Biochemical pathways represented by Gene Ontology-Causal Activity Models identify distinct phenotypes resulting from mutations in pathways.
Biochemical pathways represented by Gene Ontology-Causal Activity Models identify distinct phenotypes resulting from mutations in pathways.
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DOI：
10.1093/genetics/iyad152
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发表时间：
2023-10-04
期刊：
影响因子：
3.3
通讯作者：
D'Eustachio, Peter
中科院分区：
文献类型：
--
作者：
Hill, David P.;Drabkin, Harold J.;Smith, Cynthia L.;Van Auken, Kimberly M.;D'Eustachio, Peter
关键词：
基因失活可以影响该基因的作用过程，并导致下游基因的作用，产生不同的突变表型。确定导致特定表型的遗传途径有助于我们了解单个基因如何在功能网络中相互作用。生物途径的可计算表示包括Reactome知识库中的详细过程描述和基因本体-因果活动模型（GO-CAMs）中分子功能之间的因果活动流。已经开发了一种计算过程来将Reactome路径转换为go - cam。实验室小鼠是人类正常和病理过程的广泛模型。我们已经将人类Reactome GO-CAMs转化为同源小鼠GO-CAMs，作为人类和模式生物之间传递途径知识的资源。这些小鼠go - cam使我们能够确定以因果联系方式起作用的基因组。为了证明来自连接通路的个体变异基因会导致相似但可区分的表型，我们使用通路模型中的基因在小鼠基因组数据库（mouse Genome Database， MGD）中交叉查询小鼠表型注释。利用GO-CAM表征两种相关但不同的途径，糖异生和糖酵解，我们表明基因网络中的个体因果路径会引起糖酵解和糖酵解基因扰动引起的离散表型结果。在对充分研究过程的分析中恢复的基因相互作用的准确和详细描述表明，该策略可以应用于研究较少的模型系统中了解较少的过程，以预测新基因变异的表型结果，并识别改变过程中的潜在基因靶点。基因在相互关联的生物学途径中起作用，因此单个突变可以产生多种表型。为了使用大量的小鼠功能基因注释，我们将人类葡萄糖代谢的基因本体-因果活动模型（GO-CAMs）转换为相应的小鼠GO-CAMs。然后，我们查询了这些go - cam中小鼠基因的表型，并确定了由于糖酵解和糖异生的扰动而导致的离散表型结果相关的基因网络。这种策略可以扩展到不太了解的过程和模型系统，以预测表型结果。
Gene inactivation can affect the process(es) in which that gene acts and causally downstream ones, yielding diverse mutant phenotypes. Identifying the genetic pathways resulting in a given phenotype helps us understand how individual genes interact in a functional network. Computable representations of biological pathways include detailed process descriptions in the Reactome Knowledgebase and causal activity flows between molecular functions in Gene Ontology-Causal Activity Models (GO-CAMs). A computational process has been developed to convert Reactome pathways to GO-CAMs. Laboratory mice are widely used models of normal and pathological human processes. We have converted human Reactome GO-CAMs to orthologous mouse GO-CAMs, as a resource to transfer pathway knowledge between humans and model organisms. These mouse GO-CAMs allowed us to define sets of genes that function in a causally connected way. To demonstrate that individual variant genes from connected pathways result in similar but distinguishable phenotypes, we used the genes in our pathway models to cross-query mouse phenotype annotations in the Mouse Genome Database (MGD). Using GO-CAM representations of 2 related but distinct pathways, gluconeogenesis and glycolysis, we show that individual causal paths in gene networks give rise to discrete phenotypic outcomes resulting from perturbations of glycolytic and gluconeogenic genes. The accurate and detailed descriptions of gene interactions recovered in this analysis of well-studied processes suggest that this strategy can be applied to less well-understood processes in less well-studied model systems to predict phenotypic outcomes of novel gene variants and to identify potential gene targets in altered processes. Genes act in interconnected biological pathways, so single mutations can yield diverse phenotypes. To use the large body of mouse functional gene annotations, we converted human Gene Ontology-Causal Activity Models (GO-CAMs) of glucose metabolism to orthologous mouse GO-CAMs. We then queried phenotypes for mouse genes in these GO-CAMs and identified gene networks associated with discrete phenotypic outcomes due to perturbations of glycolysis and gluconeogenesis. This strategy can be extended to less well-understood processes and model systems to predict phenotypic outcomes.
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影响因子：
14.9
作者：
Blake JA;Baldarelli R;Kadin JA;Richardson JE;Smith CL;Bult CJ;Mouse Genome Database Group
通讯作者：
Mouse Genome Database Group
影响因子：
14.9
作者：
Groza, Tudor;Gomez, Federico Lopez;Mashhadi, Hamed Haseli;Munoz-Fuentes, Violeta;Gunes, Osman;Wilson, Robert;Cacheiro, Pilar;Frost, Anthony;Keskivali-Bond, Piia;Vardal, Bora;McCoy, Aaron;Cheng, Tsz Kwan;Santos, Luis;Wells, Sara;Smedley, Damian;Mallon, Ann-Marie;Parkinson, Helen
通讯作者：
Parkinson, Helen
影响因子：
14.9
作者：
Gillespie M;Jassal B;Stephan R;Milacic M;Rothfels K;Senff-Ribeiro A;Griss J;Sevilla C;Matthews L;Gong C;Deng C;Varusai T;Ragueneau E;Haider Y;May B;Shamovsky V;Weiser J;Brunson T;Sanati N;Beckman L;Shao X;Fabregat A;Sidiropoulos K;Murillo J;Viteri G;Cook J;Shorser S;Bader G;Demir E;Sander C;Haw R;Wu G;Stein L;Hermjakob H;D'Eustachio P
通讯作者：
D'Eustachio P
影响因子：
1.9
作者：
Bello SM;Eppig JT;the MGI Software Group
通讯作者：
the MGI Software Group
影响因子：
4.8
作者：
Arizmendi, C;Liu, S;Friedman, JE
通讯作者：
Friedman, JE