Mutagenesis最新文献

Base damage in DNA constitutes a major source of mutations, and consequently leads to cancers. In human cells, 8-oxo-7,8-dihydroguanine (8-hydroxyguanine) induces targeted G→T transversions, and untargeted base substitution mutations at positions distant from the damaged site (action-at-a-distance mutations). OGG1 is a base excision repair enzyme and suppresses the former mutations, but is involved in the latter mutations' process. In this study, 5-hydroxycytosine (CO), another oxidized base removed by base excision repair, was incorporated into the inside and outside regions of the supF gene, and the CO-plasmid DNAs were transfected into human U2OS cells. The damaged cytosine base caused base substitution mutations at the lesion site, and seemed to induce the action-at-a-distance mutations at a lower frequency than the oxidized guanine base. These results indicated that CO is mutagenic in human cells. In addition, the (6-4) photoproduct of 5'-TpT-3', the lesion repaired by another type of DNA repair pathway, nucleotide excision repair, did not cause the action-at-a-distance mutations.

Colorectal cancer (CRC) remains a major health challenge due to its late-stage diagnosis and the variability in patient prognosis. This study explores the potential of DNA damage in peripheral blood lymphocytes (PBLs) as a biomarker for CRC, comparing it with standard clinical parameters. We assessed DNA strand breaks using the alkaline comet assay in 27 CRC patients at diagnosis and post-treatment, comparing these levels with 31 healthy controls. Patients received 5-fluorouracil (5-FU) -based chemotherapy (with irinotecan or oxaliplatin), radiotherapy, or combined chemoradiotherapy. At diagnosis (t0), DNA damage in PBLs was significantly higher in CRC compared to healthy controls (mean ± SD %DNA in tail: CRC 27.9 ± 14.0%; controls 6.5 ± 3.8%; p = 0.001), and independently of common confounding factors (sex, age, smoking, and alcohol consumption). Crucially, the prognostic signal came from baseline (t0): 6 of 27 patients relapsed/metastasised within 8-10 months, and high DNA damage basal levels was the only significant prognostic predictor (p = 0.0137), yielding an infinitely elevated Odds Ratio (95% CI: ≥2.203) and 100% sensitivity. In stark contrast, carcinoembryonic antigen (CEA) and cancer antigen 19-9 (CA 19-9) showed limited performance. At t0, among patients with available serum data (n = 23), most values were below clinical cut-offs: CEA 3 ng/mL (14/23, 61%); CA19-9, 37 U/mL (19/23, 83%). Prognostic sensitivities were 50.0% (CEA) and 16.7% (CA19-9). Post-treatment (t1) increases in DNA damage are pharmacodynamically expected with DNA-damaging therapy. t1 values were higher in patients who relapsed (p < 0.001), whereas the within-patient change (Δ = t1 - t0) was not associated with outcome (p = 0.148); these post-treatment findings are exploratory. Evaluating DNA damage in PBLs, therefore, offers a valuable non-invasive biomarker for early detection, treatment monitoring, and short-term risk stratification in CRC, warranting validation in larger, stage-balanced cohorts.

Molnupiravir (MOV) is a prodrug of N-hydroxycytidine (NHC), an analog of the endogenous ribonucleoside cytidine that can be administered orally. MOV is used for treating patients infected by SARS-CoV-2, the coronavirus responsible for COVID-19. MOV and NHC are mutagenic in bacterial and in mammalian cell cultures, yet both chemicals have been negative or equivocal in nonclinical in vivo models of mutagenesis. We designed and validated a novel error-corrected sequencing (ECS) method for detecting single basepair substitutions in tissue samples obtained from genetically heterogeneous humans. The software used for this ECS method is available for free from a public on-line repository and it is suitable for analysis of in vivo and in vitro derived samples collected in various experimental scenarios. We recruited two groups of patients having COVID-19 one to three years ago, one group that received a full course of MOV therapy and the other group (matched for age and COVID-19 diagnosis date) that did not receive MOV. Using the ECS method, we determined the frequencies of basepair substitutions in nucleated cells isolated from peripheral blood of the patients. There was no observed difference in the frequency of mutations, the types of mutations, or mutational spectra between the MOV and the control groups. Also, the spectra of mutations in the MOV group did not show any evidence of the mutational signature expected from exposures to MOV or NHC based on data from mammalian cell culture models. Within the limits of this study, the dose of MOV authorized for the treatment of COVID-19 under emergency use authorization appears to have no mutational consequences for treated patients.

Nanoparticle genotoxicity can be induced through several mechanisms, but there are currently no nanoparticle positive controls available for the evaluation of in vitro genotoxicity. Tungsten carbide-cobalt (WC/Co) has been proposed as one possible candidate. The aim of this study was therefore to investigate the genotoxic profile of WC/Co (Co 8% wt.) utilizing the cytokinesis-blocked micronucleus (CBMN) assay, the mammalian cell gene mutation test, and comet assay following a 24-hour exposure. This was conducted in human lymphoblast (TK6) and Chinese hamster lung fibroblast (V79-4) cells. No cytotoxicity was observed in the TK6 CBMN assay even when significant induction of micronuclei was observed at 100 μg/ml (2-fold over control). In contrast, V79-4 cells demonstrated no significant genotoxicity or cytotoxicity in the CBMN assay. In the gene mutation assay significant mutagenicity was observed in V79-4 cells at 100 μg/ml (2-fold over control). Cellular uptake of the WC/Co nanoparticles was not qualitatively detected in either cell type when investigated with transmission electron microscopy. No genotoxicity was observed in either cell type with the comet assay. The data generated indicates that WC/Co nanoparticles may be used as a positive particulate control in the CBMN assay when using TK6 cells only; whilst in the gene mutation assay it can be used as a positive control for V79-4 cells. However, its use as a particle positive control is only possible when applying the highest test concentration of 100 μg/ml.

The standard comet assay detects DNA strand breaks and alkali-labile sites, but these lesions are nonspecific. They may result directly from genotoxic agents or arise as intermediates during the repair of other DNA damage, such as oxidized bases or bulky DNA adducts. Different approaches have been developed to generate or trap these repair intermediates, making them detectable with the comet assay. Recently, the combination of the comet assay with DNA repair inhibitors like hydroxyurea and cytosine arabinoside has been proposed to detect bulky DNA adducts. These lesions are mainly repaired through nucleotide excision repair, a process that transiently produces strand breaks when damaged oligonucleotides are excised. Normally, these intermediates are rapidly repaired by DNA resynthesis and ligation. However, by inhibiting this repair step, strand breaks persist and can be detected by the comet assay. This strategy has been applied in various fields, including genotoxicity testing, environmental toxicology, human biomonitoring, and studies on DNA repair kinetics. This review focuses specifically on the use of hydroxyurea, cytosine arabinoside, and aphidicolin in in vitro experiments to evaluate the utility and specificity of this method for detecting different types of DNA lesions. Notably, in ~70% of studies reviewed, the inclusion of DNA repair inhibitors led to a significant increase in DNA damage, highlighting the added value of this approach. However, although the method enhances sensitivity to bulky adducts, it also responds to other types of damage, such as those induced by alkylating or oxidative agents.

Microplastics are emerging pollutants of global concern, and their widespread presence poses a serious threat to aquatic and terrestrial ecosystems. The current study investigated the water quality and the presence of microplastics in water and native fish samples of the Karamana River, Kerala, India. The water quality was analyzed using various physicochemical parameters, including the dissolved oxygen, biochemical oxygen demand, and chemical oxygen demand. Microplastics isolated from water and native fish samples were characterized using Fourier transform infrared (FTIR) spectroscopy. DNA damage in fish liver and gill cells was assessed using the comet assay (single-cell gel electrophoresis). The water quality assessment revealed metals in the water within the acceptable limits, reduced dissolved oxygen, and increased biochemical oxygen demand and chemical oxygen demand, which indicate a river water ecosystem in hypoxic conditions, and the higher level of the most probable number index confirmed the presence of coliforms in this river. The microplastics isolated from the water and native fish samples were in fibers, fragments, film, pellets, and foams in nature. The abundance of microplastics in the river confirmed the load of microplastic pollution, which varied among the sites. FTIR spectroscopy analysis confirmed the presence of microplastic polymers such as polyethylene, polypropylene, polystyrene, polyamide, polyoxymethylene, and polyester in the water and native fish samples of the Karamana River. The increased percentage of tail DNA in the liver and gill cells of the fish inhabitants of the Karamana River, compared with the control fish, indicated DNA damage; this could be due to the microplastics in that aquatic ecosystem.

Micronuclei (MNi) are critical biomarkers for pathological conditions, yet their manual scoring is inherently laborious and prone to significant inter-observer variability, limiting the reliability and scalability of genotoxicity assessments. Recent advancements in deep learning and computer vision have revolutionised automated MNi detection in various assay samples, enhancing accuracy, efficiency, and reducing human bias. While these AI-powered techniques have been demonstrated in in vitro genotoxicity testing, their application to the minimally invasive Buccal Micronucleus Cytome (BMCyt) assay for human biomonitoring remains largely unexplored. The BMCyt assay, invaluable for assessing genotoxic damage in environmentally exposed populations, presents unique challenges, including sample variability, confounding factors, and the complexity of scoring multiple cytogenetic endpoints. This review covers the evolution of AI-based MNi detection, analysing key methodologies and advancements. It highlights the untapped potential of integrating AI into the BMCyt assay to overcome current analytical limitations, improve reproducibility, increase throughput, and eliminate observer bias. By facilitating more robust and scalable genomic damage monitoring, AI integration will significantly enhance the utility of the BMCyt assay in large-scale epidemiological studies and human biomonitoring.

The Buccal Micronucleus Cytome (B-MNcyt) assay is used world-wide to study chromosomal abnormalities and environmental genotoxicity and cytotoxicity in humans. The aim of this paper is to discuss the strengths and limitations of the B-MNcyt assay and to identify emerging opportunities to further improve and validate its use. This can be achieved by innovating and evolving the B-MNcyt assay by identifying and solving important knowledge and technological gaps that hinder its utility. The cells examined in the B-MNcyt assay are squamous epithelial cells that can be easily collected from the inside of the mouth. These cells are post-mitotic cells generated from the proliferative basal layer and may contain micronuclei (MN). MN can be generated during mitosis of the basal cells prior to their differentiation into squamous cells. The B-MNcyt assay is increasingly being used to measure DNA damage induced in vivo by environmental genotoxins. Results with this assay have been shown to correlate positively with MN frequency measured using the well-validated lymphocyte cytokinesis-block micronucleus cytome (L-CBMNcyt) assay. However, the B-MNcyt assay has some important limitations that need to be addressed to achieve a similar level of validation and applicability as the L-CBMNcyt assay. These include: (i) lack of evidence that the buccal MN frequency predicts disease risk in prospective studies; (ii) no automated scoring system to score MN in buccal cells which is essential to achieve statistically robust results and to improve the feasibility of the assay in population studies; (iii) kinetics of MN expression in buccal cells needs more research to define optimal time frames to score MN after acute exposure or during chronic genotoxin exposure; (iv) studies are also required to test the suitability of using the B-MNcyt assay for radiation exposure bio-dosimetry. This paper discusses these issues and provides some suggestions how to address them.

The mutagenicity of chemical compounds is a key consideration in toxicology, drug development, and environmental safety. Traditional methods such as the Ames test, while reliable, are time-intensive and costly. With advances in imaging and machine learning (ML), high-content assays like cell painting offer new opportunities for predictive toxicology. Cell painting captures extensive morphological features of cells, which can correlate with chemical bioactivity. In this study, we leveraged cell painting data to develop ML models for predicting mutagenicity and compared their performance with structure-based models. We used two datasets: a Broad Institute dataset containing profiles of over 30 000 molecules and a U.S.-Environmental Protection Agency dataset with images of 1200 chemicals tested at multiple concentrations. By integrating these datasets, we aimed to improve the robustness of our models. Among three algorithms tested-Random Forest, Support Vector Machine, and Extreme Gradient Boosting-the third showed the best performance for both datasets. Notably, selecting the most relevant concentration per compound, the phenotypic altering concentration, significantly improved prediction accuracy. Our models outperformed traditional quantitative structure activity relationship (QSAR) tools such as the Virtual models for property Evaluation of chemicals within a Global Architecture (VEGA) and the CompTox Dashboard for the majority of compounds, demonstrating the utility of cell painting features. The cell painting-based models revealed morphological changes related to DNA and RNA perturbation, especially in mitochondria, endoplasmic reticulum and nuclei, aligning with mutagenicity mechanisms. Despite this, certain compounds remained challenging to predict due to inherent dataset limitations and inter-laboratory variability in cell painting technology. The findings highlight the potential of cell painting in mutagenicity prediction, offering a complementary perspective to chemical structure-based models. Future work could involve harmonizing cell painting methodologies across datasets and exploring deep learning techniques to enhance predictive accuracy. Ultimately, integrating cell painting data with QSAR descriptors in hybrid models may unlock novel insights into chemical mutagenicity.