在生物醫學研究與應用中，了解基因與性狀之間的關係是至關重要的，然而到目前為止卻僅有一部分基因的功能被知曉。在這個研究中我們提出，基因共表現網絡(GCN, gene coexpression network)內的模組(modules)在透過基因突變所產生的性狀(mutant phenotype)加以詮釋後，可以用來預測未知基因的功能。有鑒於心血管疾病是全球排行第一的死因，且病理機制與相關致病基因尚未完全清楚，所以我們以心臟功能作為切入點，蒐集了3781筆微陣列型晶片(microarray)的轉錄體(transcriptome)資料。經由該資料所建構的基因共表現網絡，我們發現了84個基因共表現模組。針對對這些模組以人類遺傳疾病性狀與老鼠突變性狀的資料進行功能性註釋後，我們得到11,583個至少可以對應到一個性狀的人類基因。接著我們選擇一個與“異常心臟形態”的表現型（人類資料庫HPO，HP：0001627及小鼠資料庫MGI，MP：0000266）有相關的模組(含包含56個基因)進行觀察，挑選了FAM155B進行後續實驗驗證如下。我們利用Cas9/sgRNA ribonucleoprotein (RNP)的技術，於斑馬魚胚胎G0時期做基因剔除的動作，結果發現該胚胎產生心臟與肌肉的異常。此外，根據對現有的公開資料分析的結果，我們推論fam155b基因的產物是膜蛋白，根據資料此產物在早期斑馬魚心臟修復與心臟發育期間扮演了重要腳色。後續，我們進一步，將基因此預測基因功能的方法延伸應用在老鼠(Mus musculus)與大鼠(Rattus norvegicus)的轉錄組資料分析上，結果在總數16252個人類基因中，有3274個基因在人類、小鼠與大鼠的共表現網絡中有一致性的基因相關功能預測結果，其中包含670個被預測可能與心血管疾病有相關的基因。雖然這些功能還需要進一步證實，本研究的結果，可以加速心血管疾病相關之遺傳分子生物學的進展。

Although the understanding of associated phenotypes of genes in the human genome is fundamentally important to biomedical researches and applications, only a small fraction of human genes have been functionally assessed presently. Here, we postulated that there exists a significant correspondence between coexpressed gene modules and gene modules underlying a specific phenotype, and this correspondence could be used to predict human gene functions at the phenotypic level. In this study, we aimed to predict novel genes associated with "heart" phenotypes, because cardiovascular diseases (CVDs) are the leading cause of morbidity and mortality in the world populations, and the underlying mechanisms of CVDs are complicated and underestimated. To do this, we compiled 3781 human (Homo sapiens) transcriptomes to construct mRNA coexpression networks and defined 84 human coexpressed gene modules within it. Phenotypes derived from abnormalities based on Mendelian diseases in humans and mutant mice were used to annotate functions of each gene module. Consequently, 11583 human genes were associated with at least one of the 14,368 phenotypic descriptions by our approach. Focusing on genes consistently associated with the phenotypes of "abnormal heart morphology" in the human (HPO, HP:0001627) and in the mouse (MGI, MP:0000266), one heart module with 56 genes was identified. Among these genes, FAM155B which was previously unknown to perform heart-related functions, was selected for further experimental validation. After targetedly deleting ortholog of human FAM155B from the zebrafish genome by Cas9/sgRNA ribonucleoprotein (RNP), the predicted abnormal heart functions were observed in the mosaic G0 zebrafish embryos. In addition, our bioinformatic analysis showed that fam155b encodes a membrane protein which may play a critical role in the early phase of cardiac healing, regeneration, and development. In parallel with the abovementioned analysis, we applied our approach to analyze transcriptomes of the human, the mouse (Mus musculus) and, the rat (Rattus norvegicus) simultaneously. We found 3,274 genes consistently predicted to be associated with the same phenotypes across the three mammalian species. These genes included 670 potential CVD genes (from 855 predicted CVD genes) whose functions have never been assessed. Our study could accelerate a better understanding of our coding genomes, especially the subset of genes related with CVDs.

中文摘要---I
Abstract---II
致謝---III
List of Figures---VI
List of Tables---VIII
List of Appendix Figures---VIII
List of Appendix Tables---IX
Abbreviations---XI
Chapter 1 Introduction and Background---1
1.1 Phenotyped protein-coding genes---1
1.2 High throughtput gene expression profiling technologies---6
1.3 Gene coexpression network and coexpressed gene modules---9
1.4 Cardiovascular diseases---12
1.5 Zebrafish as an ideal model for heart research---13
1.6 Previous network-based analyses for gene function prediction---16
Chapter 2 MATERIALS AND METHODS---18
2.1 Defining Coexpressed Gene Modules and Network---18
2.1.1 Data resource---18
2.1.2 Processing of mRNA expression signals---19
2.1.3 Coexpression Gene Network and the Modular Structures---20
2.2 Associating GCN modules with phenotypes---21
2.2.1 The phenotypic data of humans and mice---21
2.2.2 Enrichment analyses---22
2.3 Creation of transgenic zebrafish---23
2.3.1 Design of targeted sgRNA of fam155b gene---23
2.3.2 Synthesis of sgRNA template---25
2.3.3 Zebrafish maintenance and injection of sgRNA---26
2.3.4 Cas9/gRNA mutation screen by Sanger sequencing---27
Chapter 3 Results---28
3.1 Defining Coexpressed Gene Modules and Network---28
3.2 Gene Modules Annotating---31
3.3 Prediction of novel gene with heart-related function---36
3.4 Establish transgenic knockout zebrafish model---42
3.5 Phenotyping zebrafish embryos according to enriched phenotypes of the module where fam155b was located---46
3.5.1. Heart phenotypes (cardiac contractility) of transgenic zebrafish---49
3.5.2. Heart phenotypes (temporal difference in development) of transgenic zebrafish---51
3.5.3. Heart phenotypes (temporal difference in regeneration) of transgenic zebrafish---53
3.5.4. Muscle phenotypes of transgenic zebrafish---56
3.5.5. Clusters of coexpression modules in GCNs---57
3.6 The potential molecular functions of FAM155B---58
3.6.1 Predicted molecular function from GO and KEGG---58
3.6.2 Predicted molecular function from functional domain of FAM155B protein---60
3.6.3 Predicted molecular function from paralog of FAM155B- FAM155A---61
3.6.4 Predicted molecular function from coexpressed genes of FAM155B and NEB gene family---67
Chapter 4 Discussion and Conclusion---71
4.1 FAM155B and cancers---71
4.2 FAM155B and regeneration---74
4.3 External evidence supporting the potentiality of heart repairment after disease---75
4.4 Limitations of this study---76
4.4.1. Limitations of the WGCNA approach with phenotypic data---76
4.4.2. Limitations of the usage of phenotypic data for enrichment analysis---77
4.4.3. Limitations of the zebrafish model---78
4.4.4. Limitations of the targeted gene deletion approach---80
4.5 Application of the proposed approach in the future---81
4.6 Conclusion---82
Chapter 5 Future Works---84
5.1. Data selection and Data Pre-processing---85
5.2. Construction of Weighted coexpression networks---86
5.3. Functional enrichment analysis of coexpression modules---87
5.4. Assessment of conserved phenotypes across species---89
5.5. Utility of the query interface---92
5.6. Summary and conclusion---93
References---95
Appendix Figures---117
Appendix Table---126

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