Inhibition, Intestinal FXR

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

CL:0000073

CL barrier epithelial cell

MP:0003674

MP oxidative stress

MP:0001860

MP liver inflammation

WIKI disrupted

WIKI increased

WIKI occurrence

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

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 human

WikiUser_26

ApacheUser rodents

9606

NCBI Homo sapiens

10090

NCBI mouse

10116

NCBI rat

WikiUser_28

Vertebrates

Inhibition, Intestinal FXR

FXR inhibition

Molecular

AOPWiki 2026-04-22T21:50:00

2026-04-22T21:50:00

Ileal FGF15/FGF19 secretion, decreased

Decreased FGF15/FGF19

Cellular

AOPWiki 2026-04-22T21:57:53

2026-04-22T21:57:53

Hepatic CYP7A1, increased

Cellular

AOPWiki 2026-04-22T22:26:54

2026-04-22T22:26:54

Intrahepatic bile acid burden, increased

Intrahepatic bile acids, increased

Cellular

AOPWiki 2026-04-23T22:39:25

2026-04-23T22:39:25

Hepatic BSEP (ABCB11), decreased

BSEP (ABCB11), decreased

Cellular

AOPWiki 2026-04-23T22:42:01

2026-04-23T22:42:01

Bile acid composition in bile and intestine, altered

Bile acid composition, altered

Cellular

AOPWiki 2026-04-23T22:43:26

2026-04-23T22:43:26

Intestinal barrier, disruption

Disruption of the intestinal barrier

Organ

A proper definition (and related ontology) of the intestinal barrier and permeability would benefit the understanding of this biological event central in many diseases. However, it is generally accepted that the intestinal barrier is a multilayer system encompassing : - a chemical barrier able to detoxify bacterial endotoxins, - a mucus layer providing a physical barrier against bacteria, - an one-cell-thick epithelial layer which physical barrier function is ensured by epithelial cell integrity and by tight junction proteins (occludins, claudins and zonulins), adherence junctions and desmosomes 2,4,5 - the cellular immune system present in the lamina propria underlying the epithelial cell layer - the antibacterial proteins secreted by the specialized intestinal epithelial cells or the Paneth cells. Together with the chemical barrier of the mucosal layer and the cellular immune system, the intestinal epithelial cell layer has actually two barrier functions:1–3 <ol style="list-style-type:lower-roman"> <li style="text-align:justify">It acts as a physical barrier against external factors (pathogens, toxins), </li> <li style="text-align:justify">It acts as a selective barrier by regulating the absorption of essential dietary nutrients and  ions, meaning their transport from the lumen into the blood.</li> </ol> Intestinal permeability6 describes the movement of molecules across the intestinal barrier from the lumen to the blood (Figure 1), and as such, is the measurable feature of the intestinal barrier. <img alt="" src="https://aopwiki.org/system/dragonfly/production/2022/01/05/9m0b6q2aex_Intestinal_permeability_wiki.png" style="height:508px; width:1103px" /> Figure 1. Created with Biorender.com Molecules can cross the epithelium via paracellular or transcellular route. Transcellular permeability encompass passive diffusion from the apical to the basal side (from the lumen to the blood), vesicle-mediated transcytosis and uptake mediated by a membrane receptor. Paracellular permeability is regulated by the tight junctions between adjacent cells and by the integrity of the epithelium. Alteration or disruption of one or more layers of the intestinal barrier leads to increased intestinal permeability, also called intestinal hyperpermeability or “leaky gut”, enhancing the transport of pathogens, toxins (such as lipopolysaccharides), undigested nutrients and the translocation of bacteria of the gut microbiota from the intestinal lumen into the systemic circulation3.

The definition of intestinal permeability being relatively broad includes altered paracellular route, regulated by TJ proteins, transcellular routes involving membrane transporters and channels, and endocytic mechanisms. Paracellular intestinal permeability can be assessed in vivo via different molecules and via putatiive blood biomarkers and ex vivo in Ussing chambers combining electrophysiology and probes of different molecular sizes. The latter is still the gold standard technique for assessing the epithelial barrier function, whereas in vivo techniques are also broadly used despite limitations (doi: <a href="https://doi.org/10.3389%2Ffnut.2021.717925" rel="noopener noreferrer" target="_blank">10.3389/fnut.2021.717925)</a>. In humans. Virtually all in vivo methods to assess paracellular intestinal permeability rely on the urinary excretion of orally ingested probes. Several markers, including different sizes of PEG, 51CrEDTA, and especially sugars have been used, each with advantages and disadvantages (doi: <a href="https://doi.org/10.3389%2Ffnut.2021.717925" rel="noopener noreferrer" target="_blank">10.3389/fnut.2021.717925)</a>. Intestinal Permeability Assessment (IPA) directly measures the ability of two non-metabolized sugar molecules (lactulose and mannitol) to permeate the small intestinal barrier by paracellular passage (sign of perturbed TJ-lactulose) or by transcellular passage (giving information of the whole epithelial absorptive area-mannitol), respectively. The patient drinks a premeasured amount of those sugars and 6h after, the ratio of Lactulose/Mannitol levels is measured in the urine 11. Levels in plasma/serum or in feces of: <ul> <li style="text-align:justify">Markers of epithelial cell damage, such as intestinal fatty acid binding protein (FABP)</li> <li style="text-align:justify">Markers of tight junction alterations, such as zonulin levels (doi: <a class="id-link" href="https://doi.org/10.1080/21688370.2016.1251384" rel="noopener" target="_blank">10.1080/21688370.2016.1251384</a>) </li> <li style="text-align:justify">Microbial translocation, such as peptidoglycans and lipopolysaccharides (LPS) and gut microbiota alteration.</li> </ul> In vitro systems12 Transepithelial electrical resistance (TEER) or the Lucifer Yellow (LY) leakage assay are techniques to measure barrier integrity and permeability of a cell layer13. Caco-2 cells are human epithelial colorectal adenocarcinoma cells with a structure and function similar to the differentiated small intestinal epithelial cells (e.g. exhibit microvilli). Caco-2 cells can be plated in wells as monolayers14,11. Other cell lines can be used, such as intestinal epithelial cells (IEC) or primary epithelial cells from human intestinal biopsies12. Co-culturing of enterocyte-like cells with immune cells in three-dimensional structure and within a microfluidic gut-on-chip has been shown to reflect better the physiology of the gut epithelium. Epi-IntestinalTM is an example of 3D human primary cell-based organotypic small intestinal model which allows evaluation of TEER and LY leakage assay (doi: <a href="https://doi.org/10.1007%2Fs11095-018-2362-0" rel="noopener noreferrer" target="_blank">10.1007/s11095-018-2362-0). </a> In vivo system In mice, one way to study intestinal paracellular permeability is by measuring the ability of fluorescein isothiocyanate (FITC)-dextran to cross from the lumen into the blood. After gavaging mice with FITC-dextran, the concentrations are measured in collected serum samples (doi: <a class="id-link" href="https://doi.org/10.3791/57032" rel="noopener" target="_blank">10.3791/57032</a>).

Human

UBERON:0000160

UBERON intestine

Not Specified Male Not Specified Female

Not Specified

All life stages

Not Specified

1.            Chelakkot, C., Ghim, J. & Ryu, S. H. Mechanisms regulating intestinal barrier integrity and its pathological implications. Exp. Mol. Med. 50, (2018). 2.            Groschwitz, K. R. & Hogan, S. P. Intestinal barrier function: Molecular regulation and disease pathogenesis. J. Allergy Clin. Immunol. 124, 3–20 (2009). 3.            Ghosh, S. S., Wang, J., Yannie, P. J. & Ghosh, S. Intestinal barrier dysfunction, LPS translocation, and disease development. J. Endocr. Soc. 4, 1–15 (2020). 4.            Sturgeon, C. & Fasano, A. Zonulin, a regulator of epithelial and endothelial barrier functions, and its involvement in chronic inflammatory diseases. Tissue Barriers 4, 1–19 (2016). 5.            Sturgeon, C., Lan, J. & Fasano, A. Zonulin transgenic mice show altered gut permeability and increased morbidity/mortality in the DSS colitis model. Ann N Y Acad Sci 1397, 130–142 (2017). 6.            Bischoff, S. C. et al. Intestinal permeability - a new target for disease prevention and therapy. BMC Gastroenterol. 14, 1–25 (2014). 7.            Qiu, W. et al. PUMA-mediated intestinal epithelial apoptosis contributes to ulcerative colitis in humans and mice. J. Clin. Invest. 121, 1722–1732 (2011). 8.            Hering, N. A., Fromm, M. & Schulzke, J. D. Determinants of colonic barrier function in inflammatory bowel disease and potential therapeutics. J. Physiol. 590, 1035–1044 (2012). 9.            Giron, L. B. et al. Plasma Markers of Disrupted Gut Permeability in Severe COVID-19 Patients. medRxiv 2020.11.13.20231209 (2021). 10.          Prasad, R. et al. Plasma microbiome in COVID-19 subjects: an indicator of gut barrier defects and dysbiosis Ram. BioRxiv (2021). 11.          Aguirre Valadez, J. M. et al. Intestinal permeability in a patient with liver cirrhosis. Ther. Clin. Risk Manag. 12, 1729–1748 (2016). 12.          Fedi, A. et al. In vitro models replicating the human intestinal epithelium for absorption and metabolism studies: A systematic review. J. Control. Release 335, 247–268 (2021). 13.          Lea, T. Epithelial Cell Models; General Introduction. in The Impact of Food Bioactives on Health: in vitro and ex vivo models (eds. Verhoeckx, K. et al.) 95–102 (Springer International Publishing, 2015). doi:10.1007/978-3-319-16104-4_9 14.          Li, B. R. et al. In Vitro and In Vivo Approaches to Determine Intestinal Epithelial Cell Permeability. J. Vis. Exp. 1–6 (2018). doi:10.3791/57032 15.          Ayehunie, S. et al. Human Primary Cell-Based Organotypic Microtissues for Modeling Small Intestinal Drug Absorption Seyoum. Pharm. Res. 35, 72 (2019). AOPWiki 2021-09-01T08:35:10

2025-04-27T13:31:37

Gut derived PAMPs, increased

PAMPs, increased

Cellular

AOPWiki 2026-04-23T22:47:01

2026-04-23T22:47:01

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

Inflammation, Liver

Organ

Approximately 29 million people in the European Union suffer from a chronic liver condition <a href="#cite_note-Blachier2013-1">[1]</a>. Inflammation is a crucial link that is related to many of these conditions, with the potential for the development of cirrhosis or primary liver cancer which represent the end-stage of liver pathology and are often associated with mortality: chronic hepatitis (A-E), non-alcoholic steatohepatitis (NASH) which is the progressive form of non-alcoholic fatty liver disease (NAFLD), primary biliary cirrhosis (PBC) or primary sclerosing cholangitis (PSC) <a href="#cite_note-Blachier2013-1">[1]</a>. Drug-induced liver injury (DILI) still is a major problem in drug development as its early detection is problematic, and acute liver inflammation is the most common symptom. DILI is the main cause for withdrawal of drugs from the pharmaceutical market <a href="#cite_note-2">[2]</a>. Liver inflammation is marked by an increased influx of neutrophils, following the secretion of signaling factors such as CXC chemokines and macrophage inflammatory protein 2 (MIP-2) from damaged cells <a href="#cite_note-3">[3]</a>. Kupffer cells (KCs), the resident macrophages of the liver and accounting for about 15-20% of total cell numbers in a healthy liver. They are the gatekeepers in the liver, as they monitor the blood that enters this organ <a href="#cite_note-Kermanizadeh2012-4">[4]</a><a href="#cite_note-Arrese2016-5">[5]</a>. Activation of KCs by activation of toll like receptors, for example, leads to the recruitment of further inflammatory cells as well as amplified KC activation. This, in turn, activates Hepatic stellate cells (HSCs) <a href="#cite_note-Arrese2016-5">[5]</a> which can link liver inflammation to further severe outcomes such as development of fibrosis A list of drugs generally known to induce DILI can be found here <a href="#cite_note-6">[6]</a>.

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? Liver inflammation is usually confirmed by analysis of histological features, marked by influx of inflammatory cells (mainly neutrophils) which can be stained by using Haematoxylin and eosin <a href="#cite_note-Huebsch2006-7">[7]</a>. In mice, neutrophil influx can be analysed using a mouse MPO ELISA kit for lysed tissue <a href="#cite_note-Kermanizadeh2012-4">[4]</a>. mRNA expression levels of inflammatory cytokines in tissue samples can be determined by using real-time PCR as described in <a href="#cite_note-Cui2011-8">[8]</a>. Plasma levels of pro-inflammatory cytokines can be analysed by enzyme linked immunosorbent assay) ELISA using commercial kits <a href="#cite_note-Ma2009-9">[9]</a>.

<a href="#cite_note-Huebsch2006-7">[7]</a>: human (representative for general application in patients, as liver inflammation is commonly found in patients with DILI) <a href="#cite_note-Cui2011-8">[8]</a><a href="#cite_note-Kermanizadeh2012-4">[4]</a><a href="#cite_note-Ma2009-9">[9]</a>: mouse (nanomaterial-induced) <a href="#cite_note-10">[10]</a>: rat (nanomaterial-induced)

UBERON:0002107

UBERON liver

High High Moderate

<ol class="references"> <li id="cite_note-Blachier2013-1">↑ <a href="#cite_ref-Blachier2013_1-0">1.0</a> <a href="#cite_ref-Blachier2013_1-1">1.1</a> Blachier M, Leleu H, Peck-Radosavljevic M, Valla DC, Roudot-Thoraval F. The burden of liver disease in Europe: a review of available epidemiological data. J Hepatol. 2013 Mar;58(3):593-608 </li> <li id="cite_note-2"><a href="#cite_ref-2">↑</a> Larrey D. Epidemiology and individual susceptibility to adverse drug reactions affecting the liver. Semin Liver Dis. 2002;22(2):145-55 </li> <li id="cite_note-3"><a href="#cite_ref-3">↑</a> Jaeschke H. Inflammation in response to hepatocellular apoptosis. Hepatology. 2002 Apr;35(4):964-6 </li> <li id="cite_note-Kermanizadeh2012-4">↑ <a href="#cite_ref-Kermanizadeh2012_4-0">4.0</a> <a href="#cite_ref-Kermanizadeh2012_4-1">4.1</a> <a href="#cite_ref-Kermanizadeh2012_4-2">4.2</a> Kermanizadeh A, Brown DM, Hutchison GR, Stone V. Engineered Nanomaterial Impact in the Liver following Exposure via an Intravenous Route–The Role of Polymorphonuclear Leukocytes and Gene Expression in the Organ. Journal of Nanomed & Nanotechnol 2012;04(01):1–7 </li> <li id="cite_note-Arrese2016-5">↑ <a href="#cite_ref-Arrese2016_5-0">5.0</a> <a href="#cite_ref-Arrese2016_5-1">5.1</a> Arrese M, Cabrera D, Kalergis AM, Feldstein AE. Innate Immunity and Inflammation in NAFLD/NASH. Dig Dis Sci. 2016 May;61(5):1294-303 </li> <li id="cite_note-6"><a href="#cite_ref-6">↑</a> Ortega-Alonso A, Stephens C, Lucena MI, Andrade RJ. Case Characterization, Clinical Features and Risk Factors in Drug-Induced Liver Injury. Int J Mol Sci. 2016 May 12;17(5) </li> <li id="cite_note-Huebsch2006-7">↑ <a href="#cite_ref-Huebsch2006_7-0">7.0</a> <a href="#cite_ref-Huebsch2006_7-1">7.1</a> Huebscher SG. Histological assessment of non-alcoholic fatty liver disease. Histopathol. 2006;49:450–465 </li> <li id="cite_note-Cui2011-8">↑ <a href="#cite_ref-Cui2011_8-0">8.0</a> <a href="#cite_ref-Cui2011_8-1">8.1</a> Cui Y, Liu H, Zhou M, Duan Y, Li N, Gong X, Hu R, Hong M, Hong F. Signaling pathway of inflammatory responses in the mouse liver caused by TiO2 nanoparticles. 2011; J. Biomed. Mater. Res. - Part A 96 A:221–229 </li> <li id="cite_note-Ma2009-9">↑ <a href="#cite_ref-Ma2009_9-0">9.0</a> <a href="#cite_ref-Ma2009_9-1">9.1</a> Ma L, Zhao J, Wang J, Liu J, Duan Y, Liu H, Li N, Yan J, Ruan J, Wang H, Hong F. The Acute Liver Injury in Mice Caused by Nano-Anatase TiO2. Nanoscale Res Lett. 2009 Aug 1;4(11):1275-85 </li> <li id="cite_note-10"><a href="#cite_ref-10">↑</a> Alarifi S, Ali ., Al-Doaiss AA, Ali BA, Ahmed M, Al-Khedhairy AA. Histologic and apoptotic changes induced by titanium dioxide nanoparticles in the livers of rats. Intern J Nanomed. 2013;8:3937–3943 </li> </ol> AOPWiki 2016-11-29T18:41:27

2017-09-16T10:16:41

Increase, Steatohepatitis

Organ

Steatohepatitis is characterized by hepatic steatosis accompanied by hepatocellular injury and lobular inflammation. It represents a pathological progression beyond simple steatosis and is a defining feature of metabolic dysfunction–associated steatohepatitis (MASH). Histologically, steatohepatitis includes: <ul> <li> Macrovesicular steatosis </li> <li> Hepatocyte ballooning degeneration </li> <li> Lobular inflammatory cell infiltration </li> <li> Mallory–Denk bodies (in some cases) </li> <li> Variable degrees of perisinusoidal fibrosis </li> </ul>

<h3>1. Histopathology (Primary Method)</h3> <ul> <li> Hematoxylin and eosin (H&E) staining </li> <li> NAFLD Activity Score (NAS) </li> <li> Steatosis–Activity–Fibrosis (SAF) scoring system </li> <li> Assessment of: <ul> <li> Steatosis grade </li> <li> Ballooning degeneration </li> <li> Lobular inflammation </li> </ul> </li> </ul> Histological scoring systems are considered the gold standard for detection. <h3>2. Serum Biomarkers</h3> <ul> <li> Elevated ALT (alanine aminotransferase) </li> <li> Elevated AST (aspartate aminotransferase) </li> <li> Inflammatory cytokines (TNF-α, IL-6) </li> </ul> Biochemical markers provide supportive but not definitive evidence. <h3>3. Molecular Markers</h3> <ul> <li> Increased expression of inflammatory genes (e.g., TNF-α, MCP-1) </li> <li> Markers of hepatocyte injury (e.g., cytokeratin-18 fragments) </li> </ul> <h3>4. Imaging (Clinical Context)</h3> <ul> <li> MRI-PDFF (steatosis quantification) </li> <li> Elastography (for associated fibrosis) </li> </ul> However, histology remains required for definitive diagnosis of steatohepatitis.

Steatohepatitis is a well-defined pathological entity in humans and is reproducibly induced in rodent models of metabolic dysfunction and lipotoxic stress. The KE is most applicable to: <ul> <li> Mammalian species with comparable hepatic architecture </li> <li> Conditions involving chronic metabolic stress </li> <li> Adult or metabolically mature organisms </li> </ul> The weight of evidence for this KE is strong due to consistent clinical and experimental characterization.

UBERON:0002107

UBERON liver

High Unspecific

High

All life stages

High AOPWiki 2017-11-13T12:47:00

2026-02-24T09:13:44

<upstream-id>36d1e467-ee44-4c58-901a-9aafad0cfa80</upstream-id> <downstream-id>c4241725-abd6-4425-b68c-e2d3bbfec987</downstream-id>

AOPWiki 2026-04-23T00:56:02

2026-04-23T00:56:02

<upstream-id>9e72cc88-99e1-44b7-9884-3f357ea6d4bd</upstream-id> <downstream-id>0f14ae6f-39e5-44e5-a350-51df4c4777e8</downstream-id>

AOPWiki 2026-04-27T21:19:09

2026-04-27T21:19:09

<upstream-id>c4241725-abd6-4425-b68c-e2d3bbfec987</upstream-id> <downstream-id>149f0cec-e59d-4a25-8850-a24c02e9cea5</downstream-id>

AOPWiki 2026-04-27T01:41:53

2026-04-27T01:41:53

<upstream-id>149f0cec-e59d-4a25-8850-a24c02e9cea5</upstream-id> <downstream-id>02977aab-cc0e-415f-9ebe-a81fd097d775</downstream-id>

AOPWiki 2026-04-27T01:42:17

2026-04-27T01:42:17

<upstream-id>02977aab-cc0e-415f-9ebe-a81fd097d775</upstream-id> <downstream-id>c2ac8fd5-6ce9-4962-8d0b-b3b615fa9b5c</downstream-id>

AOPWiki 2026-04-27T01:43:48

2026-04-27T01:43:48

<upstream-id>c2ac8fd5-6ce9-4962-8d0b-b3b615fa9b5c</upstream-id> <downstream-id>6e082c2d-9342-4e69-9b95-8ff4dffad639</downstream-id>

AOPWiki 2026-04-27T01:44:14

2026-04-27T01:44:14

<upstream-id>6e082c2d-9342-4e69-9b95-8ff4dffad639</upstream-id> <downstream-id>0f14ae6f-39e5-44e5-a350-51df4c4777e8</downstream-id>

AOPWiki 2026-04-27T01:44:33

2026-04-27T01:44:33

<upstream-id>0f14ae6f-39e5-44e5-a350-51df4c4777e8</upstream-id> <downstream-id>9c14a11e-88a3-45ab-95ec-8ab621a34643</downstream-id>

AOPWiki 2026-04-27T01:44:47

2026-04-27T01:44:47

<upstream-id>9c14a11e-88a3-45ab-95ec-8ab621a34643</upstream-id> <downstream-id>dbf1e7d1-e030-4b0a-81b6-235b46f4c576</downstream-id>

AOPWiki 2026-04-27T21:17:22

2026-04-27T21:17:22

<upstream-id>dbf1e7d1-e030-4b0a-81b6-235b46f4c576</upstream-id> <downstream-id>9e72cc88-99e1-44b7-9884-3f357ea6d4bd</downstream-id>

AOPWiki 2026-04-27T21:18:41

2026-04-27T21:18:41

<upstream-id>9e72cc88-99e1-44b7-9884-3f357ea6d4bd</upstream-id> <downstream-id>46fd64d5-8995-4a50-b92b-809e720a1312</downstream-id>

AOPWiki 2026-04-27T21:19:31

2026-04-27T21:19:31

Intestinal FXR inhibition leading to steatohepatitis via gut‑liver axis dysregulation

Intestinal FXR inhibition to steatohepatitis

Jung-Hwa Oh

Ga-Won Lee1,2, Jiin Lee1, Mi-Sun Choi1, Soojin Kim1, Yejin Do1,2, Hyun Jegal1,  Jung-Hwa Oh1,2   1Korea Institute of Toxicology, 141 Gajeong-ro, Yuseong-gu, Daejeon, 34114, Republic of Korea 2Department of Human and Environmental Toxicology, University of Science & Technology (UST), Daejeon, Republic of Korea

BY-SA

2.8

Steatohepatitis is the inflammatory form of steatotic liver disease and a major driver of progressive liver damage, fibrosis and hepatocellular carcinoma. The gut‑liver axis, and particularly bile acid–farnesoid X receptor (FXR) signalling, plays a central role in the development of steatohepatitis, but an AOP formalising the causal sequence from intestinal FXR inhibition to steatohepatitis has not been available.  This AOP describes how inhibition of intestinal FXR (MIE) can culminate in steatohepatitis (AO) through a linear sequence of well‑defined key events that integrate endocrine, metabolic, and barrier‑immune mechanisms. Inhibition of ileal FXR reduces FGF15/FGF19 secretion (KE1), derepressing hepatic CYP7A1 (KE2) and thereby increasing bile acid synthesis and intrahepatic bile acid burden (KE3). Under these conditions, bile salt export pump (BSEP/ABCB11) expression and function are reduced (KE4), contributing together with excess synthesis to quantitative and qualitative alterations of bile acid composition in bile and along the intestine (KE5). The resulting enrichment of hydrophobic and microbially derived bile acids, combined with loss of FXR‑mediated epithelial defence, impairs intestinal barrier integrity (KE6) and increases portal exposure to gut‑derived pathogen‑associated molecular patterns (PAMPs) such as lipopolysaccharide (KE7). On the hepatic side, bile acid overload and lipid accumulation promote oxidative stress (KE8), while PAMP‑ and damage‑associated signalling activate Kupffer cells and other immune cells, driving hepatic inflammation (KE9) that, together with steatosis, manifests histologically as steatohepatitis (AO). The biological plausibility of this AOP is high, as each KER follows established physiology of the FXR–FGF15/FGF19 axis, enterohepatic bile acid circulation, gut barrier biology, and inflammatory signalling in the liver. Essentiality of the central KEs is supported by intestinal and hepatic FXR knockout models, genetic and pharmacologic manipulation of FGF15/FGF19 and CYP7A1, experimental modulation of bile acid composition and intestinal permeability, and interventions that alter portal PAMP load, oxidative stress, and hepatic inflammation in rodent NASH and cholestasis models. Clinical and translational data showing dysregulation of FXR–FGF19 signalling, bile acid profiles, and gut–liver barrier function in patients with steatotic liver disease and steatohepatitis further support human relevance. This AOP is therefore suited to underpin assay development and integrated approaches to testing and assessment (IATA) for chemicals or drugs that perturb intestinal FXR and bile acid signalling, to help prioritise potential steatogenic or steatohepatitic liabilities and to provide a mechanistic context for regulatory interpretation of gut–liver axis–mediated liver injury. Steatohepatitis develops in the setting of steatotic liver disease when lipid overload is accompanied by hepatocellular injury and inflammation. Over the past decade, research has highlighted that this transition is strongly influenced by the gut–liver axis rather than by hepatic mechanisms alone. Intestinal FXR, highly expressed in the ileum, is a central regulator of bile acid composition, FGF15/FGF19 secretion and intestinal barrier function, all of which shape hepatic exposure to bile acids and microbial products. Dysregulation of this signalling axis has been repeatedly associated with steatosis, steatohepatitis and fibrosis in experimental models and in humans. This AOP focuses specifically on perturbations of intestinal FXR within the gut–liver axis as a biologically plausible and translationally relevant driver of steatohepatitis, with the aim of providing a structured mechanistic framework that can support assay development, IATA design and regulatory interpretation, without attempting to cover all possible causes or pathways leading to this liver outcome.

The development of this AOP was guided by a targeted, mechanism‑oriented literature strategy focused on three elements: (1) organ‑specific roles of FXR, with emphasis on intestinal FXR; (2) the FXR–FGF15/FGF19–CYP7A1 feedback loop and bile acid homeostasis; and (3) gut–liver axis processes linking bile acids, intestinal barrier function, microbial products and steatohepatitis. Initial scoping relied on expert knowledge and key reviews to propose the MIE, candidate KEs and a provisional linear sequence from intestinal FXR inhibition to steatohepatitis. Subsequent searches in major biomedical databases used combinations of terms such as “intestinal FXR”, “FGF15/FGF19”, “CYP7A1”, “bile acid pool/composition”, “BSEP/ABCB11”, “intestinal permeability”, “endotoxin/LPS”, “gut–liver axis” and “NASH/steatohepatitis”, together with “knockout”, “overexpression”, “agonist” or “inhibitor” to prioritise mechanistic and intervention studies. Titles/abstracts were first screened to retain experimental animal, in vitro and translational clinical studies with mechanistic endpoints, followed by full‑text review to identify direct gain‑ and loss‑of‑function evidence for each KE and KER, as well as consistent correlative data across models and species. The final AOP scope was intentionally limited to KEs and KERs with at least moderate support for biological plausibility, empirical linkage and essentiality in the context of intestinal FXR perturbation and steatohepatitis, in order to maximise clarity and reusability for regulatory applications.

Steatohepatitis represents a clinically recognized and pathologically defined stage of metabolic dysfunction–associated steatotic liver disease (MASLD), characterized by hepatic steatosis accompanied by hepatocellular injury and inflammation. It is a critical transition point between reversible metabolic steatosis and progressive, potentially irreversible liver pathology, including fibrosis, cirrhosis, and hepatocellular carcinoma. As such, an increase in steatohepatitis severity constitutes a biologically meaningful and adverse health outcome. <h2>Human Health Relevance</h2> Steatohepatitis (formerly NASH; now MASH under MASLD nomenclature) is associated with: <ul> <li> Increased risk of liver fibrosis progression </li> <li> Elevated liver-related morbidity and mortality </li> <li> Increased risk of hepatocellular carcinoma </li> <li> Higher overall cardiometabolic mortality </li> </ul> Histologically confirmed steatohepatitis is a strong predictor of disease progression compared to simple steatosis. Therefore, regulatory concern is substantially higher once inflammatory and hepatocellular injury components are present. <h2>Scientific Basis for Domain of Applicability</h2> <h3>Taxonomic Applicability</h3> The adverse outcome is highly relevant to mammals, particularly humans, due to conserved hepatic architecture, lipid metabolism, inflammatory signaling, and fibrogenic pathways. Rodent models (e.g., high-fat diet, Western diet, glucocorticoid exposure models) reliably reproduce key histopathological features of steatohepatitis, including: <ul> <li> Steatosis with hepatocyte ballooning </li> <li> Lobular inflammation </li> <li> Early perisinusoidal fibrosis </li> </ul> This cross-species concordance supports high biological plausibility within Mammalia. <h3>Life Stage Applicability</h3> The adverse outcome is most relevant in adolescent and adult life stages, where metabolic systems are fully developed and chronic exposure conditions can lead to progressive disease. While pediatric MASLD exists, the majority of mechanistic and regulatory evidence derives from adult populations and adult rodent models. <h3>Sex Applicability</h3> Steatohepatitis occurs in both males and females. Sex differences in susceptibility and progression rate have been reported, likely reflecting hormonal influences on lipid metabolism and inflammation. However, the pathological entity and its progression mechanisms are conserved across sexes. <h2>Weight of Evidence for Adversity</h2> The weight of evidence supporting steatohepatitis as an adverse outcome is strong based on: <ol> <li> Clinical Evidence <ul> <li> Histologically confirmed steatohepatitis predicts fibrosis progression and mortality. </li> <li> Longitudinal human studies demonstrate increased liver-related outcomes compared to simple steatosis. </li> </ul> </li> <li> Pathophysiological Evidence <ul> <li> Hepatocyte ballooning reflects cellular injury and cytoskeletal disruption. </li> <li> Inflammatory infiltration drives sustained tissue damage and fibrogenesis. </li> <li> Cytokine and TGF-β signaling link inflammation directly to fibrosis progression. </li> </ul> </li> <li> Consistency Across Models <ul> <li> Reproducible induction in multiple rodent models. </li> <li> Mechanistic concordance between experimental systems and human disease. </li> </ul> </li> <li> Irreversibility Consideration <ul> <li> While early steatohepatitis may be partially reversible, sustained inflammation significantly increases the probability of irreversible fibrosis. </li> </ul> </li> </ol> <h2>Regulatory Relevance</h2> An increase in steatohepatitis severity represents: <ul> <li> A clear adverse effect at the organ level </li> <li> A progression beyond adaptive metabolic perturbation </li> <li> A disease-defining pathological state recognized in clinical practice </li> </ul> From a regulatory perspective, this adverse outcome is relevant for: <ul> <li> Hazard identification </li> <li> Chronic toxicity assessment </li> <li> Endocrine and metabolic disruptor evaluation </li> <li> Integration into adverse outcome pathways supporting chemical prioritization </li> </ul> Because steatohepatitis is a well-defined diagnostic and pathological entity with established clinical consequences, it provides a robust anchor for AOP-based risk assessment frameworks.

adjacent

Moderate

High adjacent

Moderate

High adjacent

Moderate

High adjacent

Low

Moderate adjacent

Low

Moderate adjacent

Low

Moderate adjacent

Low

High adjacent

Low

Moderate adjacent

Low

Moderate adjacent

Moderate

High non-adjacent

Low

Moderate

This AOP is primarily applicable to mammals with a conserved FXR–FGF15/FGF19–CYP7A1 axis and enterohepatic bile acid circulation, notably rodents and humans. It is most strongly supported for adult life stages; while both sexes are represented in the underlying studies, sex‑specific differences in FXR and bile acid metabolism are not explicitly resolved and are therefore considered within the general adult domain of applicability. The overall weight of evidence is moderate‑to‑high. Biological plausibility of the key event sequence from intestinal FXR inhibition through altered bile acid synthesis and composition, intestinal barrier dysfunction, increased portal PAMP exposure, hepatic oxidative stress and inflammation to steatohepatitis is strong and aligned with current understanding of gut–liver axis biology. Empirical support for several central KERs (FXR–FGF15/FGF19–CYP7A1–bile acid burden, bile acid–barrier interactions, barrier/PAMP–driven hepatic inflammation) includes both genetic and pharmacologic gain‑ and loss‑of‑function data, as well as dose‑ and time‑dependent concordance in multiple rodent models. On this basis, the AOP is suitable as a qualitative mechanistic framework for regulatory uses such as early hazard flagging and prioritisation of intestinal FXR‑modulating substances, design and interpretation of gut–liver axis testing strategies, and weight‑of‑evidence support in risk assessment where steatohepatitis is a concern. Quantitative relationships and explicit modulating factors (diet, microbiota, genetics, sex) are not yet fully defined, so use in strictly quantitative prediction should be considered with caution. This AOP is intended to apply to species, life stages and biological contexts in which the intestinal FXR–FGF15/FGF19–CYP7A1 axis and enterohepatic bile acid circulation are conserved and functionally important. Taxa <ul> <li> The primary taxonomic domain is mammals, especially rodents (mouse, rat) and humans, where FXR, FGF15/FGF19, FGFR4/β‑Klotho, CYP7A1, BSEP and gut–liver barrier–immune mechanisms are well described. </li> <li> Applicability to non‑mammalian vertebrates (e.g. fish, birds) is uncertain, because of species differences in bile acid chemistry, FXR ligands, and the presence and function of FGF15/19 and enterohepatic circulation. At present, the AOP should not be extrapolated beyond mammals without additional species‑specific data. </li> </ul> Life stage <ul> <li> Evidence underpinning the AOP comes predominantly from adult animals and adult human patients with steatotic liver disease or cholestatic conditions. </li> <li> Data in juvenile or aged individuals are more limited, and developmental changes in bile acid metabolism and intestinal barrier function may alter sensitivity and kinetics. Consequently, the AOP is most robustly supported for adolescent to adult life stages. </li> </ul> Sex <ul> <li> Both males and females are represented in the experimental and clinical literature that informs this AOP, and the core FXR–FGF15/FGF19–CYP7A1 axis and gut–liver barrier mechanisms are conserved between sexes. </li> <li> Known sex differences in bile acid pool size, composition and FXR activity may influence quantitative responses and susceptibility, but do not invalidate the qualitative sequence of key events. Thus, the AOP is considered applicable to both sexes, while recognising that sex may act as a modulating factor. </li> </ul> Other biological context <ul> <li> The AOP is most relevant under conditions where enterohepatic bile acid cycling is intact and the intestinal barrier and microbiota are present (i.e. conventional, non‑germ‑free animals and humans). </li> <li> Dietary factors (e.g. high‑fat/high‑sugar diets), metabolic status (obesity, insulin resistance) and concomitant gut microbiota changes can modulate the sensitivity and magnitude of the key events, but the qualitative event sequence is expected to hold across a range of such backgrounds. </li> </ul>

<ul> <li>MIE – Intestinal FXR, inhibition</li> </ul> - Essentiality: Moderate (mainly indirect, pathway‑level evidence) Intestine‑specific or whole‑body FXR‑deficient mice show reduced ileal FGF15/FGF19 expression, increased hepatic CYP7A1 and bile acid synthesis, expansion of the bile acid pool, and aggravated steatosis, inflammation and fibrosis under dietary or toxic challenges compared with wild‑type controls, indicating that loss of intestinal FXR signalling worsens multiple downstream KEs and the AO. Conversely, pharmacological activation of intestinal FXR with gut‑restricted or gut‑biased agonists increases ileal FGF15/FGF19, suppresses CYP7A1, normalises bile acid synthesis and attenuates hepatic steatosis and inflammation in NASH models. Because most interventions modulate both intestinal and hepatic FXR and other pathways simultaneously, the essentiality of intestinal FXR inhibition per se is supported primarily by pathway‑level, but consistent, evidence and is therefore graded as Moderate. KE1 – Ileal FGF15/FGF19 secretion, decreased Essentiality: High (direct evidence) Mice lacking intestinal FXR or Fgf15 exhibit markedly reduced ileal FGF15 expression, increased hepatic CYP7A1 and C4, elevated bile acid synthesis, enlarged bile acid pool and worsened steatosis and liver injury under dietary challenge compared with wild‑type animals, demonstrating that loss of KE1 enhances multiple downstream KEs. Conversely, restoring or enhancing FGF15/FGF19 signalling by FGF19 analogs, adenoviral FGF19 expression or FXR agonists suppresses CYP7A1, reduces bile acid synthesis and intrahepatic bile acid burden, and improves steatosis, inflammation and fibrosis endpoints in rodent models. These gain‑ and loss‑of‑function data directly show that blocking or increasing KE1 has the expected impact on downstream KEs and the AO, supporting a High essentiality rating. <ul> <li>KE2 – Hepatic CYP7A1 expression/activity, increased</li> </ul> - Essentiality: High (direct evidence) Transgenic overexpression of Cyp7a1 increases conversion of cholesterol to bile acids, expands the bile acid pool and alters bile acid composition, which is accompanied by changes in liver injury and metabolic phenotypes, whereas Cyp7a1 knockout mice display a reduced bile acid pool and decreased bile acid synthesis. In FXR/FGF15/FGF19‑manipulated models, suppression of CYP7A1 consistently lowers bile acid synthesis and intrahepatic bile acid burden and mitigates downstream injury, while failure to suppress CYP7A1 maintains or worsens these outcomes. Together, these specifically designed gain‑ and loss‑of‑function studies show that modulating KE2 directly controls downstream KEs related to bile acid burden and contributes to the AO, justifying a High essentiality rating. <ul> <li>KE3 – Intrahepatic bile acid burden, increased</li> </ul> - Essentiality: High (direct evidence) Experimental elevation of intrahepatic bile acid levels by bile acid loading, increased synthesis or impaired export produces cholestatic liver injury with hepatocellular damage, inflammation and fibrosis in animal models, demonstrating that increased KE3 can by itself drive downstream KEs. Conversely, reducing intrahepatic bile acid burden with bile acid sequestrants, FXR or FGF19 agonists, or inhibitors of bile acid synthesis decreases hepatic bile acid content and consistently attenuates oxidative stress, inflammation, fibrosis and steatohepatitis severity. These interventions directly link quantitative changes in KE3 to corresponding changes in multiple downstream KEs and the AO, supporting High essentiality. <ul> <li>KE4 – Hepatic BSEP (ABCB11) expression/function, decreased</li> </ul> - Essentiality: Moderate (direct for cholestasis, indirect in this AOP) Genetic BSEP deficiency (PFIC2), Bsep knockout models and pharmacologic BSEP inhibition cause impaired canalicular bile acid export, marked intrahepatic bile acid accumulation and cholestatic liver injury with inflammation and fibrosis, indicating that loss of KE4 can be sufficient to exacerbate downstream KEs related to bile acid burden and liver damage. Restoration or upregulation of BSEP expression and function, for example by FXR agonists or geniposide, improves bile flow, lowers hepatic bile acid levels and ameliorates markers of liver injury and fibrosis. However, in the specific context where bile acid overproduction and other perturbations co‑occur, BSEP is one of several contributors to KE3 and KE5 rather than a uniquely isolated node, so its essentiality in this linear AOP is best characterised as Moderate, supported by strong cholestasis data but more indirect evidence for the full gut–liver steatohepatitis sequence. <ul> <li>KE5 – Bile acid composition in bile and intestine, altered</li> </ul> - Essentiality: Moderate (indirect but consistent evidence) Experimental manipulation of bile acid composition—through genetic or pharmacologic modification of bile acid synthesis enzymes, intestinal transporters or microbial bile salt hydrolases—shifts the balance between primary and secondary, conjugated and unconjugated, and hydrophilic and hydrophobic bile acids, and these shifts are associated with parallel changes in intestinal barrier integrity, gut microbiota, hepatic inflammation and fibrosis. Interventions that maintain a more conjugated, less hydrophobic bile acid pool protect barrier function and reduce liver injury, whereas enrichment of toxic secondary or hydrophobic bile acids aggravates barrier dysfunction and hepatic inflammation. Because these studies typically modulate bile acid amount, composition and receptor signalling together, the essentiality of KE5 is well supported but largely indirect, and thus graded as Moderate. <ul> <li>KE6 – Intestinal barrier integrity, decreased</li> </ul> - Essentiality: High (direct evidence) In models of fatty liver disease and cholestatic liver injury, experimental disruption of the intestinal barrier (e.g. DSS colitis, MCD diet, other mucosal insults) increases intestinal permeability, portal endotoxin levels and hepatic inflammation and fibrosis compared with barrier‑intact controls, demonstrating that inducing KE6 exacerbates downstream KEs and the AO. Conversely, preserving or restoring barrier integrity using probiotics, prebiotics, antibiotics, FXR/TGR5/PXR agonists or tight junction‑stabilising agents reduces intestinal leakiness, lowers portal PAMP exposure and significantly attenuates hepatic inflammation and fibrotic progression. These specifically designed interventions clearly show that blocking or reversing KE6 reduces the magnitude of downstream KEs and the AO, supporting a High essentiality rating. <ul> <li>KE7 – Portal exposure to gut-derived PAMPs (e.g. LPS), increased</li> </ul> - Essentiality: High (direct evidence) Diet‑induced NASH, alcohol, MCD diet and colitis models consistently demonstrate that increased portal or systemic levels of LPS and other bacterial products are associated with stronger hepatic inflammatory responses and fibrosis compared with animals with lower PAMP exposure. Direct reduction of PAMP load through broad‑spectrum antibiotics, germ‑free conditions, TLR4/MyD88 knockout or LPS neutralisation diminishes hepatic inflammatory signalling, inflammatory infiltrates and fibrotic progression, even in the presence of persistent steatosis or metabolic stress. These data provide direct evidence that modulating KE7 in either direction has predictable effects on downstream KEs and the AO, supporting High essentiality. <ul> <li>KE8 – Hepatic oxidative stress, increased</li> </ul> - Essentiality: Moderate (amplifier rather than absolute requirement) Numerous NASH, cholestasis and toxin models show that hepatic oxidative stress markers (ROS, MDA, 4‑HNE, 8‑OHdG) increase in step with steatosis, inflammation and fibrosis, and that higher levels of oxidative stress correlate with more severe disease. Antioxidant therapies, Nrf2 activators, mitochondrial protectants and inhibitors of lipid peroxidation reduce oxidative stress and often attenuate hepatic inflammation and fibrotic progression, indicating that lowering KE8 can mitigate downstream KEs and the AO. Nevertheless, some degree of steatohepatitis can still develop in models where oxidative stress is partially controlled, reflecting parallel contributions from PAMP‑driven inflammation and direct bile acid toxicity, so KE8 is best viewed as an important amplifier with Moderate essentiality rather than an absolutely indispensable step. <ul> <li>KE9 – Hepatic inflammation, increased</li> </ul> - Essentiality: High (direct evidence) Across diet‑induced NASH, cholestatic liver injury and FXR/FGF19 dysregulation models, hepatic inflammation—measured by inflammatory infiltrates, cytokine and chemokine expression and histological inflammation scores—is closely associated with progression from simple steatosis to steatohepatitis and fibrosis. Interventions that directly suppress hepatic inflammatory signalling, including FXR agonists, bile acid modulators, TLR4/MyD88 inhibitors, anti‑TNF agents and other anti‑inflammatory therapies, reduce inflammatory markers and are frequently accompanied by improvements in ballooning, steatosis and fibrosis, and even regression of established steatohepatitis in some models. These studies show that attenuating KE9 consistently reduces downstream AO severity, supporting a High essentiality rating for hepatic inflammation in this AOP.

<ul> <li>KER1: Intestinal FXR, inhibition → Ileal FGF15/FGF19 secretion, decreased</li> </ul> - Biological plausibility: Intestinal FXR directly binds FXR response elements in the FGF15/FGF19 promoter, and bile acid–induced FXR activation in the distal ileum is a well‑established driver of FGF15/FGF19 transcription and secretion. Loss or inhibition of intestinal FXR is therefore expected to reduce FGF15/FGF19 expression as a direct transcriptional consequence. - Empirical support: Intestine‑specific and whole‑body FXR knockout mice show markedly reduced ileal Fgf15 and circulating FGF15/FGF19 under basal and bile acid–stimulated conditions, whereas pharmacologic FXR agonists robustly increase FGF15/FGF19 in rodents and humans. Time‑course and diurnal studies confirm that FXR activity and FGF15/FGF19 levels fluctuate in parallel. Overall, empirical support is strong and consistent. - Quantitative understanding: Dose–response data show graded induction of ileal FGF15/FGF19 with increasing bile acid or FXR agonist exposure, but fully quantitative models linking a defined decrement in intestinal FXR activity to FGF15/FGF19 output across species and conditions are not yet established. Quantitative understanding is therefore considered Moderate. <ul> <li>KER2: Ileal FGF15/FGF19 secretion, decreased → Hepatic CYP7A1 expression/activity, increased</li> </ul> - Biological plausibility: FGF15/FGF19 is the key endocrine mediator of negative feedback from intestine to liver, acting via FGFR4/β‑Klotho and downstream signalling (e.g. ERK/JNK/Src) to repress CYP7A1 transcription. Thus, reduced FGF15/FGF19 signalling is expected to derepress CYP7A1 and increase bile acid synthesis. - Empirical support: Fgf15‑deficient or FGFR4/β‑Klotho‑deficient mice show increased hepatic Cyp7a1 expression, elevated C4 and higher bile acid synthesis, whereas exogenous FGF19 or FXR agonists that restore FGF19 signalling reduce CYP7A1 and bile acid synthesis in rodents and humans. Human studies show an inverse quantitative correlation between serum FGF19 and C4 (a CYP7A1 activity surrogate). Empirical support is strong and consistent across models. - Quantitative understanding: Good quantitative relationships between FGF19 and C4 have been demonstrated in humans, and dose–response data for FGF19 analogues on CYP7A1/C4 exist in clinical and preclinical studies. However, integrated models spanning intestinal FXR perturbation → FGF15/FGF19 → hepatic CYP7A1 across species and contexts are incomplete; quantitative understanding is graded as Moderate. <ul> <li>KER3: Hepatic CYP7A1 expression/activity, increased → Intrahepatic bile acid burden, increased</li> </ul> - Biological plausibility: CYP7A1 catalyses the rate‑limiting step in the classic pathway of bile acid synthesis from cholesterol, so increased CYP7A1 activity is expected to increase de novo bile acid production and, unless fully compensated by excretion, increase hepatic bile acid burden and pool size. - Empirical support: Cyp7a1 transgenic overexpression in mice increases bile acid synthesis, expands the bile acid pool and alters hepatic and biliary bile acid levels, whereas Cyp7a1 knockout reduces pool size and synthesis. In FXR/FGF19‑manipulated models, higher CYP7A1 expression is consistently associated with higher intrahepatic bile acid content and vice versa. Empirical support is strong. - Quantitative understanding: Transgenic and knockout studies provide semi‑quantitative relationships (e.g. approximately two‑fold increase in CYP7A1 activity leading to approximately 2–2.5‑fold expansion of the bile acid pool), but precise dose–response curves and human‑relevant quantitative functions are limited. Quantitative understanding is considered Moderate. <ul> <li>KER4: Intrahepatic bile acid burden, increased → Hepatic BSEP (ABCB11) expression/function, decreased</li> </ul> - Biological plausibility: Under physiological conditions, bile acids activate FXR to induce BSEP; however, sustained bile acid overload combined with cholestatic and inflammatory stress can downregulate BSEP transcription and impair its localisation and function via ER stress, inflammatory cytokines (e.g. IL‑1β, TNF‑α) and altered FXR signalling. Thus, pathological increases in intrahepatic bile acid burden are plausibly linked to reduced effective BSEP function. - Empirical support: Cholestatic models and drug‑induced liver injury show that intrahepatic bile acid accumulation is accompanied by decreased BSEP mRNA/protein, mislocalisation of BSEP at the canalicular membrane and worsened bile acid retention. Interventions that reduce bile acid burden or inflammation partially restore BSEP expression and function. However, many studies manipulate bile acids, inflammation and FXR together, making it difficult to isolate bile acid burden as the sole driver of BSEP changes. Empirical support is therefore Moderate and context‑dependent. - Quantitative understanding: There are no well‑defined quantitative response–response functions linking a specific rise in intrahepatic bile acid levels to a defined decrement in BSEP expression/function across conditions. Quantitative understanding is Low–Moderate and best considered Low for AOP purposes. <ul> <li>KER5: Intrahepatic bile acid burden / BSEP dysfunction → Bile acid composition in bile and intestine, altered</li> </ul> - Biological plausibility: Increased bile acid synthesis (via CYP7A1) and impaired canalicular export (via reduced BSEP) together modify the flux and routing of bile acids through the biliary tree and intestine, affecting the relative abundance of primary vs secondary, conjugated vs unconjugated and hydrophilic vs hydrophobic bile acids. These changes are further shaped by altered intestinal re‑uptake and microbial metabolism. It is therefore plausible that bile acid overproduction plus export dysfunction leads to altered bile acid composition in bile and the gut lumen. - Empirical support: Cyp7a1 transgenic and cholestatic models exhibit both increased bile acid pool size and clear shifts in bile acid species (e.g. CA/CDCA ratio, secondary bile acid fractions), while BSEP deficiency/inhibition and FXR disruption also modify biliary and intestinal bile acid profiles. Studies combining modern bile acid profiling with genetic or pharmacologic manipulations document consistent compositional changes, although amount and composition are often co‑modulated. Empirical support is Moderate‑to‑strong but usually not specific to a single upstream KE. - Quantitative understanding: High‑resolution analytical methods allow quantitative measurement of individual bile acids, but formal quantitative models predicting compositional shifts from a defined increase in CYP7A1 activity or bile acid burden are lacking. Quantitative understanding of this KER is Low–Moderate and best treated as Low in regulatory contexts. <ul> <li>KER6: Bile acid composition in bile and intestine, altered → Intestinal barrier integrity, decreased</li> </ul> - Biological plausibility: Bile acids act both as detergents and signalling molecules. Enrichment of hydrophobic, deconjugated or secondary bile acids increases epithelial toxicity, tight junction disruption and mucosal inflammation, whereas more hydrophilic/conjugated bile acids can be barrier‑protective via FXR/TGR5 signalling. A shift towards more toxic bile acid profiles is therefore mechanistically expected to impair intestinal barrier integrity. - Empirical support: In vivo, diets and interventions that increase colonic exposure to hydrophobic or secondary bile acids are associated with increased permeability and reduced tight junction proteins, while maintaining a more conjugated or less hydrophobic pool (e.g. by microbial BSH inhibition or specific bile acid supplementation) preserves or improves barrier function and reduces liver injury. In vitro, exposure of intestinal epithelial cells or organoids to higher concentrations of deoxycholic or lithocholic acid disrupts tight junctions and increases paracellular permeability, whereas more hydrophilic bile acids are less damaging and sometimes protective. Empirical support is Moderate‑to‑strong but often involves concomitant microbiota and receptor effects. - Quantitative understanding: Some studies report concentration‑dependent barrier disruption for specific bile acids, but comprehensive quantitative functions linking complex in vivo composition changes to barrier integrity are not available. Quantitative understanding is Low–Moderate and for this AOP best classified as Low. <ul> <li>KER7: Intestinal barrier integrity, decreased → Portal exposure to gut‑derived PAMPs, increased</li> </ul> - Biological plausibility: The intestinal barrier—epithelial cells, tight junctions and mucus—physically restricts luminal microbes and their products from entering the portal circulation. Barrier disruption increases paracellular and transcellular translocation of bacteria and PAMPs (e.g. LPS, peptidoglycan, flagellin), making an increase in portal PAMP exposure a direct and plausible consequence. - Empirical support: In multiple models (DSS colitis, MCD diet, high‑fat diet, alcohol), increased intestinal permeability (measured by FITC‑dextran flux, zonulin elevation and tight junction loss) coincides with higher portal or systemic LPS and bacterial DNA levels. Barrier‑protective interventions reduce both permeability and portal endotoxin levels, while deliberate barrier injury increases PAMP translocation. Empirical support is strong and consistent. - Quantitative understanding: Several studies show correlations between quantitative measures of permeability and portal endotoxin, but system‑level quantitative models are limited and often confounded by changes in microbiota, motility and bile flow. Quantitative understanding is Low–Moderate and best considered Low. <ul> <li>KER8: Portal exposure to gut‑derived PAMPs, increased → Hepatic oxidative stress, increased</li> </ul> - Biological plausibility: Portal PAMPs (e.g. LPS, peptidoglycan, bacterial DNA) engage TLR4 and other pattern‑recognition receptors on Kupffer cells and hepatic endothelial cells, triggering NADPH oxidase (NOX2) activation, mitochondrial electron transport chain disruption and NF‑κB–mediated pro‑oxidant gene expression, all of which are well‑established mechanisms of ROS generation in the liver. Increased portal PAMP load is therefore mechanistically expected to elevate intrahepatic ROS, lipid peroxidation and oxidative DNA damage independently of, and in addition to, the contribution of bile acid overload and steatosis. - Empirical support: Rodent models of endotoxaemia and gut‑derived PAMP exposure show elevated hepatic ROS, MDA, 4‑HNE and 8‑OHdG levels alongside Kupffer cell activation and inflammatory responses. TLR4/MyD88 knockout or LPS neutralisation reduces both oxidative stress markers and downstream hepatic inflammation, whereas direct LPS administration to steatotic animals increases hepatic ROS in a dose‑dependent manner. Targeted interventions using NADPH oxidase inhibitors (e.g. apocynin, DPI) or NOX2 KO models further show that blocking PAMP‑induced ROS generation can partially uncouple oxidative stress from downstream inflammation, providing mechanistically direct evidence for this KER. Overall, empirical support is Moderate. - Quantitative understanding: Some dose–time‑course studies document graded increases in hepatic oxidative stress markers in response to defined LPS doses in rodents, but fully parameterised quantitative models linking portal PAMP concentration to specific ROS output across species and metabolic states are not yet available. Confounding by simultaneous inflammatory and metabolic changes further limits quantitative interpretation. Quantitative understanding is therefore Low. <ul> <li>KER9: Hepatic oxidative stress, increased → Hepatic inflammation, increased</li> </ul> - Biological plausibility: ROS and lipid peroxidation products generate damage‑associated molecular patterns (DAMPs) that activate Kupffer cells and hepatic stellate cells via pattern‑recognition and stress‑response pathways, amplifying inflammatory cytokine production and recruiting immune cells. Oxidative stress is therefore a plausible proximal driver and amplifier of hepatic inflammation, acting in concert with PAMP‑driven and bile acid–driven immune activation. - Empirical support: In many NASH and toxin models, increased oxidative stress co‑localises with areas of inflammatory infiltration, and higher ROS/oxidative damage is associated with stronger inflammatory responses. Antioxidant and Nrf2‑activating interventions that reduce oxidative stress often decrease inflammatory cytokine expression and inflammatory cell infiltration, though not always to baseline, indicating a contributory but not exclusive role. Empirical support is Moderate. - Quantitative understanding: There is limited quantitative information on exact thresholds of oxidative stress required to trigger specific levels of hepatic inflammation, and multiple parallel pathways (e.g. PAMP‑driven signalling, direct bile acid toxicity) complicate modelling. Quantitative understanding is therefore Low. <ul> <li>KER10: Hepatic inflammation, increased → Intestinal barrier integrity, decreased (gut–liver feedback)</li> </ul> - Biological plausibility: Hepatic inflammation releases pro‑inflammatory cytokines and altered bile acid mixtures into the systemic circulation and bile, which can impair epithelial turnover, disrupt tight junctions and disturb mucus and antimicrobial defences in the intestine. These effects, together with inflammation‑induced changes in bile acid composition and microbiota, provide a coherent mechanistic basis for liver inflammation to further weaken intestinal barrier integrity and reinforce gut–liver axis dysregulation. - Empirical support: Patients with advanced chronic liver disease or severe steatohepatitis frequently show increased intestinal permeability that correlates with global liver dysfunction and systemic inflammation, and not solely with primary gut disease. In animal models, interventions that mainly reduce hepatic inflammation or cholestasis (e.g. FXR agonists, anti‑inflammatory or bile acid–modulating drugs) often improve barrier markers and microbiota composition, suggesting a liver‑to‑gut feedback effect, although most studies do not isolate hepatic inflammation as the only variable. Overall, empirical support is Moderate. - Quantitative understanding: Available human and animal data consistently show that intestinal permeability tends to be higher in more advanced or inflamed liver disease, but quantitative response–response relationships between defined changes in hepatic inflammation and specific barrier endpoints are scarce and heterogeneous. This KER currently has Low quantitative understanding: the direction and plausibility are clear, but no robust predictive function can yet be defined. <ul> <li>KER11: Hepatic inflammation, increased → Steatohepatitis (AO)</li> </ul> - Biological plausibility: Steatohepatitis is defined histologically by the co‑occurrence of hepatic steatosis, hepatocellular ballooning and lobular inflammation, with or without fibrosis; hepatic inflammation is therefore not merely associated with but definitionally required for the diagnosis of steatohepatitis. Sustained hepatic inflammatory signalling drives hepatocellular injury and ballooning via cytotoxic cytokines (e.g. TNF‑α, IL‑1β), activates hepatic stellate cells and promotes fibrogenesis, and perpetuates lipid accumulation by impairing fatty acid oxidation and lipid export, thereby completing the histological picture of steatohepatitis. Anti‑inflammatory interventions in NASH models consistently reduce NAS inflammation and ballooning sub‑scores together, confirming that inflammation is a direct upstream driver of the AO. - Empirical support: In rodent NASH models (high‑fat diet, MCD diet, STAM, CDAHFD), the severity of hepatic inflammation as measured by inflammatory cell infiltration, cytokine levels and NAS inflammation scores closely tracks the development and severity of steatohepatitis and fibrosis. Genetic or pharmacologic suppression of hepatic inflammatory signalling—including FXR agonists, TLR4/MyD88 inhibition, anti‑TNF agents, NLRP3 inflammasome inhibitors and PPAR agonists—consistently reduces hepatic inflammation scores and simultaneously improves ballooning, steatosis and fibrosis, with some studies demonstrating histologic resolution of steatohepatitis. In clinical trials for NASH (e.g. obeticholic acid, selonsertib, lanifibranor), reduction in hepatic inflammation is a key primary or secondary endpoint and correlates with overall histologic improvement, further supporting the essentiality of hepatic inflammation for the AO in humans. Empirical support is strong. - Quantitative understanding: Clinical and preclinical studies provide semi‑quantitative data showing that higher baseline inflammation scores (NAS inflammation sub‑score, lobular inflammation grade) are associated with greater likelihood and severity of steatohepatitis, and that reductions in inflammation score of ≥1 point correlate with overall histologic improvement. However, fully parameterised response–response functions that precisely translate a defined magnitude of hepatic inflammation into a predicted NAS or steatohepatitis severity score are not yet established, partly because of the composite and multi‑factorial nature of the AO definition. Quantitative understanding is therefore Moderate, being better supported than most intermediate KERs but not yet at the level required for formal qAOP modelling.

<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>Diet composition (high fat/fructose, low fibre, choline deficiency)</td> <td>Increases intestinal BA exposure and BA pool size, promotes hepatic steatosis and low‑grade inflammation; amplifies FGF15/19 suppression, CYP7A1 induction and severity of downstream inflammatory and barrier events, accelerating progression to the AO.</td> <td>KER1, KER2, KER3, KER5, KER6, KER8, KER9, KER11</td> </tr> <tr> <td>Gut microbiota composition and function (dysbiosis)</td> <td>Alters BA deconjugation/conversion (secondary BA formation), reduces SCFA production, impairs FXR/TGR5 signalling and mucus integrity; increases BA‑driven epithelial toxicity and PAMP leakage, enhancing hepatic inflammation via the gut–liver feedback loop.</td> <td>KER5, KER6, KER7, KER8, KER10</td> </tr> <tr> <td>FXR–FGF15/19 axis genetic variation (NR1H4, FGF19, FGFR4, β‑Klotho)</td> <td>Alters the sensitivity and gain of the FXR–FGF15/19–CYP7A1 feedback loop; determines how strongly a given level of intestinal FXR inhibition translates to FGF15/19 reduction, CYP7A1 derepression and intrahepatic BA burden.</td> <td>KER1, KER2, KER3</td> </tr> <tr> <td>Systemic inflammation / circulating cytokines (TNF‑α, IL‑1β, IL‑6)</td> <td>Represses BSEP expression and disrupts FXR signalling; impairs intestinal tight junctions and primes hepatic immune cells, amplifying both cholestatic and inflammatory progression along the AOP, including the liver‑to‑gut feedback.</td> <td>KER4, KER5, KER10, KER11</td> </tr> <tr> <td>Concomitant liver insults (alcohol, hepatotoxic drugs, viral hepatitis)</td> <td>Additively or synergistically increases intrahepatic BA retention, oxidative stress and hepatic inflammation; may lower the threshold at which upstream KEs lead to steatohepatitis.</td> <td>KER3, KER4, KER9, KER11</td> </tr> </tbody> </table> </div>

Based on the evidence assembled across all KERs in this AOP, the overall quantitative understanding is semi‑quantitative and heterogeneous. The proximal steps of the pathway are most consistently characterised in quantitative terms. Dose–response relationships between bile acid or FXR agonist/antagonist exposure and ileal FGF15/FGF19 expression and secretion have been demonstrated in vitro, in rodent ileal tissue and in human intervention studies (KER1). A robust inverse relationship between circulating FGF19 and serum C4 (a surrogate for hepatic CYP7A1 activity) has been documented in multiple human cohorts and pharmacologic studies, providing a reasonably well‑defined quantitative link spanning KER1 and KER2. Semi‑quantitative relationships between CYP7A1 activity and bile acid pool size (approximately two‑fold change in CYP7A1 yielding approximately two‑ to 2.5‑fold change in pool) are available from transgenic and knockout rodent models, supporting a moderate level of quantitative understanding for KER3. For the intermediate KERs involving BSEP and bile acid composition (KER4, KER5), quantitative data are more limited. While mass spectrometry–based bile acid profiling allows precise compositional measurement, formal quantitative models linking defined changes in CYP7A1 activity or BA burden to specific shifts in species composition are not yet established. For KER6, concentration–response data for specific bile acids and intestinal barrier endpoints exist in vitro, but comprehensive in vivo quantitative functions are lacking. For KER7, correlations between permeability indices and portal endotoxin levels have been reported, but predictive quantitative models are not available. For the more distal KERs (KER8 through KER11 and the feedback KER10), evidence is predominantly correlative or categorical. The magnitudes of oxidative stress and hepatic inflammation scale with the degree of BA overload and PAMP exposure, and intervention studies provide directional evidence, but precise dose–response and time–course functions applicable across species and metabolic contexts are sparse. The gut–liver feedback KER (KER10) has the weakest quantitative support, with only cross‑sectional clinical associations and no parameterised response–response function currently available. Modulating factors such as diet, metabolic status, gut microbiota composition, sex and age introduce additional variability that complicates quantitative extrapolation, particularly to humans. Species differences in bile acid pool composition and FXR ligand potency further limit direct translation of rodent dose–response data. Overall, while the direction and relative sensitivity of most KERs are supported by multiple datasets, fully parameterised response–response functions suitable for formal quantitative AOP (qAOP) modelling exist only for selected proximal parts of the pathway (KER1–KER3). The AOP is therefore currently best applied in a qualitative or semi‑quantitative manner to support mechanistic interpretation, hazard prioritisation and testing strategy design, rather than for precise numerical prediction of steatohepatitis incidence or severity.

1. Simbrunner B, Hofer BS, Schwabl P, Zinober K, Petrenko O, Fuchs C, Semmler G, Marculescu R, Mandorfer M, Datz C, Trauner M, Reiberger T. FXR-FGF19 signaling in the gut-liver axis is dysregulated in patients with cirrhosis and correlates with impaired intestinal defence. Hepatol Int. 2024 Jun;18(3):929-942. doi: 10.1007/s12072-023-10636-4. Epub 2024 Feb 8. PMID: 38332428; PMCID: PMC11126514. 2. Hernandez GV, Smith VA, Melnyk M, Burd MA, Sprayberry KA, Edwards MS, Peterson DG, Bennet DC, Fanter RK, Columbus DA, Steibel JP, Glanz H, Immoos C, Rice MS, Santiago-Rodriguez TM, Blank J, VanderKelen JJ, Kitts CL, Piccolo BD, La Frano MR, Burrin DG, Maj M, Manjarin R. Dysregulated FXR-FGF19 signaling and choline metabolism are associated with gut dysbiosis and hyperplasia in a novel pig model of pediatric NASH. Am J Physiol Gastrointest Liver Physiol. 2020 Mar 1;318(3):G582-G609. doi: 10.1152/ajpgi.00344.2019. Epub 2020 Jan 31. PMID: 32003601; PMCID: PMC7099491. 3. Kliewer SA, Mangelsdorf DJ. Bile Acids as Hormones: The FXR-FGF15/19 Pathway. Dig Dis. 2015;33(3):327-31. doi: 10.1159/000371670. Epub 2015 May 27. PMID: 26045265; PMCID: PMC4465534. 4. Schumacher JD, Guo GL. Pharmacologic Modulation of Bile Acid-FXR-FGF15/FGF19 Pathway for the Treatment of Nonalcoholic Steatohepatitis. Handb Exp Pharmacol. 2019;256:325-357. doi: 10.1007/164_2019_228. PMID: 31201553; PMCID: PMC7033713. 5. Wen YQ, Zou ZY, Zhao GG, Zhang MJ, Zhang YX, Wang GH, Shi JJ, Wang YY, Song YY, Wang HX, Chen RY, Zheng DX, Duan XQ, Liu YM, Gonzalez FJ, Fan JG, Xie C. FXR activation remodels hepatic and intestinal transcriptional landscapes in metabolic dysfunction-associated steatohepatitis. Acta Pharmacol Sin. 2024 Nov;45(11):2313-2327. doi: 10.1038/s41401-024-01329-1. Epub 2024 Jul 11. PMID: 38992119; PMCID: PMC11489735. 6. Zhu S, Zou M, Wu Q, Zou Y, Tan T, Huang Z, Gong Z, Luo H, Dong X. The Gut-Liver Axis in Metabolic Dysfunction-Associated Steatotic Liver Disease: From Mechanistic Insights to Precision Therapeutics. FASEB J. 2026 Mar 31;40(6):e71687. doi: 10.1096/fj.202503607RR. PMID: 41824007; PMCID: PMC12986715. 7. Federico A, Dallio M, Godos J, Loguercio C, Salomone F. Targeting gut-liver axis for the treatment of nonalcoholic steatohepatitis: translational and clinical evidence. Transl Res. 2016 Jan;167(1):116-24. doi: 10.1016/j.trsl.2015.08.002. Epub 2015 Aug 12. PMID: 26318867. 8. Gil-Gómez A, Brescia P, Rescigno M, Romero-Gómez M. Gut-Liver Axis in Nonalcoholic Fatty Liver Disease: the Impact of the Metagenome, End Products, and the Epithelial and Vascular Barriers. Semin Liver Dis. 2021 May;41(2):191-205. doi: 10.1055/s-0041-1723752. Epub 2021 Mar 8. PMID: 34107545. 9. Cui C, Gao S, Shi J, Wang K. Gut-Liver Axis: The Role of Intestinal Microbiota and Their Metabolites in the Progression of Metabolic Dysfunction-Associated Steatotic Liver Disease. Gut Liver. 2025 Jul 15;19(4):479-507. doi: 10.5009/gnl240539. Epub 2025 May 8. PMID: 40336226; PMCID: PMC12261135. 10. Buzzetti E, Pinzani M, Tsochatzis EA. The multiple-hit pathogenesis of non-alcoholic fatty liver disease (NAFLD). Metabolism. 2016 Aug;65(8):1038-48. doi: 10.1016/j.metabol.2015.12.012. Epub 2016 Jan 4. PMID: 26823198.     AOPWiki 2026-04-21T22:49:31

2026-04-28T04:06:08