Increased intracelluar Iron accumulation

60-35-5

DLFVBJFMPXGRIB-UHFFFAOYSA-N

Acetamide

Acetamid acetamida Acetic acid amide Acetimidic acid Ethanamide Ethanimidic acid Methanecarboxamide NSC 25945

DTXSID7020005

103-90-2

RZVAJINKPMORJF-UHFFFAOYSA-N

Acetaminophen

4-Acetamidophenol APAP Paracetamol 4-hydroxyacetanilide Acetamide, N-(4-hydroxyphenyl)- 4-(Acetylamino)phenol 4-(N-Acetylamino)phenol 4-Acetaminophenol 4'-Hydroxyacetanilide Abensanil Acetagesic Acetalgin ACETAMIDE, N-(4-HYDROXYPHENYL) Acetaminofen Acetanilide, 4'-hydroxy- ACETANILIDE, 4-HYDROXY- Algotropyl Alvedon Anaflon Apamide Banesin Ben-u-ron Bickie-mol Biocetamol Cetadol Citramon P Claratal Clixodyne Dafalgan Daphalgan Dial-a-gesic Disprol Doliprane Dolprone Dymadon Efferalgan Endophy Febrilex Febrilix Febro-Gesic Febrolin Fepanil Finimal Gattaphen T Gelocatil Gutte Enteric Homoolan Jin Gang Lestemp Liquagesic Lonarid Lyteca Syrup Minoset Momentum N-(4-Hydroxyphenyl)acetamide N-Acetyl-4-aminophenol N-Acetyl-4-hydroxyaniline N-Acetyl-p-aminophenol Napafen Naprinol Nobedon NSC 109028 NSC 3991 Ortensan p-(Acetylamino)phenol p-Aceaminophenol Pacemol p-Acetamidophenol p-Acetoaminophen P-ACETYLAMINOPHENOL Paldesic panadeine Panadol Panadol Actifast Panadol Extend Panaleve Panasorb Panodil Paracetamol DC Paracetamole Parageniol Paramol Paraspen Parelan Pasolind N Perfalgan Phenaphen Phendon p-Hydroxyacetanilide Prodafalgan Puerxitong Pyrinazine Resfenol Resprin Rhodapop NCR Salzone Tabalgin Tachipirina Tempanal Tralgon Tylenol TylolHot Valadol Valgesic Vermidon Vick Pyrena

DTXSID2020006

968-81-0

VGZSUPCWNCWDAN-UHFFFAOYSA-N

Acetohexamide

Benzenesulfonamide, 4-acetyl-N-[(cyclohexylamino)carbonyl]- 1-(p-Acetylbenzenesulfonyl)-3-cyclohexylurea 1-[(p-Acetylphenyl)sulfonyl]-3-cyclohexylurea Acetohexamid acetohexamida Dimelin Dimelor Dymelor Gamadiabet Hypoglicil Metaglucina Minoral N-(p-Acetylphenylsulfonyl)-N'-cyclohexylurea Ordimel Tsiklamid Urea, 1-[(p-acetylphenyl)sulfonyl]-3-cyclohexyl-

DTXSID7020007

67-66-3

HEDRZPFGACZZDS-UHFFFAOYSA-N

Chloroform

Trichloromethane Methane, trichloro- CARBON TRICHLORIDE Chloroforme cloroformo Formyl trichloride Methane trichloride Methane,trichloro- NSC 77361 Trichloroform UN 1888

DTXSID1020306

110-00-9

YLQBMQCUIZJEEH-UHFFFAOYSA-N

Furan

Divinylene oxide furanne Furfuran Oxacyclopentadiene Tetrole UN 2389

DTXSID6020646

7429-90-5

XAGFODPZIPBFFR-UHFFFAOYSA-N

AZDRQVAHHNSJOQ-UHFFFAOYSA-N

Aluminum

Aisin Metal Fiber Al 050P-H24 ALC Fine Alcan XI 1391 Almi-Paste SSP 303AR Aloxal 3010 Alpaste 00-0506 Alpaste 0100M Alpaste 0100MA Alpaste 0100M-C Alpaste 0200M Alpaste 0200T Alpaste 0230M Alpaste 0230T Alpaste 0241M Alpaste 0300M Alpaste 0500M Alpaste 0539X Alpaste 0620MS Alpaste 0625TS Alpaste 0638-70C Alpaste 0700M Alpaste 0780M Alpaste 0900M Alpaste 100M Alpaste 100MS Alpaste 100MSR Alpaste 1100M Alpaste 1100MA Alpaste 1100N Alpaste 1100NA Alpaste 1109MA Alpaste 1109MC Alpaste 1200M Alpaste 1200T Alpaste 1260MS Alpaste 1500MA Alpaste 1700NL Alpaste 1810YL Alpaste 1830YL Alpaste 1900M Alpaste 1900XS Alpaste 1950M Alpaste 1950N Alpaste 210N Alpaste 2172EA Alpaste 2173 Alpaste 240T Alpaste 241M Alpaste 417 Alpaste 46-046 Alpaste 4-621 Alpaste 4919 Alpaste 50-63 Alpaste 50-635 Alpaste 51-148B Alpaste 51-231 Alpaste 5205N Alpaste 5207N Alpaste 52-509 Alpaste 52-568 Alpaste 5301N Alpaste 5302N Alpaste 53-119 Alpaste 5422NS Alpaste 54-452 Alpaste 54-497 Alpaste 54-542 Alpaste 55-516 Alpaste 55-519 Alpaste 55-574 Alpaste 5620NS Alpaste 5630NS Alpaste 5640NS Alpaste 56-501 Alpaste 5650NS Alpaste 5653NS Alpaste 5654NS Alpaste 5680N Alpaste 5680NS Alpaste 60-600 Alpaste 60-760 Alpaste 60-768 Alpaste 62-356 Alpaste 6340NS Alpaste 6370NS Alpaste 6390NS Alpaste 640NS Alpaste 65-388 Alpaste 66NLB Alpaste 710N Alpaste 7130N Alpaste 7160N Alpaste 7160NS Alpaste 725N Alpaste 740NS Alpaste 7430NS Alpaste 7580NS Alpaste 7620NS Alpaste 7640NS Alpaste 7670M Alpaste 7670NS Alpaste 7675NS Alpaste 7679NS Alpaste 7680N Alpaste 7680NS Alpaste 76840NS Alpaste 7730N Alpaste 7770N Alpaste 7830N Alpaste 8004 Alpaste 8080N Alpaste 8260NAR Alpaste 891K Alpaste 91-0562 Alpaste 92-0592 Alpaste 93-0595 Alpaste 93-0647 Alpaste 94-2315 Alpaste 95-0570 Alpaste 96-0635 Alpaste 96-2104 Alpaste 97-0510 Alpaste 97-0534 Alpaste AW 520B Alpaste AW 612 Alpaste AW 9800 Alpaste F 795 Alpaste FM 7680K Alpaste FX 440 Alpaste FX 910 Alpaste FZ 0534 Alpaste FZU 40C Alpaste G Alpaste HR 8801 Alpaste HS 2 Alpaste J Alpaste K 9800 Alpaste MC 666 Alpaste MC 707 Alpaste MF 20 Alpaste MG 01 Alpaste MG 1000 Alpaste MG 1300 Alpaste MG 500 Alpaste MG 600 Alpaste MH 6601 Alpaste MH 8801 Alpaste MH 9901 Alpaste MR 7000 Alpaste MR 9000 Alpaste MS 630 Alpaste N 1700NL Alpaste NS 7670 Alpaste O 100N Alpaste O 2130 Alpaste O 300M Alpaste P 0100 Alpaste P 1950 Alpaste S Alpaste SAP 110 Alpaste SAP 414P Alpaste SAP 550N Alpaste SCR 5070 Alpaste TCR 2020 Alpaste TCR 2060 Alpaste TCR 2070 Alpaste TCR 3010 Alpaste TCR 3030 Alpaste TCR 3040 Alpaste TCR 3130 Alpaste TD 200T Alpaste UF 500 Alpaste WB 0230 Alpaste WD 500 Alpaste WJP-U 75C Alpaste WX 0630 Alpaste WX 7830 Alpaste WXA 7640 Alpaste WXM 0630 Alpaste WXM 0650 Alpaste WXM 0660 Alpaste WXM 1415 Alpaste WXM 1440 Alpaste WXM 5422 Alpaste WXM 760b Alpaste WXM 7640 Alpaste WXM 7675 Alpaste WXM-T 60B Alpaste WXM-U 75 Alpaste WXM-U 75C Altop X Aluchrome Ultrafin Super Alumat 1600 Alumet H 30 aluminio Aluminium Aluminium Flake Aluminum 27 Aluminum atom Aluminum element Aluminum Flake PCF 7620 Aluminum granules ALUMINUM METAL/GRANULE ALUMINUM PASTE ALUMINUM PIGMENT ALUMINUM TURNINGS Alumi-paste 640NS Alumipaste 91-0562 Alumipaste 98-1822T Alumipaste AW 620 Alumipaste CR 300 Alumipaste GX 180A Alumipaste GX 201A Alumipaste HR 7000 Alumipaste HR 850 Alumipaste MG 11 Alumipaste MH 8801 Aquamet NPW 2900 Aquapaste 205-5 Aquasilver LPW Astroflake 40 Astroflake Black N 020 Astroflake Black N 070 Astroflake LG 40 Astroflake LG 70 Astroflake Silver N 040 Astroshine NJ 1600 Astroshine T 8990 Atomizalumi VA 200 C.I. PIGMENT METAL 1 Chromal IV Chromal X Decomet 1001/10 Decomet 2018/10 Decomet High Gloss Al 1002/10 Ecka AS 081 Eckart 9155 Eterna Brite 301-1 Eterna Brite 601-1 Eterna Brite 651-1 Eterna Brite EBP 251PA Eterna Brite Primier 251PA Ferro FX 53-038 Friend Color F 500GR-W Friend Color F 500WT Friend Color F 700RE-W Friend Color F 701RE-W Hi Print 60T High Print 60T Hisparkle HS 2 Hydro Paste 8726 Hydrolac WHH 2153 Hydrolan 3560 Hydrolux Reflexal 100 Hydroshine WS 1001 JISA 51010P Kryal Z Lansford 243 LE Sheet 800 Leafing Alpaste LG-H Silver 25 Lunar Al-V 95 Metallux 161 Metallux 2154 Metallux 2192 Metalure Metalure 55350 Metalure L 55350 Metalure L 59510 Metalure W 2001 Metapor Metasheen 1800 Metasheen HR 0800 Metasheen KM 100 Metasheen KM 1000 Metasheen Slurry 1807 Metasheen Slurry 1811 Metasheen Slurry KM 100 Metax G Metax S Mirror Glow 1000 Mirror Glow 600 Mirrorsheen Noral Aluminium Noral Ink Grade Aluminium Obron 10890 Offset FM 4500 Puratronic Reflexal 145 Reynolds 400 Reynolds 4-301 Reynolds 4-591 Reynolds 667 SAP 260PW-HS SAP-FM 4010 SBC 516-20Z Scotchcal 7755SE Serumekku Setanium 50MIS-H8 Siberline ET 2025 Siberline ST 21030E1 Silvar A Silver VT 522 Silverline SSP 353 Silvex 793-20C Sparkle Silver 3141ST Sparkle Silver 3500 Sparkle Silver 3641 Sparkle Silver 5000AR Sparkle Silver 516AR Sparkle Silver 5242AR Sparkle Silver 5245AR Sparkle Silver 5271AR Sparkle Silver 5500 Sparkle Silver 5745 Sparkle Silver 7000AR Sparkle Silver 7005AR Sparkle Silver 7500 Sparkle Silver 960-25E1 Sparkle Silver E 1745AR Sparkle Silver L 1526AR Sparkle Silver Premier 751 Sparkle Silver SS 3130 Sparkle Silver SS 5242AR Sparkle Silver SS 5588 Sparkle Silver SSP 132AR Special PCR 507 Splendal 6001BG Spota Mobil 801 SSP 760-20C Stapa Aloxal PM 2010 Stapa Aloxal PM 3010 Stapa Aloxal PM 4010 Stapa Hydrolac BG 8n.1 Stapa Hydrolac BGH Chromal X Stapa Hydrolac PM Chromal VIII Stapa Hydrolac W 60NL Stapa Hydrolac WH 16 Stapa Hydrolac WH 66NL Stapa Hydrolux 2192 Stapa Hydrolux 8154 Stapa IL Hydrolan 2192-55900G Stapa Metallic R 607 Stapa Metallux 1050 Stapa Metallux 211 Stapa Metallux 212 Stapa Metallux 2196 Stapa Metallux 274 Stapa Mobilux 181 Stapa Offset 3000 Stapa PV 10 Stapa VP 46432G Starbrite 2100 Super Fine 18000 Super Fine 22000 Supramex 2022 Toyo Aluminum 02-0005 Toyo Aluminum 93-3040 Transmet K 102HE Tufflake 3645 Tufflake 5843 UN 1396 US Aluminum 809 Valimet H 2 Valimet H 3 White Silver 7080N White Silver 7130N

DTXSID3040273

7440-43-9

BDOSMKKIYDKNTQ-UHFFFAOYSA-N

Cadmium

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

DTXSID1023940

7439-97-6

QSHDDOUJBYECFT-UHFFFAOYSA-N

Mercury

Liquid silver Mercure MERCURIC METAL TRIPLE DISTILLED mercurio Mercury element Quecksilber Quicksilver UN 2024 UN 2809

DTXSID1024172

7440-61-1

JFALSRSLKYAFGM-UHFFFAOYSA-N

Uranium

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

DTXSID1042522

7440-38-2

RQNWIZPPADIBDY-UHFFFAOYSA-N

Arsenic

As Arsenic black ARSENIC METAL arsenico Grey arsenic UN 1558

DTXSID4023886

7440-22-4

BQCADISMDOOEFD-UHFFFAOYSA-N

Silver

Ag Nanopaste NPS-J 90 Ag Sphere 2 Ag-C-GS Algaedyn Arctic Silver 3 Argentum Astroflake 5 Carey Lea silver Colloidal silver Dotite XA 208 Du Pont 4943 ECM 100AF4810 Enlight 600 Enlight silver plate 600 Epinall Finesphere SVND 102 Fordel DC FP 5369-502 Jelcon SH 1 Jungindai Takasago 300 KS (metal) LCP 1-19SFS Metz 3000-1 Nanomelt AGC-A Nanomelt Ag-XA 301 Nanomelt Ag-XF 301 Nanomelt Ag-XF 301H Nanopaste NPS-J 90 Perfect Silver Puff Silver X 1200 RT 1710S-C1 SD (metal) Shell Silver Silbest E 20 Silbest F 20 Silbest J 18 Silbest TC 12 Silbest TC 20E Silbest TC 25A Silbest TCG 1 Silbest TCG 7 Silcoat AgC 103 Silcoat AgC 2011 Silcoat AgC 209 Silcoat AgC 2190 Silcoat AgC 222 Silcoat AgC 2411 Silcoat AgC 74T Silcoat AgC-A Silcoat AgC-AO Silcoat AgC-B Silcoat AgC-BO Silcoat AgC-D Silcoat AgC-G Silcoat AgC-GS Silcoat AgC-L Silcoat AgC-O Silcoat GS Silcoat RF 200 Silflake 135 Silsphere 514 Silver atom Silver element Silver Flake 1 Silver Flake 25 Silver Flake 52 Silver Flake 7A SILVER FLAKES Silver metal Silvest TCG 11N Technic 299 Technic 450 Techno Alpha 175

DTXSID4024305

7439-96-5

PWHULOQIROXLJO-UHFFFAOYSA-N

Manganese

Colloidal manganese Cutaval Manganese element Manganese fulleride Manganese metal alloy Manganese-55 manganeso

DTXSID2024169

7440-02-0

PXHVJJICTQNCMI-UHFFFAOYSA-N

Nickel

Carbonyl 255 Carbonyl Ni 123 Carbonyl Ni 283 Carbonyl Nickel 123 Carbonyl Nickel 283 Carbonyl Nickel 287 Cerac N 2003 CNS 10 Micron Exmet 4 Ni X-4/0 Fibrex P Incofoam Nickel element NICKEL ROUND ANODES Nicrobraz LM:BNi 2 Ni-Flake 95 Novamet 123 Novamet 4SP Novamet 4SP10 Novamet 525 Novamet CNS 400 Novamet HCA 1 Novamet NI 255 Raney nickel Raney nickel 2800 UN 1325 UN 2881

DTXSID2020925

7440-66-6

HCHKCACWOHOZIP-UHFFFAOYSA-N

Zinc

Zn Asarco L 15 C.I. Pigment Black 16 Merrillite NC-Zinc Rheinzink Stapa TE Zinc AT UF (metal) UN 1436 Zinc dust Zinc Dust 3 Zinc Dust 500 mesh Zinc Dust LS 2 Zinc Dust MCS Zinc Flakes GTT ZINC METAL ZINC MOSSY ZINC STRIP ZINC, MOSSY Zincsalt GTT

DTXSID7035012

GO:0099536

GO synaptic signaling

MP:0003674

MP oxidative stress

WIKI decreased

WIKI increased

Acetaminophen

2016-11-29T18:42:26

Chloroform

2016-11-29T18:42:27

furan

2020-05-01T14:35:22

Platinum

2022-02-04T14:36:54

Aluminum

2022-02-04T14:42:11

Cadmium

2017-10-25T08:33:12

Mercury

2016-11-29T18:42:19

Uranium

2021-08-05T14:28:50

Arsenic

2021-04-27T00:15:21

Silver

2022-02-03T11:20:11

Manganese

2022-02-04T14:47:23

Nickel

2022-02-04T14:47:59

Zinc

2022-02-04T15:05:00

nanoparticles

2016-12-21T09:40:06

WCS_9606

common toxicological species humans

10116

NCBI rat

10095

NCBI mice

9685

NCBI cat

WikiUser_26

ApacheUser rodents

9606

NCBI Homo sapiens

Increased intracelluar Iron accumulation

Increased intracelluar Iron

Cellular

AOPWiki 2023-06-15T04:38:20

2023-06-15T04:38:20

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

Neurological disorder

Individual

AOPWiki 2023-06-15T04:43:04

2023-06-15T04:43:04

Increase, Oxidative Stress

Molecular

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 on neighboring amino acids (Antelmann & 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).    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 (Pi) (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 has its own set of DNA and it is a prime target of oxidative damage (Guo et al., 2013). ROS is 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).

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  <ul> <li>Detection of ROS by chemiluminescence (https://www.sciencedirect.com/science/article/abs/pii/S0165993606001683) </li> <li>Detection of ROS by chemiluminescence is also described in OECD TG 495 to assess phototoxic potential. </li> <li>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). </li> <li>TBARS. Oxidative damage to lipids can be measured by assaying for lipid peroxidation using TBARS (thiobarbituric acid reactive substances) using a commercially available kit. </li> <li>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). </li> </ul>    Molecular Biology: Nrf2. Nrf2’s transcriptional activity is controlled post-translationally by oxidation of Keap1. Assay for Nrf2 activity include:  <ul> <li>Immunohistochemistry for increases in Nrf2 protein levels and translocation into the nucleus Western blot for increased Nrf2 protein levels </li> <li>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) </li> <li>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) </li> <li>OECD TG422D describes an ARE-Nrf2 Luciferase test method </li> </ul> In general, there are a variety of commercially available colorimetric or fluorescent kits for detecting Nrf2 activation. <table border="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> ROS  Formation in the Mitochondria assay (Shaki et al., 2012)  </td> <td> “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, 10mM 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.”    </td> <td> 0, 50,100 and 200 µM of Uranyl Acetate    </td> <td>  Long/ Easy High accuracy    </td> </tr> <tr> <td> Mitochondrial Antioxidant Content Assay Measuring GSH content (Shaki et al., 2012)    </td> <td> “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.”  </td> <td> 0, 50,  100, or  200 µM  Uranyl Acetate  </td> <td>   </td> </tr> <tr> <td> H2O2 Production Assay Measuring H2O2 Production in isolated mitochondria (Heyno et al., 2008)    </td> <td> “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.”   </td> <td> 0, 10, 30  µM Cd2+     2 µM antimycin A  </td> <td>   </td> </tr> <tr> <td> Flow Cytometry ROS & Cell Viability (Kruiderig et al., 1997)    </td> <td> “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)”“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)”  </td> <td>   </td> <td>           Strong/easy medium  </td> </tr> <tr> <td> DCFH-DA  Assay Detection of hydrogen peroxide production (Yuan et al.,  2016)  </td> <td> 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.    </td> <td> 0-400  µM  </td> <td> Long/ Easy High accuracy  </td> </tr> <tr> <td> H2-DCF-DAAssay Detection of superoxide production (Thiebault etal., 2007)    </td> <td> 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.  </td> <td> 0–600  µM  </td> <td> Long/ Easy High accuracy  </td> </tr> <tr> <td> CM-H2DCFDA  Assay (Eruslanov  & Kusmartsev, 2009)  </td> <td> The dye (CM-H2DCFDA) diffuses into the cell and is cleaved by esterases, the thiol reactive chlormethyl group reacts with intracellular glutathione which can be detected using flow cytometry.  </td> <td>   </td> <td> Long/Easy/ High Accuracy  </td> </tr> </tbody> </table>   <table border="1"> <tbody> <tr> <td> Method of Measurement   </td> <td> References   </td> <td> Description   </td> <td colspan="2"> OECD-Approved Assay  </td> </tr> <tr> <td> Chemiluminescence   </td> <td> (Lu, C. et al., 2006;   Griendling, K. K., et al., 2016)  </td> <td> 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 luminol and lucigenin are commonly used to amplify the signal.   </td> <td colspan="2"> No    </td> </tr> <tr> <td> Spectrophotometry   </td> <td> (Griendling, K. K., et al., 2016)  </td> <td> 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.   </td> <td colspan="2"> No  </td> </tr> <tr> <td> Direct or Spin Trapping-Based electron paramagnetic resonance (EPR) Spectroscopy   </td> <td> (Griendling, K. K., et al., 2016)  </td> <td> 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.   </td> <td colspan="2"> No  </td> </tr> <tr> <td> Nitroblue Tetrazolium Assay   </td> <td> (Griendling, K. K., et al., 2016)  </td> <td> 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.   </td> <td colspan="2"> No  </td> </tr> <tr> <td> Fluorescence analysis of dihydroethidium (DHE) or Hydrocyans   </td> <td> (Griendling, K. K., et al., 2016)  </td> <td> 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.   </td> <td colspan="2"> No  </td> </tr> <tr> <td> Amplex Red Assay   </td> <td> (Griendling, K. K., et al., 2016)  </td> <td> 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.   </td> <td colspan="2"> No  </td> </tr> <tr> <td> Dichlorodihydrofluorescein Diacetate (DCFH-DA)   </td> <td> (Griendling, K. K., et al., 2016)  </td> <td> 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.   </td> <td colspan="2"> No  </td> </tr> <tr> <td> HyPer Probe   </td> <td> (Griendling, K. K., et al., 2016)  </td> <td> Fluorescent measurement of intracellular H2O2 levels. HyPer is a genetically encoded fluorescent sensor that can be used for in vivo and in situ imaging.   </td> <td colspan="2"> No  </td> </tr> <tr> <td> Cytochrome c Reduction Assay   </td> <td> (Griendling, K. K., et al., 2016)  </td> <td> The cytochrome c reduction assay is used to measure O2.− levels. O 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.   </td> <td colspan="2"> No  </td> </tr> <tr> <td> Proton-electron double-resonance imaging (PEDRI)   </td> <td> (Griendling, K. K., et al., 2016)  </td> <td> 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.   </td> <td colspan="2"> No              </td> </tr> <tr> <td> Glutathione (GSH) depletion   </td> <td> (Biesemann, N. et al., 2018)   </td> <td> 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., <a href="http://www.abcam.com/gshgssg-ratio-detection-assay-kit-fluorometric-green-ab138881.html" rel="noreferrer noopener" target="_blank">http://www.abcam.com/gshgssg-ratio-detection-assay-kit-fluorometric-green-ab138881.html</a>).    </td> <td colspan="2"> No  </td> </tr> <tr> <td> Thiobarbituric acid reactive substances (TBARS)   </td> <td> (Griendling, K. K., et al., 2016)  </td> <td> Oxidative damage to lipids can be measured by assaying for lipid peroxidation with TBARS using a commercially available kit.    </td> <td colspan="2"> No  </td> </tr> <tr> <td> Protein oxidation (carbonylation)  </td> <td> (Azimzadeh et al., 2017; Azimzadeh et al., 2015; Ping et al., 2020)  </td> <td> Can be determined with ELISA or a commercial assay kit. Protein oxidation can indicate the level of oxidative stress.  </td> <td colspan="2"> No  </td> </tr> <tr> <td> Seahorse XFp Analyzer  </td> <td> Leung et al. 2018  </td> <td> 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).  </td> <td> No  </td> </tr> </tbody> </table>   Molecular Biology: Nrf2. Nrf2’s transcriptional activity is controlled post-translationally by oxidation of Keap1. Assays for Nrf2 activity include:   <table border="1"> <tbody> <tr> <td> Method of Measurement   </td> <td> References   </td> <td> Description   </td> <td> OECD-Approved Assay  </td> </tr> <tr> <td> Immunohistochemistry   </td> <td> (Amsen, D., de Visser, K. E., and Town, T., 2009)  </td> <td> Immunohistochemistry for increases in Nrf2 protein levels and translocation into the nucleus    </td> <td> No  </td> </tr> <tr> <td> qPCR   </td> <td> (Forlenza et al., 2012)  </td> <td> 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)   </td> <td> No  </td> </tr> <tr> <td> Whole transcriptome profiling via microarray or via RNA-seq followed by a pathway analysis  </td> <td> (Jackson, A. F. et al., 2014)  </td> <td> 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  </td> <td> No  </td> </tr> </tbody> </table>

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).

High Mixed

High

All life stages

High High

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, <a href="https://doi.org/10.1093/jisesa/ieab080" rel="noreferrer noopener" target="_blank">https://doi.org/10.1093/jisesa/ieab080</a>  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, <a href="https://doi.org/10.1089/ars.2010.3400" rel="noreferrer noopener" target="_blank">https://doi.org/10.1089/ars.2010.3400</a>  Amsen, D., de Visser, K. E., and Town, T. (2009), “Approaches to determine expression of inflammatory cytokines”, in Inflammation and Cancer, Humana Press, Totowa, <a href="https://doi.org/10.1007/978-1-59745-447-6_5" rel="noreferrer noopener" target="_blank">https://doi.org/10.1007/978-1-59745-447-6_5</a>   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, <a href="https://doi.org/10.1021/pr501141b" rel="noreferrer noopener" target="_blank">https://doi.org/10.1021/pr501141b</a>  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, <a href="https://doi.org/10.1080/09553002.2017.1339332" rel="noreferrer noopener" target="_blank">https://doi.org/10.1080/09553002.2017.1339332</a>  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, <a href="https://doi.org/10.1152/ajpcell.00520.2019." rel="noreferrer noopener" target="_blank">https://doi.org/10.1152/ajpcell.00520.2019.</a>  Balasubramanian, D (2000), “Ultraviolet radiation and cataract”, Journal of ocular pharmacology and therapeutics, Vol. 16/3, Mary Ann Liebert Inc., Larchmont, <a href="https://doi.org/10.1089/jop.2000.16.285.%22%20/t%20%22_blank" rel="noreferrer noopener" target="_blank">https://doi.org/10.1089/jop.2000.16.285.</a>    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, <a href="https://doi.org/10.1038/s41598-018-27614-8" rel="noreferrer noopener" target="_blank">https://doi.org/10.1038/s41598-018-27614-8</a>.   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  Eruslanov, E., & Kusmartsev, S. (2010). Identification of ROS using oxidized DCFDA and flow-cytometry. Methods in molecular biology ,N.J.,  Vol. 594,  https://doi.org/10.1007/978-1-60761-411-1_4  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, <a href="https://doi.org/10.1159/000316476.%22%20/t%20%22_blank" rel="noreferrer noopener" target="_blank">https://doi.org/10.1159/000316476.</a>   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, <a href="https://doi.org/10.1152/physrev.00038.201" rel="noreferrer noopener" target="_blank">https://doi.org/10.1152/physrev.00038.201</a>  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, <a href="https://doi.org/10.1080/02713680500477347.%22%20/t%20%22_blank" rel="noreferrer noopener" target="_blank">https://doi.org/10.1080/02713680500477347.</a>   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, <a href="https://doi.org/10.1161/RES.0000000000000110" rel="noreferrer noopener" target="_blank">https://doi.org/10.1161/RES.0000000000000110</a>   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, <a href="https://doi.org/10.3969/j.issn.1673-5374.2013.21.009" rel="noreferrer noopener" target="_blank">https://doi.org/10.3969/j.issn.1673-5374.2013.21.009</a>  Hargreaves, M., and L. L. Spriet (2020), “Skeletal muscle energy metabolism during exercise.”, Nature Metabolism, Vol. 2, Nature Portfolio, London, <a href="https://doi.org/10.1038/s42255-020-0251-4" rel="noreferrer noopener" target="_blank">https://doi.org/10.1038/s42255-020-0251-4</a>  Hladik, D. and S. Tapio (2016), “Effects of ionizing radiation on the mammalian brain”, Mutation Research/Reviews in Mutation Research, Vol. 770, Elsevier, Amsterdam, <a href="https://doi.org/10.1016/j.mrrev.2016.08.003" rel="noreferrer noopener" target="_blank">https://doi.org/10.1016/j.mrrev.2016.08.003</a>  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, <a href="https://doi.org/10.1089/ars.2010.3222" rel="noreferrer noopener" target="_blank">https://doi.org/10.1089/ars.2010.3222</a>   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, <a href="https://doi.org/10.1016/j.taap.2013.10.019" rel="noreferrer noopener" target="_blank">https://doi.org/10.1016/j.taap.2013.10.019</a>  Jacobsen, N.R. et al. (2008), “Genotoxicity, cytotoxicity, and reactive oxygen species induced by single-walled carbon nanotubes and C60 fullerenes in the FE1-MutaTM Mouse lung epithelial cells”, Environmental and Molecular Mutagenesis, Vol. 49/6, John Wiley & Sons, Inc., Hoboken, <a href="https://doi.org/10.1002/em.20406" rel="noreferrer noopener" target="_blank">https://doi.org/10.1002/em.20406</a>  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, <a href="https://doi.org/10.4103/jphi.JPHI_60_17.%E2%80%AF" rel="noreferrer noopener" target="_blank">https://doi.org/10.4103/jphi.JPHI_60_17. </a>  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, <a href="https://doi.org/10.1016/j.trac.2006.07.007" rel="noreferrer noopener" target="_blank">https://doi.org/10.1016/j.trac.2006.07.007</a>  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, <a href="https://doi.org/10.1089/ars.2012.4641" rel="noreferrer noopener" target="_blank">https://doi.org/10.1089/ars.2012.4641</a>  Ping, Z. et al. (2020), “Oxidative Stress in Radiation-Induced Cardiotoxicity”, Oxidative Medicine and Cellular Longevity, Vol. 2020, Hindawi, <a href="https://doi.org/10.1155/2020/3579143" rel="noreferrer noopener" target="_blank">https://doi.org/10.1155/2020/3579143</a>  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, <a href="https://doi.org/10.1152/physrev.00031.2007" rel="noreferrer noopener" target="_blank">https://doi.org/10.1152/physrev.00031.2007</a>  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, <a href="https://doi.org/10.1038/s41416-019-0651-y" rel="noreferrer noopener" target="_blank">https://doi.org/10.1038/s41416-019-0651-y</a>  Seen, S. and L. Tong. (2018), “Dry eye disease and oxidative stress”, Acta Ophthalmologica, Vol. 96/4, John Wiley & Sons, Inc., Hoboken, <a href="https://doi.org/10.1111/aos.13526" rel="noreferrer noopener" target="_blank">https://doi.org/10.1111/aos.13526</a>  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, <a href="https://doi.org/10.1093/gerona/glt057." rel="noreferrer noopener" target="_blank">https://doi.org/10.1093/gerona/glt057.</a>     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, <a href="https://doi.org/10.3390/life11111269" rel="noreferrer noopener" target="_blank">https://doi.org/10.3390/life11111269</a>  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, <a href="https://doi.org/10.7150/ijbs.35460" rel="noreferrer noopener" target="_blank">https://doi.org/10.7150/ijbs.35460</a>   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, <a href="https://doi.org/10.1152/japplphysiol.01278.2007" rel="noreferrer noopener" target="_blank">https://doi.org/10.1152/japplphysiol.01278.2007</a>.  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, <a href="https://doi.org/10.3892/ijmm.2019.4188" rel="noreferrer noopener" target="_blank">https://doi.org/10.3892/ijmm.2019.4188</a>  AOPWiki 2017-05-30T13:58:17

2026-02-11T07:05:27

<upstream-id>6710bfcb-ca3b-438c-a3ab-63589c1013f7</upstream-id> <downstream-id>fd5056ef-0fb8-4de3-b78c-95c132837c82</downstream-id>

AOPWiki 2024-07-23T22:32:15

2024-07-23T22:32:15

<upstream-id>fd5056ef-0fb8-4de3-b78c-95c132837c82</upstream-id> <downstream-id>b354c32f-c7f9-4d9c-a785-d39ddb0ec74e</downstream-id>

AOPWiki 2024-07-23T22:36:35

2024-07-23T22:36:35

<upstream-id>b354c32f-c7f9-4d9c-a785-d39ddb0ec74e</upstream-id> <downstream-id>b3a38af8-16b8-4edc-9bd7-4473216c5a4b</downstream-id>

AOPWiki 2023-06-21T09:55:28

2023-06-21T09:55:28

Excessive iron accumulation leading to neurological disorders

Excessive iron accumulation in Neuron, Neurological disorders

Young Jun Kim

Young Jun Kim1 and Bongsuk Choi2 1 KIST Europe, Saarbruecken 66123, Germany 2. Hanpoong Pharm & Foods Co., Ltd.11 Guretdeul 3-gil, Deokjin-gu, Jeonju-si, Jeollabuk-do, 54843, Republic of Korea. email : bongsuk333@hanpoong.co.kr

Open for adoption

2.6

The Adverse Outcome Pathway (AOP) for Excessive Iron Accumulation Leading to Neurological Disorders describes a mechanistic sequence linking the Molecular Initiating Event (MIE)—brain iron overload with elevation of the labile iron pool (LIP)—to the adverse outcome (AO) of neurological disorder. Excess iron catalyzes Fenton/Fenton-like chemistry and impairs iron–sulfur protein function, producing oxidative stress (Key Event 1, KE1) characterized by reactive oxygen species (ROS), lipid peroxidation, and oxidative damage to proteins/DNA. Persistent oxidative stress perturbs synaptic homeostasis (glutamatergic/GABAergic balance), mitochondrial bioenergetics, and membrane excitability, driving decrease of neuronal network function (Key Event 2, KE2)—measured as reduced synaptic transmission, impaired long-term potentiation (LTP), diminished firing/synchrony on MEAs, and connectivity loss. These network-level deficits translate to neurological disorders (AO) including cognitive impairment, movement disorders, and neurobehavioral syndromes. The sequence is supported by strong biological plausibility (iron-catalyzed ROS; vulnerability of PUFA-rich neuronal membranes) and broad empirical evidence in cellular and in vivo models. Iron chelators (e.g., deferoxamine, deferiprone) and lipid peroxidation/ferroptosis inhibitors functionally rescue early and intermediate KEs, strengthening causality. Prototypical stressors include genetic iron-handling defects (e.g., HFE, ceruloplasmin, ferritin L-chain, SLC40A1), hemorrhagic/iatrogenic iron loading, chronic inflammation with hepcidin dysregulation, and environmental/occupational sources. This AOP supports hazard identification, disease-mechanism alignment (e.g., Parkinsonian phenotypes), and screening of neuroprotective strategies targeting iron–redox balance. This AOP frames how excessive iron in the CNS elevates the labile iron pool, amplifies oxidative stress, and degrades neuronal network function, culminating in neurological disorders. Neurons and oligodendrocytes are rich in PUFA and iron-dependent enzymes; microglia/astrocytes modulate iron flux (transferrin, ferritin, ferroportin, DMT1, hepcidin). Dysregulation at this node perturbs redox homeostasis, synaptic plasticity, and circuit integrity—key determinants of cognitive and motor function. Applications span regulatory neurotoxicity, disease modeling (iron-accumulation syndromes), and therapeutic design (iron chelation, antioxidant/anti-ferroptotic strategies).

1. Identify and Characterize Key Events (KEs) 1.1 Molecular Initiating Event (MIE) Focus: Excessive iron accumulation / ↑Labile iron pool (LIP) in the brain. Approach: <ul> <li> Quantify LIP (calcein-AM quench; ferritinophagy markers), total iron (AAS/ICP-MS), and iron distribution (MRI-R2*, QSM; Perls’/DAB-enhanced). </li> <li> Manipulate iron flux (hepcidin–ferroportin axis; DMT1) and verify directionality with iron chelators (DFO, DFP). Outcome: Define thresholds of LIP increase that trigger oxidative stress. </li> </ul> 1.2 Downstream KEs Focus: KE1: Oxidative stress → KE2: Decrease of neuronal network function → AO: Neurological disorder. Approach: <ul> <li> KE1 metrics: ROS (DCFH-DA, MitoSOX), lipid peroxidation (BODIPY-C11, 4-HNE/MDA), protein/DNA oxidation (protein carbonyls, 8-oxo-dG). </li> <li> KE2 metrics: synaptic proteins (PSD-95, synaptophysin), LTP/LTD (field EPSP), MEA (firing rate, burst index, synchrony), calcium imaging (ΔF/F), resting membrane/excitability. </li> <li> AO metrics: behavioral/cognitive (Morris water maze/novel object recognition), motor (rotarod/open field), neurologic exam; imaging of atrophy/iron accumulation. Outcome: Establish temporal and quantitative links among KEs and AO; demonstrate rescue by chelation/antioxidants. </li> </ul> 2. Define Key Event Relationships (KERs) 2.1 Biological Plausibility: Iron catalyzes ROS via Fenton chemistry; neurons are ROS-sensitive; oxidative damage impairs synaptic proteins/mitochondria → network failure. 2.2 Empirical Support: Dose–response/time-course (iron ↑ → ROS ↑ → synaptic/MEA ↓); reversibility with chelators and lipid-ROS scavengers. 2.3 Quantitative Understanding: Build response–response models (LIP vs BODIPY-C11; BODIPY-C11 vs LTP/MEA; MEA/LTP vs behavioral scores). 3. Address Modulating Factors Iron status (systemic and CNS), age, sex hormones, diet (PUFA), antioxidant capacity (GSH/NADPH), neuroinflammation (microglial activation), co-exposures (pesticides, solvents), and genetics (iron handling variants). 4. Expand Domain of Applicability Taxonomic: Human, rodent, zebrafish; Life Stage: adult/aged > juvenile; Sex: threshold shifts via iron stores/hormones.

adjacent

Not Specified

Moderate adjacent

Not Specified

High adjacent

Not Specified

High

High Mixed

High

Old Age

High

This AOP links brain iron overload to neurological disorders via well-established chemistry (iron-driven ROS) and neurophysiology (synaptic/circuit degradation). Biological plausibility is high; empirical support is strong for early KEs (oxidative stress) and moderate–strong for network impairment. Quantitative mapping from network metrics to complex clinical phenotypes remains to be refined. <table> <thead> <tr> <th>Domain</th> <th>Relevance</th> <th>Evidence</th> </tr> </thead> <tbody> <tr> <td>Taxonomic</td> <td>Humans, rodents (primary)</td> <td>Conserved iron homeostasis and redox pathways</td> </tr> <tr> <td>Life Stage</td> <td>Adults/elderly</td> <td>Age-related iron accumulation; antioxidant decline</td> </tr> <tr> <td>Sex</td> <td>Both</td> <td>Differences mainly shift thresholds (iron burden, hormones)</td> </tr> <tr> <td>Molecular/Cellular</td> <td>Neurons, astrocytes, microglia, oligodendrocytes</td> <td>Iron transporters (DMT1, TfR, ferroportin), ferritin, antioxidant/ferroptosis machinery</td> </tr> <tr> <td>Stressors</td> <td>Genetic, acquired iron loading</td> <td>Align with clinical and preclinical observations</td> </tr> </tbody> </table>

<table cellspacing="0" style="border-collapse:collapse; width:541px"> <tbody> <tr> <td style="border-bottom:1px solid black; border-left:1px solid black; border-right:1px solid black; border-top:1px solid black; height:37px; text-align:center; vertical-align:middle; white-space:normal; width:235px">Key Event (KE)</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:1px solid black; text-align:center; vertical-align:middle; white-space:normal; width:115px">Essentiality</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:1px solid black; text-align:center; vertical-align:middle; white-space:normal; width:191px">Rationale and Evidence</td> </tr> <tr> <td style="border-bottom:1px solid black; border-left:1px solid black; border-right:1px solid black; border-top:none; height:37px; vertical-align:middle; white-space:normal; width:235px">MIE: Brain iron overload (↑LIP)</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:none; vertical-align:middle; white-space:normal; width:115px">Strong</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:none; vertical-align:middle; white-space:normal; width:191px">Necessary driver of iron-catalyzed ROS; chelation reduces KE1.</td> </tr> <tr> <td style="border-bottom:1px solid black; border-left:1px solid black; border-right:1px solid black; border-top:none; height:37px; vertical-align:middle; white-space:normal; width:235px">KE1: Oxidative stress</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:none; vertical-align:middle; white-space:normal; width:115px">Strong</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:none; vertical-align:middle; white-space:normal; width:191px">Required for lipid/protein/DNA damage; antioxidants/iron chelators prevent KE2.</td> </tr> <tr> <td style="border-bottom:1px solid black; border-left:1px solid black; border-right:1px solid black; border-top:none; height:37px; vertical-align:middle; white-space:normal; width:235px">KE2: Network function decrease</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:none; vertical-align:middle; white-space:normal; width:115px">Strong</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:none; vertical-align:middle; white-space:normal; width:191px">Central determinant of cognition/motor control; synaptic/circuit rescue improves outcomes.</td> </tr> <tr> <td style="border-bottom:1px solid black; border-left:1px solid black; border-right:1px solid black; border-top:none; height:37px; vertical-align:middle; white-space:normal; width:235px">AO: Neurological disorder</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:none; vertical-align:middle; white-space:normal; width:115px">Outcome</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:none; vertical-align:middle; white-space:normal; width:191px">Emerges from cumulative network failure across regions (e.g., cortex, basal ganglia).</td> </tr> </tbody> </table>

1. MIE: Brain iron overload → Oxidative stress Biological Plausibility: Strong (Fenton chemistry; iron–sulfur protein disruption). Empirical Support: Strong (LIP↑ precedes ROS/lipid-ROS↑; reversed by chelation). 2. KE1: Oxidative stress → KE2: Network function decrease Biological Plausibility: Strong (oxidative damage to synapses/mitochondria/ion channels). Empirical Support: Strong (BODIPY-C11/4-HNE↑ correlates with ↓LTP, ↓MEA spiking/synchrony; antioxidant rescue). 3. KE2 → AO: Neurological disorder Biological Plausibility: Moderate–Strong (network integrity underlies behavior). Empirical Support: Moderate (improvements in LTP/MEA associate with behavioral rescue in models).

<table cellspacing="0" style="border-collapse:collapse; width:541px"> <tbody> <tr> <td style="border-bottom:1px solid black; border-left:1px solid black; border-right:1px solid black; border-top:1px solid black; height:39px; text-align:center; vertical-align:middle; white-space:normal; width:235px">Modulating Factor</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:1px solid black; text-align:center; vertical-align:middle; white-space:normal; width:115px">Influence/Outcome</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:1px solid black; text-align:center; vertical-align:middle; white-space:normal; width:191px">KER(s) involved</td> </tr> <tr> <td style="border-bottom:1px solid black; border-left:1px solid black; border-right:1px solid black; border-top:none; height:39px; vertical-align:middle; white-space:normal; width:235px">Labile iron pool (↑)</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:none; vertical-align:middle; white-space:normal; width:115px">Lowers threshold for KE1/KE2</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:none; vertical-align:middle; white-space:normal; width:191px">MIE→KE1; KE1→KE2</td> </tr> <tr> <td style="border-bottom:1px solid black; border-left:1px solid black; border-right:1px solid black; border-top:none; height:39px; vertical-align:middle; white-space:normal; width:235px">PUFA/ACSL4 (↑)</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:none; vertical-align:middle; white-space:normal; width:115px">Amplifies lipid peroxidation</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:none; vertical-align:middle; white-space:normal; width:191px">KE1→KE2</td> </tr> <tr> <td style="border-bottom:1px solid black; border-left:1px solid black; border-right:1px solid black; border-top:none; height:39px; vertical-align:middle; white-space:normal; width:235px">Antioxidant capacity (GSH/NADPH) (↓)</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:none; vertical-align:middle; white-space:normal; width:115px">Worsens ROS handling</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:none; vertical-align:middle; white-space:normal; width:191px">MIE→KE1; KE1→KE2</td> </tr> <tr> <td style="border-bottom:1px solid black; border-left:1px solid black; border-right:1px solid black; border-top:none; height:58px; vertical-align:middle; white-space:normal; width:235px">Neuroinflammation</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:none; vertical-align:middle; white-space:normal; width:115px">Microglial ROS/RNS amplify damage</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:none; vertical-align:middle; white-space:normal; width:191px">KE1→KE2</td> </tr> <tr> <td style="border-bottom:1px solid black; border-left:1px solid black; border-right:1px solid black; border-top:none; height:39px; vertical-align:middle; white-space:normal; width:235px">Mitochondrial dysfunction</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:none; vertical-align:middle; white-space:normal; width:115px">Increases mtROS; network fragility</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:none; vertical-align:middle; white-space:normal; width:191px">KE1→KE2</td> </tr> <tr> <td style="border-bottom:1px solid black; border-left:1px solid black; border-right:1px solid black; border-top:none; height:77px; vertical-align:middle; white-space:normal; width:235px">Age/sex/hormones</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:none; vertical-align:middle; white-space:normal; width:115px">Shift thresholds via iron/hormonal milieu</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:none; vertical-align:middle; white-space:normal; width:191px">All</td> </tr> </tbody> </table>

<table cellspacing="0" style="border-collapse:collapse; width:641px"> <tbody> <tr> <td style="border-bottom:1px solid black; border-left:1px solid black; border-right:1px solid black; border-top:1px solid black; height:77px; text-align:center; vertical-align:middle; white-space:normal; width:160px">Key Event / Relationship</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:1px solid black; text-align:center; vertical-align:middle; white-space:normal; width:185px">Quantitative Evidence</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:1px solid black; text-align:center; vertical-align:middle; white-space:normal; width:191px">Thresholds</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:1px solid black; text-align:center; vertical-align:middle; white-space:normal; width:105px">Temporal Concordance</td> </tr> <tr> <td style="border-bottom:1px solid black; border-left:1px solid black; border-right:1px solid black; border-top:none; height:58px; vertical-align:middle; white-space:normal; width:160px">MIE (LIP↑)</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:none; vertical-align:middle; white-space:normal; width:185px">% calcein-quench loss vs ROS indices</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:none; vertical-align:middle; white-space:normal; width:191px">LIP ≥120–150% baseline → ROS↑</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:none; vertical-align:middle; white-space:normal; width:105px">Minutes–hours</td> </tr> <tr> <td style="border-bottom:1px solid black; border-left:1px solid black; border-right:1px solid black; border-top:none; height:39px; vertical-align:middle; white-space:normal; width:160px">KE1 (Oxidative stress)</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:none; vertical-align:middle; white-space:normal; width:185px">BODIPY-C11/4-HNE vs LTP/MEA</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:none; vertical-align:middle; white-space:normal; width:191px">BODIPY ≥150–200% & 4-HNE↑ predict LTP↓ ≥20–30%</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:none; vertical-align:middle; white-space:normal; width:105px">Hours–days</td> </tr> <tr> <td style="border-bottom:1px solid black; border-left:1px solid black; border-right:1px solid black; border-top:none; height:39px; vertical-align:middle; white-space:normal; width:160px">KE1→KE2</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:none; vertical-align:middle; white-space:normal; width:185px">ROS/lipid-ROS vs network metrics</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:none; vertical-align:middle; white-space:normal; width:191px">≥30% LTP decline or ≥25% MEA firing↓ indicates KE2</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:none; vertical-align:middle; white-space:normal; width:105px">Hours–days</td> </tr> <tr> <td style="border-bottom:1px solid black; border-left:1px solid black; border-right:1px solid black; border-top:none; height:58px; vertical-align:middle; white-space:normal; width:160px">KE2→AO</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:none; vertical-align:middle; white-space:normal; width:185px">Network composite vs behavior</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:none; vertical-align:middle; white-space:normal; width:191px">Region-specific; composite drop associates with cognitive/motor deficits</td> <td style="border-bottom:1px solid black; border-left:none; border-right:1px solid black; border-top:none; vertical-align:middle; white-space:normal; width:105px">Days–weeks</td> </tr> </tbody> </table>

<ul> <li> Screening: Prioritize compounds that raise LIP or lipid-ROS in neuron–glia co-cultures/brain organoids; apply MEA/LTP readouts. </li> <li> Risk management: Incorporate iron chelation and anti-ferroptotic strategies in mitigation studies. </li> <li> Translational markers: MRI-QSM/R2* for brain iron; plasma/CSF 4-HNE/MDA; electrophysiological network metrics. </li> </ul>

<ol> <li> Levi S, et al. "Iron imbalance in neurodegeneration." Molecular Psychiatry, 2024. </li> <li> Ward RJ, et al. "The role of iron in brain ageing and neurodegenerative disorders." PMC, 2014. </li> <li> Stankiewicz J, et al. "Iron in Chronic Brain Disorders." Neurobiology of Disease, 2007. </li> <li> Mohammadi S, et al. "Iron accumulation/overload and Alzheimer's disease risk." Alzheimer's & Dementia, 2024. </li> <li> Daglas M, et al. "The Involvement of Iron in Traumatic Brain Injury and Neurodegeneration." Frontiers in Neuroscience, 2018. </li> <li> Levi S, et al. "Neurodegeneration with brain iron accumulation." Frontiers in Pharmacology, 2014. </li> <li> Gao G, et al. "Brain Iron Metabolism, Redox Balance and Neurological Diseases." PMC, 2023. </li> <li> Gao Q, et al. "Role of iron in brain development, aging, and neurodegeneration." Annals of Medicine, 2025. </li> <li> Medlink Editors. "Neurodegeneration with brain iron accumulation." Medlink Neurology, 2025. </li> <li> Loughnan R, et al. "Hemochromatosis neural archetype reveals iron disruption in the brain." Science Advances, 2024. </li> <li> Riederer P, et al. "Iron in neurodegeneration - cause or consequence?", Journal of Neural Transmission, 2021. </li> <li> Rouault TA. "Iron metabolism in the CNS: implications for neurodegenerative diseases." Nature Reviews Neuroscience, 2013. </li> <li> Papanikolaou G, et al. "Iron metabolism and toxicity in Parkinson's disease." Acta Neurologica Scandinavica, 2020. </li> <li> Ayton S, et al. "Brain iron accumulation and its impact on neurodegenerative diseases." Trends in Neurosciences, 2017. </li> <li> Hare D, et al. "Iron in Alzheimer's disease: from pathogenesis to therapeutic approaches." Lancet Neurology, 2013. </li> <li> Que EL, et al. "Iron Homeostasis: Mechanisms and Disease." Annual Review of Biochemistry, 2018. </li> <li> Zecca L, et al. "Iron, brain ageing and neurodegenerative disorders." Nature Reviews Neuroscience, 2004. </li> <li> Forni GL, et al. "Neurological complications in hereditary hemochromatosis." Hematology and Therapy, 2019. </li> <li> Bush AI. "Metals and neurodegeneration." Current Opinion in Chemical Biology, 2017. </li> <li> Kaur D, et al. "Iron in Parkinson's disease: causes and consequences." Neurochemical Research, 2015. </li> <li> Walker Z, et al. "Neuroferritinopathy: clinical features and genetics." Neurology, 2019. </li> <li> Miyajima H, et al. "Aceruloplasminemia as a genetic iron overload disorder." Nature Genetics, 1987. </li> <li> Möller HE, et al. "Brain iron mapping using MRI: clinical relevance." Radiology, 2020. </li> <li> You LH, et al. "Ferroptosis and neurodegeneration: iron toxicity mechanisms." Free Radical Biology & Medicine, 2022. </li> <li> Zimmermann MB. "Iron deficiency and excess: neurological effects." Annals of Nutrition & Metabolism, 2017. </li> <li> Gregory A, et al. "Neurodegeneration with brain iron accumulation syndromes." Brain, 2009. </li> <li> Pandolfo M, et al. "Friedreich ataxia and iron toxicity." Annals of Neurology, 2012. </li> <li> Morey TM, et al. "Iron overload and cognitive decline." Neuropsychopharmacology, 2018. </li> <li> Langkammer C, et al. "Quantitative MR imaging of brain iron." Radiology, 2010. </li> <li> Cossu G, et al. "Pantothenate kinase-associated neurodegeneration: A clinical review." Movement Disorders Clinical Practice, 2020. </li> </ol> AOPWiki 2023-06-21T09:43:46

2025-11-05T10:16:41