AOP ID and Title:

Short Title: Deposition of energy leading to bone loss

Authors

Snehpal Sandhu¹, Mitchell Keyworth¹, Syna Karimi-Jashni¹, Dalya Alomar¹, Benjamin Smith¹, Tatiana Kozbenko¹, Robyn Hocking², Carole Yauk³, Ruth C. Wilkins¹, Vinita Chauhan¹

(1) Consumer and Clinical Radiation Protection Bureau, Health Canada, Ottawa, Ontario, Canada

(2) Learning and Knowledge and Library Services, Health Canada, Ottawa, Ontario, Canada

(3) Department of Biology, University of Ottawa, Ottawa, Ontario, Canada

Consultants

Stephen Doty¹, Nobuyuki Hamada², Robert Reynolds³ , Ryan T. Scott⁴, Sylvain V. Costes⁵, Afshin Beheshti^6,7

(1) Hospital for Special Surgery Research Institute, New York City, New York, USA

(2) Biology and Environmental Chemistry Division, Sustainable System Research Laboratory, Central Research Institute of Electric Power Industry (CRIEPI), Tokyo, Japan

(3) KBR, NASA Johnson Space Center, Houston, TX 77058 USA; 

(4) KBR, NASA Ames Research Center, Moffett Field, CA 94035 USA; 

(5) NASA Ames Research Center, Space Biosciences Research Branch, Mountain View, CA, USA;  

(6) KBR, NASA Ames Research Center, Space Biosciences Division, Moffett Field, CA 94035, USA;  

(7) Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA;

Status

Abstract

The present AOP describes Key Events (KEs) from deposition of energy, the moleculare initiating event (MIE), to bone loss, the adverse outcome (AO) and is part of a broader network to three other AOs relevant to radiation exposures: impaired learning and memory, cataracts, and vascular remodeling. The AOP begins with the deposition of energy (KE#1686) that can lead directly to oxidative stress (KE#1392), defined as an imbalance of oxidants and antioxidants. Oxidation of key functional amino acids can alter signaling proteins, resulting in downstream effects in bone-regulating signaling pathways (KE#2066), specifically the Wnt/β-catenin pathway and the receptor activator of nuclear factor kappa B ligand (RANK-L) pathway. Concurrently, oxidative damage to vital cellular components, such as the nucleus, mitochondria or cell membrane, can induce oxidative stress-driven cell death (KE#1825), such as apoptosis, autophagy, and necrosis. Cell death can reduce osteocyte and osteoblast cell numbers or initiate the secretion of osteoclast-stimulatory molecules that can alter bone cell homeostasis (KE#2089). Impaired activity and differentiation of osteoblasts decreases bone formation, while increased activity and differentiation of osteoclasts increases bone destruction. Subsequent bone remodeling (KE#2090) is then altered, defined by bone resorption being increased above bone formation. Bone density and quality can then be changed, leading to bone loss (KE#2091), the AO. The overall evidence for this AOP is moderate based on the literature to support the pathway. Although biological plausibility is well established and the evidence supporting the essentiality of most KEs is high or moderate, the quantitative understanding of the AOP is weak. Modulating factors for this relationship include age and genotype. Overall, the AOP identifies data gaps that can inform new experiments to improve quantitative understanding and could serve as a basis for developing strategies mitigating the risks of long duration spaceflight and radiotherapy treatments. 

Background

Bone loss, as observed in a variety of conditions such as osteopenia and osteoporosis, is a skeletal disorder characterized by decreased bone density and quality resulting in porous, fracture-prone bones (Rachner, Khosla, and Hofbauer, 2011). In the United States, it has been estimated that 2 million fractures per year are due to osteoporosis, costing $57 billion per year from direct medical costs combined with productivity losses and informal caregiving (Lewiecki et al., 2019). Bone loss is more common in Caucasians, women, and older people (Sozen, Ozisik, and Basaran, 2017). Risk factors for fractures include low body mass index, previous fractures, glucocorticoid treatment, and other conditions like rheumatoid arthritis and type 1 diabetes mellitus (Sozen, Ozisik, and Basaran, 2017).  

Growing evidence suggests that acute and chronic radiation exposure can contribute to the loss of bone mass (Donaubauer et al., 2020; Willey et al., 2011; Wissing, 2015). Clinical studies have shown that skeletal sites receiving high doses of ionizing radiation (25 Gy or higher) have increased fracture risk (Baxter et al., 2005; Oeffinger et al., 2006; Willey et al., 2011). For example, radiotherapy for pelvic malignancies causes an increased risk of hip fractures (Baxter et al., 2005; Williams and Davies, 2006). Similarly, radiotherapy for breast cancer or rectal carcinoma has been shown to increase the risk of fracture to the ribs or pelvis/femoral neck, respectively (Holm et al., 1996; Overgaard, 1988). Low to moderate doses of radiation as received during long-term spaceflight contribute to bone loss (Stavnichuk et al., 2020; Willey et al., 2011), but is the focus of fewer studies. Therefore, identifying essential early endpoints relevant to radiation-induced bone loss through the development of AOPs can inform mitigation strategies to reduce the risks from radiation exposures. 

Summary of the AOP

Events

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

Key Event Relationships

Stressors

Overall Assessment of the AOP

This AOP collates peer-reviewed published data in the space field and studies from other radiation exposure scenarios that are not encountered during space travel to strengthen the evidence. The search priotized chronic low- to moderate-dose radiation emitted from high linear energy transfer (LET) particles, which is most applicable to long-term spaceflight. High doses from low-LET acute radiation studies were included as well; thus, AOP is also relevant to bone loss from radiotherapy. Other stressors that are space-relevant but not radiation-related like microgravity are also used to strengthen the AOP. However, not all KERs are equally supported by the multitude of stressors encountered during space travel, as some KERs have different responses dependent on the stressor. A few studies show additive effects when combining radiation and microgravity stressors in animal models, demonstrating that these stressors may encourage bone loss through separate pathways (Willey et al., 2021). However, particularly in studies using chronic or fractionated exposures, radiation did not exacerbate the effects of microgravity (Kondo et al., 2010; Willey et al., 2021). This could be because the identical components of each mechanism are saturated by the individual stressor (Kondo et al., 2010). 

Biological Plausibility 

Overall, each KER in the AOP is well understood mechanistically and biological plausibility is high. Mechanisms such as altered bone cell homeostasis and bone remodeling are well accepted biological events contributing to bone loss (details provided in tables). The deposition of energy (MIE) causes the ionization of water molecules within cells, producing free radicals that combine to more stable reactive oxygen species (ROS) (Eaton, 1994; Padgaonkar et al., 2015; Rehman et al., 2016; Varma et al., 2011). Additionally, deposited energy can directly upregulate enzymes involved in reactive oxygen and nitrogen species (RONS) production (de Jager, Cockrell and Du Plessis, 2017). This, along with positive feedback loops that further generate RONS, contributes to oxidative stress as RONS overwhelm the cells’ antioxidant defense systems and subsequently damage macromolecules and organelles (Balasubramanian, 2000; Ganea and Harding, 2006; Karimi et al., 2017; Zigman et al., 2000). 

It is well established that oxidative damage can cause both cell death and altered signaling. Oxidation of key amino acids in proteins from major signaling pathways will cause conformational and functional changes to these signaling molecules, inducing changes in the activity of the entire pathway (Ping et al., 2020; Schmidt-Ullrich et al., 2000; Valerie et al., 2007). Oxidative stress can indirectly affect signaling through oxidative DNA damage, which influences the expression and activity of signaling molecules, such as the molecules involved in the MAPK pathway (Nagane et al., 2021; Ping et al., 2020; Schmidt-Ullrich et al., 2000; Valerie et al., 2007). Additionally, extensive damage to DNA, mitochondria, or the cell membrane can induce cell death (Jilka, Noble and Weinstein, 2013). 

In bones, the combined influence of altered signaling pathways and increased cell death will alter bone cell homeostasis, characterized by an increase in osteoclasts (bone resorbing cells) and a decrease in osteoblasts (bone forming cells). Upregulated signaling from the RANK-L pathway will increase osteoclastogenesis, while impaired Wnt/β-catenin signaling will decrease osteoblastogenesis (Arfat et al., 2014; Bellido, 2014; Boyce and Xing, 2007; Chatziravdeli, Katsaras and Lambrou, 2019; Chen, Deng and Li, 2012; Donaubauer et al., 2020; Maeda et al., 2019; Manolagas and Almeida, 2007; Smith, 2020a; Smith, 2020b; Willey et al., 2011). Osteoblast death will reduce osteoblast numbers, while osteocyte death will free osteoclast-stimulating molecules (Jilka, Noble, and Weinstein, 2013; Komori, 2013; Li et al., 2015; O’Brien, Nakashima, and Takayanagi, 2013; Plotkin, 2014; Wang et al., 2020; Xiong and O’Brien, 2012). As bone cells are dysregulated, subsequent bone remodeling results in a greater rate of resorption than formation of bone (Bikle and Halloran, 1999; Donaubauer et al., 2020; Morey-Holton et al., 1991; Smith, 2020b; Tian et al., 2017). Consequently, bones exhibit reduced volume, density, mineralization, and strength as bone loss occurs (Bikle and Halloran, 1999; Donaubauer et al., 2020; Morey-Holton and Arnaud, 1991; Tian et al., 2017). A complete understanding of the relationship across taxonomy and sex is lacking at the time of AOP development; this is an area that requires further research. 

Temporal, Dose, and Incidence Concordance 

Evidence for time, dose, and incidence concordance in this AOP is moderate, as evidence to support the modified Bradford Hill criteria is often limited due to space travel conditions, where there are restrictions on the number of animals, doses and timepoints represented. For this reason, data from other exposure scenarios are used to help strengthen the adjacent relationships, in keeping with the principles of AOP development. In contrast, there was a larger evidence base for the non-adjacent relationships that were directly linked to the MIE, as there is much radiobiological research to support MIE’s causal association to each of the KEs in the AOP. 

In general, many studies demonstrated that the upstream KEs occurred earlier than the downstream KEs in time course experiments. It is well accepted that deposition of energy occurs immediately following irradiation, and downstream changes will always occur later in a time course. The subsequent radical formation occurs within microseconds (Azzam, Jay-Gerin, and Pain, 2012), and studies have observed the resulting oxidative stress as early as 2 minutes post-irradiation (Wortel et al., 2019). Altered signaling is a molecular-level KE like oxidative stress, and both KEs occur with a similar time course, making the assessment of time concordance difficult between these KEs. However, oxidative stress can still be observed slightly earlier than altered signaling (Wortel et al., 2019). The ensuing cell death due to oxidative stress often occurs within days post-irradiation, while altered bone cell homeostasis owing to both altered signaling and cell death is subsequently observed about a week after irradiation (Liu et al., 2018). Then, from multiple weeks to a month post-irradiation, bone remodeling is observed to favor resorption over formation (Alwood et al., 2010; Chandra et al., 2017; Chandra et al., 2014; Zhai et al., 2019). The resulting bone loss presents after this, with the greatest bone loss and risk of fractures observed months to years following irradiation (Holm et al., 1996; Nishiyama et al., 1992; Oest et al., 2018; Zou et al., 2016). 

Radiation at any dose and dose rate will deposit energy. Extensive evidence shows that upstream KEs can be observed at the same doses or lower doses as downstream KEs. For example, Kondo et al. (2009) and Kondo et al. (2010) showed that ROS levels and osteoclastogenesis were increased by both 1 and 2 Gy of gamma radiation, while bone loss and remodeling endpoints occurred at 2 Gy but not 1 Gy. In another study, X-ray irradiation at both 2 and 24 Gy led to increased osteoclast activity, while only 24 Gy led to consistent decreases in areal bone mineral density (aBMD) and mineral apposition rate (MAR) (Zhai et al., 2019). Dose concordance is not consistently observed across studies, but this may be due to different models, timepoints, and radiation types used. 

A few studies support incidence concordance. Although many studies demonstrate equal changes between the two KEs, less than half of the studies across KERs show that the upstream KE produces a greater change than the downstream KE following a stressor. One KER showing strong incidence concordance is altered signaling leading to altered bone cell homeostasis. For example, Sambandam et al. (2016) showed that tumor necrosis factor receptor-associated factor 6 (TRAF6) and tumor necrosis factor-related apoptosis inducing ligand (TRAIL) signaling molecules were increased 6 and 14.5-fold, respectively, while tartrate-resistant acid phosphatase (TRAP) staining (indicative of bone cell homeostasis) was just increased 1.7-fold by microgravity. 

Uncertainties and Inconsistencies

There are some notable knowledge gaps in the understanding of the biological mechanism involved in the deposition of energy leading to bone loss. In the space environment, both microgravity and radiation stressors are present. However, the differences in the underlying molecular changes following each stressor are currently uncertain (Willey et al., 2021). More research should be focused on understanding differential effects of microgravity and radiation on bone loss. Furthermore, studies using multi-ion radiation and chronic radiation exposure in addition to microgravity could better represent the space environment (Willey et al., 2021). 

Some studies also show conflicting results. For example, a few studies demonstrate bone cell differentiation and activity at doses of ionizing radiation at 2 Gy or below (Li et al., 2020b), while others show no effects (Kook et al., 2015; He et al., 2019). Differences may be due to experimental designs related to timepoints, histology measurements, models, radiation quality, doses, and dose rates. This often complicates the ability to evaluate the strength of the evidence due to inconsistent results. Studies were also limited in the range of doses or timepoints used, which challenged the identification of dose and time concordance data. Often studies measured KEs at a single dose or timepoint. Furthermore, no single study evaluated all KEs in the AOP, which would have provided ideal evidence to determine the weight of evidence supporting this AOP. 

Other aspects for consideration are interspecies differences. During the first few months of spaceflight, bone resorption increases greatly in humans (Stavnichuk et al., 2020). In rats, however, resorption does not change during spaceflight (Fu et al., 2021). Mouse models are more representative of the altered bone cell homeostasis KE than rat models, as they show consistent increases in resorption during spaceflight (Vico and Hargens, 2018). In addition, there are differences in measurements used to assess the resorption of bone in humans and experimental animals (Fu et al., 2021). 

Lastly, the bone remodeling KE includes endpoints to measure changes in the bone formation rate but has fewer endpoints to measure bone resorption. Resorption endpoints are often cell-level and are included in the altered bone cell homeostasis KE. Changes to resorption in the bone remodeling KE are determined indirectly through changes to bone formation and bone volume. Consequently, it is difficult to quantify bone resorption in the bone remodeling KE, even though it is an important contributor to bone loss. Further efforts could be directed to developing mthods that are able to assess bone resorption at the tissue level. 

Domain of Applicability

Life Stage Applicability Taxonomic Applicability Sex Applicability

This AOP is relevant to vertebrates, such as humans, mice, and rats. The taxonomic evidence supporting the AOP is derived from studies in human (Homo sapiens) and human-derived cell lines, mouse (Mus musculus), rat (Rattus orvegicus), and rhesus monkey (Macaca mulatta) (Chandra et al., 2014; Nishiyama et al., 1992; Willey et al., 2011; Zerath et al., 2002). Across all species, most available data was from adult and adolescent models with less available data from preadolescent models. 

The AOP is applicable to both sexes, with most studies using either male or female animal models but not both. In humans, spaceflight-induced bone loss has also been observed in both sexes (Smith et al., 2014). 

The AOP is applicable to all life stages, with extensive studies in adult humans and animals and fewer studies in adolescent and preadolescent animals. However, bone loss can be more prevalent in the aging population (>~50 years) (Riggs, Khosla, and Melton, 1998; Pacheco and Stock, 2013).

Essentiality of the Key Events

Modulation of upstream KEs often influences the occurrence or extent of downstream KEs, making the evidence of essentiality moderate for the KEs in the AOP. Below are a few examples showing how downstream KEs are affected by upstream modulation. 

Essentiality of the Deposition of Energy (MIE#1686) 

• The effect of radiation shielding on altered bone cell homeostasis (KE#2089) 

• Increased osteoclast numbers were not observed in shielded contralateral bones following irradiation (Wright et al., 2015). However, a few studies show equal changes to osteoblast and osteoclast number in vivo in irradiated and contralateral limbs, possibly due to the abscopal effects of radiation (Zhang et al., 2019; Zou et al., 2016). 

• The effect of radiation shielding on increased bone remodeling (KE#2090) 

• Shielded limbs show a higher bone formation rate than directly irradiated limbs (Wright et al., 2015; Zhai et al., 2019). 

• The effect of radiation shielding on occurrence of bone loss (AO#2091) 

• Multiple studies measuring bone loss in shielded limbs contralateral to the irradiation show a greater loss of bone in the irradiated limb (Baxter et al., 2005; Oest et al., 2018; Wright et al., 2015). Although a few studies find equal changes in irradiated and contralateral limbs, this may be due to the abscopal effects of radiation (Zhang et al., 2019; Zou et al., 2016). 

Essentiality of Oxidative Stress (KE#1392) 

• The effect of antioxidants on altered signaling pathways (KE#2066) 

• Antioxidants including N-acetyl cysteine, curcumin, melatonin, polyphenol S3, and hydrogen water restore signaling in the Wnt/β-catenin pathway and inhibit signaling in the RANK/RANK-L pathway (Diao et al., 2018; Kook et al., 2015; Sun et al., 2013; Xin et al., 2015; Yoo, Han & Kim, 2016). 

• The effect of antioxidants on increased cell death (KE#1825). 

• Antioxidants including α-2-macroglobulin (α2M), semaphorin 3A (sema3a), amifostine (AMI), and melatonin reduce apoptosis levels induced by radiation or microgravity (Huang et al., 2019; Huang et al., 2018; Liu et al., 2018; Li et al., 2018a; Yoo, Han and Kim, 2016). 

• The effect of antioxidants on altered bone cell homeostasis (KE#2089) 

• Antioxidants including N-acetyl cysteine, α2M, AMI, curcumin, cerium (IV) oxide, and hydrogen water restore osteoblastogenesis and reduce osteoclastogenesis following radiation or microgravity (Diao et al., 2018; Huang et al., 2019; Huang et al., 2018; Kook et al., 2015; Liu et al., 2018; Sun et al., 2013; Wang et al., 2016; Xin et al., 2015; Zhang et al., 2020). 

Essentiality of Altered Signaling Pathways (KE#2066) 

• The effect of modulated signaling on altered bone cell homeostasis (KE#2089) 

• Modulation of osteoclastogenesis-related signaling – Inhibitors of the RANK/RANK-L pathway or other osteoclast-stimulating molecules reduce osteoclast activity after it is increased by exposure to gamma rays, X-rays, and microgravity (He et al., 2019; Li et al., 2018b; Rucci et al., 2007; Sambandam et al., 2016; Zhang et al., 2019; Zhou et al., 2008). 

• Modulation of osteoblastogenesis-related signaling – Activation of pathways leading to runt-related transcription factor 2 (Runx2) activation or the Wnt/β-catenin pathway restored osteoblast activity after it is decreased by exposure to X-rays and microgravity (Chandra et al., 2017; Chen et al., 2020; Li et al., 2020b; Li et al., 2018b; Liu et al., 2018). In contrast, direct inhibition of the Wnt/β-catenin pathway impairs osteoblast activity (Chen et al., 2020). 

Essentiality of Increase, Cell Death (KE#1825) 

• The effect of modulating cell death on altered bone cell homeostasis (KE#2089) 

• Osteoblast cell death decreases the number of osteoblasts, while osteocyte cell death can stimulate osteoclastogenesis. Inhibition of cell death by using drugs that promote cell survival or by inhibiting autophagy restores osteoblast numbers and activity as well as reducing osteoclast numbers and activity (Chandra et al., 2014; Huang et al., 2019; Li et al., 2020b; Liu et al., 2018; Wang et al., 2020; Wu et al., 2020; Yang et al., 2020). 

Essentiality of Altered Bone Cell Homeostasis (KE#2089) 

• No study directly modulating the changes to osteoblasts and osteoclasts and observing the results on downstream KEs was identified in the literature search. 

Essentiality of Increase, Bone Remodeling (KE#2090) 

• The effect of modulated bone remodeling on bone loss (AO#2091) 

• Bone remodeling blocked by knockout of osteopontin, a mediator of bone remodeling, restores the bone volume after microgravity (Ishijima et al., 2001). Similarly, inhibition of Calponin h1, an inhibitor of bone formation, increases BMD following microgravity (Yotsumoto, Takeoka, and Yokoyama, 2010). 

Weight of Evidence Summary

1. Support for Biological Plausibility of KERs			Defining Question	High (Strong)				Moderate		Low (Weak)
			Is there a mechanistic relationship between KEupstream and KEdownstream consistent with established biological knowledge?	Extensive understanding of the KER based on extensive previous documentation and broad acceptance; Established mechanistic basis				KER is plausible based on analogy to accepted biological relationships, but scientific understanding is not completely established		There is empirical support for statistical association between KEs, but the structural or functional relationship between them is not understood
MIE#1686 → KE#1392:  Deposition of Energy leads to Oxidative Stress			High  There is strong evidence of the biological plausibility of deposition of energy leading to oxidative stress. It is well understood that when deposited energy reaches a cell it reacts with water and organic materials to produce free radicals such as ROS. If the ROS cannot be eliminated quickly and efficiently enough by the cell’s defense system, oxidative stress may ensue.
KE#1392 → KE#1825:  Oxidative stress leads to Increase, cell death			High  It is well known that oxidative stress can lead to cell death. ROS lead to the release of pro-apoptotic factors, and enough ROS accumulation can lead to necrosis. Lipid and protein oxidation of key structures within the cell will also lead to cell death.
KE#1392 → KE#2066:  Oxidative stress leads to Altered Signaling Pathways			High  There is much evidence demonstrating the biological plausibility of the link between oxidative stress and altered signaling pathways. The direct and indirect mechanisms of oxidative stress leading to altered signaling are well known. Directly, oxidative stress conditions can lead to oxidation of amino acid residues. This can cause conformational changes, protein modifications, protein degradation, and impaired activity, leading to changes in the activity and level of signaling proteins. Indirectly, oxidative stress can damage DNA causing changes in the expression of signaling proteins as well as the activation of DNA damage response signaling.
Non-adjacent KE#1392 → KE#2089:  Oxidative stress leads to → Altered Bone Cell Homeostasis			High  It is well understood that an increase in cellular oxidative stress indirectly leads to altered bone cell homeostasis. An increase in oxidative stress and the resulting decrease in osteoblast activity and increase in osteoclast activity have been discussed and well documented, in several reviews.
KE#1825 → KE#2089:  Increase, Cell Death leads to Altered Bone Cell Homeostasis			High  It is well understood that the induction of different forms of cell death of osteoblasts, osteoclasts, and osteocytes leads to an increase in bone resorption and decrease in bone deposition. Osteocyte apoptosis results in rupture of the plasma membrane as phagocytes are unable to engulf these cells, allowing for the release of osteoclast-stimulatory molecules. Apoptotic osteocytes also signal to viable osteocytes in the vicinity to express osteoclast-stimulatory signals. Osteoblast death reduces the overall pool of active osteoblasts. Autophagy can also lead to cell death, and a few studies associate it with cell death in bone cells.
KE#2066 → KE#2089: Altered Signaling Pathways leads to Altered Bone Cell Homeostasis			High  It is very well understood that changes in osteoblast and osteoclast signaling pathways lead to decreased bone deposition and increased bone resorption. A few highly characterized pathways that are important for osteoblast and osteoclast differentiation are the Wnt/β-catenin pathway and the RANK/RANK-L pathway, respectively. Alterations in signaling from these pathways will alter bone cell numbers and activity.
KE#2089 → KE#2090:  Altered Bone Cell Homeostasis leads to Increase, Bone Remodeling			High  Review papers strongly support the structural and functional relationship between altered bone cell homeostasis and bone remodeling. Decreased activity and differentiation of osteoblasts and increased activity and differentiation of osteoclasts lead to increased overall destruction of bone. Bone remodeling is therefore imbalanced to favor bone resorption over formation.
KE#2090 → AO#2091:  Increase, Bone Remodeling leads to Occurrence, Bone Loss			High   The structural and functional relationship between bone remodeling and bone loss is well supported by review articles. Current literature on the subject establishes bone loss due to a decrease in bone formation and an increase in bone resorption by bone remodeling cells.
Non-adjacent MIE#1686 → KE#2089:  Deposition of Energy leads to Altered Bone Cell Homeostasis			High  The structural and functional relationships connecting energy deposition to the loss of homeostasis among bone cells is well supported by several reviews on the subject related to space travel and clinical treatment. More specifically, reviews on ionizing radiation exposure have defined the biological mechanisms by which these stressors can indirectly induce the loss of homeostasis among bone cells.
Non-adjacent MIE#1686 → KE#2090:  Deposition of Energy leads to Increase, Bone Remodeling			High  The biological plausibility for the indirect relationship between deposition of energy and imbalanced remodeling is strong. Reviews describe the impact of radiation on bone formation and resorption as well as the mechanisms involved.
Non-adjacent MIE#1686 → AO#2091:  Deposition of Energy leads to Bone Loss			High  There is a high level of structural and functional evidence for the indirect relationship between deposition of energy and bone loss.
2. Support for Essentiality of KEs	Defining Question				High (Strong)	Moderate			Low (Weak)
	Are downstream KEs and/or the AO prevented if an upstream KE is blocked?				Direct evidence from specifically designed experimental studies illustrating essentiality for at least one of the important KEs	Indirect evidence that sufficient modification of an expected modulating factor attenuates or augments a KE			No or contradictory experimental evidence of the essentiality of any of the KEs
MIE#1686: Deposition of energy		Moderate  Numerous studies show that physical shielding or attenuating the amount of deposited energy can modulate the downstream KEs. However, some studies still show significant bone loss in shielded limbs, possibly due to the abscopal effects of radiation.
KE#1392: Oxidative Stress		High  Essentiality of oxidative stress is well-supported within literature. Many studies have shown that adding or withholding antioxidants such as catalase and glutathione peroxidase will decrease and increase the level of oxidative stress, respectively. Studies using antioxidants to attenuate oxidative stress show restored signaling and bone cell homeostasis, as well as reduced apoptosis.
KE#2066: Altered Signaling Pathways		High  Studies strongly support the essentiality of altered signaling pathways on downstream effects. Studies have used inhibitors or activators of various signaling pathways and observed attenuation of downstream KEs, particularly altered bone cell homeostasis.
KE#1825: Increase, Cell Death		High  Essentiality of increased cell death is well supported within literature through evidence that inhibiting cell death attenuates downstream KEs. Multiple studies inhibit osteoblast and osteocyte cell death by preventing apoptosis or autophagy and find restored osteocyte numbers as well as restored osteoblast numbers and activity.
KE#2089: Altered Bone Cell Homeostasis		Low  There were no studies found on the essentiality of this event; i.e., there were no studies that inhibited the alteration of bone cell homeostasis and measured the downstream KE.
KE#2090: Increase, Bone Remodeling		Moderate  Essentiality of bone remodeling is moderately supported within literature. A small number of studies that inhibit bone resorption or induce bone formation show a reduction in bone loss.
3. Empirical support for KERs			Defining Question	High (Strong)			Moderate			Low (Weak)
			Does KEupstream occur at lower doses and earlier timepoints than KEdownstream; is the incidence or frequency of KEupstream greater than that for KEdownstream for the same dose of tested stressor?	There is a dependent change in both events following exposure to a wide range of specific stressors (extensive evidence for temporal, dose-response and incidence concordance) and no or few data gaps or conflicting data.			There is demonstrated dependent change in both events following exposure to a small number of specific stressors and some evidence inconsistent with the expected pattern that can be explained by factors such as experimental design, technical considerations, differences among laboratories, etc.			There are limited or no studies reporting dependent change in both events following exposure to a specific stressor (i.e., endpoints never measured in the same study or not at all), and/or lacking evidence of temporal or dose-response concordance, or identification of significant inconsistencies in empirical support across taxa and species that don’t align with the expected pattern for the hypothesized AOP.
MIE#1686 → KE#1392:  Deposition of Energy leads to Oxidative Stress			High  There is a large body of evidence that supports an understanding of the time and dose relationship from deposition of energy leading to oxidative stress. The evidence collected to support this relationship was gathered from various studies using in vitro and in vivo rat, mice, rabbit, squirrel, bovine and human models. Various stressors were applied, including ultraviolet (UV) light (UV-B and UV-A) and ionizing radiation (gamma rays, X-rays, protons, photons, neutrons, and heavy ions). Studies that examined the effects of range of ionizing radiation doses (0-10 Gy) discovered that oxidative stress increases in a dose-dependent matter.
KE#1392 → KE#1825:   Oxidative Stress leads to Increase, Cell Death			Moderate  There is moderate empirical evidence to support the relationship between oxidative stress and increased cell death. Many studies demonstrate incidence concordance, dose concordance, and time concordance. However, there are limited data pertaining to low doses of the radiation stressors (X-rays, gamma rays, 12C ions) used to investigate the relationship.
KE#1392 → KE#2066:   Oxidative Stress leads to Altered Signaling Pathways			High  There is strong empirical evidence for this relationship. A number of studies demonstrated incidence concordance. Most studies that examined the effects of a range of stressor doses showed dose concordance, and most studies that analyzed oxidative stress and signaling pathways over multiple timepoints supported temporal concordance. This evidence was collected from studies using a variety of stressors, including ionizing radiation in doses as low as 0.125 Gy, in in vitro cell and in vivo mouse, rat, and pig models.
Non-adjacent KE#1392 → KE#2089:   Oxidative Stress leads to Altered Bone Cell Homeostasis			Moderate  There is a moderate body of evidence showing concordance between oxidative stress and altered bone cell homeostasis. A few studies demonstrated incidence concordance, most studies that examined the effects of a range of doses demonstrated dose concordance, and most studies that examined oxidative stress and bone cell dysfunction over multiple timepoints provided evidence in support of temporal concordance. However, the evidence for dose concordance is weak as only a single study measured the KEs at multiple doses. Ionizing radiation (X-rays and gamma rays) in doses as low as 1 Gy and microgravity were the stressors used in studies. The models used included in vitro cells and in vivo rats and mice.
KE#1825 → KE#2089:   Increase, Cell Death leads to Altered Bone Cell Homeostasis			High  There is a large body of evidence indicating concordance between increased cell death to altered bone cell homeostasis. Most studies demonstrated time, dose, and incidence concordance. Ionizing radiation (X-rays and gamma rays) in doses as low as 0.5 Gy and microgravity were the stressors used in studies. The models used included in vitro cells and in vivo rats and mice.
KE#2066 → KE#2089: Altered Signaling Pathways leads to Altered Bone Cell Homeostasis			High  There is strong evidence showing concordance to support the KER. Evidence in most of the studies collected supported time, dose, and incidence concordance. Ionizing radiation (X-rays and gamma rays) at doses as low as 0.5 Gy and microgravity were the stressors used in studies. The models used included in vitro cells and in vivo rats and mice.
KE#2089 → KE#2090:   Altered Bone Cell Homeostasis leads to Increase, Bone Remodeling			Moderate  Dose and time concordance between altered bone cell homeostasis and bone remodeling are currently supported by moderate evidence. A number of studies demonstrate incidence concordance and most studies that analyzed altered bone cell homeostasis and bone remodeling over multiple timepoints demonstrated time concordance. However, some studies showed changes to one or more endpoints that were inconsistent with the change expected following the stressors. Also, there were no studies that could be used to evaluate the dose concordance of the KEs at multiple doses. The relationship was demonstrated using X-rays at doses as low as 2 Gy and microgravity in in vitro cell and in vivo rat and mouse models.
KE#2090 → AO#2091:   Increase, Bone Remodeling leads to Occurrence, Bone Loss			Moderate  There is moderate evidence for concordance between bone remodeling and bone loss. Most studies demonstrate time and incidence concordance. However, no studies measured both KEs at multiple doses of the stressor. The relationship was demonstrated using X-rays at doses as low as 2 Gy and microgravity in in vitro cell and in vivo rat, mouse, and monkey models.
Non-adjacent MIE#1686 → KE#2089:   Deposition of Energy leads to Altered Bone Cell Homeostasis			High  A strong body of evidence shows dose- and time-response effects of ionizing radiation. Data from studies show that radiation exposure indirectly increases osteoclast activity and decreases osteoblast activity in a dose-dependent manner. X-rays and gamma rays in doses ranging from 0-30 Gy were used to study the effects of radiation on bone cells in in vitro and ex vivo cell models, in vivo mouse and rat models, and human models. About a week after radiation exposure, with increasing radiation doses, numbers and activity of osteoblasts decrease, while numbers of osteoclasts increase.
Non-adjacent MIE#1686 → KE#2090:   Deposition of Energy leads to Increase, Bone Remodeling			High  The empirical evidence for deposition of energy leading to bone remodeling is high. Imbalanced bone remodeling caused by ionizing radiation is directly related to the absorbed dose. Bone remodeling is affected after exposure of mice and rats to 0.5-24 Gy of X-ray, gamma ray, proton, and 56Fe ion radiation. Few studies examine the time course of this KER, but changes to bone remodeling occur from 7 to 60 days post-irradiation.
Non-adjacent MIE#1686 → AO#2091:   Deposition of Energy leads to Occurrence, Bone Loss			High  There is strong evidence for the deposition of energy leading to bone loss. X-rays, gamma rays, protons, and heavy ions from 0.05 to 64 Gy delivered to rat, mouse, and human models were used to assess this relationship. By comparing the results of studies using either high or low dose radiation, there is a consensus that bone loss from low dose exposure is less than that from high dose exposure. Studies also show that bone loss can be observed from 1 week to years after irradiation but is mostly found in the first few months after exposure.

Quantitative Consideration

There is a low quantitative understanding for the KERs in this AOP. Many studies have quantified the changes in consecutive KEs after a specific stressor dose. However, due to varying experimental parameters, including experimental model, radiation type, doses, dose rate, and timepoints, a quantitative relationship is difficult to determine between most adjacent KEs in the pathway. KERs between the MIE and downstream KEs are readily quantified, as changes to the upstream KE, in this case the dose, dose rate, and radiation type applied to the model, are determined in the experimental method and can be more easily standardized across studies. KERs that do not include the MIE are more difficult to quantify, as the perturbation to the upstream KE cannot be standardized to determine its effects on a downstream KE, as it is the product of the applied stressor and the resulting changes to KEs that came before it in the pathway.

Review of the Quantitative Understanding for each KER	Defining Question	High (Strong)	Moderate	Low (Weak)
	To what extent can a change in KEdownstream be predicted from KEupstream? With what precision can the uncertainty in the prediction of KEdownstream be quantified? To what extent are the known modulating factors of feedback mechanisms accounted for? To what extent can the relationships described be reliably generalized across the applicability domain of the KER?	Change in KEdownstream can be precisely predicted based on a relevant measure of KEupstream; uncertainty in the quantitative prediction can be precisely estimated from the variability in the relevant KEupstream measure. Known modulating factors and feedback/feedforward mechanisms are accounted for in the quantitative description. Evidence that the quantitative relationship between the KEs generalizes across the relevant applicability domain of the KER.	Change in KEdownstream can be precisely predicted based on a relevant measure of KEupstream; uncertainty in the quantitative prediction is influenced by factors other than the variability in the relevant KEupstream measure. Quantitative description does not account for all known modulating factors and/or known feedback/feedforward mechanisms. The quantitative relationship has only been demonstrated for a subset of the overall applicability domain of the KER.	Only a qualitative or semi-quantitative prediction of the change in KEdown can be determined from a measure of KEup. Known modulating factors and feedback/feedforward mechanisms are not accounted for. Quantitative relationship has only been demonstrated for a narrow subset of the overall applicability domain of the KER.
MIE#1686 → KE#1392:   Deposition of Energy leads to Oxidative Stress	Moderate  The quantitative understanding of the MIE leading to oxidative stress is moderate. The most common dose of radiation applied to models when examining the effects of energy deposition on oxidative stress is 2 Gy. In general, exposure to 2 Gy of low LET radiation, such as X-rays, gamma rays, or protons, resulted in increased ROS production compared to high LET radiation, such as heavy ions. 2 Gy of low LET radiation results in increases of ~15-200% to ROS production and ~136-433% to levels of other oxidative stress markers, as well as decreases of ~9-70% to levels of antioxidants, with some studies not demonstrating significant changes to any oxidative stress endpoints. 2 Gy of high LET radiation results in increases of ~120-125% to ROS production.
KE#1392 → KE#1825:   Oxidative Stress leads to Increase, Cell Death	Low  The quantitative understanding of oxidative stress leading to cell death is low. Increases of ~20-400% in ROS levels and ~100% in other oxidative stress markers as well as decreases of ~34-75% in antioxidants cause a ~60-440% increase in apoptosis and a ~125% increase in autophagy. Some studies show significant changes to one or more endpoints that are inconsistent with the expected effect of the stressor.
KE#1392 → KE#2066:   Oxidative Stress leads to Altered Signaling Pathways	Low  The quantitative understanding of oxidative stress leading to altered signaling pathways is low. A ~35-260% increase in RONS, a ~20-110% increase in oxidative stress markers (such as malondialdehyde (MDA), protein carbonylation, p67 levels), and/or a ~10-76% decrease in antioxidants results in a ~20-500% increase in expression and activity of osteoclast differentiation signaling molecules and/or a ~10-96% decrease in expression and activity of osteoblast differentiation signaling molecules. Some studies show significant changes to one or more endpoints that are inconsistent with the expected effect of the stressor.
Non-adjacent KE#1392 → KE#2089:   Oxidative Stress leads to Altered Bone Cell Homeostasis	Low  The quantitative understanding of oxidative stress leading to altered bone cell homeostasis is low. Many studies quantify oxidative stress and altered bone cell homeostasis following a stressor; however, studies often measure different endpoints in different experimental models and the change to bone cell homeostasis cannot be precisely predicted from the level of oxidative stress. Furthermore, the effect of modulating factors is not well quantified in studies.
KE#1825 → KE#2089: Increase, Cell Death leads to Altered Bone Cell Homeostasis	Low  The quantitative understanding of increased cell death leading to altered bone cell homeostasis is low. Increases of ~100-600% in osteoblast apoptosis and/or ~50-1500% osteocyte apoptosis result in decreases of ~30-63% in osteoblastogenesis markers and ~47-73% in osteoblast/osteocyte number, as well as increases of ~200-250% in osteoclastogenesis markers and ~50-1100% in osteoclast number.
KE#2066 → KE#2089: Altered Signaling Pathways leads to Altered Bone Cell Homeostasis	Moderate  The quantitative understanding of altered signaling pathways leading to altered bone cell homeostasis is moderate. Altered bone cell homeostasis can be roughly predicted from measures of the protein expression and activity of key signaling molecules for osteoblasts and osteoclasts. Decreases of ~40-90% to expression and activity of osteoblast differentiation signaling molecules result in decreases of ~48.2-93.9% in osteoblastogenesis markers. Increases of ~30-300% to expression and activity of osteoclast differentiation signaling molecules result in increases of ~30-460% in osteoclastogenesis markers.
KE#2089 → KE#2090: Altered Bone Cell Homeostasis leads to Increase, Bone Remodeling	Low  The quantitative understanding of altered bone cell homeostasis to bone remodeling is low. Decreases of ~17-75% in osteoblastogenesis markers and/or increases of ~22-300% in osteoclastogenesis markers resulted in decreases of ~16-100% in bone formation and increases of ~6-26% in the structural modeling index (SMI). Both microgravity and ionizing radiation exposure have the same effect on altered bone cell homeostasis and bone remodeling markers. However, these effects are more significant for ionizing radiation exposure.
KE#2090 → AO#2091: Increase, Bone Remodeling leads to Occurrence, Bone Loss	Low  The quantitative understanding of bone remodeling leading to bone loss is low. There is an abundance of quantitative data pertaining to the effects of stressor-induced bone remodeling on bone loss. However, the decreases in bone formation do not precisely predict the resulting bone loss. Decreases of ~20-100% in bone formation and increases of ~6-26% in SMI, cause decreases of ~9-82% in bone structure. Some studies showed changes to one or more endpoints that are inconsistent with the expected effect of the stressor.
Non-adjacent MIE#1686 → KE#2089:   Deposition of Energy leads to Altered Bone Cell Homeostasis	Low  The quantitative understanding of the deposition of energy leading to altered bone cell homeostasis is low. Many studies quantify altered bone cell homeostasis following radiation exposure; however, it is difficult to compare results and quantify relationships as each study uses different models, stressors, doses and time points. In addition, the influence of modulating factors has not been completely assessed. Thus, no model has been established to predict the changes in bone cell homeostasis after the deposition of energy.
Non-adjacent MIE#1686 → KE#2090:   Deposition of Energy leads to Increase, Bone Remodeling	Low  The quantitative understanding of the deposition of energy leading to bone remodeling is low. Many studies quantify bone remodeling; however, it is difficult to compare results and quantify relationships as each study uses different stressors, doses and time points. In addition, the influence of modulating factors such as sex have not been completely assessed. Thus, no model has been established to predict the changes in bone remodeling after the deposition of energy.
Non-adjacent MIE#1686 → AO#2091:   Deposition of Energy leads to Occurrence, Bone Loss	Moderate  The quantitative understanding of the deposition of energy leading to bone loss is moderate. Bone loss can be partially predicted by the dose of deposited energy. For example, a 2 Gy dose of 56Fe ions will consistently reduce BV/TV by about 20-30%. However, these changes depend on the bone studied, the dose, the radiation type, and the time point. No model has been established to precisely predict the changes in bone loss after the deposition of energy.

Considerations for Potential Applications of the AOP (optional)

The present AOP is one of four built to describe the causal connectivity of KEs leading to adverse health outcomes relevant to space travel and radiotherapy. In constructing the AOP, critical and well-understood biological events and data gaps in empirical evidence were identified. The evidence summary for this AOP can thus be used to justify areas for future work. For example, studies using multi-ion radiation at sustained deliveries and at chronic low doses under microgravity conditions would better represent the space environment and could clarify uncertainties observed in current studies. In addition, a standard range of stressor doses and measurement timepoints would allow for more dose and time response/concordance data and would facilitate more accurate comparisons of evidence between KEs. This  should include low doses, as existing low-dose evidence is often inconsistent. Quantitative understanding of each KER  could be improved through experiemtns designed to measure mulitple endpoints across dose- and time-ranges. Future studies should also strive to use models that are more applicable for assessing the risks of human space flight, as the proportion of human studies for each KER ranged from 0-33.3%, with only a few KERs containing human studies. In addition, further investigations are needed to consider sex differences in the study design, thereby strengthening  the understanding of the sex differences within the AOP. An uncertainty in the bone remodeling KE is that changes to the rate of resorption are not directly determined and are instead assumed based on changes to the bone formation rate and bone volume. Future work should identify a direct tissue-level measure of the bone resorption rate. The modulating factors and domain of applicability of this AOP can be used to develop risk mitigation strategies.  

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Appendix 1

List of MIEs in this AOP

Event: 1686: Deposition of Energy

Short Name: Energy Deposition

AOPs Including This Key Event

Stressors

Biological Context

Evidence for Perturbation by Stressor

Overview for Molecular Initiating Event

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.

Domain of Applicability

Taxonomic Applicability Life Stage Applicability Sex Applicability

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. 

Key Event Description

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 (X-X nm) is, in contrast to UVB and UVA, considered to be a type of ionizing radiation.

How it is Measured or Detected

Radiation type	Assay Name	References	Description	OECD Approved Assay
Ionizing radiation	Monte Carlo Simulations (Geant4)	Douglass et al., 2013; Douglass et al. 2012; Zyla et al., 2020	Monte Carlo simulations are based on a computational algorithm that mathematically models the deposition of energy into materials.	No
Ionizing radiation	Fluorescent Nuclear Track Detector (FNTD)	Sawakuchi, 2016; Niklas, 2013; Koaira & Konishi, 2015	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.	No
Ionizing radiation	Tissue equivalent proportional counter (TEPC)	Straume et al, 2015	Measure the LET spectrum and calculate the dose equivalent.	No
Ionizing radiation	alanine dosimeters/NanoDots	Lind et al. 2019; Xie et al., 2022		No
Non-ionizing radiation	UV meters or radiameters	Xie et at., 2020	UVA/UVB (irradiance intensity), UV dosimeters (accumulated irradiance over time), Spectrophoto meter (absorption of UV by a substance or material)	No

 

References

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. 

 

 

List of Key Events in the AOP

Event: 1392: Oxidative Stress

Short Name: Oxidative Stress

Key Event Component

AOPs Including This Key Event

Stressors

Biological Context

Evidence for Perturbation by Stressor

Platinum

Kruidering et al. (1997) examined the effect of platinum on pig kidneys and found that it was able to induce significant dose-dependant ROS formation within 20 minutes of treatment administration.

Aluminum

In a study of the effects of aluminum treatment on rat kidneys, Al Dera (2016) found that renal GSH, SOD, and GPx levels were significantly lower in the treated groups, while lipid peroxidation levels were significantly increased.

Cadmium

Belyaeva et al. (2012) investigated the effect of cadmium treatment on human kidney cells. They found that cadmium was the most toxic when the sample was treated with 500 μM for 3 hours (Belyaeva et al., 2012). As this study also looked at mercury, it is worth noting that mercury was more toxic than cadmium in both 30-minute and 3-hour exposures at low concentrations (10-100 μM) (Belyaeva et al., 2012).

Wang et al. (2009) conducted a study evaluating the effects of cadmium treatment on rats and found that the treated group showed a significant increase in lipid peroxidation. They also assessed the effects of lead in this study, and found that cadmium can achieve a very similar level of lipid peroxidation at a much lower concentration than lead can, implying that cadmium is a much more toxic metal to the kidney mitochondria than lead is (Wang et al., 2009). They also found that when lead and cadmium were applied together they had an additive effect in increasing lipid peroxidation content in the renal cortex of rats (Wang et al., 2009).

Jozefczak et al. (2015) treated Arabidopsis thaliana wildtype, cad2-1 mutant, and vtc1-1 mutant plants with cadmium to determine the effects of heavy metal exposure to plant mitochondria in the roots and leaves. They found that total GSH/GSG ratios were significantly increased after cadmium exposure in the leaves of all sample varieties and that GSH content was most significantly decreased for the wildtype plant roots (Jozefczak et al., 2015).

Andjelkovic et al. (2019) also found that renal lipid peroxidation was significantly increased in rats treated with 30 mg/kg of cadmium.

Mercury

Belyaeva et al. (2012) conducted a study which looked at the effects of mercury on human kidney cells, they found that mercury was the most toxic when the sample was treated with 100 μM for 30 minutes.

Buelna-Chontal et al. (2017) investigated the effects of mercury on rat kidneys and found that treated rats had higher lipid peroxidation content and reduced cytochrome c content in their kidneys.

Uranium

In Shaki et al.’s article (2012), they found rat kidney mitochondria treated with uranyl acetate caused increased formation of ROS, increased lipid peroxidation, and decreased GSH content when exposed to 100 μM or more for an hour.

Hao et al. (2014), found that human kidney proximal tubular cells (HK-2 cells) treated with uranyl nitrate for 24 hours with 500 μM showed a 3.5 times increase in ROS production compared to the control. They also found that GSH content was decreased by 50% of the control when the cells were treated with uranyl nitrate (Hao et al., 2014).

Arsenic

Bhadauria and Flora (2007) studied the effects of arsenic treatment on rat kidneys. They found that lipid peroxidation levels were increased by 1.5 times and the GSH/GSSG ratio was decreased significantly (Bhadauria and Flora, 2007).

Kharroubi et al. (2014) also investigated the effect of arsenic treatment on rat kidneys and found that lipid peroxidation was significantly increased, while GSH content was significantly decreased.

In their study of the effects of arsenic treatment on rat kidneys, Turk et al. (2019) found that lipid peroxidation was significantly increased while GSH and GPx renal content were decreased.

Silver

Miyayama et al. (2013) investigated the effects of silver treatment on human bronchial epithelial cells and found that intracellular ROS generation was increased significantly in a dose-dependant manner when treated with 0.01 to 1.0 μM of silver nitrate.

Manganese

Chtourou et al. (2012) investigated the effects of manganese treatment on rat kidneys. They found that manganese treatment caused significant increases in ROS production, lipid peroxidation, urinary H₂O₂ levels, and PCO production. They also found that intracellular GSH content was depleted in the treated group (Chtourou et al., 2012).

Nickel

Tyagi et al. (2011) conducted a study of the effects of nickel treatment on rat kidneys. They found that the treated rats showed a significant increase in kidney lipid peroxidation and a significant decrease in GSH content in the kidney tissue (Tyagi et al., 2011).

Zinc

Yeh et al. (2011) investigated the effects of zinc treatment on rat kidneys and found that treatment with 150 μM or more for 2 weeks or more caused a time- and dose-dependant increase in lipid peroxidation. They also found that renal GSH content was decreased in the rats treated with 150 μM or more for 8 weeks (Yeh et al., 2011).

It should be noted that Hao et al. (2014) found that rat kidneys exposed to lower concentrations of zinc (such as 100 μM) for short time periods (such as 1 day), showed a protective effect against toxicity induced by other heavy metals, including uranium. Soussi, Gargouri, and El Feki (2018) also found that pre-treatment with a low concentration of zinc (10 mg/kg treatment for 15 days) protected the renal cells of rats were from changes in varying oxidative stress markers, such as lipid peroxidation, protein carbonyl, and GPx levels.

nanoparticles

Huerta-García et al. (2014) conducted a study of the effects of titanium nanoparticles on human and rat brain cells. They found that both the human and rat cells showed time-dependant increases in ROS when treated with titanium nanoparticles for 2 to 6 hours (Huerta-García et al., 2014). They also found elevated lipid peroxidation that was induced by the titanium nanoparticle treatment of human and rat cell lines in a time-dependant manner (Huerta-García et al., 2014).

Liu et al. (2010) also investigated the effects of titanium nanoparticles, however they conducted their trials on rat kidney cells. They found that ROS production was significantly increased in a dose dependant manner when treated with 10 to 100 μg/mL of titanium nanoparticles (Liu et al., 2010).

Pan et al. (2009) treated human cervix carcinoma cells with gold nanoparticles (Au1.4MS) and found that intracellular ROS content in the treated cells increased in a time-dependant manner when treated with 100 μM for 6 to 48 hours. They also compared the treatment with Au1.4MS gold nanoparticles to treatment with Au15MS treatment, which are another size of gold nanoparticle (Pan et al., 2009). The Au15MS nanoparticles were much less toxic than the Au1.4MS gold nanoparticles, even when the Au15MS nanoparticles were applied at a concentration of 1000 μM (Pan et al., 2009). When investigating further markers of oxidative stress, Pan et al. (2009) found that GSH content was greatly decreased in cells treated with gold nanoparticles.

Ferreira et al. (2015) also studied the effects of gold nanoparticles. They exposed rat kidneys to GNPs-10 (10 nm particles) and GNPs-30 (30 nm particles), and found that lipid peroxidation and protein carbonyl content in the rat kidneys treated with GNPs-30 and GNPs-10, respectively, were significantly elevated.

Domain of Applicability

Taxonomic Applicability Life Stage Applicability Sex Applicability

Taxonomic applicability: Occurrence of oxidative stress is not species specific.  

Life stage applicability: Occurrence of oxidative stress is not life stage specific. 

Sex applicability: Occurrence of oxidative stress is not sex specific. 

Evidence for perturbation by prototypic stressor: There is evidence of the increase of oxidative stress following perturbation from a variety of stressors including exposure to ionizing radiation and altered gravity (Bai et al., 2020; Ungvari et al., 2013; Zhang et al., 2009).  

Key Event Description

Oxidative stress is defined as an imbalance in the production of reactive oxygen species (ROS) and antioxidant defenses. High levels of oxidizing free radicals can be very damaging to cells and molecules within the cell.  As a result, the cell has important defense mechanisms to protect itself from ROS. For example, Nrf2 is a transcription factor and master regulator of the oxidative stress response. During periods of oxidative stress, Nrf2-dependent changes in gene expression are important in regaining cellular homeostasis (Nguyen, et al. 2009) and can be used as indicators of the presence of oxidative stress in the cell.

In addition to the directly damaging actions of ROS, cellular oxidative stress also changes cellular activities on a molecular level. Redox sensitive proteins have altered physiology in the presence and absence of ROS, which is caused by the oxidation of sulfhydryls to disulfides (2SH àSS) on neighboring amino acids (Antelmann and Helmann 2011). Importantly Keap1, the negative regulator of Nrf2, is regulated in this manner (Itoh, et al. 2010).

ROS also undermine the mitochondrial defense system from oxidative damage. The antioxidant systems consist of superoxide dismutase, catalase, glutathione peroxidase and glutathione reductase, as well as antioxidants such as α-tocopherol and ubiquinol, or antioxidant vitamins and minerals including vitamin E, C, carotene, lutein, zeaxanthin, selenium, and zinc (Fletcher, 2010). The enzymes, vitamins and minerals catalyze the conversion of ROS to non-toxic molecules such as water and O₂. However, these antioxidant systems are not perfect and endogenous metabolic processes and/or exogenous oxidative influences can trigger cumulative oxidative injuries to the mitochondria, causing a decline in their functionality and efficiency, which further promotes cellular oxidative stress (Balasubramanian, 2000; Ganea & Harding, 2006; Guo et al., 2013; Karimi et al., 2017).

However, an emerging viewpoint suggests that ROS-induced modifications may not be as detrimental as previously thought, but rather contribute to signaling processes (Foyer et al., 2017). 

Protection against oxidative stress is relevant for all tissues and organs, although some tissues may be more susceptible. For example, the brain possesses several key physiological features, such as high O2 utilization, high polyunsaturated fatty acids content, presence of autooxidable neurotransmitters, and low antioxidant defenses as compared to other organs, that make it highly susceptible to oxidative stress (Halliwell, 2006; Emerit and al., 2004; Frauenberger et al., 2016).

Sources of ROS Production

Direct Sources: Direct sources involve the deposition of energy onto water molecules, breaking them into active radical species. When ionizing radiation hits water, it breaks it into hydrogen (H*) and hydroxyl (OH*) radicals by destroying its bonds. The hydrogen will create hydroxyperoxyl free radicals (HO₂*) if oxygen is available, which can then react with another of itself to form hydrogen peroxide (H₂O₂) and more O₂ (Elgazzar and Kazem, 2015). Antioxidant mechanisms are also affected by radiation, with catalase (CAT) and peroxidase (POD) levels rising as a result of exposure (Seen et al. 2018; Ahmad et al. 2021).

Indirect Sources: An indirect source of ROS is the mitochondria, which is one of the primary producers in eukaryotic cells (Powers et al., 2008).  As much as 2% of the electrons that should be going through the electron transport chain in the mitochondria escape, allowing them an opportunity to interact with surrounding structures. Electron-oxygen reactions result in free radical production, including the formation of hydrogen peroxide (H₂O₂) (Zhao et al., 2019). The electron transport chain, which also creates ROS, is activated by free adenosine diphosphate (ADP), O₂, and inorganic phosphate (P_i) (Hargreaves et al. 2020; Raimondi et al. 2020; Vargas-Mendoza et al. 2021). The first and third complexes of the transport chain are the most relevant to mammalian ROS production (Raimondi et al., 2020). The mitochondria have its own set of DNA and it is a prime target of oxidative damage (Guo et al., 2013). ROS are also produced through nicotinamide adenine dinucleotide phosphate oxidase (NOX) stimulation, an event commenced by angiotensin II, a product/effector of the renin-angiotensin system (Nguyen Dinh Cat et al. 2013; Forrester et al. 2018). Other ROS producers include xanthine oxidase, immune cells (macrophage, neutrophils, monocytes, and eosinophils), phospholipase A₂ (PLA₂), monoamine oxidase (MAO), and carbon-based nanomaterials (Powers et al. 2008; Jacobsen et al. 2008; Vargas-Mendoza et al. 2021).

How it is Measured or Detected

Oxidative Stress. Direct measurement of ROS is difficult because ROS are unstable. The presence of ROS can be assayed indirectly by measurement of cellular antioxidants, or by ROS-dependent cellular damage. Listed below are common methods for detecting the KE, however there may be other comparable methods that are not listed

• Detection of ROS by chemiluminescence (https://www.sciencedirect.com/science/article/abs/pii/S0165993606001683)
• Detection of ROS by chemiluminescence is also described in OECD TG 495 to assess phototoxic potential.
• Glutathione (GSH) depletion. GSH can be measured by assaying the ratio of reduced to oxidized glutathione (GSH:GSSG) using a commercially available kit (e.g., http://www.abcam.com/gshgssg-ratio-detection-assay-kit-fluorometric-green-ab138881.html). 
• TBARS. Oxidative damage to lipids can be measured by assaying for lipid peroxidation using TBARS (thiobarbituric acid reactive substances) using a commercially available kit. 
• 8-oxo-dG. Oxidative damage to nucleic acids can be assayed by measuring 8-oxo-dG adducts (for which there are a number of ELISA based commercially available kits),or  HPLC, described in Chepelev et al. (Chepelev, et al. 2015).

Molecular Biology: Nrf2. Nrf2’s transcriptional activity is controlled post-translationally by oxidation of Keap1. Assay for Nrf2 activity include:

• Immunohistochemistry for increases in Nrf2 protein levels and translocation into the nucleus
• Western blot for increased Nrf2 protein levels
• Western blot of cytoplasmic and nuclear fractions to observe translocation of Nrf2 protein from the cytoplasm to the nucleus
• qPCR of Nrf2 target genes (e.g., Nqo1, Hmox-1, Gcl, Gst, Prx, TrxR, Srxn), or by commercially available pathway-based qPCR array (e.g., oxidative stress array from SABiosciences)
• Whole transcriptome profiling by microarray or RNA-seq followed by pathway analysis (in IPA, DAVID, metacore, etc.) for enrichment of the Nrf2 oxidative stress response pathway (e.g., Jackson et al. 2014)
• OECD TG422D describes an ARE-Nrf2 Luciferase test method
• In general, there are a variety of commercially available colorimetric or fluorescent kits for detecting Nrf2 activation

 

Assay Type & Measured Content	Description	Dose Range Studied	Assay Characteristics (Length / Ease of use/Accuracy)
ROS Formation in the Mitochondria assay (Shaki et al., 2012)	“The mitochondrial ROS measurement was performed flow cytometry using DCFH-DA. Briefly, isolated kidney mitochondria were incubated with UA (0, 50, 100 and 200 μM) in respiration buffer containing (0.32 mM sucrose, 10 mM Tris, 20 mM Mops, 50 μM EGTA, 0.5 mM MgCl2, 0.1 mM KH2PO4 and 5 mM sodium succinate) [32]. In the interval times of 5, 30 and 60 min following the UA addition, a sample was taken and DCFH-DA was added (final concentration, 10 μM) to mitochondria and was then incubated for 10 min. Uranyl acetate-induced ROS generation in isolated kidney mitochondria were determined through the flow cytometry (Partec, Deutschland) equipped with a 488-nm argon ion laser and supplied with the Flomax software and the signals were obtained using a 530-nm bandpass filter (FL-1 channel). Each determination is based on the mean fluorescence intensity of 15,000 counts.”	0, 50, 100 and 200 μM of Uranyl Acetate	Long/ Easy High accuracy
Mitochondrial Antioxidant Content Assay Measuring GSH content (Shaki et al., 2012)	“GSH content was determined using DTNB as the indicator and spectrophotometer method for the isolated mitochondria. The mitochondrial fractions (0.5 mg protein/ml) were incubated with various concentrations of uranyl acetate for 1 h at 30 °C and then 0.1 ml of mitochondrial fractions was added into 0.1 mol/l of phosphate buffers and 0.04% DTNB in a total volume of 3.0 ml (pH 7.4). The developed yellow color was read at 412 nm on a spectrophotometer (UV-1601 PC, Shimadzu, Japan). GSH content was expressed as μg/mg protein.”	0, 50, 100, or 200 μM Uranyl Acetate
H2O2 Production Assay Measuring H2O2 Production in isolated mitochondria (Heyno et al., 2008)	“Effect of CdCl2 and antimycin A (AA) on H2O2 production in isolated mitochondria from potato. H2O2 production was measured as scopoletin oxidation. Mitochondria were incubated for 30 min in the measuring buffer (see the Materials and Methods) containing 0.5 mM succinate as an electron donor and 0.2 µM mesoxalonitrile 3‐chlorophenylhydrazone (CCCP) as an uncoupler, 10 U horseradish peroxidase and 5 µM scopoletin.” (	0, 10, 30  μM Cd2+ 2  μM antimycin A
Flow Cytometry ROS & Cell Viability (Kruiderig et al., 1997)	“For determination of ROS, samples taken at the indicated time points were directly transferred to FACScan tubes. Dih123 (10 mM, final concentration) was added and cells were incubated at 37°C in a humidified atmosphere (95% air/5% CO2) for 10 min. At t 5 9, propidium iodide (10 mM, final concentration) was added, and cells were analyzed by flow cytometry at 60 ml/min. Nonfluorescent Dih123 is cleaved by ROS to fluorescent R123 and detected by the FL1 detector as described above for Dc (Van de Water 1995)”		Strong/easy medium
DCFH-DA Assay Detection of hydrogen peroxide production (Yuan et al., 2016)	Intracellular ROS production was measured using DCFH-DA as a probe. Hydrogen peroxide oxidizes DCFH to DCF. The probe is hydrolyzed intracellularly to DCFH carboxylate anion. No direct reaction with H2O2 to form fluorescent production.	0-400 µM	Long/ Easy High accuracy
H2-DCF-DA Assay Detection of superoxide production (Thiebault et al., 2007)	This dye is a stable nonpolar compound which diffuses readily into the cells and yields H2-DCF. Intracellular OH or ONOO- react with H2-DCF when cells contain peroxides, to form the highly fluorescent compound DCF, which effluxes the cell. Fluorescence intensity of DCF is measured using a fluorescence spectrophotometer.	0–600 µM	Long/ Easy High accuracy
CM-H2DCFDA Assay	**Come back and explain the flow cytometry determination of oxidative stress from Pan et al. (2009)**

Direct Methods of Measurement

Method of Measurement	References	Description	OECD-Approved Assay
Chemiluminescence	(Lu, C. et al., 2006;  Griendling, K. K., et al., 2016)	ROS can induce electron transitions in molecules, leading to electronically excited products. When the electrons transition back to ground state, chemiluminescence is emitted and can be measured. Reagents such as uminol and lucigenin are commonly used to amplify the signal.	No
Spectrophotometry	(Griendling, K. K., et al., 2016)	NO has a short half-life. However, if it has been reduced to nitrite (NO2-), stable azocompounds can be formed via the Griess Reaction, and further measured by spectrophotometry.	No
Direct or Spin Trapping-Based Electron Paramagnetic Resonance (EPR) Spectroscopy	(Griendling, K. K., et al., 2016)	The unpaired electrons (free radicals) found in ROS can be detected with EPR, and is known as electron paramagnetic resonance. A variety of spin traps can be used.	No
Nitroblue Tetrazolium Assay	(Griendling, K. K., et al., 2016)	The Nitroblue Tetrazolium assay is used to measure O2•– levels. O2•– reduces nitroblue tetrazolium (a yellow dye) to formazan (a blue dye), and can be measured at 620 nm.	No
Fluorescence analysis of dihydroethidium (DHE) or Hydrocyans	(Griendling, K. K., et al., 2016)	Fluorescence analysis of DHE is used to measure O2•– levels. O2•–  is reduced to O2 as DHE is oxidized to 2-hydroxyethidium, and this reaction can be measured by fluorescence. Similarly, hydrocyans can be oxidized by any ROS, and measured via fluorescence.	No
Amplex Red Assay	(Griendling, K. K., et al., 2016)	Fluorescence analysis to measure extramitochondrial or extracellular H2O2 levels. In the presence of horseradish peroxidase and H2O2, Amplex Red is oxidized to resorufin, a fluorescent molecule measurable by plate reader.	No
Dichlorodihydrofluorescein Diacetate (DCFH-DA)	(Griendling, K. K., et al., 2016)	An indirect fluorescence analysis to measure intracellular H2O2 levels. H2O2 interacts with peroxidase or heme proteins, which further react with DCFH, oxidizing it to dichlorofluorescein (DCF), a fluorescent product.	No
HyPer Probe	(Griendling, K. K., et al., 2016)	Fluorescent measurement of intracellular H2O2 levels. HyPer is a genetically encoded fluorescent sensor that can be used for in vivo and in situ imaging.	No
Cytochrome c Reduction Assay	(Griendling, K. K., et al., 2016)	The cytochrome c reduction assay is used to measure O2•– levels. O2•–  is reduced to O2 as ferricytochrome c is oxidized to ferrocytochrome c, and this reaction can be measured by an absorbance increase at 550 nm.	No
Proton-electron double-resonance imagine (PEDRI)	(Griendling, K. K., et al., 2016)	The redox state of tissue is detected through nuclear magnetic resonance/magnetic resonance imaging, with the use of a nitroxide spin probe or biradical molecule.	No
Glutathione (GSH) depletion	(Biesemann, N. et al., 2018)	A downstream target of the Nrf2 pathway is involved in GSH synthesis. As an indication of oxidation status, GSH can be measured by assaying the ratio of reduced to oxidized glutathione (GSH:GSSG) using a commercially available kit (e.g., http://www.abcam.com/gshgssg-ratio-detection-assay-kit-fluorometric-green-ab138881.html).	No
Thiobarbituric acid reactive substances (TBARS)	(Griendling, K. K., et al., 2016)	Oxidative damage to lipids can be measured by assaying for lipid peroxidation with TBARS using a commercially available kit.	No
Protein oxidation (carbonylation)	(Azimzadeh et al., 2017; Azimzadeh etal., 2015; Ping et al., 2020)	Can be determined with enzyme-linked immunosorbent assay (ELISA) or a commercial assay kit. Protein oxidation can indicate the level of oxidative stress.	No
Seahorse XFp Analyzer	Leung et al. 2018	The Seahorse XFp Analyzer provides information on mitochondrial function, oxidative stress, and metabolic dysfunction of viable cells by measuring respiration (oxygen consumption rate; OCR) and extracellular pH (extracellular acidification rate; ECAR).	No

Molecular Biology: Nrf2. Nrf2’s transcriptional activity is controlled post-translationally by oxidation of Keap1. Assays for Nrf2 activity include: 

Method of Measurement	References	Description	OECD-Approved Assay
Immunohistochemistry	(Amsen, D., de Visser, K. E., and Town, T., 2009)	Immunohistochemistry for increases in Nrf2 protein levels and translocation into the nucleus	No
Quantitative polymerase chain reaction (qPCR)	(Forlenza et al., 2012)	qPCR of Nrf2 target genes (e.g., Nqo1, Hmox-1, Gcl, Gst, Prx, TrxR, Srxn), or by commercially available pathway-based qPCR array (e.g., oxidative stress array from SABiosciences)	No
Whole transcriptome profiling via microarray or via RNA-seq followed by a pathway analysis	(Jackson, A. F. et al., 2014)	Whole transcriptome profiling by microarray or RNA-seq followed by pathway analysis (in IPA, DAVID, metacore, etc.) for enrichment of the Nrf2 oxidative stress response pathway	No

References

Ahmad, S. et al. (2021), “60Co-γ Radiation Alters Developmental Stages of Zeugodacus cucurbitae (Diptera: Tephritidae) Through Apoptosis Pathways Gene Expression”, Journal Insect Science, Vol. 21/5, Oxford University Press, Oxford, https://doi.org/10.1093/jisesa/ieab080

Antelmann, H. and J. D. Helmann (2011), “Thiol-based redox switches and gene regulation.”, Antioxidants & Redox Signaling, Vol. 14/6, Mary Ann Leibert Inc., Larchmont, https://doi.org/10.1089/ars.2010.3400

Amsen, D., de Visser, K. E., and Town, T. (2009), “Approaches to determine expression of inflammatory cytokines”, in Inflammation and Cancer, Humana Press, Totowa, https://doi.org/10.1007/978-1-59745-447-6_5 

Azimzadeh, O. et al. (2015), “Integrative Proteomics and Targeted Transcriptomics Analyses in Cardiac Endothelial Cells Unravel Mechanisms of Long-Term Radiation-Induced Vascular Dysfunction”, Journal of Proteome Research, Vol. 14/2, American Chemical Society, Washington, https://doi.org/10.1021/pr501141b

Azimzadeh, O. et al. (2017), “Proteome analysis of irradiated endothelial cells reveals persistent alteration in protein degradation and the RhoGDI and NO signalling pathways”, International Journal of Radiation Biology, Vol. 93/9, Informa, London, https://doi.org/10.1080/09553002.2017.1339332

Azzam, E. I. et al. (2012), “Ionizing radiation-induced metabolic oxidative stress and prolonged cell injury”, Cancer Letters, Vol. 327/1-2, Elsevier, Ireland, https://doi.org/10.1016/j.canlet.2011.12.012 

Bai, J. et al. (2020), “Irradiation-induced senescence of bone marrow mesenchymal stem cells aggravates osteogenic differentiation dysfunction via paracrine signaling”, American Journal of Physiology - Cell Physiology, Vol. 318/5, American Physiological Society, Rockville, https://doi.org/10.1152/ajpcell.00520.2019.

Balasubramanian, D (2000), “Ultraviolet radiation and cataract”, Journal of ocular pharmacology and therapeutics, Vol. 16/3, Mary Ann Liebert Inc., Larchmont, https://doi.org/10.1089/jop.2000.16.285. 

Biesemann, N. et al., (2018), “High Throughput Screening of Mitochondrial Bioenergetics in Human Differentiated Myotubes Identifies Novel Enhancers of Muscle Performance in Aged Mice”, Scientific Reports, Vol. 8/1, Nature Portfolio, London, https://doi.org/10.1038/s41598-018-27614-8. 

Elgazzar, A. and N. Kazem. (2015), “Chapter 23: Biological effects of ionizing radiation” in The Pathophysiologic Basis of Nuclear Medicine, Springer, New York, pp. 540-548

Fletcher, A. E (2010), “Free radicals, antioxidants and eye diseases: evidence from epidemiological studies on cataract and age-related macular degeneration”, Ophthalmic Research, Vol. 44, Karger International, Basel, https://doi.org/10.1159/000316476.  

Forlenza, M. et al. (2012), “The use of real-time quantitative PCR for the analysis of cytokine mRNA levels” in Cytokine Protocols, Springer, New York, https://doi.org/10.1007/978-1-61779-439-1_2 

Forrester, S.J. et al. (2018), “Angiotensin II Signal Transduction: An Update on Mechanisms of Physiology and Pathophysiology”, Physiological Reviews, Vol. 98/3, American Physiological Society, Rockville, https://doi.org/10.1152/physrev.00038.201

Foyer, C. H., A. V. Ruban, and G. Noctor (2017), “Viewing oxidative stress through the lens of oxidative signalling rather than damage”, Biochemical Journal, Vol. 474/6, Portland Press, England, https://doi.org/10.1042/BCJ20160814 

Ganea, E. and J. J. Harding (2006), “Glutathione-related enzymes and the eye”, Current eye research, Vol. 31/1, Informa, London, https://doi.org/10.1080/02713680500477347. 

Griendling, K. K. 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, Vol. 119/5, Lippincott Williams & Wilkins, Philadelphia, https://doi.org/10.1161/RES.0000000000000110

Guo, C. et al. (2013), “Oxidative stress, mitochondrial damage and neurodegenerative diseases”, Neural regeneration research, Vol. 8/21, Publishing House of Neural Regeneration Research, China, https://doi.org/10.3969/j.issn.1673-5374.2013.21.009

Hargreaves, M., and L. L. Spriet (2020), “Skeletal muscle energy metabolism during exercise.”, Nature Metabolism, Vol. 2, Nature Portfolio, London, https://doi.org/10.1038/s42255-020-0251-4

Hladik, D. and S. Tapio (2016), “Effects of ionizing radiation on the mammalian brain”, Mutation Research/Reviews in Mutation Research, Vol. 770, Elsevier, Amsterdam, https://doi.org/10.1016/j.mrrev.2016.08.003

Itoh, K., J. Mimura and M. Yamamoto (2010), “Discovery of the negative regulator of Nrf2, Keap1: a historical overview”, Antioxidants & Redox Signaling, Vol. 13/11, Mary Ann Leibert Inc., Larchmont, https://doi.org/10.1089/ars.2010.3222

Jackson, A.F. et al. (2014), “Case study on the utility of hepatic global gene expression profiling in the risk assessment of the carcinogen furan.”, Toxicology and Applied Pharmacology, Vol. 274/11, Elsevier, Amsterdam, https://doi.org/10.1016/j.taap.2013.10.019

Jacobsen, N.R. et al. (2008), “Genotoxicity, cytotoxicity, and reactive oxygen species induced by single-walled carbon nanotubes and C₆₀ fullerenes in the FE1-Muta^TM Mouse lung epithelial cells”, Environmental and Molecular Mutagenesis, Vol. 49/6, John Wiley & Sons, Inc., Hoboken, https://doi.org/10.1002/em.20406

Karimi, N. et al. (2017), “Radioprotective effect of hesperidin on reducing oxidative stress in the lens tissue of rats”, International Journal of Pharmaceutical Investigation, Vol. 7/3, Phcog Net, Bengaluru, https://doi.org/10.4103/jphi.JPHI_60_17.

Leung, D.T.H., and Chu, S. (2018), “Measurement of Oxidative Stress: Mitochondrial Function Using the Seahorse System” In: Murthi, P., Vaillancourt, C. (eds) Preeclampsia. Methods in Molecular Biology, vol 1710. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7498-6_22 

Lu, C., G. Song, and J. Lin (2006), “Reactive oxygen species and their chemiluminescence-detection methods”, TrAC Trends in Analytical Chemistry, Vol. 25/10, Elsevier, Amsterdam, https://doi.org/10.1016/j.trac.2006.07.007

Nguyen Dinh Cat, A. et al. (2013), “Angiotensin II, NADPH oxidase, and redox signaling in the vasculature”, Antioxidants & redox signaling, Vol. 19/10, Mary Ann Liebert, Larchmont, https://doi.org/10.1089/ars.2012.4641

Ping, Z. et al. (2020), “Oxidative Stress in Radiation-Induced Cardiotoxicity”, Oxidative Medicine and Cellular Longevity, Vol. 2020, Hindawi, https://doi.org/10.1155/2020/3579143

Powers, S.K. and M.J. Jackson. (2008), “Exercise-Induced Oxidative Stress: Cellular Mechanisms and Impact on Muscle Force Production”, Physiological Reviews, Vol. 88/4, American Physiological Society, Rockville, https://doi.org/10.1152/physrev.00031.2007

Raimondi, V., F. Ciccarese and V. Ciminale. (2020), “Oncogenic pathways and the electron transport chain: a dangeROS liason”, British Journal of Cancer, Vol. 122/2, Nature Portfolio, London, https://doi.org/10.1038/s41416-019-0651-y

Seen, S. and L. Tong. (2018), “Dry eye disease and oxidative stress”, Acta Ophthalmologica, Vol. 96/4, John Wiley & Sons, Inc., Hoboken, https://doi.org/10.1111/aos.13526

Ungvari, Z. et al. (2013), “Ionizing Radiation Promotes the Acquisition of a Senescence-Associated Secretory Phenotype and Impairs Angiogenic Capacity in Cerebromicrovascular Endothelial Cells: Role of Increased DNA Damage and Decreased DNA Repair Capacity in Microvascular Radiosensitivity”, The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, Vol. 68/12, Oxford University Press, Oxford, https://doi.org/10.1093/gerona/glt057.

 

Vargas-Mendoza, N. et al. (2021), “Oxidative Stress, Mitochondrial Function and Adaptation to Exercise: New Perspectives in Nutrition”, Life, Vol. 11/11, Multidisciplinary Digital Publishing Institute, Basel, https://doi.org/10.3390/life11111269

Wang, H. et al. (2019), “Radiation-induced heart disease: a review of classification, mechanism and prevention”, International Journal of Biological Sciences, Vol. 15/10, Ivyspring International Publisher, Sydney, https://doi.org/10.7150/ijbs.35460

Zhang, R. et al. (2009), “Blockade of AT1 receptor partially restores vasoreactivity, NOS expression, and superoxide levels in cerebral and carotid arteries of hindlimb unweighting rats”, Journal of applied physiology, Vol. 106/1, American Physiological Society, Rockville, https://doi.org/10.1152/japplphysiol.01278.2007.

Zhao, R. Z. et al. (2019), “Mitochondrial electron transport chain, ROS generation and uncoupling”, International journal of molecular medicine, Vol. 44/1, Spandidos Publishing Ltd., Athens, https://doi.org/10.3892/ijmm.2019.4188

Event: 2066: Altered Signaling Pathways

Short Name: Altered Signaling

AOPs Including This Key Event

Biological Context

Domain of Applicability

Taxonomic Applicability Life Stage Applicability Sex Applicability

Taxonomic applicability: Altered signaling is applicable to all animals as cell signaling occurs among animal cells. This includes vertebrates such as humans, mice and rats (Nair et al., 2019).

Life stage applicability: This key event is not life stage specific.

Sex applicability: This key event is not sex specific.

Evidence for perturbation by a stressor: Multiple studies show that signaling pathways can be disrupted by many types of stressors including ionizing radiation and altered gravity (Cheng et al., 2020; Coleman et al., 2021; Su et al., 2020; Yentrapalli et al., 2013).

Key Event Description

Cells receive, process, and transmit signals to respond to their environment via signaling pathways. Signaling pathways are groups of molecules that work together in a cell to control physiological and metabolic processes. Kinases, for example, are important signaling molecules that can phosphorylate other proteins (Svoboda & Reenstra, 2002). Initiation of signaling pathways is an important component of cellular homeostasis including normal cell development and the response to cellular damage from exposure to external stressors (Esbenshade & Duzic, 2006). Signaling pathways are themselves activated by signals and the same signal can lead to different responses depending on the tissue type (Hamada, et al. 2011; Svoboda & Reenstra, 2002). Examples of signals include the activation of receptors to activate transcriptional targets, induction of receptor-ligand interactions and the initiation of cell-cell contact, or cell-extracellular matrix contact (Hunter, 2000). Many signalling pathways are crucial to intercellular communication via membrane receptors that transduce signals into the cell, while others are activated in an intracellular manner (Svoboda & Reenstra, 2002). Altered signalling (i.e., increase/decrease) can lead to different physiological outcomes, meaning that the directionality of the signaling response, determines the end outcome. For example, increase of the PI3K/Akt/mTOR pathway, which under physiological conditions is responsible for regulating the cell cycle, can lead to increased proliferation and decreased apoptosis. However, a decrease expression of this pathway can lead to an increase in apoptosis and decreased proliferation (Porta et al., 2014; Venkatesulu et al., 2018).

How it is Measured or Detected

Method of Measurement	Reference	Description	OECD Approved Assay
Kinase assays	(Svoboda & Reenstra, 2002)	Block kinase with inhibitors to monitor the activity of a kinase of interest.	No
Cell behaviour assays	(Svoboda & Reenstra, 2002)	Signal transduction events of cells are monitored. Cells are exposed to varying levels of signaling proteins and the resulting actions of a cell are observed (changes in structure, cell shape, matrix binding etc.).	No
Ratiometric or single-wavelength dyes	(Svoboda & Reenstra, 2002)	Detects alterations in signal-transduction activities via monitoring changes in detectable wavelengths.	No
Fluorescence microscopy/spectroscopy	(Oksvold et al., 2002)	Measures cell localization, protein interactions, signal propagation, amplification, and integration in the cell in real-time, or upon stimulation.	Yes
Green Fluorescent Protein (GFP)	(Zaccolo and Pozzan, 2000)	GFP assays act as fluorescent reporters but also as a marker of intracellular signalling events i.e. second messengers Ca2+ and cAMP, or for pH in different various cell compartments	No
Fluorescence Resonance Energy Transfer (FRET)	(Bunt and Wouters, 2017)	Assay helps illuminate the interactions between biological molecules	No
Fluorescence recovery after photobleaching (FRAP)	(Svoboda & Reenstra, 2002)	Determines mobility and diffusion of small molecules.	No
Immunoprecipitation	(Svoboda & Reenstra, 2002)	Involves isolating and concentrating a particular protein from mixed samples to detect changes in signalling molecule activity.	Chromatin immunoprecipitation approved for analyzing histone modifications
Immunohistochemistry	(Kurien et al., 2011; Svoboda & Reenstra, 2002)	Northern, western and southern blotting techniques can be used to visualize signal transduction events. For example, antibodies with recognition epitopes can be used to locate active configurations or phosphorylated proteins within a cell or cell lysate.	No
Reverse transcription-quantitative polymerase chain reaction (RT-qPCR)	(Veremeyko et al., 2012; Alwine et al, 1977)	Measures mRNA expression of the gene of interest.	No
Enzyme-linked immunosorbent assay (ELISA)	(Amsen et al., 2009; Engvall & Perlmann, 1972)	Plate-based assay technique using antibodies to detect presence of a protein in a liquid sample. Can be used to identify presence of a protein of interest especially in when in low concentrations	No

References

Alwine, J. C., D. J. Kemp and G. R. Stark (1977), “Method for detection of specific RNAs in agarose gels by transfer to diazobenzyloxymethyl-paper and hybridization with DNA probes”, Proceedings of the National Academy of Sciences of the United States of America, Vol. 74/12, United States National Academy of Sciences, Washington, D.C., https://doi.org/10.1073/pnas.74.12.5350

Amsen, D., de Visser, K. E., and Town, T. (2009), “Approaches to determine expression of inflammatory cytokines”, in Inflammation and Cancer, Humana Press, Totowa, https://doi.org/10.1007/978-1-59745-447-6_5

Bunt, G., and F. S. Wouters (2017), “FRET from single to multiplexed signaling events”, Biophysical reviews, Vol. 9, Springer, London, https://doi.org/10.1007/s12551-017-0252-z

Cheng, Y. P. et al. (2017), “Acid sphingomyelinase/ceramide regulates carotid intima-media thickness in simulated weightless rats”, Pflugers Archiv European Journal of Physiology, Vol. 469, Springer, New York, https://doi.org/10.1007/s00424-017-1969-z

Coleman, M. A. et al. (2015), “Low-dose radiation affects cardiac physiology: gene networks and molecular signaling in cardiomyocytes”,  American Journal of Physiology - Heart and Circulatory Physiology, Vol. 309/11, American Physiological Society, Rockville, https://doi.org/10.1152/ajpheart.00050.2015

Engvall, E., and P. Perlmann (1972), “Enzyme-Linked Immunosorbent Assay, Elisa”, The Journal of Immunology, Vol. 109/1, American Association of Immunologists, Minneapolis, pp. 129-135

Esbenshade, T. A., and E. Duzic (2006), “Overview of signal transduction”, Current Protocols in Pharmacology, Vol. 31/1, John Wiley & Sons, Ltd., Hoboken, https://doi.org/10.1002/0471141755.ph0201s31

Hamada, N. et al. (2011), “Signaling pathways underpinning the manifestations of ionizing radiation-induced bystander effects”, Current Molecular Pharmacology, Vol. 4/2, Bentham Science Publishers, Sharjah UAE, https://doi.org/10.2174/1874467211104020079

Hunter, T. (2000), “Signaling - 2000 and beyond”, Cell, Vol. 100/1, Cell Press, Cambridge, https://doi.org/10.1016/s0092-8674(00)81688-8

Kurien, B. T. et al. (2011), “An overview of Western blotting for determining antibody specificities for immunohistochemistry”, in Signal Transduction Immunohistochemistry Methods and Protocols, Springer, London, https://doi.org/10.1007/978-1-61779-024-9_3

Nair, A. et al. (2019), “Conceptual Evolution of Cell Signaling”, International journal of molecular sciences, Vol. 20/13, Multidisciplinary Digital Publishing Institute, Basel, https://doi.org/10.3390/ijms20133292

Oksvold, M. P. et al. (2002), “Fluorescent histochemical techniques for analysis of intracellular signaling”, The Journal of Histochemistry and Cytochemistry, Vol. 50/3, SAGE Publications, Thousand Oaks, https://doi.org/10.1177/002215540205000301

Porta, C., C. Paglino and A. Mosca (2014), “Targeting PI3K/Akt/mTOR Signaling in Cancer”, Frontiers in Oncology, Vol. 4, Frontiers Media SA, Lausanne, https://doi.org/10.3389/fonc.2014.00064

Su, Y. T. et al. (2020), “Acid sphingomyelinase/ceramide mediates structural remodeling of cerebral artery and small mesenteric artery in simulated weightless rats”, Life Sciences, Vol. 243, Elsevier, Amsterdam, https://doi.org/10.1016/j.lfs.2019.117253

Svoboda, K. K. and W. R. Reenstra (2002), “Approaches to studying cellular signaling: a primer for morphologists”, The Anatomical record, Vol. 269/2, John Wiley & Sons, Ltd., Hoboken, https://doi.org/10.1002/ar.10074

Venkatesulu, B. P. et al. (2018), “Radiation-Induced Endothelial Vascular Injury: A Review of Possible Mechanisms”, JACC: Basic to Translational Science, Vol. 3/4, Elsevier, Amsterdam, https://doi.org/10.1016/j.jacbts.2018.01.014

Veremeyko, T. et al. (2012), “Detection of microRNAs in microglia by real-time PCR in normal CNS and during neuroinflammation”, Journal of Visualized Experiments: JoVE, Vol. 65, MyJove Corporation, Cambridge, https://doi.org/10.3791/4097

Yentrapalli, R. et al. (2013), “The PI3K/Akt/mTOR pathway is implicated in the premature senescence of primary human endothelial cells exposed to chronic radiation”, PloS one, Vol. 8/8, PLOS, San Francisco, https://doi.org/10.1371/journal.pone.0070024

Zaccolo, M. and T. Pozzan (2000), “Imaging signal transduction in living cells with GFP-based probes”, IUBMB life, Vol. 49/5, John Wiley & Sons, Ltd., Hoboken, https://doi.org/10.1080/152165400410218

Event: 1825: Increase, Cell death

Short Name: Increase, Cell death

AOPs Including This Key Event

Stressors

Biological Context

Cell term

Organ term

Evidence for Perturbation by Stressor

Food deprivation

Autophagy can be initiated by a variety of stressors, most notably by nutrient deprivation (caloric restriction) or can result from signals present during cellular differentiation and embryogenesis and on the surface of damaged organelles (Mizushima et al., 2008).

Gentamicin

Gentamicin causes significant inner ear sensory hair cell death and auditory dysfunction in zebrafish (Uribe et al., 2013).

Domain of Applicability

Taxonomic Applicability Life Stage Applicability Sex Applicability

The process of cell death is highly conserved within multi‐cellular organisms. (Lockshin & Zakeri, 2004).

 

Taxonomic applicability: Increased cell death is applicable to all animals. This includes vertebrates such as humans, mice and rats (Alberts et al., 2002).  

Life stage applicability: There is insufficient data on life stage applicability of this KE. 

Sex applicability: This key event is not sex specific (Forger and de Vries, 2010; Ortona Matarrese, and Malorni, 2014).  

Evidence for perturbation by a stressor: Multiple studies show that cell death can be increased or disrupted by many types of stressors including ionizing radiation and altered gravity (Zhu et al., 2016).  

Key Event Description

Cell death is part of normal development and maturation cycle, and is the component of many response patterns of living tissues to xenobiotic agents (i.e.. micro organisms and chemicals) and to endogenous modulations, such as inflammation and disturbed blood supply (Kanduc et al., 2002). Many physiological processes require cell death for their function (e.g.., embryonal development and immune selection of B and T cells) (Bertheloot et al., 2021). Defects in cells that result in their inappropriate survival or untimely death can negatively impact development or contribute to a variety of human pathologies, including cancer, AIDS, autoimmune disorders, and chronic infection. Cell death may also occur following exposure to environmental toxins or cytotoxic chemicals. Although this is often harmful, it can be beneficial in some cases, such as in the treatment of cancer (Crowley et al., 2016).

Cell death can be divided into: programmed cell death (cell death as a normal component of development) and non-programmed cell death (uncontrolled death of the cell). Although this simplistic view has blurred the intricate mechanisms separating these forms of cell death, studies have and will uncover new effectors, cell death pathways and reveal a more complex and intertwined landscape of processes involving cell death (Bertheloot et al., 2021).

Programmed cell death: is a form of cell death in which the dying cell plays an active part in its own demise (Cotter & Al-Rubeai, 1995).

Apoptosis At a morphological level, it is characterized by cell shrinkage rather than the swelling seen in necrotic cell death. It is characterized by a number of characteristic morphological changes in the structure of the cell, together with a number of enzyme‐dependent biochemical processes. The result of it being the clearance of cells from the body, with minimal damage to surrounding tissues. An essential feature of apoptosis is the release of cytochrome c from mitochondria, regulated by a balance between proapoptotic and antiapoptotic proteins of the BCL-2 family, initiator caspases (caspase-8, -9 and -10) and effector caspases (caspase-3, -6 and -7). Apoptosis culminates in the breakdown of the nuclear membrane by caspase-6, the cleavage of many intracellular proteins (e.g., PARP and lamin), membrane blebbing, and the breakdown of genomic DNA into nucleosomal structures (Bertheloot et al., 2021). Mechanistically, two main pathways contribute to the caspase activation cascade downstream of mitochondrial cytochrome c release:

• Intrinsic pathway is triggered by dysregulation of or imbalance in intracellular homeostasis by toxic agents or DNA damage. It is characterized by mitochondrial outer membrane permeabilization (MOMP), which results in the release of cytochrome c into the cytosol.
• Extrinsic pathway is initiated by activation of cell surface death receptors. Proapoptotic death receptors include TNFR1/2, Fas and the TNF-related apoptosis-inducing ligand (TRAIL) receptors DR4 and DR5.

Other pathways of programmed cell death are called »non-apoptotic programmed cell-death« or »caspase-independent programmed cell-death« (Blank & Shiloh, 2007).

Necroptosis: This type of regulated cell death, occurs following the activation of the tumor necrosis receptor (TNFR1) by TNFα. Activation of other cellular receptors triggers necroptosis. These receptors include death receptors (i.e., Fas/FasL), Toll-like receptors (TLR4 and TLR3) and cytosolic nucleic acid sensors such as RIG-I and STING, which induce type I interferon (IFN-I) and TNFα production and thus promote necroptosis in an autocrine feedback loop. Most of these pathways trigger NFκB- dependent proinflammatory and prosurvival signals.

Pyroptosis is a type of cell death culminating in the loss of plasma membrane integrity and induced by activation of so-called inflammasome sensors. These include the Nod-like receptor (NLR) family, the DNA receptor Absent in Melanoma 2 (AIM2) and the Pyrin receptor.

Autophagy: is a process where cellular components such as macro proteins or even whole organelles are sequestered into lysosomes for degradation (Mizushima et al., 2008; Shintani & Klionsky, 2004). The lysosomes are then able to digest these substrates, the components of which can either be recycled to create new cellular structures and/or organelles or alternatively can be further processed and used as a source of energy (D’Arcy, 2019).

Anoikis is apoptosis induced by loss of attachment to substrate or to other cells (anoikis). Anoikis overlaps with apoptosis in molecular terms, but is classified as a separate entity because of its specific form od induction (Blank & Shiloh, 2007). Induction of anoikis occurs when cells lose attachment to ECM, or adhere to an inappropriate type of ECM, the latter being the more relevant in vivo (Gilmore, 2005).

Cornification: is programmed cell death of keratinocytes. Cell death in the context of cornification involves distinct enzyme classes such as transglutaminases, proteases, DNases and others (Eckhart et al., 2013).

Non-programmed cell death: occurs accidentally in an unplanned manner.

Necrosis is generally characterized to be the uncontrolled death of the cell, usually following a severe insult, resulting in spillage of the contents of the cell into surrounding tissues and subsequent damage thereof (D’Arcy, 2019).

 

 

How it is Measured or Detected

Assays for Quantitating Cell Death:

• Cell death can be measured by staining a sample of cells with trypan blue, assay is described in protocol: Measuring Cell Death by Trypan Blue Uptake and Light Microscopy (Crowley, Marfell, Christensen, et al., 2015d). Or with propidium Iodide, assay is described in protocol: Measuring Cell Death by Propidium Iodide (PI) Uptake and Flow Cytometry (Crowley & Waterhouse, 2015a)
• TUNEL technique: in situ terminal deoxynucleotidyl transferase (TdT)-mediated dUTP nick-end labeling can be used to detect apoptotic cells (Bever & Fekete, 1999; Uribe et al., 2013).

Assays for Quantitating Cell Survival          

Colony-forming assay can be used to define the number of cells in a population that are capable of proliferating and forming large groups of cells. Described in Protocol: Measuring Survival of Adherent Cells with the Colony-Forming Assay (Crowley, Christensen, & Waterhouse, 2015c); Measuring Survival of Hematopoietic Cancer Cells with the Colony-Forming Assay in Soft Agar (Crowley & Waterhouse, 2015b).

ASSAYS TO DISTINGUISH APOPTOSIS FROM NECROSIS AND OTHER DEATH MODALITIES

Detecting Nuclear Condensation: The nucleus is generally round in healthy cells but fragmented in apoptotic cells. Dyes such as Giemsa or hematoxylin, which are purple in color and therefore easily viewed using light microscopy, are commonly used to stain the nucleus. Other features of apoptosis and necrosis, such as plasma membrane blebbing or rupture, can be identified by staining the cytoplasm with eosin. Eosin is pinkish in color and can also be viewed using light microscopy. Hematoxylin and eosin are, therefore, commonly used together to stain cells. Assay is described in Protocol: Morphological Analysis of Cell Death by Cytospinning Followed by Rapid Staining (Crowley, Marfell, & Waterhouse, 2015c); Analyzing Cell Death by Nuclear Staining with Hoechst 33342 (Crowley, Marfell, & Waterhouse, 2015a).

Detection of DNA Fragmentation: Apoptotic cells with fragmented DNA can be identified and distinguished from live cells by staining with Propidium Iodide (PI) and measuring DNA content by flow cytometry. This assay is described in Protocol: Measuring the DNA Content of Cells in Apoptosis and at Different Cell-Cycle Stages by Propidium Iodide Staining and Flow Cytometry (Crowley, Chojnowski, & Waterhouse, 2015a). TUNEL technique can also be used: in situ terminal deoxynucleotidyl transferase (TdT)-mediated dUTP nick-end labeling can be used to detect apoptotic cells (Bever & Fekete, 1999; Crowley, Marfell, & Waterhouse, 2015b; Uribe et al., 2013).

Detecting Phosphatidylserine Exposure: Apoptosis is also characterized by exposure of phosphatidylserine (PS) on the outside of apoptotic cells, which acts as a signal that triggers removal of the dying cell by phagocytosis. Annexin V, can selectively bind to PS to label apoptotic cells in which PS is exposed. Purified annexin V can be conjugated to various fluorochromes, which can then be visualized by fluorescence microscopy or detected by flow cytometry. This assay is described in protocol: Quantitation of Apoptosis and Necrosis by Annexin V Binding, Propidium Iodide Uptake, and Flow Cytometry (Crowley, Marfell, Scott, et al., 2015e).

Detecting Caspase Activity: antibodies that specifically recognize the cleaved fragments of caspases and their substrates can be used to specifically detect caspase activity in apoptotic cells by immunocytochemistry. Flow cytometry (using primary antibodies conjugated to fluorescent molecules, or by counter staining with fluorescently labeled antibodies against the primary antibody) can then be used to quantitate the number of apoptotic cells. This assay is described in protocol: Detecting Cleaved Caspase-3 in Apoptotic Cells by Flow Cytometry (Crowley & Waterhouse, 2015a).

Detecting Mitochondrial Damage: flow cytometry can be used to quantitate the number of cells that have reduced mitochondrial transmembrane potential, which is commonly associated with cytochrome c release during apoptosis. For this assay see protocol: Measuring Mitochondrial Transmembrane Potential by TMRE Staining (Crowley, Christensen, & Waterhouse, 2015b).

 

 

Listed below are common methods for detecting the KE, however there may be other comparable methods that are not listed. 

 

 Measures of apoptotic cytomorphological alterations: 

 Apoptotic cells exhibit electron dense nuclei, nuclear fragmentation, intact cell membrane up to the disintegration phase, disorganized cytoplasmic organelles, large clear vacuoles, blebs at cell surface, and apoptotic bodies, which can be visualized with various methods. (Elmore, 2007; Watanabe et al., 2002) 

Method of Measurement	Reference	Description	OECD Approved Assay
Transmission electron microscopy (TEM) / Scanning electron microscopy (SEM)/ Fluorescence microscopy	Martinez, Reif, and Pappas, 2010; Watanabe et al., 2002	TEM and SEM can image the cytomorphological alterations caused by apoptosis.	No
Stains:
Hematoxylin with eosin	Elmore, 2007	Hematoxylin stains nuclei blue and eosin stains the cytoplasm/extracellular matrix pink, allowing for the visualization of the cytomorphological alterations of cells.	No
Toluidine blue or methylene blue	Watanabe et al., 2002	Toluidine blue stains cellular nuclei, and identifies malignant tissue, which has an increased DNA content and a higher nuclear-to-cytoplasmic ratio.  Methylene blue stain applied to a healthy cell sample results in a colorless stain. This is due to the cell's enzymes, which reduce the methylene blue, thereby, reducing its color. Methylene blue stain applied to a dead cell sample turns blue.	No
DAPI	Crowley, Marfell, and Waterhouse, 2016	Binds strongly to adenine–thymine-rich regions in the DNA. DAPI can stain live and fixed cells. It passes less efficiently through the membrane in live cells.	Yes
Hoescht 33342	Crowley, Marfell, and Waterhouse, 2016	Binds to DNA in live and fixed cells, used to measure DNA condensation.	Yes
Acridine Orange (AO)	Watanabe et al., 2002	Interacts with DNA/RNA through intercalation/electrostatic interaction, is able to penetrate cell membranes. Stains live cells green and dead cells red.	No
Nile blue sulfate	Watanabe et al., 2002	Stains cell nuclei and lysosomes, indicating apoptotic bodies.	No
Neutral red	Watanabe et al., 2002	Measures lysosomal membrane integrity	No
LysoTracker Red	Watanabe et al., 2002	Measures phagolysosomal activity that occurs due to the engulfment of apoptotic bodies.	No

 

DNA damage/fragmentation assays: 

Assay	Reference	Description	OECD Approved Assay
Terminal deoxynucleotidyl transferase-mediated dUTP nick end-labeling (TUNEL) assay	Kressel and Groscurth, 1994	Apoptosis is detected with the TUNEL method to assay the endonuclease cleavage products by enzymatically end-labeling the DNA strand breaks.	Yes
Nicoletti Assay (SubG1 cell fragment measurement)	Nicoletti et al., 1991	Measures DNA content in nuclei at the pre-G1 phase of the cell cycle (apoptotic nuclei have less DNA than nuclei in healthy cells).	No
Cell Death Detection ELISA kit	Parajuli, 2014	Apoptotic nucleosomes are detected using the Cell Death Detection ELISA kit, which were calculated as absorbance subtraction at 405 nm and 490 nm.	No

 

Measurement of apoptotic markers through immunochemistry: 

Method of Measurement	Reference	Description	OECD Approved Assay
Western blot / immunofluorescence microscopy / immunohistochemistry	Elmore 2007; Martinez, Reif, and Pappas, 2010; Parajuli et al, 2014	Apoptosis can be detected with the expression of various apoptotic markers by western blotting using antibodies. Markers can include: cytosolic cytochrome-c; caspases 2, 3, 6, 7, 8, 9, 10; Bax; Bcl-2 (apoptosis inhibitor); BIRC2; BIRC3; GAPDH; PARP; CDK2; CDK4; cyclin D1; p53; p63; p73; cytokeratin-18	No

 

Measures of altered caspase activity: 

Method of Measurement	Reference	Description	OECD Approved Assay
Caspase-3 and caspase-9 activity is measured with the enzyme-catalyzed release of p-nitroanilide (pNA) and quantified at 405 nm	Wu, 2016	Visualizes caspase-3 and caspase-9 activity	No
PhiPhiLux Assay	Watanabe et al., 2002	The PhiPhiLux molecule becomes fluorescent once it is cleaved by caspase-3, indicating caspase activity.	No
Ferrocene reporter	Martinez, Reif, and Pappas, 2010	An electrochemical method to detect apoptosis. Ferrocene is attached to a peptide. The peptide sequence is a caspase 3 cleavage site and the ferrocene acts as the electrochemical reporter. The more caspase cleavage that occurs, the more ferrocene molecules are cleaved, the stronger the signal.	No
Self-assembled monolayers for matrix assisted laser desorption ionization time-of-flight mass spectrometry (SAMDI-MS) assay	Martinez, Reif, and Pappas, 2010	This assay detects caspase activity.	No

 

Measures of altered mitochondrial physiology: 

Method of Measurement	Reference	Description	OECD Approved Assay
Laser scanning confocal microscopy (LSCM)	Watanabe et al., 2002	LCSM can monitor many mitochondrial events following staining of cells, such as: mitochondrial permeability transition, depolarization of the inner mitochondrial membrane, which may be indicative of apoptosis.	No
Fluorescent, cationic, lipophilic mitochondrial dyes, such as: JC-1 dye, Rhodamine, DiOC6, Mitotracker red	Martinez, Reif, and Pappas, 2010; Sivandzade, Bhalerao, and Cucullo, 2019	These mitochondrial dyes can indicate disintegration of the mitochondrial outer membrane’s electrochemical gradient, as different fluorescence is observed between healthy and apoptotic cells. In healthy cells the dye accumulates in aggregates, but in apoptotic cells missing the electrochemical membrane, the dye will spread out into the cytoplasm providing different fluorescent signals.	No

 

Other measures: 

Method of measurement	Reference	Description	OECD Approved Assay
Apoptosis PCR microarray	Elmore, 2007	A method to profile the gene expression of many apoptotic-related genes, for example: ligands, receptors, intracellular modulators, and transcription factors.	No
Fluorescence correlation spectroscopy (FCS) or dual-colour fluorescence cross-correlation spectroscopy (dcFCCS)	Martinez, Reif, and Pappas, 2010	Used to measure protease activity.	No
Apoptosis is measured with Annexin V-FITC probes	Elmore, 2007; Wu et al., 2016	A measure of apoptotic membrane alterations. Annexin-V detects externalized phosphatidylserine residues, a result of apoptosis. Can be conducted in conjunction with propidium iodide (PI) staining. The relative percentage of Annexin V-FITC-positive/PI-negative cells is analyzed by flow cytometry.	Yes

References

Alberts, B. et al. (2002), “Programmed Cell Death (Apoptosis)”, in Molecular Biology of the Cell. 4th edition, Garland Science, New York, https://www.ncbi.nlm.nih.gov/books/NBK26873/  

Bertheloot, D., Latz, E., & Franklin, B. S. (2021). Necroptosis, pyroptosis and apoptosis: an intricate game of cell death. Cellular & Molecular Immunology, 18, 1106–1121. https://doi.org/10.1038/s41423-020-00630-3

Bever, M. M., & Fekete, D. M. (1999). Ventromedial focus of cell death is absent during development of Xenopus and zebrafish inner ears. Journal of Neurocytology, 28(10–11), 781–793. https://doi.org/10.1023/a:1007005702187

Blank, M., & Shiloh, Y. (2007). Cell Cycle Programs for Cell Death: Apoptosis is Only One Way to Go. Cell Cycle, 6(6), 686–695. https://doi.org/10.4161/cc.6.6.3990

Cotter, T. G., & Al-Rubeai, M. (1995). Cell death (apoptosis) in cell culture systems. Trends in Biotechnology, 13(4), 150–155. https://doi.org/10.1016/S0167-7799(00)88926-X

Crowley, L. C., Chojnowski, G., & Waterhouse, N. J. (2015a). Measuring the DNA content of cells in apoptosis and at different cell-cycle stages by propidium iodide staining and flow cytometry. Cold Spring Harbor Protocols, 10, 905–910. https://doi.org/10.1101/pdb.prot087247

Crowley, L. C., Christensen, M. E., & Waterhouse, N. J. (2015b). Measuring mitochondrial transmembrane potential by TMRE staining. Cold Spring Harbor Protocols, 12, 1092–1096. https://doi.org/10.1101/pdb.prot087361

Crowley, L. C., Christensen, M. E., & Waterhouse, N. J. (2015c). Measuring survival of adherent cells with the Colony-forming assay. Cold Spring Harbor Protocols, 8, 721–724. https://doi.org/10.1101/pdb.prot087171

Crowley, L. C., Marfell, B. J., Christensen, M. E., & Waterhouse, N. J. (2015d). Measuring cell death by trypan blue uptake and light microscopy. Cold Spring Harbor Protocols, 7, 643–646. https://doi.org/10.1101/pdb.prot087155

Crowley, L. C., Marfell, B. J., Scott, A. P., Boughaba, J. A., Chojnowski, G., Christensen, M. E., & Waterhouse, N. J. (2016). Dead cert: Measuring cell death. Cold Spring Harbor Protocols, 2016(12), 1064–1072. https://doi.org/10.1101/pdb.top070318

Crowley, L. C., Marfell, B. J., Scott, A. P., & Waterhouse, N. J. (2015e). Quantitation of apoptosis and necrosis by annexin V binding, propidium iodide uptake, and flow cytometry. Cold Spring Harbor Protocols, 11, 953–957. https://doi.org/10.1101/pdb.prot087288

Crowley, L. C., Marfell, B. J., & Waterhouse, N. J. (2015a). Analyzing cell death by nuclear staining with Hoechst 33342. Cold Spring Harbor Protocols, 9, 778–781. https://doi.org/10.1101/pdb.prot087205

Crowley, L. C., Marfell, B. J., & Waterhouse, N. J. (2015b). Detection of DNA fragmentation in apoptotic cells by TUNEL. Cold Spring Harbor Protocols, 10, 900–905. https://doi.org/10.1101/pdb.prot087221

Crowley, L. C., Marfell, B. J., & Waterhouse, N. J. (2015c). Morphological analysis of cell death by cytospinning followed by rapid staining. Cold Spring Harbor Protocols, 9, 773–777. https://doi.org/10.1101/pdb.prot087197

Crowley, L. C., & Waterhouse, N. J. (2015a). Detecting cleaved caspase-3 in apoptotic cells by flow cytometry. Cold Spring Harbor Protocols, 11, 958–962. https://doi.org/10.1101/pdb.prot087312

Crowley, L. C., & Waterhouse, N. J. (2015b). Measuring survival of hematopoietic cancer cells with the Colony-forming assay in soft agar. Cold Spring Harbor Protocols, 8, 725. https://doi.org/10.1101/pdb.prot087189

D’Arcy, M. S. (2019). Cell death: a review of the major forms of apoptosis, necrosis and autophagy. Cell Biology International, 43(6), 582–592. https://doi.org/10.1002/cbin.11137

Eckhart, L., Lippens, S., Tschachler, E., & Declercq, W. (2013). Cell death by cornification. Biochimica et Biophysica Acta - Molecular Cell Research, 1833(12), 3471–3480. https://doi.org/10.1016/j.bbamcr.2013.06.010

Elmore, S. (2007), “Apoptosis: A Review of Programmed Cell Death”, Toxical Pathology, Vol. 35/4, SAGE, https://doi.org/10.1080/01926230701320337. 

Forger, N. G. and G. J. de Vries (2010), “Cell death and sexual differentiation of behavior: worms, flies, and mammals”, Current opinion in neurobiology, Vol. 20/6, Elsevier, Amsterdam, https://doi.org/10.1016/j.conb.2010.09.006  

Gilmore, A. P. (2005). Anoikis. Cell Death and Differentiation, 12, 1473–1477. https://doi.org/10.1038/sj.cdd.4401723

Kanduc, D., Mittelman, A., Serpico, R., Sinigaglia, E., Sinha, A. A., Natale, C., Santacroce, R., Di Corcia, M. G., Lucchese, A., Dini, L., Pani, P., Santacroce, S., Simone, S., Bucci, R., & Farber, E. (2002). Cell death: apoptosis versus necrosis (review). International Journal of Oncology, 21(1), 165–170. https://doi.org/10.3892/ijo.21.1.165

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Event: 2089: Altered Bone Cell Homeostasis

Short Name: Altered Bone Cell Homeostasis

AOPs Including This Key Event

Biological Context

Cell term

Domain of Applicability

Taxonomic Applicability Life Stage Applicability Sex Applicability

Taxonomic applicability: Altered bone cell homeostasis is applicable to all vertebrates such as humans, mice, and rats (Donaubauer et al., 2020; Smith, 2020).  

Life stage applicability: There is insufficient data on life stage applicability of this KE. 

Sex applicability: Osteoblast/osteoclastogenesis is sexually dimorphic and influenced by genetic factors (Lorenzo J. 2020; Zanotti et al., 2014; Steppe et al., 2022; Mun et al., 2021). 

Evidence for perturbation by a stressor: Multiple studies show that bone cell homeostasis can be disrupted by many types of stressors including ionizing radiation and altered gravity (Donaubauer et al., 2020; Smith, 2020). 

Key Event Description

Osteogenesis is the process by which new bone is formed through the balanced action of bone depositing osteoblasts and bone resorbing osteoclasts. Osteogenesis is regulated by the differentiation and activity of osteoblasts/clasts. Dysregulation of bone cell differentiation and functional activity leads to imbalanced osteogenesis and altered bone matrix (Smith, 2020).  

Osteoclast precursors are of hematopoietic origin and differentiated into mature, multi-nucleated osteoclasts based on external signals in the microenvironment, of which the cytokine macrophage colony stimulating factor (M-CSF, also known as CSF-1) and receptor activator of NF-κB ligand (RANKL, aka TNFSF11) are key components (Donaubauer et al., 2020; Smith, 2020). Osteoclasts bone resorbing activity is a result of enzymes expressed in cellular lysosomes that are involved in the degradation extracellular components, including tartrate-resistant acid phosphatase (TRAP), cathepsin K (CTSK), and matrix metalloproteinases (MMPs), among others. Cellular lysosomes are shuttled to the resorption lacunae, located under the ruffled osteoclast membrane, from which they begin degrading the bone matrix (Lacombe, Karsenty, and Ferron, 2013; Smith, 2020).  

Osteoblasts differentiate from precursors of mesenchymal origin through various differentiation pathways activated by growth factors and signaling proteins such as bone morphogenic protein 2 (BMP-2) and transforming growth factor B (TGF-ß), among others. Pre-osteoblasts migrate to the site of bone resorption, where they become fully functioning osteoblasts capable of depositing new bone matrix (Donaubauer et al., 2020). Osteoblasts will synthesize and secrete bone matrix, most importantly collagen, and participate in the mineralization of bone to regulate the balance of calcium and phosphate ions in bone. Key molecular components involved in bone formation are alkaline phosphatase (ALP), osteocalcin (OCN), and procollagen type I C- and N-terminal propeptides (PICP and PINP), among others (Chen, Deng, and Ling, 2012; Rowe et al., 2021). 

How it is Measured or Detected

Listed below are common methods for detecting the KE; however, there may be other comparable methods that are not listed.

Markers of Osteoblast differentiation and activity: 

Method(s) of Measurement	References	Description / Marker	OECD-Approved Assay
L-type Wako ALP J2 assay   Iso-ALP assay    Tandem-R Ostase assay   Alkphase-B assay	Abe et al., 2019      Calvo, Eyre, and Gundberg, 1996	These assays measure a mineralization protein produced by osteoblasts, Alkaline phosphatase (ALP).	No
Tandem-MP Ostase immunoassay	Broyles et al., 1998	This assay measures a mineralization protein produced by osteoblasts, bone-specific alkaline phosphatase (BAP)	No
Bovine assays:  Ostk-PR assay   NovoCalcin assay   Human assays:  OSCAtest osteocalcin assay   Intact osteocalcin assay   ELISA-OST-NAT assay   ELIS-OSTEO assay   Mid-Tact osteocalcin assay	Calvo, Eyre, and Gundberg, 1996	These assays measure a mineralization protein produced by osteoblasts, osteocalcin (OCN).	No
Procollagen PICP assay   Prolagen-C assay	Calvo, Eyre, and Gundberg, 1996	Type I collagen (COL1A1 gene) is the most common form of collagen found in bone. During osteoblastic collagen production and processing, procollagen type I N-terminal peptide (PINP) and procollagen I C-terminal (PICP) are generated and released into the blood stream.	No
Proliferation assay:   Bromodeoxyuridine (BrdU) labeling	Bodine and Komm, 2006	Measures cell proliferations.	No
Osteoblast numbers and surface	Willey et al., 2011	Osteoblast formation can be determined by comparing the number of osteoblasts before and after a stressor in cell culture and histological bone samples.	No
Alizarin red stain for calcium deposition	Huang et al., 2019	Alizarin red staining can be used to visualize calcified elements of the bone, the final step of osteoblastic bone formation and mineralization activity.	No

 

Markers of Osteoclast differentiation and activity:  

Method(s) of Measurement	References	Description / Marker	OECD-Approved Assay
BoneTRAP assay	Calvo, Eyre, and Gundberg, 1996    Wu et al., 2009	Measures tartrate-resistant acid phosphatase (TRAP), an osteoclast specific bone-resorbing molecule.	No
Pirijinorin ICTP via RIA2 antibody assay    ICTP assay   Crosslap assay  CTX assays	Abe et al., 2019 Calvo, Eyre, and Gundberg, 1996 Seibel, 2005	Measures C-terminal type I collagen telopeptide (ICTP or CTX), a product of bone collagen degradation.	No
Osteomark Ntx urine or serum ELISA assay NTX assays	Calvo, Eyre, and Gundberg, 1996 Seibel, 2005	Measures N-terminal type I collagen telopeptide (NTX), a product of bone collagen degradation.	No
Colorimetric assays   HPLC-UV   Hypronosticon assay	Calvo, Eyre, and Gundberg, 1996	Measures hydroxyproline, a product of bone collagen degradation.	No
HPLC   ELISA	Seibel, 2005	Measures hydroxylysine glycosides, products of bone collagen degradation.    Hydroxylysine glycosides include:   Galactosyl hydroxylysine (GHYL or GHL)   Glycosyl-galactosyl-hydroxylysine (GGHL)	No
Pyrilinks assay   Pyrilinks D assay   Total Dpy assay   Free Dpy assay	Seibel, 2005	Measures deoxypyridinoline (dpy), a product of bone collagen degradation.	No
Immunocytochemical assays for cathepsin K	Seibel, 2005	Measures cathepsin K, a collagen cleaving molecule.	No
Immunoassays for non-collagenous matrix proteins	Seibel, 2005	Non-collagenous matrix proteins, such as bone sialoprotein (BSP), osteonectin, osteopontin, and matrix gla protein (MGP) can be measured via immunoassays. Changes in the amount of non-collagenous matrix proteins before and after a stressor indicate alterations in bone formation.	No
Osteoclast numbers and surface	Willey et al., 2011	Osteoclast formation can be determined by comparing the number of osteoclasts before and after a stressor.	No

References

Abe, Y., et al. (2019), “Increase in Bone Metabolic Markers and Circulating Osteoblast-Lineage Cells after Orthognathic Surgery”, Scientific Reports, Vol. 9, Nature, https://doi.org/10.1038/s41598-019-56484-x.

Bodine, P. V. N., and B. S. Komm (2006), “Wnt Signaling and Osteoblastogenesis”, Reviews in Endocrine and Metabolic Disorders, Vol. 7, Nature, https://doi.org/10.1007/s11154-006-9002-4.  

Broyles, D. L., et al. (1998), “Analytical and Clinical Performance Characteristics of Tandem-MP Ostase, a New Immunoassay for Serum Bone Alkaline Phosphatase”, Clinical Chemistry, Vol. 44/10, Oxford University Press, Oxford, https://doi.org/10.1093/clinchem/44.10.2139.  

Calvo, M. S., D. R. Eyre, and C. M. Gundberg (1996), “Molecular Basis and Clinical Application of Biological Markers of Bone Turnover”, Endocrine Reviews, Vol. 17, Oxford University Press, Oxford, https://doi.org/10.1210/edrv-17-4-333 

Chen, G., C. Deng, and Y.-P. Ling (2012), “TGF-ß and BMP signaling in osteoblast differentiation and bone formation”, International Journal of Biological Sciences. Vol. 8/2, Ivyspring International Publisher, https://doi.org/10.7150/ijbs.2929  

Donaubauer, A., et al. (2020), “The Influence of Radiation on Bone and Bone cells – Differential Effects on Osteoclasts and Osteoblasts”, International Journal of Molecular Sciences, Vol. 21/17, MDPI, Basel, https://doi.org/10.3390/ijms21176377  

Huang, B. et al. (2019), “Amifostine Suppresses the Side Effects of Radiation on BMSCs by Promoting Cell Proliferation and Reducing ROS Production”, Stem cells international, Vol. 2019, Hindawi, https://doi.org/10.1155/2019/8749090 

Lacombe, J., G. Karsenty, and M. Ferron (2013), “Regulation of Lysosome Biogenesis and Functions in Osteoclasts”, Cell Cycle, Vol. 12/17, Informa, London, https://doi.org/10.4161/cc.25825 

Lorenzo J. (2020), “Sexual Dimorphism in Osteoclasts” Cells, 9(9), 2086. https://doi.org/10.3390/cells9092086 

Mun, S. H. et al., (2021) “Sexual Dimorphism in Differentiating Osteoclast Precursors Demonstrates Enhanced Inflammatory Pathway Activation in Female Cells” Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research, 36(6), 1104–1116. https://doi.org/10.1002/jbmr.4270 

Rowe, P., A. Koller, and S. Sharma (Updated January 2022), “Physiology, Bone Remodeling”, StatPearls Publishing, www.ncbi.nlm.nih.gov/books/NBK499863/  

Seibel, M. J. (2005), “Biochemical Markers of Bone Turnover: Part I: Biochemistry and Variability”, The Clinical Biochemist Reviews, Vol. 26/4, pp. 97–122.  

Smith, J.K. (2020), “Osteoclasts and Microgravity”, Life, Vol. 10/9, MDPI, Basel, https://doi.org/10.3390/life10090207  

Steppe, L. et al., (2022) "Bone Mass and Osteoblast Activity Are Sex-Dependent in Mice Lacking the Estrogen Receptor α in Chondrocytes and Osteoblast Progenitor Cells" International Journal of Molecular Sciences 23, no. 5: 2902. https://doi.org/10.3390/ijms23052902 

Willey, J. S. et al. (2011), "Space Radiation and Bone Loss", Gravitational and space biology bulletin, Vol. 25/1, pp. 14-21. 

Wu, Y., et al. (2009), “Tartrate-Resistant Acid Phosphatase (TRACP 5b): A Biomarker of Bone Resorption Rate in Support of Drug Development: Modification, Validation and Application of the BoneTRAP® Kit Assay”, Journal of Pharmaceutical and Biomedical Analysis, Vol. 49/5, Elsevier, Amsterdam, https://doi.org/10.1016/j.jpba.2009.03.002. 

Zanotti, S. et al., (2014) “Sex and genetic factors determine osteoblastic differentiation potential of murine bone marrow stromal cells” PloS one, 9(1), e86757. https://doi.org/10.1371/journal.pone.0086757

Event: 2090: Increase, Bone Remodeling

Short Name: Bone Remodeling

AOPs Including This Key Event

Biological Context

Domain of Applicability

Taxonomic Applicability Life Stage Applicability Sex Applicability

Taxonomic applicability: Bone remodeling is applicable to all vertebrates such as humans, mice and rats (Bikle and Halloran, 1999; Donaubauer et al., 2020).  

Life stage applicability: There is insufficient data on life stage applicability of this KE. 

Sex applicability: There is insufficient data on sex applicability of this KE. 

Evidence for perturbation by a stressor: Multiple studies show that bone remodeling can be disrupted by many types of stressors including ionizing radiation and altered gravity (Bikle and Halloran, 1999; Donaubauer et al., 2020). 

Key Event Description

Bone remodeling is a lifelong process where mature bone tissue is removed by bone resorbing osteoclasts and new bone is formed by bone forming osteoblasts. Each local remodeling event involves a team called the basic multicellular unit (BMU) (Slyfield et al., 2012). Each BMU consists of several morphologically and functionally different cell types, mainly osteoblasts and osteoclasts, that act in coordination on the bone remodeling compartment to replace old bone by new bone.

Physiological bone remodeling, responsible for repairing damaged bone and for mineral homeostasis, is a highly coordinated process that requires balance between bone resorption and bone formation (Raggatt and Partridge, 2010). This tight regulation is necessary to maintain skeletal size, shape, and structural integrity (Raggatt and Partridge, 2010). Mechanical strain or stimulation of bone cells by hormones activates bone remodeling and causes the recruitment of osteoclast precursors, like hematopoietic stem cells (HSCs), to the remodeling site to initiate resorption (Raggatt and Partridge, 2010). Osteocytes, mechanosensory cells that regulate bone homeostasis, basally produce transforming growth factor beta (TGF-β) which inhibits osteoclastogenesis. TGF-β levels are lowered following damage to the bone matrix through osteocyte apoptosis, removing this inhibitory signal (Raggatt and Partridge, 2010). Osteoblasts recruit osteoclast precursors to the remodeling site through the production of monocyte chemoattractant protein-1 (MCP-1). Osteoblasts can then induce osteoclastogenesis through the increased expression of colony stimulating factor 1 (CSF-1) and the receptor activator of nuclear factor kappa B ligand (RANK-L), as well as the decreased expression of osteoprotegerin (OPG), the inhibitor of RANK-L (Donaubauer et al., 2020; Raggatt and Partridge, 2010). Mature osteoclasts produce resorption pits also called resorption bays or Howship’s lacunae (Slyfield et al., 2012). Matrix metalloproteinases (MMPs) secreted by osteoblasts degrade the osteoid lining the bone surface, exposing the bone for osteoclast attachment. A resorption cavity is formed as mature osteoclasts degrade the matrix (Raggatt and Partridge, 2010; Slyfield et al., 2012). The acidic environment produced by osteoclasts dissolves the mineralized matrix, while enzymes like Cathepsin K (CTSK) degrade the organic matrix. Reversal cells then remove the undigested demineralized collagen matrix to prepare for bone formation by osteoblasts. TGF-β acts as the signal for the recruitment of osteoblast progenitor mesenchymal stem cells (MSCs). Osteocytes also basally secrete sclerostin, which inhibits the Wnt pathway for osteoblastogenesis. Mechanical strain and parathyroid hormone (PTH) signaling contribute to suppression of sclerostin and subsequent osteoblastogenesis (Raggatt and Partridge, 2010). Mature osteoblasts create the osteoid (unmineralized) matrix with collagen and subsequently mineralize new bone tissue with hydroxyapatite, involving various enzymes including alkaline phosphatase (ALP) (Donaubauer et al., 2020; Raggatt and Partridge, 2010). 

Disruption to this process results in an imbalance in bone remodeling. For example, increased resorption by osteoclasts and increased mineralization by osteoblasts will increase the rate of bone resorption and decrease the rate of bone formation. 

How it is Measured or Detected

Bone remodeling can be measured by the detection of biochemical markers of bone formation and bone resorption in blood serum, dynamic bone histomorphometry in bone biopsies, or via X-ray imaging techniques in vivo. Listed below are common methods for detecting the KE; however, there may be other comparable methods that are not listed.

Method of Measurement	References	Description	OECD Approved Assay
X-ray and imaging options:  Single-energy x-ray absorptiometry (S[E]XA)  Dual-energy x-ray absorptiometry (D[E]XA)  Single-photon absorptiometry (SPA)  Dual-photon absorptiometry (DPA)  Quantitative computed tomography (QCT)	Carter, Bouxsein and, Marcus, 1992    Cummings et al., 2002	Recurrent imaging of the same bone region in a specific time interval and subsequent overlay of these images, allows for the identification of bone remodeling units and state of bone remodeling.	No
Measurements of bone minerals in bodily fluids:  Calcium stable isotope tracers  Spectrophotometry  Ion-sensitive electrode techniques for ionized calcium	Smith et al., 2005	Measurement of inorganic skeletal matrix markers such as calcium, phosphorus which, above all, reflect calcium-phosphorus homeostasis and are indicators for the status of bone mineralization.	No
Dynamic bone histomorphometry (2D and 3D kinetic measurements) include:   Mineral apposition rate  MAR  Mineral formation rate  Mineralization lag time  Adjusted apposition rate  Osteoid apposition rate  Osteoid maturation time  Bone formation rate  Double-labeled formation events  Formation period  Bone resorption rate  Resorption period  Reversal period  Remodeling period  Quiescent period  Total period  Activation frequency  Structural modeling index (SMI)  Serial block imaging (also known as serial block-face scanning electron microscopy)	Dempster et al., 2013	Dynamic histomorphometry comprised the evaluation of bone mineralization from fluorochrome labeled samples. Thus, it is a quantitative measure of bone remodeling in addition to evaluation of bone structure over time. Dynamic histomorphometry can be performed in trabecular and cortical bone.	No
Trabeculae measurements:  Rod volume density (Ro.BV/TV)   Plate volume density (Pl.BV/TV) % rod volume fraction (Ro.BV/BV)   % plate volume fraction (Pl.BV/BV)   Rod volume (Ro.V)   Rod surface (Ro.S)   Rod thickness (Ro.Th)   Rod orientation (Ro.θ)   Rod slenderness (Ro.Sl)   Rod mean curvature (Ro.<H>)   Plate volume (Pl.V)    Plate surface (Pl.S)   Plate thickness (Pl.Th)   Plate mean curvature (Pl.<H>)	Stauber et al., 2006	Rods and plates forming the trabecular can indicate bone remodeling by altering the bone turnover states (bone formation and resorption) and microarchitecture (Compston, 2016).	No

References

Bikle, D. D., and B.P. Halloran (1999), “The response of bone to unloading”, Journal of Bone and Mineral Metabolism, Vol. 17/4, Springer Nature, https://doi.org/10.1007/s007740050090. 

Carter, D. R., M.L. Bouxsein and R. Marcus (1992), “New Approaches for Interpreting Projected Bone Densitometry Data”, Journal of Bone and Mineral Research, Vol. 7/2, Wiley, https://doi.org/10.1002/jbmr.5650070204. 

Compston, Juliet (2006), “Bone quality: what is it and how is it measured?”, Arquivos Brasileiros de Endocrinologia & Metabologia, Vol. 50/4, https://doi.org/10.1590/S0004-27302006000400003  

Cummings, S. R., D. Bates and D.M. Black (2002), “Clinical Use of Bone Densitometry: Scientific Review”, Journal of the Americal Medical Association, Vol. 288/15, JAMA Network, https://doi.org/10.1001/jama.288.15.1889. 

Dempster, D. W. et al.  (2013), “Standardized Nomenclature, Symbols, and Units for Bone Histomorphometry: A 2012 Update of the Report of the ASBMR Histomorphometry Nomenclature Committee”, Journal of Bone and Mineral Research, Vol. 28, Wiley, https://doi.org/10.1002/jbmr.1805. 

Donaubauer, A. J., et al. (2020), “The Influence of Radiation on Bone and Bone Cells-Differential Effects on Osteoclasts and Osteoblasts”, International journal of molecular sciences, Vol. 21/17, MDPI, Basel https://doi.org/10.3390/ijms21176377. 

Raggatt, L. J., and N.C. Partridge (2010), “Cellular and Molecular Mechanisms of Bone Remodeling”, Journal of Biological Chemistry, Vol. 285/33, Elsevier, Amsterdam, https://doi.org/10.1074/jbc.R109.041087 . 

Slyfield, C. R. et al. (2012), “Three-Dimensional Dynamic Bone Histomorphometry”, Journal of Bone and Mineral Research, Vol. 27/2, Wiley, https://doi.org/10.1002/jbmr.553 

Smith, S. M., et al. (2005), “Bone Markers, Calcium Metabolism, and Calcium Kinetics during Extended-Duration Space Flight on the Mir Space Station”, Journal of Bone and Mineral Research, Vol. 20/2, Wiley, https://doi.org/10.1359/JBMR.041105. 

Stauber et al. (2006), “Importance of Individual Rods and Plates in the Assessment of Bone Quality and Their Contribution to Bone Stiffness”, Journal of Bone and Mineral Research, Vol. 21/4, Wiley, https://doi.org/10.1359/jbmr.060102. 

Wang, Y. H. et al. (2006), “Examination of Mineralized Nodule Formation in Living Osteoblastic Cultures Using Fluorescent Dyes”, Biotechnology Progress, Vol. 22/6, Wiley, https://doi.org/10.1021/bp060274b 

List of Adverse Outcomes in this AOP

Event: 2091: Occurrence, Bone Loss

Short Name: Bone Loss

AOPs Including This Key Event

Biological Context

Domain of Applicability

Taxonomic Applicability Life Stage Applicability Sex Applicability

Taxonomic applicability: Bone loss is applicable to all vertebrates such as humans, mice and rats.   

Life stage applicability: There is insufficient data on life stage applicability of this KE.  

Sex applicability: According to a study of astronauts who spent 170 days living in the international space station, women demonstrated greater preservation of their musculoskeletal tissues during the mission compared to males, (33 men, 9 women) (Lang et al., 2017). However, other studies have indicated that the rates of regional and whole-body bone loss were similar in male and female astronauts (Lang et al., 2017).

Evidence for perturbation by a stressor: Multiple studies showed that many types of stressors including ionizing radiation and altered gravity (Bikle and Halloran, 1999; Donaubauer et al., 2020) can interfere with bone remodeling.  

Key Event Description

Bone loss describes the reduction in bone mass or density, which can be caused by various processes and is a characteristic of osteopenia, and osteoporosis, and can lead to bone fracture. An imbalance between bone resorption and formation towards higher bone abrasion contributes to bone loss (Bikle and Halloran, 1999). A decline of bone mineralization and bone density over time or a significant deviation from established reference ranges are direct indicators of bone loss (Cummings, Bates, and Black, 2002). In addition, bone loss can lead to increased risk of bone fractures as bone loss interferes with overall bone integrity and its capacity to withstand mechanical load (Cummings, Bates, and Black, 2002).

How it is Measured or Detected

Listed below are common methods for detecting the KE; however, there may be other comparable methods that are not listed 

Measurement method	Reference	Description	OECD Approved Assay
X-ray and imaging options:   Single energy x-ray absorptiometry (S[E]XA)   Dual-energy x-ray absorptiometry (D[E]XA)   Single-photon absorptiometry (SPA)   Dual-photon absorptiometry (DPA)   Quantitative computed tomography (QCT)   Radiographic absorptiometry   Ultrasound (quantitative bone ultrasonography)   Magnetic resonance imaging (MRI)	Carter, Bouxsein, and Marcus, 1992      Cummings, Bates, and Black, 2002      Russo, 2009      Rho, Ashman, and Turner, 1993	Bone mineral density (BMD) is a direct measurement of bone matrix composition. Less mineral dense bones indicate bone loss.	No
Measurement of bone minerals via staining methods:   Xylenol orange (bone formation marker)   Calcein green (bone formation marker)   Tetracycline (bone formation marker)   Von Kossa (calcium salt stain, non-specific)   Alizarin red (calcium cation stain)   All listed chemicals stain calcium.	Kulak and Dempster, 2010      Wang et al., 2006	Less bone deposition and/or reduced mineral dense bones indicate bone loss.    Comment: xylenol orange, calcein green, and tetracycline are calcium binding fluorescent dyes that are used to label new bone deposition.  Von Kossa method is based on the binding of silver ions to anions (phosphates, sulfates, or carbonates) of calcium salts and the reduction of silver salts to form dark brown or black metallic silver staining. Unlike the non-specificity of von Kossa for calcium, alizarin red reacts with calcium cation to form a chelate.	No
Static bone histomorphometry of an intact iliac crest bone biopsy (2D and 3D Structural measurements):   Marrow diameter  Marrow area  Marrow volume  Trabecular number, spacing, width, diameter, thickness   Cortical thickness, area, and porosity (bone-specific surface)  Cancellous bone volume   Mineralized volume, thickness   Osteoid surface, volume, thickness   Interstitial thickness   Bone volume fraction (BV/TV)  Wall width, thickness   Percent eroded surface  Serial block imaging (aka serial block-face scanning electron microscopy)	Dempster et al., 2013	Static bone histomorphometry with structural measurements is the quantitative measure of bone structure at a fixed time point.  Bone histomorphometry is most useful when interpreted in the context with other data such as structural analysis (CT, DEXA), serum markers of bone turnover etc.	No
Measurements of bone mechanical resistance:    Energy-absorbing bone capacity. Bones that cannot absorb as much energy after trauma are more likely to fracture.  Stress-strain curve. Measures the strain exhibited on a bone according to increasing applied stress until fracture.  Three-point bending test. Is a structural mechanical test where the entire bone is hold in a fixture attached to a material testing machine and the mid-diaphysis is loaded until broken. 3PB measures applied load and corresponding bone displacement indicating bone mechanical properties. Combination of 3PB and microCT data of the mid-diaphysis allows to calculate bone material properties.	Fonseca et al., 2013; Sharir, Barak, and Shahar, 2008; Walker et al., 2015; Turner, 2002	Measurements of bone mechanical resistance indicates changes in bone integrity possibly due to bone loss, as weaker bones are unable to withstand to as much mechanical force as healthy bones. Often measures Young’s modulus (E) which indicates the property of an object to stretch and deform and is defined as the ratio of applied stress to measured strain on an object.	No
Measurements of bone connectivity:   Euler’s characteristic   Betti numbers  Connectivity density (Conn. D)	Odgaard and Gundersen, 1993	These mathematical models allow for the 3D reconstruction of connectivity in cancellous bone. Bone loss, as seen as a decrease in strength and bone stiffness, can result from a decrease in connective bone tissue.	No

References

Bikle, D. D., & B. P. Halloran (1999), “The response of bone to unloading”, Journal of Bone and Mineral Metabolism, Vol. 17, Nature,  https://doi.org/10.1007/s007740050090 

Carter, D. R., M. L. Bouxsein, and R. Marcus. (1992), “New Approaches for Interpreting Projected Bone Densitometry Data”, Journal of Bone and Mineral Research, Vol. 7/2, Wiley, https://doi.org/10.1002/jbmr.5650070204.  

Cummings, S. R., D. Bates, and D. M. Black (2002), “Clinical Use of Bone Densitometry: Scientific Review”, Journal of the American Medical Association, Vol. 288/15, https://doi.org/10.1001/jama.288.15.1889.  

Dempster, D. W., et al. (2013), “Standardized Nomenclature, Symbols, and Units for Bone Histomorphometry: A 2012 Update of the Report of the ASBMR Histomorphometry Nomenclature Committee”, American Society for Bone and Mineral Research, Vol. 28/1, Wiley, https://doi.org/10.1002/jbmr.1805.  

Fonseca, H., et al. (2013), “Bone Quality: The Determinants of Bone Strength and Fragility”, Sports Medicine, Vol. 44/1, Nature, https://doi.org/10.1007/s40279-013-0100-7 

Kulak, C. A. M and D. W. Dempster (2010), “Bone histomorphometry: a concise review for endocrinologists and clinicians”, Arquivos Brasileiros de Endocrinologia & Metabologia, Vol. 54/2, Sociedade Brasileira de Endocrinologia e Metabologia, Sao Paulo, https://doi.org/10.1590/S0004-27302010000200002 

 Lang, T. et al. (2017), "Towards human exploration of space: The THESEUS review series on muscle and bone research priorities", npj Microgravity, Vol. 3/1, Nature, https://doi.org/10.1038/s41526-017-0013-0. 

Odgaard, A. and H. J. G. Gundersen (1993), “Quantification of connectivity in cancellous bone, with special emphasis on 3-D reconstructions”, Bone, Vol. 14, Elsevier, Amsterdam, https://doi.org/10.1016/8756-3282(93)90245-6 

Rho, J. Y., R. B. Ashman, and C. H. Turner (1993), “Young’s modulus of trabecular and cortical bone material: Ultrasonic and microtensile measurements”, Journal of Biomechanics, Vol. 26/2, Elsevier, Amsterdam, https://doi.org/10.1016/0021-9290(93)90042-D 

Russo, C. R. (2009), “The effects of exercise on bone. Basic concepts and implications for the prevention of fractures”, Clinical Cases Mineral and Bone Metabolism, Vol. 6/3, pp. 223-228. 

Sharir, A., M. M. Barak, and R. Shahar (2008), “Whole bone mechanics and mechanical testing”, The Veterinary Journal, Vol. 177/1, Elsevier, Amsterdam, https://doi.org/10.1016/j.tvjl.2007.09.012 

Turner, C. H. (2002), “Biomechanics of Bone: Determinants of Skeletal Fragility and Bone Quality”, Osteoporosis International, Vol. 13, Nature, https://doi.org/10.1007/s001980200000 

Walker, A. H. et al. (2015), “Changes in Mechanical Properties of Rat Bones under Simulated Effects of Microgravity and Radiation” Physics Procedia, Vol. 66, Elsevier, Amsterdam, https://doi.org/10.1016/j.phpro.2015.05.081 

Wang, Y. H., et al. (2006), “Examination of Mineralized Nodule Formation in Living Osteoblastic Cultures Using Fluorescent Dyes”, Biotechnology Progress, Vol. 22/6, Wiley, https://doi.org/10.1021/bp060274b 

Appendix 2

List of Key Event Relationships in the AOP