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

Bayesian network learning with nominal, ordinal and continuous data

Bayesian networks are a type of probabilistic graphic models composed of nodes and directed edges that serve to represent dependencies between the di erent random variables of the problem, as well as the conditional probability distributions associated to said variables. Originally, both the structure of the Network and the probability distributions were thought for discrete data, but due to the wide eld of application of Bayesian Networks, methods have been developed for other types of variables, as well as for problems with variables of various types. In this Master's Degree Dissertation, we seek to understand the operation of the di erent methods that have been developed to deal with the learning of Bayesian Networks with not only discrete variables, but also continuous, ordinal and mixed variables. Although one of the most common methods to treat Bayesian Networks with continuous variables is discretization, and some methods will be brie y explained, an attempt will be made to explain alternative methods to avoid the loss of information and the error with respect to the modelling of reality that it entails. After the completion of the thesis, it is expected to have an understanding of the subject, and to be able to transmit this knowledge to the reader in a clear and concise way. Structure learning methods will be explained, as well as the conditional probability distributions used to represent each kind of variable, together with the parameters to be learned. Also, methods of study of independence such as Mutual Information, and their estimation from a training database will be explained, as well as the Jonckheere-Terpstra Test for ordinal variables

Diseño y desarrollo de un simulador de vehículo autónomo en Python

El objetivo de este trabajo es desarrollar un simulador en Python capaz de crear una red neuronal que permita a un coche conducirse de manera autónoma sin colisionar. Este programa simple servirá para definir una base sobre la cual el usuario podrá aprender y estudiar el funcionamiento de las redes neuronale

Diseño y desarrollo de un simulador de vehículo autónomo en Python

El objetivo de este trabajo es desarrollar un simulador en Python capaz de crear una red neuronal que permita a un coche conducirse de manera autónoma sin colisionar. Este programa simple servirá para definir una base sobre la cual el usuario podrá aprender y estudiar el funcionamiento de las redes neuronale