Assessment of the adverse impacts of aflatoxin B1 on gut-microbiota dependent metabolism in F344 rats Original paper
Researched by:
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Dr. Umar
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Dr. Umar
Read MoreClinical Pharmacist and Clinical Pharmacy Master’s candidate focused on antibiotic stewardship, AI-driven pharmacy practice, and research that strengthens safe and effective medication use. Experience spans digital health research with Bloomsbury Health (London), pharmacovigilance in patient support programs, and behavioral approaches to mental health care. Published work includes studies on antibiotic use and awareness, AI applications in medicine, postpartum depression management, and patient safety reporting. Developer of an AI-based clinical decision support system designed to enhance antimicrobial stewardship and optimize therapeutic outcomes.
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Dr. Umar
Read More
Researched by:
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Dr. Umar
ID
Dr. Umar
Read More
Microbiome Signatures identifies and validates condition-specific microbiome shifts and interventions to accelerate clinical translation. Our multidisciplinary team supports clinicians, researchers, and innovators in turning microbiome science into actionable medicine.
Dr. Umar
What was studied
This study examined aflatoxin B1 gut microbiota metabolism by testing whether repeated oral exposure to aflatoxin B1 (AFB1) disrupts gut–microbiota–dependent metabolic functions in rats, using high-throughput fecal metabolomics. Male F344 rats were gavaged daily for 5 weeks with 0, 5, 25, or 75 µg AFB1/kg body weight, and fecal pellets collected (weeks 2–4) were analyzed with ultra-high performance liquid chromatography (UHPLC) metabolic profiling plus UHPLC–mass spectrometry (MS) metabolomics. The investigators quantified global metabolite shifts, applied multivariate models (OPLS-DA, random forest) to identify discriminatory metabolite “signatures,” and mapped altered features to biochemical pathways using enrichment analysis (MetaboAnalyst/KEGG) to characterize microbiota-dependent metabolic disruption.
Who was studied
The experimental model used 100 male Fischer 344 (F344) rats (initially ~100–120 g at purchase; ~150 g at treatment start), housed under controlled temperature, humidity, and 12-hour light/dark cycles, fed an AIN 76A purified diet ad libitum. Rats were assigned into four groups (control plus three AFB1 doses) with five cages per group; fecal and urine samples were collected via metabolic cages and pooled by cage/day for analysis. This design emphasized dose–response effects and leveraged fecal metabolomics as a noninvasive proxy for gut microbial metabolic output that can influence host energy balance, immune regulation, and liver physiology.
Most important findings
AFB1 produced a strong, dose-dependent collapse in fecal metabolic diversity, with far more metabolites decreasing than increasing. In UHPLC profiling, the number of significantly reduced peaks rose from 24 (low dose) to 71 (high dose), indicating broad loss of fecal nutrient/metabolite signals. UHPLC–MS detected 494 significantly altered features, with ~234–178 imputed metabolite identifications used for pathway analysis. The most disrupted pathways centered on amino acid handling and core energy biochemistry, including protein biosynthesis, methionine metabolism, pantothenate/CoA biosynthesis, glycine–serine–threonine metabolism, and pyruvate metabolism; additional disturbances involved betaine and cysteine metabolism, the urea cycle, and fatty-acid oxidation. Predictive “indicator” metabolites (useful for a microbiome signatures database) repeatedly highlighted small alcohols/sugars, carnitine species, polyamines, and vitamin-related compounds: 3-decanol, xanthylic acid, norspermidine, nervonyl carnitine, pantothenol, D-threitol, 2-hexenoyl carnitine, and 1-nitrohexane. The authors also linked these metabolite shifts to previously observed microbial community changes (e.g., depletion of lactic-acid–producing taxa and relative enrichment of Bacteroides), consistent with reduced short-chain fatty acids (SCFAs) such as lactic, acetic, and valeric acids—metabolites critical for epithelial integrity and systemic metabolic signaling.
| Microbiome-linked signal | Direction with AFB1 exposure |
|---|---|
| SCFAs (e.g., lactic/acetic/valeric acids) | Decreased |
| Polyamines (e.g., norspermidine/putrescine/spermine) | Decreased |
| Carnitine-linked lipids (e.g., nervonyl carnitine, 2-hexenoyl carnitine) | Altered (signature markers) |
| Amino acid & CoA-related pathways (methionine, pantothenate/CoA, pyruvate) | Disrupted |
Key implications
Clinically, these findings support a mechanistic bridge between dietary AFB1 exposure and systemic outcomes (growth impairment, immune dysfunction, liver injury) through microbiota-dependent metabolic derailment. The signature pattern—loss of SCFAs and polyamines alongside disrupted amino acid/one-carbon metabolism and CoA biochemistry—suggests reduced nutrient harvesting, impaired barrier-supporting metabolites, and altered substrates entering hepatic detoxification pathways. For translational databases, the highlighted indicator metabolites provide a tractable fecal “fingerprint” of toxin-associated microbiome dysfunction that could inform exposure surveillance, risk stratification, and evaluation of interventions aimed at restoring microbial metabolic resilience.
Citation
Zhou J, Tang L, Wang J-S. Assessment of the adverse impacts of aflatoxin B1 on gut-microbiota dependent metabolism in F344 rats. Chemosphere. 2019;226:301-314. doi:10.1016/j.chemosphere.2018.11.044
Aflatoxin
Aflatoxin is a carcinogenic foodborne mycotoxin that damages the liver through DNA-reactive metabolites. It also disrupts gut microbiome metabolism and gut–liver signaling, potentially contributing to inflammation and barrier dysfunction. Microbiome medicine integrates exposure biomarkers with microbial and metabolic signatures for risk assessment.
Short-chain Fatty Acids (SCFAs)
Short-chain fatty acids are microbially derived metabolites that regulate epithelial integrity, immune signaling, and microbial ecology. Their production patterns and mechanistic roles provide essential functional markers within microbiome signatures and support the interpretation of MBTIs, MMAs, and systems-level microbial shifts across clinical conditions.