Increase, D1 protein deactivation

PCO:0000001

PCO population of organisms

CHEBI:26523

CHEBI reactive oxygen species

PCO:0000008

PCO population growth rate

GO:1903409

GO reactive oxygen species biosynthetic process

WIKI decreased

WIKI increased

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

Gamma radiation

2017-04-15T16:04:31

Ultraviolet B radiation

2017-04-15T16:04:52

WikiUser_22

all species

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_28

Vertebrates

Increase, D1 protein deactivation

D1 protein deactivation

Cellular

AOPWiki 2021-04-11T08:16:58

2021-04-11T08:16:58

Decrease, Photosystem II efficiency

Cellular

AOPWiki 2021-04-11T08:18:07

2021-04-11T17:32:34

Decrease, Photosynthesis

Cellular

AOPWiki 2017-10-10T07:51:34

2017-10-10T07:51:34

Decrease, ATP production

Cellular

AOPWiki 2017-10-10T07:49:23

2021-04-11T17:36:16

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

2023-01-03T09:09:06

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

Increased, Reactive oxygen species

Cellular

Biological State: increased reactive oxygen species (ROS) Biological compartment: an entire cell -- may be cytosolic, may also enter organelles. Reactive oxygen species (ROS) are O2- derived molecules that can be both free radicals (e.g. superoxide, hydroxyl, peroxyl, alcoxyl) and non-radicals (hypochlorous acid, ozone and singlet oxygen) (Bedard and Krause 2007; Ozcan and Ogun 2015). ROS production occurs naturally in all kinds of tissues inside various cellular compartments, such as mitochondria and peroxisomes (Drew and Leeuwenburgh 2002; Ozcan and Ogun 2015). Furthermore, these molecules have an important function in the regulation of several biological processes – they might act as antimicrobial agents or triggers of animal gamete activation and capacitation (Goud et al. 2008; Parrish 2010; Bisht et al. 2017).  However, in environmental stress situations (exposure to radiation, chemicals, high temperatures) these molecules have its levels drastically increased, and overly interact with macromolecules, namely nucleic acids, proteins, carbohydrates and lipids, causing cell and tissue damage (Brieger et al. 2012; Ozcan and Ogun 2015).

Photocolorimetric assays (Sharma et al. 2017; Griendling et al. 2016) or through commercial kits purchased from specialized companies. Yuan, Yan, et al., (2013) described ROS monitoring by using H2-DCF-DA, a redox-sensitive fluorescent dye. Briefly, the harvested cells were incubated with H2-DCF-DA (50 µmol/L final concentration) for 30 min in the dark at 37°C. After treatment, cells were immediately washed twice, re-suspended in PBS, and analyzed on a BD-FACS Aria flow cytometry. ROS generation was based on fluorescent intensity which was recorded by excitation at 504 nm and emission at 529 nm. Lipid peroxidation (LPO) can be measured as an indicator of oxidative stress damage Yen, Cheng Chien, et al., (2013). Chattopadhyay, Sukumar, et al. (2002) assayed the generation of free radicals within the cells and their extracellular release in the medium by addition of yellow NBT salt solution (Park et al., 1968). Extracellular release of ROS converted NBT to a purple colored formazan. The cells were incubated with 100 ml of 1 mg/ml NBT solution for 1 h at 37 °C and the product formed was assayed at 550 nm in an Anthos 2001 plate reader. The observations of the ‘cell-free system’ were confirmed by cytological examination of parallel set of explants stained with chromogenic reactions for NO and ROS.

ROS is a normal constituent found in all organisms.

High Unspecific

High

All life stages

High

B.H. Park, S.M. Fikrig, E.M. Smithwick Infection and nitroblue tetrazolium reduction by neutrophils: a diagnostic aid Lancet, 2 (1968), pp. 532-534 Bedard, Karen, and Karl-Heinz Krause. 2007. “The NOX Family of ROS-Generating NADPH Oxidases: Physiology and Pathophysiology.” Physiological Reviews 87 (1): 245–313. Bisht, Shilpa, Muneeb Faiq, Madhuri Tolahunase, and Rima Dada. 2017. “Oxidative Stress and Male Infertility.” Nature Reviews. Urology 14 (8): 470–85. Brieger, K., S. Schiavone, F. J. Miller Jr, and K-H Krause. 2012. “Reactive Oxygen Species: From Health to Disease.” Swiss Medical Weekly 142 (August): w13659. Chattopadhyay, Sukumar, et al. "Apoptosis and necrosis in developing brain cells due to arsenic toxicity and protection with antioxidants." Toxicology letters 136.1 (2002): 65-76. Drew, Barry, and Christiaan Leeuwenburgh. 2002. “Aging and the Role of Reactive Nitrogen Species.” Annals of the New York Academy of Sciences 959 (April): 66–81. Goud, Anuradha P., Pravin T. Goud, Michael P. Diamond, Bernard Gonik, and Husam M. Abu-Soud. 2008. “Reactive Oxygen Species and Oocyte Aging: Role of Superoxide, Hydrogen Peroxide, and Hypochlorous Acid.” Free Radical Biology & Medicine 44 (7): 1295–1304. Griendling, Kathy K., Rhian M. Touyz, Jay L. Zweier, Sergey Dikalov, William Chilian, Yeong-Renn Chen, David G. Harrison, Aruni Bhatnagar, and American Heart Association Council on Basic Cardiovascular Sciences. 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. Ozcan, Ayla, and Metin Ogun. 2015. “Biochemistry of Reactive Oxygen and Nitrogen Species.” In Basic Principles and Clinical Significance of Oxidative Stress, edited by Sivakumar Joghi Thatha Gowder. Rijeka: IntechOpen. Parrish, A. R. 2010. “2.27 - Hypoxia/Ischemia Signaling.” In Comprehensive Toxicology (Second Edition), edited by Charlene A. McQueen, 529–42. Oxford: Elsevier. Sharma, Gunjan, Nishant Kumar Rana, Priya Singh, Pradeep Dubey, Daya Shankar Pandey, and Biplob Koch. 2017. “p53 Dependent Apoptosis and Cell Cycle Delay Induced by Heteroleptic Complexes in Human Cervical Cancer Cells.” Biomedicine & Pharmacotherapy = Biomedecine & Pharmacotherapie 88 (April): 218–31. Yen, Cheng Chien, et al. "Inorganic arsenic causes cell apoptosis in mouse cerebrum through an oxidative stress-regulated signaling pathway." Archives of toxicology 85 (2011): 565-575. Yuan, Yan, et al. "Cadmium-induced apoptosis in primary rat cerebral cortical neurons culture is mediated by a calcium signaling pathway." PloS one 8.5 (2013): e64330. AOPWiki 2016-11-29T18:41:29

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Deposition of ionizing energy leading to population decline via inhibition of photosynthesis

ROS production leading to population decline via photosynthesis inhibition

Arthur Author

<h3 style="text-align:justify">Li Xie1,3, You Song1,3, Knut Erik <a href="https://www.google.no/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0ahUKEwju4ty69sXMAhWEVywKHQHMAVEQFggcMAA&url=http%3A%2F%2Fwww.niva.no%2Fse-ansatt%3Fnavn%3DKnut-Erik%2520Tollefsen&usg=AFQjCNEcD7iL_TI4IPB7RrXApxFvdea9ZA&sig2=W3HomTsh5RonGjOyuROqOQ">Tollefsen</a>1,2,3</h3> 1 Norwegian Institute for Water Research (NIVA), Section of Ecotoxicology and Risk Assessment, Gaustadalléen 21, N-0349 Oslo, Norway 2 Norwegian University of Life Sciences (NMBU), Faculty of Environmental Sciences and Natural Resource Management (MINA), P.O. Box 5003, N-1432 Ås, Norway 3. Centre for Environmental Radioactivity, Norwegian University of Life Sciences (NMBU), Post box 5003, N-1432 Ås, Norway

BY-SA

2.0

Ionising and non-ionizing radiation may excite and/or ionize intracellular water molecules to increase production of reactive oxygen species (ROS) in cells of primary producers. Such excessive ROS production can trigger oxidative damage of proteins (such as the D1 protein) in photosystem II (PSII) reaction centers and result in a reduction in PSII efficiency, total photosynthesis and ATP production. Reduction in the ATP pool may consequently reduce available energy to support development (size and weight) and/or reproduction (number of organisms and/or leaves) to reduce the overall population.

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.

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.

adjacent

High

High adjacent

High

High adjacent

High

High adjacent

High

High adjacent

Moderate

Moderate adjacent

High

High Unspecific Low Hermaphrodite

High

Adult, reproductively mature

Not Specified Not Specified

The evidence for the MIE, KE and AO were considered Moderate to High for all Key Events and Key Event Relationships. The overall assessment of the AOP were considered moderate.  Taxonomic: all primary producers Life stage: all stages Sex: both genders (dioecious plants) and not relevant (hermaphrodites) Stressors: Ionizing radiation

The essentiality of all key events was considered as Moderate to High. Essentiality evaluations were mainly based on specifically designed studies demonstrating the expected effect pattern predicted by the AOP to occur after exposure to Cobalt-60 external radiation.

Biological Plausibility: Empirical Evidence: Overall confidence in the AOP:

<div> <table class="table table-bordered table-fullwidth"> <thead> <tr> <th>Modulating Factor (MF)</th> <th>Influence or Outcome</th> <th>KER(s) involved</th> </tr> </thead> <tbody> <tr> <td> </td> <td> </td> <td> </td> </tr> </tbody> </table> </div>

Quantitative data were generated in studies with Lemna minor and the freshwater algae Chlamydomonas reinhardtii exposed to external gamma radiation from a Cobalt-60 source and artifical ultraviolet B radiation (UVB). The quantitative understanding of the AOP was therefore considered to be Moderate for these species and stressors.

High

Moderate

Xie, L., Solhaug, K. A., Song, Y., Brede, D. A., Lind, O. C., Salbu, B., & Tollefsen, K. E. (2019). Modes of action and adverse effects of gamma radiation in an aquatic macrophyte Lemna minor. Science of the Total Environment, 680, 23-34. <div>T. Gomes, L. Xie, D. Brede, O.-C. Lind, K.A. Solhaug, B. Salbu, K.E. Tollefsen (2017). Sensitivity of the green algae Chlamydomonas reinhardtii to gamma radiation: photosynthetic performance and ROS formation. Aquat. Toxicol., 183 (2017), pp. 1-10.</div> <div> </div> <div>Xie, L., Solhaug, K. A., Song, Y., Johnsen, B., Olsen, J. E., & Tollefsen, K. E. (2020). Effects of artificial ultraviolet B radiation on the macrophyte Lemna minor: a conceptual study for toxicity pathway characterization. Planta, 252(5), 1-18.</div> <div> </div> <div> </div> <div> </div> AOPWiki 2021-04-11T07:13:32

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