Identification of lysophospholipid receptors in human platelets: the relation of two agonists, lysophosphatidic acid and sphingosine 1-phosphate

AbstractLysophosphatidic acid (LPA) and sphingosine 1-phosphate (Sph-1-P) are known as structurally related bio-active lipids activating platelets through their respective receptors. Although the receptors for LPA and Sph-1-P have been recently identified in various cells, the identification and characterization of ones in platelets have been reported only preliminarily. In this report, we first investigated the distinct modes of LPA and Sph-1-P actions in platelet activation and found that LPA functioned as a much stronger agonist than Sph-1-P, and high concentrations of Sph-1-P specifically desensitized LPA-induced intracellular Ca2+ mobilization. In order to identify the responsible receptors underlying these observations, we analyzed the LPA and Sph-1-P receptors which might be expressed in human platelets, by RT-PCR. We found for the first time that Edg2, 4, 6 and 7 mRNA are expressed in human platelets

Curated genome annotation of Oryza sativa ssp. japonica and comparative genome analysis with Arabidopsis thaliana

We present here the annotation of the complete genome of rice Oryza sativa L. ssp. japonica cultivar Nipponbare. All functional annotations for proteins and non-protein-coding RNA (npRNA) candidates were manually curated. Functions were identified or inferred in 19,969 (70%) of the proteins, and 131 possible npRNAs (including 58 antisense transcripts) were found. Almost 5000 annotated protein-coding genes were found to be disrupted in insertional mutant lines, which will accelerate future experimental validation of the annotations. The rice loci were determined by using cDNA sequences obtained from rice and other representative cereals. Our conservative estimate based on these loci and an extrapolation suggested that the gene number of rice is ~32,000, which is smaller than previous estimates. We conducted comparative analyses between rice and Arabidopsis thaliana and found that both genomes possessed several lineage-specific genes, which might account for the observed differences between these species, while they had similar sets of predicted functional domains among the protein sequences. A system to control translational efficiency seems to be conserved across large evolutionary distances. Moreover, the evolutionary process of protein-coding genes was examined. Our results suggest that natural selection may have played a role for duplicated genes in both species, so that duplication was suppressed or favored in a manner that depended on the function of a gene

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

The Macro-and Micro-Language Learning Counseling: An Autoethnographic Account

This article describes an example of the counselor’s role in a relatively small Self-Access Center (SAC) for language learning in universities in Japan. The author has been involved with establishing and running two SACs in Japanese universities. The study used autoethnography as its research method to look closely at the counselor’s role. This study eventually helped the author to analyze the counseling she has been providing and to realize that the counselor is required to provide not only macro-counseling but also micro-counseling. Micro-counseling consists of short, informal interactions with learners which connect the learner to elements in SACs, such as teaching assistants, other language learners, and language learning materials. These micro-counseling encounters can help to create a secure space which encourages learners to engage in macro-counseling sessions which support their language learning

Asymmetric PTEN Distribution Regulated by Spatial Heterogeneity in Membrane-Binding State Transitions

<div><p>The molecular mechanisms that underlie asymmetric PTEN distribution at the posterior of polarized motile cells and regulate anterior pseudopod formation were addressed by novel single-molecule tracking analysis. Heterogeneity in the lateral mobility of PTEN on a membrane indicated the existence of three membrane-binding states with different diffusion coefficients and membrane-binding lifetimes. The stochastic state transition kinetics of PTEN among these three states were suggested to be regulated spatially along the cell polarity such that only the stable binding state is selectively suppressed at the anterior membrane to cause local PTEN depletion. By incorporating experimentally observed kinetic parameters into a simple mathematical model, the asymmetric PTEN distribution can be explained quantitatively to illustrate the regulatory mechanisms for cellular asymmetry based on an essential causal link between individual stochastic reactions and stable localizations of the ensemble.</p> </div

Lifetime-diffusion analysis assuming a two-state model.

<p>Kinetic model describing the state transitions and membrane dissociations in non-polarized cells and at the pseudopod and tail of polarized cells. All kinetic parameter values estimated are summarized in the scheme.</p

Lifetime-diffusion analysis of PTEN_G129E in polarized cells.

<p>(A) The decay profiles of three subpopulations obtained from molecules observed at the pseudopod (<i>crosses</i>) and fitted to Eq. S12 (<i>solid lines</i>). (B) The decay profiles of three subpopulations obtained from molecules observed at the tail (<i>crosses</i>) and fitted to Eq. S12 (<i>solid lines</i>). (C) Kinetic model describing the state transitions and membrane dissociation in polarized cells. All kinetic parameter values estimated in (A) and (B) are summarized in the scheme. See also Movie S1.</p

Models of membrane-bound signaling molecules exhibiting diffusion, state transitions and membrane dissociation.

<p>(A) Schematic view of the three principle models. (B,D,F) The probability density function (PDF) of molecular position at <i>t</i> = 0.033, 0.3 and 1 (B) or 0.033, 0.3 and 3 (D,F). (C,E,G) The membrane residence probability, <i>R</i>(<i>t</i>) (<i>black</i>), and the subpopulation probability, <i>Q</i>(<i>t</i>), for state 1 (<i>green</i>) and state 2 (<i>orange</i>). (insets in E and G) Time series of the subpopulation ratios. (B,C) Model S1. The PDFs before (<i>dotted lines</i>) and after (<i>solid lines</i>) incorporating the measurement error are shown. <i>D</i> = 0.01, <i>λ</i> = 1.00 and <i>ε</i> = 0.04. (D,E) Model S2. <i>D</i>₁ = 0.01, <i>D</i>₂ = 0.10, <i>λ</i>₁ = 0.10, <i>λ</i>₂ = 1.00, <i>q</i>₁ = 0.20 and <i>ε</i> = 0.04. (F,G) Model S3. <i>D</i>₁ = 0.01, <i>D</i>₂ = 0.10, <i>λ</i>₁ = 0.10, <i>λ</i>₂ = 1.00, <i>k</i>₁₂ = 0.10, <i>k</i>₂₁ = 0.50, <i>q</i>₁ = 0.20 and <i>ε</i> = 0.04. <i>D</i>, µm²s⁻¹; <i>λ</i>, <i>k</i>, s⁻¹; <i>ε</i>, µm. PDFs for Models S2 and S3 incorporate the measurement error. See also Figures S1.</p