Frontiers in Molecular Biosciences

Detection of mutations: from Ames test to duplex sequencing

2026-04-02

Niketa Bhawsinghka, Roel M. Schaaper

Mutation is a biological phenomenon observed in all life forms from viruses to humans. This inescapable process has fascinated scientists for nearly a century. Mutagenicity has become a concern since the 1940s following the discovery that chemicals can cause mutations, because of which the scientific community has ventured into finding effective methods of detecting harmful mutagens. The earlier studies in this field were carried out using organisms like Escherichia coli, Drosophila, and Neurospora. Later, the breakthrough development of an assay using bacteria allowed researchers to detect the abilities of chemical compounds or mixtures to induce DNA mutations. This assay came to be named as the Ames test after its developer Bruce Ames; since then, it has been widely adopted for mutagenicity testing. The introduction of Sanger sequencing technology enabled researchers to explore beyond phenotypic changes and uncover detailed information on DNA sequence changes and mutational spectra. With the advent of next-generation sequencing (NGS), it has become possible to expand mutation analysis to the larger genome without the need for phenotypic selection, particularly given the development of various error-corrected NGS (ecNGS) techniques. Duplex sequencing (DS) is a relatively new ecNGS technique that can detect mutations at low frequencies in isolated DNA. In this mini review, we briefly explore the genetics of the Ames test and shed light on DS as an emerging tool for detecting mutations.

Platelet-related hematologic markers and genetic associations of aspirin resistance in kawasaki disease

2026-04-01

Linjie Jiang, Xiaoting Ding, Wan Yang, Xilian Luo, Kaining Chen, Lanyan Fu, Yufen Xu, Huazhong Zhou, Xiaoxue Li, Caiting Xiao, Xiaoqiong Gu, Xiangna Yang, Chunjiao Wei, Zhouping Wang, Jianrui Wei, Lei Pi

Kawasaki disease (KD) is the leading cause of acquired cardiovascular disease in children and is characterized by intense immune activation and platelet dysfunction. High platelet reactivity (HPR) is increasingly recognized as a biological basis of aspirin resistance (AR), which may increase the risk of adverse coronary outcomes, including coronary artery aneurysms (CAA). However, the hematologic dynamics and genetic determinants underlying AR in KD remain unclear. In this study, the association between AR and CAA was assessed using chi-square analysis. We compared platelet parameters between KD aspirin-resistant (KD-AR) and KD non-aspirin-resistant (KD-NAR) patients across different disease phases using linear mixed-effects models (LMM). Baseline complete blood count (CBC) derived inflammatory indices, including the systemic immune-inflammation index (SII), platelet-to-lymphocyte ratio (PLR), and neutrophil-to-lymphocyte ratio (NLR), were evaluated using restricted cubic spline (RCS) and receiver operating characteristic (ROC) analyses. Integrated transcriptomic and expression quantitative trait loci (eQTL) analyses were performed to identify candidate genetic factors associated with the KD-AR phenotype. The results showed that AR was significantly associated with CAA formation. LMM showed significant phase-dependent changes in platelet parameters, with distinct longitudinal trajectories between KD-AR and KD-NAR patients. Between-group differences were mainly observed during the subacute phase (D7–14), when KD-AR patients showed lower platelet count (PLT) and plateletcrit (PCT), but higher platelet distribution width (PDW) and platelet large cell ratio (PLCR). Baseline SII, PLR, and NLR were significantly elevated in KD-AR patients (all P < 0.001). RCS analyses demonstrated significant overall associations between these indices and AR risk (all Poverall < 0.001). ROC analyses showed moderate discrimination for SII (AUC = 0.702) and NLR (AUC = 0.722), whereas PLR showed lower performance (AUC = 0.626). MBP was consistently upregulated in HPR-associated samples, and eQTL integration identified MBP/rs8090438 as a candidate variant linked to KD-AR. These findings suggest that AR in KD represents a multifactorial phenotype involving immune-driven platelet dysregulation and genetic susceptibility. Baseline inflammatory indices, particularly NLR and SII, may assist in early identification of KD patients at increased likelihood of AR.

Pitfalls of onco-metabolomics: impact of sample integrity on metabolomic investigations in more than 4500 human serum samples from ten different cohorts

2026-03-31

Michael Ladurner, Selina Strathmeyer, Tobias Ameismeier, Helmut Klocker, Eberhard Steiner, Gerhard Aigner, Martin Puhr, Tina Böld, Diana Drettwan, Franziska Sommermeyer, Iris E. Eder

IntroductionMetabolomics such as nuclear magnetic resonance spectroscopy or mass spectrometry (MS) in different body fluids are considered potentially useful diagnostic techniques for various diseases including cancer. One of the most important prerequisites of metabolomics is a high sample quality, for which reason explicit care must be taken during pre-analytical/analytical handling.MethodsIn the present study, we investigated the influence of pre-processing (PPT), and pre-centrifugation time (PCT), sample storage time (SST), and sample texture on NMR-based metabolite levels in 4,658 long-term and short-term stored retrospectively and prospectively collected serum samples from breast and prostate cancer patients as well as from healthy men.ResultsWe found that the majority of the metabolites were highly stable with regard to variations in PCT, PPT, or SST. PCT and PPT significantly affected the concentrations of only a few individual metabolites, including ascorbic acid, asparagine, glucose, glutamic acid, glutamine, lactate, phenylalanine, pyruvic acid, and serine, indicating that pre-analytical protocol variations need to be considered for the quantitative analysis of metabolites. Notably, the glucose:lactate and glutamine:glutamic acid ratios were found to be suitable to assess sample quality in case of high PCT or PPT. Importantly, the highest sample quality was detected in prospectively collected serum samples with strict protocol adherence and a total PPT of only 1.2 h. Specific care must also be taken with the analysis of lipemic samples, in which strong variations in the concentrations of lipid metabolites, albumin, and valine were observed.DiscussionIn summary, our data show that the majority of metabolites are mostly stable with regard to variations in pre-analytical processing, indicating that retrospective biobank samples are suitable for metabolomics studies. However, individual metabolites are strongly dependent on PCT and PPT, suggesting that a short PPT may be mandatory for clinical diagnosis, depending on the individual metabolite to be measured.

Editorial: In honor of Dr. Bruce Ames: innovations in mutagenesis and DNA repair

2026-03-27

Cecilia Giulivi, Maret G. Traber

Editorial: Molecular studies and therapeutic approaches on BRCA-associated cancers

2026-03-26

Audesh Bhat, Xiaoqiang Wang, Preyesh Stephen

Revealing key genes and molecular mechanisms associated with dietary restriction in ulcerative colitis

2026-03-25

Yingying Li, Min Xu, Wen Li, Hao Zhang, Qijin He, Shuyi Zhang

IntroductionUlcerative colitis (UC) is characterized by chronic colonic mucosal inflammation, with its pathogenesis involving multidimensional interactions and limitations in clinical treatment. Dietary restriction (DR) is a commonly used approach for UC patients to alleviate symptoms, and exploring the role of DR-related genes in UC could provide new directions for the development of precision therapies.MethodsBioinformatics analysis was performed on UC-related datasets (GSE75214, GSE73661) obtained from the GEO database. Candidate genes were acquired by intersecting differentially expressed genes (DEGs) with dietary restriction-related genes (DRRGs). Subsequently, key genes were identified via machine learning algorithms and ROC curve analysis. A deep neural network (DNN) model and a diagnostic nomogram were constructed. In addition, gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), immune infiltration analysis, and single-cell RNA sequencing (scRNA-seq) analysis were conducted. Finally, the expression of key genes was validated through experiments.ResultsCPT1A, ANGPTL4, and CLDN1 were identified as the key genes. The deep neural network (DNN) model achieved area under the curve (AUC) values of 0.914 and 0.933 in the two datasets, respectively; the diagnostic nomogram exhibited high predictive performance (AUC > 0.7), and decision curve analysis (DCA) revealed its potential clinical net benefit. Enrichment analyses demonstrated that the key genes were significantly enriched in dietary restriction (DR)-related pathways, including cytokine-receptor interaction, the IL2-STAT5 signaling pathway, and fatty acid metabolism. Thirty-two activated pathways and five inhibited pathways were detected in UC patients (e.g., the oxidative phosphorylation pathway was suppressed). Immune infiltration analysis identified 27 differentially infiltrating immune cell types. CLDN1 was localized to epithelial cells, ANGPTL4 to fibroblasts, and CPT1A to endothelial cells. Macrophages were identified as a signaling hub in UC, showing intensified crosstalk with stromal and vascular cells via pathways such as ACKR1. Experimental validation confirmed that ANGPTL4 and CLDN1 were highly expressed in UC, whereas CPT1A was lowly expressed, a pattern consistent with the expression trends observed in public database analyses.DiscussionThese results indicated that CPT1A, ANGPTL4, and CLDN1 are involved in the pathological regulation of UC by DR through modulating the metabolism-immune-barrier axis, providing novel biomarkers and potential intervention targets for the clinical diagnosis and targeted therapy of UC.

Mining personalized core traditional Chinese medicine prescriptions for rheumatoid arthritis and elucidating their mechanisms via frequent closed Itemset compression and multilevel network pharmacology

2026-03-23

Xu Chen, Jinlong Yu, Xin Dong, Zhangfan Chen, Jiangshan Tian, Min Zhao, Miao Jiang, Hongtao Guo, Xuezhong Zhou, Lifeng Fa, Yuqiu Li, Lei Zhang

IntroductionRheumatoid arthritis (RA) is a complex immune-mediated inflammatory disease involving multiple dysregulated signaling pathways and marked inter-individual heterogeneity in treatment response. In real-world clinical practice in China, traditional Chinese medicine (TCM) is widely used for RA management in the form of multi-herbal prescriptions; however, systematic approaches that link heterogeneous TCM prescription patterns to objective clinical signals and underlying molecular mechanisms remain limited.MethodsIn this study, large-scale inpatient electronic medical records from two tertiary hospitals were analyzed to identify representative TCM prescriptions used for RA treatment. Frequent closed itemset mining combined with compression strategies was applied to extract stable and non-redundant core prescription patterns across different physicians. Retrospective clinical validation was conducted using longitudinal changes in C-reactive protein (CRP) as an objective biomarker of inflammatory activity. Systems pharmacology approaches—including network pharmacology, network topology-based proximity analysis, and molecular docking—were integrated to characterize shared and prescription-specific molecular targets, signaling pathways, and compound-target interaction feasibility.ResultsFive representative core TCM prescriptions were identified. Among 614 eligible patients receiving these prescriptions, all groups exhibited significant post-treatment reductions in CRP levels (p < 0.05), indicating consistent anti-inflammatory signals in real-world settings. Network pharmacology analysis revealed substantial overlap between prescription targets and RA-associated genes (65–115 targets per prescription), with convergent enrichment in key inflammatory pathways, including Toll-like receptor, IL-17, and TNF signaling pathway. Network proximity metrics demonstrated close associations between prescription targets and the RA disease module. Molecular docking further supported the structural plausibility of direct interactions between representative active compounds—such as quercetin and berberine—and core RA-related targets, including TNF-α and PTGS2.DiscussionThis integrative analysis demonstrates that heterogeneous TCM prescriptions used in RA converge on shared inflammatory regulatory networks while retaining prescription-specific mechanistic features. By linking real-world clinical evidence with systems-level and structural analyses, this study provides a reproducible framework for mechanistic interpretation of TCM-based therapeutic heterogeneity and generates testable hypotheses for future prospective and stratified RA studies incorporating standardized clinical outcomes.

Transcriptomic profiling of chlorogenic acid and taurine treatment in human skin cells provides insights into cellular senescence mechanisms

2026-03-20

Beomsu Kim, Joong-Gon Shin, In-Shik Hong, Yeeun Ahn, Jung Yeon Seo, Jae Young Shin, Sooyeon Lee, Seung-Hyun Jun, Eui Taek Jeong, Hyeonbin Jo, Mi-So Park, Dan Say Kim, Nae Gyu Kang, Yunkwan Kim, Hong-Hee Won

BackgroundChlorogenic acid (CGA) and taurine are well-known antioxidant compounds reported to reduce skin cellular senescence. However, the biological mechanisms underlying their skin-protective effects remain unclear.MethodsIn this study, we conducted transcriptome-wide RNA sequencing to profile gene expression changes in human epidermal keratinocytes, melanocytes, and fibroblasts following treatment with CGA, taurine, or their combination. To identify aging-related genes, we integrated evidence from aging databases, perceived-age GWAS, enrichment in aging-related gene ontology and pathways, and drug-gene interaction annotations. Validation of representative genes was performed using quantitative real-time PCR.ResultsA total of 197 differentially expressed genes (DEGs) were identified, of which 62 were prioritized as aging-related DEGs (AR-DEGs) based on their relevance to skin aging anti-senescence-associated pathways, highlighting regulatory transcription factors including TGFB2, ETS1, and EGR1. Co-treatment enhanced the transcriptional effects of CGA and taurine, with several genes exhibiting synergistic responses. Targeted transcriptome-wide association analysis indicated potential links between specific AR-DEGs, such as FST, and phenotypes including perceived age and skin pigmentation.ConclusionBy identifying key genes and pathways that contribute to cellular longevity in human skin, this study provides molecular insights for developing anti-aging strategies with potential applications in dermatology.