Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, 63110, USA.
BMC Genomics. 2019 Feb 15;20(1):137. doi: 10.1186/s12864-019-5497-4.
Long non-coding RNAs (lncRNAs) exhibit remarkable cell-type specificity and disease association. LncRNA's functional versatility includes epigenetic modification, nuclear domain organization, transcriptional control, regulation of RNA splicing and translation, and modulation of protein activity. However, most lncRNAs remain uncharacterized due to a shortage of predictive tools available to guide functional experiments.
To address this gap for lymphoma-associated lncRNAs identified in our studies, we developed a new computational method, Predicting LncRNA Activity through Integrative Data-driven 'Omics and Heuristics (PLAIDOH), which has several unique features not found in other methods. PLAIDOH integrates transcriptome, subcellular localization, enhancer landscape, genome architecture, chromatin interaction, and RNA-binding (eCLIP) data and generates statistically defined output scores. PLAIDOH's approach identifies and ranks functional connections between individual lncRNA, coding gene, and protein pairs using enhancer, transcript cis-regulatory, and RNA-binding protein interactome scores that predict the relative likelihood of these different lncRNA functions. When applied to 'omics datasets that we collected from lymphoma patients, or to publicly available cancer (TCGA) or ENCODE datasets, PLAIDOH identified and prioritized well-known lncRNA-target gene regulatory pairs (e.g., HOTAIR and HOX genes, PVT1 and MYC), validated hits in multiple lncRNA-targeted CRISPR screens, and lncRNA-protein binding partners (e.g., NEAT1 and NONO). Importantly, PLAIDOH also identified novel putative functional interactions, including one lymphoma-associated lncRNA based on analysis of data from our human lymphoma study. We validated PLAIDOH's predictions for this lncRNA using knock-down and knock-out experiments in lymphoma cell models.
Our study demonstrates that we have developed a new method for the prediction and ranking of functional connections between individual lncRNA, coding gene, and protein pairs, which were validated by genetic experiments and comparison to published CRISPR screens. PLAIDOH expedites validation and follow-on mechanistic studies of lncRNAs in any biological system. It is available at https://github.com/sarahpyfrom/PLAIDOH .
长非编码 RNA（lncRNA）表现出显著的细胞类型特异性和与疾病的关联。lncRNA 的功能多样性包括表观遗传修饰、核域组织、转录控制、RNA 剪接和翻译的调节以及蛋白质活性的调节。然而，由于缺乏可用于指导功能实验的预测工具，大多数 lncRNA 仍然没有得到描述。
为了解决我们研究中鉴定的淋巴瘤相关 lncRNA 的这一空白，我们开发了一种新的计算方法，即通过整合数据驱动的“Omics 和启发式方法预测 lncRNA 活性（Predicting LncRNA Activity through Integrative Data-driven 'Omics and Heuristics，PLAIDOH），该方法具有其他方法所没有的几个独特特征。PLAIDOH 整合了转录组、亚细胞定位、增强子景观、基因组结构、染色质相互作用和 RNA 结合（eCLIP）数据，并生成具有统计学定义的输出分数。PLAIDOH 的方法使用增强子、转录顺式调控和 RNA 结合蛋白互作网络分数来识别和对单个 lncRNA、编码基因和蛋白质对之间的功能连接进行排序，这些分数预测了这些不同 lncRNA 功能的相对可能性。当应用于我们从淋巴瘤患者收集的‘omics 数据集或公开的癌症（TCGA）或 ENCODE 数据集时，PLAIDOH 识别并优先考虑了众所周知的 lncRNA-靶基因调控对（例如，HOTAIR 和 HOX 基因、PVT1 和 MYC），验证了多个 lncRNA 靶向 CRISPR 筛选的命中，并鉴定了 lncRNA-蛋白结合伙伴（例如，NEAT1 和 NONO）。重要的是，PLAIDOH 还识别了新的潜在功能相互作用，包括基于我们人类淋巴瘤研究数据的分析的一个淋巴瘤相关 lncRNA。我们使用淋巴瘤细胞模型中的敲低和敲除实验验证了 PLAIDOH 对该 lncRNA 的预测。
我们的研究表明，我们已经开发了一种新的方法来预测和对单个 lncRNA、编码基因和蛋白质对之间的功能连接进行排序，这些预测已通过遗传实验和与已发表的 CRISPR 筛选进行比较得到验证。PLAIDOH 加快了任何生物系统中 lncRNA 的验证和后续机制研究。它可在 https://github.com/sarahpyfrom/PLAIDOH 获得。
