以基因體親緣分層法解析人類蛋白質交互作用體與微核糖核酸調控體

近年來，基因體親源分層法已被成功應用於生物學中多種子領域，用以探討各種基因體親源相關之研究問題，或藉以提供演化歷史之足跡與線索。本研究試圖應用此分析方法，結合生物網路模型，以探討蛋白質交互作用體與微核糖核酸調控體中尚未被充分了解之特性與集體動態。蛋白質交互作用網路提供了一個概念性的分析框架，有助於理解蛋白質體中的功能性結構。然而，傳統網路分析多著重於二維拓樸空間中的相對關係，且受限於生物網路模型的高度複雜性，使得系統化分析過程存在不少困難。由於生物網路如同生物基因體，實乃演化過程下的產物，蛋白質親緣資訊可望提供額外的線索，幫助吾人分析此類生物網路。在此研究中，我們採用了親緣分層的方法，搭配拓樸空間中的物理模擬，將人類蛋白質交互作用網路進行了三維分解。我們發現，古老的蛋白質傾向佔據網路中的核心位置，組成主要的網路階層結構；而晚期出現的蛋白質則傾向依附於網路的周邊位置。拓樸分析結果亦顯示，蛋白質的演化年齡與其在網路空間中的重要性存在一致性。大部份的網路中心節點均由資深的蛋白質所擔任，且對於生物網路典型的無尺度與階層特性有著主要貢獻。網路演化模擬結果顯示，天擇壓力可能作用於網路演化過程，亦即高中心性的網路節點會避免受到擾動。此結果有助於改進傳統複製─分歧的網路演化模型。最後，透過功能性分析，我們發現各年齡群蛋白質具有高度的功能特異性，可能對應於動物細胞在演化過程中所扮演的不同角色。更有趣的是，網路拓樸空間與蛋白質在細胞中的空間定位存在類似的模式。這些研究結果顯示了蛋白質網路概念性的框架實際應用於理解真實細胞中功能性組織的潛力。接著，我們將研究目標轉向於人類微核糖核酸調控體，透過應用親源分層法研究人類胚胎發育與腦部發育過程中的轉錄體表現時序圖譜。我們發現人類胚胎發育所展現之轉錄體年齡變化趨勢與前人於斑馬魚研究所觀察到的發育砂時漏模型趨勢一致，而後期人類腦部各區域亦普遍存在類似的轉錄體年齡變化趨勢。更有趣的是，微核糖核酸轉錄體所呈現之年齡變化，竟與前述蛋白質編碼基因所呈現之趨勢正好相反。此結果暗示了動物微核糖核酸可能於發育過程中，對於整體基因表達之形塑有所貢獻。因此，我們透過重建微核糖核酸調控網路模型，描述了人類微核糖核酸調控之年齡偏好。我們發現同齡調控普遍存在於微核糖核酸調控體中，意即微核糖核酸偏好調控年齡相近之標的基因。此特性可用以解釋前述觀察之微核糖核酸轉錄體與蛋白質編碼基因轉錄體所呈現之年齡變化相反的趨勢，並加強了微核糖核酸實際參與形塑發育過程轉錄體之論述。整體而言，本研究探討基因體親緣分層法應用於生物網路分析研究之可行性，並藉此於多個研究面向均提出了新的洞見。隨著未來可預見之各種生物體學數據逐漸累積，本研究所建立之分析框架可望逐步提升其解析度與精準度，並擴大其適用範圍，以探討更多重要的生物問題。

關鍵字

基因體親源分層；蛋白質交互作用網路；微核糖核酸基因調控網路；同齡交往；網路演化；發育砂時漏模型；轉錄體年齡圖譜

並列摘要

Genomic phylostratigraphy has been demonstrated as a powerful strategy to systematically study various biology questions. Here we made an attempt to utilize this technique in biological network contexts to study several issues that still remained unexplored in the fields of protein interactome and miRNA reg-ulome. In network biology, the protein-protein interaction (PPI) network offers a conceptual framework for better understanding the functional organization of the proteome. However, typical network analyses focused only on the topo-logical space, thus limiting the scope of biological implication. Moreover, the complexity of tangled interactions could hinder comprehensive analysis. Here, we adopted a genomic phylostratigraphic approach combined with force-directed graph simulation to decompose the human PPI network in a multi-dimensional manner. This network model enabled us to associate the network topological properties with evolutionary and biological implications. First, we found that ancient proteins occupy the core of the network, whereas young proteins tend to reside on the periphery. Topological analysis also re-vealed a positive correlation between protein age and network centrality. Second, the scale-free and hierarchical properties of the PPI network are ubiq-uitous across age groups, whereas each group still contributes dependently to the global properties. Third, the presence of age homophily suggests a possible selection pressure may have acted on the duplication and divergence process during the PPI network evolution. Lastly, functional analysis revealed that each age group possesses high specificity of enriched biological processes and path-way engagements, which could correspond to their evolutionary roles in eu-karyotic cells. More interestingly, the network landscape closely coincides with the subcellular localization of proteins. Together, these findings suggest the potential of using conceptual frameworks to mimic the true functional organ-ization in a living cell. Next, we shifted our focus onto the human miRNA regulome and its im-plication in human embryogenesis and brain development. By adopting the phylostratigraphy framework to study the temporal transcriptomic profiles during human embryogenesis and brain development, we showed that the temporal patterns of transcriptomic age profiles of different brain structures generally follow a similar trajectory. In addition, the oscillation of this trajectory during the infancy and childhood periods coincides with brain energy con-sumption pattern, implying the transcriptomic age profile can capture im-portant development stages of human brain. Our next aim was to search the regulatory force that shapes the intrinsic pattern of those transcriptomic age profiles. By applying a similar phylostratifigraphic approach to stratificate hu-man miRNA genes and performing network analysis, we found a strong age homophily phenomenon among human miRNA regulome. More importantly, the transcriptomic age profile of miRNAs showed a striking anti-correlation against the one of protein-coding genes, which could be explained by age ho-mophily that we identified in the miRNA regulome. Taken together, these re-sults reveal a consistent pattern of transcriptomic age profiles across human brain regions, which corroborate the development hourglass model during animal embryogenesis. Our work expands the applicability of this model from embryogenesis to follow-up development stages of human brain. These results also suggest a possibility that miRNAs may shape the transcriptomic landscape during human brain development.

並列關鍵字

Genomic phylostratigraphy ； protein‐protein interaction network ； network evolution ； microRNA regulatory network ； age homophily ； development hourglass ； transcriptomic age profile

參考文獻

1. Barabasi, A.-L. & Oltvai, Z. N. Network biology: understanding the cell's functional organization. Nat Rev Genet 5, 101-113, (2004).

2. Kunin, V., Pereira-Leal, J. B. & Ouzounis, C. A. Functional evolution of the yeast protein interaction network. Mol Biol Evol 21, 1171-1176, (2004).

3. Butland, G. et al. Interaction network containing conserved and essential protein complexes in Escherichia coli. Nature 433, 531-537, (2005).

4. Stelzl, U. et al. A human protein-protein interaction network: a resource for annotating the proteome. Cell 122, 957-968, (2005).

5. Bork, P. et al. Protein interaction networks from yeast to human. Curr Opin Struct Biol 14, 292-299, (2004).

國際替代計量

以基因體親緣分層法解析人類蛋白質交互作用體與微核糖核酸調控體

全文下載

主題瀏覽