Thessen Anne E, Cooper Laurel, Swetnam Tyson L, Hegde Harshad, Reese Justin, Elser Justin, Jaiswal Pankaj
Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States.
Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, United States.
Front Artif Intell. 2023 Jun 13;6:1201002. doi: 10.3389/frai.2023.1201002. eCollection 2023.
Climate change is already affecting ecosystems around the world and forcing us to adapt to meet societal needs. The speed with which climate change is progressing necessitates a massive scaling up of the number of species with understood genotype-environment-phenotype (G×E×P) dynamics in order to increase ecosystem and agriculture resilience. An important part of predicting phenotype is understanding the complex gene regulatory networks present in organisms. Previous work has demonstrated that knowledge about one species can be applied to another using ontologically-supported knowledge bases that exploit homologous structures and homologous genes. These types of structures that can apply knowledge about one species to another have the potential to enable the massive scaling up that is needed through experimentation.
We developed one such structure, a knowledge graph (KG) using information from Planteome and the EMBL-EBI Expression Atlas that connects gene expression, molecular interactions, functions, and pathways to homology-based gene annotations. Our preliminary analysis uses data from gene expression studies in and plants exposed to drought conditions.
A graph query identified 16 pairs of homologous genes in these two taxa, some of which show opposite patterns of gene expression in response to drought. As expected, analysis of the upstream cis-regulatory region of these genes revealed that homologs with similar expression behavior had conserved cis-regulatory regions and potential interaction with similar trans-elements, unlike homologs that changed their expression in opposite ways.
This suggests that even though the homologous pairs share common ancestry and functional roles, predicting expression and phenotype through homology inference needs careful consideration of integrating cis and trans-regulatory components in the curated and inferred knowledge graph.
气候变化已经在影响世界各地的生态系统，并迫使我们做出调整以满足社会需求。气候变化的进展速度要求大规模增加对具有已知基因型 - 环境 - 表型（G×E×P）动态的物种数量的研究，以提高生态系统和农业的恢复力。预测表型的一个重要部分是了解生物体中存在的复杂基因调控网络。先前的工作表明，利用本体支持的知识库，通过利用同源结构和同源基因，可以将关于一个物种的知识应用于另一个物种。这些能够将一个物种的知识应用于另一个物种的结构类型有潜力通过实验实现所需的大规模扩展。
我们开发了一种这样的结构，即知识图谱（KG），它使用来自植物基因组数据库（Planteome）和欧洲生物信息研究所（EMBL-EBI）表达图谱的信息，将基因表达、分子相互作用、功能和通路与基于同源性的基因注释联系起来。我们的初步分析使用了来自暴露于干旱条件下的[具体植物名称1]和[具体植物名称2]植物的基因表达研究数据。
通过图谱查询在这两个分类单元中鉴定出16对同源基因，其中一些在干旱响应中表现出相反的基因表达模式。正如预期的那样，对这些基因上游顺式调控区域的分析表明，具有相似表达行为的同源基因具有保守的顺式调控区域，并且与相似的反式元件存在潜在相互作用，这与那些以相反方式改变其表达的同源基因不同。
这表明，尽管同源基因对具有共同的祖先和功能作用，但通过同源性推断来预测表达和表型需要在经过整理和推断的知识图谱中仔细考虑整合顺式和反式调控成分。
