Global analyses of human immune variation reveal baseline predictors of postvaccination responses.
Global analyses of human immune variation reveal baseline predictors of postvaccination responses.
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DOI：
10.1016/j.cell.2014.03.031
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发表时间：
2014-04-10
期刊：
影响因子：
64.5
通讯作者：
CHI Consortium
中科院分区：
文献类型：
--
作者：
Tsang JS;Schwartzberg PL;Kotliarov Y;Biancotto A;Xie Z;Germain RN;Wang E;Olnes MJ;Narayanan M;Golding H;Moir S;Dickler HB;Perl S;Cheung F;Baylor HIPC Center;CHI Consortium
系统生物学的一个主要目标是发展能够准确预测扰动反应的模型。构建这样的模型需要收集系统状态的密集测量，但将数据转换为预测结构仍然是一个挑战。为了开始模拟人体免疫，我们深入分析了基线和流感疫苗接种后的免疫参数。在63名个体中评估了疫苗接种前后的外周血单核细胞转录组、血清滴度、细胞亚群频率和B细胞应答，并用于开发系统框架，以剖析个体间和个体内变异，并建立疫苗接种后抗体应答的预测模型。引人注目的是，独立于年龄和预先存在的抗体滴度，可以单独使用扰动前的细胞群体构建准确的模型，其使用独立的基线时间点进行验证。大多数有助于预测的参数描绘了个体间暂时稳定的基线差异，提高了干预前免疫监测的前景。
A major goal of systems biology is the development of models that accurately predict responses to perturbation. Constructing such models requires collection of dense measurements of system states, yet transformation of data into predictive constructs remains a challenge. To begin to model human immunity, we analyzed immune parameters in depth both at baseline and in response to influenza vaccination. Peripheral blood mononuclear cell transcriptomes, serum titers, cell subpopulation frequencies, and B cell responses were assessed in 63 individuals before and after vaccination and used to develop a systematic framework to dissect inter- and intra-individual variation and build predictive models of post-vaccination antibody responses. Strikingly, independent of age and pre-existing antibody titers, accurate models could be constructed using pre-perturbation cell populations alone, which were validated using independent baseline time-points. Most of the parameters contributing to prediction delineated temporally-stable baseline differences across individuals, raising the prospect of immune monitoring before intervention.
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影响因子：
16.8
作者：
Gerriets VA;Rathmell JC
通讯作者：
Rathmell JC
DOI：
10.1097/qad.0b013e328342328b
发表时间：
2011-01-28
期刊：
AIDS (London, England)
影响因子：
--
作者：
Ho J;Moir S;Wang W;Posada JG;Gu W;Rehman MT;Dewar R;Kovacs C;Sneller MC;Chun TW;Follmann DA;Fauci AS
通讯作者：
Fauci AS
影响因子：
4.4
作者：
Frasca, Daniela;Diaz, Alain;Blomberg, Bonnie B.
通讯作者：
Blomberg, Bonnie B.
影响因子：
32.4
作者：
Obermoser, Gerlinde;Presnell, Scott;Chaussabel, Damien
通讯作者：
Chaussabel, Damien
通过替代变量分析捕获基因表达研究中的异质性。
影响因子：
4.5
作者：
Leek, Jeffrey T.;Storey, John D.
通讯作者：
Storey, John D.