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

JASMINE: Near-infrared astrometry and time-series photometry science

The Japan Astrometry Satellite Mission for INfrared Exploration (JASMINE) is a planned M-class science space mission by the Institute of Space and Astronautical Science, the Japan Aerospace Exploration Agency. JASMINE has two main science goals. One is Galactic archaeology with a Galactic Center survey, which aims to reveal the Milky Way’s central core structure and formation history from Gaia-level (∼25 ${\mu}$as) astrometry in the near-infrared (NIR) Hw band (1.0–1.6 ${\mu}$m). The other is an exoplanet survey, which aims to discover transiting Earth-like exoplanets in the habitable zone from NIR time-series photometry of M dwarfs when the Galactic Center is not accessible. We introduce the mission, review many science objectives, and present the instrument concept. JASMINE will be the first dedicated NIR astrometry space mission and provide precise astrometric information on the stars in the Galactic Center, taking advantage of the significantly lower extinction in the NIR. The precise astrometry is obtained by taking many short-exposure images. Hence, the JASMINE Galactic Center survey data will be valuable for studies of exoplanet transits, asteroseismology, variable stars, and microlensing studies, including discovery of (intermediate-mass) black holes. We highlight a swath of such potential science, and also describe synergies with other missions

Problem-based Learning and Problem Finding Among University Graduate Students

In recent years, problem-based learning (PBL) techniques have been gaining momentum in schools and university curricula around the world. The main advantage of the PBL method is that it promotes creative problem solving, improves cognition and enhances overall thought processes in learners. For most PBL-style programmes, problem solving is at the core, although the notion of problem discovery or problem finding is not seriously considered. In most cases, students are always presented with a structured and welldefined problem, but have no experience of solving an ill-structured problem or ʻwicked’ problem. The present study focuses on problem finding as a critical step towards developing problem solving skills in university graduate students. The study aims at understanding the importance of problem formulation and creativity, and focuses as well on our attempt to teach problem finding as an important tool in the development of creative thinking and problem solving among graduate students. The study is part of a special graduate programme called the Nitobe School at Hokkaido University in Japan, which started in 2015. In an active learning classroom setting, this course is intended to support graduate students in their discovery of illstructured problems, help them to understand their formulation and thereby improve their problem solving skills. We present the results of our teaching method for the first year at the Nitobe School and share our findings through this work

Low temperature hydrogenation of iron nanoparticles on graphene

Hydrogenation of iron nanoparticles was performed both computationally and experimentally where previously chemically-bonded iron hydride is considered to be unachievable under ordinary conditions. Density functional theory (DFT) calculations predict that hydrogenated iron nanoparticles are stabilized on a single-layer graphene/Cu substrate. Experimentally, iron nanoparticles were deposited onto a graphene/Cu substrate by vacuum deposition. Hydrogenation was done at 1atm of hydrogen gas and under liquid nitrogen. Mass spectrometry peak confirmed the hydrogen release from hydrogenated iron nanoparticles while a scanning transmission electron microscopy is used in order to link a geometrical shape of iron hydride nanoparticles between experimental and theoretical treatments. The hydrogenated iron nanoparticles were successfully synthesized where hydrogenated iron nanoparticles are stable under ordinary conditions

Time-Varying Transition Probability Matrix Estimation and Its Application to Brand Share Analysis

<div><p>In a product market or stock market, different products or stocks compete for the same consumers or purchasers. We propose a method to estimate the time-varying transition matrix of the product share using a multivariate time series of the product share. The method is based on the assumption that each of the observed time series of shares is a stationary distribution of the underlying Markov processes characterized by transition probability matrices. We estimate transition probability matrices for every observation under natural assumptions. We demonstrate, on a real-world dataset of the share of automobiles, that the proposed method can find intrinsic transition of shares. The resulting transition matrices reveal interesting phenomena, for example, the change in flows between TOYOTA group and GM group for the fiscal year where TOYOTA group’s sales beat GM’s sales, which is a reasonable scenario.</p></div

Observed sales share data of manufacturers from the year 2007 to the year 2015.

<p>Observed sales share data of manufacturers from the year 2007 to the year 2015.</p

Visualization of estimated consumers’ flow from a series of data in 2008-4Q and 2009-1Q.

<p>Visualization of estimated consumers’ flow from a series of data in 2008-4Q and 2009-1Q.</p