A network-based approach to identify expression modules underlying rejection in pediatric liver transplantation.
A network-based approach to identify expression modules underlying rejection in pediatric liver transplantation.
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DOI：
10.1016/j.xcrm.2022.100605
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发表时间：
2022-04-19
影响因子：
14.3
通讯作者：
Das, Jishnu
中科院分区：
文献类型：
--
作者：
Ningappa, Mylarappa;Rahman, Syed A.;Higgs, Brandon W.;Ashokkumar, Chethan S.;Sahni, Nidhi;Sindhi, Rakesh;Das, Jishnu
选择合适的免疫抑制剂以确保无排斥反应的结果对儿科肝移植(LT)接受者提出了独特的挑战。分子预报器可以全面解决这些挑战。目前，还没有经过充分验证的血液生物标志物用于儿童肝移植受者的肝移植前或术后。在这里，我们发现和验证单独的移植前和移植后的排斥转录签名。使用一种综合的机器学习方法，我们将转录数据与参考的高质量人类蛋白质相互作用组相结合，以识别网络模块签名，这是拒绝的基础。与基因签名不同，我们的方法天生就是多变量的，对复制更健壮，并捕获了底层网络结构，封装了相加效应。我们还以特定于个人的方式确定了目前的抗排斥药物和其他可以改变用途的药物所针对的签名。我们的方法可以为儿童肝移植前后的主要靶向途径实现个性化的药物方案调整。揭示儿童肝移植排斥反应的潜在信号表明，网络模块签名比单个基因更具预测性，识别哪些模块可以成为当前抗排斥药物的靶向，使宁帕等人能够针对主要的靶向通路对药物方案进行个性化调整。发现并验证儿科肝移植受者排斥反应的可靠多变量转录组特征。该方法结合了潜在的蛋白质相互作用组网络的结构，封装了加性效应。他们还以特定于个人的方式识别可以成为当前抗排斥药物靶标的签名。
Selecting the right immunosuppressant to ensure rejection-free outcomes poses unique challenges in pediatric liver transplant (LT) recipients. A molecular predictor can comprehensively address these challenges. Currently, there are no well-validated blood-based biomarkers for pediatric LT recipients before or after LT. Here, we discover and validate separate pre- and post-LT transcriptomic signatures of rejection. Using an integrative machine learning approach, we combine transcriptomics data with the reference high-quality human protein interactome to identify network module signatures, which underlie rejection. Unlike gene signatures, our approach is inherently multivariate and more robust to replication and captures the structure of the underlying network, encapsulating additive effects. We also identify, in an individual-specific manner, signatures that can be targeted by current anti-rejection drugs and other drugs that can be repurposed. Our approach can enable personalized adjustment of drug regimens for the dominant targetable pathways before and after LT in children. Uncover signatures that underlie rejection in pediatric liver transplantation Demonstrate network module signatures are more predictive than individual genes Identify which modules can be targeted by current anti-rejection drugs Enable personalized adjustment of drug regimens for dominant targetable pathways Ningappa et al. discover and validate robust multivariate transcriptomics signatures of rejection in pediatric liver transplant recipients. The approach incorporates the structure of the underlying protein interactome network, encapsulating additive effects. They also identify, in an individual-specific manner, signatures that can be targeted by current anti-rejection drugs.
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影响因子：
--
作者：
Das J;Yu H
通讯作者：
Yu H
免疫途径定义了疫苗介导的对SIV的保护的多种机制。
影响因子：
82.9
作者：
Ackerman ME;Das J;Pittala S;Broge T;Linde C;Suscovich TJ;Brown EP;Bradley T;Natarajan H;Lin S;Sassic JK;O'Keefe S;Mehta N;Goodman D;Sips M;Weiner JA;Tomaras GD;Haynes BF;Lauffenburger DA;Bailey-Kellogg C;Roederer M;Alter G
通讯作者：
Alter G
影响因子：
6.2
作者：
Ashokkumar C;Soltys K;Mazariegos G;Bond G;Higgs BW;Ningappa M;Sun Q;Brown A;White J;Levy S;Fazzolare T;Remaley L;Dirling K;Harris P;Hartle T;Kachmar P;Nicely M;OʼToole L;Boehm B;Jativa N;Stanley P;Jaffe R;Ranganathan S;Zeevi A;Sindhi R
通讯作者：
Sindhi R
影响因子：
1.3
作者：
Newland, David M.;Royston, Macy J.;Horslen, Simon
通讯作者：
Horslen, Simon
影响因子：
8.8
作者：
Madill-Thomsen, Katelynn;Abouljoud, Marwan;Halloran, Philip F.
通讯作者：
Halloran, Philip F.