背景:代谢产物相关的细胞通讯在维持人类生物学功能方面发挥着关键作用。然而,大多数现有的工具和资源只关注配体-受体相互作用对,其中两个伴侣都是蛋白质的,忽略其他非蛋白质分子。为了解决这个差距,我们介绍了MRCLinkdb数据库和算法,它聚集和组织细胞间通讯中与非蛋白质L-R相互作用相关的数据,为基于代谢物相关的配体-受体相互作用预测细胞间通讯提供了有价值的资源。
结果:这里,我们从文献和已知数据库中手动策划了代谢物-配体-受体(ML-R)相互作用,最终收集超过790人和670只小鼠ML-R相互作用。此外,我们汇编了与这些代谢物相关的超过1900种酶和260种转运蛋白条目的信息.我们开发了基于代谢物受体的细胞链接数据库(MRCClinkdb)来存储这些ML-R相互作用数据。同时,该平台还提供了广泛的信息来呈现ML-R交互,包括基本代谢物信息和代谢物相关基因集的整体表达景观(如受体,酶,和转运蛋白)基于单细胞转录组学测序(覆盖35个人类和26个小鼠组织,52种人类和44种小鼠细胞类型)和大量RNA-seq/微阵列数据(包括62种人类和39种小鼠组织)。此外,MRCLinkdb引入了一个专门用于分析基于ML-R交互的细胞间通信的Web服务器。MRCLinkdb可在https://www上免费获得。cellknowledge.com.cn/mrclinkdb/.
结论:除了补充配体-受体数据库,MRCClinkdb可以提供用于解码蜂窝间通信和基于ML-R交互推进相关预测工具的新观点。
BACKGROUND: Metabolite-associated cell communications play critical roles in maintaining human biological function. However, most existing tools and resources focus only on ligand-receptor interaction pairs where both partners are proteinaceous, neglecting other non-protein molecules. To address this gap, we introduce the MRCLinkdb database and algorithm, which aggregates and organizes data related to non-protein L-R interactions in cell-cell communication, providing a valuable resource for predicting intercellular communication based on metabolite-related ligand-receptor interactions.
RESULTS: Here, we manually curated the metabolite-ligand-receptor (ML-R) interactions from the literature and known databases, ultimately collecting over 790 human and 670 mouse ML-R interactions. Additionally, we compiled information on over 1900 enzymes and 260 transporter entries associated with these metabolites. We developed Metabolite-Receptor based Cell Link Database (MRCLinkdb) to store these ML-R interactions data. Meanwhile, the platform also offers extensive information for presenting ML-R interactions, including fundamental metabolite information and the overall expression landscape of metabolite-associated gene sets (such as receptor, enzymes, and transporter proteins) based on single-cell transcriptomics sequencing (covering 35 human and 26 mouse tissues, 52 human and 44 mouse cell types) and bulk RNA-seq/microarray data (encompassing 62 human and 39 mouse tissues). Furthermore, MRCLinkdb introduces a web server dedicated to the analysis of intercellular communication based on ML-R interactions. MRCLinkdb is freely available at https://www.cellknowledge.com.cn/mrclinkdb/ .
CONCLUSIONS: In addition to supplementing ligand-receptor databases, MRCLinkdb may provide new perspectives for decoding the intercellular communication and advancing related prediction tools based on ML-R interactions.