2014-08-012024-05-13https://scholars.lib.ntu.edu.tw/handle/123456789/648621摘要：關於病毒的研究發現，短時間內的演化速率會比長時間內分化累積的速率還許多，但是其根本原因卻仍未知。我們認為這現象源自於病毒生活史中的多樣型態，包含宿主間的傳遞以及宿主內的競爭。部分病毒株可能因為擁有高效的複製能力而擅於傳遞與感染(拓殖者)，而其他病毒株則可能專精於在宿主免疫選汰之下的競爭(競爭者)。但是，沒有病毒株有辦法同時成功扮演兩個角色。這猶如生態系統中的競爭拓荒取捨模型。在這兩種角色間快速變換則病毒需要有快速的適應能力以加速短期演化時間內的替換率。如果因為功能上限制，使得可能的拓殖者與競爭者的種類受到限制，類似的適應僅會是暫時的，而無法對長時間演化有所貢獻。 我們所提的競爭拓殖病毒演化模型預測 (1) 不同病毒株專精於不同生物功能，及 (2) 病毒族群內序列變化模式和病毒族群間分化不相關。要測試我們的模型假設，必須藉由追蹤一系列連續受到傳染的宿主並分析其宿主間與宿主內病毒之動態變化。不幸的是，此類資訊通常無法取得。慢性 B型肝炎病毒(HBV)感染通常藉由母親生產時垂直傳染給新生兒，而且因為傳染的次序以及時間都可以簡單取得，而提供了此類研究理想的模型。為了試圖解答病毒演化的爭論，我們目標是研究家族內傳遞的 HBV 族群(quasispecies) 演化。我們將記錄 HBV病毒在多個家族成員間的動態模式。 為了獲得詳細 HBV動態資訊，我們應用了次世代定序技術。目前分析次世代定序的方法對於高歧異度與覆蓋深度資料的組裝表現不佳，而這二因子是總體基因體(metagenomic)與病毒研究中主要的焦點。為了填補這個空隙，我們針對病毒與總體基因體開發一個全新的組裝流程(目標 1)。此外，我們將測試不同生物尺度下 HBV演化模式的差異(目標 2)。我們要估算 HBV族群數量、替換率與評估天擇的信號與強度。隨著一連串感染傳遞與突變頻率的改變，我們可以評估病毒基因體不同區域異質性程度。我們將剖析不同演化尺度下影響病毒適應與演化的因子。這些結果將有助於產生一個預測HBV長期演化的模型。最後這個模型將用來檢測人類與 HBV共同演化各種情節 (目標3)。<br> Abstract: It is well known that short-term rates of evolution appear orders of magnitude faster than rates at which divergence accumulates over the longer term. Nevertheless, the reason behind is still elusive. We hypothesize that this is stemmed from the dual demands of the virus during its life cycle, i.e., transmission between hosts and competition among hosts. Some viral strain may excel at transmission and infection due to their high replicative ability (colonizer), whereas others excel at competition under host immune selection (competitor). But, no viral strain is able to simultaneously excel at both. This notion is parallel to competition colonization trade-off model previously recognized in ecological systems. Shifting between two phases need rapid adaptation of the virus which increases substitution rate in the short-term evolution. However, if there are limited number of possible colonizers and competitors due to functional constraint, such adaptation will be transient and, thus, fail to contribute to long-term evolution. This competition colonization trade-off model of virus evolution predicts that (1) different viral strains excel at different biological scales, i.e., competition within hosts and colonization among hosts; and (2) the patterns of viral sequence variations within population and divergence between populations are not correlated. To test this model, one has to follow viral dynamics within and among hosts from a chain of sequentially infected transmission. Unfortunately, such information is generally not available. Chronic Hepatits B virus (HBV) infection, which is usually caused by transmission from a carrier mother to her child at birth, is, therefore, an idea model for such study, because both order of transmissions and time of infection can be readily inferred. In an attempt to answer the controversy in viral evolution, we aim to study quasispecies evolution of HBV transmitted within families. To that end, we will enroll several families with HBV transmitted among family members. Quasispecies dynamic within and among hosts are studied by HBV genome recovered by traditional PCR-cloning and next-generation sequencing (NGS) methods. In order to get the detail picture of HBV quasispecies dynamic, we will apply NGS technology. Current available methods performed poorly on assembling data sets of high diversity and coverage depth, both of which are the main focuses of metagenomic and viral quasispecies studies. In order to fill this gap, we develop a whole new assembly pipeline for viral quaspecies as well as metagenomic assembling (Aim 1). Next, we will test whether HBV quasispecies dynamics are different at different biological scales (Aim 2). We estimate HBV demography and substitution rates and assess the signal and strength of selection. By following mutation frequencies change in a chain of sequentially infected transmission, we can evaluate the extent of heterogeneity across different regions in the viral genome. We will dissect factors that contribute to viral adaptation and evolution at different levels. The results will help to generate a model to predict the long-term pattern of HBV evolution. Finally, the model will be tested under different scenarios of HBV/human coevolution (Aim 3).