Casey Fergal, Krogan Nevan, Shields Denis C, Cagney Gerard
1Complex and Adaptive Systems Laboratory, University College Dublin, Belfield, Dublin 4, Ireland.
BMC Syst Biol. 2011 Aug 22;5:133. doi: 10.1186/1752-0509-5-133.
Gene and protein interactions are commonly represented as networks, with the genes or proteins comprising the nodes and the relationship between them as edges. Motifs, or small local configurations of edges and nodes that arise repeatedly, can be used to simplify the interpretation of networks.
We examined triplet motifs in a network of quantitative epistatic genetic relationships, and found a non-random distribution of particular motif classes. Individual motif classes were found to be associated with different functional properties, suggestive of an underlying biological significance. These associations were apparent not only for motif classes, but for individual positions within the motifs. As expected, NNN (all negative) motifs were strongly associated with previously reported genetic (i.e. synthetic lethal) interactions, while PPP (all positive) motifs were associated with protein complexes. The two other motif classes (NNP: a positive interaction spanned by two negative interactions, and NPP: a negative spanned by two positives) showed very distinct functional associations, with physical interactions dominating for the former but alternative enrichments, typical of biochemical pathways, dominating for the latter.
We present a model showing how NNP motifs can be used to recognize supportive relationships between protein complexes, while NPP motifs often identify opposing or regulatory behaviour between a gene and an associated pathway. The ability to use motifs to point toward underlying biological organizational themes is likely to be increasingly important as more extensive epistasis mapping projects in higher organisms begin.
基因和蛋白质相互作用通常以网络形式呈现,其中基因或蛋白质构成节点,它们之间的关系为边。基序,即反复出现的边和节点的小局部构型,可用于简化网络的解释。
我们在定量上位遗传关系网络中研究了三联体基序,发现特定基序类别的分布并非随机。发现各个基序类别与不同的功能特性相关,这暗示了潜在的生物学意义。这些关联不仅在基序类别中明显,在基序内的各个位置也很明显。正如预期的那样,NNN(全为负)基序与先前报道的遗传(即合成致死)相互作用密切相关,而PPP(全为正)基序与蛋白质复合物相关。另外两个基序类别(NNP:由两个负相互作用跨越的正相互作用,以及NPP:由两个正相互作用跨越的负相互作用)显示出非常不同的功能关联,前者以物理相互作用为主,而后者以生化途径典型的其他富集为主。
我们提出了一个模型,展示了NNP基序如何用于识别蛋白质复合物之间的支持性关系,而NPP基序通常识别基因与相关途径之间的相反或调节行为。随着高等生物中更广泛的上位性图谱绘制项目的开展,利用基序指向潜在生物学组织主题的能力可能会变得越来越重要。