臺灣博碩士論文加值系統

吳郭魚是全球重要的蛋白質供應來源，隨著地球資源日益匱乏和糧食需求逐漸攀升，提高吳郭魚的產量變得至關重要。目前，吳郭魚養殖以GIFT品系為主，其特點為生長快速但抗病力較不佳。為了大幅推動台灣吳郭魚產業的發展，本研究利用分子標記輔助選育和營養基因體學同時提升台灣鯛的抗病與生長表現。本研究針對三個台灣鯛品系進行海豚鏈球菌（Streptococcus iniae）的攻毒試驗，並建立了11 個與抗病相關微衛星標記，包括 3 個 OnHAMP2、4 個 OnHAMP1、1 個 PGRN2、2 個 PGRN1 及 1 個 TP4 基因關聯的微衛星。攻毒的結果顯示三個品系的抗病能力高低依序為A、B、N2，三個品系70-80的致死劑量（LD）為A品系 2 × 106 CFU/g （LD71.3），B品系6 × 105 CFU/g（LD73.2)， N2品系 6.5 × 105 CFU/g （LD79.7）。接著透過基因型與存活率的關聯性分析，發現 55 個與存活率顯著相關的基因型，在A、B、N2品系分別有37、9、9個基因型具有顯著差異。透過這些微衛星的基因型資料我們成功建立了台灣鯛的SVM抗病預測模型。最終也經由實際攻毒海豚鏈球菌於吳郭魚抗鏈球菌子代SR-ANT1、SR-AB、SR-BB，證實了抗病預測模型的準確性與實用性。
為了進一步提高台灣鯛的成長和抗病能力，並減少抗生素與化學藥品的使用，達到永續養殖，可食用草本植物大花咸豐草 (Bidens pilosa) 被選擇作為多功能飼料添加劑使用。經過8週的餵食試驗後，結果顯示投餵0.5% 和1% 大花咸豐草會顯著提升台灣鯛的生長性能，包括最終體重、增重百分比、特定生長率和飼料轉換率，而攝食量則無影響。除了成長外，本研究也發現到投餵大花咸豐草具有促進脂質代謝和抗氧化的作用。經8週的投餵實驗，台灣鯛的膽固醇與三酸甘油脂接顯著降低，而血清中超氧化物歧化酶的活性則增加。在肝臟和肌肉組織轉錄組的功能富集分析則發現到，投餵大花咸豐草主要會影響肝臟和肌肉與胺基酸代謝、脂質代謝、碳水化合物代謝、內分泌系統、信號傳導等相關的途徑和基因。qRT-PCR的結果顯示大花咸豐草會增強內分泌IGF1/IGF1Rb信號的基因 (肝臟的igf1和肌肉的igf1rb) 及抑制肌肉自分泌/旁分泌MSTN信號的負調節因子(mstnb) 的表現，進而促進肌生成調節因子如myod1、myog和mrf4的表達，最終提高台灣鯛的肌肉生長。此外，以控制組無添加及添加0.5% 與1% 大花咸豐草飼料餵食台灣鯛8週後再以腹腔注射海豚鏈球菌（6×105 CFU/g） 攻毒，可將致死率自67% 降低至37% 與27%。
本研究也利用第三代牛津奈米孔定序技術建立了台灣鯛餵食大花咸豐草的腸道菌資料庫。結果顯示在投餵大花咸豐草後，除了台灣鯛腸道微生物相多樣性下降外，吳郭魚的其中一種致病菌-產氣單胞菌（Aeromonas）相對豐度也下降，與此同時，已經被用作吳郭魚益生菌的檸檬酸桿菌（Citrobacter）相對豐度則上升。透過腸道菌的功能預測分析，我們發現腸道微生物群的功能預測結果和台灣鯛的肝臟轉錄組富集KEGG途徑結果極為相似，同時被顯著富集的代謝途徑包含胺基酸代謝的Arginine and proline metabolism、Glycine, serine and threonine metabolism、Tryptophan metabolism、Valine, leucine and isoleucine degradation；脂質代謝中的Fatty acid degradation和Glycerophospholipid metabolism；碳水化合物代謝的Amino sugar and nucleotide sugar、Glyoxylate and dicarboxylate、Starch and sucrose metabolism；內分泌系統的Adipocytokine signaling pathway和PPAR signaling pathway；信號傳導FoxO 和MAPK signaling pathway；其他胺基酸代謝的beta-Alanine metabolism途徑。因此我們認為大花咸豐草可能可以透過改變腸道微生物的組成，再經由腸肝軸影響台灣鯛肝臟的營養代謝進而促進成長。
綜上所述，本研究透過分子標記輔助選育和營養基因體學研究，成功增強了台灣鯛的抗病性與生長性能，將可有效減少抗生素和化學藥品的使用。重大的研究成果包括建立台灣鯛的微衛星標記、轉錄組和腸道微生物群的綜合資料庫，並發展出抗病預測模型及可提升養殖效果的多功能飼料添加劑。抗病預測模型可以做為一個節省時間與符合經濟效應的方法幫助選育。而大花咸豐草的添加不僅顯著提高了台灣鯛的生長效率、脂質代謝和抗氧化能力，還有效抑制腸道有害菌的增長，間接促進魚體健康。此外，腸道微生物群的調控進一步佐證了腸肝軸在促進台灣鯛生長和免疫力中的潛在作用。這些成果有望加速台灣鯛產業的生產效能，更可為永續水產養殖模式帶來實質的貢獻。

Tilapia plays a crucial role in the global protein supply. As the demand for food continues to rise and natural resources become increasingly scarce, enhancing tilapia production is of paramount importance. The primary strain of tilapia currently cultivated is the Genetically Improved Farmed Tilapia (GIFT), which is known for its rapid growth but exhibits limited resistance to diseases. This research aims to significantly advance the development of the tilapia industry in Taiwan by improving disease resistance and growth performance through the application of molecular marker-assisted selection and nutrigenomics.
The present study employed a challenge test on Streptococcus iniae on three strains of Taiwan tilapia, simultaneously establishing 11 microsatellite markers pertinent to disease resistance. The identified markers included three OnHAMP2, four OnHAMP1, one PGRN2, two PGRN1, and one TP4. The findings from the challenge test indicated that the disease resistance of the three strains ranked as follows: strain A exhibited the highest resistance, followed by strain B, and lastly strain N2. The lethal dose required to achieve 70-80% mortality (LD70-80) for strain A was determined to be 2 × 106 CFU/g (LD71.3), for strain B it was 6 × 105 CFU/g (LD73.2), and for strain N2 it was 6.5 × 105 CFU/g (LD79.7). A correlation analysis of genotypes and survival revealed that a total of 55 genotypes related to survival were found—37 genotypes in strain A, 9 in strain B, and 9 in strain N2. Then, a Support Vector Machine (SVM) predictive model was built by the genotypes of the parental generation. Furthermore, the accuracy and practicality of the prediction model was confirmed by S. iniae challenge test in the Streptococcus-resistant offspring of Taiwan tilapia, namely SR-ANT1, SR-AB, and SR-BB.
To further improve growth and disease resistance while simultaneously reducing reliance on chemicals and antibiotics in aquaculture, the Bidens pilosa has been selected as a multifunctional additive. An 8-week trial demonstrated that feeding Taiwan tilapia with 0.5% and 1% B. pilosa significantly enhanced their growth performance, including final body weight, percentage weight gain, specific growth rate, and feed conversion ratio, whereas no increase in food intake. B. pilosa also promoted lipid metabolism and antioxidant effects. Taiwan tilapia after fed with BP diet for 8 weeks, cholesterol and triglycerides were decreased and simultaneously increase the activity of superoxide dismutase in serum. Functional enrichment analysis of the transcriptomes of liver and muscle tissues indicated that B. pilosa primarily affects pathways associated with amino acid metabolism, carbohydrate metabolism, lipid metabolism, endocrine system, and signal transduction. Additionally, the results of qRT-PCR revealed that B. pilosa enhance the expression of genes involved in the endocrine IGF1/IGF1Rb signaling pathway (specifically igf1 in the liver and igf1rb in muscle), while inhibiting the negative regulator of autocrine/paracrine MSTN signaling (mstnb) in muscle tissue. Furthermore, the expression of high levels of myogenic regulatory factors (MRFs), including mrf4, myog, and myod1, was found to promote muscle growth in Taiwan tilapia. In addition, the mortality of tilapia after challenged with Streptococcus iniae (6×105 CFU/g) by IP injection was significantly suppressed from 67% in control group to 37% and 27% in the additive group fed with 0.5% and 1% BP, respectively.
Moreover, we employed third-generation Oxford Nanopore Technology (ONT) to establish a gut microbiota database for Taiwan tilapia fed with B. pilosa. The findings showed a decline in intestinal microbial diversity in Taiwan tilapia and a reduction in the relative abundance of Aeromonas, a pathogenic bacterium in tilapia, after B. pilosa. feeding. The relative abundance of Citrobacter, which has been utilized as a probiotic for tilapia, has increased. Additionally, the functional predictions of the intestinal microbiota were found to be similar to those of the KEGG pathway for liver transcriptome enrichment in Taiwanese tilapia. The significant enriched pathways were following: arginine and proline metabolism, glycine, serine and threonine metabolism, tryptophan metabolism, valine, leucine and isoleucine degradation in amino acid metabolism; fatty acid degradation and glycerophospholipid metabolism in lipid metabolism; amino sugar and nucleotide sugar, glyoxylate and dicarboxylate, Starch and sucrose metabolism in carbohydrate metabolism; adipocytokine signaling pathway and PPAR signaling pathway in endocrine system; FoxO and MAPK signaling pathway in signal transduction; beta-Alanine metabolism in metabolism of other amino acids. Therefore, we propose that B. pilosa may alter the composition of gut microbiota and affect the metabolism and growth of Taiwan tilapia by influencing nutritional processes in the liver through the gut-liver axis.
In conclusion, this study effectively improved the disease resistance and growth performance of Taiwan tilapia through the application of molecular marker-assisted selection and nutrigenomics. This approach presents a viable strategy for minimizing the reliance on antibiotics and chemicals. Notable accomplishments include the establishment of a comprehensive database encompassing microsatellite markers, transcriptomic data, and gut microbiome for Taiwan tilapia, as well as the development of a predictive model for disease resistance and a multifunctional feed additive. The predictive model serves as an efficient and cost-effective tool for selective breeding. Furthermore, the incorporation of B. pilosa into the diet significantly enhanced growth efficiency, lipid metabolism, and antioxidant capacity in Taiwan tilapia, while concurrently inhibiting harmful gut bacteria, thereby promoting overall fish health. Furthermore, the modulation of the gut microbiome plays a significant role in supporting the gut-liver axis, which enhances growth and immunity in Taiwan tilapia. These advancements indicate the potential to improve production efficiency and contribute to sustainable aquaculture practices.

致謝 I
摘要 II
Abstract IV
Graphical abstract of the dissertation VII
Table of contents VIII
List of Tables XI
List of Figures XII
Abbreviations XIV
Chapter 1. General Introduction 1
1.1 Tilapia industry 1
1.1.1 Tilapia aquaculture production 1
1.1.2 Genetically improved farmed tilapia 1
1.1.3 Current issues in developing tilapia farming 2
1.2 Selective breeding 2
1.2.1 Traditional breeding 2
1.2.2 Selective breeding programs in aquaculture 3
1.2.3 Marker-assisted selection (MAS) 3
1.3 Feed additive in aquaculture 4
1.3.1 Function additives of feedstuff 4
1.3.2 Phytogenics in aquaculture 5
1.3.3 Nutrigenomics 5
1.4 Studies of gut microbiota in aquaculture 6
1.4.1 Probiotics in aquaculture 6
1.4.2 Correlation between intestinal bacteria and host 7
1.5 The motivation and aims of this study 7
Chapter 2. Development of Disease-Resistance-Associated Microsatellite DNA Markers for Selective Breeding 8
2.1 Introduction 8
2.2 Material and method 10
2.2.1 Experimental animal 10
2.2.2 Selecting the disease-resistant associated microsatellites 11
2.2.3 Challenge test by Streptococcus iniae 11
2.2.4 Streptococcus-resistant associated microsatellites analysis 12
2.2.5 The genetic diversity analysis 15
2.2.6 The correlation analysis of genotype and survival rate 17
2.2.7 The new disease-resistant Taiwan tilapia 17
2.2.8 The Effectiveness Analysis of Genotypes by Predictive Models 18
2.2.9 Detecting the Streptococcus Resistance of New Strains 21
2.3 Result 21
2.3.1 Seventeen microsatellites linked to disease resistance in tilapia 21
2.3.2 Challenge test results for selecting Streptococcus-resistant tilapia 21
2.3.3 Genotypic variation in disease-resistance-related microsatellites in tilapia 22
2.3.4 Genetic diversity among microsatellites linked to Streptococcus-resistance 22
2.3.5 The correlation between genotype and survival rates of Streptococcus-resistance-associated microsatellites in tilapia 23
2.3.6 Predictive model and effectiveness analysis of genotypes 24
2.4 Discussion 25
2.4.1 Microsatellites linked to disease resistance in tilapia 25
2.4.2 The effect of SSRs on gene expression 25
2.4.3 The correlation between SSRs on Streptococcus resistance in tilapia 26
2.4.4 Predictive model and effectiveness in tilapia 27
2.4.5 Application marker-assisted selection in aquaculture 30
2.5 Conclusion 30
Chapter 3. The Nutrigenomics Study of Multifunctional Additive in Tilapia 32
3.1 Introduction 32
3.2 Material and method 34
3.2.1 Experimental animal 34
3.2.2 Preparation of additives and feedstuffs 34
3.2.3 Growth performance, hepatosomatic index, and viscerosomatic index 35
3.2.4 Blood biochemistry analysis 35
3.2.5 Transcriptome analysis 36
3.2.6 The gene expression analysis in tilapia 37
3.2.7 Streptococcus iniae challenge 38
3.2.8 DNA extraction 38
3.2.9 Oxford Nanopore Technologies 38
3.2.10 The diversity analysis of gut microbiota 39
3.2.11 Functional prediction 39
3.2.12 Statistical analysis 40
3.3 Result 40
3.3.1 The growth performance of tilapia following an eight-week feeding period with a B. pilosa diet 40
3.3.2 The effects of an eight-week feeding period with a B. pilosa diet on the biochemical variables and antioxidative responses of tilapia 41
3.3.3 Transcriptome analysis in liver, muscle, and spleen tissues 42
3.3.4 Gene expression in liver, muscle, and spleen tissues 44
3.3.5 The optimal dietary level of B. pilosa 46
3.3.6 Relative percent survival of tilapia by Streptococcus iniae infection 47
3.3.7 The diversity and composition of gut microbiota in tilapia 47
3.3.8 The predicted function of gut microbiota following an eight-week feeding period with a B. pilosa diet in tilapia 48
3.3.9 Potential probiotics and pathogens in tilapia at species level 49
3.4 Discussion 49
3.4.1 The impact of B. pilosa on the growth of tilapia 49
3.4.2 The mechanism by which B. pilosa promotes the growth of tilapia 51
3.4.3 The impact of B. pilosa on the antioxidant capacity of tilapia 52
3.4.4 The impact of B. pilosa on the lipid metabolism of tilapia 54
3.4.5 The impact of B. pilosa on the disease resistance of tilapia 55
3.4.6 Effects of B. pilosa feeding on gut microbiota hosts 57
3.5 Conclusion 59
General conclusion 61
Reference 62
Tables 80
Figures 109

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