Translation initiation from the ribosomal P‐site is the specialty of the initiator tRNAs (tRNAfMet). Presence of the three consecutive G‐C base pairs (G29‐C41, G30‐C40 and G31‐C39) in their anticodon stems, a highly conserved feature of the initiator tRNAs across the three kingdoms of life, has been implicated in their preferential binding to the P‐site. How this feature is exploited by ribosomes has remained unclear. Using a genetic screen, we have isolated an Escherichia coli strain, carrying a G122D mutation in folD, which allows initiation with the tRNAfMet containing mutations in one, two or all the three G‐C base pairs. The strain shows a severe deficiency of methionine and S‐adenosylmethionine, and lacks nucleoside methylations in rRNA. Targeted mutations in the methyltransferase genes have revealed a connection between the rRNA modifications and the fundamental process of the initiator tRNA selection by the ribosome.
Breast and/or ovarian cancer (BOC) are among the most frequently diagnosed forms of hereditary cancers and leading cause of death in India. This emphasizes on the need for a cost-effective method for early detection of these cancers. We sequenced 141 unrelated patients and families with BOC using the TruSight Cancer panel, which includes 13 genes strongly associated with risk of inherited BOC. Multi-gene sequencing was done on the Illumina MiSeq platform. Genetic variations were identified using the Strand NGS software and interpreted using the StrandOmics platform. We were able to detect pathogenic mutations in 51 (36.2%) cases, out of which 19 were novel mutations. When we considered familial breast cancer cases only, the detection rate increased to 52%. When cases were stratified based on age of diagnosis into three categories, ⩽40 years, 40-50 years and >50 years, the detection rates were higher in the first two categories (44.4% and 53.4%, respectively) as compared with the third category, in which it was 26.9%. Our study suggests that next-generation sequencing-based multi-gene panels increase the sensitivity of mutation detection and help in identifying patients with a high risk of developing cancer as compared with sequential tests of individual genes.
The accuracy of the initiator tRNA (tRNAfMet) selection in the ribosomal P-site is central to the fidelity of protein synthesis. A highly conserved occurrence of three consecutive G–C base pairs in the anticodon stem of tRNAfMet contributes to its preferential selection in the P-site. In a genetic screen, using a plasmid borne copy of an inactive tRNAfMet mutant wherein the three G–C base pairs were changed, we isolated Escherichia coli strains that allow efficient initiation with the tRNAfMet mutant. Here, extensive characterization of two such strains revealed novel mutations in the metZWV promoter severely compromising tRNAfMet levels. Low cellular abundance of the chromosomally encoded tRNAfMet allows efficient initiation with the tRNAfMet mutant and an elongator tRNAGln, revealing that a high abundance of the cellular tRNAfMet is crucial for the fidelity of initiator tRNA selection on the ribosomal P-site in E. coli. We discuss possible implications of the changes in the cellular tRNAfMet abundance in proteome remodeling.
Background: Yearly death rate is increasing due to heart disease. Major factors for the increasing death rate due to heart disease are (a) misdiagnosed by the medical doctors or (b) ignorance by the patients. Heart diseases can be described as any kind of disorder which affects the heart. Methods: The dataset of 'statlog' from the UCI Machine Learning with 270 patients related to heart disease isused in this article. The dataset comprises attributes of patients diagnosed with heart diseases. The diagnosis was used to confirm whether heart disease is present or absent in the patient. The present article aims to identify the risk factors/variables which influence this diagnosis. Classification is a very important part of the disease diagnosis but it is also relevant to identify the risk factors/variables. Two classification techniques namely Support Vector Machines (SVM), Multi-Layer Perceptrons ensembles (MLPE) and one advanced regression technique,Generalized additive model (GAM) with binomial distribution and'logit' link have been introduced for diagnosis and risk factors/variables identification. Results: GAM explains 65% deviance with adjusted R square value 0.70 approximately. Sensitivity analysis has been performed under SVM, which is the best model for this dataset with approximately 85% classification accuracy rate. MLPE gives 82% classification accuracy rate approximately.Maximum heart rate, vessel, old peak, chest pain, thallium scan are the most important factors/variables find through both sensitivity analysis under SVM and GAM. Conclusion: The present article attempt to remove some new information regarding heart disease through probabilistic modeling which may provide better assistance for treatment decision making using the individual patient risk factors and the benefits of a specific treatment. These findings may help the medical practitioners for better medical treatment.
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