Gene Expression Nebulas
基于标准化流程分析的转录图谱综合数据库

Gene Expression Nebulas

多物种转录图谱整合数据库

PRJNA722046: Identification of driver genes for severe forms of COVID-19 in a deeply phenotyped young patient cohort

来源: NCBI / GSE172114
提交时间: Apr 14 2021
释放时间: Oct 26 2021
最后更新时间: Oct 27 2021

概要: The etiology of severe forms of COVID19, especially in young patients, remains a salient unanswered question. Here we build built on a 3-tier cohort where all individuals/patients were strictly below 50 years of age and where a number of comorbidities were excluded at study onset. Besides healthy controls (N=22), these included patients in the intensive care unit with Acute Respiratory Distress Syndrome (ARDS) (“critical group”; N=47), and those in a non-critical care ward under supplemental oxygen (“non-critical group”, N=25). This highly curated cohort allowed us to perform a deep multi-omics approach, which included whole genome sequencing, whole blood RNA-sequencing, plasma and peripheral-blood mononuclear cells proteomics, multiplex cytokine profiling, mass-cytometry-based immune cell profiling in conjunction with viral parameters i.e. anti-SARS-Cov-2 neutralizing antibodies and multi-target antiviral serology. Critical patients were characterized by an exacerbated inflammatory state, perturbed lymphoid and myeloid cell compartments, signatures of dysregulated blood coagulation and active regulation of viral entry into the cells. A unique gene signature that differentiates critical from non-critical patients was identified by an ensemble of machine learning, deep learning and quantum annealing approachmethods. Within this gene networksignature, Sstructural Causal causal Modeling modeling identified several genes that may potentially drivepromote ARDS driver genes etiology, among which the up-regulated metalloprotease ADAM9 seems to be a key driver. Inhibition of ADAM9 ex vivo interfered with SARS-Cov-2 uptake and replication in human epithelial cells. In brief, Hence we applyied an advanced integrated machine learning approach and probabilistic programming strategy to identify causal molecular driver geness for of severe forms of COVID-19 in a small, uncluttered tightly controlled cohort of patients, the importance of which were then validated with experiments.

项目整体设计: RNA-seq was performed on 69 whole blood RNA samples corresponding to 46 critical and 23 non-critical patients at hospitalization.

GEN 数据集:
GEND000369
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提取方案: PAXgene Blood RNA Kit (Qiagen), TruSeq Stranded Total RNA with Ribo-Zero Globin kit (Illumina),151bp paired-end
建库方案: -
测序信息
分子类型: rRNA- RNA
库的片段类型: PAIRED
库的链类型: Forward
测序平台: ILLUMINA
测序仪型号: Illumina NovaSeq 6000
链特异性: Specific
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数据来源 GEN样本编号 GEN数据集编号 系列编号 项目编号 样本编号 样本名称 生物样本编号 样本访问号 实验访问号 释放时间 提交时间 最后更新时间 物种 种族 族裔 年龄 年龄单位 性别 来源名称 组织 细胞类型 细胞亚型 细胞系 疾病 疾病状态 发育阶段 突变/变异 表型 Condition Detail 生长方案 处理方案 提取方案 建库方案 分子类型 库的片段类型 链特异性 库的链类型 加标(Spike-In) 测序方法 测序平台 测序仪型号 细胞数 测序片段数 碱基数 平均测序片段长度_1 平均测序片段长度_2 唯一比对率 多重比对率 覆盖度
文章
Identification of driver genes for critical forms of COVID-19 in a deeply phenotyped young patient cohort.
Science translational medicine . 2021-10-26 [PMID: 34698500]