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

Gamma-rays induced genome wide stable mutations in cowpea deciphered through whole genome sequencing

PURPOSE

Gamma rays are the most widely exploited physical mutagen in plant mutation breeding. They are known to be involved in the development of more than 60% of global cowpea (Vigna unguiculata (L.) Walp.) mutant varieties. Nevertheless, the nature and type of genome-wide mutations induced by gamma rays have not been studied in cowpea and therefore, the present investigation was undertaken.

MATERIALS AND METHODS

Genomic DNAs from three stable gamma rays-induced mutants (large seed size, small seed size and disease resistant mutant) of cowpea cultivar 'CPD103' in M6 generation along with its progenitor were used for Illumina-based whole-genome resequencing.

RESULTS

Gamma rays induced a relatively higher frequency (88.9%) of single base substitutions (SBSs) with an average transition to transversion ratio (Ti/Tv) of 3.51 in M6 generation. A > G transitions, including its complementary T > C transitions, predominated the transition mutations, while all four types of transversion mutations were detected with frequencies over 6.5%. Indels (small insertions and deletions) constituted about 11% of the total induced variations, wherein small insertions (6.3%) were relatively more prominent than small deletions (4.8%). Among the indels, single-base indels and, in particular, those involving A/T bases showed a preponderance, albeit indels of up to three bases were detected in low proportions. Distributed across all 11 chromosomes, only a fraction of SBSs (19.45%) and indels (20.2%) potentially altered the encoded amino acids/peptides. The inherent mutation rate induced by gamma rays in cowpea was observed to be in the order of 1.4 × 10-7 per base pair in M6 generation.

CONCLUSION

Gamma-rays with a greater tendency to induce SBSs and, to a lesser extent, indels could be efficiently and effectively exploited in cowpea mutation breeding.

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

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.

Deep Learning Approaches for Detection of Breast Adenocarcinoma Causing Carcinogenic Mutations

Genes are composed of DNA and each gene has a specific sequence. Recombination or replication within the gene base ends in a permanent change in the nucleotide collection in a DNA called mutation and some mutations can lead to cancer. Breast adenocarcinoma starts in secretary cells. Breast adenocarcinoma is the most common of all cancers that occur in women. According to a survey within the United States of America, there are more than 282,000 breast adenocarcinoma patients registered each 12 months, and most of them are women. Recognition of cancer in its early stages saves many lives. A proposed framework is developed for the early detection of breast adenocarcinoma using an ensemble learning technique with multiple deep learning algorithms, specifically: Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Bi-directional LSTM. There are 99 types of driver genes involved in breast adenocarcinoma. This study uses a dataset of 4127 samples including men and women taken from more than 12 cohorts of cancer detection institutes. The dataset encompasses a total of 6170 mutations that occur in 99 genes. On these gene sequences, different algorithms are applied for feature extraction. Three types of testing techniques including independent set testing, self-consistency testing, and a 10-fold cross-validation test is applied to validate and test the learning approaches. Subsequently, multiple deep learning approaches such as LSTM, GRU, and bi-directional LSTM algorithms are applied. Several evaluation metrics are enumerated for the validation of results including accuracy, sensitivity, specificity, Mathew’s correlation coefficient, area under the curve, training loss, precision, recall, F1 score, and Cohen’s kappa while the values obtained are 99.57, 99.50, 99.63, 0.99, 1.0, 0.2027, 99.57, 99.57, 99.57, and 99.14 respectively.

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

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.

Unitary structure of palindromes in DNA

We investigate the quantum behavior encountered in palindromes within DNA structure. In particular, we reveal the unitary structure of usual palindromic sequences found in genomic DNAs of all living organisms, using the Schwinger's approach. We clearly demonstrate the role played by palindromic configurations with special emphasis on physical symmetries, in particular subsymmetries of unitary structure. We unveil the prominence of unitary structure in palindromic sequences in the sense that vitally significant information endowed within DNA could be transformed unchangeably in the process of transcription. We introduce a new symmetry relation, namely purine-purine or pyrimidine-pyrimidine symmetries (p-symmetry) in addition to the already known symmetry relation of purine-pyrimidine symmetries (pp-symmetry) given by Chargaff's rule. Therefore, important vital functions of a living organisms are protected by means of these symmetric features. It is understood that higher order palindromic sequences could be generated in terms of the basis of the highest prime numbers that make up the palindrome sequence number. We propose that violation of this unitary structure of palindromic sequences by means of our proposed symmetries leads to a mutation in DNA, which could offer a new perspective in the scientific studies on the origin and cause of mutation.

The damage of the Watson-Crick base pairs by nickel nanoparticles: A first-principles molecular dynamics study

The nickel nanoparticles are harmful atmospheric pollutants, and the damage caused by them in humans has become a topic of great relevance. In this study we investigate the interaction of the Ni2 and Ni3 clusters with the AT and GC Watson-Crick base pairs in an aqueous medium. Molecular dynamics in combination with density functional theory are employed. A novel method is implemented to create realistic thermodynamic conditions (NVT) in the simulations. The energies, the charges of the interacting compounds, the temperature changes, and the geometric rearrangements are reported. The results show the formation of stable organometallic compounds of the nickel nanoparticles with the DNA nucleic acid bases. In this respect, the biological processes where the DNA is implicated may be altered by the formation of such super-structures.