7722-84-1MHAJPDPJQMAIIY-UHFFFAOYSA-NMHAJPDPJQMAIIY-UHFFFAOYSA-N
Hydrogen peroxideHydrogen peroxide,
Adeka Super EL
Albone DS
Anti-Keim 50
Asepticper
Baquashock
Clarigel Gold
Crest whitestrips
Crestal Whitestrips
Crystacide
Dentasept
Deslime LP
Dihydrogen dioxide
Hybrite
Hydrogen dioxide
Hydrogen peroxide aq. solns.
Hydroperoxide
Inhibine
Interox ST 50
Lase Peroxide
Lensan A
Magic Bleaching
Metrokur
Microcyn 60
Mirasept
Nite White Excel 2
NSC 19892
Odosat D
Opalescence Xtra
Opalescence Xtra Boost
OxiDate
Oxigenal
Oxyfull
Oxysept
Oxysept I
Pegasyl
PERHYDROL
Peroxaan
Peroxclean
peroxido de hidrogeno
peroxyde d'hydrogene
Quasar Brite
Select Bleach
Superoxol
T-Stuff
UN 2014
UN 2015
UN 2984
Wasserstoffperoxid
WASSERSTOFFPEROXID <5%
WASSERSTOFFPEROXID >20-35%
WASSERSTOFFPEROXID >35%
WASSERSTOFFPEROXID 5-20%
Whiteness HP
Whitespeed
Xtra White
Zerosil
DTXSID20207157758-01-2OCATYIAKPYKMPG-UHFFFAOYSA-MOCATYIAKPYKMPG-UHFFFAOYSA-M
Potassium bromateBromate, potassium
Bromic acid, potassium salt
Bromate de potassium
bromato de potasio
Bromic acid, potassium salt (1:1)
Kaliumbromat
UN 1484
DTXSID60201957784-46-5PTLRDCMBXHILCL-UHFFFAOYSA-MPTLRDCMBXHILCL-UHFFFAOYSA-M
Sodium arseniteArsenenous acid, sodium salt (1:1)
Sodium metaarsenite
Arsenic sodium oxide
Arsenenous acid, sodium salt
Arsenite sodium
Dioxoarsenate de sodium
dioxoarsenato de sodio
NATRIUMARSENIT
Natriumdioxoarsenat
Sodium arsenic oxide
sodium dioxoarsenate
Sodium meta-arsenite
UN 1686
UN 2027
DTXSID502010413674-87-8ASLWPAWFJZFCKF-UHFFFAOYSA-NASLWPAWFJZFCKF-UHFFFAOYSA-N
TDCPPTris(1,3-dichloro-2-propyl)phosphate
2-Propanol, 1,3-dichloro-, phosphate (3:1)
DTXSID9026261CHEBI:26523reactive oxygen speciesPCO:0000001population of organismsGO:1903409reactive oxygen species biosynthetic processGO:1901031regulation of response to reactive oxygen speciesVT:1000294egg quantityPCO:0000008population growth rate1increased3occurrence2decreasedGamma radiation2017-04-15T16:04:312017-04-15T16:04:31Ionizing Radiation<p>Ionizing radiation can vary in energy, dose, charge, and in the spatial distributions of energy transferred to other matter (linear energy transfer per unit length or LET) (ICRU 1970). At the same dose, low and high LET both generate energy deposition events, including many higher energy events (Goodhead and Nikjoo 1989). However, they differ in the spatial distribution and upper range of intensity of energy deposited. Lower LET such as gamma rays sparsely deposit many individual excitations or small clusters of excitations of low energy (Goodhead 1988). In contrast, high LET such as alpha particles have fewer tracks but readily transfer their energy to matter and therefore deposit their energy over a much smaller area (Goodhead 1994). Consequently, alpha and other high LET particles penetrate less deeply into tissue, interactions are densely focused on a narrow track, and individual energy depositions can be large (Goodhead 1988). These different energy deposition patterns can lead to differences in radiation effects including the pattern of DNA damage.</p>
<p>Exposure to ionizing radiation can come from natural and industrial sources. Space and terrestrial radiation includes a range of LET particles, while diagnostic radiation methods such as X-ray imaging, mammography and CT scans use low LET X-rays. Radiation therapy can use an external beam to direct radiation on a focused tissue area, or deposit solid or liquid radioactive materials in the body that release (mostly gamma) radiation internally. External radiotherapy typically uses X-rays but is moving towards higher LET charged particles such as protons and heavy ions (Durante, Orecchia et al. 2017).</p>
2019-05-03T12:36:362019-05-07T12:12:13Hydrogen peroxide2019-05-19T17:21:212019-05-19T17:21:21Potassium bromate2019-05-19T17:21:432019-05-19T17:21:43Sodium arsenite2019-05-19T18:27:362019-05-19T18:27:36Reactive oxygen species2017-06-16T08:32:102017-08-15T10:43:27Tris(1,3-dichloropropyl)phosphate - TDCPP2018-06-19T07:35:302018-06-19T07:59:12WCS_9606human10116rat10090mouse6239nematodeWCS_7955zebrafish3702thale-cress3349Scotch pineWCS_35525Daphnia magna3055Chlamydomonas reinhardtiiWCS_6396common brandling wormWCS_4472Lemna minor8030Salmo salarWikiUser_25human and other cells in culture4932yeast9913bovineWCS_9986rabbitWCS_90988fathead minnow8078Fundulus heteroclitus8090Oryzias latipesWikiUser_22all speciesWikiUser_6fishDeposition of EnergyEnergy DepositionMolecular<p><span style="color:#0000ff"><span style="font-family:Arial,Helvetica,sans-serif"><span style="font-size:12px">Deposition of energy refers to events where subatomic particles or electromagnetic radiation deposit energy in the media through which they transverse. The energy may either be sufficient (e.g. ionizing radiation) or insufficient (e.g. non-ionizing radiation) to ionize atoms or molecules (Beir, et al. 1999). </span></span></span></p>
<p><span style="color:#0000ff"><span style="font-family:Arial,Helvetica,sans-serif"><span style="font-size:12px">Ionizing radiation can cause the ejection of electrons from atoms and molecules, thereby </span></span></span><span style="color:#e74c3c"><span style="font-family:Arial,Helvetica,sans-serif"><span style="font-size:12px">resulting in their ionization and the breakage of chemical bonds</span></span></span><span style="color:#0000ff"><span style="font-family:Arial,Helvetica,sans-serif"><span style="font-size:12px">. The energy of these subatomic particles or electromagnetic waves ranges from 124 KeV to 5.4 MeV, and is dependent on the source and type of radiation (Zyla et al., 2020). Not all electromagnetic radiation is ionizing; as the incident radiation must have sufficient energy to free electrons from the atom or molecule’s electron orbitals. The energy can induce direct and indirect ionization events and </span></span></span><span style="color:#e74c3c"><span style="font-family:Arial,Helvetica,sans-serif"><span style="font-size:12px">can be via internal (injections, inhalation, injection) or external exposure</span></span></span><span style="color:#0000ff"><span style="font-family:Arial,Helvetica,sans-serif"><span style="font-size:12px">. Direct ionization is the principal path where charged particles interact with DNA to cause a biological damage. Photons, which are electromagnetic waves can also cause direct ionization. Indirect ionization produces free radicals of other molecules, specifically water, which can transform to damage critical targets such as DNA (Beir, et al. 1999; </span></span></span><span style="color:#e74c3c"><span style="font-family:Arial,Helvetica,sans-serif"><span style="font-size:12px">Balagamwala et al., 2013</span></span></span><span style="color:#0000ff"><span style="font-family:Arial,Helvetica,sans-serif"><span style="font-size:12px">). Given the fundamental nature of energy deposition by nuclei, nucleons or elementary particles in material, this process is universal to all biological contexts.</span></span></span></p>
<p><span style="color:#e74c3c"><span style="font-family:Arial,Helvetica,sans-serif"><span style="font-size:12px">Energy deposition is influenced by the linear energy transfer (LET) (Hall and Giaccia, 2018 UNSCEAR, 2020). High LET refers to energy above 10 keV μm-1 which produces more complex, dense structural damage than low LET radiation (below 10 keV μm-1). Low-LET particles produce sparse ionization events such as photons (X- and gamma rays), as well as high-energy protons. Low LET radiation travels farther into tissue but deposits smaller amounts of energy, whereas high LET radiation, which includes heavy ions, alpha particles and high-energy neutrons, does not travel as far but deposits larger amounts of energy into tissue at the same absorbed dose. The biological effect of the deposition of energy can be modulated by varying dose and dose rate of exposure, such as acute, chronic, or fractionated exposures (Hall and Giaccia, 2018). </span></span></span></p>
<p><span style="font-family:Arial,Helvetica,sans-serif"><span style="font-size:12px"><span style="color:#e74c3c">Non-ionizing radiation is a type of electromagnetic radiation that lacks the energy to ionize atoms or molecules. Examples of non-ionizing radiation include radio waves (wavelength: 100 km-1m), microwaves (wavelength: 1m-1mm), infrared radiation (wavelength: 1mm- 1 um), visible light (wavelengths: 400-700 nm), and ultraviolet radiation of longer wavelengths such as UVB (wavelengths: 315-400nm) and UVA (wavelengths: 280-315 nm). UVC radiation (X-X nm) is, in contrast to UVB and UVA, considered to be a type of ionizing radiation. Exposure to non-ionizing radiation occurs either from natural or anthropogenic sources, and include radio waves used for communication (broadcasting and cell phones), microwaves used in cooking food and in radar systems, infrared radiation emitted by warm objects or used in remote controls, thermal imaging and medical treatments. Visible light is the range of electromagnetic radiation that we can see and that is commonly used in photosynthesis in primary producers. UV radiation has key functions in melanisation (tanning) of a number of species, and exhibit key signalling roles in navigation and communication (e.g insects, aquatic invertebrates and fish), locomotory and predatory behavior (e.g. reptiles, birds and crustaceans) and growth and development (e.g. plants). UV radiation is also used in some medical treatments such as skin diseases (e.g. psoriasis, eczema, vitiligo and skin cancers). </span></span></span></p>
<table border="1" bordercolor="#ccc" cellpadding="5" cellspacing="0" style="border-collapse:collapse">
<tbody>
<tr>
<td style="background-color:#eeeeee; text-align:center"><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif"><strong>Radiation type</strong></span></span></td>
<td style="background-color:#eeeeee; text-align:center">
<p><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif"><strong>Assay Name</strong></span></span></p>
</td>
<td style="background-color:#eeeeee; text-align:center">
<p><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif"><strong>References</strong></span></span></p>
</td>
<td style="background-color:#eeeeee; text-align:center">
<p><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif"><strong>Description</strong></span></span></p>
</td>
<td style="background-color:#eeeeee; text-align:center">
<p><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif"><strong>OECD Approved Assay</strong></span></span></p>
</td>
</tr>
<tr>
<td style="text-align:center"><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">Ionizing radiation</span></span></td>
<td style="text-align:center">
<p><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">Monte Carlo Simulations (Geant4)</span></span></p>
</td>
<td style="text-align:center">
<p><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">Douglass et al., 2013; Douglass et al. 2012; <span style="color:#e74c3c">Zyla et al., 2020</span></span></span></p>
</td>
<td style="text-align:center">
<p><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">Monte Carlo simulations are based on a computational algorithm that mathematically models the deposition of energy into materials.</span></span></p>
</td>
<td style="text-align:center">
<p><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">No</span></span></p>
</td>
</tr>
<tr>
<td style="text-align:center"><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">Ionizing radiation</span></span></td>
<td style="text-align:center">
<p><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">Fluorescent Nuclear Track Detector (FNTD)</span></span></p>
</td>
<td style="text-align:center">
<p><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">Sawakuchi, 2016; Niklas, 2013; Koaira & Konishi, 2015</span></span></p>
</td>
<td style="text-align:center">
<p><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">FNTDs are biocompatible chips with crystals of aluminium oxide doped with carbon and magnesium; used in conjuction with fluorescent microscopy, these FNTDs allow for the visualization and the linear energy transfer (LET) quantification of tracks produced by the deposition of energy into a material.</span></span></p>
</td>
<td style="text-align:center">
<p><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">No</span></span></p>
</td>
</tr>
<tr>
<td style="text-align:center"><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">Ionizing radiation</span></span></td>
<td style="text-align:center"><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">Tissue equivalent proportional counter (TEPC)</span></span></td>
<td style="text-align:center"><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">Straume et al, 2015</span></span></td>
<td style="text-align:center"><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">Measure the LET spectrum and calculate the dose equivalent.</span></span></td>
<td style="text-align:center"><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">No</span></span></td>
</tr>
<tr>
<td style="text-align:center"><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">Ionizing radiation</span></span></td>
<td style="text-align:center"><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">alanine dosimeters/NanoDots</span></span></td>
<td style="text-align:center">
<p><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">Lind et al. 2019; Xie et al., 2022</span></span></p>
</td>
<td style="text-align:center"> </td>
<td style="text-align:center"><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">No</span></span></td>
</tr>
<tr>
<td style="text-align:center"><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">Non-ionizing radiation</span></span></td>
<td style="text-align:center"><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">UV meters or radiameters</span></span></td>
<td style="text-align:center"><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">Xie et at., 2020</span></span></td>
<td style="text-align:center"><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">UVA/UVB (irradiance intensity), UV dosimeters (accumulated irradiance over time), Spectrophoto meter (absorption of UV by a substance or material)</span></span></td>
<td style="text-align:center"><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">No</span></span></td>
</tr>
</tbody>
</table>
<p> </p>
<p><span style="color:#0000ff"><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">Energy can be deposited into any substrate, both living and non-living; it is independent of age, taxa, sex, or life-stage. </span></span></span></p>
<p><span style="color:#e74c3c"><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif"><strong>Taxonomic applicability: </strong>This MIE is not taxonomically specific. </span></span></span></p>
<p><span style="color:#e74c3c"><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif"><strong>Life stage applicability: </strong>This MIE is not life stage specific. </span></span></span></p>
<p><span style="color:#e74c3c"><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif"><strong>Sex applicability: </strong>This MIE is not sex specific. </span></span></span></p>
LowUnspecificHighAll life stagesModerateModerateModerateHighHighHighModerateHighModerateModerateHighLow<p><span style="color:#e74c3c"><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">Balagamwala, E. H. et al. (2013), “Introduction to radiotherapy and standard teletherapy techniques”,<em> Dev Ophthalmol,</em> Vol. 52, Karger, Basel, https://doi.org/10.1159/000351045 </span></span></span></p>
<p><span style="color:#0000ff"><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">Beir, V. et al. (1999), “The Mechanistic Basis of Radon-Induced Lung Cancer”, in <em>Health Risks of Exposure to Radon: BEIR V</em>I, National Academy Press, Washington, D.C., https://doi.org/10.17226/5499 </span></span></span></p>
<p><span style="color:#0000ff"><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">Douglass, M. et al. (2013), “Monte Carlo investigation of the increased radiation deposition due to gold nanoparticles using kilovoltage and megavoltage photons in a 3D randomized cell model”<em>, Medical Physics</em>, Vol. 40/7, American Institute of Physics, College Park, https://doi.org/10.1118/1.4808150 </span></span></span></p>
<p><span style="color:#0000ff"><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">Douglass, M. et al. (2012), “Development of a randomized 3D cell model for Monte Carlo microdosimetry simulations.”, <em>Medical Physics</em>, Vol. 39/6, American Institute of Physics, College Park, https://doi.org/10.1118/1.4719963 </span></span></span></p>
<p><span style="color:#e74c3c"><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">Hall, E. J. and Giaccia, A.J. (2018), <em>Radiobiology for the Radiologist</em>, 8th edition, Wolters Kluwer, Philadelphia. </span></span></span></p>
<p><span style="color:#0000ff"><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">Kodaira, S. and Konishi, T. (2015), “Co-visualization of DNA damage and ion traversals in live mammalian cells using a fluorescent nuclear track detector.”, <em>Journal of Radiation Research</em>, Vol. 56/2, Oxford University Press, Oxford, https://doi.org/10.1093/jrr/rru091 </span></span></span></p>
<p><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">Lind, O.C., D.H. Oughton and Salbu B. (2019), "The NMBU FIGARO low dose irradiation facility", <em>International Journal of Radiation Biology</em>, Vol. 95/1, Taylor & Francis, London, https://doi.org/10.1080/09553002.2018.1516906.</span></span></p>
<p><span style="color:#0000ff"><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">Sawakuchi, G.O. and Akselrod, M.S. (2016), “Nanoscale measurements of proton tracks using fluorescent nuclear track detectors.”,<em> Medical Physics</em>, Vol. 43/5, American Institute of Physics, College Park, https://doi.org/10.1118/1.4947128 </span></span></span></p>
<p><span style="color:#0000ff"><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">Straume, T. et al. (2015), “Compact Tissue-equivalent Proportional Counter for Deep Space Human Missions.”,<em> Health physics,</em> Vol. 109/4, Lippincott Williams & Wilkins, Philadelphia, https://doi.org/10.1097/HP.0000000000000334 </span></span></span></p>
<p><span style="color:#0000ff"><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">Niklas, M. et al. (2013), “Engineering cell-fluorescent ion track hybrid detectors.”, <em>Radiation Oncology</em>, Vol. 8/104, BioMed Central, London, https://doi.org/10.1186/1748-717X-8-141 </span></span></span></p>
<p><span style="color:#e74c3c"><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">UNSCEAR (2020), <em>Sources, effects and risks of ionizing radiation</em>, United Nations. </span></span></span></p>
<p><span style="font-family:Arial,Helvetica,sans-serif"><span style="font-size:12px">Xie, Li. et al. (2022), "Ultraviolet B Modulates Gamma Radiation-Induced Stress Responses in Lemna Minor at Multiple Levels of Biological Organisation", <em>SSRN</em>, Elsevier, Amsterdam, http://dx.doi.org/10.2139/ssrn.4081705 .</span></span></p>
<p><span style="color:#e74c3c"><span style="font-size:12px"><span style="font-family:Arial,Helvetica,sans-serif">Zyla, P.A. et al. (2020)<em>, Review of particle physics: Progress of Theoretical and Experimental Physics,</em> 2020 Edition, Oxford University Press, Oxford. </span></span></span></p>
<p> </p>
<p> </p>
2019-08-22T09:44:232023-04-28T08:40:04Increase, Reactive Oxygen Species productionIncrease, ROS productionMolecularCL:0000255eukaryotic cell2016-11-29T18:41:232021-04-11T18:03:23Increase, Oxidative damage to DNAIncrease, Oxidative DNA damageMolecular<p>The nitrogenous bases of DNA are susceptible to oxidation in the presence of oxidizing agents. Oxidative adducts form mainly on C5 and to a lesser degree on C6 of thymine and cytosine, and on C8 of guanine and adenine. Guanine is most prone to oxidation due to its low oxidation potential (Jovanovic and Simic, 1986). Indeed, 8-oxo-2’-deoxyguanosine (8-oxodG)/8-hydroxy-2’-deoxyguanosine (8-OHdG) is the most abundant and well-studied oxidative DNA lesion in the cell (Swenberg et al., 2011). It causes an A(anti):8-oxo-G(syn) mispair instead of the normal C(anti):8-oxo-G(syn) pair. This pairing does not cause large structural changes to the DNA backbone, and therefore remains undetected by the polymerase’s proofreading mechanism. Consequently, one of the daughter strands will have an AT pair instead of the correct GC pair after replication (Markkanen, 2017). </p>
<p>Formamidopyrimidine lesions on guanine and adenine (FaPyG and FaPyA), 8-hydroxy-2'-deoxyadenine (8-oxodA), and thymidine glycol (Tg) are other common oxidative lesions. We refer the reader to reviews on this topic to see the full set of potential oxidative DNA lesions (Whitaker et al., 2017). Oxidative DNA lesions are present in the cell at a steady state due to endogenous redox processes (Swenberg et al., 2010). Under normal conditions, cells are able to withstand the baseline level of oxidized bases through efficient repair and regulation of free radicals in the cell. However, direct chemical insult from specific compounds, or induction of reactive oxygen species (ROS) from the reduction of endogenous molecules, as well as through the release of inflammatory cell-derived oxidants, can lead to increased DNA oxidation, a state known as oxidative stress (Turner et al., 2002; Schoenfeld et al., 2012; Tangvarasittichai and Tangvarasittichai, 2019). Furthermore, although cells do possess repair mechanisms to deal with oxidative DNA damage, sometimes the repair intermediates can interfere with genome function or decrease stability of the genome. This creates a balancing act between when it is best to repair damage and when it is best to leave it (Poetsch, 2020a). </p>
<p>This KE describes an increase in oxidative lesions in the nuclear DNA above the steady-state level. Oxidative DNA damage can occur in any cell type with nuclear DNA under oxidative stress.</p>
<p>Relative Quantification of Oxidative DNA Lesions</p>
<ul>
<li>Comet assay (single cell gel electrophoresis) with Fpg and hOGG1 modifications (Smith et al., 2006; Platel et al., 2011)
<ul style="list-style-type:circle">
<li>Oxoguanine glycosylase (hOGG1) and formamidopyrimidine-DNA glycosylase (Fpg) are base excision repair (BER) enzymes in eukaryotic and prokaryotic cells, respectively</li>
<li>Both enzymes are bi-functional; the glycosylase function cleaves the glycosidic bond between the ribose and the oxidized base, giving rise to an abasic site, and the apurinic/apymidinic (AP) site lyase function cleaves the phosphodiester bond via β-elimination reaction and creates a single strand break</li>
<li>Treatment of DNA with either enzyme prior to performing the electrophoresis step of the comet assay allows detection of oxidative lesions by measuring the increase in comet tail length when compared against untreated samples.</li>
</ul>
</li>
<li>Enzyme-linked immunosorbant assay (ELISA) (Dizdaroglu et al., 2002; Breton et al., 2003; Xu et al., 2008; Zhao et al. 2017)
<ul style="list-style-type:circle">
<li>8-oxodG can be detected using immunoassays, such as ELISA, that use antibodies against 8-oxodG lesions. It has been noted that immunodetection of 8-oxodG can be interfered by certain compounds in biological samples.</li>
</ul>
</li>
</ul>
<p>Absolute Quantification of Oxidative DNA Lesions</p>
<ul>
<li>Quantification of 8-oxodG using HPLC-EC (Breton et al., 2003; Chepelev et al., 2015)
<ul style="list-style-type:circle">
<li>8-oxodG can be separated from digested DNA and precisely quantified using high performance liquid chromatography (HPLC) with electrochemical detection</li>
</ul>
</li>
<li>Mass spectrometry LC-MRM/MS (Mangal et al., 2009)
<ul style="list-style-type:circle">
<li>Liquid chromatography can also be coupled with multiple reaction monitoring/ mass spectrometry to detect and quantify oxidative lesions. Correlation between lesions measured by hOGG1-modified comet assay and LC-MS has been reported</li>
</ul>
</li>
</ul>
<p>Gas chromatography-mass spectrometry (GC-MS) </p>
<ul>
<li>DNA is hydrolyzed to release either free bases or nucleosides and then undergoes derivatization in order to increase their volatility. Finally, samples run through a gas chromatograph and then a mass spectrometer. The mass spectrometer results are used to determine oxidative DNA damage by identifying modified bases or nucleosides (Dizdaroglu, 1994). </li>
</ul>
<p>Sequencing assays </p>
<ul>
<li>Various markers are used to detect and highlight sites of DNA damage; the result is then processed and sequenced. This category encompasses a wide range of assays such as snAP-seq, OGG1-AP-seq, oxiDIP-seq, OG-seq, and click-code-seq (Yun et al., 2017; Wu et al., 2018; Amente et al., 2019; Poetsch, 2020b). </li>
<li>We note that other types of oxidative lesions can be quantified using the methods described above.</li>
</ul>
<p><strong>Taxonomic applicability:</strong> Theoretically, DNA oxidation can occur in any cell type, in any organism. Oxidative DNA lesions have been measured in mammalian cells (human, mouse, calf, rat) in vitro and in vivo, and in prokaryotes.</p>
<p><strong>Life stage applicability:</strong> This key event is not life stage specific (Mesa & Bassnett, 2013; Suman et al., 2019). </p>
<p><strong>Sex applicability:</strong> This key event is not sex specific (Mesa & Bassnett, 2013). </p>
<p><strong>Evidence for Perturbation by Prototypic Stressor:</strong> H<sub>2</sub>O<sub>2</sub> and KBrO<sub>3</sub> – A concentration-dependent increase in oxidative lesions was observed in both Fpg- and hOGG1-modified comet assays of TK6 cells treated with increasing concentrations of glucose oxidase (an enzyme that generates H<sub>2</sub>O<sub>2</sub>) and potassium bromate for 4 h (Platel et al., 2011). </p>
<p>Evidence indicates that oxidative DNA damage is also induced by X-rays (Bahia et al., 2018), <sup>60</sup>Co γ-rays, <sup>12</sup>C ions, α particles, electrons (Georgakilas, 2013), UVB (Mesa and Bassnett, 2013), γ-rays, <sup>56</sup>Fe ions (Datta et al., 2012), and protons (Suman et al., 2019). </p>
UBERON:0000062organCL:0000255eukaryotic cellModerateUnspecificHighAll life stagesModerateLowHighLowLowHighLow<p>Amente, S. et al. (2019), “Genome-wide mapping of 8-oxo-7,8-dihydro-2’-deoxyguanosine reveals accumulation of oxidatively-generated damage at DNA replication origins within transcribed long genes of mammalian cells”, <em>Nucleic Acids Research 2019</em>, Vol. 47/1, Oxford University Press, England, https://doi.org/10.1093/nar/gky1152 </p>
<p>Bahia, S. et al. (2018), “Oxidative and nitrative stress-related changes in human lens epithelial cells following exposure to X-rays”, <em>International journal of radiation biology</em>, Vol. 94/4, England, https://doi.org/10.1080/09553002.2018.1439194 </p>
<p>Breton J, Sichel F, Bainchini F, Prevost V. (2003). Measurement of 8-Hydroxy-2′-Deoxyguanosine by a Commercially Available ELISA Test: Comparison with HPLC/Electrochemical Detection in Calf Thymus DNA and Determination in Human Serum. Anal Lett 36:123-134.</p>
<p>Cabrera, M. P., R. Chihuailaf and F. Wittwer Menge (2011), “Antioxidants and the integrity of ocular tissues”, <em>Veterinary medicine international</em>, Vol. 2011, SAGE-Hindawi Access to Research, United States, https://doi.org/10.4061/2011/905153 </p>
<p>Cadet, J. et al. (2012), “Oxidatively generated complex DNA damage: tandem and clustered lesions”, <em>Cancer letters</em>, Vol. 327/1, Elsevier Ireland Ltd, Ireland. https://doi.org/10.1016/j.canlet.2012.04.005 </p>
<p>Chepelev N, Kennedy D, Gagne R, White T, Long A, Yauk C, White P. (2015). HPLC Measurement of the DNA Oxidation Biomarker, 8-oxo-7,8-dihydro-2'-deoxyguanosine, in Cultured Cells and Animal Tissues. Journal of Visualized Experiments 102:e52697.</p>
<p>Collins, A. R. (2014), “Measuring oxidative damage to DNA and its repair with the comet assay”, <em>Biochimica et biophysica acta. General subjects</em>, Vol. 1840/2, Elsevier B.V., https://doi.org/10.1016/j.bbagen.2013.04.022 </p>
<p>Datta, K. et al. (2012), “Exposure to heavy ion radiation induces persistent oxidative stress in mouse intestive”, <em>PloS One</em>, Vol. 7/8, Public Library of Science, United States, https://doi.org/10.1371/journal.pone.0042224 </p>
<p>Dizdaroglu, M. (1994), “Chemical determination of oxidative DNA damage by gas chromatography-mass spectrometry”, <em>Methods in Enzymology</em>, Vol. 234, Elsevier Science & Technology, United States, https://doi.org/ 10.1016/0076-6879(94)34072-2 </p>
<p>Dizdaroglu, M. et al. (2002), “Free radical-induced damage to DNA : mechanisms and measurement”, <em>Free radical biology & medicine</em>, Vol. 32/11, United States, pp. 1102-1115 </p>
<p>Eaton, J. W. (1995), “UV-mediated cataractogenesis: a radical perspective”, <em>Documenta ophthalmologica</em>, Vol. 88/3-4, Springer, Dordrecht, https://doi.org/10.1007/BF01203677 </p>
<p>Fletcher, A. E. (2010), “Free radicals, antioxidants and eye diseases: evidence from epidemiological studies on cataract and age-related macular degeneration”, <em>Ophthalmic Research</em>, Vol. 44/3, Karger international, Basel, https://doi.org/10.1159/000316476 </p>
<p>Georgakilas, A. G et al. (2013), “Induction and repair of clustered DNA lesions: what do we know so far?”, Radiation Research, Vol. 180/1, <em>The Radiation Research Society</em>, United States, https://doi.org/10.1667/RR3041.1 </p>
<p>Jose, D. et al. (2009). “Spectroscopic studies of position-specific DNA “breathing” fluctuations at replication forks and primer-template junctions”, <em>Proceedings of the National Academy of Sciences of the United States of America</em>, Vol. 106/11, https://doi.org/10.1073/pnas.0900803106 </p>
<p>Jovanovic S, Simic M. (1986). One-electron redox potential of purines and pyrimidines. J Phys Chem 90:974-978.</p>
<p>Kruk, J., K. Kubasik-Kladna and H. Y. Aboul-Enein (2015), “The role oxidative stress in the pathogenesis of eye diseases: current status and a dual role of physical activity”, <em>Mini-reviews in medicinal chemistry</em>, Vol. 16/3, Bentham Science Publishers Ltd, Netherlands, https://doi.org/10.2174/1389557516666151120114605 </p>
<p>Lee, J. et al. (2004), “Reactive oxygen species, aging, and antioxidative nutraceuticals”, <em>Comprehensive reviews in food science and food safety</em>, Vol. 3/1, Blackwell Publishing Ltd, Oxford, https://doi.org/10.1111/j.1541-4337.2004.tb00058.x </p>
<p>Mangal D, Vudathala D, Park J, Lee S, Penning T, Blair I. (2009). Analysis of 7,8-Dihydro-8-oxo-2′-deoxyguanosine in Cellular DNA during Oxidative Stress. Chem Res Toxicol 22:788-797.</p>
<p>Markkanen, E. (2017), “Not breathing is not an option: How to deal with oxidative DNA damage”, <em>DNA repair</em>, Vol. 59, Elsevier B.V., Netherlands, https://doi.org/10.1016/j.dnarep.2017.09.007 </p>
<p>Mesa, R. and S. Bassnett (2013), “UV-B induced DNA damage and repair in the mouse lens”,<em> Investigative ophthalmology & visual science</em>, Vol. 54/10, the Association for Research in Vision and Ophthalmology, United States, https://doi.org/10.1167/iovs.13-12644 </p>
<p>Pendergrass, W. et al. (2010), “X-ray induced cataract is preceded by LEC loss, and coincident with accumulation of cortical DNA, and ROS; similarities with age-related cataracts”,<em> Molecular vision</em>, Vol. 16, Molecular Vision, United States, pp. 1496-1513 </p>
<p>Platel A, Nesslany F, Gervais V, Claude N, Marzin D. (2011). Study of oxidative DNA damage in TK6 human lymphoblastoid cells by use of the thymidine kinase gene-mutation assay and the <em>in vitro </em>modified comet assay: Determination of No-Observed-Genotoxic-Effect-Levels. Mutat Res 726:151-159.</p>
<p>Poetsch, Anna R. (2020a), “The genomics of oxidative DNA damage, repair, and resulting mutagenesis”, <em>Computational and structural biotechnology journal 2020</em>, Vol. 18, Elsevier B.V., Netherlands https://doi.org/10.1016/j.csbj.2019.12.013 </p>
<p>Poetsch, A. R. (2020b), “AP-Seq: A method to measure apurinic sites and small base adducts genome-wide”, The Nucleus, Springer US, New York, Sacca, S. C. et al. (2009), “Gene-environment interactions in ocular diseases”, <em>Mutation research – fundamental and molecular mechanisms of mutagenesis</em>, Vol. 667/1-2, Elsevier, Amsterdam, https://doi.org/10.1016/j.mrfmmm.2008.11.002 </p>
<p>Schoenfeld, M. P. et al. (2012), “A hypothesis on biological protection from space radiation through the use of new therapeutic gases as medical counter measures”, <em>Medical gas research</em>, Vol. 2/1, BioMed Central Ltd, India, https://doi.org/10.1186/2045-9912-2-8 </p>
<p>Smith C, O'Donovan M, Martin E. (2006). hOGG1 recognizes oxidative damage using the comet assay with greater specificity than FPG or ENDOIII. Mutagenesis 21:185-190.</p>
<p>Stohs, S. J. (1995), “The role of free radicals in toxicity and disease”, <em>Journal of Basic and Clinical Physiology and Pharmacology</em>, Vol. 6/3-4, Freund Publishing House Ltd, https://doi.org/10.1515/JBCPP.1995.6.3-4.205 </p>
<p>Suman, S. et al. (2019), “Fractionated and acute proton radiation show differential intestinal tumorigenesis and DNA damage and repair pathway response in ApcMin/+ mice”, <em>International Journal of Radiation Oncology</em>, Biology, Physics, Vol. 105/3, Elsevier Inc, https://doi.org/10.1016/j.ijrobp.2019.06.2532 </p>
<p>Swenberg J, Lu K, Moeller B, Gao L, Upton P, Nakamura J, Starr T. (2011). Endogenous versus Exogenous DNA Adducts: Their Role in Carcinogenesis, Epidemiology, and Risk Assessment. Toxicol Sci 120:S130-S145.</p>
<p>Tangvarasittichai, O and S. Tangvarasittichai (2018), “Oxidative stress, ocular disease, and diabetes retinopathy”,<em> Current Pharmaceutical Design</em>, Vol. 24/40, Bentham Science Publishers, https://doi.org/10.2174/1381612825666190115121531 </p>
<p>Turner, N. D. et al. (2002), “Opportunities for nutritional amelioration of radiation-induced cellular damage”, <em>Nutrition</em>, Vol. 18/10, Elsevier Inc, New York, https://doi.org/10.1016/S0899-9007(02)00945-0 </p>
<p>Whitaker A, Schaich M, Smith MS, Flynn T, Freudenthal B. (2017). Base excision repair of oxidative DNA damage: from mechanism to disease. Front Biosci 22:1493-1522.</p>
<p>Wu, J. (2018), “Nucleotide-resolution genome-wide mapping of oxidative DNA damage by click-code-seq”,<em> Journal of the American Chemical Society 2018</em>, American Chemical Society, United States https://doi-org.proxy.bib.uottawa.ca/10.1021/jacs.8b03715 </p>
<p>Xu, X. et al. (2008). “Fluorescence recovery assay for the detection of protein-DNA binding”, <em>Analytical Chemistry</em>, Vol. 80/14, https://doi.org/10.1021/ac8007016 </p>
<p>Zhao M, Howard E, Guo Z, Parris A, Yang X. (2017). p53 pathway determines the cellular response to alcohol-induced DNA damage in MCF-7 breast cancer cells. PLoS One 12:e0175121.</p>
2019-05-19T16:31:302023-01-09T20:30:24Increase, Oocyte apoptosisIncrease, Oocyte apoptosisCellular2020-04-30T16:41:182020-04-30T16:41:18Decrease, OogenesisDecrease, OogenesisOrgan2017-04-15T16:20:502020-04-30T16:41:53Reduction, Cumulative fecundity and spawningReduction, Cumulative fecundity and spawningIndividual<p>Spawning refers to the release of eggs. Cumulative fecundity refers to the total number of eggs deposited by a female, or group of females over a specified period of time.</p>
<p>In laboratory-based reproduction assays (e.g., OECD Test No. 229; OECD Test No. 240), spawning and cumulative fecundity can be directly measured through daily observation of egg deposition and egg counts.</p>
<p>In some cases, fecundity may be estimated based on gonado-somatic index (<a href="http://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=env/jm/mono(2008)22&doclanguage=en">OECD 2008</a>).</p>
<p>Cumulative fecundity and spawning can, in theory, be evaluated for any egg laying animal.</p>
HighFemaleHighAdult, reproductively matureHighHighHigh<ul>
<li>OECD 2008. Series on testing and assessment, Number 95. Detailed Review Paper on Fish Life-cycle Tests. OECD Publishing, Paris. ENV/JM/MONO(2008)22.</li>
<li>OECD (2015), <em>Test No. 240: Medaka Extended One Generation Reproduction Test (MEOGRT)</em>, OECD Publishing, Paris.<br />
DOI: <a href="http://dx.doi.org/10.1787/9789264242258-en" target="_blank" title="http://dx.doi.org/10.1787/9789264242258-en">http://dx.doi.org/10.1787/9789264242258-en</a></li>
<li>OECD. 2012a. Test no. 229: Fish short term reproduction assay. Paris, France:Organization for Economic Cooperation and Development.</li>
</ul>
2016-11-29T18:41:222017-03-20T17:52:57Decrease, Population growth rateDecrease, Population growth ratePopulation<p style="text-align:start"><span style="font-size:medium"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><span style="color:black">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</span> <span style="color:black">assessments, population growth rate (and hence population size over time) is important to consider within the context of applied conservation practices.</span></span></span></span></p>
<p style="text-align:start"><span style="font-size:medium"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><span style="color:black">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). </span></span></span></span></p>
<p style="margin-left:144px; text-align:start"><span style="font-size:medium"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><span style="color:black">Equation 1: r = b - d</span></span></span></span></p>
<p style="text-align:start"><span style="font-size:medium"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><span style="color:black">This key event refers to scenarios where r < 0 (instantaneous death rate exceeds instantaneous birth rate).</span></span></span></span></p>
<p style="text-align:start"><span style="font-size:medium"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><span style="color:black">Examining r in the context of population growth rate:</span></span></span></span></p>
<p style="margin-left:48px; text-align:start"><span style="font-size:medium"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><span style="color:black">● A population will decrease to extinction when the instantaneous death rate exceeds the instantaneous birth rate (r < 0). </span></span></span></span></p>
<p style="text-align:start"><span style="font-size:medium"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><span style="color:black"> ● The smaller the value of r below 1, the faster the population will decrease to zero. </span></span></span></span></p>
<p style="margin-left:48px; text-align:start"><span style="font-size:medium"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><span style="color:black">● A population will increase when resources are available and the instantaneous birth rate exceeds the instantaneous death rate (r > 0)</span></span></span></span></p>
<p style="text-align:start"><span style="font-size:medium"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><span style="color:black"> ● The larger the value that r exceeds 1, the faster the population can increase over time </span></span></span></span></p>
<p style="margin-left:48px; text-align:start"><span style="font-size:medium"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><span style="color:black">● 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). </span></span></span></span></p>
<p style="text-align:start"><span style="font-size:medium"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><span style="color:black">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. </span></span></span></span></p>
<p style="margin-left:48px; text-align:start"><span style="font-size:medium"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><span style="color:black">● 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). </span></span></span></span></p>
<p style="text-align:start"><span style="font-size:medium"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><span style="color:black">Alternatively, a stressor could indirectly impact survival and/or reproduction. </span></span></span></span></p>
<p style="margin-left:48px; text-align:start"><span style="font-size:medium"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><span style="color:black">● 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)</span></span></span></span></p>
<p style="text-align:start"><span style="font-size:medium"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><span style="color:black">Density dependence can be an important consideration:</span></span></span></span></p>
<p style="margin-left:48px; text-align:start"><span style="font-size:medium"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><span style="color:black">● 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. </span></span></span></span></p>
<p style="margin-left:48px; text-align:start"><span style="font-size:medium"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><span style="color:black">● 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). </span> </span></span></span></p>
<p style="text-align:start"><span style="font-size:medium"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><span style="color:black">Closed versus open systems:</span></span></span></span></p>
<p style="margin-left:48px; text-align:start"><span style="font-size:medium"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><span style="color:black">● 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. </span></span></span></span></p>
<p style="margin-left:48px; text-align:start"><span style="font-size:medium"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><span style="color:black">● When individuals depart (emigrate out of a population) the loss will diminish population growth rate. </span></span></span></span></p>
<p style="text-align:start"><span style="font-size:medium"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><span style="color:black">Population growth rate applies to all organisms, both sexes, and all life stages.</span></span></span></span></p>
<p> </p>
<p style="text-align:start"><span style="font-size:medium"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><span style="color:black">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, N</span><sub><span style="font-size:9pt"><span style="color:black">t=0 </span></span></sub><span style="color:black">(population size at time t=0), and the population size at the end of the interval, N</span><sub><span style="font-size:9pt"><span style="color:black">t=1 </span></span></sub><span style="color:black">(population size at time t = 1), and then subsequently dividing by the initial population size. </span></span></span></span></p>
<p style="margin-left:96px; text-align:start"><span style="font-size:medium"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><span style="color:black">Equation 2: r = (N</span><sub><span style="font-size:9pt"><span style="color:black">t=1 </span></span></sub><span style="color:black">- N</span><sub><span style="font-size:9pt"><span style="color:black">t=0</span></span></sub><span style="color:black">) / N</span><sub><span style="font-size:9pt"><span style="color:black">t=0</span></span></sub></span></span></span></p>
<p style="text-align:start"><span style="font-size:medium"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><span style="color:black">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). </span></span></span></span></p>
<p style="margin-left:48px; text-align:start"><span style="font-size:medium"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><span style="color:black">● 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. </span></span></span></span></p>
<p style="margin-left:48px; text-align:start"><span style="font-size:medium"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><span style="color:black">● 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). </span></span></span></span></p>
<p style="text-align:start"><span style="font-size:medium"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><span style="color:black">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). </span></span></span></span></p>
<p style="text-align:start"><span style="font-size:medium"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><span style="color:black">Some examples of modeling constructs used to investigate population growth rate:</span></span></span></span></p>
<p style="margin-left:48px; text-align:start"><span style="font-size:medium"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><span style="color:black">● 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 (<em>Pimephales promelas</em>) exposed to endocrine disrupting chemicals in combination with a population model to examine projected alterations in population growth rate. </span></span></span></span></p>
<p style="margin-left:48px; text-align:start"><span style="font-size:medium"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><span style="color:black">● 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. </span></span></span></span></p>
<p style="margin-left:48px; text-align:start"><span style="font-size:medium"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><span style="color:black">● 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).</span></span></span></span></p>
<p style="margin-left:48px; text-align:start"><span style="font-size:medium"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><span style="color:black">● 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.</span></span></span></span></p>
<p style="margin-left:48px; text-align:start"><span style="font-size:medium"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><span style="color:black">● 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).</span></span></span></span></p>
<p> </p>
<p>Consideration of population size and changes in population size over time is potentially relevant to all living organisms.</p>
Not SpecifiedUnspecificNot SpecifiedAll life stagesHigh<ul>
<li><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><span style="color:black">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.</span></span></span></li>
<li><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><span style="color:black">Bookhout TA. 1994. Research and management techniques for wildlife and habitats. The Wildlife Society, Bethesda, Maryland. 740 pp.</span></span></span></li>
<li><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><span style="color:black">Caswell H. 2001. Matrix Population Models. Sinauer Associates, Inc., Sunderland, MA, USA</span></span></span></li>
<li><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><span style="color:black">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.</span></span></span></li>
<li><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><span style="color:black">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.</span></span></span></li>
<li><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><span style="color:black">Etterson MA, Ankley GT. 2021. Endogenous Lifecycle Models for Chemical Risk Assessment. Environ. Sci. Technol. 55: 15596-15608. </span></span></span></li>
<li><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><span style="color:black">Gotelli NJ, 2008. A Primer of Ecology. Sinauer Associates, Inc., Sunderland, MA, USA.</span></span></span></li>
<li><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><span style="color:black">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 (<em>Danio rerio</em>) and fathead minnow <em>(Pimephales promelas</em>) as test species in the Fish Sexual Development Test (FSDT). Comp. Biochem. Physiol. C Toxicol. Pharmacol. 155: 407–415.</span></span></span></li>
<li><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><span style="color:black">Kramer VJ, Etterson MA, Hecker M, Murphy CA, Roesijadi G, Spade DJ, Stromberg JA, Wang M, Ankley GT. </span><span style="color:black">2011. Adverse outcome pathways and risk assessment: Bridging to population level effects. Environ. Toxicol. Chem. 30, 64-76.</span></span></span></li>
<li><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><span style="color:black">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. </span></span></span></li>
<li><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><span style="color:black">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.</span></span></span></li>
<li><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><span style="color:black">Miller DH, Tietge JE, McMaster ME, Munkittrick KR, Xia X, Griesmer DA, Ankley GT. 2015. </span><span style="color:black">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.</span></span></span></li>
<li><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><span style="color:black">Miller DH, Jensen KM, Villeneuve DE, Kahl MD, Makynen EA, Durhan EJ, Ankley GT. 2007. </span><span style="color:black">Linkage of biochemical responses to population-level effects: A case study with vitellogenin in the fathead minnow (<em>Pimephales promelas</em>). Environ Toxicol Chem 26: 521–527.</span></span></span></li>
<li><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><span style="color:black">Miller DH, Ankley GT. 2004. Modeling impacts on populations: Fathead minnow (<em>Pimephales promelas</em>) exposure to the endocrine disruptor 17b-trenbolone as a case study. Ecotox Environ Saf 59: 1–9.</span></span></span></li>
<li><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><span style="color:black">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.</span></span></span></li>
<li><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><span style="color:black">Murray DL, Sandercock BK (editors). 2020. Population ecology in practice. Wiley-Blackwell, Oxford UK, 448 pp.</span></span></span></li>
<li><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><span style="color:black">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.</span></span></span></li>
<li><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><span style="color:black">Nisbet RM, Muller EB, Lika K, Kooijman SALM. 2000. </span><span style="color:black">From molecules to ecosystems through dynamic energy budgets. J Anim Ecol 69: 913–926.</span></span></span></li>
<li><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><span style="color:black">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. </span></span></span></li>
<li><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><span style="color:black">Vandermeer JH, Goldberg DE. 2003. Population ecology: first principles. Princeton University Press, Princeton NJ, 304 pp.</span></span></span></li>
<li><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><span style="color:black">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</span></span></span></li>
<li><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><span style="color:black">Watanabe KH, Mayo M, Jensen KM, Villeneuve DL, Ankley GT, Perkins EJ. 2016. Predicting fecundity of fathead minnows (<em>Pimephales promelas</em>) exposed to endocrine‐disrupting chemicals using a MATLAB(R)‐based model of oocyte growth dynamics. PLoS One 11: e0146594.</span></span></span></li>
</ul>
2016-11-29T18:41:242023-01-03T09:09:06d13a5715-a386-4d44-9436-65200de694ba04f1cbe6-b208-40b8-9002-ed1bcd75980f2022-02-04T07:23:432022-02-04T07:23:4304f1cbe6-b208-40b8-9002-ed1bcd75980f591be6d0-7e88-4ad0-a927-4ea5ff7ad5472022-03-01T16:12:522022-03-01T16:12:52591be6d0-7e88-4ad0-a927-4ea5ff7ad54777c5db1e-9962-465b-ba82-9deb3ade8bb12022-03-01T16:23:212022-03-01T16:23:2177c5db1e-9962-465b-ba82-9deb3ade8bb107cea225-9fa0-4cfe-8ed0-1ede75c7399b2020-04-30T16:44:142020-04-30T16:44:1407cea225-9fa0-4cfe-8ed0-1ede75c7399b24c49697-4a66-4d69-ba00-3b26665384642022-03-01T12:49:102022-03-01T12:49:1024c49697-4a66-4d69-ba00-3b26665384641879df79-3574-4813-91bc-a99dcb9fc2f9<p>SEE BIOLOGICAL PLAUSIBILITY BELOW</p>
<p>Updated 03/20/2017</p>
<p>Using a relatively simple density-dependent population model and assuming constant young of year survival with no immigration/emigration, reductions in cumulative fecundity have been predicted to yield declines in population size over time (Miller and Ankley 2004). Under real-world environmental conditions, outcomes may vary depending on how well conditions conform with model assumptions. Nonetheless, cumulative fecundity can be considered one vital rate that contributes to overall population trajectories (Kramer et al. 2011).</p>
<ul>
<li>Using a relatively simple density-dependent population model and assuming constant young of year survival with no immigration/emigration, reductions in cumulative fecundity have been predicted to yield declines in population size over time (Miller and Ankley 2004). However, it should be noted that the model was constructed in such a way that predicted population size is dependent on cumulative fecundity, therefore this is a fairly weak form of empirical support.</li>
<li>In a study in which an entire lake was treated with 17alpha-ethynyl estradiol, Kidd et al. (2007) declines in fathead minnow population size were associated with signs of reduced fecundity.</li>
</ul>
<ul>
<li>Wester et al. (2003) and references cited therein suggest that although egg production is an endpoint of demographic significance, incomplete reductions of egg production may not translate in a simple manner to population reductions. Compensatory effects of reduced predation and reduced competition for limited food and/or habitat resources may offset the effects of incomplete reductions in egg production.</li>
<li>Fish and other egg laying animals employ a diverse range of reproductive strategies and life histories. The nature of the relationship between reduced spawning frequency and cumulative fecundity and overall population trajectories will depend heavily on the life history and reproductive strategy of the species in question. Relationships developed for one species will not necessarily hold for other species, particularly those with differing life histories.</li>
</ul>
<ul>
<li>Cumulative fecundity is one example of a vital rate that can influence population size over time. A variety of population model constructs can be adapted to utilize measurements or estimates of cumulative fecundity as a predictor of population trends over time (e.g., (Miller and Ankley 2004; Miller et al. 2013).</li>
<li>The model of Miller et al. 20014 uses a relatively simple density-dependent population model and assuming constant young of year survival with no immigration/emigration, use measures of cumulative fecundity to predict relative change in in population size over time (Miller and Ankley 2004).</li>
</ul>
Not SpecifiedUnspecificNot SpecifiedAll life stagesModerate<p>Spawning generally refers to the release of eggs and/or sperm into water, generally by aquatic or semi-aquatic organisms. Consequently, by definition, this KER is likely applicable only to organisms that spend a portion of their life-cycle in or near aquatic environments.</p>
<ul>
<li>Kidd KA, Blanchfield KH, Palace VP, Evans RE, Lazorchak JM, Flick RW. 2007. Collapse of a fish population after exposure to a synthetic estrogen. PNAS 104:8897-8901.</li>
<li>Kramer VJ, Etterson MA, Hecker M, Murphy CA, Roesijadi G, Spade DJ, Spromberg JA, Wang M, Ankley GT. Adverse outcome pathways and ecological risk assessment: bridging to population-level effects. Environ Toxicol Chem. 2011 Jan;30(1):64-76. doi: 10.1002/etc.375. PubMed PMID: 20963853</li>
<li>Miller DH, Ankley GT. 2004. Modeling impacts on populations: fathead minnow (Pimephales promelas) exposure to the endocrine disruptor 17b-trenbolone as a case study. Ecotoxicology and Environmental Safety 59: 1-9.</li>
<li>Miller DH, Tietge JE, McMaster ME, Munkittrick KR, Xia X, Ankley GT. 2013. Assessment of Status of White Sucker (Catostomus Commersoni) Populations Exposed to Bleached Kraft Pulp Mill Effluent. Environmental toxicology and chemistry / SETAC (in press).</li>
<li>Wester P, van den Brandhof E, Vos J, van der Ven L. 2003. Identification of endocrine disruptive effects in the aquatic environment - a partial life cycle assay with zebrafish. (RIVM Report). Bilthoven, the Netherlands: Joint Dutch Environment Ministry.</li>
</ul>
2016-11-29T18:41:332017-03-20T13:49:05Deposition of energy leading to population decline via DNA oxidation and oocyte apoptosisEnergy deposition leading to population decline via DNA oxidation and oocyte apoptosis<p>You Song<sup>1</sup>, Knut Erik Tollefsen<sup>1,2,3</sup></p>
<p><sup>1</sup>Norwegian Institute for Water Research (NIVA), Økernveien 94, 0579 Oslo, Norway</p>
<p><sup>2</sup>Centre for Environmental Radioactivity (CERAD), Norwegian University of Life Sciences (NMBU), Post box 5003, N-1432 Ås, Norway</p>
<p><sup>3</sup>Norwegian University of Life Sciences (NMBU), Faculty of Environmental Sciences and Natural Resource Management (MINA), Post box 5003, N-1432 Ås, Norway<br />
</p>
Under development: Not open for comment. Do not cite<p><span style="font-family:Arial,Helvetica,sans-serif"><span style="font-size:12px">It is well documented that ionizing radiation( (eg. X-rays, gamma, photons, alpha, beta, neutrons, heavy ions) leads to energy deposition on the atoms and molecules of the substrate. Many studies, have demonstrated that the type of radiation and distance from source has an impact on the pattern of energy deposition (Alloni, et al. 2014). High linear energy transfer (LET) radiation has been associated with higher-energy deposits (Liamsuwan et al., 2014) that are more densely-packed and cause more complex effects within the particle track (Hada and Georgakilas, 2008; Okayasu, 2012ab; Lorat et al., 2015; Nikitaki et al., 2016) in comparison to low LET radiation. Parameters such as mean lineal energy, dose mean lineal energy, frequency mean specific energy and dose mean specific energy can impact track structure of the traversed energy into a medium (Friedland et al., 2017)</span></span><span style="font-family:Arial,Helvetica,sans-serif"><span style="font-size:12px">. The detection of energy deposition by ionizing radiation can be demonstrated with the use of fluorescent nuclear track detectors (FNTDs). FNTDs used in conjunction with fluorescent microscopy, are able to visualize radiation tracks produced by ionizing radiation (Niklas et al., 2013; Kodaira et al., 2015; Sawakuchi and Akselrod, 2016). In addition, these FNTD chips can quantify the LET of primary and secondary radiation tracks up to 0.47 keV/um (Sawakuchi and Akselrod, 2016). This co-visualization of the radiation tracks and the cell markers enable the mapping of the radiation trajectory to specific cellular compartments, and the identification of accrued damage (Niklas et al., 2013; Kodaira et al., 2015). There are no known chemical initiators or prototypes that can mimic the MIE.</span></span></p>
<p>Cumulative fecundity is the most apical endpoint considered in the OECD 229 Fish Short Term Reproduction Assay. The OECD 229 assay serves as screening assay for endocrine disruption and associated reproductive impairment (<a href="http://www.oecd-ilibrary.org/environment/test-no-229-fish-short-term-reproduction-assay_9789264185265-en">OECD 2012</a>). Fecundity is also an important apical endpoint in the Medaka Extended One Generation Reproduction Test (MEOGRT; <a href="http://www.oecd-ilibrary.org/environment/test-no-240-medaka-extended-one-generation-reproduction-test-meogrt_9789264242258-en">OECD Test Guideline 240</a>; OECD 2015).</p>
<p>A variety of fish life cycle tests also include cumulative fecundity as an endpoint (<a href="http://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=env/jm/mono(2008)22&doclanguage=en">OECD 2008</a>).</p>
<p> </p>
<p>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.</p>
adjacentNot SpecifiedNot SpecifiedadjacentNot SpecifiedNot SpecifiedadjacentNot SpecifiedNot SpecifiedadjacentNot SpecifiedNot SpecifiedadjacentNot SpecifiedNot SpecifiedadjacentNot SpecifiedNot SpecifiedNot SpecifiedFemaleNot SpecifiedAdult, reproductively matureNot SpecifiedNot SpecifiedHigh2019-11-18T04:01:382023-04-29T13:02:17