Deposition of Energy

CHEBI:16991

CHEBI deoxyribonucleic acid

PCO:0000001

PCO population of organisms

MP:0003674

MP oxidative stress

PCO:0000008

PCO population growth rate

WIKI functional change

WIKI increased

WIKI decreased

Ionizing Radiation 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.

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).

2019-05-03T12:36:36

2019-05-07T12:12:13

Estrogen

2019-05-08T11:40:27

WCS_9606

common toxicological species human

10116

NCBI rat

10090

NCBI mouse

6239

NCBI nematode

WCS_7955

common ecological species zebrafish

3702

NCBI thale-cress

3349

NCBI Scotch pine

WCS_35525

common ecological species Daphnia magna

3055

NCBI Chlamydomonas reinhardtii

WCS_6396

common ecological species common brandling worm

WCS_4472

common ecological species Lemna minor

8030

NCBI Salmo salar

WikiUser_22

all species

Deposition of Energy

Energy Deposition

Molecular

Deposition of energy refers to events where energetic subatomic particles, nuclei, 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).   Ionizing radiation can cause the ejection of electrons from atoms and molecules, thereby resulting in their ionization and the breakage of chemical bonds. The energy of these subatomic particles or electromagnetic waves mostly range from 124 KeV to 5.4 MeV and is dependent on the source and type of radiation (Zyla et al., 2020). To be ionizing the incident radiation must have sufficient energy to free electrons from atomic or molecular electron orbitals. The energy deposited can induce direct and indirect ionization events and this can be via internal (injections, inhalation, or absorption of radionuclides) or external exposure from radiation fields -- this also applies to non-ionizing radiation. Direct ionization is the principal path where charged particles interact with biological structures such as DNA, proteins or membranes to cause biological damage. Photons, which are electromagnetic waves can also deposit energy to cause direct ionization. Ionization of water, which is a major constituent of tissues and organs, produces free radical and molecular species, which themselves can indirectly damage critical targets such as DNA (Beir et al., 1999; Balagamwala et al., 2013) or alter cellular processes. Given the fundamental nature of energy deposition by radioactive/unstable nuclei, nucleons or elementary particles in material, this process is universal to all biological contexts. The spatial structure of ionizing energy deposition along the resulting particle track is represented as linear energy transfer (LET) (Hall and Giaccia, 2018 UNSCEAR, 2020). High LET refers to energy mostly 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). Non-ionizing radiation is electromagnetic waves that does not have enough energy to break bonds and induce ion formation but it can cause molecules to excite and vibrate faster resulting in biological effects. 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 (200-280 nm) is, in contrast to UVB and UVA, considered to be a type of ionizing radiation.

<table border="1" bordercolor="#ccc" cellpadding="5" cellspacing="0" style="border-collapse:collapse"> <tbody> <tr> <td style="background-color:#eeeeee; text-align:center">Radiation type</td> <td style="background-color:#eeeeee; text-align:center"> Assay Name </td> <td style="background-color:#eeeeee; text-align:center"> References </td> <td style="background-color:#eeeeee; text-align:center"> Description </td> <td style="background-color:#eeeeee; text-align:center"> OECD Approved Assay </td> </tr> <tr> <td style="text-align:center">Ionizing radiation</td> <td style="text-align:center"> Monte Carlo Simulations (Geant4) </td> <td style="text-align:center"> Douglass et al., 2013; Douglass et al. 2012; Zyla et al., 2020 </td> <td style="text-align:center"> Monte Carlo simulations are based on a computational algorithm that mathematically models the deposition of energy into materials. </td> <td style="text-align:center"> No </td> </tr> <tr> <td style="text-align:center">Ionizing radiation</td> <td style="text-align:center"> Fluorescent Nuclear Track Detector (FNTD) </td> <td style="text-align:center"> Sawakuchi, 2016; Niklas, 2013; Koaira & Konishi, 2015 </td> <td style="text-align:center"> 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. </td> <td style="text-align:center"> No </td> </tr> <tr> <td style="text-align:center">Ionizing radiation</td> <td style="text-align:center">Tissue equivalent proportional counter (TEPC)</td> <td style="text-align:center">Straume et al, 2015</td> <td style="text-align:center">Measure the LET spectrum and calculate the dose equivalent.</td> <td style="text-align:center">No</td> </tr> <tr> <td style="text-align:center">Ionizing radiation</td> <td style="text-align:center">alanine dosimeters/NanoDots</td> <td style="text-align:center"> Lind et al. 2019; Xie et al., 2022 </td> <td style="text-align:center"> </td> <td style="text-align:center">No</td> </tr> <tr> <td style="text-align:center">Non-ionizing radiation</td> <td style="text-align:center">UV meters or radiameters</td> <td style="text-align:center">Xie et at., 2020</td> <td style="text-align:center">UVA/UVB (irradiance intensity), UV dosimeters (accumulated irradiance over time), Spectrophoto meter (absorption of UV by a substance or material)</td> <td style="text-align:center">No</td> </tr> </tbody> </table>

Energy can be deposited into any substrate, both living and non-living; it is independent of age, taxa, sex, or life-stage. Taxonomic applicability: This MIE is not taxonomically specific.   Life stage applicability: This MIE is not life stage specific.  Sex applicability: This MIE is not sex specific.

Low Unspecific

High

All life stages

Moderate Moderate Moderate High High High Moderate High Moderate Moderate High Low Balagamwala, E. H. et al. (2013), “Introduction to radiotherapy and standard teletherapy techniques”, Dev Ophthalmol, Vol. 52, Karger, Basel, https://doi.org/10.1159/000351045  Beir, V. et al. (1999), “The Mechanistic Basis of Radon-Induced Lung Cancer”, in Health Risks of Exposure to Radon: BEIR VI, National Academy Press, Washington, D.C., https://doi.org/10.17226/5499  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”, Medical Physics, Vol. 40/7, American Institute of Physics, College Park, https://doi.org/10.1118/1.4808150  Douglass, M. et al. (2012), “Development of a randomized 3D cell model for Monte Carlo microdosimetry simulations.”, Medical Physics, Vol. 39/6, American Institute of Physics, College Park, https://doi.org/10.1118/1.4719963  Hall, E. J. and Giaccia, A.J. (2018), Radiobiology for the Radiologist, 8th edition, Wolters Kluwer, Philadelphia.   Kodaira, S. and Konishi, T. (2015), “Co-visualization of DNA damage and ion traversals in live mammalian cells using a fluorescent nuclear track detector.”, Journal of Radiation Research, Vol. 56/2, Oxford University Press, Oxford, https://doi.org/10.1093/jrr/rru091  Lind, O.C., D.H. Oughton and Salbu B. (2019), "The NMBU FIGARO low dose irradiation facility", International Journal of Radiation Biology, Vol. 95/1, Taylor & Francis, London, https://doi.org/10.1080/09553002.2018.1516906. Sawakuchi, G.O. and Akselrod, M.S. (2016), “Nanoscale measurements of proton tracks using fluorescent nuclear track detectors.”, Medical Physics, Vol. 43/5, American Institute of Physics, College Park, https://doi.org/10.1118/1.4947128  Straume, T. et al. (2015), “Compact Tissue-equivalent Proportional Counter for Deep Space Human Missions.”, Health physics, Vol. 109/4, Lippincott Williams & Wilkins, Philadelphia, https://doi.org/10.1097/HP.0000000000000334  Niklas, M. et al. (2013), “Engineering cell-fluorescent ion track hybrid detectors.”, Radiation Oncology, Vol. 8/104, BioMed Central, London, https://doi.org/10.1186/1748-717X-8-141  UNSCEAR (2020), Sources, effects and risks of ionizing radiation, United Nations.  Xie, Li. et al. (2022), "Ultraviolet B Modulates Gamma Radiation-Induced Stress Responses in Lemna Minor at Multiple Levels of Biological Organisation", SSRN, Elsevier, Amsterdam, http://dx.doi.org/10.2139/ssrn.4081705 . Zyla, P.A. et al. (2020), Review of particle physics: Progress of Theoretical and Experimental Physics, 2020 Edition, Oxford University Press, Oxford.      AOPWiki 2019-08-22T09:44:23

2023-08-09T15:06:01

Increase in reactive oxygen and nitrogen species (RONS)

Increase in RONS

Molecular

Reactive oxygen and nitrogen species (RONS) are highly reactive oxygen- and nitrogen-based molecules that often contain or generate free radicals. Key molecules include superoxide ([O2]•−), hydrogen peroxide (H2O2), hydroxyl radical ([OH]•), lipid peroxide (ROOH), nitric oxide ([NO]•, and peroxynitrite ([ONOO-]) (Dickinson and Chang 2011; Egea, Fabregat et al. 2017) RONS are generated in the course of cellular respiration, metabolism, cell signaling, and inflammation (Dickinson and Chang 2011; Egea, Fabregat et al. 2017). Superoxide and hydrogen peroxide are commonly produced by the mitochondrial electron transport chain and cytochrome c and by membrane bound NADPH oxidases and related molecules. Hydrogen peroxide is also made by the endoplasmic reticulum in the course of protein folding. Nitric oxide is produced at the highest levels by nitric oxide synthase in endothelial cells and phagocytes. The other species are produced by reactions with superoxide or peroxide, or by other free radicals or enzymes. RONS activity is principally local. Most reactive oxygen species (ROS) have short half-lives, ranging from nano- to milliseconds, so diffusion is limited, while reactive nitrogen species (RNS) nitric oxide or peroxynitrate can survive long enough to diffuse across membranes (Calcerrada, Peluffo et al. 2011). Consequently, local concentrations of ROS are much higher than average cellular concentrations and signaling is typically controlled by colocalization with redox buffers (Dickinson and Chang 2011; Egea, Fabregat et al. 2017). The effects of ROS and RNS are countered by cellular antioxidants, with glutathione and peroxiredoxins playing a major role (Dickinson and Chang 2011). Glutathione is slower but broad acting, while peroxiredoxins act quickly and are specific to peroxides. Peroxiredoxins are effective at low peroxide concentrations but can be deactivated at higher concentrations, suggesting the cellular response to peroxides may sometimes be non-linear. Although their existence is limited temporally and spatially, reactive oxygen species (ROS) interact with other RONS or with other nearby molecules to produce more ROS and participate in a feedback loop to amplify the ROS signal, which can increase Reactive Nitrogen Species (RNS). Both ROS and RNS also move into neighboring cells and ROS can increase intracellular RONS signaling in neighboring cells (Egea, Fabregat et al. 2017). RONS can modify a range of targets including amino acids, lipids, and nucleic acids to inactivate or alter target functionality (Calcerrada, Peluffo et al. 2011; Dickinson and Chang 2011; Go and Jones 2013; Ravanat, Breton et al. 2014; Egea, Fabregat et al. 2017). For example, phosphatases including the tumor suppressor PTEN can be reversibly deactivated by oxidation, and the movement of HDAC4 is peroxide dependent. Elevated ROS are implicated in proliferation and maintenance of stem cell population size (Dickinson and Chang 2011) and conversely in differentiation of stem cells and oncogene-induced senescence (Egea, Fabregat et al. 2017).

RONS is typically measured using fluorescent or other probes that react with RONS to change state, or by measuring the redox state of proteins or DNA (Dickinson and Chang 2011; Wang, Fang et al. 2013; Griendling, Touyz et al. 2016). Optimal methods for RONS detection have high sensitivity, selectivity, and spatiotemporal resolution to distinguish transient and localized activity, but most methods lack one or more of these parameters. Molecular probes that indicate the presence of RONS species vary in specificity and kinetics (Dickinson and Chang 2011; Wang, Fang et al. 2013; Griendling, Touyz et al. 2016). Small molecule fluorescent probes can be applied to any tissue in vitro, but cannot be finely targeted to different cellular compartments. The non-selective probe DCHF was widely used in the past, but can produce false positive signals and is no longer recommended. Newer more selective small molecule probes such as boronate-based molecules are being developed but are not yet widely used. Alternatively, fluorescent protein-based probes can be genetically engineered, expressed in vivo, and targeted to cellular compartments and specific cells. However, these probes are very sensitive to pH in the physiological range and must be carefully controlled.  EPR (electron paramagnetic resonance spectroscopy) provide the most direct and specific detection of free radicals, but requires specialized equipment. Alternative methods involve the detection of redox-dependent changes to cellular constituents such as proteins, DNA, lipids, or glutathione (Dickinson and Chang 2011; Wang, Fang et al. 2013; Griendling, Touyz et al. 2016). However, these methods cannot generally distinguish between the oxidative species behind the changes, and cannot provide good resolution for kinetics of oxidative activity. Table 1. Common methods for detecting oxidative activity <table border="1" cellpadding="0" cellspacing="0"> <tbody> <tr> <td style="height:22px; width:133px"> Target </td> <td style="height:22px; width:126px"> Name </td> <td style="height:22px; width:144px"> Method </td> <td style="height:22px; width:235px"> Strengths/Weaknesses </td> </tr> <tr> <td style="height:22px; width:133px"> Hydrogen peroxide- extracellular </td> <td style="height:22px; width:126px"> AmplexRed </td> <td style="height:22px; width:144px"> Small molecule fluorescent probes </td> <td style="height:22px; width:235px"> Can be applied to any tissue in vitro. </td> </tr> <tr> <td style="height:22px; width:133px"> Hydrogen peroxide- mitochondrial </td> <td style="height:22px; width:126px"> MitoPy1 </td> <td style="height:22px; width:144px"> Small molecule fluorescent probes </td> <td style="height:22px; width:235px"> Can be applied to any tissue in vitro. </td> </tr> <tr> <td style="height:22px; width:133px"> Hydrogen peroxide </td> <td style="height:22px; width:126px"> HyPer </td> <td style="height:22px; width:144px"> Protein-based fluorescent probes </td> <td style="height:22px; width:235px"> Sensitive, can be targeted to specific cells and compartments. Slower and pH sensitive. </td> </tr> <tr> <td style="height:22px; width:133px"> Hydrogen peroxide </td> <td style="height:22px; width:126px"> HyPer3 </td> <td style="height:22px; width:144px"> Protein-based fluorescent probes </td> <td style="height:22px; width:235px"> Rapid kinetics and larger dynamic range, can be targeted to specific cells and compartments. Sensitive to pH, less sensitive to H2O2. </td> </tr> <tr> <td style="height:22px; width:133px"> Hydrogen peroxide </td> <td style="height:22px; width:126px"> Boronate-based indicators </td> <td style="height:22px; width:144px"> Small molecule fluorescent probe </td> <td style="height:22px; width:235px"> Selective for H2O2 but can interact with peroxynitrite. </td> </tr> <tr> <td style="height:22px; width:133px"> Superoxide- intracellular </td> <td style="height:22px; width:126px"> DHE (dihydroethidium) </td> <td style="height:22px; width:144px"> Small molecule fluorescent probe </td> <td style="height:22px; width:235px"> Can be applied to any tissue in vitro, but not targeted to different compartments. </td> </tr> <tr> <td style="height:22px; width:133px"> Superoxide- intracellular </td> <td style="height:22px; width:126px"> cpYFP </td> <td style="height:22px; width:144px"> Protein-based fluorescent probes </td> <td style="height:22px; width:235px"> Reversible. Can be targeted to specific cells and compartments. </td> </tr> <tr> <td style="height:22px; width:133px"> Superoxide- mitochondrial </td> <td style="height:22px; width:126px"> MitoSox </td> <td style="height:22px; width:144px"> Small molecule fluorescent probe </td> <td style="height:22px; width:235px"> Can be applied to any tissue in vitro. </td> </tr> <tr> <td style="height:22px; width:133px"> Superoxide- mitochondrial </td> <td style="height:22px; width:126px"> mt-cpYFP </td> <td style="height:22px; width:144px"> Protein-based fluorescent probes </td> <td style="height:22px; width:235px"> Reversible. Can be targeted to specific cells and compartments. </td> </tr> <tr> <td style="height:22px; width:133px"> Superoxide- extracellular </td> <td style="height:22px; width:126px"> nitroblue tetrazolium </td> <td style="height:22px; width:144px"> Small molecule fluorescent probe </td> <td style="height:22px; width:235px"> Can be applied to any tissue in vitro. </td> </tr> <tr> <td style="height:22px; width:133px"> Superoxide- intracellular or extracelluar </td> <td style="height:22px; width:126px"> various trityl probes </td> <td style="height:22px; width:144px"> EPR </td> <td style="height:22px; width:235px"> Very specific, but requires specialized equipment, not as sensitive in tissue. </td> </tr> <tr> <td style="height:22px; width:133px"> Nitric oxide </td> <td style="height:22px; width:126px"> Fe[DETC]2 and Fe[MGD]2, </td> <td style="height:22px; width:144px"> EPR </td> <td style="height:22px; width:235px"> Very specific, but requires specialized equipment, not as sensitive in tissue. </td> </tr> <tr> <td style="height:22px; width:133px"> Nitric oxide </td> <td style="height:22px; width:126px"> DAF-FM </td> <td style="height:22px; width:144px"> Small molecule fluorescent probe </td> <td style="height:22px; width:235px"> Can be applied to any tissue in vitro, but not targeted to different compartments </td> </tr> <tr> <td style="height:22px; width:133px"> Peroxynitrite </td> <td style="height:22px; width:126px"> EMPO </td> <td style="height:22px; width:144px"> EPR </td> <td style="height:22px; width:235px"> Very specific, but requires specialized equipment, not as sensitive in tissue. </td> </tr> <tr> <td style="height:22px; width:133px"> Peroxynitrite </td> <td style="height:22px; width:126px"> Boronate-based indicators </td> <td style="height:22px; width:144px"> Small molecule fluorescent probe </td> <td style="height:22px; width:235px"> Selective for H2O2 but can interact with (is inhibited by) peroxynitrite. </td> </tr> <tr> <td style="height:22px; width:133px"> Peroxynitrite </td> <td style="height:22px; width:126px"> 8-nitroguanine (DNA) content </td> <td style="height:22px; width:144px"> HPLC-MS/MS </td> <td style="height:22px; width:235px"> Destruction of sample required for measurement. </td> </tr> <tr> <td style="height:22px; width:133px"> Non-specific oxidation </td> <td style="height:22px; width:126px"> DCHF </td> <td style="height:22px; width:144px"> Small molecule fluorescent probe </td> <td style="height:22px; width:235px"> Very non selective, and can produce false positive signals. </td> </tr> <tr> <td style="height:22px; width:133px"> Non-specific oxidation </td> <td style="height:22px; width:126px"> roGFP or FRET </td> <td style="height:22px; width:144px"> Protein-based fluorescent probes </td> <td style="height:22px; width:235px"> Slow acting. Good to look at steady state activity. </td> </tr> <tr> <td style="height:22px; width:133px"> Non-specific oxidation </td> <td style="height:22px; width:126px"> ratio of reduced to oxidized glutathione or cysteine </td> <td style="height:22px; width:144px"> Redox state detectors </td> <td style="height:22px; width:235px"> Slow acting. Good to look at steady state activity. Destruction of sample required for measurement. </td> </tr> <tr> <td style="height:22px; width:133px"> Non-specific oxidation </td> <td style="height:22px; width:126px"> 8-oxoguanine (DNA) or protein carbonyl content </td> <td style="height:22px; width:144px"> HPLC-MS/MS </td> <td style="height:22px; width:235px"> Destruction of sample required for measurement. </td> </tr> <tr> <td style="height:22px; width:133px"> Non-specific oxidation </td> <td style="height:22px; width:126px"> TBARS (thiobarbituric acid reactive substance) </td> <td style="height:22px; width:144px"> Lipid peroxidation </td> <td style="height:22px; width:235px"> Destruction of sample required for measurement. </td> </tr> </tbody> </table>

This KE is broadly applicable across species.

<a name="_ENREF_1">Calcerrada, P., G. Peluffo, et al. (2011). "Nitric oxide-derived oxidants with a focus on peroxynitrite: molecular targets, cellular responses and therapeutic implications." Curr Pharm Des 17(35): 3905-3932.</a> <a name="_ENREF_2">Dickinson, B. C. and C. J. Chang (2011). "Chemistry and biology of reactive oxygen species in signaling or stress responses." Nature chemical biology 7(8): 504-511.</a> <a name="_ENREF_3">Egea, J., I. Fabregat, et al. (2017). "European contribution to the study of ROS: A summary of the findings and prospects for the future from the COST action BM1203 (EU-ROS)." Redox biology 13: 94-162.</a> <a name="_ENREF_4">Go, Y. M. and D. P. Jones (2013). "The redox proteome." J Biol Chem 288(37): 26512-26520.</a> <a name="_ENREF_5">Griendling, K. K., R. M. Touyz, et al. (2016). "Measurement of Reactive Oxygen Species, Reactive Nitrogen Species, and Redox-Dependent Signaling in the Cardiovascular System: A Scientific Statement From the American Heart Association." Circulation research 119(5): e39-75.</a> <a name="_ENREF_6">Ravanat, J. L., J. Breton, et al. (2014). "Radiation-mediated formation of complex damage to DNA: a chemical aspect overview." Br J Radiol 87(1035): 20130715.</a> <a name="_ENREF_7">Wang, X., H. Fang, et al. (2013). "Imaging ROS signaling in cells and animals." Journal of molecular medicine 91(8): 917-927.</a> AOPWiki 2019-05-03T14:00:04

2019-05-08T12:30:20

Increase, DNA damage

Increase, DNA Damage

Molecular

DNA nucleotide damage, single, and double strand breaks occur in the course of cellular operations such as DNA repair and replication and can be induced directly and in neighboring “bystander” cells by internal or external stressors like reactive oxygen species, chemicals, and radiation. Ionizing radiation and RONS such as hydroxyl radicals or peroxide can create a range of lesions (a change in molecular structure) in the base of the nucleotide, with guanine particularly vulnerable because of its low redox potential (David, O'Shea et al. 2007). The same stressors can also break the sugar (deoxyribose)-phosphate backbone creating a single strand break. Simultaneous proximal breaks in both strands of DNA form double strand breaks, which are considered to be more destructive and mutagenic than lesions or single strand breaks. Double strand breaks can generate chromosomal abnormalities including changes in chromosomal number, breaks and gaps, translocations, inversions, and deletions (Yang, Craise et al. 1992; Haag, Hsu et al. 1996; Ponnaiya, Cornforth et al. 1997; Yang, Georgy et al. 1997; Unger, Wienberg et al. 2010; Behjati, Gundem et al. 2016; Morishita, Muramatsu et al. 2016). However, DNA lesions and single strand breaks can also be destructive and mutagenic. Lesions can lead to point mutations (David, O'Shea et al. 2007) or single strand breaks (Regulus, Duroux et al. 2007). Lesions and single strand breaks can also promote the formation of double strand breaks: replication fork collapse and double strand breaks sometimes occur during mitosis when the replisome encounters an unrepaired single strand break (Kuzminov 2001), and clustered lesions and closely opposed single strand breaks can also form double strand breaks (Chaudhry and Weinfeld 1997; Vispe and Satoh 2000; Shiraishi, Shikazono et al. 2017). Complex damage consists of any combination of closely opposed DNA lesions, abasic sites, crosslinks, single, or double strand breaks in proximity. While classically induced by ionizing radiation, there is also evidence that it can be induced by oxidative activity (Sharma, Collins et al. 2016) or even by a single oxidizing particle (Ravanat, Breton et al. 2014). Complex damage is more difficult to repair (Kuhne, Rothkamm et al. 2000; Stenerlow, Hoglund et al. 2000; Pinto, Prise et al. 2005; Rydberg, Cooper et al. 2005). DNA damage and resulting repair activity can trigger a halt in the cell cycle, cell death (apoptosis), and cause permanent changes to DNA including deletions, translocations, and sequence changes. DNA damage is also associated with an increase in genomic instability - the new appearance of DNA damage including double strand breaks, mutations, and chromosomal damage following repair of initial damage in affected cells or in clonal descendants or neighbors of DNA damaged cells. The mechanism behind this long term DNA damage is not clear, but telomere erosion appears to play a major role (Murnane 2012; Sishc, Nelson et al. 2015). Genomic instability is more common and longer lasting following complex damage (Ponnaiya, Cornforth et al. 1997), and is influenced by multiple factors including variants in DNA repair genes (Ponnaiya, Cornforth et al. 1997; Yu, Okayasu et al. 2001; Yin, Menendez et al. 2012), RONS (Dayal, Martin et al. 2008), estrogen (Kutanzi and Kovalchuk 2013), caspases (Liu, He et al. 2015), and telomeres (Sishc, Nelson et al. 2015).

DNA damage can be studied in isolated DNA, fixed cells, or living cells. Types of damage that can be detected include single and double strand breaks, nucleotide damage, complex damage, and chromosomal or telomere damage. The OECD test guideline for DNA synthesis Test No. 486 (OECD 1997) detects nucleotide excision repair, so it will reflect the formation of bulky DNA adducts but not the majority of oxidative damage to nucleotides, which is typically repaired via the Base Excision Repair pathway. The OECD test guideline alkaline comet assay Test No. 489 (OECD 2016) detects single and double strand breaks, including those arising from repair as well as some (alkali sensitive) nucleotide lesions including some lesions from oxidative damage. OECD tests for chromosomal damage and micronuclei Test No. 473, 475, 483, and 487 measure longer term effects of DNA damage but these tests require the damaged cell to subsequently undergo replication (OECD 2016; OECD 2016; OECD 2016; OECD 2016).  They can therefore reflect a wider range of sources of DNA damage including changes in mitosis. Finally, tests for mutations reveal past DNA damage that resulted in a heritable change, and these are described in the key event ‘Increase in Mutation’. Many other (non-test guideline) techniques have been used to examine specific forms of DNA damage (Table 1). Double strand breaks are commonly reported because of the significant risk attributed to breaks and the relative ease of detecting and quantifying them. Historically, single and double strand breaks were measured using gel electrophoresis, but are now commonly visualized microscopically using fluorescent or other labeled probes for double and single strand break repair such as H2AX and XRCC2.  Base lesions can also be detected using labeled probes for base excision repair enzymes, or by chemical methods such as mass spectroscopy. Refinements on these methods can be used to characterize complex or clustered damage, in which various forms of damage occur in close proximity on a DNA molecule (Lorat, Timm et al. 2016; Nikitaki, Nikolov et al. 2016). Certain challenges are common to all methods of detecting DNA damage. In the time required to initiate the detection method, some DNA may already be repaired, leading to undercounting of damage. On the other hand, apoptotic DSBs may be incorrectly included in a measurement of direct (non-apoptotic) induction of DSB damage unless controlled. All methods have difficulty distinguishing individual components of clustered lesions, and microscopic methods may undercount disparate breaks that are processed together in repair centers (Barnard, Bouffler et al. 2013). Methods that use isolated DNA (gel electrophoresis, analytical chemistry) are vulnerable to artifacts and must ensure that the DNA sample is protected from oxidative damage during extraction (Pernot, Hall et al. 2012; Barnard, Bouffler et al. 2013; Ravanat, Breton et al. 2014). Table 1. Common methods of detecting DNA damage <table border="1" cellpadding="0" cellspacing="0"> <tbody> <tr> <td style="height:22px; width:127px"> Target </td> <td style="height:22px; width:167px"> Name </td> <td style="height:22px; width:133px"> Method </td> <td style="height:22px; width:211px"> Strengths/Weaknesses </td> </tr> <tr> <td style="height:22px; width:127px"> Nucleotide damage </td> <td style="height:22px; width:167px"> Single cell gel electrophoresis (comet assay) with restriction enzymes (Collins 2004) </td> <td style="height:22px; width:133px"> Gel electrophoresis   </td> <td style="height:22px; width:211px"> A variant of the comet assay in which restriction enzymes allow the identification of different types of nucleotide damage. The comet assay is more sensitive than PFGE, detecting damage from 0.1 Gy ionizing radiation (Pernot, Hall et al. 2012). A reproducible high-throughput application of the assay is available (Ge, Prasongtanakij et al. 2014; Sykora, Witt et al. 2018), and the test requires only a small (single cell) sample. Requires destruction of the cell. </td> </tr> <tr> <td style="height:22px; width:127px"> Nucleotide damage </td> <td style="height:22px; width:167px"> Labeled probes including Biotrin OxyDNA and anti- 8-oxoguanine-DNA glycosylase (OGG1) for oxidative damage and AP endonuclease (APE1) for Base Excision Repair of less bulky lesions such as oxidative damage. </td> <td style="height:22px; width:133px"> Microscopy, FACS </td> <td style="height:22px; width:211px"> Most useful with FACS or other measures of average or relative intensity, as locations and numbers of damaged nucleotides can be difficult to distinguish using fluorescence microscopy. (Ogawa, Kobayashi et al. 2003; Nikitaki, Nikolov et al. 2016). </td> </tr> <tr> <td style="height:22px; width:127px"> Nucleotide damage </td> <td style="height:22px; width:167px"> High performance liquid chromatography (HPLC), tandem mass spectrometry (MS/MS) </td> <td style="height:22px; width:133px"> Analytical chemistry </td> <td style="height:22px; width:211px"> Capable of quantifying low levels of specific nucleotide lesions (Madugundu, Cadet et al. 2014; Ravanat, Breton et al. 2014). Requires destruction of the cell. </td> </tr> <tr> <td style="height:22px; width:127px"> Nucleotide damage </td> <td style="height:22px; width:167px"> Unscheduled DNA synthesis test OECD Test Guideline 486 (OECD 1997) </td> <td style="height:22px; width:133px"> Autoradiography </td> <td style="height:22px; width:211px"> Measures DNA damage that is repaired using Nucleotide Excision Repair - mostly bulky adducts (OECD (Organisation for Economic Co-operation and Development) 2016). </td> </tr> <tr> <td style="height:22px; width:127px"> Non-specific DNA strand breaks </td> <td style="height:22px; width:167px"> Single cell gel electrophoresis (comet assay), alkali conditions OECD Test Guideline 489 (OECD 2016) </td> <td style="height:22px; width:133px"> Gel electrophoresis </td> <td style="height:22px; width:211px"> When used in alkali conditions, the comet assay reveals single and double strand breaks and alkali-sensitive nucleotide lesions. See single cell gel electrophoresis (comet assay) with restriction enzymes above for further comments.     </td> </tr> <tr> <td style="height:22px; width:127px"> Single strand breaks </td> <td style="height:22px; width:167px"> Labeled probe pXRCC1 (Lorat, Brunner et al. 2015) </td> <td style="height:22px; width:133px"> Microscopy </td> <td style="height:22px; width:211px"> Fluorescent probes can label single strand breaks in cells, while immunogold labeling is able to distinguish multiple single strand breaks in clusters (Lorat, Timm et al. 2016; Nikitaki, Nikolov et al. 2016). </td> </tr> <tr> <td style="height:22px; width:127px"> Double strand breaks </td> <td style="height:22px; width:167px"> Single cell gel electrophoresis (comet assay), neutral conditions </td> <td style="height:22px; width:133px"> Gel electrophoresis </td> <td style="height:22px; width:211px"> Neutral conditions help minimize the release of single strand breaks coiled DNA and alkali lesions, allowing the measurement of double strand breaks. Since single strand breaks can still appear, assay is not very sensitive or specific to double strand breaks (Pernot, Hall et al. 2012). See single cell gel electrophoresis (comet assay) with restriction enzymes above for further comments. </td> </tr> <tr> <td style="height:22px; width:127px"> Double strand breaks </td> <td style="height:22px; width:167px"> Pulsed field gel electrophoresis (PFGE) </td> <td style="height:22px; width:133px"> Gel electrophoresis </td> <td style="height:22px; width:211px"> Permits the quantitative measurement of double strand breaks, and can be combined with immunoblotting to detect DNA-associated proteins (Lobrich, Rydberg et al. 1995; Kawashima, Yamaguchi et al. 2017). Considered less sensitive than comet assay, but detected damage from 0.25 Gy ionizing radiation (Gradzka and Iwanenko 2005). Requires destruction of the cell. </td> </tr> <tr> <td style="height:22px; width:127px"> Double strand breaks </td> <td style="height:22px; width:167px"> Labeled probes including phosphorylated H2AX, 53BP1, Ku70, ATM (Lorat, Brunner et al. 2015) </td> <td style="height:22px; width:133px"> Microscopy </td> <td style="height:22px; width:211px"> Fluorescent probes can label individual double breaks in cells allowing for quantification, with immunogold labeling resolving breaks in clusters (Lorat, Timm et al. 2016; Nikitaki, Nikolov et al. 2016). Sensitive: detects damage from 0.001 Gy ionizing radiation (Rothkamm and Lobrich 2003; Ojima, Ban et al. 2008). </td> </tr> <tr> <td style="height:22px; width:127px"> Chromosomal damage </td> <td style="height:22px; width:167px"> Chromosomal aberrations and micronuclei OECD Test Guidelines 473, 475, 483, and 487 (OECD 2016; OECD 2016; OECD 2016; OECD 2016) </td> <td style="height:22px; width:133px"> Microscopy </td> <td style="height:22px; width:211px"> Detects major DNA damage resulting from large breaks and rearrangements, or mitotic failures. Damage does not appear until DNA undergoes mitosis, so slower and limited to damage in replicating cells. Insensitive tosmall deletions and substitutions. </td> </tr> </tbody> </table>

CL:0000255

CL eukaryotic cell

<a name="_ENREF_1">Barnard, S., S. Bouffler, et al. (2013). "The shape of the radiation dose response for DNA double-strand break induction and repair." Genome integrity 4(1): 1.</a> <a name="_ENREF_2">Behjati, S., G. Gundem, et al. (2016). "Mutational signatures of ionizing radiation in second malignancies." Nat Commun 7: 12605.</a> <a name="_ENREF_3">Chaudhry, M. A. and M. Weinfeld (1997). "Reactivity of human apurinic/apyrimidinic endonuclease and Escherichia coli exonuclease III with bistranded abasic sites in DNA." The Journal of biological chemistry 272(25): 15650-15655.</a> <a name="_ENREF_4">Collins, A. R. (2004). "The comet assay for DNA damage and repair: principles, applications, and limitations." Molecular biotechnology 26(3): 249-261.</a> <a name="_ENREF_5">David, S. S., V. L. O'Shea, et al. (2007). "Base-excision repair of oxidative DNA damage." Nature 447(7147): 941-950.</a> <a name="_ENREF_6">Dayal, D., S. M. Martin, et al. (2008). "Hydrogen peroxide mediates the radiation-induced mutator phenotype in mammalian cells." Biochem J 413(1): 185-191.</a> <a name="_ENREF_7">Ge, J., S. Prasongtanakij, et al. (2014). "CometChip: a high-throughput 96-well platform for measuring DNA damage in microarrayed human cells." Journal of visualized experiments : JoVE(92): e50607.</a> <a name="_ENREF_8">Gradzka, I. and T. Iwanenko (2005). "A non-radioactive, PFGE-based assay for low levels of DNA double-strand breaks in mammalian cells." DNA repair 4(10): 1129-1139.</a> <a name="_ENREF_9">Haag, J. D., L. C. Hsu, et al. (1996). "Allelic imbalance in mammary carcinomas induced by either 7,12-dimethylbenz[a]anthracene or ionizing radiation in rats carrying genes conferring differential susceptibilities to mammary carcinogenesis." Mol Carcinog 17(3): 134-143.</a> <a name="_ENREF_10">Kawashima, Y., N. Yamaguchi, et al. (2017). "Detection of DNA double-strand breaks by pulsed-field gel electrophoresis." Genes to cells : devoted to molecular & cellular mechanisms 22(1): 84-93.</a> <a name="_ENREF_11">Kuhne, M., K. Rothkamm, et al. (2000). "No dose-dependence of DNA double-strand break misrejoining following alpha-particle irradiation." International journal of radiation biology 76(7): 891-900.</a> <a name="_ENREF_12">Kutanzi, K. and O. Kovalchuk (2013). "Exposure to estrogen and ionizing radiation causes epigenetic dysregulation, activation of mitogen-activated protein kinase pathways, and genome instability in the mammary gland of ACI rats." Cancer Biol Ther 14(7): 564-573.</a> <a name="_ENREF_13">Kuzminov, A. (2001). "Single-strand interruptions in replicating chromosomes cause double-strand breaks." Proceedings of the National Academy of Sciences of the United States of America 98(15): 8241-8246.</a> <a name="_ENREF_14">Liu, X., Y. He, et al. (2015). "Caspase-3 promotes genetic instability and carcinogenesis." Mol Cell 58(2): 284-296.</a> <a name="_ENREF_15">Lobrich, M., B. Rydberg, et al. (1995). "Repair of x-ray-induced DNA double-strand breaks in specific Not I restriction fragments in human fibroblasts: joining of correct and incorrect ends." Proceedings of the National Academy of Sciences of the United States of America 92(26): 12050-12054.</a> <a name="_ENREF_16">Lorat, Y., C. U. Brunner, et al. (2015). "Nanoscale analysis of clustered DNA damage after high-LET irradiation by quantitative electron microscopy--the heavy burden to repair." DNA repair 28: 93-106.</a> <a name="_ENREF_17">Lorat, Y., S. Timm, et al. (2016). "Clustered double-strand breaks in heterochromatin perturb DNA repair after high linear energy transfer irradiation." Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology 121(1): 154-161.</a> <a name="_ENREF_18">Madugundu, G. S., J. Cadet, et al. (2014). "Hydroxyl-radical-induced oxidation of 5-methylcytosine in isolated and cellular DNA." Nucleic acids research 42(11): 7450-7460.</a> <a name="_ENREF_19">Morishita, M., T. Muramatsu, et al. (2016). "Chromothripsis-like chromosomal rearrangements induced by ionizing radiation using proton microbeam irradiation system." Oncotarget 7(9): 10182-10192.</a> <a name="_ENREF_20">Murnane, J. P. (2012). "Telomere dysfunction and chromosome instability." Mutation research 730(1-2): 28-36.</a> <a name="_ENREF_21">Nikitaki, Z., V. Nikolov, et al. (2016). "Measurement of complex DNA damage induction and repair in human cellular systems after exposure to ionizing radiations of varying linear energy transfer (LET)." Free radical research 50(sup1): S64-S78.</a> <a name="_ENREF_22">OECD (1997). Test No. 486: Unscheduled DNA Synthesis (UDS) Test with Mammalian Liver Cells in vivo.</a> <a name="_ENREF_23">OECD (2016). Test No. 473: In Vitro Mammalian Chromosomal Aberration Test.</a> <a name="_ENREF_24">OECD (2016). Test No. 475: Mammalian Bone Marrow Chromosomal Aberration Test.</a> <a name="_ENREF_25">OECD (2016). Test No. 483: Mammalian Spermatogonial Chromosomal Aberration Test.</a> <a name="_ENREF_26">OECD (2016). Test No. 487: In Vitro Mammalian Cell Micronucleus Test.</a> <a name="_ENREF_27">OECD (2016). Test No. 489: In Vivo Mammalian Alkaline Comet Assay.</a> <a name="_ENREF_28">OECD (Organisation for Economic Co-operation and Development) (2016). Overview of the set of OECD Genetic Toxicology Test Guidelines and updates performed in 2014–2015. No. 238.</a> <a name="_ENREF_29">Ogawa, Y., T. Kobayashi, et al. (2003). "Radiation-induced oxidative DNA damage, 8-oxoguanine, in human peripheral T cells." International journal of molecular medicine 11(1): 27-32.</a> <a name="_ENREF_30">Ojima, M., N. Ban, et al. (2008). "DNA double-strand breaks induced by very low X-ray doses are largely due to bystander effects." Radiation research 170(3): 365-371.</a> <a name="_ENREF_31">Pernot, E., J. Hall, et al. (2012). "Ionizing radiation biomarkers for potential use in epidemiological studies." Mutation research 751(2): 258-286.</a> <a name="_ENREF_32">Pinto, M., K. M. Prise, et al. (2005). "Evidence for complexity at the nanometer scale of radiation-induced DNA DSBs as a determinant of rejoining kinetics." Radiation research 164(1): 73-85.</a> <a name="_ENREF_33">Ponnaiya, B., M. N. Cornforth, et al. (1997). "Induction of chromosomal instability in human mammary cells by neutrons and gamma rays." Radiation research 147(3): 288-294.</a> <a name="_ENREF_34">Ponnaiya, B., M. N. Cornforth, et al. (1997). "Radiation-induced chromosomal instability in BALB/c and C57BL/6 mice: the difference is as clear as black and white." Radiation research 147(2): 121-125.</a> <a name="_ENREF_35">Ravanat, J. L., J. Breton, et al. (2014). "Radiation-mediated formation of complex damage to DNA: a chemical aspect overview." Br J Radiol 87(1035): 20130715.</a> <a name="_ENREF_36">Regulus, P., B. Duroux, et al. (2007). "Oxidation of the sugar moiety of DNA by ionizing radiation or bleomycin could induce the formation of a cluster DNA lesion." Proceedings of the National Academy of Sciences of the United States of America 104(35): 14032-14037.</a> <a name="_ENREF_37">Rothkamm, K. and M. Lobrich (2003). "Evidence for a lack of DNA double-strand break repair in human cells exposed to very low x-ray doses." Proceedings of the National Academy of Sciences of the United States of America 100(9): 5057-5062.</a> <a name="_ENREF_38">Rydberg, B., B. Cooper, et al. (2005). "Dose-dependent misrejoining of radiation-induced DNA double-strand breaks in human fibroblasts: experimental and theoretical study for high- and low-LET radiation." Radiation research 163(5): 526-534.</a> <a name="_ENREF_39">Sharma, V., L. B. Collins, et al. (2016). "Oxidative stress at low levels can induce clustered DNA lesions leading to NHEJ mediated mutations." Oncotarget 7(18): 25377-25390.</a> <a name="_ENREF_40">Shiraishi, I., N. Shikazono, et al. (2017). "Efficiency of radiation-induced base lesion excision and the order of enzymatic treatment." International journal of radiation biology 93(3): 295-302.</a> <a name="_ENREF_41">Sishc, B. J., C. B. Nelson, et al. (2015). "Telomeres and Telomerase in the Radiation Response: Implications for Instability, Reprograming, and Carcinogenesis." Front Oncol 5: 257.</a> <a name="_ENREF_42">Stenerlow, B., E. Hoglund, et al. (2000). "Rejoining of DNA fragments produced by radiations of different linear energy transfer." International journal of radiation biology 76(4): 549-557.</a> <a name="_ENREF_43">Sykora, P., K. L. Witt, et al. (2018). "Next generation high throughput DNA damage detection platform for genotoxic compound screening." Sci Rep 8(1): 2771.</a> <a name="_ENREF_44">Unger, K., J. Wienberg, et al. (2010). "Novel gene rearrangements in transformed breast cells identified by high-resolution breakpoint analysis of chromosomal aberrations." Endocrine-related cancer 17(1): 87-98.</a> <a name="_ENREF_45">Vispe, S. and M. S. Satoh (2000). "DNA repair patch-mediated double strand DNA break formation in human cells." The Journal of biological chemistry 275(35): 27386-27392.</a> <a name="_ENREF_46">Yang, T.-H., L. M. Craise, et al. (1992). "Chromosomal changes in cultured human epithelial cells transformed by low- and high-LET radiation." Adv Space Res 12(2-3): 127-136.</a> <a name="_ENREF_47">Yang, T. C., K. A. Georgy, et al. (1997). "Initiation of oncogenic transformation in human mammary epithelial cells by charged particles." Radiat Oncol Investig 5(3): 134-138.</a> <a name="_ENREF_48">Yin, Z., D. Menendez, et al. (2012). "RAP80 is critical in maintaining genomic stability and suppressing tumor development." Cancer research 72(19): 5080-5090.</a> <a name="_ENREF_49">Yu, Y., R. Okayasu, et al. (2001). "Elevated breast cancer risk in irradiated BALB/c mice associates with unique functional polymorphism of the Prkdc (DNA-dependent protein kinase catalytic subunit) gene." Cancer Res 61(5): 1820-1824.</a> AOPWiki 2016-11-29T18:41:30

2019-05-08T12:28:46

Increased, Oxidative Stress

Molecular

CL:0000255

CL eukaryotic cell

AOPWiki 2016-11-29T18:41:29

2022-02-03T14:20:13

Increase, Apoptosis

Cellular

AOPWiki 2017-04-15T16:17:34

2017-04-15T16:17:34

Decreased spermatogenesis

Organ

AOPWiki 2020-07-13T04:32:49

2021-02-09T08:36:06

Decrease, Fecundity

Individual

AOPWiki 2016-11-29T18:41:24

2018-06-12T04:39:53

Decrease, Reproduction

Individual

AOPWiki 2021-04-11T08:21:37

2021-04-11T17:38:35

Decrease, Population growth rate

Population

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.

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).

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

Not Specified Unspecific

Not Specified

All life stages

High

<ul> <li>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.</li> <li>Bookhout TA. 1994. Research and management techniques for wildlife and habitats. The Wildlife Society, Bethesda, Maryland. 740 pp.</li> <li>Caswell H. 2001. Matrix Population Models. Sinauer Associates, Inc., Sunderland, MA, USA</li> <li>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.</li> <li>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.</li> <li>Etterson MA, Ankley GT.  2021.  Endogenous Lifecycle Models for Chemical Risk Assessment. Environ. Sci. Technol. 55:  15596-15608. </li> <li>Gotelli NJ, 2008. A Primer of Ecology. Sinauer Associates, Inc., Sunderland, MA, USA.</li> <li>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.</li> <li>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.</li> <li>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. </li> <li>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.</li> <li>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.</li> <li>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.</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. Ecotox Environ Saf 59: 1–9.</li> <li>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.</li> <li>Murray DL, Sandercock BK (editors).  2020.  Population ecology in practice.  Wiley-Blackwell, Oxford UK, 448 pp.</li> <li>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.</li> <li>Nisbet RM, Muller EB, Lika K, Kooijman SALM. 2000. From molecules to ecosystems through dynamic energy budgets. J Anim Ecol 69:  913–926.</li> <li>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. </li> <li>Vandermeer JH, Goldberg DE. 2003.  Population ecology: first principles.  Princeton University Press, Princeton NJ, 304 pp.</li> <li>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</li> <li>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.</li> </ul> AOPWiki 2016-11-29T18:41:24

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#<Reference::ActiveRecord_Associations_CollectionProxy:0x00007b42eb11aab8> AOPWiki 2022-03-28T07:19:51

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#<Reference::ActiveRecord_Associations_CollectionProxy:0x00007b42eb138838> AOPWiki 2022-03-28T07:46:41

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#<Reference::ActiveRecord_Associations_CollectionProxy:0x00007b42eb168e48> AOPWiki 2022-01-21T07:18:45

2022-01-21T07:18:45

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#<Reference::ActiveRecord_Associations_CollectionProxy:0x00007b42eb1af988> AOPWiki 2019-04-22T05:00:22

2019-04-22T05:00:22

<upstream-id>34710c86-bd15-48c6-93f7-5189e291b289</upstream-id> <downstream-id>edd56cdd-1914-4ba4-b3ed-77e2932c2e6d</downstream-id> Increased RONS leads to an increase in DNA damage.

Biological plausibiltiy is High. Reactive oxygen and nitrogen species from oxygen and respiratory activity are generally acknowledged to damage DNA under a range of cellular conditions. Empirical support is High. Multiple studies show an increase in DNA damage with RONS treatment as well as dependent changes in both RONS and DNA damage in response to stressors. DNA damage increases with RONS dose, and temporal concordance between RONS and DNA damage events following ionizing radiation is consistent with a causative relationship, although few studies examine multiple doses and time points. A small number of studies do not find double strand breaks at physiological doses, or report an increase in one key event but not the other.

High. Reactive oxygen and nitrogen species from oxygen and respiratory activity are generally acknowledged to damage DNA under typical cellular conditions (Dickinson and Chang 2011; Aziz, Nowsheen et al. 2012; Tubbs and Nussenzweig 2017). Damage commonly occurs via oxidation of a nucleotide by the hydroxyl radical (or by radicals created by nitric oxide), or can occur indirectly in nearby nucleotides following the secondary reaction of a radical created in nucleotides (Cadet, Davies et al. 2017). Oxidative damage predominantly consists of DNA lesions (structural modifications to nucleotides) including single strand breaks, although double strand breaks can occur when transcription or translation machinery encounters damaged strands (Tubbs and Nussenzweig 2017).

High. Multiple studies show an increase in DNA damage with RONS treatment as well as dependent changes in both RONS and DNA damage in response to stressors. DNA damage increases with RONS dose, and temporal concordance between RONS and DNA damage events following ionizing radiation is consistent with a causative relationship, although few studies examine multiple doses and time points. A small number of studies do not find double strand breaks at physiological doses, or report an increase in one key event but not the other. Treatment with H2O2 or other RONS inducers increase DNA damage and double strand breaks. H2O2 treatment reaches the nucleus where it can damage DNA (Ameziane-El-Hassani, Boufraqech et al. 2010; Ameziane-El-Hassani, Talbot et al. 2015). Oxidized nucleotides (including clusters) and single strand breaks are commonly reported following H2O2 treatment (Dahm-Daphi, Sass et al. 2000; Nakamura, Purvis et al. 2003; Yang, Durando et al. 2013; Sharma, Collins et al. 2016), and double strand breaks can occur when transcription and translation machinery encounters damaged strands (Berdelle, Nikolova et al. 2011; Yang, Durando et al. 2013; Tubbs and Nussenzweig 2017). However, it is less clear whether H2O2 or RONS cause a measurable increase in double strand breaks, particularly at physiologically relevant concentrations (in the range of 12 uM) (Liu and Zweier 2001; Ameziane-El-Hassani, Talbot et al. 2015). Studies report double strand breaks following treatment with 15 uM- 1mM H2O2 (Oya, Yamamoto et al. 1986; Driessens, Versteyhe et al. 2009; Seager, Shah et al. 2012; Werner, Wang et al. 2014; Ameziane-El-Hassani, Talbot et al. 2015; Sharma, Collins et al. 2016) as well as parallel increases in RONS and double strand breaks (Han, Chen et al. 2010; Berdelle, Nikolova et al. 2011; Stanicka, Russell et al. 2015). DNA damage including double strand breaks and mutations increase with H2O2 dose (Sandhu and Birnboim 1997; Dahm-Daphi, Sass et al. 2000; Driessens, Versteyhe et al. 2009; Seager, Shah et al. 2012; Lorat, Brunner et al. 2015; Sharma, Collins et al. 2016). RONS is dose-dependently and reversibly associated with increased genomic instability (Dayal, Martin et al. 2008; Dayal, Martin et al. 2009; Buonanno, de Toledo et al. 2011; Pazhanisamy, Li et al. 2011; Datta, Suman et al. 2012; Bensimon, Biard et al. 2016) and with DNA damage in bystander cells (Azzam, De Toledo et al. 2002; Yang, Asaad et al. 2005; Yang, Anzenberg et al. 2007; Han, Chen et al. 2010; Buonanno, de Toledo et al. 2011) although other non-RONS factors such as telomere erosion and breakage-fusion-bridge events may be sufficient to maintain genomic instability (Suzuki, Kashino et al. 2009; Murnane 2012). To our knowledge no experiments have tested whether elevating intracellular RONS alone in one group of cells can cause DNA damage in nearby cells. Antioxidants and other interventions to reduce RONS production also reduce or block the effect of RONS treatment on DNA base damage (Berdelle, Nikolova et al. 2011) and double-strand breaks (Ameziane-El-Hassani, Boufraqech et al. 2010; Ameziane-El-Hassani, Talbot et al. 2015; Stanicka, Russell et al. 2015). Similarly, nitric oxide scavengers can reduce DNA damage in cells treated with nitric oxide producers (Han, Chen et al. 2010) or in bystander cells. Further support for a causative relationship between RONS and DNA damage comes from many studies showing that antioxidants and other interventions capable of reducing RONS can also reduce DNA damage following IR. Antioxidant reduction of nucleotide damage from IR occurs in isolated DNA (Winyard, Faux et al. 1992; Douki, Ravanat et al. 2006), and in vitro and in vivo antioxidants reduce nucleotide damage, double strand breaks, micronuclei, chromosomal damage, and mutations when added before (Azzam, De Toledo et al. 2002; Choi, Kang et al. 2007; Jones, Riggs et al. 2007; Ameziane-El-Hassani, Boufraqech et al. 2010; Ozyurt, Cevik et al. 2014; Ameziane-El-Hassani, Talbot et al. 2015; Fetisova, Antoschina et al. 2015; Manna, Das et al. 2015), or in the case of delayed (15 min to days) or bystander DNA damage, added after radiation (Yang, Asaad et al. 2005; Han, Chen et al. 2010; Pazhanisamy, Li et al. 2011; Ameziane-El-Hassani, Talbot et al. 2015). Interestingly, NO specific blockers reduce DNA damage and mutations in bystander cells but not in directly IR cells, suggesting that NO specifically contributes to the bystander effect (Zhou, Ivanov et al. 2008; Han, Chen et al. 2010). Temporal concordance between RONS and DNA damage events following a stressor (ionizing radiation) is consistent with a causative relationship between RONS and DNA damage. Following ionizing radiation, an increase in RONS typically occurs coincident with DNA damage. Few studies examine multiple doses and time points, and detection methods have differing sensitivities. However, both RONS and double strand breaks appear rapidly after IR (Ameziane-El-Hassani, Boufraqech et al. 2010; Denissova, Nasello et al. 2012; Martin, Nakamura et al. 2014), and in several studies RONS and DNA single and double strand breaks, chromosomal damage, and micronuclei appear at the same time points over several days following IR (Choi, Kang et al. 2007; Jones, Riggs et al. 2007; Du, Gao et al. 2009; Saenko, Cieslar-Pobuda et al. 2013; Ameziane-El-Hassani, Talbot et al. 2015; Manna, Das et al. 2015). RONS also appears coincident with longer term DNA damage including nucleotide damage, double strand breaks, and micronuclei, both in IR exposed (Dayal, Martin et al. 2008; Pazhanisamy, Li et al. 2011; Datta, Suman et al. 2012; Werner, Wang et al. 2014; Ameziane-El-Hassani, Talbot et al. 2015) and in bystander cells not directly exposed to IR (Buonanno, de Toledo et al. 2011).

While the bulk of the evidence support a mechanism where RONS increases DNA damage, including double strand DNA breaks, not all studies report these effects. Some studies report the induction of single strand breaks by H2O2, but only show double strand breaks with H2O2 doses at or above 1 mM H2O2 (Dahm-Daphi, Sass et al. 2000; Lorat, Brunner et al. 2015) or do not find an effect of H2O2 on double strand breaks at any concentration (Gradzka and Iwanenko 2005; Ismail, Nystrom et al. 2005). These conflicting results may be partially explained by experimental variations including temperature (two of the studies showing reduced or no effect were exposed to H2O2 at 4C or colder) or other factors including catalysts required to transform H2O2 into DNA damaging OH radicals (Nakamura, Purvis et al. 2003). The reduction of IR-induced DNA damage (including double strand breaks) by antioxidants is strong evidence for an essential role of RONS in DNA damage, but antioxidants don’t reduce all DNA damage from IR and anti-oxidants that reduce double strand breaks and chromosomal aberrations after IR don’t necessarily reduce baseline DNA damage (Fetisova, Antoschina et al. 2015). This incomplete effect suggests either that antioxidants are unable to fully reduce endogenous RONS, or that additional sources of DNA damage are also at work. Furthermore, RONS can be observed following IR in the absence of DNA nucleotide damage (Yoshida, Goto et al. 2012) and counter to expectations lower (10 uM) doses of H2O2 applied six days after IR were associated with a decrease in detectable micronuclei (Werner, Wang et al. 2014), suggesting that additional factors (such as repair and apoptosis or changes in endogenous antioxidants) may influence the effect of RONS on IR-induced DNA damage. Finally, double strand breaks and chromosomal damage can be observed following IR in the absence of measured RONS (Suzuki, Kashino et al. 2009), although since antioxidants are still capable of reducing DNA damage in the absence of measurable RONS, such a discrepancy might be attributable to a lack of sensitivity in RONS detection methods (Yang, Asaad et al. 2005).

RONS activates or is essential to many inflammatory pathways including TGF-β  (Barcellos-Hoff and Dix 1996; Jobling, Mott et al. 2006), TNF (Blaser, Dostert et al. 2016), Toll-like receptor (TLR) (Park, Jung et al. 2004; Nakahira, Kim et al. 2006; Powers, Szaszi et al. 2006; Miller, Goodson et al. 2017; Cavaillon 2018), and NF-kB signaling (Gloire, Legrand-Poels et al. 2006; Morgan and Liu 2011). These interactions principally involve ROS, but RNS can indirectly activate TLRs and possibly NF-kB. Since inflammatory signaling and activated immune cells can also increase the production of RONS, positive feedback and feedforward loops can occur (Zhao and Robbins 2009; Ratikan, Micewicz et al. 2015; Blaser, Dostert et al. 2016). Damage inflicted by RONS on cells activate TLRs and other receptors to promote release of cytokines (Ratikan, Micewicz et al. 2015). For example, oxidized lipids or oxidative stress-induced heat shock proteins can activate TLR4 (Miller, Goodson et al. 2017; Cavaillon 2018). ROS is essential to TLR4 activation of downstream signals including NF-kB. Activation of TLR4 promotes the surface expression and movement of TLR4 into signal-promoting lipid rafts (Nakahira, Kim et al. 2006; Powers, Szaszi et al. 2006). This signal promotion requires NADPH-oxidase and ROS (Park, Jung et al. 2004; Nakahira, Kim et al. 2006; Powers, Szaszi et al. 2006). ROS is also required for the TLR4/TRAF6/ASK-1/p38 dependent activation of inflammatory cytokines (Matsuzawa, Saegusa et al. 2005). ROS therefore amplifies the inflammatory process. RONS can also fail to activate or actively inhibit inflammatory pathways, and the circumstances determining response to RONS are not well known (Gloire, Legrand-Poels et al. 2006).   <a name="_ENREF_1">Barcellos-Hoff, M. H. and T. A. Dix (1996). "Redox-mediated activation of latent transforming growth factor-beta 1." Mol Endocrinol 10(9): 1077-1083.</a> <a name="_ENREF_2">Blaser, H., C. Dostert, et al. (2016). "TNF and ROS Crosstalk in Inflammation." Trends in cell biology 26(4): 249-261.</a> <a name="_ENREF_3">Cavaillon, J.-M. (2018). Damage-associated Molecular Patterns. Inflammation: From Molecular and Cellular Mechanisms to the Clinic. J.-M. Cavaillon and M. Singer, Wiley-VCHVerlagGmbH&Co.KGaA.: 57-80.</a> <a name="_ENREF_4">Gloire, G., S. Legrand-Poels, et al. (2006). "NF-kappaB activation by reactive oxygen species: fifteen years later." Biochem Pharmacol 72(11): 1493-1505.</a> <a name="_ENREF_5">Jobling, M. F., J. D. Mott, et al. (2006). "Isoform-specific activation of latent transforming growth factor beta (LTGF-beta) by reactive oxygen species." Radiation research 166(6): 839-848.</a> <a name="_ENREF_6">Matsuzawa, A., K. Saegusa, et al. (2005). "ROS-dependent activation of the TRAF6-ASK1-p38 pathway is selectively required for TLR4-mediated innate immunity." Nat Immunol 6(6): 587-592.</a> <a name="_ENREF_7">Miller, M. F., W. H. Goodson, et al. (2017). "Low-Dose Mixture Hypothesis of Carcinogenesis Workshop: Scientific Underpinnings and Research Recommendations." Environmental health perspectives 125(2): 163-169.</a> <a name="_ENREF_8">Morgan, M. J. and Z. G. Liu (2011). "Crosstalk of reactive oxygen species and NF-kappaB signaling." Cell Res 21(1): 103-115.</a> <a name="_ENREF_9">Nakahira, K., H. P. Kim, et al. (2006). "Carbon monoxide differentially inhibits TLR signaling pathways by regulating ROS-induced trafficking of TLRs to lipid rafts." J Exp Med 203(10): 2377-2389.</a> <a name="_ENREF_10">Park, H. S., H. Y. Jung, et al. (2004). "Cutting edge: direct interaction of TLR4 with NAD(P)H oxidase 4 isozyme is essential for lipopolysaccharide-induced production of reactive oxygen species and activation of NF-kappa B." J Immunol 173(6): 3589-3593.</a> <a name="_ENREF_11">Powers, K. A., K. Szaszi, et al. (2006). "Oxidative stress generated by hemorrhagic shock recruits Toll-like receptor 4 to the plasma membrane in macrophages." J Exp Med 203(8): 1951-1961.</a> <a name="_ENREF_12">Ratikan, J. A., E. D. Micewicz, et al. (2015). "Radiation takes its Toll." Cancer Lett 368(2): 238-245.</a> <a name="_ENREF_13">Zhao, W. and M. E. Robbins (2009). "Inflammation and chronic oxidative stress in radiation-induced late normal tissue injury: therapeutic implications." Curr Med Chem 16(2): 130-143.</a>

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#<Reference::ActiveRecord_Associations_CollectionProxy:0x00007b42eb357150> AOPWiki 2016-11-29T18:41:37

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#<Reference::ActiveRecord_Associations_CollectionProxy:0x00007b42ebeb9bd8> AOPWiki 2021-04-11T08:26:55

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Ionizing radiation leads to reduced reproduction in Eisenia fetida via reduced spermatogenesis and cocoon hatchability

Deposition of energy leads to reduced cocoon hatchability

Allie Always

BY-SA

2.0

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). 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.

The following stressors increase this key event: ionizing radiation.

Stressors include: Ionizing radiation Estrogen

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.

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2023-09-25T16:27:11