mutation analysis of the androgen receptor gene in 17 Chinese patients with androgen insensitivity syndrome. Clinica Chimica Acta, 506, 180–186. https://doi.org/10.1016/j.cca.2020.03.036

Juengel, J. L., Heath, D. A., Quirke, L. D., & McNatty, K. P. (2006). Oestrogen receptor alpha and beta, androgen receptor and progesterone receptor mRNA and protein localisation within the developing ovary and in small growing follicles of sheep. Reproduction, 131(1), 81–92. Retrieved from http://ovidsp.ovid.com/ovidweb.cgiT=JS&CSC=Y&NEWS=N&PAGE=fulltext&D=me d6&AN=16388012

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