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We report two structures of the human voltage-gated potassium channel (Kv) Kv1.3 in immune cells alone (apo-Kv1.3) and bound to an immunomodulatory drug called dalazatide (dalazatide-Kv1.3). Both the apo-Kv1.3 and dalazatide-Kv1.3 structures are in an activated state based on their depolarized voltage sensor and open inner gate. In apo-Kv1.3, the aromatic residue in the signature sequence (Y447) adopts a position that diverges 11 Å from other K+ channels. The outer pore is significantly rearranged, causing widening of the selectivity filter and perturbation of ion binding within the filter. This conformation is stabilized by a network of intrasubunit hydrogen bonds. In dalazatide-Kv1.3, binding of dalazatide to the channel's outer vestibule narrows the selectivity filter, Y447 occupies a position seen in other K+ channels, and this conformation is stabilized by a network of intersubunit hydrogen bonds. These remarkable rearrangements in the selectivity filter underlie Kv1.3's transition into the drug-blocked state.

Voltage-gated potassium (Kv) channels modulate the function of electrically-excitable and non-excitable cells by using several types of gates to regulate ion flow through the channels. An important gating mechanism, C-type inactivation, limits ion flow by transitioning Kv channels into a non-conducting inactivated state. Here, we highlight two recent papers, one on the human Kv1.3 channel and the second on the Drosophila Shaker Kv channel, that combined cryogenic electron microscopy and molecular dynamics simulation to define mechanisms underlying C-type inactivation. In both channels, the transition to the non-conducting inactivated conformation begins with the rupture of an intra-subunit hydrogen bond that fastens the selectivity filter to the pore helix. The freed filter swings outwards and gets tethered to an external residue. As a result, the extracellular end of the selectivity filter dilates and K+ permeation through the pore is impaired. Recovery from inactivation may entail a reversal of this process. Such a reversal, at least partially, is induced by the peptide dalazatide. Binding of dalazatide to external residues in Kv1.3 frees the filter to swing inwards. The extracellular end of the selectivity filter narrows allowing K+ to move in single file through the pore typical of conventional knock-on conduction. Inter-subunit hydrogen bonds that stabilize the outer pore in the dalazatide-bound structure are equivalent to those in open-conducting conformations of Kv channels. However, the intra-subunit bond that fastens the filter to the pore-helix is absent, suggesting an incomplete reversal of the process. These mechanisms define how Kv channels self-regulate the flow of K+ by changing the conformation of the selectivity filter.

Alternative splicing (AS) is a crucial process in genetic information processing that generates multiple mRNA molecules from a single gene, producing diverse proteins. Accurate prediction of AS events is essential for understanding various physiological aspects, including disease progression and prognosis. Machine learning (ML) techniques have been widely employed in bioinformatics to address this challenge. However, existing models have limitations in capturing AS events in the presence of mutations and achieving high prediction performance. To overcome these limitations, this research presents deep splicing code (DSC), a deep learning (DL)-based model for AS prediction. The proposed model aims to improve predictive ability by investigating state-of-the-art techniques in AS and developing a DL model specifically designed to predict AS events accurately. The performance of the DSC model is evaluated against existing techniques, revealing its potential to enhance the understanding and predictive power of DL algorithms in AS. It outperforms other models by achieving an average AUC score of 92%. The significance of this research lies in its contribution to identifying functional implications and potential therapeutic targets associated with AS, with applications in genomics, bioinformatics, and biomedical research. The findings of this study have the potential to advance the field and pave the way for more precise and reliable predictions of AS events, ultimately leading to a deeper understanding of genetic information processing and its impact on human physiology and disease. [ABSTRACT FROM AUTHOR]

Stevia rebaudiana Bertoni is a perennial herb, widely used as a natural sweetener around the globe. The key compounds responsible for its sweetness includes stevioside and rebaudioside-A. In order to improve these steviol glycosides, the present study was initiated to study the effect of induced mutagenesis on growth parameters, steviol glycosides and nuclear DNA content in Stevia rebaudiana Bertoni using ten doses of gamma-rays (5-100 kR). Healthy seeds of 'Madhuguna' variety of Stevia rebaudiana Bertoni developed and maintained at stevia breeding farm, Agrotechnology division, CSIR-Institute of Himalayan Bioresource Technology, Palampur (HP), India were irradiated with ten doses of gamma rays (600 seeds each/dose) ranging from 5 kR to 100 kR (i.e., 5, 10, 15, 20, 30, 40, 50, 60, 80 and 100 kR) using Co60 gamma irradiation chamber at CCS Haryana Agricultural University, Hisar, (Haryana), India. Significant variations were recorded for all the seedling traits studied while major impact was noticed on the seedling after reaching the cotyledonary stage and doses above 40 kR showed absolute mortality of the seedlings. Based on probit analysis, the optimum LD50 dose lies in the range of 20-23 kR. Glycosidic profiling of 296 mutants using high-performance liquid chromatography showed decreased total steviol glycoside content with increased radiation dose. Doses 5 kR and 10 kR, were found to be effective in increasing the overall glycosidic content. A total of 72 promising mutants were also screened for increased rebaudioside-A stevioside ratio. Comparison of nuclear DNA content using flow cytometry revealed a similar decrease in the total nuclear DNA content with increase in dosage of gamma rays. The average genome size at 5, 10, 15, 20 and 30 kR treatments were 2.72, 2.69, 2.68, 2.70 and 2.66 pg as compared to 2.72 pg in control. Mild dose of gamma rays (5 and 10 kR) in stevia were found to be effective in improving the mean steviol glycoside content and may be used in future stevia mutation programmes. [ABSTRACT FROM AUTHOR]

AbstractVery little attention has been paid by the authors for providing improved imputation methods under correlated measurement errors (CMEs) using various sampling designs. This article addresses several power ratio imputation methods and the resulting estimators under ranked set sampling (RSS) when CMEs are present. The mean square error (MSE) is developed to assess how well the suggested estimators work under CMEs. The effectiveness of the proposed imputation methods and corresponding resultant estimators is assessed by a comprehensive simulation utilizing an artificially generated population. Further, an application of the proposed imputation methods is also provided using real data from Sweden’s municipalities consisting of the total number of seats in 1982 in municipal council taken as study variable and the number of conservative seats in 1982 in municipal council taken as auxiliary variable. The CMEs might arise due to common surveyor biases if the same surveyor collects information on both the “total number of seats in municipal council” and “the number of conservative seats in that municipal council”. The simulation and real data results show that the proposed power ratio imputation methods outperform the mean, conventional ratio, and product imputation methods. [ABSTRACT FROM AUTHOR]