| 摘要: | 細菌的運動來自於鞭毛，而鞭毛的幾何結構如同一個中空的管子（外徑大約20個奈米，內徑大約2個奈米）形成螺旋推進器，在流體中產生剪力產生推進力。而鞭毛的形成是一種自我組裝（self-assembly）的一維系統。這系統藉由離子電動勢作為輸出鞭毛蛋白分子的能量來源。然後，鞭毛蛋白分子從鞭毛馬達的輸出裝置藉由這個管道送至鞭毛尾端然後形成鞭毛通道的一部分，而使得鞭毛長度增長。
在研究奈米尺度的自我組裝系統，我們在觀察及分析上，分別開發了快速螢光標記鞭毛的方法及根據光學影像原理自動追蹤鞭毛影像的骨幹。由於以往的螢光標記鞭毛的手法採用胺基酸特異性染料（amino-specific dye），此種染色手法為了要有良好的螢光訊號，染色時間是需要半小時到一小時之久。因此，我們巧妙地選用了溶藻弧菌（Vibrio alginolyticus）作為我們的系統，因為此菌種的鞭毛外包覆著一層鞘（sheath），其成分類似細胞膜的脂質，所以我們使用厭水性的螢光染料（lipophilic dye，FM4-64），作為我們標定鞭毛的方法。基於這方法的開發，染色時間大大縮短為隨染隨標定的快速，使得時間解析度大幅提升，有助於研究自我組裝系統的量測和觀測的精準度。另外，過去分析鞭毛的長度來自於手動量測，不是基於固定的演算法，而導致量測數據伴隨人為誤差。但是我們所開發的螢光標記手法，會連同細胞膜一同染色，可以用為細胞邊界的認定。所以我們基於光學原理撰寫專門處理這種問體的演算法，使得細菌鞭毛分析數據更為可靠。
最後，我們對於溶藻弧菌的鞭毛自我組裝系統的量測結果顯示，鞭毛的生長速率和鞭毛的長度是有相關性的。實驗上，我們也同時比較兩種鞭毛長短突變株的鞭毛生長速率。因此，我們建立Injection-diffusion模型並使用Brownian dynamics計算模擬計算此一模型。模型的主要想法是，注入(Injection)和擴散(Diffusion)兩種機制彼此競爭導致自我組裝的速率在不同時期有不同的速率。在鞭毛生長的前期，由於管道較短，在管道中的鞭毛蛋白分子較少，因此輸入裝置上的蛋白分子進入管道受到的推擠較少，所以這個時期就是以輸入裝置上的輸出力所主宰；但是，一旦鞭毛長度變得較長時，在鞭毛管道內的蛋白分子數量上升，導致輸入裝置上的輸出力無法順利送入新的蛋白分子，除非管道內的蛋白分子受擴散往前進空出空間，新的蛋白分子才有機會送入，因此這時期將會是擴散所主導，因此鞭毛自我組裝系統的組裝速率就下降。
我們的實驗程序，分析手法，實驗結果及模型的建立，完整的解釋鞭毛自我組成系統機制。
;Bacteria move in the fluid by means of flagella. The flagellar geometry is a hollow tube which outer diameter is about 20 nanometers and the inner diameter is about 2 nanometers to form a helical propeller that produces a thrust force in the fluid. The formation of a flagellum is a one-dimensional self-assembly system. This system uses the ion electromotive force and ATP hydrolysis as the energy sources for the secretion of flagellin subunits. During flagella growth, flagellar monomer delivery to the distal end of the filament and folded into the flagella.
To study this self-assembly system, we developed a fast flagella labeling method and constructed an image processing protocol to track flagella automatically. For E. coli, there is a flagella labeling method using amino acid-specific dye (amino-specific dye). But, this dye spent about half to one hour to loading if you want to get a good signal to noise ratio. Therefore, we choose Vibrio alginolyticus as our system because they have sheathed flagella which are covered by a layer membrane-like structure. Therefore, we developed a flagella labeling protocol using lipophilic dye FM4-64 to monitor flagellar growth in real-time. This labeling protocol is greatly shortened to stain fluorescent dye, so that the time-resolution is greatly improved. Hence, we can obtain the accuracy measurement and observation for the self-assembly system. Besides, in the past, analysis is not based on a fixed algorithm resulting in the measurement accompanied by human error. But, the lipophilic dye also can stain the cell membrane. So, we cannot use define a threshold to pick out flagella. Then, based on the principle of optical imaging, we remove a halo effect between the cell and flagellum to make the bacterial flagella analysis data more reliable.
Further, we measured the flagellum self-assembly system of Vibrio alginolyticus, showing that the growth rate of flagellum is length dependent. From our experimental result, we compared the flagellar growth rate in different mutants. And then, we set up the Injection-diffusion model and use Brownian Dynamics simulation to fit the model. The main idea of the model is that both injection and diffusion mechanisms compete with each other to cause the rate of self-assembly to have different rate-limiting in different duration. In the early stages of flagellation, because the flagellin molecules are at low occupancy in the channel, so the export apparatus can smoothly pump the subunits into the channel. Hence, the secretion force dominates the flagellar growth rate at short flagellation. However, when the flagellar length becomes longer, the number of protein molecules are jammed in the flagellum tube. The secretion force cannot be successfully loaded into the new protein molecules into channel. Until the protein molecules in the channel are diffused, the new protein molecules have the opportunity to enter. Therefore, this time would be dominated by diffusion resulting in the flagellar self-assembly rate decay.
Our fluorescent labeling protocol, image process, experimental results, and the injection-diffusion model shed the light of understanding bacterial flagella self-assembly mechanisms. |
