Divisions of Human Genetics and Immunobiology, Center for Circadian Medicine, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA.
Department of Medicine, Chronobiology and Sleep Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Bioinformatics. 2022 Dec 13;38(24):5375-5382. doi: 10.1093/bioinformatics/btac686.
Years of time-series gene expression studies have built a strong understanding of clock-controlled pathways across species. However, comparatively little is known about how 'non-clock' pathways influence clock function. We need a strong understanding of clock-coupled pathways in human tissues to better appreciate the links between disease and clock function.
We developed a new computational approach to explore candidate pathways coupled to the clock in human tissues. This method, termed LTM, is an in silico screen to infer genetic influences on circadian clock function. LTM uses natural variation in gene expression in human data and directly links gene expression variation to clock strength independent of longitudinal data. We applied LTM to three human skin and one melanoma datasets and found that the cell cycle is the top candidate clock-coupled pathway in healthy skin. In addition, we applied LTM to thousands of tumor samples from 11 cancer types in the TCGA database and found that extracellular matrix organization-related pathways are tightly associated with the clock strength in humans. Further analysis shows that clock strength in tumor samples is correlated with the proportion of cancer-associated fibroblasts and endothelial cells. Therefore, we show both the power of LTM in predicting clock-coupled pathways and classify factors associated with clock strength in human tissues.
LTM is available on GitHub (https://github.com/gangwug/LTMR) and figshare (https://figshare.com/articles/software/LTMR/21217604) to facilitate its use.
Supplementary data are available at Bioinformatics online.
多年的时间序列基因表达研究已经对跨物种的时钟控制途径有了深入的了解。然而，关于“非时钟”途径如何影响时钟功能，我们知之甚少。我们需要深入了解人类组织中与时钟相关的途径，以便更好地理解疾病与时钟功能之间的联系。
我们开发了一种新的计算方法来探索与人类组织钟相关的候选途径。这种方法称为 LTM，是一种推断遗传对生物钟功能影响的计算筛选。LTM 使用人类数据中的基因表达自然变异，并直接将基因表达变异与时钟强度联系起来，而无需纵向数据。我们将 LTM 应用于三个人类皮肤和一个黑色素瘤数据集，发现细胞周期是健康皮肤中顶级候选时钟相关途径。此外，我们将 LTM 应用于 TCGA 数据库中 11 种癌症类型的数千个肿瘤样本，发现细胞外基质组织相关途径与人类时钟强度密切相关。进一步的分析表明，肿瘤样本中的时钟强度与癌症相关成纤维细胞和内皮细胞的比例相关。因此，我们展示了 LTM 在预测时钟相关途径和分类与人类组织中时钟强度相关的因素方面的强大功能。
LTM 可在 GitHub（https://github.com/gangwug/LTMR）和 figshare（https://figshare.com/articles/software/LTMR/21217604）上获得，以方便使用。
补充数据可在生物信息学在线获得。
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