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

Polynomial Regression Techniques for Environmental Data Recovery in Wireless Sensor Networks

In the near feature, large-scale wireless sensor networks will play an important role in our lives by monitoring our environment with large numbers of sensors. However, data loss owing to data collision between the sensor nodes and electromagnetic noise need to be addressed. As the interval of aggregate data is not fixed, digital signal processing is not possible and noise degrades the data accuracy. To overcome these problems, we have researched an environmental data recovery technique using polynomial regression based on the correlations among environmental data. The reliability of the recovered data is discussed in the time, space and frequency domains. The relation between the accuracy of the recovered characteristics and the polynomial regression order is clarified. The effects of noise, data loss and number of sensor nodes are quantified. Clearly, polynomial regression offers the advantage of low-pass filtering and enhances the signal-to-noise ratio of the environmental data. Furthermore, the polynomial regression can recover arbitrary environmental characteristics. Measured temperature and accelerator characteristics were recovered successfully

CCM.FF-K3.2011 Intercomparison for airspeed

The CCM.FF-K3.2011 comparison was organized for the purpose of determination of the degree of equivalence of the national standards for air speed over the range 0.5 m/s to 40 m/s. An ultrasonic anemometer and a Laser Doppler anemometer were used as transfer standards. Nine laboratories from three RMOs participated between July 2013 and July 2015 – EURAMET: PTB, Germany; LNE-CETIAT, France; INRIM, Italy; VSL, The Netherlands; E+E, Austria; SIM: NIST, measurements were provided at ambient conditions. All results of independent participants were used in the determination of the key comparison reference value (KCRV) and the uncertainty of the KCRV. The reference value was determined at each air speed separately following “procedure A” presented by M. G. Cox. The degree of equivalence with the KCRV was calculated for each air speed and laboratory. Almost all reported results were consistent with the KCRV