Interaction Effects of AFB1 and MC-LR Co-exposure with Polymorphism of Metabolic Genes on Liver Damage: focusing on SLCO1B1 and GSTP1

Impact of SLCO1B1*5 on Flucloxacillin and Co-Amoxiclav-Related Liver Injury

Background: Idiosyncratic drug-induced liver injury (DILI) is a serious uncommon disease that may develop as a result of the intake of certain drugs such as the antimicrobials flucloxacillin and co-amoxiclav. The reported cases showed significant associations between DILI and various human leukocyte (HLA) markers. The solute carrier organic anion transporter 1B1 (SLCO1B1), a non-HLA candidate gene, was previously reported as a risk factor for liver injury induced by rifampin and methimazole. This study presumed that SLCO1B1 may play a general role in the DILI susceptibility and therefore investigated the association of rs4149056 (SLCO1B1*5, T521C) polymorphism with flucloxacillin- and co-amoxiclav-induced liver injury. Methodology: We recruited 155 and 165 DILI cases of white ancestral origin from various European countries but mainly from the United Kingdom owing to flucloxacillin and co-amoxiclav, respectively. Only adult patients (≥18 years) who were diagnosed with liver injury and who showed i) clinical jaundice or bilirubin >2x the upper limit of normal (ULN), ii) alanine aminotransferase (ALT) >5x ULN or iii) alkaline phosphatase (ALP) >2x ULN and bilirubin > ULN were selected. The population reference sample (POPRES), a European control group (n = 282), was used in comparison with the investigated cases. TaqMan SNP genotyping custom assay designed by Applied Biosystems was used to genotype both DILI cohorts for SLCO1B1 polymorphism (rs4149056). Allelic discrimination analysis was performed using a step one real-time PCR machine. Genotype differences between cases and controls were examined using Fisher's exact test. GraphPad Prism version 5.0 was used to determine the p-value, odds ratio, and 95% confidence interval. Compliance of the control group with Hardy-Weinberg equilibrium was proven using a web-based calculator available at https://wpcalc.com/en/equilibrium-hardy-weinberg/. Results: A small number of cases failed genotyping in each cohort. Thus, only 149 flucloxacillin and 162 co-amoxiclav DILI cases were analyzed. Genotyping of both DILI cohorts did not show evidence of association with the variant rs4149056 (T521C) (OR = 0.71, 95% CI = 0.46-1.12; p = 0.17 for flucloxacillin cases and OR = 0.87, 95% CI = 0.56-1.33; p = 0.58 for co-amoxiclav), although slightly lower frequency (22.8%) of positive flucloxacillin cases was noticed than that of POPRES controls (29.4%). Conclusion: Carriage of the examined allele SLCO1B1*5 is not considered a risk factor for flucloxacillin DILI or co-amoxiclav DILI as presumed. Testing a different allele (SLCO1B1*1B) and another family member gene (SLCO1B3) may still be needed to provide a clearer role of SLCO1B drug transporters in DILI development-related to the chosen antimicrobials.

CYP1A2, 2A13, and 3A4 network and interaction with aflatoxin B1

Aspergillus fungi are known to produce aflatoxins, among which aflatoxin B1 (AFB1) is the most potent carcinogen that is metabolised by cytochrome P450 (CYP450). In the liver, AFB1 is metabolised into exo-8,9-epoxide by the CYP1A2 enzymes. The resulting epoxide can react with guanine to cause DNA damage. Natural inhibitors are being identified. However, the modes of action are poorly understood. In the current study, we have investigated the mode of action of AFB1 with CYP1A2, CYP3A4 and CYP2A13 using molecular dynamic simulation (MD simulation) approaches. The interaction network and paths among CYP1A2, CYP3A4, and CYP2A13 have been investigated using the STRING database and PathLinker plugin of Cytoscape. CYP3A4 is the most active protein involved in interactions with AFB1 during its metabolism. Residues 362ARG, 445SER, 450LEU and 451PHE of CYP1A2 are important, interacting with AFB1 and converting it to toxic exo-AFB1-8,9-epoxide (AFBEX). The pathway shows that microsomal epoxide hydrolase (EPHX1) may acts as initiator in the signalling pathway where CYP1A2, CYP3A4 and CYP2A13 interact in a sequential order. The interaction network shows there to be a strong association in expression among CYP1A2, CYP3A4 and CYP2A13 along with other metabolising enzymes. The complex of AFB1 and CYP1A2 was found to be stable during the MD simulation. This study provides a better understanding of the mode of action between AFB1 and CYP1A2, CYP3A4 and CYP2A13 which relates to the effective management of AFB1 toxicity. EPHX1 in the protein network may be an ideal target when designing inhibitors to prevent the toxin’s activation. Peptide inhibitors may be designed to block the substrate site residues of CYP1A2 in order to prevent the conversion from AFB1 into AFBEX. This would either neutralise or reduce its toxicity.

Risk assessment of aflatoxins in food

EFSA was asked to deliver a scientific opinion on the risks to public health related to the presence of aflatoxins in food. The risk assessment was confined to aflatoxin B1 (AFB1), AFB2, AFG1, AFG2 and AFM1. More than 200,000 analytical results on the occurrence of aflatoxins were used in the evaluation. Grains and grain-based products made the largest contribution to the mean chronic dietary exposure to AFB1 in all age classes, while 'liquid milk' and 'fermented milk products' were the main contributors to the AFM1 mean exposure. Aflatoxins are genotoxic and AFB1 can cause hepatocellular carcinomas (HCCs) in humans. The CONTAM Panel selected a benchmark dose lower confidence limit (BMDL) for a benchmark response of 10% of 0.4 μg/kg body weight (bw) per day for the incidence of HCC in male rats following AFB1 exposure to be used in a margin of exposure (MOE) approach. The calculation of a BMDL from the human data was not appropriate; instead, the cancer potencies estimated by the Joint FAO/WHO Expert Committee on Food Additives in 2016 were used. For AFM1, a potency factor of 0.1 relative to AFB1 was used. For AFG1, AFB2 and AFG2, the in vivo data are not sufficient to derive potency factors and equal potency to AFB1 was assumed as in previous assessments. MOE values for AFB1 exposure ranged from 5,000 to 29 and for AFM1 from 100,000 to 508. The calculated MOEs are below 10,000 for AFB1 and also for AFM1 where some surveys, particularly for the younger age groups, have an MOE below 10,000. This raises a health concern. The estimated cancer risks in humans following exposure to AFB1 and AFM1 are in-line with the conclusion drawn from the MOEs. The conclusions also apply to the combined exposure to all five aflatoxins.

Liver-Metabolizing Genes and Their Relationship to the Performance of Elite Spanish Male Endurance Athletes; a Prospective Transversal Study

Background

The genetic profile that is needed to define an endurance athlete has been studied during recent years. The main objective of this work is to approach for the first time the study of genetic variants in liver-metabolizing genes and their role in endurance performance by comparing the allelic and genotypic frequencies in elite endurance athletes to the non-athlete population.

Methods

Genotypic and allelic frequencies were determined in 123 elite endurance athletes (75 professional road cyclists and 48 endurance elite runners) and 122 male non-athlete subjects (sedentary). Genotyping of cytochrome P450 family 2 subfamily D member 6 (CYP2D6 rs3892097), glutathione-S transferase mu isoform 1 (GSTM1), glutathione S-transferase pi (GSTP rs1695) and glutathione S-transferase theta (GSTT) genes was performed by polymerase chain reaction (PCR). The combination of the polymorphisms for the “optimal” polygenic profile has been quantified using the genotype score (GS).

Results

Statistical differences were found in the genetic distributions between elite endurance athletes and non-athletes in CYP2D6 (p < 0.001) and GSTT (p = 0.014) genes. The binary logistic regression model showed a favourable OR (odds ratio) of being an elite endurance runner against a professional road cyclist (OR: 2.403, 95% CI: 1.213–4.760 (p = 0.002)) in the polymorphisms studied.

Conclusions

Genotypic distribution of liver-metabolizing genes in elite endurance athletes is different to non-athlete subjects, with a favourable gene profile in elite endurance athletes in terms of detoxification capacity.