Biology subjects

Plata, D. L.

Publications and source records attributed to Plata, D. L..

2 recordsLinked to original sources

bioRxiv2025

DNA adduct and mutational profiles reveal a threshold of cellular defenses against N-nitrosodimethylamine administered to mice in drinking water

N-Nitrosodimethylamine (NDMA) is classified as an animal and probable human carcinogen. Murine liver DNA adducts, mutations, MGMT and CYP2E1 were evaluated following chronic administration of NDMA in drinking water. In a dose-escalation study, 7-methylguanine (m7G) increased linearly with NDMA dose. O6-Methylguanine (m6G) remained near background for NDMA doses up to [~]1 ppm, beyond which its level, and corresponding mutations, rose steeply. An extended study was done with 5 ppm NDMA, in which adducts were measured at 3 weeks and mutations at 10 weeks. While the level of CYP2E1 was unchanged, MGMT gene transcription was induced in females at 10 weeks. Homologous recombination-mediated chromosomal rearrangements did not increase over background. Point mutations, however, were elevated substantially in both sexes. Mutational analysis over 96 trinucleotide contexts revealed predominantly GC[->]AT mutations in 5-purine-G-3 contexts in a pattern matching human COSMIC cancer mutational signature SBS11, with secondary features resembling SBS119 (AT[->]GC). Taken together, the data implicate m6G as the dominant mutagenic adduct under chronic dosing with NDMA. Furthermore, the genomic m6G level was identified at which its dedicated repair protein, MGMT, became saturated. The coordinated application of DNA adduct, mutational and biochemical analyses provides a new approach for early detection and cancer management. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=103 SRC="FIGDIR/small/687536v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@1e6ae72org.highwire.dtl.DTLVardef@1c6fad6org.highwire.dtl.DTLVardef@7e3716org.highwire.dtl.DTLVardef@52a87b_HPS_FORMAT_FIGEXP M_FIG C_FIG

pharmacology and toxicology↗

bioRxiv2025

Neural Spectral Prediction for Structure Elucidation with Tandem Mass Spectrometry

Structural elucidation using untargeted tandem mass spectrometry (MS/MS) has played a critical role in advancing scientific discovery [1, 2]. However, differentiating molecular fragmentation patterns between isobaric structures remains a prominent challenge in metabolomics [3-10], drug discovery [11-13], and reaction screening [14-17], presenting a significant barrier to the cost-effective and rapid identification of unknown molecular structures. Here, we present a geometric deep learning model, ICEBERG, that simulates high-energy collision-induced dissociation in mass spectrometry to generate chemically plausible fragments and their relative intensities with awareness of collision energies and polarities. We utilize ICEBERG predictions to facilitate structure elucidation by ranking a set of candidate structures based on the similarity between their predicted in silico MS/MS spectra and an experimental MS/MS spectrum of interest. This integrated elucidation pipeline enables state-of-the-art performance in compound annotation, with 40% top-1 accuracy on the NIST20 [M+H]+ adduct subset and with 92% of correct structures appearing in the top ten predictions in the same dataset. It achieves 46% top-1 and 86% top-10 accuracies when tested on the open-access MassSpecGym benchmark, and outperforms SIRIUS on a recently released test set with previously uncharacterized structures. We demonstrate several real-world case studies, including identifying clinical biomarkers of depression and tuberculous meningitis, annotating an aqueous abiotic degradation product of the pesticide thiophanate methyl, disambiguating isobaric products in pooled reaction screening, and annotating biosynthetic pathways in Withania somnifera. Overall, this deep learning-based paradigm for structural elucidation enables rapid molecular annotation from complex mixtures, driving discoveries across diverse scientific domains.

bioinformatics↗