AOP-Wiki

AOP ID and Title:

AOP 536: Estrogen receptor agonism leading to reduced survival and population growth due to renal failure
Short Title: ER agonism leads to reduced survival/population growth

Authors

Camille Baettig

Status

Author status OECD status OECD project SAAOP status
Under development: Not open for comment. Do not cite

Summary of the AOP

Events

Molecular Initiating Events (MIE), Key Events (KE), Adverse Outcomes (AO)

Sequence Type Event ID Title Short name
MIE 111 Agonism, Estrogen receptor Agonism, Estrogen receptor
KE 307 Increase, Vitellogenin synthesis in liver Increase, Vitellogenin synthesis in liver
KE 220 Increase, Plasma vitellogenin concentrations Increase, Plasma vitellogenin concentrations
KE 252 Increase, Renal pathology due to VTG deposition Increase, Renal pathology due to VTG deposition
KE 351 Increased Mortality Increased Mortality
KE 360 Decrease, Population growth rate Decrease, Population growth rate

Key Event Relationships

Upstream Event Relationship Type Downstream Event Evidence Quantitative Understanding
Agonism, Estrogen receptor adjacent Increase, Vitellogenin synthesis in liver High Low
Increase, Vitellogenin synthesis in liver adjacent Increase, Plasma vitellogenin concentrations High Moderate
Increase, Plasma vitellogenin concentrations adjacent Increase, Renal pathology due to VTG deposition Moderate Low
Increase, Renal pathology due to VTG deposition adjacent Increased Mortality Moderate Low
Increased Mortality adjacent Decrease, Population growth rate Moderate Moderate

Overall Assessment of the AOP

References

Appendix 1

List of MIEs in this AOP

Event: 111: Agonism, Estrogen receptor

Short Name: Agonism, Estrogen receptor

Key Event Component

Process Object Action
estrogen receptor activity estrogen receptor increased

AOPs Including This Key Event

Biological Context

Level of Biological Organization
Molecular

Cell term

Cell term
hepatocyte

Domain of Applicability

Taxonomic applicability: In mammals there are two ER subtypes, ER alpha (ERα) and ER beta (ERβ), which are located on chromosome 6 and 14 and encoded by two different genes (ESR1 and ESR2) (Ascenzi et al., 2006). ERs were conventionally identified as mammal specific, but most vertebrates contain functional ERs. However, although teleost fish have receptors homologous to mammilian ERα, ERβ is divided into ERβ1 and ERβ2 resulting in three distinct ERs (Asnake et al., 2019; Menuet et al., 2004; Menuet et al., 2002). The majority of invertebrates (i.e. mollusks) possess a gene that is the orthologue of the vertebrate ER but in many species it has been demonstrated to only have constitutive transcriptional activity, and is not activated by ligand binding (Balbi et al., 2019). However, ERs in annelids share functional characteristics with vertebrate ERs and its transcriptional activity can be disrupted by known endocrine-disrupting substances (Keay & Thornton, 2009).

This MIE would generally be viewed as relevant to vertebrates, but not invertebrates.

Life stage: This MIE is applicable to all life stages.

Sex: This MIE is applicable to both sexes.

Key Event Description

Site of action: The molecular site of action is the estrogen receptor (ER). ERs are members of the steroid hormone receptor family which belongs to a group of nuclear receptors that are transcriptionally activated by ligands leading to downstream activation of many cellular processes. ERs are composed of three principal domains – N-terminal domain (NTD), DNA binding domain (DBD), and the ligand binding domain (LBD). ER binds to specific DNA sequences known as estrogen response elements (EREs); EREs are generally short sequences located in the promoter region but can also exist in introns or exons (Klinge, 2001). ER-mediated gene transcription is initiated by binding of the DBD to an ERE with two distinct transcriptional activation domains, AF1 and AF2, located on the NTD and LBD respectively (Kumar et al., 2011).

Responses at the macromolecular level: ER’s bind to endogenous and exogenous compounds and are activated by endogenous ligands such as estrone (E1), estradiol (E2) and estriol (E3) (Ng et al., 2014). There are numerous compounds (e.g., natural or pharmaceutical estrogens, alkylphenols, organochlorine pesticides, phthalates, etc.) that can act as estrogen agonists or antagonists, and effectively mimic or block the natural effects of estrogens on the ER (Pillon et al., 2005; Schmieder et al., 2014).

ER is part of a multi-protein complex consisting of HSP 90, HSP 70, and immunophilins (Stice & Knowlton, 2008). In this multi-protein complex HSP 90 is the dominant protein and its binding to ER is essential for ER conformational binding of 17β-estradiol (Segnitz & Gehring, 1997). When binding on the LBD receptor occurs ER dissociates from HSP 90 and leads to receptor dimerization which can either be homodimers from the same isoform (ERα-Erα) or heterodimers containing one unit from both isoforms (ERα-Erβ) (Fliss et al., 2000). The translocation of these dimers into the nucleus modulates gene transcription (Aranda & Pascual, 2001).

How it is Measured or Detected

  • OECD Test No. 455: Performance-based test guideline for stably transfected transactivation in vitro assays to detect estrogen receptor agonists and antagonists (OECD 2021).
  • OECD Test No. 457: BG1Luc Estrogen Receptor Transactivation Test Method for Identifying Estrogen Receptor Agonists and Antagonists (OECD 2012).
  • Standard Evaluation Procedure (SEP) for estrogen receptor transcriptional activation (Human Cell Line HeLa-9903) assay was developed by the U.S. Environmental Protection Agency (EPA).
  • ER-based transactivation assays that have been used to detect ER agonists and antagonist using cell lines include T47D-Kbluc assay (Wehmas et al., 2011), the ERα CALUX assay (Van et al.); MELN assay (Berckmans et al., 2007); and the yeast estrogen screen (YES; (De Boever et al., 2001)). The T47D-Kbluc assay responds to both ERα and ERß agonists but support the assumption that ERα is inducing more reporter expression than ERß. Each of these assays have undergone some level of validation.
  • Browne et al. (2015) integrated 18 ER ToxCast high-throughput screening (HTS) assays, measuring ER binding, dimerization, chromatin binding, transcriptional activation and ER-dependent cell proliferation, into the ToxCast ER pathway model. This mathematical model that in vitro assays to predict whether a chemical is an ER agonist or antagonist.

References

  • Aranda, A., & Pascual, A. (2001). Nuclear hormone receptors and gene expression. Physiological reviews, 81(3), 1269-1304.
  • Ascenzi, P., Bocedi, A., & Marino, M. (2006). Structure–function relationship of estrogen receptor α and β: Impact on human health. Molecular aspects of medicine, 27(4), 299-402.
  • Asnake, S., Modig, C., & Olsson, P.-E. (2019). Species differences in ligand interaction and activation of estrogen receptors in fish and human. The Journal of steroid biochemistry and molecular biology, 195, 105450.
  • Balbi, T., Ciacci, C., & Canesi, L. (2019). Estrogenic compounds as exogenous modulators of physiological functions in molluscs: Signaling pathways and biological responses. Comparative Biochemistry and Physiology Part C: Toxicology & Pharmacology, 222, 135-144.
  • Berckmans, P., Leppens, H., Vangenechten, C., & Witters, H. (2007). Screening of endocrine disrupting chemicals with MELN cells, an ER-transactivation assay combined with cytotoxicity assessment. Toxicology in vitro, 21(7), 1262-1267.
  • Browne, P., Judson, R. S., Casey, W. M., Kleinstreuer, N. C., & Thomas, R. S. (2015). Screening Chemicals for Estrogen Receptor Bioactivity Using a Computational Model. Environmental Science & Technology, 49(14), 8804-8814. https://doi.org/10.1021/acs.est.5b02641
  • De Boever, P., Demaré, W., Vanderperren, E., Cooreman, K., Bossier, P., & Verstraete, W. (2001). Optimization of a yeast estrogen screen and its applicability to study the release of estrogenic isoflavones from a soygerm powder. Environmental Health Perspectives, 109(7), 691-697.
  • Fliss, A. E., Benzeno, S., Rao, J., & Caplan, A. J. (2000). Control of estrogen receptor ligand binding by Hsp90. The Journal of steroid biochemistry and molecular biology, 72(5), 223-230.
  • Keay, J., & Thornton, J. W. (2009). Hormone-activated estrogen receptors in annelid invertebrates: implications for evolution and endocrine disruption. Endocrinology, 150(4), 1731-1738.
  • Klinge, C. M. (2001). Estrogen receptor interaction with estrogen response elements. Nucleic Acids Res, 29(14), 2905-2919. https://doi.org/10.1093/nar/29.14.2905
  • Kumar, R., Zakharov, M. N., Khan, S. H., Miki, R., Jang, H., Toraldo, G., Singh, R., Bhasin, S., & Jasuja, R. (2011). The dynamic structure of the estrogen receptor. Journal of amino acids, 2011.
  • Menuet, A., Le Page, Y., Torres, O., Kern, L., Kah, O., & Pakdel, F. (2004). Analysis of the estrogen regulation of the zebrafish estrogen receptor (ER) reveals distinct effects of ERalpha, ERbeta1 and ERbeta2. Journal of Molecular Endocrinology, 32(3), 975-986.
  • Menuet, A., Pellegrini, E., Anglade, I., Blaise, O., Laudet, V., Kah, O., & Pakdel, F. (2002). Molecular characterization of three estrogen receptor forms in zebrafish: binding characteristics, transactivation properties, and tissue distributions. Biology of reproduction, 66(6), 1881-1892.
  • Ng, H. W., Perkins, R., Tong, W., & Hong, H. (2014). Versatility or Promiscuity: The Estrogen Receptors, Control of Ligand Selectivity and an Update on Subtype Selective Ligands. International Journal of Environmental Research and Public Health, 11(9), 8709-8742. https://www.mdpi.com/1660-4601/11/9/8709
  • Pillon, A., Boussioux, A.-M., Escande, A., Aït-Aïssa, S., Gomez, E., Fenet, H., Ruff, M., Moras, D., Vignon, F., & Duchesne, M.-J. (2005). Binding of estrogenic compounds to recombinant estrogen receptor-α: application to environmental analysis. Environmental Health Perspectives, 113(3), 278-284.
  • Schmieder, P. K., Kolanczyk, R. C., Hornung, M. W., Tapper, M. A., Denny, J. S., Sheedy, B. R., & Aladjov, H. (2014). A rule-based expert system for chemical prioritization using effects-based chemical categories. SAR and QSAR in Environmental Research, 25(4), 253-287. https://doi.org/10.1080/1062936X.2014.898691
  • Segnitz, B., & Gehring, U. (1997). The function of steroid hormone receptors is inhibited by the hsp90-specific compound geldanamycin. Journal of Biological Chemistry, 272(30), 18694-18701.
  • Stice, J. P., & Knowlton, A. A. (2008). Estrogen, NFκB, and the heat shock response. Molecular Medicine, 14, 517-527.
  • Van, d., Winter, R., Weimer, M., Beckmanns, P., Suzuki, G., Gijsberg, L., Jonas, A., Van, d. W., Hilda, & Aarts, J. Optimization and Prevalidation of the in Vitro ER CALUX Method to Test Estrogenic and Antiestrogenic Activity of Compounds.
  • Wehmas, L. C., Cavallin, J. E., Durhan, E. J., Kahl, M. D., Martinovic, D., Mayasich, J., Tuominen, T., Villeneuve, D. L., & Ankley, G. T. (2011). Screening complex effluents for estrogenic activity with the T47D‐KBluc cell bioassay: Assay optimization and comparison with in vivo responses in fish. Environmental toxicology and chemistry, 30(2), 439-445.

List of Key Events in the AOP

Event: 307: Increase, Vitellogenin synthesis in liver

Short Name: Increase, Vitellogenin synthesis in liver

Key Event Component

Process Object Action
gene expression vitellogenins increased

AOPs Including This Key Event

Biological Context

Level of Biological Organization
Tissue

Organ term

Organ term
liver

Domain of Applicability

Taxonomic applicability: Oviparous vertebrates.

  • Although vitellogenin is conserved among oviparous vertebrates and many invertebrates, liver is not a relevant tissue for the production of vitellogenin in invertebrates (Wahli, 1988).

Life stage: This KE is applicable to all life stages following the differentiation of the liver. Embryos prior to liver differentiation should not be included.

Sex: This KE is applicable to both sexes.

Key Event Description

Vitellogenin (VTG) is an egg yolk precursor protein synthesized by hepatocytes of oviparous vertebrates (Hara et al., 2016). Transcription of vtg is regulated by estrogens and their interaction on ERs. In males expression can be modulated by exogenous compounds. Under high estrogen stimulation the fold increase of vtg transcripts increases by orders of magnitude (Brock & Shapiro, 1983).

How it is Measured or Detected

Relative abundance of vitellogenin transcripts or protein can be measured in liver tissue (e.g., Miracle et al., 2006), hepatocytes (e.g., Vaillant et al., 1988), exposed in vitro, or whole-body homogenates from organisms exposed in vivo (Holbech et al., 2001).

mRNA transcripts can be measured using real-time quantitative polymerase chain reaction (qPCR) while protein quantification can be measured using alkali-labile phosphoprotein (e.g., Kramer et al., 1998), or immunochemical methods such as radioimmunoassay (RIA; e.g., Tyler & Sumpter, 1990), enzyme linked immunosorbent assay (ELISA; e.g., Denslow et al., 1999), and Western blotting (e.g., Heppell et al., 1995).

References

  • Brock, M. L., & Shapiro, D. (1983). Estrogen regulates the absolute rate of transcription of the Xenopus laevis vitellogenin genes. Journal of Biological Chemistry, 258(9), 5449-5455.
  • Denslow, N. D., Chow, M. C., Kroll, K. J., & Green, L. (1999). Vitellogenin as a biomarker of exposure for estrogen or estrogen mimics. Ecotoxicology, 8, 385-398.
  • Hara, A., Hiramatsu, N., & Fujita, T. (2016). Vitellogenesis and choriogenesis in fishes. Fisheries Science, 82(2), 187-202. https://doi.org/10.1007/s12562-015-0957-5
  • Heppell, S. A., Denslow, N. D., Folmar, L. C., & Sullivan, C. V. (1995). Universal assay of vitellogenin as a biomarker for environmental estrogens. Environmental Health Perspectives, 103(suppl 7), 9-15.
  • Holbech, H., Andersen, L., Petersen, G. I., Korsgaard, B., Pedersen, K. L., & Bjerregaard, P. (2001). Development of an ELISA for vitellogenin in whole body homogenate of zebrafish (Danio rerio). Comparative Biochemistry and Physiology Part C: Toxicology & Pharmacology, 130(1), 119-131.
  • Kramer, V., Miles-Richardson, S., Pierens, S., & Giesy, J. (1998). Reproductive impairment and induction of alkaline-labile phosphate, a biomarker of estrogen exposure, in fathead minnows (Pimephales promelas) exposed to waterborne 17β-estradiol. Aquatic Toxicology, 40(4), 335-360.
  • Miracle, A., Ankley, G., & Lattier, D. (2006). Expression of two vitellogenin genes (vg1 and vg3) in fathead minnow (Pimephales promelas) liver in response to exposure to steroidal estrogens and androgens. Ecotoxicology and environmental safety, 63(3), 337-342.
  • Tyler, C. R., & Sumpter, J. P. (1990). The development of a radioimmunoassay for carp, Cyprinus carpio, vitellogenin. Fish Physiology and Biochemistry, 8, 129-140.
  • Vaillant, C., Le Guellec, C., Pakdel, F., & Valotaire, Y. (1988). Vitellogenin gene expression in primary culture of male rainbow trout hepatocytes. General and Comparative Endocrinology, 70(2), 284-290.
  • Wahli, W. (1988). Evolution and expression of vitellogenin genes. Trends in Genetics, 4(8), 227-232.

 

Event: 220: Increase, Plasma vitellogenin concentrations

Short Name: Increase, Plasma vitellogenin concentrations

Key Event Component

Process Object Action
vitellogenins increased

AOPs Including This Key Event

Biological Context

Level of Biological Organization
Organ

Organ term

Organ term
blood plasma

Domain of Applicability

Taxonomic applicability: Oviparous vertebrates synthesize yolk precursor proteins that are transported in the circulation for uptake by developing oocytes. Many invertebrates also synthesize vitellogenins that are taken up into developing oocytes via active transport mechanisms. However, invertebrate vitellogenins are transported in hemolymph or via other transport mechanisms rather than plasma.

Life stage: This KE is applicable to all life stages following the differentiation of the liver. Embryos prior to liver differentiation should not be included.

Sex: This KE is applicable to both sexes.

Key Event Description

Vitellogenins are large serum phospholipoglycoprotein that are encoded by a family of paralog genes whose number varies in the different vertebrate lineages resulting in numerous isoforms (Wahli, 1988). Vtg is synthesized in the liver and is secreted into the blood as ~500 kDa homodimers which circulate to the ovaries for uptake and bind to receptors on the surface of growing oocytes (Wallace, 1985).

How it is Measured or Detected

Vitellogenin concentrations in plasma are typically measured using enzyme linked immunosorbent assay (ELISA; e.g., Denslow et al., 1999; Holbech et al., 2001). Less specific and/or sensitive assays such as determination of alkali-labile phosphoprotein (e.g., Kramer et al., 1998) and Western blotting (e.g., Heppell et al., 1995) may also be used.

There are also several standardized test guidelines that measure vtg including: Fish Short Term Reproduction Assay (OECD, 2009a), 21-day Fish Assay (OECD, 2009b); Fish Sexual Development Test (OECD, 2011), Medaka Extended One Generation Reproduction Test (OECD, 2015a). Measurement of vtg is also an optional parameter in the Larval Amphibian Growth and Development Assay (OECD, 2015b). The US Environmental Protection Agency (EPA) has similar standardized guidelines (US EPA, 2009, US EPA, 2014) as does the EU as part of the Guidance For The Identification Of Endocrine Disruptors In The Context Of Regulations (EC 2013, EC 2018).

References

  • Denslow, N. D., Chow, M. C., Kroll, K. J., & Green, L. (1999). Vitellogenin as a biomarker of exposure for estrogen or estrogen mimics. Ecotoxicology, 8, 385-398.
  • Heppell, S. A., Denslow, N. D., Folmar, L. C., & Sullivan, C. V. (1995). Universal assay of vitellogenin as a biomarker for environmental estrogens. Environmental Health Perspectives, 103(suppl 7), 9-15.
  • Holbech, H., Andersen, L., Petersen, G. I., Korsgaard, B., Pedersen, K. L., & Bjerregaard, P. (2001). Development of an ELISA for vitellogenin in whole body homogenate of zebrafish (Danio rerio). Comparative Biochemistry and Physiology Part C: Toxicology & Pharmacology, 130(1), 119-131.
  • Kramer, V., Miles-Richardson, S., Pierens, S., & Giesy, J. (1998). Reproductive impairment and induction of alkaline-labile phosphate, a biomarker of estrogen exposure, in fathead minnows (Pimephales promelas) exposed to waterborne 17β-estradiol. Aquatic Toxicology, 40(4), 335-360.
  • Wahli, W. (1988). Evolution and expression of vitellogenin genes. Trends in Genetics, 4(8), 227-232.
  • Wallace, R. A. (1985). Vitellogenesis and oocyte growth in nonmammalian vertebrates. Oogenesis, 127-177.

Event: 252: Increase, Renal pathology due to VTG deposition

Short Name: Increase, Renal pathology due to VTG deposition

Key Event Component

Process Object Action
Kidney Diseases increased

AOPs Including This Key Event

Biological Context

Level of Biological Organization
Organ

Organ term

Organ term
kidney

Domain of Applicability

Taxonomic applicability: All vertebrates with functional kidneys.

Life stage: This KE is applicable to all life stages following the differentiation of the kidney.

Sex: This KE is applicable to both sexes.

Key Event Description

Renal pathology deals with the characterization of the kidneys. The kidneys perform a suite of physiological roles that are critical for organismal homeostasis including waste excretion, osmoregulation, and fluid homeostasis (Preuss, 1993). Each kidney is made up of specialized epithelial cells known as nephrons and while nephron numbers can vary greatly between species their overall function remains conserved in vertebrates (Desgrange & Cereghini, 2015). Nephrons act as filtering units that are composed of glomeruli and tubules which are responsible for removing metabolic waste from the bloodstream, regulating fluids, and balancing electrolytes (Wesselman et al., 2023). Organ tissue damage can occur after exposure to toxins, parasites, or be caused by disease. If pathology is measurable this would be an indication of damage or diseased tissue state and a departure from normal/healthy tissue.

How it is Measured or Detected

Histopathology focuses on the changes in tissues and is a technique used for identifying correlations with biochemical markers. Generally renal pathology is measured after either whole organism or specific tissue of interest is fixed, dehydrated, and then embedded in wax, commonly paraffin wax. Sections are then cut to approximately 3–5 μm in thickness and stained before being examined under a microscope (e.g., Folmar et al., 2001; Mihaich et al., 2012; Zha et al., 2007).

  • OECD Test No. 123: Guidance document on the diagnosis of endocrine-related histopathology in fish gonads (OECD 2010).
  • OECD Test No. 227: Guidance document on medaka histopathology techniques and evaluation for the medaka extended one-generation reproduction test (OECD 2015)
  • Crissman et al. (2004) describes best practice guidelines for toxicologic histopathology.
  • Fiedler et al. (2023) have written standardized tissue sampling guidelines for histopathological analyses using rainbow trout.

References

  • Crissman, J. W., Goodman, D. G., Hildebrandt, P. K., Maronpot, R. R., Prater, D. A., Riley, J. H., Seaman, W. J., & Thake, D. C. (2004). Best Practices Guideline: Toxicologic Histopathology. Toxicologic Pathology, 32(1), 126-131. https://doi.org/10.1080/01926230490268756
  • Desgrange, A., & Cereghini, S. (2015). Nephron patterning: lessons from Xenopus, zebrafish, and mouse studies. Cells, 4(3), 483-499.
  • Fiedler, S., Schrader, H., Theobalt, N., Hofmann, I., Geiger, T., Arndt, D., Wanke, R., Schwaiger, J., & Blutke, A. (2023). Standardized tissue sampling guidelines for histopathological and molecular analyses of rainbow trout (Oncorhynchus mykiss) in ecotoxicological studies. PLOS ONE, 18(7), e0288542. https://doi.org/10.1371/journal.pone.0288542
  • Folmar, L. C., Gardner, G. R., Schreibman, M. P., Magliulo-Cepriano, L., Mills, L. J., Zaroogian, G., Gutjahr-Gobell, R., Haebler, R., Horowitz, D. B., & Denslow, N. D. (2001). Vitellogenin-induced pathology in male summer flounder (Paralichthys dentatus). Aquatic Toxicology, 51(4), 431-441.
  • Mihaich, E., Rhodes, J., Wolf, J., van der Hoeven, N., Dietrich, D., Hall, A. T., Caspers, N., Ortego, L., Staples, C., & Dimond, S. (2012). Adult fathead minnow, Pimephales promelas, partial life‐cycle reproductive and gonadal histopathology study with bisphenol A. Environmental toxicology and chemistry, 31(11), 2525-2535.
  • Preuss, H. G. (1993). Basics of renal anatomy and physiology. Clinics in laboratory medicine, 13(1), 1-11.
  • Wesselman, H. M., Gatz, A. E., Pfaff, M. R., Arceri, L., & Wingert, R. A. (2023). Estrogen signaling influences nephron segmentation of the zebrafish embryonic kidney. Cells, 12(4), 666.
  • Zha, J., Wang, Z., Wang, N., & Ingersoll, C. (2007). Histological alternation and vitellogenin induction in adult rare minnow (Gobiocypris rarus) after exposure to ethynylestradiol and nonylphenol. Chemosphere, 66(3), 488-495.

Event: 351: Increased Mortality

Short Name: Increased Mortality

Key Event Component

Process Object Action
mortality increased

AOPs Including This Key Event

AOP ID and Name Event Type
Aop:16 - Acetylcholinesterase inhibition leading to acute mortality AdverseOutcome
Aop:96 - Axonal sodium channel modulation leading to acute mortality AdverseOutcome
Aop:104 - Altered ion channel activity leading impaired heart function AdverseOutcome
Aop:113 - Glutamate-gated chloride channel activation leading to acute mortality AdverseOutcome
Aop:160 - Ionotropic gamma-aminobutyric acid receptor activation mediated neurotransmission inhibition leading to mortality AdverseOutcome
Aop:161 - Glutamate-gated chloride channel activation leading to neurotransmission inhibition associated mortality AdverseOutcome
Aop:138 - Organic anion transporter (OAT1) inhibition leading to renal failure and mortality AdverseOutcome
Aop:177 - Cyclooxygenase 1 (COX1) inhibition leading to renal failure and mortality AdverseOutcome
Aop:186 - unknown MIE leading to renal failure and mortality AdverseOutcome
Aop:312 - Acetylcholinesterase Inhibition leading to Acute Mortality via Impaired Coordination & Movement​ AdverseOutcome
Aop:320 - Binding of SARS-CoV-2 to ACE2 receptor leading to acute respiratory distress associated mortality AdverseOutcome
Aop:155 - Deiodinase 2 inhibition leading to increased mortality via reduced posterior swim bladder inflation AdverseOutcome
Aop:156 - Deiodinase 2 inhibition leading to increased mortality via reduced anterior swim bladder inflation AdverseOutcome
Aop:157 - Deiodinase 1 inhibition leading to increased mortality via reduced posterior swim bladder inflation AdverseOutcome
Aop:158 - Deiodinase 1 inhibition leading to increased mortality via reduced anterior swim bladder inflation AdverseOutcome
Aop:159 - Thyroperoxidase inhibition leading to increased mortality via reduced anterior swim bladder inflation AdverseOutcome
Aop:363 - Thyroperoxidase inhibition leading to altered visual function via altered retinal layer structure AdverseOutcome
Aop:377 - Dysregulated prolonged Toll Like Receptor 9 (TLR9) activation leading to Multi Organ Failure involving Acute Respiratory Distress Syndrome (ARDS) AdverseOutcome
Aop:364 - Thyroperoxidase inhibition leading to altered visual function via decreased eye size AdverseOutcome
Aop:365 - Thyroperoxidase inhibition leading to altered visual function via altered photoreceptor patterning AdverseOutcome
Aop:399 - Inhibition of Fyna leading to increased mortality via decreased eye size (Microphthalmos) AdverseOutcome
Aop:413 - Oxidation and antagonism of reduced glutathione leading to mortality via acute renal failure AdverseOutcome
Aop:410 - GSK3beta inactivation leading to increased mortality via defects in developing inner ear AdverseOutcome
Aop:450 - Inhibition of AChE and activation of CYP2E1 leading to sensory axonal peripheral neuropathy and mortality AdverseOutcome
Aop:536 - Estrogen receptor agonism leading to reduced survival and population growth due to renal failure KeyEvent

Biological Context

Level of Biological Organization
Population

Domain of Applicability

Taxonomic Applicability
Term Scientific Term Evidence Links
all species all species High NCBI
Life Stage Applicability
Life Stage Evidence
All life stages High
Sex Applicability
Sex Evidence
Unspecific Moderate

All living things are susceptible to mortality.

Key Event Description

Increased mortality refers to an increase in the number of individuals dying in an experimental replicate group or in a population over a specific period of time.

How it is Measured or Detected

Mortality of animals is generally observed as cessation of the heart beat, breathing (gill or lung movement) and locomotory movements. Mortality is typically measured by observation. Depending on the size of the organism, instruments such as microscopes may be used. The reported metric is mostly the mortality rate: the number of deaths in a given area or period, or from a particular cause.

Depending on the species and the study setup, mortality can be measured:

  • in the lab by recording mortality during exposure experiments
  • in dedicated setups simulating a realistic situation such as mesocosms or drainable ponds for aquatic species
  • in the field, for example by determining age structure after one capture, or by capture-mark-recapture efforts. The latter is a method commonly used in ecology to estimate an animal population's size where it is impractical to count every individual.

Regulatory Significance of the AO

Increased mortality is one of the most common regulatory assessment endpoints, along with reduced growth and reduced reproduction.

Event: 360: Decrease, Population growth rate

Short Name: Decrease, Population growth rate

Key Event Component

Process Object Action
population growth rate population of organisms decreased

AOPs Including This Key Event

AOP ID and Name Event Type
Aop:23 - Androgen receptor agonism leading to reproductive dysfunction (in repeat-spawning fish) AdverseOutcome
Aop:25 - Aromatase inhibition leading to reproductive dysfunction AdverseOutcome
Aop:29 - Estrogen receptor agonism leading to reproductive dysfunction AdverseOutcome
Aop:30 - Estrogen receptor antagonism leading to reproductive dysfunction AdverseOutcome
Aop:100 - Cyclooxygenase inhibition leading to reproductive dysfunction via inhibition of female spawning behavior AdverseOutcome
Aop:122 - Prolyl hydroxylase inhibition leading to reproductive dysfunction via increased HIF1 heterodimer formation AdverseOutcome
Aop:123 - Unknown MIE leading to reproductive dysfunction via increased HIF-1alpha transcription AdverseOutcome
Aop:155 - Deiodinase 2 inhibition leading to increased mortality via reduced posterior swim bladder inflation AdverseOutcome
Aop:156 - Deiodinase 2 inhibition leading to increased mortality via reduced anterior swim bladder inflation AdverseOutcome
Aop:157 - Deiodinase 1 inhibition leading to increased mortality via reduced posterior swim bladder inflation AdverseOutcome
Aop:158 - Deiodinase 1 inhibition leading to increased mortality via reduced anterior swim bladder inflation AdverseOutcome
Aop:159 - Thyroperoxidase inhibition leading to increased mortality via reduced anterior swim bladder inflation AdverseOutcome
Aop:101 - Cyclooxygenase inhibition leading to reproductive dysfunction via inhibition of pheromone release AdverseOutcome
Aop:102 - Cyclooxygenase inhibition leading to reproductive dysfunction via interference with meiotic prophase I /metaphase I transition AdverseOutcome
Aop:63 - Cyclooxygenase inhibition leading to reproductive dysfunction AdverseOutcome
Aop:103 - Cyclooxygenase inhibition leading to reproductive dysfunction via interference with spindle assembly checkpoint AdverseOutcome
Aop:292 - Inhibition of tyrosinase leads to decreased population in fish AdverseOutcome
Aop:310 - Embryonic Activation of the AHR leading to Reproductive failure, via epigenetic down-regulation of GnRHR AdverseOutcome
Aop:16 - Acetylcholinesterase inhibition leading to acute mortality AdverseOutcome
Aop:312 - Acetylcholinesterase Inhibition leading to Acute Mortality via Impaired Coordination & Movement​ AdverseOutcome
Aop:334 - Glucocorticoid Receptor Agonism Leading to Impaired Fin Regeneration AdverseOutcome
Aop:336 - DNA methyltransferase inhibition leading to population decline (1) AdverseOutcome
Aop:337 - DNA methyltransferase inhibition leading to population decline (2) AdverseOutcome
Aop:338 - DNA methyltransferase inhibition leading to population decline (3) AdverseOutcome
Aop:339 - DNA methyltransferase inhibition leading to population decline (4) AdverseOutcome
Aop:340 - DNA methyltransferase inhibition leading to transgenerational effects (1) AdverseOutcome
Aop:341 - DNA methyltransferase inhibition leading to transgenerational effects (2) AdverseOutcome
Aop:289 - Inhibition of 5α-reductase leading to impaired fecundity in female fish AdverseOutcome
Aop:297 - Inhibition of retinaldehyde dehydrogenase leads to population decline AdverseOutcome
Aop:346 - Aromatase inhibition leads to male-biased sex ratio via impacts on gonad differentiation AdverseOutcome
Aop:326 - Thermal stress leading to population decline (3) AdverseOutcome
Aop:325 - Thermal stress leading to population decline (2) AdverseOutcome
Aop:324 - Thermal stress leading to population decline (1) AdverseOutcome
Aop:363 - Thyroperoxidase inhibition leading to altered visual function via altered retinal layer structure AdverseOutcome
Aop:349 - Inhibition of 11β-hydroxylase leading to decresed population trajectory AdverseOutcome
Aop:348 - Inhibition of 11β-Hydroxysteroid Dehydrogenase leading to decreased population trajectory AdverseOutcome
Aop:376 - Androgen receptor agonism leading to male-biased sex ratio AdverseOutcome
Aop:386 - Deposition of ionizing energy leading to population decline via inhibition of photosynthesis AdverseOutcome
Aop:387 - Deposition of ionising energy leading to population decline via mitochondrial dysfunction AdverseOutcome
Aop:388 - Deposition of ionising energy leading to population decline via programmed cell death AdverseOutcome
Aop:389 - Oxygen-evolving complex damage leading to population decline via inhibition of photosynthesis AdverseOutcome
Aop:364 - Thyroperoxidase inhibition leading to altered visual function via decreased eye size AdverseOutcome
Aop:365 - Thyroperoxidase inhibition leading to altered visual function via altered photoreceptor patterning AdverseOutcome
Aop:399 - Inhibition of Fyna leading to increased mortality via decreased eye size (Microphthalmos) AdverseOutcome
Aop:410 - GSK3beta inactivation leading to increased mortality via defects in developing inner ear AdverseOutcome
Aop:216 - Deposition of energy leading to population decline via DNA strand breaks and follicular atresia AdverseOutcome
Aop:238 - Deposition of energy leading to population decline via DNA strand breaks and oocyte apoptosis AdverseOutcome
Aop:299 - Deposition of energy leading to population decline via DNA oxidation and follicular atresia AdverseOutcome
Aop:311 - Deposition of energy leading to population decline via DNA oxidation and oocyte apoptosis AdverseOutcome
Aop:444 - Ionizing radiation leads to reduced reproduction in Eisenia fetida via reduced spermatogenesis and cocoon hatchability AdverseOutcome
Aop:138 - Organic anion transporter (OAT1) inhibition leading to renal failure and mortality AdverseOutcome
Aop:177 - Cyclooxygenase 1 (COX1) inhibition leading to renal failure and mortality AdverseOutcome
Aop:97 - 5-hydroxytryptamine transporter (5-HTT; SERT) inhibition leading to population decline AdverseOutcome
Aop:203 - 5-hydroxytryptamine transporter inhibition leading to decreased reproductive success and population decline AdverseOutcome
Aop:218 - Inhibition of CYP7B activity leads to decreased reproductive success via decreased locomotor activity AdverseOutcome
Aop:219 - Inhibition of CYP7B activity leads to decreased reproductive success via decreased sexual behavior AdverseOutcome
Aop:323 - PPARalpha Agonism Leading to Decreased Viable Offspring via Decreased 11-Ketotestosterone AdverseOutcome
Aop:536 - Estrogen receptor agonism leading to reduced survival and population growth due to renal failure KeyEvent
Aop:540 - Oxidative Stress in the Fish Ovary Leads to Reproductive Impairment via Reduced Vitellogenin Production AdverseOutcome

Biological Context

Level of Biological Organization
Population

Domain of Applicability

Taxonomic Applicability
Term Scientific Term Evidence Links
all species all species High NCBI
Life Stage Applicability
Life Stage Evidence
All life stages Not Specified
Sex Applicability
Sex Evidence
Unspecific Not Specified

Consideration of population size and changes in population size over time is potentially relevant to all living organisms.

Key Event Description

A population can be defined as a group of interbreeding organisms, all of the same species, occupying a specific space during a specific time (Vandermeer and Goldberg 2003, Gotelli 2008).  As the population is the biological level of organization that is often the focus of ecological risk assessments, population growth rate (and hence population size over time) is important to consider within the context of applied conservation practices.

If N is the size of the population and t is time, then the population growth rate (dN/dt) is proportional to the instantaneous rate of increase, r, which measures the per capita rate of population increase over a short time interval. Therefore, r, is a difference between the instantaneous birth rate (number of births per individual per unit of time; b) and the instantaneous death rate (number of deaths per individual per unit of time; d) [Equation 1]. Because  r is an instantaneous rate, its units can be changed via division.  For example, as there are 24 hours in a day, an r of 24 individuals/(individual x day) is equal to an r of 1 individual/(individual/hour) (Caswell 2001, Vandermeer and Goldberg 2003, Gotelli 2008, Murray and Sandercock 2020). 

Equation 1:  r = b - d

This key event refers to scenarios where r < 0 (instantaneous death rate exceeds instantaneous birth rate).

Examining r in the context of population growth rate:

● A population will decrease to extinction when the instantaneous death rate exceeds the instantaneous birth rate (r < 0).  

           ● The smaller the value of r below 1, the faster the population will decrease to zero.  

● A population will increase when resources are available and the instantaneous birth rate exceeds the instantaneous death rate (r > 0)

           ● The larger the value that r exceeds 1, the faster the population can increase over time      

● A population will neither increase or decrease when the population growth rate equals 0 (either due to N = 0, or if the per capita birth and death rates are exactly balanced).  For example, the per capita birth and death rates could become exactly balanced due to density dependence and/or to the effect of a stressor that reduces survival and/or reproduction (Caswell 2001, Vandermeer and Goldberg 2003, Gotelli 2008, Murray and Sandercock 2020).     

Effects incurred on a population from a chemical or non-chemical stressor could have an impact directly upon birth rate (reproduction) and/or death rate (survival), thereby causing a decline in population growth rate.  

● Example of direct effect on r:  Exposure to 17b-trenbolone reduced reproduction (i.e., reduced b) in the fathead minnow over 21 days at water concentrations ranging from 0.0015 to about 41 mg/L (Ankley et al. 2001; Miller and Ankley 2004).             

Alternatively, a stressor could indirectly impact survival and/or reproduction.  

● Example of indirect effect on r:  Exposure of non-sexually differentiated early life stage fathead minnow to the fungicide prochloraz has been shown to produce male-biased sex ratios based on gonad differentiation, and resulted in projected change in population growth rate (decrease in reproduction due to a decrease in females and thus recruitment) using a population model. (Holbech et al., 2012; Miller et al. 2022)

Density dependence can be an important consideration:

● The effect of density dependence depends upon the quantity of resources present within a landscape.  A change in available resources could increase or decrease the effect of density dependence and therefore cause a change in population growth rate via indirectly impacting survival and/or reproduction.  

● This concept could be thought of in terms of community level interactions whereby one species is not impacted but a competitor species is impacted by a chemical stressor resulting in a greater availability of resources for the unimpacted species.  In this scenario, the impacted species would experience a decline in population growth rate. The unimpacted species would experience an increase in population growth rate (due to a smaller density dependent effect upon population growth rate for that species).       

Closed versus open systems:

● The above discussion relates to closed systems (there is no movement of individuals between population sites) and thus a declining population growth rate cannot be augmented by immigration.  

● When individuals depart (emigrate out of a population) the loss will diminish population growth rate.  

Population growth rate applies to all organisms, both sexes, and all life stages.

 

How it is Measured or Detected

Population growth rate (instantaneous growth rate) can be measured by sampling a population over an interval of time (i.e. from time t = 0 to time t = 1).  The interval of time should be selected to correspond to the life history of the species of interest (i.e. will be different for rapidly growing versus slow growing populations). The population growth rate, r, can be determined by taking the difference (subtracting) between the initial population size, Nt=0 (population size at time t=0), and the population size at the end of the interval, Nt=1 (population size at time t = 1), and then subsequently dividing by the initial population size. 

Equation 2:  r = (Nt=1 - Nt=0) / Nt=0

The diversity of forms, sizes, and life histories among species has led to the development of a vast number of field techniques for estimation of population size and thus population growth over time (Bookhout 1994, McComb et al. 2021).  

● For stationary species an observational strategy may involve dividing a habitat into units. After setting up the units, samples are performed throughout the habitat at a select number of units (determined using a statistical sampling design) over a time interval (at time t = 0 and again at time t = 1), and the total number of organisms within each unit are counted. The numbers recorded are assumed to be representative for the habitat overall, and can be used to estimate the population growth rate within the entire habitat over the time interval.  

● For species that are mobile throughout a large range, a strategy such as using a mark-recapture method may be employed (i.e. tags, bands, transmitters) to determine a count over a time interval (at time = 0 and again at time =1).   

Population growth rate can also be estimated using mathematical model constructs (for example, ranging from simple differential equations to complex age or stage structured matrix projection models and individual based modeling approaches), and may assume a linear or nonlinear population increase over time (Caswell 2001, Vandermeer and Goldberg 2003, Gotelli 2008, Murray and Sandercock 2020). The AOP framework can be used to support the translation of pathway-specific mechanistic data into responses relevant to population models and output from the population models, such as changing (declining) population growth rate, can be used to assess and manage risks of chemicals (Kramer et al. 2011). As such, this translational capability can increase the capacity and efficiency of safety assessments both for single chemicals and chemical mixtures (Kramer et al. 2011).  

Some examples of modeling constructs used to investigate population growth rate:

● A modeling construct could be based upon laboratory toxicity tests to determine effect(s) that are then linked to the population model and used to estimate decline in population growth rate.  Miller et al. (2007) used concentration–response data from short term reproductive assays with fathead minnow (Pimephales promelas) exposed to endocrine disrupting chemicals in combination with a population model to examine projected alterations in population growth rate.  

● A model construct could be based upon a combination of effects-based monitoring at field sites (informed by an AOP) and a population model.  Miller et al. (2015) applied a population model informed by an AOP to project declines in population growth rate for white suckers (Catostomus commersoni) using observed changes in sex steroid synthesis in fish exposed to a complex pulp and paper mill effluent in Jackfish Bay, Ontario, Canada. Furthermore, a model construct could be comprised of a series of quantitative models using KERs that culminates in the estimation of change (decline) in population growth rate.  

● A quantitative adverse outcome pathway (qAOP) has been defined as a mathematical construct that models the dose–response or response–response relationships of all KERs described in an AOP (Conolly et al. 2017, Perkins et al. 2019). Conolly et al. (2017) developed a qAOP using data generated with the aromatase inhibitor fadrozole as a stressor and then used it to predict potential population‐level impacts (including decline in population growth rate). The qAOP modeled aromatase inhibition (the molecular initiating event) leading to reproductive dysfunction in fathead minnow (Pimephales promelas) using 3 computational models: a hypothalamus–pituitary–gonadal axis model (based on ordinary differential equations) of aromatase inhibition leading to decreased vitellogenin production (Cheng et al. 2016), a stochastic model of oocyte growth dynamics relating vitellogenin levels to clutch size and spawning intervals (Watanabe et al. 2016), and a population model (Miller et al. 2007).

● Dynamic energy budget (DEB) models offer a methodology that reverse engineers stressor effects on growth, reproduction, and/or survival into modular characterizations related to the acquisition and processing of energy resources (Nisbet et al. 2000, Nisbet et al. 2011).  Murphy et al. (2018) developed a conceptual model to link DEB and AOP models by interpreting AOP key events as measures of damage-inducing processes affecting DEB variables and rates.

● Endogenous Lifecycle Models (ELMs), capture the endogenous lifecycle processes of growth, development, survival, and reproduction and integrate these to estimate and predict expected fitness (Etterson and Ankley, 2021).  AOPs can be used to inform ELMs of effects of chemical stressors on the vital rates that determine fitness, and to decide what hierarchical models of endogenous systems should be included within an ELM (Etterson and Ankley, 2021).

 

Regulatory Significance of the AO

Maintenance of sustainable fish and wildlife populations (i.e., adequate to ensure long-term delivery of valued ecosystem services) is a widely accepted regulatory goal upon which risk assessments and risk management decisions are based.

References

  • Ankley GT, Jensen KM, Makynen EA, Kahl MD, Korte JJ, Hornung MW, Henry TR, Denny JS, Leino RL, Wilson VS, Cardon MD, Hartig PC, Gray LE. 2003. Effects of the androgenic growth promoter 17b-trenbolone on fecundity and reproductive endocrinology of the fathead minnow. Environ. Toxicol. Chem. 22: 1350–1360.
  • Bookhout TA. 1994. Research and management techniques for wildlife and habitats. The Wildlife Society, Bethesda, Maryland. 740 pp.
  • Caswell H. 2001. Matrix Population Models. Sinauer Associates, Inc., Sunderland, MA, USA
  • Cheng WY, Zhang Q, Schroeder A, Villeneuve DL, Ankley GT, Conolly R.  2016.  Computational modeling of plasma vitellogenin alterations in response to aromatase inhibition in fathead minnows. Toxicol Sci 154: 78–89.
  • Conolly RB, Ankley GT, Cheng W-Y, Mayo ML, Miller DH, Perkins EJ, Villeneuve DL, Watanabe KH. 2017. Quantitative adverse outcome pathways and their application to predictive toxicology. Environ. Sci. Technol. 51:  4661-4672.
  • Etterson MA, Ankley GT.  2021.  Endogenous Lifecycle Models for Chemical Risk Assessment. Environ. Sci. Technol. 55:  15596-15608. 
  • Gotelli NJ, 2008. A Primer of Ecology. Sinauer Associates, Inc., Sunderland, MA, USA.
  • Holbech H, Kinnberg KL, Brande-Lavridsen N, Bjerregaard P, Petersen GI, Norrgren L, Orn S, Braunbeck T, Baumann L, Bomke C, Dorgerloh M, Bruns E, Ruehl-Fehlert C, Green JW, Springer TA, Gourmelon A. 2012 Comparison of zebrafish (Danio rerio) and fathead minnow (Pimephales promelas) as test species in the Fish Sexual Development Test (FSDT). Comp. Biochem. Physiol. C Toxicol. Pharmacol. 155:  407–415.
  • Kramer VJ, Etterson MA, Hecker M, Murphy CA, Roesijadi G, Spade DJ, Stromberg JA, Wang M, Ankley GT.  2011.  Adverse outcome pathways and risk assessment: Bridging to population level effects.  Environ. Toxicol. Chem. 30, 64-76.
  • McComb B, Zuckerberg B, Vesely D, Jordan C.  2021.  Monitoring Animal Populations and their Habitats: A Practitioner's Guide.  Pressbooks, Oregon State University, Corvallis, OR Version 1.13, 296 pp. 
  • Miller DH, Villeneuve DL, Santana Rodriguez KJ, Ankley GT. 2022.  A multidimensional matrix model for predicting the effect of male biased sex ratios on fish populations. Environmental Toxicology and Chemistry 41(4): 1066-1077.
  • Miller DH, Tietge JE, McMaster ME, Munkittrick KR, Xia X, Griesmer DA, Ankley GT. 2015. Linking mechanistic toxicology to population models in forecasting recovery from chemical stress: A case study from Jackfish Bay, Ontario, Canada. Environmental Toxicology and Chemistry 34(7):  1623-1633.
  • Miller DH, Jensen KM, Villeneuve DE, Kahl MD, Makynen EA, Durhan EJ, Ankley GT. 2007. Linkage of biochemical responses to population-level effects: A case study with vitellogenin in the fathead minnow (Pimephales promelas). Environ Toxicol Chem 26:  521–527.
  • Miller DH, Ankley GT. 2004. Modeling impacts on populations: Fathead minnow (Pimephales promelas) exposure to the endocrine disruptor 17b-trenbolone as a case study. Ecotox Environ Saf 59: 1–9.
  • Murphy CA, Nisbet RM, Antczak P, Garcia-Reyero N, Gergs A, Lika K, Mathews T, Muller EB, Nacci D, Peace A, Remien CH, Schultz IR, Stevenson LM, Watanabe KH.  2018.  Incorporating suborganismal processes into dynamic energy budget models for ecological risk assessment.  Integrated Environmental Assessment and Management 14(5):  615–624.
  • Murray DL, Sandercock BK (editors).  2020.  Population ecology in practice.  Wiley-Blackwell, Oxford UK, 448 pp.
  • Nisbet RM, Jusup M, Klanjscek T, Pecquerie L.  2011.  Integrating dynamic energy budget (DEB) theory with traditional bioenergetic models.  The Journal of Experimental Biology 215: 892-902.
  • Nisbet RM, Muller EB, Lika K, Kooijman SALM. 2000. From molecules to ecosystems through dynamic energy budgets. J Anim Ecol 69:  913–926.
  • Perkins EJ,  Ashauer R, Burgoon L, Conolly R, Landesmann B,, Mackay C, Murphy CA, Pollesch N, Wheeler JR, Zupanic A, Scholzk S.  2019.  Building and applying quantitative adverse outcome pathway models for chemical hazard and risk assessment.  Environmental Toxicology and Chemistry 38(9): 1850–1865. 
  • Vandermeer JH, Goldberg DE. 2003.  Population ecology: first principles.  Princeton University Press, Princeton NJ, 304 pp.
  • Villeneuve DL, Crump D, Garcia-Reyero N, Hecker M, Hutchinson TH, LaLone CA, Landesmann B, Lattieri T, Munn S, Nepelska M, Ottinger MA, Vergauwen L, Whelan M. Adverse outcome pathway (AOP) development 1: Strategies and principles. Toxicol Sci. 2014: 142:312–320
  • Watanabe KH, Mayo M, Jensen KM, Villeneuve DL, Ankley GT, Perkins EJ.  2016.  Predicting fecundity of fathead minnows (Pimephales promelas) exposed to endocrine‐disrupting chemicals using a MATLAB(R)‐based model of oocyte growth dynamics. PLoS One 11:  e0146594.

Appendix 2

List of Key Event Relationships in the AOP