在植物中，調控因子(包含轉錄因子、組蛋白修飾和其他DNA結合蛋白)可以藉由結合目標基因的DNA來控制基因轉錄活性或染色質重塑。在系統生物學領域中，運用電腦計算技術，預測轉錄因子結合位置是否在目標基因的啟動子上，是建構基因調控網路的普遍方法。然而許多預測結果存在高比例的偽陽性，讓只利用DNA序列作為預測轉錄因子結合位置的模式成為生物資訊預測的瓶頸。為解決此問題，此研究的第一部份著重在整合植物染色質免疫沉澱測序資料與蛋白質資訊，並建立完善的轉錄因子資料庫，命名為PlantPAN 3.0。PlantPAN 3.0資料庫收集了662植物染色質免疫沉澱測序樣品與2,449轉錄因子結合陣列(position weight matrices，PWMs)，前者可用於查詢基因體上的調控位置，後者(PWMs)則可運用於預測其他植物啟動子上的轉錄因子結合位置。為了提供使用者更多的轉錄因子結構特性，資料庫新增3D結構與蛋白質結構域(domain)資料。PlantPAN 3.0也收錄多方資料，如: 基因表現量與不同物種間同源基因的資訊，並提供基因共表達分析、跨物種的轉錄因子調控網路比較分析功能。我們希望藉由PlantPAN 3.0來建構複雜的轉錄調控網路，且協助使用者排除偽陽性的預測結果。由於PlantPAN 3.0的染色質免疫沉澱測序資料涵蓋多種轉錄因子與組蛋白修飾，在第二部分的研究，我們藉由收集的測序資料探索轉錄因子結合序列的特異性，同時分析全基因組上轉錄因子結合位點與組蛋白修飾沉積位置的特性。在分析152個阿拉伯芥轉錄因子樣品的定序片段峰區(peak)於基因組上的落點分布後，我們在77%的轉錄因子樣品中發現70%的結合峰區落在蛋白質編碼的基因區與其上下游一千鹼基對的區域，另外約有10%在非基因區、8%在轉座子、12%在其他的非編碼基因上。接著探索蛋白質編碼基因的7個基因區域(例如: 編碼區、5’非轉譯區、3’非轉譯區等)，很驚訝地發現大部分的轉錄因子竟少於一半的結合區域落在基因上游一千鹼基對與5’非轉譯區(啟動子區)，同樣的分析結果揭露其他區域(如: 下游一千鹼基對的區域和基因編碼區)也存在轉錄因子結合位。與轉錄因子不同，組蛋白修飾有比較高的比例落在蛋白質編碼基因的編碼區上。這些結果暗示轉錄因子與組蛋白利用不同基因區域來調控蛋白質編碼的基因。在轉錄起始與結束位點的上下游兩千五百鹼基對的區間分析也進一步證實這個發現。另一方面，比較不同基因群(如: 管家基因和熱逆境反應基因)的結果顯示，基因上受調控的區塊會隨著轉錄因子與基因功能而有所不同。此外，轉錄因子結合位點在染色體的分布圖說明，轉錄因子的結合區域主要都在染色體臂，真染色質富集的區域。有趣的是，我們在著絲點上發現可能與轉座子的調控相關的轉錄因子結合區間。根據本研究成果，我們提出一個植物轉錄因子多面向的結合特性模型，未來可應用於提高轉錄調控預測之準確性。

In plants, regulatory factors, including transcription factors (TFs), modified histones, and other DNA-binding proteins, control gene transcriptional activity and chromatin remodeling by binding to DNA sequences of target genes. In systems biology, gene regulatory networks are usually reconstructed based on the presence of transcription factor binding sites (TFBSs) on the proximal promoters of target genes by using computational methods. Unfortunately, due to high false-positive rate, TFBSs predicted solely by DNA sequence-based approach has become a bottleneck in bioinformatics predictions. To address this issue, the first part of this study focuses on the construction of a comprehensive database, named as PlantPAN 3.0, for TFs and their regulatory networks by integrating chromatin immunoprecipitation sequencing (ChIP-seq) data, protein information of TFs, etc. In PlantPAN 3.0 system, the genomic regulatory maps and plant promoter sequence analysis were supplied from 662 ChIP-seq samples and 2,449 position weight matrices (PWMs), respectively. To provide more structure characteristics of TFs, the structure-based information and protein sequence annotation (such as functional domains, secondary structures, post-translational modification, and variants, etc.) were newly added into TF/TFBS Search function. Significantly, PlantPAN 3,0 also integrated homologous genes and gene expressions to provide the comparative analysis of transcription regulatory networks across different species and co-expression profiles among A. thaliana, Oryza sativa, Zea mays, and Glycine max. We hope this system can help users to reconstruct complex transcriptional regulatory networks and decrease the false positives in TFBS predictions. Since PlantPAN 3.0 contains several ChIP-seq data of TFs and histone marks of A. thaliana, the investigation of TF-DNA binding specificities and genome-wide binding profiles of TFs and histone marks become available. Thus, in the second part of this study, the TF-DNA binding specificity is characterized in statistical terms by using our collected ChIP-seq data, gene annotation, and genome sequence annotation. Based on the genome-wide ChIP-seq profiles, 77% TF samples show most TFs tend to bind at protein-coding genes and their upstream/downstream 1k bps (over 70% binding peaks), distal intergenic regions (about 10%), transposon fragments (8%), and the other gene types (e.g., ribosomal RNA genes) (12%). Surprisingly, the regulatory regions of protein-coding genes show less than half TF peaks appeared at expected promoter regions (i.e., upstream 1k bps and 5’ UTRs). The result also reveals TF bindings at other genetic regions (e.g., downstream 1k bps, and CDS). In contrast to TFs, both of repressive and activating histone marks tend to be located at CDSs of protein-coding genes. The results suggest that TFs and histone marks use different genetic regulatory regions of protein-coding genes to control gene transcription. This finding was further confirmed by distributions of binding peaks within 5k bps around transcription start sites and transcription terminal sites. The comparisons between different gene groups, such as housekeeping gene and heat stress responsive genes, show that the regulatory regions vary with the regulated TFs and gene functions. Moreover, the chromosomal depositions illustrate that TF binding peaks are predominantly located at chromosome arms, where euchromatin is enriched. Interestingly, the consecutive bins of TF bindings were observed at the centromeres, and associated with transcriptional regulations of TEs. Based on the results of this study, we proposed a plant TF binding specificity model, which might be applied to improve prediction accuracy of transcriptional regulation in the future.

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