Ovens Katie, Eames B Frank, McQuillan Ian
Augmented Intelligence & Precision Health Laboratory (AIPHL), Research Institute of the McGill University Health Centre, Montreal, QC, Canada.
Department of Anatomy, Physiology, & Pharmacology, University of Saskatchewan, Saskatoon, SK, Canada.
Front Genet. 2021 Aug 13;12:695399. doi: 10.3389/fgene.2021.695399. eCollection 2021.
Similarities and differences in the associations of biological entities among species can provide us with a better understanding of evolutionary relationships. Often the evolution of new phenotypes results from changes to interactions in pre-existing biological networks and comparing networks across species can identify evidence of conservation or adaptation. Gene co-expression networks (GCNs), constructed from high-throughput gene expression data, can be used to understand evolution and the rise of new phenotypes. The increasing abundance of gene expression data makes GCNs a valuable tool for the study of evolution in non-model organisms. In this paper, we cover motivations for why comparing these networks across species can be valuable for the study of evolution. We also review techniques for comparing GCNs in the context of evolution, including local and global methods of graph alignment. While some protein-protein interaction (PPI) bioinformatic methods can be used to compare co-expression networks, they often disregard highly relevant properties, including the existence of continuous and negative values for edge weights. Also, the lack of comparative datasets in non-model organisms has hindered the study of evolution using PPI networks. We also discuss limitations and challenges associated with cross-species comparison using GCNs, and provide suggestions for utilizing co-expression network alignments as an indispensable tool for evolutionary studies going forward.
物种间生物实体关联的异同能让我们更好地理解进化关系。新表型的进化通常源于现有生物网络中相互作用的变化，比较不同物种的网络可以识别出保守或适应的证据。由高通量基因表达数据构建的基因共表达网络（GCN）可用于理解进化和新表型的出现。基因表达数据的日益丰富使GCN成为研究非模式生物进化的宝贵工具。在本文中，我们阐述了跨物种比较这些网络对进化研究具有价值的原因。我们还回顾了在进化背景下比较GCN的技术，包括局部和全局的图比对方法。虽然一些蛋白质-蛋白质相互作用（PPI）生物信息学方法可用于比较共表达网络，但它们往往忽略了高度相关的属性，包括边权重存在连续值和负值的情况。此外，非模式生物中缺乏比较数据集阻碍了使用PPI网络进行进化研究。我们还讨论了使用GCN进行跨物种比较相关的局限性和挑战，并为将共表达网络比对作为未来进化研究不可或缺的工具提供了建议。
