High-Density Blood Transcriptomics Reveals Precision Immune Signatures of SARS-CoV-2 Infection in Hospitalized Individuals.
High-Density Blood Transcriptomics Reveals Precision Immune Signatures of SARS-CoV-2 Infection in Hospitalized Individuals.
复制标题
DOI：
10.3389/fimmu.2021.694243
复制
发表时间：
2021
影响因子：
7.3
通讯作者：
Rajasekaran S
中科院分区：
文献类型：
--
作者：
Prokop JW;Hartog NL;Chesla D;Faber W;Love CP;Karam R;Abualkheir N;Feldmann B;Teng L;McBride T;Leimanis ML;English BK;Holsworth A;Frisch A;Bauss J;Kalpage N;Derbedrossian A;Pinti RM;Hale N;Mills J;Eby A;VanSickle EA;Pageau SC;Shankar R;Chen B;Carcillo JA;Sanfilippo D;Olivero R;Bupp CP;Rajasekaran S
对COVID-19感染的免疫反应是可变的。COVID-19如何影响住院患者的临床结果，需要通过容易获得的生物材料（如血液）来了解。我们假设，当结合先进的RNA多维生物信息学时，对SARS-CoV-2住院患者的宿主（和病原体）血液RNA进行高密度分析，将为COVID-19患者的反应异质性提供机制性见解。我们招募了36名住院的COVID-19患者（11名死亡）和15名对照，在多个时间点收集了74支PAXgene RNA管，其中一个时间点是早期，23名患者在接受各种治疗后。总RNAseq以高密度进行，每个样品具有> 1.6亿个配对末端，150个碱基对读数，代表任何公开存放的血液PAXgene管研究的每个样品的最多测序碱基。在COVID-19患者的血液中有770个基因发生了显著改变，这些基因与抗病毒防御、有丝分裂细胞周期、I型干扰素信号传导和严重病毒感染有关。激活的免疫基因包括与中性粒细胞机制、分泌颗粒和中性粒细胞胞外陷阱（NET）相关的那些，沿着淋巴细胞中基因表达降低和获得性免疫应答的克隆扩增。恢复期血清和地塞米松等疗法减少了COVID-19的许多血液表达特征。重症或死亡患者的特征是各种继发感染、独特的基因模式、先天反应失调和外周器官损伤，这些在队列中没有发现。高密度转录组数据提供了共享的基因表达特征，提供了对免疫系统的独特见解和患者的个性化特征，可用于了解患者的临床状况。全血转录组学为免疫激活、免疫库和继发性感染提供了患者层面的见解，可以进一步指导精准治疗。
The immune response to COVID-19 infection is variable. How COVID-19 influences clinical outcomes in hospitalized patients needs to be understood through readily obtainable biological materials, such as blood. We hypothesized that a high-density analysis of host (and pathogen) blood RNA in hospitalized patients with SARS-CoV-2 would provide mechanistic insights into the heterogeneity of response amongst COVID-19 patients when combined with advanced multidimensional bioinformatics for RNA. We enrolled 36 hospitalized COVID-19 patients (11 died) and 15 controls, collecting 74 blood PAXgene RNA tubes at multiple timepoints, one early and in 23 patients after treatment with various therapies. Total RNAseq was performed at high-density, with >160 million paired-end, 150 base pair reads per sample, representing the most sequenced bases per sample for any publicly deposited blood PAXgene tube study. There are 770 genes significantly altered in the blood of COVID-19 patients associated with antiviral defense, mitotic cell cycle, type I interferon signaling, and severe viral infections. Immune genes activated include those associated with neutrophil mechanisms, secretory granules, and neutrophil extracellular traps (NETs), along with decreased gene expression in lymphocytes and clonal expansion of the acquired immune response. Therapies such as convalescent serum and dexamethasone reduced many of the blood expression signatures of COVID-19. Severely ill or deceased patients are marked by various secondary infections, unique gene patterns, dysregulated innate response, and peripheral organ damage not otherwise found in the cohort. High-density transcriptomic data offers shared gene expression signatures, providing unique insights into the immune system and individualized signatures of patients that could be used to understand the patient’s clinical condition. Whole blood transcriptomics provides patient-level insights for immune activation, immune repertoire, and secondary infections that can further guide precision treatment.
登录
查看更多内容
危及生命的Covid-19患者中针对I型IFN的自身抗体。
DOI：
10.1126/science.abd4585
发表时间：
2020-10-23
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Bastard P;Rosen LB;Zhang Q;Michailidis E;Hoffmann HH;Zhang Y;Dorgham K;Philippot Q;Rosain J;Béziat V;Manry J;Shaw E;Haljasmägi L;Peterson P;Lorenzo L;Bizien L;Trouillet-Assant S;Dobbs K;de Jesus AA;Belot A;Kallaste A;Catherinot E;Tandjaoui-Lambiotte Y;Le Pen J;Kerner G;Bigio B;Seeleuthner Y;Yang R;Bolze A;Spaan AN;Delmonte OM;Abers MS;Aiuti A;Casari G;Lampasona V;Piemonti L;Ciceri F;Bilguvar K;Lifton RP;Vasse M;Smadja DM;Migaud M;Hadjadj J;Terrier B;Duffy D;Quintana-Murci L;van de Beek D;Roussel L;Vinh DC;Tangye SG;Haerynck F;Dalmau D;Martinez-Picado J;Brodin P;Nussenzweig MC;Boisson-Dupuis S;Rodríguez-Gallego C;Vogt G;Mogensen TH;Oler AJ;Gu J;Burbelo PD;Cohen JI;Biondi A;Bettini LR;D'Angio M;Bonfanti P;Rossignol P;Mayaux J;Rieux-Laucat F;Husebye ES;Fusco F;Ursini MV;Imberti L;Sottini A;Paghera S;Quiros-Roldan E;Rossi C;Castagnoli R;Montagna D;Licari A;Marseglia GL;Duval X;Ghosn J;HGID Lab;NIAID-USUHS Immune Response to COVID Group;COVID Clinicians;COVID-STORM Clinicians;Imagine COVID Group;French COVID Cohort Study Group;Milieu Intérieur Consortium;CoV-Contact Cohort;Amsterdam UMC Covid-19 Biobank;COVID Human Genetic Effort;Tsang JS;Goldbach-Mansky R;Kisand K;Lionakis MS;Puel A;Zhang SY;Holland SM;Gorochov G;Jouanguy E;Rice CM;Cobat A;Notarangelo LD;Abel L;Su HC;Casanova JL
通讯作者：
Casanova JL
影响因子：
24.8
作者：
Lee, Jeong Seok;Park, Seongwan;Shin, Eui-Cheol
通讯作者：
Shin, Eui-Cheol
影响因子：
14.9
作者：
Franceschini A;Szklarczyk D;Frankild S;Kuhn M;Simonovic M;Roth A;Lin J;Minguez P;Bork P;von Mering C;Jensen LJ
通讯作者：
Jensen LJ
DOI：
10.1126/science.abc6261
发表时间：
2020-09-04
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Arunachalam PS;Wimmers F;Mok CKP;Perera RAPM;Scott M;Hagan T;Sigal N;Feng Y;Bristow L;Tak-Yin Tsang O;Wagh D;Coller J;Pellegrini KL;Kazmin D;Alaaeddine G;Leung WS;Chan JMC;Chik TSH;Choi CYC;Huerta C;Paine McCullough M;Lv H;Anderson E;Edupuganti S;Upadhyay AA;Bosinger SE;Maecker HT;Khatri P;Rouphael N;Peiris M;Pulendran B
通讯作者：
Pulendran B
影响因子：
120.7
作者：
LEGALL, JR;LEMESHOW, S;SAULNIER, F
通讯作者：
SAULNIER, F