Spin density functional study on magnetism of potassium loaded Zeolite A

In order to clarify the mechanism of spin polarization in potassium-loaded zeolite A, we perform {\em ab initio} density-functional calculations. We find that (i) the system comprising only non-magnetic elements (Al, Si, O and K) can indeed exhibit ferromagnetism, (ii) while the host cage makes a confining quantum-well potential in which $s$- and $p$-like states are formed, the potassium-4$s$ electrons accommodated in the p-states are responsible for the spin polarization, and (iii) the size of the magnetic moment sensitively depends on the atomic configuration of the potassium atoms. We show that the spin polarization can be described systematically in terms of the confining potential and the crystal field splitting of the p-states

Glycolysis Inhibition Inactivates ABC Transporters to Restore Drug Sensitivity in Malignant Cells

Cancer cells eventually acquire drug resistance largely via the aberrant expression of ATP-binding cassette (ABC) transporters, ATP-dependent efflux pumps. Because cancer cells produce ATP mostly through glycolysis, in the present study we explored the effects of inhibiting glycolysis on the ABC transporter function and drug sensitivity of malignant cells. Inhibition of glycolysis by 3-bromopyruvate (3BrPA) suppressed ATP production in malignant cells, and restored the retention of daunorubicin or mitoxantrone in ABC transporter-expressing, RPMI8226 (ABCG2), KG-1 (ABCB1) and HepG2 cells (ABCB1 and ABCG2). Interestingly, although side population (SP) cells isolated from RPMI8226 cells exhibited higher levels of glycolysis with an increased expression of genes involved in the glycolytic pathway, 3BrPA abolished Hoechst 33342 exclusion in SP cells. 3BrPA also disrupted clonogenic capacity in malignant cell lines including RPMI8226, KG-1, and HepG2. Furthermore, 3BrPA restored cytotoxic effects of daunorubicin and doxorubicin on KG-1 and RPMI8226 cells, and markedly suppressed subcutaneous tumor growth in combination with doxorubicin in RPMI8226-implanted mice. These results collectively suggest that the inhibition of glycolysis is able to overcome drug resistance in ABC transporter-expressing malignant cells through the inactivation of ABC transporters and impairment of SP cells with enhanced glycolysis as well as clonogenic cells

The whole blood transcriptional regulation landscape in 465 COVID-19 infected samples from Japan COVID-19 Task Force

「コロナ制圧タスクフォース」COVID-19患者由来の血液細胞における遺伝子発現の網羅的解析 --重症度に応じた遺伝子発現の変化には、ヒトゲノム配列の個人差が影響する--. 京都大学プレスリリース. 2022-08-23.Coronavirus disease 2019 (COVID-19) is a recently-emerged infectious disease that has caused millions of deaths, where comprehensive understanding of disease mechanisms is still unestablished. In particular, studies of gene expression dynamics and regulation landscape in COVID-19 infected individuals are limited. Here, we report on a thorough analysis of whole blood RNA-seq data from 465 genotyped samples from the Japan COVID-19 Task Force, including 359 severe and 106 non-severe COVID-19 cases. We discover 1169 putative causal expression quantitative trait loci (eQTLs) including 34 possible colocalizations with biobank fine-mapping results of hematopoietic traits in a Japanese population, 1549 putative causal splice QTLs (sQTLs; e.g. two independent sQTLs at TOR1AIP1), as well as biologically interpretable trans-eQTL examples (e.g., REST and STING1), all fine-mapped at single variant resolution. We perform differential gene expression analysis to elucidate 198 genes with increased expression in severe COVID-19 cases and enriched for innate immune-related functions. Finally, we evaluate the limited but non-zero effect of COVID-19 phenotype on eQTL discovery, and highlight the presence of COVID-19 severity-interaction eQTLs (ieQTLs; e.g., CLEC4C and MYBL2). Our study provides a comprehensive catalog of whole blood regulatory variants in Japanese, as well as a reference for transcriptional landscapes in response to COVID-19 infection

DOCK2 is involved in the host genetics and biology of severe COVID-19

「コロナ制圧タスクフォース」COVID-19疾患感受性遺伝子DOCK2の重症化機序を解明 --アジア最大のバイオレポジトリーでCOVID-19の治療標的を発見--. 京都大学プレスリリース. 2022-08-10.Identifying the host genetic factors underlying severe COVID-19 is an emerging challenge. Here we conducted a genome-wide association study (GWAS) involving 2, 393 cases of COVID-19 in a cohort of Japanese individuals collected during the initial waves of the pandemic, with 3, 289 unaffected controls. We identified a variant on chromosome 5 at 5q35 (rs60200309-A), close to the dedicator of cytokinesis 2 gene (DOCK2), which was associated with severe COVID-19 in patients less than 65 years of age. This risk allele was prevalent in East Asian individuals but rare in Europeans, highlighting the value of genome-wide association studies in non-European populations. RNA-sequencing analysis of 473 bulk peripheral blood samples identified decreased expression of DOCK2 associated with the risk allele in these younger patients. DOCK2 expression was suppressed in patients with severe cases of COVID-19. Single-cell RNA-sequencing analysis (n = 61 individuals) identified cell-type-specific downregulation of DOCK2 and a COVID-19-specific decreasing effect of the risk allele on DOCK2 expression in non-classical monocytes. Immunohistochemistry of lung specimens from patients with severe COVID-19 pneumonia showed suppressed DOCK2 expression. Moreover, inhibition of DOCK2 function with CPYPP increased the severity of pneumonia in a Syrian hamster model of SARS-CoV-2 infection, characterized by weight loss, lung oedema, enhanced viral loads, impaired macrophage recruitment and dysregulated type I interferon responses. We conclude that DOCK2 has an important role in the host immune response to SARS-CoV-2 infection and the development of severe COVID-19, and could be further explored as a potential biomarker and/or therapeutic target

Phospholipid Flippases Lem3p-Dnf1p and Lem3p-Dnf2p Are Involved in the Sorting of the Tryptophan Permease Tat2p in Yeast

The type 4 P-type ATPases are flippases that generate phospholipid asymmetry in membranes. In budding yeast, heteromeric flippases, including Lem3p-Dnf1p and -Dnf2p translocate phospholipids to the cytoplasmic leaflet of membranes. Here we report that Lem3p-Dnf1/2p are involved in transport of the tryptophan permease Tat2p to the plasma membrane. The lem3Δ mutant exhibited a tryptophan requirement due to the mislocalization of Tat2p to intracellular membranes. Tat2p was relocalized to the plasma membrane when trans-Golgi network (TGN)-to-endosome transport was inhibited. Inhibition of ubiquitination by mutations in ubiquitination machinery also rerouted Tat2p to the plasma membrane. Lem3p-Dnf1/2p are localized to endosomal/TGN membranes in addition to the plasma membrane. Endocytosis mutants, in which Lem3p-Dnf1/2p are sequestered to the plasma membrane. Endocytosis mutants, in which Lem3p-Dnf1/2p are sequestered to the plasma membrane, also exhibited the ubiquitination-dependent missorting of Tat2p. These results suggest that Tat2p is ubiquitinated at the TGN and missorted to the vacuolar pathway in the lem3Δ mutant. The NH2-terminal cytoplasmic region of Tat2p containing ubiquitination acceptor lysines interacted with liposomes containing acidic phospholipids, including phosphatidylserine. This interaction was abrogated by alanine substitution mutations in the basic amino acids downstream of the ubiquitination sites. Interestingly, a mutant Tat2p containing these substitutions was missorted in a ubiquitination-dependent manner. We propose the following model based on these results; Tat2p is not ubiquitinated when the NH2-terminal region is bound to membrane phospholipids, but if it dissociates from the membrane due to a low level of phosphatidylserine caused by perturbation of phospholipid asymmetry in the lem3Δ mutant, Tat2p is ubiquitinated and then transported from the TGN to the vacuole

Automatic dental age calculation from panoramic radiographs using deep learning: a two-stage approach with object detection and image classification

Abstract Background Dental age is crucial for treatment planning in pediatric and orthodontic dentistry. Dental age calculation methods can be categorized into morphological, biochemical, and radiological methods. Radiological methods are commonly used because they are non-invasive and reproducible. When radiographs are available, dental age can be calculated by evaluating the developmental stage of permanent teeth and converting it into an estimated age using a table, or by measuring the length between some landmarks such as the tooth, root, or pulp, and substituting them into regression formulas. However, these methods heavily depend on manual time-consuming processes. In this study, we proposed a novel and completely automatic dental age calculation method using panoramic radiographs and deep learning techniques. Methods Overall, 8,023 panoramic radiographs were used as training data for Scaled-YOLOv4 to detect dental germs and mean average precision were evaluated. In total, 18,485 single-root and 16,313 multi-root dental germ images were used as training data for EfficientNetV2 M to classify the developmental stages of detected dental germs and Top-3 accuracy was evaluated since the adjacent stages of the dental germ looks similar and the many variations of the morphological structure can be observed between developmental stages. Scaled-YOLOv4 and EfficientNetV2 M were trained using cross-validation. We evaluated a single selection, a weighted average, and an expected value to convert the probability of developmental stage classification to dental age. One hundred and fifty-seven panoramic radiographs were used to compare automatic and manual human experts’ dental age calculations. Results Dental germ detection was achieved with a mean average precision of 98.26% and dental germ classifiers for single and multi-root were achieved with a Top-3 accuracy of 98.46% and 98.36%, respectively. The mean absolute errors between the automatic and manual dental age calculations using single selection, weighted average, and expected value were 0.274, 0.261, and 0.396, respectively. The weighted average was better than the other methods and was accurate by less than one developmental stage error. Conclusion Our study demonstrates the feasibility of automatic dental age calculation using panoramic radiographs and a two-stage deep learning approach with a clinically acceptable level of accuracy