探討多巴胺神經訊號的阻斷對果蠅嗅覺關聯性學習的影響

張盈恩(Ying-En Zhang)

指導教授 ： 江安世

國立清華大學/生命科學院/生物科技研究所/碩士(2007年)

摘要

多巴胺(dopamine)在果蠅的嗅覺關聯性學習中扮演很重要的角色，特別在多巴胺在處罰性的學習的最初階段才被需要。因此，在果蠅的處罰式學習中，多巴胺神經元只負責把非制約刺激(unconditioned stimulus)以及制約刺激(conditioned stimulus)連結在一起，至於其他學習階段則是沒有參與。當我們用螢光顯微技術，將多巴胺在果蠅腦的分佈做了詳細分析之後，發現TH-GAL4, Cha-GAL80/ UAS-mCD8::GFP跟原本TH-GAL4/ UAS-mCD8::GFP相比，少了果蠅蕈狀體calyx區域的螢光表現。利用這點差異，以及Shits1與行為實驗，我們發現TH-GAL4/ UAS- Shits1與TH-GAL4, Cha-GAL80/ UAS- Shits1在限制溫度上的學習分數有差異，同時也發現了要使用UAS- Shits1在生長環境上需要注意的問題。而除了多巴胺，章魚胺(octopamine)在嗅覺關聯性學習中也扮演很重要的角色，與多巴胺不同的是，章魚胺是在獎勵性的學習中才被需要，為了將來可以進一步探討處罰性學習與獎勵性學習在果蠅腦中的形成有何差異，需要有一套好的獎勵性行為實驗流程，所以我們試著發展出最基本的獎勵性行為實驗，但最後得到的分數只有處罰性學習的一半分數，仍是有許多需要改進的地方。

關鍵字

果蠅 ； 學習 ； 多巴胺

並列摘要

It has been known that dopamine is very important in olfactory associated learning of drosophila. And in the recently research, it has been postulated that dopamine is only needed in the acquisition of punish learning. Therefore, dopaminergic neurons has been implicated to play a key role in delivering aversive unconditional stimulus (US) to the MBs to associate with olfactory conditional stimulus (CS) during classical associated learning in the drosophila. By using fluorescence microscopy, we found that the pattern of TH-GAL4, Cha-GAL80/ UAS-mCD8::GFP was different with TH-GAL4 / UAS-mCD8::GFP. In the TH-GAL4, Cha-GAL80/ UAS-mCD8::GFP line, dopamine neurons innervating in MB calyx were subtracted, and dopamine neurons innervating MB lobes were not . To find out where CS/US associates, we were performing behavior analysis by expressing Shits1 in TH-Gal4, Cha-Gal80 line and TH-Gal4 line. And we find learning of TH-GAL4, Cha-GAL80/ UAS- Shits1 was normal at restricted temperature while learning of TH-GAL4/ UAS- Shits1 was blocked. Besides dopamine, octopamine play a key role in olfactory associated learning of drosophila, too. But octopamine is only needed in appetitive learning. If we want to perform behavior analysis in octopamine neurons, we must perform a good protocol of reward leaning. Unfortunately, the protocol of reward learning is too rough to gain stable Performing index (PI). In this research we can only show how to get stable PI in wild type.

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