Increase, Reactive oxygen species

7440-61-1

JFALSRSLKYAFGM-UHFFFAOYSA-N

Uranium

Uranium, isotope of mass 238 238U Element UN 2979 (DOT) Uranium I

DTXSID1042522

7440-43-9

BDOSMKKIYDKNTQ-UHFFFAOYSA-N

Cadmium

Cadimium CADMIUM BLUE CADMIUM, IN PLATTEN, STANGEN, BROCKEN,KOERNER

DTXSID1023940

CHEBI:26523

CHEBI reactive oxygen species

GO:0005739

GO mitochondrion

CL:0000129

CL microglial cell

CL:0000127

CL astrocyte

GO:1903409

GO reactive oxygen species biosynthetic process

MP:0001847

MP brain inflammation

GO:0099536

GO synaptic signaling

WIKI increased

WIKI functional change

WIKI pathological

WIKI decreased

Uranium

2021-08-05T14:28:50

Nanoparticles and Micrometer Particles

2022-02-04T13:43:43

Cadmium

2017-10-25T08:33:12

SARS-CoV

2020-03-01T10:42:46

Sars-CoV-2 Virus from the coronaviridae family related to SARS-CoV, 229E, NL63, OC43, HKU1 and MERS.

Transmitted by aerosols

2021-02-23T04:50:40

2022-09-09T05:09:36

Chemical

2017-02-07T13:22:42

Virus

2018-05-29T07:10:01

bacteria

2021-02-23T05:15:41

WikiUser_28

Vertebrates

WCS_9606

common toxicological species human

WikiUser_25

Wikiuser: Cyauk human and other cells in culture

10090

NCBI mouse

WCS_35525

common ecological species crustaceans

WCS_4472

common ecological species Lemna minor

WCS_7955

common ecological species zebrafish

10116

NCBI rat

WCS_7227

common ecological species Drosophila melanogaster

6239

NCBI Caenorhabditis elegans

9541

NCBI Macaca fascicularis

WCS_9606

common toxicological species humans

10095

NCBI mice

9685

NCBI cat

Increase, Reactive oxygen species

Increase, ROS

Cellular

Biological State: increased reactive oxygen species (ROS) Biological compartment: an entire cell -- may be cytosolic, may also enter organelles. Reactive oxygen species (ROS) are O2- derived molecules that can be both free radicals (e.g. superoxide, hydroxyl, peroxyl, alcoxyl) and non-radicals (hypochlorous acid, ozone and singlet oxygen) (Bedard and Krause 2007; Ozcan and Ogun 2015). ROS production occurs naturally in all kinds of tissues inside various cellular compartments, such as mitochondria and peroxisomes (Drew and Leeuwenburgh 2002; Ozcan and Ogun 2015). Furthermore, these molecules have an important function in the regulation of several biological processes – they might act as antimicrobial agents or triggers of animal gamete activation and capacitation (Goud et al. 2008; Parrish 2010; Bisht et al. 2017).  However, in environmental stress situations (exposure to radiation, chemicals, high temperatures) these molecules have its levels drastically increased, and overly interact with macromolecules, namely nucleic acids, proteins, carbohydrates and lipids, causing cell and tissue damage (Brieger et al. 2012; Ozcan and Ogun 2015).  <div> Reactive oxygen species (ROS) refers to the chemical species superoxide, hydrogen peroxide, and their secondary reactive products. In the biological context, ROS are signaling molecules with important roles in cell energy metabolism, cell proliferation, and fate. Therefore, balancing ROS levels at the cellular and tissue level is an important part of many biological processes. Disbalance, mainly an increase in ROS levels, can cause cell dysfunction and irreversible cell damage. ROS are produced from both exogenous stressors and normal endogenous cellular processes, such as the mitochondrial electron transport chain (ETC). Inhibition of the ETC can result in the accumulation of ROS. Exposure to chemicals, heavy metal ions, or ionizing radiation can also result in increased production of ROS. Chemicals and heavy metal ions can deplete cellular antioxidants reducing the cell’s ability to control cellular ROS and resulting in the accumulation of ROS. Cellular antioxidants include glutathione (GSH), protein sulfhydryl groups, superoxide dismutase (SOD). ROS are radicals, ions, or molecules that have a single unpaired electron in their outermost shell of electrons, which can be categorized into two groups: free oxygen radicals and non-radical ROS [Liou et al., 2010]. <Free oxygen radicals> <div> <table cellspacing="0" class="MsoTableGrid" style="border-collapse:collapse; border:none"> <tbody> <tr> <td style="border-bottom:2px solid black; border-left:2px solid black; border-right:2px solid black; border-top:2px solid black; vertical-align:top; width:290px"> superoxide </td> <td style="border-bottom:2px solid black; border-left:none; border-right:2px solid black; border-top:2px solid black; vertical-align:top; width:290px"> O2·- </td> </tr> <tr> <td style="border-bottom:2px solid black; border-left:2px solid black; border-right:2px solid black; border-top:none; vertical-align:top; width:290px"> hydroxyl radical </td> <td style="border-bottom:2px solid black; border-left:none; border-right:2px solid black; border-top:none; vertical-align:top; width:290px"> ·OH </td> </tr> <tr> <td style="border-bottom:2px solid black; border-left:2px solid black; border-right:2px solid black; border-top:none; vertical-align:top; width:290px"> nitric oxide </td> <td style="border-bottom:2px solid black; border-left:none; border-right:2px solid black; border-top:none; vertical-align:top; width:290px"> NO· </td> </tr> <tr> <td style="border-bottom:2px solid black; border-left:2px solid black; border-right:2px solid black; border-top:none; vertical-align:top; width:290px"> organic radicals </td> <td style="border-bottom:2px solid black; border-left:none; border-right:2px solid black; border-top:none; vertical-align:top; width:290px"> R· </td> </tr> <tr> <td style="border-bottom:2px solid black; border-left:2px solid black; border-right:2px solid black; border-top:none; vertical-align:top; width:290px"> peroxyl radicals </td> <td style="border-bottom:2px solid black; border-left:none; border-right:2px solid black; border-top:none; vertical-align:top; width:290px"> ROO· </td> </tr> <tr> <td style="border-bottom:2px solid black; border-left:2px solid black; border-right:2px solid black; border-top:none; vertical-align:top; width:290px"> alkoxyl radicals </td> <td style="border-bottom:2px solid black; border-left:none; border-right:2px solid black; border-top:none; vertical-align:top; width:290px"> RO· </td> </tr> <tr> <td style="border-bottom:2px solid black; border-left:2px solid black; border-right:2px solid black; border-top:none; vertical-align:top; width:290px"> thiyl radicals </td> <td style="border-bottom:2px solid black; border-left:none; border-right:2px solid black; border-top:none; vertical-align:top; width:290px"> RS· </td> </tr> <tr> <td style="border-bottom:2px solid black; border-left:2px solid black; border-right:2px solid black; border-top:none; vertical-align:top; width:290px"> sulfonyl radicals </td> <td style="border-bottom:2px solid black; border-left:none; border-right:2px solid black; border-top:none; vertical-align:top; width:290px"> ROS· </td> </tr> <tr> <td style="border-bottom:2px solid black; border-left:2px solid black; border-right:2px solid black; border-top:none; vertical-align:top; width:290px"> thiyl peroxyl radicals </td> <td style="border-bottom:2px solid black; border-left:none; border-right:2px solid black; border-top:none; vertical-align:top; width:290px"> RSOO· </td> </tr> <tr> <td style="border-bottom:2px solid black; border-left:2px solid black; border-right:2px solid black; border-top:none; vertical-align:top; width:290px"> disulfides </td> <td style="border-bottom:2px solid black; border-left:none; border-right:2px solid black; border-top:none; vertical-align:top; width:290px"> RSSR </td> </tr> </tbody> </table> </div> <Non-radical ROS> <div> <table cellspacing="0" class="MsoTableGrid" style="border-collapse:collapse; border:none"> <tbody> <tr> <td style="border-bottom:2px solid black; border-left:2px solid black; border-right:2px solid black; border-top:2px solid black; vertical-align:top; width:290px"> hydrogen peroxide </td> <td style="border-bottom:2px solid black; border-left:none; border-right:2px solid black; border-top:2px solid black; vertical-align:top; width:290px"> H2O2 </td> </tr> <tr> <td style="border-bottom:2px solid black; border-left:2px solid black; border-right:2px solid black; border-top:none; vertical-align:top; width:290px"> singlet oxygen </td> <td style="border-bottom:2px solid black; border-left:none; border-right:2px solid black; border-top:none; vertical-align:top; width:290px"> 1O2 </td> </tr> <tr> <td style="border-bottom:2px solid black; border-left:2px solid black; border-right:2px solid black; border-top:none; vertical-align:top; width:290px"> ozone/trioxygen </td> <td style="border-bottom:2px solid black; border-left:none; border-right:2px solid black; border-top:none; vertical-align:top; width:290px"> O3 </td> </tr> <tr> <td style="border-bottom:2px solid black; border-left:2px solid black; border-right:2px solid black; border-top:none; vertical-align:top; width:290px"> organic hydroperoxides </td> <td style="border-bottom:2px solid black; border-left:none; border-right:2px solid black; border-top:none; vertical-align:top; width:290px"> ROOH </td> </tr> <tr> <td style="border-bottom:2px solid black; border-left:2px solid black; border-right:2px solid black; border-top:none; vertical-align:top; width:290px"> hypochlorite </td> <td style="border-bottom:2px solid black; border-left:none; border-right:2px solid black; border-top:none; vertical-align:top; width:290px"> ClO- </td> </tr> <tr> <td style="border-bottom:2px solid black; border-left:2px solid black; border-right:2px solid black; border-top:none; vertical-align:top; width:290px"> peroxynitrite </td> <td style="border-bottom:2px solid black; border-left:none; border-right:2px solid black; border-top:none; vertical-align:top; width:290px"> ONOO- </td> </tr> <tr> <td style="border-bottom:2px solid black; border-left:2px solid black; border-right:2px solid black; border-top:none; vertical-align:top; width:290px"> nitrosoperoxycarbonate anion </td> <td style="border-bottom:2px solid black; border-left:none; border-right:2px solid black; border-top:none; vertical-align:top; width:290px"> O=NOOCO2- </td> </tr> <tr> <td style="border-bottom:2px solid black; border-left:2px solid black; border-right:2px solid black; border-top:none; vertical-align:top; width:290px"> nitrocarbonate anion </td> <td style="border-bottom:2px solid black; border-left:none; border-right:2px solid black; border-top:none; vertical-align:top; width:290px"> O2NOCO2- </td> </tr> <tr> <td style="border-bottom:2px solid black; border-left:2px solid black; border-right:2px solid black; border-top:none; vertical-align:top; width:290px"> dinitrogen dioxide </td> <td style="border-bottom:2px solid black; border-left:none; border-right:2px solid black; border-top:none; vertical-align:top; width:290px"> N2O2 </td> </tr> <tr> <td style="border-bottom:2px solid black; border-left:2px solid black; border-right:2px solid black; border-top:none; vertical-align:top; width:290px"> nitronium </td> <td style="border-bottom:2px solid black; border-left:none; border-right:2px solid black; border-top:none; vertical-align:top; width:290px"> NO2+ </td> </tr> <tr> <td colspan="2" style="border-bottom:2px solid black; border-left:2px solid black; border-right:2px solid black; border-top:none; vertical-align:top; width:580px"> highly reactive lipid- or carbohydrate-derived carbonyl compounds </td> </tr> </tbody> </table> </div> Potential sources of ROS include NADPH oxidase, xanthine oxidase, mitochondria, nitric oxide synthase, cytochrome P450, lipoxygenase/cyclooxygenase, and monoamine oxidase [Granger et al., 2015]. ROS are generated through NADPH oxidases consisting of p47phox and p67phox. ROS are generated through xanthine oxidase activation in sepsis [Ramos et al., 2018]. Arsenic produces ROS [Zhang et al., 2011]. Mitochondria-targeted paraquat and metformin mediate ROS production [Chowdhury et al., 2020]. ROS are generated by bleomycin [Lu et al., 2010]. Radiation induces dose-dependent ROS production [Ji et al., 2019]. ROS are generated in the course of cellular respiration, metabolism, cell signaling, and inflammation [Dickinson and Chang 2011; Egea et al. 2017]. Hydrogen peroxide is also made by the endoplasmic reticulum in the course of protein folding. Nitric oxide (NO) is produced at the highest levels by nitric oxide synthase in endothelial cells and phagocytes. NO production is one of the main mechanisms by which phagocytes kill bacteria [Wang et al., 2017]. The other species are produced by reactions with superoxide or peroxide, or by other free radicals or enzymes. ROS activity is principally local. Most ROS have short half-lives, ranging from nano- to milliseconds, so diffusion is limited, while reactive nitrogen species (RNS) nitric oxide or peroxynitrite can survive long enough to diffuse across membranes [Calcerrada et al. 2011]. Consequently, local concentrations of ROS are much higher than average cellular concentrations, and signaling is typically controlled by colocalization with redox buffers [Dickinson and Chang 2011; Egea et al. 2017]. Although their existence is limited temporally and spatially, ROS interact with other ROS or with other nearby molecules to produce more ROS and participate in a feedback loop to amplify the ROS signal, which can increase RNS. Both ROS and RNS also move into neighboring cells, and ROS can increase intracellular ROS signaling in neighboring cells [Egea et al. 2017]. In the primary event, photoreactive chemicals are excited by the absorption of photon energy.  The energy of the photoactivated chemicals transfer to oxygen and then generates the reactive oxygen species (ROS), including superoxide (O2−) via type I reaction and singlet oxygen (1O2) via type II reaction, as principal intermediate species in phototoxic reaction (Foote, 1991, Onoue et al. , 2009). </div>

Photocolorimetric assays (Sharma et al. 2017; Griendling et al. 2016) or through commercial kits purchased from specialized companies. Yuan, Yan, et al., (2013) described ROS monitoring by using H2-DCF-DA, a redox-sensitive fluorescent dye. Briefly, the harvested cells were incubated with H2-DCF-DA (50 µmol/L final concentration) for 30 min in the dark at 37°C. After treatment, cells were immediately washed twice, re-suspended in PBS, and analyzed on a BD-FACS Aria flow cytometry. ROS generation was based on fluorescent intensity which was recorded by excitation at 504 nm and emission at 529 nm. Lipid peroxidation (LPO) can be measured as an indicator of oxidative stress damage Yen, Cheng Chien, et al., (2013). Chattopadhyay, Sukumar, et al. (2002) assayed the generation of free radicals within the cells and their extracellular release in the medium by addition of yellow NBT salt solution (Park et al., 1968). Extracellular release of ROS converted NBT to a purple colored formazan. The cells were incubated with 100 ml of 1 mg/ml NBT solution for 1 h at 37 °C and the product formed was assayed at 550 nm in an Anthos 2001 plate reader. The observations of the ‘cell-free system’ were confirmed by cytological examination of parallel set of explants stained with chromogenic reactions for NO and ROS. On the basis of the pathogenesis of drug-induced phototoxicity, a reactive oxygen species (ROS) assay was proposed to evaluate the phototoxic risk of chemicals. The ROS assay can monitor generation of ROS, such as singlet oxygen and superoxide, from photoirradiated chemicals, and the ROS data can be used to evaluate the photoreactivity of chemicals (Onoue et al. , 2014, Onoue et al. , 2013, Onoue and Tsuda, 2006).  The ROS assay is a recommended approach by guidelines to evaluate the phototoxic risk of chemicals (ICH, 2014, PCPC, 2014). <div> <Direct detection> Many fluorescent compounds can be used to detect ROS, some of which are specific, and others are less specific. ・ROS can be detected by fluorescent probes such as p-methoxy-phenol derivative [Ashoka et al., 2020]. ・Chemiluminescence analysis can detect the superoxide, where some probes have a wider range for detecting hydroxyl radical, hydrogen peroxide, and peroxynitrite [Fuloria et al., 2021]. ・ROS in the blood can be detected using superparamagnetic iron oxide nanoparticles (SPION)-based biosensor [Lee et al., 2020]. ・Hydrogen peroxide (H2O2) can be detected with a colorimetric probe, which reacts with H2O2 in a 1:1 stoichiometry to produce a bright pink colored product, followed by the detection with a standard colorimetric microplate reader with a filter in the 540-570 nm range. ・The levels of ROS can be quantified using multiple-step amperometry using a stainless steel counter electrode and non-leak Ag|AgCl reference node [Flaherty et al., 2017]. ・Singlet oxygen can be measured by monitoring the bleaching of p-nitrosodimethylaniline at 440 nm using a spectrophotometer with imidazole as a selective acceptor of singlet oxygen [Onoue et al., 2014]. <Indirect Detection> Alternative methods involve the detection of redox-dependent changes to cellular constituents such as proteins, DNA, lipids, or glutathione [Dickinson and Chang 2011; Wang et al. 2013; Griendling et al. 2016]. However, these methods cannot generally distinguish between the oxidative species behind the changes and cannot provide good resolution for the kinetics of oxidative activity. </div>

ROS is a normal constituent found in all organisms, lifestages, and sexes.

UBERON:0000062

UBERON organ

CL:0000000

CL cell

High Unspecific High Mixed

High

All life stages

High Moderate Moderate Moderate High High High

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2025-06-12T01:27:08

Increase, Mitochondrial dysfunction

Cellular

Mitochondrial dysfunction is a consequence of inhibition of the respiratory chain leading to oxidative stress. Mitochondria can be found in all cells and are considered the most important cellular consumers of oxygen. Furthermore, mitochondria possess numerous redox enzymes capable of transferring single electrons to oxygen, generating the superoxide (O2-). Some mitochondrial enzymes that are involved in reactive oxygen species (ROS) generation include the electron-transport chain (ETC) complexes I, II and III; pyruvate dehydrogenase (PDH) and glycerol-3-phosphate dehydrogenase (GPDH). The transfer of electrons to oxygen, generating superoxide, happens mainly when these redox carriers are charged enough with electrons and the potential energy for transfer is elevated, like in the case of high mitochondrial membrane potential. In contrast, ROS generation is decreased if there are not enough electrons and the potential energy for the transfer is not sufficient (reviewed in Lin and Beal, 2006). Cells are also able to detoxify the generated ROS due to an extensive antioxidant defence system that includes superoxide dismutases, glutathione peroxidases, catalase, thioredoxins, and peroxiredoxins in various cell organelles (reviewed in Lin and Beal, 2006). It is worth mentioning that, as in the case of ROS generation, antioxidant defences are also closely related to the redox and energetic status of mitochondria. If mitochondria are structurally and functionally healthy, an antioxidant defence mechanism balances ROS generation, and there is not much available ROS production. However, in case of mitochondrial damage, the antioxidant defence capacity drops and ROS generation takes over. Once this happens, a vicious cycle starts and ROS can further damage mitochondria, leading to more free-radical generation and further loss of antioxidant capacity. During mitochondrial dysfunction the availability of ATP also decreases, which is considered necessary for repair mechanisms after ROS generation. A number of proteins bound to the mitochondria or endoplasmic reticulum (ER), especially in the mitochondria-associated ER membrane (MAM), are playing an important role of communicators between these two organelles (reviewed Mei et al., 2013). ER stress induces mitochondrial dysfunction through regulation of Ca2+ signaling and ROS production (reviewed Mei et al., 2013). Prolonged ER stress leads to release of Ca2+ at the MAM and increased Ca2+ uptake into the mitochondrial matrix, which induces Ca2+-dependent mitochondrial outer membrane permeabilization and apoptosis. At the same, ROS are produced by proteins in the ER oxidoreductin 1 (ERO1) family. ER stress activates ERO1 and leads to excessive production of ROS, which, in turn, inactivates SERCA and activates inositol-1,4,5- trisphosphate receptors (IP3R) via oxidation, resulting in elevated levels of cytosolic Ca2+, increased mitochondrial uptake of Ca2+, and ultimately mitochondrial dysfunction. Just as ER stress can lead to mitochondrial dysfunction, mitochondrial dysfunction also induces ER Stress (reviewed Mei et al., 2013). For example, nitric oxide disrupts the mitochondrial respiratory chain and causes changes in mitochondrial Ca2+ flux which induce ER stress. Increased Ca2+ flux triggers loss of mitochondrial membrane potential (MMP), opening of mitochondrial permeability transition pore (mPTP), release of cytochrome c and apoptosis inducing factor (AIF), decreasing ATP synthesis and rendering the cells more vulnerable to both apoptosis and necrosis (Wang and Qin, 2010). Metal-induced Mitochondrial Dysfunction Mitochondria are an important site of Ca2+ regulation and storage, taking up Ca2+ ions electrophoretically from the cytosol through a Ca2+ uniporter, which can then accumulate in the mitochondria (Roos et al., 2012; Orrenius et al., 2015). Similarities between calcium and metals, such as cadmium and lead, makes the entrance and accumulation of these metals into the mitochondria via calcium metals possible by mode of molecular mimicry (Mathews et al., 2013; Adiele et al., 2012). The outer mitochondrial membrane also contains the divalent metal transporter (DMT1), which allows for mitochondrial uptake of divalent metals such as Fe and Mn. When cells are under heavy metal-induced stress, DMT has been shown to be overexpressed in the mitochondrial membrane, making the mitochondria targets of metal toxicity and accumulation. Heavy metal exposure in aerobic organisms increases ROS formation through redox cycling, where metals with different valence states (Fe, Cu, Cr, etc.) directly produce ROS as they are reduced by cellular antioxidants and then react with oxygen (Shaki et al., 2012; Shaki et al., 2013; Pourahmad et al., 2006; Santos et al., 2007). The production of highly reactive hydroxyl radicals under mitochondrial oxidative stress and in the presence of transition metals occurs via the Fenton reaction or Haber-Weiss reaction (Hancock et al., 2001; Valko et al., 2005; Adam-Vizi et al., 2010). Metals and ROS are capable of damaging mitochondrial DNA as well as mechanisms of DNA repair and proliferation arrest (Valko et al., 2005). Metals and ROS have the potential to directly damage mitochondrial membranes and structure by binding to and oxidizing membrane lipids and proteins. This structural damage can collapse the MMP and lead to the opening of the MPTP (Orrenius et al., 2015; Roos et al., 2012; Pourahmad et al., 2006). Uranium and mercury, for example, have both been shown to directly inhibit the mitochondrial electron transport chain and interfere with ATP production (Shaki et al., 2012; Roos et al., 2012). Furthermore, as previously mentioned, metals have been shown to inhibit ROS-detoxifying enzymes. By binding to these enzymes, metals can inhibit their antioxidant functions, and cause an accumulation of ROS and increased synthesis of more antioxidant enzymes in order to combat the oxidative stress (Blajszczak and Bonini, 2017). Summing up: Mitochondria play a pivotal role in cell survival and cell death because they are regulators of both energy metabolism and apoptotic/necrotic pathways (Fiskum, 2000; Wieloch, 2001; Friberg and Wieloch, 2002). The production of ATP via oxidative phosphorylation is a vital mitochondrial function (Kann and Kovács, 2007; Nunnari and Suomalainen, 2012). The ATP is continuously required for signalling processes (e.g. Ca2+ signalling), maintenance of ionic gradients across membranes, and biosynthetic processes (e.g. protein synthesis, heme synthesis or lipid and phospholipid metabolism) (Kang and Pervaiz, 2012), and (Green, 1998; McBride et al., 2006). Inhibition of mitochondrial respiration contributes to various cellular stress responses, such as deregulation of cellular Ca2+ homeostasis (Graier et al., 2007) and ROS production (Nunnari and Suomalainen, 2012; reviewed Mei et al., 2013).). It is well established in the existing literature that mitochondrial dysfunction may result in: (a) an increased ROS production and a decreased ATP level, (b) the loss of mitochondrial protein import and protein biosynthesis, (c) the reduced activities of enzymes of the mitochondrial respiratory chain and the Krebs cycle, (d) the loss of the mitochondrial membrane potential, (e) the loss of mitochondrial motility, causing a failure to re-localize to the sites with increased energy demands (f) the destruction of the mitochondrial network, and (g) increased mitochondrial Ca2+ uptake, causing Ca2+ overload (reviewed in Lin and Beal, 2006; Graier et al., 2007), (h) the rupture of the mitochondrial inner and outer membranes, leading to (i) the release of mitochondrial pro-death factors, including cytochrome c (Cyt. c), apoptosis-inducing factor, or endonuclease G (Braun, 2012; Martin, 2011; Correia et al., 2012; Cozzolino et al., 2013), which eventually leads to apoptotic, necrotic or autophagic cell death (Wang and Qin, 2010). Due to their structural and functional complexity, mitochondria present multiple targets for various compounds.

Mitochondrial dysfunction can be detected using isolated mitochondria, intact cells or cells in culture as well as in vivo studies. Such assessment can be performed with a large range of methods (revised by Brand and Nicholls, 2011) for which some important examples are given. All approaches to assess mitochondrial dysfunction fall into two main categories: the first assesses the consequences of a loss-of-function, i.e. impaired functioning of the respiratory chain and processes linked to it. Some assay to assess this have been described for KE1, with the limitation that they are not specific for complex I. In the context of overall mitochondrial dysfunction, the same assays provide useful information, when performed under slightly different assay conditions (e.g. without addition of complex III and IV inhibitors). The second approach assesses a ‘non-desirable gain-of-function’, i.e. processes that are usually only present to a very small degree in healthy cells, and that are triggered in a cell, in which mitochondria fail. I. Mitochondrial dysfunction assays assessing a loss-of function. 1. Cellular oxygen consumption. See KE1 for details of oxygen consumption assays. The oxygen consumption parameter can be combined with other endpoints to derive more specific information on the efficacy of mitochondrial function. One approach measures the ADP-to-O ratio (the number of ADP molecules phosphorylated per oxygen atom reduced (Hinkle, 1995 and Hafner et al., 1990). The related P/O ratio is calculated from the amount of ADP added, divided by the amount of O2 consumed while phosphorylating the added ADP (Ciapaite et al., 2005; Diepart et al., 2010; Hynes et al., 2006; James et al., 1995; von Heimburg et al., 2005). 2. Mitochondrial membrane potential (Δψm ). - Revision of AOP3 (Project: <a href="https://www.efsa.europa.eu/en/call/npefsaprev202402-development-aop-network-parkinsonian-motor-symptoms" rel="noreferrer noopener" target="_blank">NP/EFSA/PREV/2024/02</a>): The mitochondrial membrane potential (Δψm) is the electric potential difference across the inner mitochondrial membrane. It requires a functioning respiratory chain in the absence of mechanisms that dissipate the proton gradient without coupling it to ATP production. Quantitative assessment of ΔΨm in living cells is most commonly achieved through the use of cationic, lipophilic fluorescent probes that accumulate within the mitochondrial matrix in proportion to the electrochemical gradient (Leonard et al., 2014). Among these, tetramethylrhodamine derivatives such as TMRE (tetramethylrhodamine ethyl ester) and TMRM (tetramethylrhodamine methyl ester) are widely employed due to their reversible, potential-dependent distribution across the inner mitochondrial membrane (Scaduto and Grotyohann, 1999; Creed and McKenzie, 2019). When applied at non-quenching, nanomolar concentrations, these dyes allow linear and quantitative detection of ΔΨm, as fluorescence intensity directly correlates with mitochondrial polarization. Detection can be performed by flow cytometry for population-level quantification, by high-content microscopy for spatially resolved analysis, or by fluorescence plate readers for higher throughput (Wong and Cortopassi, 2002; Valdebenito and Dunchen, 2022). Quantitative interpretation requires the use of appropriate controls, typically involving treatment with protonophores such as FCCP or CCCP, which fully dissipate ΔΨm and thereby establish baseline fluorescence, and inhibitors such as oligomycin or antimycin A to reveal different components of mitochondrial respiration. In parallel, dyes such as JC-1 are also used, though their ratiometric readout is less sensitive at low potentials and more prone to artifacts compared with TMRE or TMRM (Leonard et al., 2022). For accurate normalization, measurements are often corrected for cell number, mitochondrial content, or total protein, and fluorescence changes are expressed relative to maximal depolarization. In addition to chemical probes, genetically encoded sensors, such as mitochondria-targeted fluorescent proteins fused to potential-sensitive domains, provide complementary tools for ΔΨm monitoring in live-cell and in vivo contexts (Leonard et al., 2022). - Not endorsed  3. Enzymatic activity of the electron transport system (ETS). Determination of ETS activity can be dene following Owens and King's assay (1975). The technique is based on a cell-free homogenate that is incubated with NADH to saturate the mitochondrial ETS and an artificial electron acceptor [l - (4 -iodophenyl) -3 - (4 -nitrophenyl) -5-phenylte trazolium chloride (INT)] to register the electron transmission rate. The oxygen consumption rate is calculated from the molar production rate of INT-formazan which is determined spectrophotometrically (Cammen et al., 1990). 4. ATP content. For the evaluation of ATP levels, various commercially-available ATP assay kits are offered  based on luciferin and luciferase activity. For isolated mitochondria various methods are available to continuously measure ATP with electrodes (Laudet 2005), with luminometric methods, or for obtaining more information on different nucleotide phosphate pools (e.g. Ciapaite et al., (2005). <div> - Revision of AOP3 (Project: <a href="https://www.efsa.europa.eu/en/call/npefsaprev202402-development-aop-network-parkinsonian-motor-symptoms">NP/EFSA/PREV/2024/02</a>): Determination of mitochondrial ATP production based on extracellular flux analysis   The method is based on the detection of OCR (Oxygen Consumption Rate) that represents mitochondrial respiration as well as on the detection of ECAR (extracellular acidification rate) / proton efflux rate (PER): reflects extracellular acidification, a proxy for glycolysis (lactate release) plus contributions from CO₂/HCO₃⁻. PER is preferred over raw ECAR since it corrects for CO₂-derived acidification (Desousa et al., 2023; Espinosa et al., 2022). Application of inhibitors of individual complexes of the respiratory chain allows the detection of ATP-linked OCR: portion of oxygen consumption directly driving ATP synthesis (lost after ATP synthase inhibition) (Yoo et al., 2024). The proton leak & non-mitochondrial OCR represents remaining oxygen consumption after ATP synthase and electron transport chain inhibitor addition. The difference yields the ATP-coupled respiration component.   Calculation of mitochondrial ATP production  Mito ATP production rate (pmol ATP/min) = OCRATP (pmol O2/min) × 2 × P/O    OCR_ATP: ATP-coupled portion of OCR.   Factor 2: each O₂ molecule contains two oxygen atoms.   P/O ratio: number of ATP molecules synthesized per oxygen atom reduced. A mean P/O ≈ 2.75 is typically assumed (validated across many cell types but substrate- and condition-dependent) (Plitzko and Loesgen, 2018; Mookerjee et al., 2017; Motawe et al., 2024).    Limitations   P/O ratio varies by substrate (glucose vs. fatty acids), cell type, and conditions. Fixed values are approximations.   Non-mitochondrial oxygen consumption (oxidases, peroxidases, etc.) can confound OCR, hence use of ETC inhibitors.   PER vs. ECAR: CO₂-driven acidification must be corrected to avoid overestimating glycolytic ATP.   Normalization: results are usually expressed per cell, protein content, DNA, or mitochondrial mass — interpretation depends on normalization method.  - Not endorsed </div> II. Mitochondrial dysfunction assays assessing a gain-of function. 1. Mitochondrial permeability transition pore opening (PTP). The opening of the PTP is associated with a permeabilization of mitochondrial membranes, so that different compounds and cellular constituents can change intracellular localization. This can be measured by assessment of the translocation of cytochrome c, adenylate kinase or AIF from mitochondria to the cytosol or nucleus. The translocation can be assessed biochemically in cell fractions, by imaging approaches in fixed cells or tissues or by life-cell imaging of GFP fusion proteins (Single 1998; Modjtahedi 2006). An alternative approach is to measure the accessibility of cobalt to the mitochondrial matrix in a calcein fluorescence quenching assay in live permeabilized cells (Petronilli et al., 1999). 2. mtDNA damage as a biomarker of mitochondrial dysfunction. Various quantitative polymerase chain reaction (QPCR)-based assays have been developed to detect changes of DNA structure and sequence in the mitochondrial genome. mtDNA damage can be detected in blood after low-level rotenone exposure, and the damage persists even after CI activity has returned to normal. With a more sustained rotenone exposure, mtDNA damage is also detected in skeletal muscle. These data support the idea that mtDNA damage in peripheral tissues in the rotenone model may provide a biomarker of past or ongoing mitochondrial toxin exposure (Sanders et al., 2014a and 2014b). 3. Generation of ROS and resultant oxidative stress. a. General approach. Electrons from the mitochondrial ETS may be transferred ‘erroneously’ to molecular oxygen to form superoxide anions. This type of side reaction can be strongly enhanced upon mitochondrial damage. As superoxide may form hydrogen peroxide, hydroxyl radicals or other reactive oxygen species, a large number of direct ROS assays and assays assessing the effects of ROS (indirect ROS assays) are available (Adam-Vizi, 2005; Fan and Li 2014). Direct assays are based on the chemical modification of fluorescent or luminescent reporters by ROS species. Indirect assays assess cellular metabolites, the concentration of which is changed in the presence of ROS (e.g. glutathione, malonaldehyde, isoprostanes,etc.) At the animal level the effects of oxidative stress are measured from biomarkers in the blood or urine. b. Measurement of the cellular glutathione (GSH) status. GSH is regenerated from its oxidized form (GSSH) by the action of an NADPH dependent reductase (GSSH + NADPH + H+ à 2 GSH + NADP+). The ratio of GSH/GSSG is therefore a good indicator for the cellular NADH+/NADPH ratio (i.e. the redox potential). GSH and GSSH levels can be determined by HPLC, capillary electrophoresis, or biochemically with DTNB (Ellman’s reagent). As excess GSSG is rapidly exported from most cells to maintain a constant GSH/GSSG ratio, a reduction of total glutathione (GSH/GSSG) is often a good surrogate measure for oxidative stress. c. Quantification of lipid peroxidation. Measurement of lipid peroxidation has historically relied on the detection of thiobarbituric acid (TBA)-reactive compounds such as malondialdehyde generated from the decomposition of cellular membrane lipid under oxidative stress (Pryor et al., 1976). This method is quite sensitive, but not highly specific. A number of commercial assay kits are available for this assay using absorbance or fluorescence detection technologies. The formation of F2-like prostanoid derivatives of arachidonic acid, termed F2-isoprostanes (IsoP) has been shown to be more specific for lipid peroxidation. A number of commercial ELISA kits have been developed for IsoPs, but interfering agents in samples requires partial purification before analysis. Alternatively, GC/MS may be used, as robust (specific) and sensitive method. d. Detection of superoxide production. Generation of superoxide by inhibition of complex I and the methods for its detection are described by Grivennikova and Vinogradov (2014). A range of different methods is also described by BioTek (<a class="external free" href="http://www.biotek.com/resources/articles/reactive-oxygen-species.html" rel="nofollow" target="_blank">http://www.biotek.com/resources/articles/reactive-oxygen-species.html</a>). The reduction of ferricytochrome c to ferrocytochrome c may be used to assess the rate of superoxide formation (McCord, 1968). Like in other superoxide assays, specificity can only be obtained by measurements in the absence and presence of superoxide dismutase. Chemiluminescent reactions have been used for their increased sensitivity. The most widely used chemiluminescent substrate is lucigenin. Coelenterazine has also been used as a chemiluminescent substrate. Hydrocyanine dyes are fluorogenic sensors for superoxide and hydroxyl radical, and they become membrane impermeable after oxidation (trapping at site of formation). The best characterized of these probes are Hydro-Cy3 and Hydro-Cy5. generation of superoxide in mitochondria can be visualized using fluorescence microscopy with MitoSOX™ Red reagent (Life Technologies). MitoSOX™ Red reagent is a cationic derivative of dihydroethidium that permeates live cells and accumulates in mitochondria. e. Detection of hydrogen peroxide (H2O2) production. There are a number of fluorogenic substrates, which serve as hydrogen donors that have been used in conjunction with horseradish peroxidase (HRP) enzyme to produce intensely fluorescent products in the presence of hydrogen peroxide (Zhou et al., 1997: Ruch et al., 1983). The more commonly used substrates include diacetyldichloro-fluorescein, homovanillic acid, and Amplex® Red. In these examples, increasing amounts of H2O2 form increasing amounts of fluorescent product (Tarpley et al., 2004). Summing up, mitochondrial dysfunction can be measured by: • ROS production: superoxide (O2-), and hydroxyl radicals (OH−) • Nitrosative radical formation such as ONOO− or directly by: • Loss of mitochondrial membrane potential (MMP) • Opening of mitochondrial permeability transition pores (mPTP) • ATP synthesis • Increase in mitochondrial Ca2+ • Cytochrome c release • AIF (apoptosis inducing factor) release from mitochondria • Mitochondrial Complexes enzyme activity • Measurements of mitochondrial oxygen consumption • Ultrastructure of mitochondria using electron microscope and mitochondrial fragmentation measured by labelling with DsRed-Mito expression (Knott et al, 2008) Mitochondrial dysfunction-induced oxidative stress can be measured by: • Reactive carbonyls formations (proteins oxidation) • Increased 8-oxo-dG immunoreactivity (DNA oxidation) • Lipid peroxidation (formation of malondialdehyde (MDA) and 4- hydroxynonenal (HNE) • 3-nitrotyrosine (3-NT) formation, marker of protein nitration • Translocation of Bid and Bax to mitochondria • Measurement of intracellular free calcium concentration ([Ca2+]i): Cells are loaded with 4 μM fura-2/AM). • Ratio between reduced and oxidized form of glutathione (GSH depletion) (Promega assay, TB369; Radkowsky et al., 1986) • Neuronal nitric oxide synthase (nNOS) activation that is Ca2+-dependent. All above measurements can be performed as the assays for each readout are well established in the existing literature (e.g. Bal-Price and Brown, 2000; Bal-Price et al., 2002; Fujikawa, 2015; Walker et al., 1995). See also KE <a href="/wiki/index.php/Event:209" title="Event:209"> Oxidative Stress, Increase</a> <table border="1" cellpadding="1" cellspacing="1"> <tbody> <tr> <td> Assay Type & Measured Content </td> <td>Description</td> <td>Dose Range Studied</td> <td> Assay Characteristics (Length/Ease of use/Accuracy) </td> </tr> <tr> <td> Rhodamine 123 Assay Measuring Mitochondrial membrane potential (MMP) and its collapse  (Shaki et al., 2012) </td> <td> Mitochondrial uptake of cationic fluorescent dye, rhodamine 123, is used for estimation of mitochondrial membrane potential. The fluorescence was monitored using Schimadzou RF-5000U fluorescence spectrophotometer at the excitation and emission wavelength of 490 nm and 535 nm, respectively. </td> <td>50, 100 and 500 μM of uranyl acetate</td> <td> Short / easy Medium accurancy </td> </tr> <tr> <td> TMRE fluorescence Assay Measuring Mitochondrial permeability transition pore (mPTP) opening (Huser et al., 1998) </td> <td>Laser scanning confocal microscopy in combination with the potentiometric fluorescence dye tetramethylrhodamine ethyl ester to monitor relative changes in membrane potential in single isolated cardiac mitochondria. The cationic dye distributes across the membrane in a voltage-dependent manner. Therefore, the large potential gradient across the inner mitochondrial membrane results in the accumulation of the fluorescent dye within the matrix compartment. Rapid depolarizations are caused by the opening of the transition pore.</td> <td>1 µM cyclosporin A</td> <td> Short / easy Low accurancy </td> </tr> <tr> <td> GSH / GSSG Determination Assay Measuring  cellular glutathione (GSH) status; ratio of GSH/GSSG (Owen & Butterfield, 2010; Shaki et al., 2013) </td> <td>GSH and GSSG levels are determinted biochemically with DTNB (Ellman’s reagent). The developed yellow color was read at 412 nm on a spectrophotometer.</td> <td>100 µM uranyl acetate</td> <td> Short / easy Low accurancy </td> </tr> <tr> <td> TBARS Assay Quantification of lipid peroxidation (Yuan et al., 2016) </td> <td>MDA content, a product of lipid peroxidation, was measured using a thiobarbituric acid reactive substances (TBARS) assay. Briefly, the kidney cells were collected in 1 ml PBS buffer solution (pH 7.4) and sonicated. MDA reacts with thiobarbituric acid forming a colored product which can be measured at an absorbance of 532 nm.</td> <td>200, 400, 800 µM uranyl acetate</td> <td> Medium / medium High accurancy </td> </tr> <tr> <td> Aequorin-based bioluminescence assay Increase in mitochondrial Ca2+ influx (Pozzan & Rudolf, 2009) </td> <td>Together with GFP, the aequorin moiety acts as Ca2+ sensor in vivo, which delivers emission energy to the GFP acceptor molecule in a BRET (Bioluminescence Resonance Energy Transfer) process; the Ca2+ can then be visualized with fluorescence microscopy.</td> <td> </td> <td> Short / easy Low accurancy </td> </tr> <tr> <td> Western blot & immunostaining analyses Measuring cytochrome c release (Chen et al., 2000)</td> <td>Examining the redistribution of Cyto c in cytosolic and mitochondrial cellular fractions. Cells are homogenized and centrifuged, then prepared for immunoblots. Cellular fractions were washed in PBS and lysed in 1% NP-40 buffer. Cellular proteins were separated by SDS–PAGE, transferred onto nitrocellulose membranes, probed using immunoblot analyses with antibodies specific to cyto c (6581A for Western and 65971A for immunostaining; Pharmingen)</td> <td> </td> <td> Short / easy Medium accurancy </td> </tr> <tr> <td> Quantikine Rat/Mouse Cytochrome c Immunoassay Measuring cytochrome c release (Shaki et al., 2012) </td> <td>Cytochrome C release was measured a monoclonal antibody specific for rat/mouse cytochrome c was precoated onto the microplate. Seventy-five microliter of conjugate (containing mono- clonal antibody specific for cytochrome c conjugated to horseradish peroxidase). After 2 h of incubation, the substrate solution (100 μl) was added to each well and incubated for 30 min. After 100 μl of the stop solution was added to each well; the optical density of each well was determined by the aforementioned microplate spectrophotometer set to 450 nm.</td> <td> </td> <td> Short / easy Low accurancy </td> </tr> <tr> <td> Membrane potential and cell viability – Flow Cytometry Measuring cytochrome c release (Kruidering et al., 1997) </td> <td>“Dc and viability were determined by analyzing the R123 and propidium iodide fluorescence intensity with a FACScan flow cytometer (Becton Dickinson, San Jose, CA) equipped with an argon laser, with the Lysis software program (Becton Dickinson). R123 is a cationic dye that accumulates in the negatively charged inner side of the mitochondria. When the potential drops, less R123 accumulates in the mitochondria, which results in a lower fluorescence signal. The potential was measured as follows: at the indicated times, a 500-ml sample of the cell suspension was taken and transferred to an Eppendorf minivial. To this sample, 100 ml of 6 mM R123 in buffer D was added. After incubation for 10 min at 37°C, the cell suspension was centrifuged for 5 min at 80 3 g. The cell pellet was resuspended in 200 ml of buffer D, containing 0.2 mM R123 and 10 mM propidium iodide, to prevent loss of R123 and to stain nonviable cells, respectively. The samples were transferred to FACScan tubes and analyzed immediately. Analysis was performed at a flow rate of 60 ml/min. R123 fluorescence was detected by the FL1 detector with an emission detection limit below 560 nm. Propidium iodide fluorescence was detected by the FL3 detector, with emission detection above 620 nm. Per sample 3,000 to 5,000 cells were counted (Van de Water et al., 1993)”</td> <td> </td> <td> Short / easy Medium accurancy </td> </tr> </tbody> </table>

Mitochondrial dysfunction is a universal event occurring in cells of any species (Farooqui and Farooqui, 2012). Many invertebrate species (drosophila, C, elegans) are considered as potential models to study mitochondrial function. New data on marine invertebrates, such as molluscs and crustaceans and non-Drosophila species, are emerging (Martinez-Cruz et al., 2012). Mitochondrial dysfunction can be measured in animal models used for toxicity testing (Winklhofer and Haass, 2010; Waerzeggers et al., 2010) as well as in humans (Winklhofer and Haass, 2010). - Revision of AOP3 (Project: <a href="https://www.efsa.europa.eu/en/call/npefsaprev202402-development-aop-network-parkinsonian-motor-symptoms" rel="noreferrer noopener" target="_blank">NP/EFSA/PREV/2024/02</a>): Endogenous ROS formation by complex I: In mammals, complex I is a dominant site of mitochondrial ROS, especially via RET. In plants (Senkler et al. 2017; Maldonado), mitochondria contain alternative NAD(P)H dehydrogenases and an alternative oxidase (AOX) that bypass Complex I and III These pathways reduce ROS formation by preventing over-reduction of the ETC. Complex I still produces ROS, but generally less damaging due to AOX. Yeast: S. cerevisiae lacks a canonical Complex I entirely, relying instead on alternative NADH dehydrogenases. Consequently, mitochondrial ROS production from a Complex I-like source is absent. Other fungi with true Complex I (e.g., Neurospora crassa) do generate ROS similar to animals. - Not endorsed

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Espinosa JA, Pohan G, Arkin MR, Markossian S. Real-Time Assessment of Mitochondrial Toxicity in HepG2 Cells Using the Seahorse Extracellular Flux Analyzer. Curr Protoc. 2021 Mar;1(3):e75. doi: 10.1002/cpz1.75. Erratum in: Curr Protoc. 2022 Aug;2(8):e551. doi: 10.1002/cpz1.551. PMID: 33735523.  Farooqui T. and . Farooqui, A. A (2012) Oxidative stress in Vertebrates and Invertebrate: molecular aspects of cell signalling. Wiley-Blackwell,Chapter 27, pp:377- 385. Fan LM, Li JM. Evaluation of methods of detecting cell reactive oxygen species production for drug screening and cell cycle studies. J Pharmacol Toxicol Methods. 2014 Jul-Aug;70(1):40-7. Fiskum G. Mitochondrial participation in ischemic and traumatic neural cell death. J Neurotrauma. 2000 Oct;17(10):843-55. Friberg H, Wieloch T. (2002). Mitochondrial permeability transition in acute neurodegeneration. Biochimie 84:241–250. Fujikawa DG, The Role of Excitotoxic Programmed Necrosis in Acute Brain Injury. 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Role of reactive oxygen species in cell signalling pathways. Biochemical Society Transactions, 29(Pt 2), 345-350. doi:10.1042/0300-5127:0290345 [doi] Hao, Y., Huang, J., Liu, C., Li, H., Liu, J., Zeng, Y., . . . Li, R. (2016). Differential protein expression in metallothionein protection from depleted uranium-induced nephrotoxicity. Scientific Reports, doi:10.1038/srep38942 Hao, Y., Ren, J., Liu, C., Li, H., Liu, J., Yang, Z., . . . Su, Y. (2014). Zinc protects human kidney cells from depleted uranium induced apoptosis. Basic & Clinical Pharmacology & Toxicology, 114, 271-280. doi:10.1111/bcpt.12167 Hinkle PC (1995) Measurement of ADP/O ratios. In Bioenergetics: A Practical Approach. Brown GC, Cooper CE, Eds. Oxford, U.K., IRL Press, p.5 –6. Huerta-García, E., Perez-Arizti, J. A., Marquez-Ramirez, S. G., Delgado-Buenrostro, N. L., Chirino, Y. I., Iglesias, G. G., & Lopez-Marure, R. (2014). 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(2013) Endoplasmic Reticulum Stress and Related Pathological Processes. J Pharmacol Biomed Anal.. 1:100-107. Miccadei, S., & Floridi, A. (1993). Sites of inhibition of mitochondrial electron transport by cadmium. Elsevier Scientific Publishers Ireland Ltd., 89, 159-167.Xu, X. M., & Møller, S. G. (2010). ROS removal by DJ-1: Arabidopsis as a new model to understand Parkinson's Disease. Plant signaling & behavior, 5(8), 1034–1036. doi:10.4161/psb.5.8.12298 Modjtahedi N, Giordanetto F, Madeo F, Kroemer G. Apoptosis-inducing factor: vital and lethal. Trends Cell Biol. 2006 May;16(5):264-72. Mookerjee SA, Gerencser AA, Nicholls DG, Brand MD. Quantifying intracellular rates of glycolytic and oxidative ATP production and consumption using extracellular flux measurements. J Biol Chem. 2017 Apr 28;292(17):7189-7207. doi: 10.1074/jbc.M116.774471. Epub 2017 Mar 7. Erratum in: J Biol Chem. 2018 Aug 10;293(32):12649-12652. doi: 10.1074/jbc.AAC118.004855. PMID: 28270511; PMCID: PMC5409486.  Motawe ZY, Abdelmaboud SS, Breslin JW. Evaluation of Glycolysis and Mitochondrial Function in Endothelial Cells Using the Seahorse Analyzer. Methods Mol Biol. 2024;2711:241-256. doi: 10.1007/978-1-0716-3429-5_20. PMID: 37776463; PMCID: PMC11368073. Nunnari J, Suomalainen A. (2012). Mitochondria: in sickness and in health. Cell 148:1145–1159. Hajnóczky G, Csordás G, Das S, Garcia-Perez C, Saotome M, Sinha Roy S, Yi M. (2006). Mitochondrial calcium signalling and cell death: approaches for assessing the role of mitochondrial Ca2+ uptake in apoptosis. Cell Calcium 40:553-560. Oliviert Martinez-Cruz, Arturo Sanchez-Paz, Fernando Garcia-Carreño, Laura Jimenez-Gutierrez, Ma. de los Angeles Navarrete del Toro and Adriana Muhlia-Almazan. 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Small, 5(8), 2067-2076. doi:10.1002/smll.200900466 Petronilli V, Miotto G, Canton M, Brini M, Colonna R, Bernardi P, Di Lisa F: Transient and long-lasting openings of the mitochondrial permeability transition pore can be monitored directly in intact cells by changes in mitochondrial calcein fluorescence. Biophys J 1999, 76:725-734. Plitzko B, Loesgen S. Measurement of Oxygen Consumption Rate (OCR) and Extracellular Acidification Rate (ECAR) in Culture Cells for Assessment of the Energy Metabolism. Bio Protoc. 2018 May 20;8(10):e2850. doi: 10.21769/BioProtoc.2850. PMID: 34285967; PMCID: PMC8275291.  Pourahmad, J., Ghashang, M., Ettehadi, H. A., & Ghalandari, R. (2006). A search for cellular and molecular mechanisms involved in depleted uranium (DU) toxicity. Environmental Toxicology, 21(4), 349-354. doi:10.1002/tox.20196 Pozzan, T., & Rudolf, R. (2009). 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2026-02-11T07:06:25

Neuroinflammation

Tissue

Neuroinflammation or brain inflammation differs from peripheral inflammation in that the vascular response and the role of peripheral bone marrow-derived cells are less conspicuous. The most easily detectable feature of neuroinflammation is activation of microglial cells and astrocytes. It is evidenced by changes in shape, increased expression of certain antigens, and accumulation and proliferation of the glial cells in affected regions (Aschner, 1998; Graeber & Streit, 1990; Monnet-Tschudi et al, 2007; Streit et al, 1999; Kraft and Harry, 2011; Claycomb et al., 2013). Upon stimulation by cytokines or inflammogens (e.g. from pathogens or from damaged neurons), both glial cell types activate inflammatory signalling pathways, which result in increased expression and/or release of inflammatory mediators such as cytokines, eicosanoids, and metalloproteinases (Dong & Benveniste, 2001), as well as in the production of reactive oxygen (ROS) and nitrogen species (RNS) (Brown & Bal-Price, 2003). Different types of activation states are possible for microglia and astrocytes, resulting in pro-inflammatory or anti-inflammatory signalling and other cellular functions (such as phagocytosis) (Streit et al., 1999; Nakajima and Kohsaka, 2004). Therefore, neuroinflammation can have both neuroprotective/neuroreparative and neurodegenerative consequences (Carson et al., 2006 ; Monnet-Tschudi et al, 2007; Aguzzi et al., 2013 ; Glass et al., 2010). Under normal physiological conditions, microglial cells scan the nervous system for neuronal integrity (Nimmerjahn et al, 2005) and for invading pathogens (Aloisi, 2001; Kreutzberg, 1995; Kreutzberg, 1996; Rivest, 2009). They are the first type of cell activated (first line of defence), and can subsequently induce astrocyte activation (Falsig, 2008). Two distinct states of microglial activation have been described (Gordon, 2003; Kigerl et al, 2009; Maresz et al, 2008; Mosser & Edwards, 2008; Perego et al; Ponomarev et al, 2005; Moehle and West, 2015): The M1 state is classically triggered by interferon-gamma and/or other pro-inflammatory cytokines, and this state is characterized by increased expression of integrin alpha M (Itgam) and CD86, as well as the release of pro-inflammatory cytokines (TNF-alpha, IL-1beta, IL-6), and it is mostly associated with neurodegeneration. The M2 state is triggered by IL-4 and IL-13 (Maresz et al, 2008; Perego et al, 2011; Ponomarev et al, 2007) and induces the expression of mannose receptor 1 (MRC1), arginase1 (Arg 1) and Ym1/2; it is involved in repair processes. The activation of astrocytes by microglia-derived cytokines or TLR agonists resembles the microglial M1 state (Falsig 2006). Although classification of the M1/M2 polarization of microglial cells may be considered as a simplification of authentic microglial reaction states (Ransohoff, 2016), a similar polarization of reactive astrocytes has been descibed recently Liddlelow et al., 2017): Interleukin-1 alpha (IL-1alpha), TNF and subcomponent q (C1q) released by activated microglial cells induce A1-reactive astrocytes, which lose the ability to promote neuronal survival, outgrowth, synaptogenesis and phagocytosis and induce the death of neurons and oligodendrocytes.   Neuroinflammation and Brain development During brain development, microglia are known to play a critical role as shapers of neural circuits, by providing trophic factors and by remodeling and pruning synapses (Rajendran and Paolicelli, 2018). In addition to playing a role in synaptic management, microglia are important for the pruning of dying neurons and in the clearance of debris (<a href="#_ENREF_43" title="Harry, 2013 #5042">Harry, 2013</a>). Microglia seem to affect also processes associated with neuronal proliferation and differentiation (Harry and Kraft, 2012). Similarly to microglia, astrocytes have instructive roles in neurogenesis, gliogenesis, angiogenesis, axonal outgrowth, synaptogenesis, and synaptic pruning (Reemst et al., 2016). The development-dependent reactivity of microglial cells and astrocytes is not well known. Ischemic insult appears to elicit similar microglial reactivity both during brain development and in adulthood (<a href="#_ENREF_3" title="Baburamani, 2014 #6737">Baburamani et al, 2014</a>; <a href="#_ENREF_54" title="Leonardo, 2009 #6879">Leonardo & Pennypacker, 2009</a>). In contrast, treatment with lead acetate was previously shown to result in a more pronounced microglial and astrocyte reactivity in immature 3D rat brain cell cultures as compared to mature ones (<a href="#_ENREF_101" title="Zurich, 2002 #3368">Zurich et al, 2002</a>). Astrocyte reactivity was also more pronounced in immature 3D rat brain cell cultures following paraquat exposure, whereas development-dependent differences in the phenotype of reactive microglia were observed (Sandström et al., 2017). This suggests that neuroinflammation is occurring during brain development and may express a different phenotype than in adulthood, and that dysfunction of microglia and astrocyte during brain development could contribute to neurodevelopmental disorders and potentially to late-onset neuropathology (Reemst et al., 2016).   Neuroinflammation in relation to COVID19 SARS-CoV-2 patients with moderate and severe COVID-19 presented an elevated plasma levels of glial fibrillary acidic protein (GFAP), which is known as biochemical indicator of glial activation (Kanberg et al., 2020).

Neuroinflammation, i.e. the activation of glial cells can be measured by quantification of cellular markers (most commonly), or of released mediators (less common). As multiple activation states exist for the two main cell types involved, it is necessary to measure several markers of neuroinflammation: <ul> <li>Microglial activation can be detected based on the increased numbers of labeled microglia per volume element of brain tissue (due to increase of binding sites, proliferation, and immigration of cells) or on morphological changes. A specific microglial marker, used across different species, is CD11b. Alternatively various specific carbohydrate structures can be stained by lectins (e.g. IB4). Beyond that, various well-established antibodies are available to detect microglia in mouse tissue (F4/80), phagocytic microglia in rat tissue (ED1) or more generally microglia across species (Iba1). Transgenic mice are available with fluorescent proteins under the control of the CD11b promoter to easily quantify microglia without the need for specific stains.</li> <li>The most frequently used astrocyte marker is GFAP (99% of all studies) (Eng et al., 2000). This protein is highly specific for astrocytes in the brain, and antibodies are available for immunocytochemical detection. In neuroinflammatory brain regions, the stain becomes more prominent, due to an upregulation of the protein, a shape change/proliferation of the cells, and/or better accessibility of the antibody. Various histological quantification approaches can be used. Occasionally, alternative astrocytic markers, such as vimentin of the S100beta protein, have been used for staining of astrocytes (Struzynska et al., 2007). Antibodies for complement component 3 (C3), the most characteristic and highly upregulated marker of A1 neurotoxic reactive astrocytes are commercially available.</li> <li>All immunocytochemical methods can also be applied to cell culture models.</li> <li>In patients, microglial accumulation can be monitored by PET imaging, using [11C]-PK 11195 as a microglial marker (Banati et al., 2002).</li> <li>Activation of glial cells can be assessed in tissue or cell culture models also by quantification of sets of activation markers. This can for instance be done by PCR quantification of inflammatory factors, by measurement of the respective mediators, e.g. by ELISA-related immuno-quantification. Such markers include:</li> <li>Pro- and anti-inflammatory cytokine expression (IL-1β; TNF-α, Il-6, IL-4); or expression of immunostimmulatory proteins (e.g. MHC-II)</li> <li>Itgam, CD86 expression as markers of M1 microglial phenotype</li> <li>Arg1, MRC1, as markers of M2 microglial phenotype</li> </ul> For descriptions of techniques, see Falsig 2004; Lund 2006 ; Kuegler 2010; Monnet-Tschudi et al., 2011; Sandström et al., 2014; von Tobel et al.,  2014   Regulatory example using the KE Measurement of glial fibrillary acidic protein (GFAP) in brain tissue, whose increase is a marker of astrocyte reactivity, is required by the US EPA in rodent toxicity studies for fuel additives (40 CFR 79.67). It has been used on rare occasions for other toxicant evaluations.

Neuroinflammation is observed in human, monkey, rat, mouse, and zebrafish, in association with neurodegeneration or following toxicant exposure, or SARS-CoV-2 and other coronavirus infection. Some references (non-exhaustive list) are given below for illustration: Human: Vennetti et al., 2006 Monkey (Macaca fascicularis): Charleston et al., 1994, 1996 Rat: Little et al., 2012; Zurich et al., 2002; Eskes et al., 2002 Mouse: Liu et al., 2012 Zebrafish: Xu et al., 2014.

UBERON:0000955

UBERON brain

High Mixed

High

During brain development, adulthood and aging

High High Moderate Low Moderate

Aguzzi, A., Barres, B.A., Bennett, M.L., 2013. Microglia: scapegoat, saboteur, or something else? Science 339(6116), 156-161. Aloisi, F., 2001. Immune function of microglia. Glia 36, 165-179. Aschner M (1998) Immune and inflammatory responses in the CNS: modulation by astrocytes. ToxicolLett 103: 283-287 Banati, R. B. (2002). "Visualising microglial activation in vivo." Glia 40: 206-217.    Baburamani AA, Supramaniam VG, Hagberg H, Mallard C (2014) Microglia toxicity in preterm brain injury. Reprod Toxicol 48: 106-112 Brown GC, Bal-Price A (2003) Inflammatory neurodegeneration mediated by nitric oxide, glutamate, and mitochondria. Mol Neurobiol 27: 325-355 Carson, M.J., Thrash, J.C., Walter, B., 2006. The cellular response in neuroinflammation: The role of leukocytes, microglia and astrocytes in neuronal death and survival. Clin Neurosci Res 6(5), 237-245. Charleston JS, Body RL, Bolender RP, Mottet NK, Vahter ME, Burbacher TM. 1996. Changes in the number of astrocytes and microglia in the thalamus of the monkey Macaca fascicularis following long-term subclinical methylmercury exposure. NeuroToxicology 17: 127-138. Charleston JS, Bolender RP, Mottet NK, Body RL, Vahter ME, Burbacher TM. 1994. Increases in the number of reactive glia in the visual cortex of Macaca fascicularis following subclinical long-term methyl mercury exposure. ToxicolApplPharmacol 129: 196-206. Claycomb, K.I., Johnson, K.M., Winokur, P.N., Sacino, A.V., Crocker, S.J., 2013. Astrocyte regulation of CNS inflammation and remyelination. Brain Sci 3(3), 1109-1127. Dong Y, Benveniste EN (2001) Immune Function of Astrocytes. Glia 36: 180-190 Eng LF, Ghirnikar RS, Lee YL (2000) Glial Fibrillary Acidic Protein: GFAP-Thirty-One Years (1969-2000). NeurochemRes 25: 1439-1451 Eskes C, Honegger P, Juillerat-Jeanneret L, Monnet-Tschudi F. 2002. Microglial reaction induced by noncytotoxic methylmercury treatment leads to neuroprotection via interactions with astrocytes and IL-6 release. Glia 37(1): 43-52. Falsig J, Latta M, Leist M. Defined inflammatory states in astrocyte cultures correlation with susceptibility towards CD95-driven apoptosis. J Neurochem. 2004  Jan;88(1):181-93. Falsig J, Pörzgen P, Lund S, Schrattenholz A, Leist M. The inflammatory transcriptome of reactive murine astrocytes and implications for their innate immune function. J Neurochem. 2006 Feb;96(3):893-907. Falsig J, van Beek J, Hermann C, Leist M. Molecular basis for detection of invading pathogens in the brain. J Neurosci Res. 2008 May 15;86(7):1434-47. Glass CK, Saijo K, Winner B, Marchetto MC, Gage FH (2010). Mechanisms underlying inflammation in neurodegeneration. Cell. 2010 Mar 19;140(6):918-34. Gordon S (2003) Alternative activation of macrophages. Nat Rev Immunol 3: 23-35 Graeber MB, Streit WJ (1990) Microglia: immune network in the CNS. Brain Pathol 1: 2-5 Harry GJ and Kraft AD (2012) Microglia in the developing brain: apotential target with lifetime effects. <a href="https://www.ncbi.nlm.nih.gov/pubmed/22322212" title="Neurotoxicology.">Neurotoxicology.</a> 33(2):191-206. Harry GJ (2013) Microglia during development and aging. Pharmacology & therapeutics 139: 313-326 Kanberg N, et al. Neurochemical evidence of astrocytic and neuronal injury commonly found in COVID-19. Neurology. 2020 Sep 22;95(12):e1754-e1759 Kigerl KA, Gensel JC, Ankeny DP, Alexander JK, Donnelly DJ, Popovich PG (2009) Identification of two distinct macrophage subsets with divergent effects causing either neurotoxicity or regeneration in the injured mouse spinal cord. J Neurosci 29: 13435-13444 Kraft AD, Harry GJ., Features of microglia and neuroinflammation relevant to environmental exposure and neurotoxicity. International Journal of Environmental research and Public Health., 2011, 8(7): 2980-3018. Kreutzberg GW (1995) Microglia, the first line of defence in brain pathologies. Arzneimttelforsch 45: 357-360 Kreutzberg GW (1996) Microglia : a sensor for pathological events in the CNS. Trends Neurosci 19: 312-318 Kuegler PB, Zimmer B, Waldmann T, Baudis B, Ilmjärv S, Hescheler J, Gaughwin P, Brundin P, Mundy W, Bal-Price AK, Schrattenholz A, Krause KH, van Thriel C, Rao MS, Kadereit S, Leist M. Markers of murine embryonic and neural stem cells, neurons and astrocytes: reference points for developmental neurotoxicity testing. ALTEX. 2010;27(1):17-42 Leonardo CC, Pennypacker KR (2009) Neuroinflammation and MMPs: potential therapeutic targets in neonatal hypoxic-ischemic injury. J Neuroinflammation 6: 13 Liddelow SA, Guttenplan KA, Clarke LE, Bennett FC, Bohlen CJ, Schirmer L, et al. 2017. Neurotoxic reactive astrocytes are induced by activated microglia. Nature 541(7638): 481-487. Little AR, Miller DB, Li S, Kashon ML, O'Callaghan JP. 2012. Trimethyltin-induced neurotoxicity: gene expression pathway analysis, q-RT-PCR and immunoblotting reveal early effects associated with hippocampal damage and gliosis. Neurotoxicol Teratol 34(1): 72-82. Liu Y, Hu J, Wu J, Zhu C, Hui Y, Han Y, et al. 2012. alpha7 nicotinic acetylcholine receptor-mediated neuroprotection against dopaminergic neuron loss in an MPTP mouse model via inhibition of astrocyte activation. J Neuroinflammation 9: 98. Lund S, Christensen KV, Hedtjärn M, Mortensen AL, Hagberg H, Falsig J, Hasseldam H, Schrattenholz A, Pörzgen P, Leist M. The dynamics of the LPS triggered inflammatory response of murine microglia under different culture and in vivo conditions. J Neuroimmunol. 2006 Nov;180(1-2):71-87. Maresz K, Ponomarev ED, Barteneva N, Tan Y, Mann MK, Dittel BN (2008) IL-13 induces the expression of the alternative activation marker Ym1 in a subset of testicular macrophages. J Reprod Immunol 78: 140-148 Moehle MS, West AB (2015) M1 and M2 immune activation in Parkinson's Disease: Foe and ally? Neuroscience 302:59-73 doi:10.1016/j.neuroscience.2014.11.018 Monnet-Tschudi F, Zurich MG, Honegger P (2007) Neurotoxicant-induced inflammatory response in three-dimensional brain cell cultures. Hum Exp Toxicol 26: 339-346 Monnet-Tschudi, F., A. Defaux, et al. (2011). "Methods to assess neuroinflammation." Curr Protoc Toxicol Chapter 12: Unit12 19.          Mosser DM, Edwards JP (2008) Exploring the full spectrum of macrophage activation. Nat Rev Immunol 8: 958-969 Nakajima K, Kohsaka S. 2004. Microglia: Neuroprotective and neurotrophic cells in the central nervous system. Current Drug Targets-Cardiovasc & Haematol Disorders 4: 65-84. Perego C, Fumagalli S, De Simoni MG (2011) Temporal pattern of expression and colocalization of microglia/macrophage phenotype markers following brain ischemic injury in mice. J Neuroinflammation 8: 174 Ponomarev ED, Maresz K, Tan Y, Dittel BN (2007) CNS-derived interleukin-4 is essential for the regulation of autoimmune inflammation and induces a state of alternative activation in microglial cells. J Neurosci 27: 10714-10721 Ponomarev ED, Shriver LP, Maresz K, Dittel BN (2005) Microglial cell activation and proliferation precedes the onset of CNS autoimmunity. J Neurosci Res 81: 374-389 <a href="https://www.ncbi.nlm.nih.gov/pubmed/?term=Rajendran%20L%5BAuthor%5D&cauthor=true&cauthor_uid=29563239">Rajendran L</a>1, <a href="https://www.ncbi.nlm.nih.gov/pubmed/?term=Paolicelli%20RC%5BAuthor%5D&cauthor=true&cauthor_uid=29563239">Paolicelli RC</a> (2018). Microglia-Mediated Synapse Loss in Alzheimer's Disease. <a href="https://www.ncbi.nlm.nih.gov/pubmed/29563239" title="The Journal of neuroscience : the official journal of the Society for Neuroscience.">J Neurosci.</a>  38:2911-2919. Ransohoff RM. 2016. A polarizing question: do M1 and M2 microglia exist? Nat Neurosci 19(8): 987-991. <a href="https://www.ncbi.nlm.nih.gov/pubmed/?term=Reemst%20K%5BAuthor%5D&cauthor=true&cauthor_uid=27877121">Reemst K</a>, <a href="https://www.ncbi.nlm.nih.gov/pubmed/?term=Noctor%20SC%5BAuthor%5D&cauthor=true&cauthor_uid=27877121">Noctor SC</a>, <a href="https://www.ncbi.nlm.nih.gov/pubmed/?term=Lucassen%20PJ%5BAuthor%5D&cauthor=true&cauthor_uid=27877121">Lucassen PJ</a>, <a href="https://www.ncbi.nlm.nih.gov/pubmed/?term=Hol%20EM%5BAuthor%5D&cauthor=true&cauthor_uid=27877121">Hol EM</a>. (2016) The Indispensable Roles of Microglia and Astrocytes during Brain Development. <a href="https://www.ncbi.nlm.nih.gov/pubmed/27877121" title="Frontiers in human neuroscience.">Front Hum Neurosci.</a>  10:566. DOI:10.3389/fnhum.2016.00566 Rivest, S., 2009. Regulation of innate immune responses in the brain. Nat Rev Immunol 9(6), 429-439. Sandstrom von Tobel, J., D. Zoia, et al. (2014). "Immediate and delayed effects of subchronic Paraquat exposure during an early differentiation stage in 3D-rat brain cell cultures." Toxicol Lett. DOI : 10.1016/j.toxlet.2014.02.001 Sandstrom J, Broyer A, Zoia D, et al. (2017a) Potential mechanisms of development-dependent adverse effects of the herbicide paraquat in 3D rat brain cell cultures. Neurotoxicology 60:116-124 doi:10.1016/j.neuro.2017.04.010 Sandstrom J, Eggermann E, Charvet I, et al. (2017b) Development and characterization of a human embryonic stem cell-derived 3D neural tissue model for neurotoxicity testing. Toxicol In Vitro 38:124-135 doi:10.1016/j.tiv.2016.10.001 Streit, W.J., Walter, S.A., Pennell, N.A., 1999. Reactive microgliosis. Progress in Neurobiol. 57, 563-581. Struzynska L, Dabrowska-Bouta B, Koza K, Sulkowski G (2007) Inflammation-Like Glial Response in Lead-Exposed Immature Rat Brain. Toxicol Sc 95:156-162 Venneti S, Lopresti BJ, Wiley CA. 2006. The peripheral benzodiazepine receptor (Translocator protein 18kDa) in microglia: from pathology to imaging. Prog Neurobiol 80(6): 308-322. von Tobel, J. S., P. Antinori, et al. (2014). "Repeated exposure to Ochratoxin A generates a neuroinflammatory response, characterized by neurodegenerative M1 microglial phenotype." Neurotoxicology 44C: 61-70. Xu DP, Zhang K, Zhang ZJ, Sun YW, Guo BJ, Wang YQ, et al. 2014. A novel tetramethylpyrazine bis-nitrone (TN-2) protects against 6-hydroxyldopamine-induced neurotoxicity via modulation of the NF-kappaB and the PKCalpha/PI3-K/Akt pathways. Neurochem Int 78: 76-85. Zurich M-G, Eskes C, Honegger P, Bérode M, Monnet-Tschudi F. 2002. Maturation-dependent neurotoxicity of lead aceate in vitro: Implication of glial reactions. J Neurosc Res 70: 108-116. AOPWiki 2016-11-29T18:41:23

2022-07-15T09:54:27

Decrease of neuronal network function

Neuronal network function, Decreased

Organ

Biological state: There are striking differences in neuronal network formation and function among the developing and mature brain. The developing brain shows a slow maturation and a transient passage from spontaneous, long-duration action potentials to synaptically-triggered, short-duration action potentials. Furthermore, at this precise developmental stage the neuronal network is characterised by "hyperexcitability”, which is related to the increased number of local circuit recurrent excitatory synapses and the lack of γ-amino-butyric acid A (GABAA)-mediated inhibitory function that appears much later. This “hyperexcitability” disappears with maturation when pairing of the pre- and postsynaptic partners occurs and synapses are formed generating population of postsynaptic potentials and population of spikes followed by developmental GABA switch. Glutamatergic neurotransmission is dominant at early stages of development and NMDA receptor-mediated synaptic currents are far more times longer than those in maturation, allowing more calcium to enter the neurons. The processes that are involved in increased calcium influx and the subsequent intracellular events seem to play a critical role in establishment of wiring of neural circuits and strengthening of synaptic connections during development (reviewed in Erecinska et al., 2004). Neurons that do not receive glutaminergic stimulation are undergoing developmental apoptosis. During the neonatal period, the brain is subject to profound alterations in neuronal circuitry due to high levels of synaptogenesis and gliogenesis. For example, in neuroendocrine regions such as the preoptic area-anterior hypothalamus (POA-AH), the site of gonadotropin-releasing hormone (GnRH) system is developmentally regulated by glutamatergic neurons. The changes in the expression of the N-methyl-D-aspartate (NMDA) receptor subunits NR1 and NR2B system begin early in postnatal development, before the onset of puberty, thereby playing a role in establishing the appropriate environment for the subsequent maturation of GnRH neurons (Adams et al., 1999). Biological compartments: Neural network formation and function happen in all brain regions but it appears to onset at different time points of development (reviewed in Erecinska et al., 2004). Glutamatergic neurotransmission in hippocampus is poorly developed at birth. Initially, NMDA receptors play important role but the vast majority of these premature glutamatergic synapses are “silent” possibly due to delayed development of hippocampal AMPA receptors. In contrast, in the cerebral cortex the maturation of excitatory glutamatergic neurotransmission happens much earlier. The “silent” synapses disappear by PND 7-8 in both brain regions mentioned above. There is strong evidence suggesting that NMDA receptor subunit composition controls synaptogenesis and synapse stabilization (Gambrill and Barria, 2011). It is established fact that during early postnatal development in the rat hippocampus, synaptogenesis occurs in parallel with a developmental switch in the subunit composition of NMDA receptors from NR2B to NR2A. It is suggested that early expression of NR2A in organotypic hippocampal slices reduces the number of synapses and the volume and dynamics of spines. In contrast, overexpression of NR2B does not affect the normal number and growth of synapses. However, it does increase spine motility, adding and retracting spines at a higher rate. The C terminus of NR2B, and specifically its ability to bind CaMKII, is sufficient to allow proper synapse formation and maturation. Conversely, the C terminus of NR2A was sufficient to stop the development of synapse number and spine growth. These results indicate that the ratio of synaptic NR2B over NR2A controls spine motility and synaptogenesis, and suggest a structural role for the intracellular C terminus of NR2 in recruiting the signalling and scaffolding molecules necessary for proper synaptogenesis. Interestingly, it was found that genetic deletion of NR3A accelerates glutamatergic synaptic transmission, as measured by AMPAR-mediated postsynaptic currents recorded in hippocampal CA1. Consistent, the deletion of NR3A accelerates the expression of the glutamate receptor subunits NR1, NR2A, and GluR1 sugesting that glutamatergic synapse maturation is critically dependent upon activation of NMDA-type glutamate receptors (Henson et al., 2012). General role in biology: The development of neuronal networks can be distinguished into two phases: an early ‘establishment’ phase of neuronal connections, where activity-dependent and independent mechanisms could operate, and a later ‘maintenance’ phase, which appears to be controlled by neuronal activity (Yuste and Sur, 1999). These neuronal networks facilitate information flow that is necessary to produce complex behaviors, including learning and memory (Mayford et al., 2012).

Methods that have been previously reviewed and approved by a recognized authority should be included in the Overview section above. All other methods, including those well established in the published literature, should be described here. Consider the following criteria when describing each method: 1. Is the assay fit for purpose? 2. Is the assay directly or indirectly (i.e. a surrogate) related to a key event relevant to the final adverse effect in question? 3. Is the assay repeatable? 4. Is the assay reproducible? In vivo: The recording of brain activity by using electroencephalography (EEG), electrocorticography (ECoG) and local field potentials (LFP) assists towards the collection of signals generated by multiple neuronal cell networks. Advances in computer technology have allowed quantification of the EEG and expansion of quantitative EEG (qEEG) analysis providing a sensitive tool for time-course studies of different compounds acting on neuronal networks' function (Binienda et al., 2011). The number of excitatory or inhibitory synapses can be functionally studied at an electrophysiological level by examining the contribution of glutamatergic and GABAergic synaptic inputs. The number of them can be determined by variably clamping the membrane potential and recording excitatory and inhibitory postsynaptic currents (EPSCs or IPSCs) (Liu, 2004). In vitro: Microelectrode array (MEA) recordings are also used to measure electrical activity in cultured neurons (Keefer et al., 2001, Gramowski et al., 2000; Gopal, 2003; Johnstone et al., 2010). MEAs can be applied in high throughput platforms to facilitate screening of numerous chemical compounds (McConnell et al., 2012). Using selective agonists and antagonists of different classes of receptors their response can be evaluated in a quantitative manner (Novellino et al., 2011; Hogberg et al., 2011). Patch clamping technique can also be used to measure neuronal network activity.In some cases, if required, planar patch clamping technique can also be used to measure neuronal networks activity (e.g., Bosca et al., 2014).

In vitro studies in brain slices applying electrophysiological techniques showed significant variability among species (immature rats, rabbits and kittens) related to synaptic latency, duration, amplitude and efficacy in spike initiation (reviewed in Erecinska et al., 2004).

UBERON:0000955

UBERON brain

High Mixed

High

During brain development

High High High High

Adams MM, Flagg RA, Gore AC., Perinatal changes in hypothalamic N-methyl-D-aspartate receptors and their relationship to gonadotropin-releasing hormone neurons. Endocrinology. 1999 May;140(5):2288-96. Binienda ZK, Beaudoin MA, Thorn BT, Ali SF. (2011) Analysis of electrical brain waves in neurotoxicology: γ-hydroxybutyrate. Curr Neuropharmacol. 9: 236-239. Bosca, A., M. Martina, and C. Py (2014) Planar patch clamp for neuronal networks--considerations and future perspectives. Methods Mol Biol, 2014. 1183: p. 93-113. Erecinska M, Cherian S, Silver IA. (2004) Energy metabolism in mammalian brain during development. Prog Neurobiol. 73: 397-445. Gambrill AC, Barria A. NMDA receptor subunit composition controls synaptogenesis and synapse stabilization. Proc Natl Acad Sci U S A. 2011:108(14):5855-60. Gopal K. (2003) Neurotoxic effects of mercury on auditory cortex networks growing on microelectrode arrays: a preliminary analysis. Neurotoxicol Teratol. 25: 69-76. Gramowski A, Schiffmann D, Gross GW. (2000) Quantification of acute neurotoxic effects of trimethyltin using neuronal networks cultures on microelectrode arrays. Neurotoxicology 21: 331-342. Henson MA, Larsen RS, Lawson SN, Pérez-Otaño I, Nakanishi N, Lipton SA, Philpot BD. (2012) Genetic deletion of NR3A accelerates glutamatergic synapse maturation. PLoS One. 7(8). Hogberg HT, Sobanski T, Novellino A, Whelan M, Weiss DG, Bal-Price AK. (2011) Application of micro-electrode arrays (MEAs) as an emerging technology for developmental neurotoxicity: evaluation of domoic acid-induced effects in primary cultures of rat cortical neurons. Neurotoxicology 32: 158-168. Johnstone AFM, Gross GW, Weiss D, Schroeder O, Shafer TJ. (2010) Use of microelectrode arrays for neurotoxicity testing in the 21st century Neurotoxicology 31: 331-350. Keefer E, Norton S, Boyle N, Talesa V, Gross G. (2001) Acute toxicity screening of novel AChE inhibitors using neuronal networks on microelectrode arrays. Neurotoxicology 22: 3-12. Liu G. (2004) Local structural balance and functional interaction of excitatory and inhibitory synapses in hippocampal dendrites. Nat Neurosci. 7: 373-379. Mayford M, Siegelbaum SA, Kandel ER. (2012) Synapses and memory storage. Cold Spring Harb Perspect Biol. 4. pii: a005751. McConnell ER, McClain MA, Ross J, LeFew WR, Shafer TJ. (2012) Evaluation of multi-well microelectrode arrays for neurotoxicity screening using a chemical training set. Neurotoxicology 33: 1048-1057. Novellino A, Scelfo B, Palosaari T, Price A, Sobanski T, Shafer TJ, Johnstone AF, Gross GW, Gramowski A, Schroeder O, Jügelt K, Chiappalone M, Benfenati F, Martinoia S, Tedesco MT, Defranchi E, D'Angelo P, Whelan M. (2011) Development of micro-electrode array based tests for neurotoxicity: assessment of interlaboratory reproducibility with neuroactive chemicals. Front Neuroeng. 4: 4. Yuste R, Peinado A, Katz LC. (1992) Neuronal domains in developing neocortex. Science 257: 665-669. AOPWiki 2016-11-29T18:41:24

2018-05-28T11:36:00

human amyotrophic lateral sclerosis (ALS)

ALS

Individual

AOPWiki 2026-03-30T20:30:18

2026-03-30T20:30:18

<upstream-id>e33b4fea-46a7-4dfd-89a3-06ba9010fc66</upstream-id> <downstream-id>e5347d99-dcb1-4c1e-98a4-36e5a039b4d9</downstream-id>

AOPWiki 2025-02-25T14:26:33

2025-02-25T14:26:33

<upstream-id>e5347d99-dcb1-4c1e-98a4-36e5a039b4d9</upstream-id> <downstream-id>aa5227fb-612a-4239-a1db-d1e01d6449f9</downstream-id>

AOPWiki 2026-04-02T01:40:26

2026-04-02T01:40:26

<upstream-id>aa5227fb-612a-4239-a1db-d1e01d6449f9</upstream-id> <downstream-id>35c91c18-8e5d-4950-86c2-2ff97bf0c766</downstream-id>

AOPWiki 2021-10-26T03:50:51

2021-10-26T03:50:51

<upstream-id>35c91c18-8e5d-4950-86c2-2ff97bf0c766</upstream-id> <downstream-id>3cf4bbfe-dbb6-45d4-ada4-622cc09cc583</downstream-id>

AOPWiki 2026-04-02T01:41:07

2026-04-02T01:41:07

Increase in reactive oxygen species (ROS) leading to human amyotrophic lateral sclerosis (ALS)

Increase in ROS leading to human ALS

Shihori Tanabe

Shihori Tanabe1, Satoru Morimoto2, Yoshitsugu Aoki3, Shinichi Takahashi2,4, Yoichi Saito2, Katarina Živančević5, Minoru Sakuragi6, Yasushi Okuno6, Hideyuki Okano2, Horacio Cabral7 1. National Institute of Health Sciences, Japan 2. Keio University Regenerative Medicine Research Center, Japan 3. National Center of Neurology and Psychiatry (NCNP), Japan 4. Saitama Medical University International Medical Center, Japan 5. University of Belgrade, Republic of Serbia 6. Kyoto University, Japan 7. The University of Tokyo, Japan   You Song

2.8

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by the impairment of motor neurons in the brain and spinal cord, leading to skeletal muscle atrophy and eventual death due to respiratory muscle paralysis. Currently, fundamental curative treatments remain unestablished. The objective of this study is to develop an Adverse Outcome Pathway (AOP) for ALS to elucidate its underlying pathological mechanisms. By analyzing molecular networks using RNA-seq data and synthesizing existing literature, we have proposed a draft AOP. This pathway delineates the progression from an initial rise in reactive oxygen species (ROS) and oxidative stress to neurodegeneration and motor neuron death, mediated by key events such as TDP-43 aggregation, mitochondrial dysfunction, neuroinflammation, and axonal transport failure. In developing a quantitative AOP (qAOP), it is essential to assess the quantifiability of each Key Event (KE) and Key Event Relationship (KER) to accurately model the network's functional relationships. Current AOP636 starts with MIE1115, an increase in ROS, links to KE177, an increase in mitochondrial dysfunction, KE188, neuroinflammation, and KE386, a decrease in neuronal network function, and leads to AO2416 human ALS. We need to develop a new KE2416 on ALS and KERs with a quantitative understanding.

adjacent

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High adjacent

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High adjacent

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High adjacent

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High

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

AOPWiki 2026-03-13T02:16:33

2026-05-19T02:06:35