Theoretical Study of Adenine to Guanine Transition Assisted by Water and Formic Acid Using Steered Molecular Dynamic Simulations

Tolosa, S.; Sansón, J.A.; Hidalgo, Antonio

doi:10.3389/fchem.2019.00414

Cited by 13 publications

(8 citation statements)

References 42 publications

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“…Then, an intramolecular proton was transferred from the OH group to the N10 nitrogen, resulting in the replacement of NH 2 by the OH group, as well as the generation of intermediate metabolite and the release of ammonia molecules. Finally, M124 was formed by the rearrangement of intramolecular proton and reduction. ,, …”

Section: Results and Discussionmentioning

confidence: 99%

“…Finally, M124 was formed by the rearrangement of intramolecular proton and reduction. 17,29,30 Theoretically, according to the law of conservation of mass, when guvermectin completely reacted with H S2). 31 But, in fact, we did not find it in the samples, which may be due to the fact that it was hard to ionize and carry the charge, resulting in its absence in the QTOF/MS chromatogram.…”

Section: ■ Materials and Methodsmentioning

confidence: 99%

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Hydrolysis, Photolysis, and Biotoxicity Assessment of a Novel Biopesticide, Guvermectin

Shi

Zhang

et al. 2022

J. Agric. Food Chem.

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Guvermectin is a biopesticide isolated from the secondary metabolites of Streptomyces sp. NEAU6, an endogenous actinomyces of a Chinese medicine named Paris polyphylla. However, the environmental degradation behavior and biotoxicity of guvermectin are still unclear, which may affect its rational application. Therefore, the degradation of guvermectin in water at different pH values (pH 4, pH 6, pH 7, and pH 9) and with or without light was investigated in the laboratory. The results showed that guvermectin could be degraded in pH 4 solution, and the presence of light irradiation enhanced the degradation process with a DT50 of 2.95 and 12 days for photolysis and hydrolysis, respectively. However, guvermectin was fairly stable in other conditions. Three products transformed from guvermectin degradation were identified by UPLC-QTOF/MS. Biotoxicity assessment was performed on Danio rerio and Daphnia magna Straus by ECOSAR prediction and in vivo biological tests. The test data showed that guvermectin and its transformation products exhibited low toxicities to D. rerio and D. magna Straus (LC50/EC50 > 100 mg a.i./L), and the transformation products had lower toxicity than their parent substance. The results provided a reference for elucidating the potential risk of guvermectin to nontarget organisms and promoting its rational use.

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Section: Results and Discussionmentioning

confidence: 99%

Section: ■ Materials and Methodsmentioning

confidence: 99%

Hydrolysis, Photolysis, and Biotoxicity Assessment of a Novel Biopesticide, Guvermectin

Shi

Zhang

et al. 2022

J. Agric. Food Chem.

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“…Of the various disinfectants widely in use for public sanitization during the ongoing Covid-19 pandemic (www.epa.gov/pesticide-registration/list-n-disinfectants-useagainst-sars-cov-2), quaternary ammonium compounds such as benzalkonium chloride and dimethyldioctadecyl-ammonia bromide, being cationic surfactants, can cause genotoxic effects (Ferk et al, 2007); chlorine (as a mixture of HClO and ClO -) can preferentially attack exocyclic-NH 2 groups of adenosine (Phe et al, 2009). Based on this chemistry, a themodynamically and kinetically favorable, three-step transition route can be conceived for A G via hydrolytic deamination involving water and formic acid (an age old biocide and ensilation agent of forage crops that is abundant in the environment) and ammonia (Figure S1B; for details see Tolosa et al, 2019).…”

Section: High Frequency Of C U Transition and A G Transversion Mutationsmentioning

confidence: 99%

Comparative Genomics of Global SARS-CoV-2 Quasispecies Offers Insights into Its Microevolution and Holds Implications for Pathogenesis and Control

Mandal¹,

Mondol²,

Mukherjee³

et al. 2020

Preprint

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In the wake of the current SARS-CoV-2 pandemic devastating the world, it is imperative to elucidate the comparative genomics of geographically-diverse strains of this novel coronavirus to gain insights into its microevolution, pathogenesis and control. Here we explore the molecular nature, genome-wide frequency, and gene-wise distribution of mutations in three distinct datasets encompassing 68 SARS-CoV-2 RNA-genomes altogether. While phylogenomic analysis revealed parallelism between the evolutionary paths charted by distinct quasispecies clusters of the virus, occurrence of mutations across genomes was found to be non-random. Whereas deletion mutations are extremely scarce and insertions totally absent, of all the instances of single nucleotide substitution detected, the overwhelming majority were transition mutations with cytidine to uridine being the most prevalent type. Propensity of this transition could be attributed to hydrolytic deamination mediated by ultra-violet irradiation or bisulfite reagent, both of which find wide usage as sterilizer/disinfectant. Transversions, albeit few and predominated by the guanosine to uridine form, were found concentrated in loci encoding the structural proteins of the virus, so might confer versatile tissue-colonization potentials. Mutation frequency of the three distinct genome-sets ranged narrowly between 0.07-1.08 × 10-4 nucleotides positions mutated per nucleotide aligned. Gene-wise mapping of the global mutations illuminated the highly conserved nature of the genes encoding the non-structural proteins Nsp7, Nsp8 (two essential cofactors of the viral RNA-dependent RNA-polymerase) and Nsp9 (Nsp8-interacting single-strand RNA-binding protein), plus the envelope protein E (involved in SARS-CoV-2 assembly, budding and pathogenesis). These mutation-free genomic loci and/or their protein products could be potent targets for future drug designing/targeting.

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“…DNA is a sequence of 4 basic nucleotides Adenine (A), Guanine (G), Thymine (T), and Cytosine (C) 4 . Any change in the base sequence in genes leads to mutation.…”

Section: Introductionmentioning

confidence: 99%

Machine learning techniques for identification of carcinogenic mutations, which cause breast adenocarcinoma

Shah

Malik

Mohammad

et al. 2022

Sci Rep

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Breast adenocarcinoma is the most common of all cancers that occur in women. According to the United States of America survey, more than 282,000 breast cancer patients are registered each year; most of them are women. Detection of cancer at its early stage saves many lives. Each cell contains the genetic code in the form of gene sequences. Changes in the gene sequences may lead to cancer. Replication and/or recombination in the gene base sometimes lead to a permanent change in the nucleotide sequence of the genome, called a mutation. Cancer driver mutations can lead to cancer. The proposed study develops a framework for the early detection of breast adenocarcinoma using machine learning techniques. Every gene has a specific sequence of nucleotides. A total of 99 genes are identified in various studies whose mutations can lead to breast adenocarcinoma. This study uses the dataset taken from 4127 human samples, including men and women from more than 12 cohorts. A total of 6170 mutations in gene sequences are used in this study. Decision Tree, Random Forest, and Gaussian Naïve Bayes are applied to these gene sequences using three evaluation methods: independent set testing, self-consistency testing, and tenfold cross-validation testing. Evaluation metrics such as accuracy, specificity, sensitivity, and Mathew’s correlation coefficient are calculated. The decision tree algorithm obtains the best accuracy of 99% for each evaluation method.

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Theoretical Study of Adenine to Guanine Transition Assisted by Water and Formic Acid Using Steered Molecular Dynamic Simulations

Cited by 13 publications

References 42 publications

Hydrolysis, Photolysis, and Biotoxicity Assessment of a Novel Biopesticide, Guvermectin

Hydrolysis, Photolysis, and Biotoxicity Assessment of a Novel Biopesticide, Guvermectin

Comparative Genomics of Global SARS-CoV-2 Quasispecies Offers Insights into Its Microevolution and Holds Implications for Pathogenesis and Control

Machine learning techniques for identification of carcinogenic mutations, which cause breast adenocarcinoma

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