In Orbit Timing Calibration of the Hard X-Ray Detector on Board Suzaku

The hard X-ray detector (HXD) on board the X-ray satellite Suzaku is designed to have a good timing capability with a 61 $\mu$s time resolution. In addition to detailed descriptions of the HXD timing system, results of in-orbit timing calibration and performance of the HXD are summarized. The relative accuracy of time measurements of the HXD event was confirmed to have an accuracy of $1.9\times 10^{-9}$ s s$^{-1}$ per day, and the absolute timing was confirmed to be accurate to 360 $\mu$s or better. The results were achieved mainly through observations of the Crab pulsar, including simultaneous ones with RXTE, INTEGRAL, and Swift.Comment: Accepted for publication on PASJ Vol.60, SP-1, 200

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

Hemostatic function to regulate perioperative bleeding in patients undergoing spinal surgery: A prospective observational study.

Although bleeding is a common complication of surgery, routine laboratory tests have been demonstrated to have a low ability to predict perioperative bleeding. Better understanding of hemostatic function during surgery would lead to identification of high-risk patients for bleeding. Here, we aimed to elucidate hemostatic mechanisms to determine perioperative bleeding. We prospectively enrolled 104 patients undergoing cervical spinal surgery without bleeding diathesis. Blood sampling was performed just before the operation. Volumes of perioperative blood loss were compared with the results of detailed laboratory tests assessing primary hemostasis, secondary hemostasis, and fibrinolysis. Platelet aggregations induced by several agonists correlated with each other, and only two latent factors determined inter-individual difference. Platelet aggregability independently determined perioperative bleeding. We also identified low levels of plasminogen-activator inhibitor-1 (PAI-1) and α2-plasmin inhibitor to be independent risk factors for intraoperative and postoperative bleeding, respectively. Most important independent factor to determine postoperative bleeding was body weight. Of note, obese patients with low levels of PAI-1 became high-risk patients for bleeding during surgery. Our data suggest that bleeding after surgical procedure may be influenced by inter-individual differences of hemostatic function including platelet function and fibrinolysis, even in the patients without bleeding diathesis

Low level of PAI-1 and obesity synergistically increase intraoperative bleeding.

<p>(A) Plasma PAI-1 level is significantly associated with BMI (<i>r</i> = 0.294, <i>P</i> = 0.002, n = 104). Patients are categorized into four groups by a median split of each value. (B) Blood-loss volume for each category is expressed as a box-and-whisker plot. The bottom and top of the box are the first and third quartiles, and the horizontal line inside the box represents the median. The ends of the whiskers represent the minimum and maximum values for all data. Statistical significance is observed between group 2 and group 4. BMI, body mass index; PAI-1, plasminogen activator inhibitor-1.</p

Correlations of patient variables with perioperative bleeding^*.

<p>Correlations of patient variables with perioperative bleeding^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0179829#t002fn001" target="_blank">*</a>}.</p