Ochsner Scott A, Pillich Rudolf T, Rawool Deepali, Grethe Jeffrey S, McKenna Neil J
Department of Molecular, Baylor College of Medicine, Houston, TX 77030, USA.
Cellular Biology and Medicine, Baylor College of Medicine, Houston, TX 77030, USA.
iScience. 2022 Jun 11;25(7):104581. doi: 10.1016/j.isci.2022.104581. eCollection 2022 Jul 15.
Investigator-generated transcriptomic datasets interrogating circulating immune cell (CIC) gene expression in clinical type 1 diabetes (T1D) have underappreciated re-use value. Here, we repurposed these datasets to create an open science environment for the generation of hypotheses around CIC signaling pathways whose gain or loss of function contributes to T1D pathogenesis. We firstly computed sets of genes that were preferentially induced or repressed in T1D CICs and validated these against community benchmarks. We then inferred and validated signaling node networks regulating expression of these gene sets, as well as differentially expressed genes in the original underlying T1D case:control datasets. In a set of three use cases, we demonstrated how informed integration of these networks with complementary digital resources supports substantive, actionable hypotheses around signaling pathway dysfunction in T1D CICs. Finally, we developed a federated, cloud-based web resource that exposes the entire data matrix for unrestricted access and re-use by the research community.
研究人员生成的转录组数据集，用于研究临床1型糖尿病（T1D）中循环免疫细胞（CIC）的基因表达，其再利用价值未得到充分重视。在此，我们重新利用这些数据集，创建了一个开放科学环境，以围绕CIC信号通路生成假设，这些信号通路的功能获得或丧失有助于T1D发病机制。我们首先计算了在T1D CIC中优先诱导或抑制的基因集，并根据社区基准对其进行验证。然后，我们推断并验证了调节这些基因集表达的信号节点网络，以及原始潜在T1D病例对照数据集中的差异表达基因。在一组三个用例中，我们展示了这些网络与互补数字资源的明智整合如何支持围绕T1D CIC中信号通路功能障碍的实质性、可操作的假设。最后，我们开发了一个基于云的联合网络资源，公开整个数据矩阵，供研究社区无限制访问和再利用。
