Functional genomics for understanding behaviour and learning in a single-celled brainless blob, the slime mould Physarum polycephalum

进化生物学家长期以来一直对一个问题很感兴趣，即看似复杂的行为是如何通过简单的规则和过程产生的。一个解决这些问题的有趣模型系统是黏液霉菌多头绒泡菌（Physarum polycephalum），这是一种单细胞进化上古老的真核生物，在其生命周期的很长一段时间里，它可以作为一个被称为疟原虫的大型无定形团块。疟原虫含有数百万个细胞核和一个广泛的脉动细胞质网络，使其能够迅速改变形状并在其基质上移动以寻找食物和庇护所。这种运动行为可以受到环境刺激的影响，研究表明疟原虫能够做出决策，学习和解决问题[1]，着名的是找到通过迷宫的最短路径[2]（https：www.youtube.com/watch? v=F3z_mdaQ5ac）！虽然多头绒泡菌的原质团长期以来一直被用作细胞运动、行为和学习的模式生物，但负责产生这些显著特性和行为的功能遗传机制尚不清楚。最近发表的绒泡菌基因组草图提供了对生物体遗传库的初步调查，并为研究基因表达机制和支持疟原虫行为的基因组组织开辟了一系列广泛的途径[3]。该项目将开发对基本基因组动力学的编排的关键见解，以在绒泡菌中产生行为，并为细胞行为，通信和学习的进化起源定义新的机制模型。对照实验试验将与高通量测序相结合，以检查功能反应（即，基因表达的变化和/或基因组组织的表观遗传变化）。要解决的关键问题是：1）哪些基因参与了行为变化，它们在基因组中是如何组织的？2)在原质团的细胞核之间，功能反应在空间上是如何变化的？以及3）适应行为是否存在表观遗传成分？多头绒泡菌是一种有趣的生物，在实验室中很容易维持。您将获得启动和维护活黏菌培养以及在受控条件下设计和进行实验试验的经验。您将进一步接触开发适用于高通量测序方法（如Illumina或Oxford NanoPore测序）的DNA和RNA提取优化实验室方案。该项目的很大一部分将涉及测序数据的生物信息学处理，包括基因组比对、差异基因表达、生理途径和网络分析以及基因组组织的探索。将提供全面的生物信息学培训。[1]白川，T.，Gunji，Y. P.和Miyake，Y.，2011.利用多头绒泡菌原质团进行联想学习实验。Nano communication networks，2（2-3），pp.99-105. [2]Nakagaki，T.，Yamada，H.和托特，2000.智能：变形虫生物体的迷宫解决。Nature，407（6803），p.470. [3]Schaap，P.，巴兰特斯岛Minx，P.，Sasaki，N.，安德森，R.W.，Bénard，M.，比格尔，K.K.，Buchler，N.E.，邦舒河Chen，X.和Fronick，C.，2015.多头绒泡菌基因组揭示了原核双组分和后生动物型酪氨酸激酶信号的广泛使用。基因组生物学和进化，8（1），第109 -125页

Evolutionary biologists have long been intrigued by the question of how seemingly complex behaviour could come about by simple rules and processes. A fascinating model system for addressing these questions is the slime mould Physarum polycephalum, a single-celled evolutionarily ancient eukaryotic organism that can spend long periods of its life cycle as a large amorphous blob called a plasmodium. The plasmodium contains millions of nuclei and an extensive pulsating cytoplasmic network that allows it to rapidly change shape and move across its substrate on the search for food and shelter. This motility behaviour can be affected by environmental stimuli, and research has suggested that the plasmodium is capable of decision making, learning and problem solving [1], famously finding the shortest path through a maze [2] (https://www.youtube.com/watch?v=F3z_mdaQ5ac)! While the plasmodium of Physarum polycephalum has been used as a model organism for cell motility, behaviour and learning for a long time, the functional genetic mechanisms responsible for producing these remarkable properties and behaviours are unknown. The recently published draft genome of Physarum has provided an initial survey of the genetic repertoire of the organism and has opened a broad suite of avenues for investigating the mechanics of gene expression and genome organisation underpinning behaviour across the plasmodium [3]. This project will develop key insights into the orchestration of fundamental genome dynamics to generate behaviour in Physarum and define novel mechanistic models for the evolutionary origin of cellular behaviour, communication and learning. Controlled experimental trials will be combined with high-throughput sequencing to examine functional responses (i.e., changes in gene expression and/or epigenetic changes in genome organisation) of the plasmodium to environmental stimuli and problem-solving situations. The key questions to be addressed are: 1) what genes are involved in behavioural changes and how are they organised in the genome?; 2) how do functional responses vary spatially among nuclei within the plasmodium?; and 3) is there an epigenetic component to adaptive behaviour?Physarum polycephalum is an intriguing organism that is simple to maintain in the lab. You will gain experience in initiating and maintaining live slime mould cultures and designing and carrying out experimental trials under controlled conditions. You will further be exposed to developing optimised lab protocols for DNA and RNA extraction suitable for high-throughput sequencing approaches such as Illumina or Oxford NanoPore sequencing. A large part of the project will involve bioinformatics processing of sequencing data, including genome alignment, differential gene expression, physiological pathway and network analysis and exploration of genome organisation. Full bioinformatics training will be provided.[1] Shirakawa, T., Gunji, Y.P. and Miyake, Y., 2011. An associative learning experiment using the plasmodium of Physarum polycephalum. Nano communication networks, 2(2-3), pp.99-105.[2] Nakagaki, T., Yamada, H. and Tóth, Á., 2000. Intelligence: Maze-solving by an amoeboid organism. Nature, 407(6803), p.470.[3] Schaap, P., Barrantes, I., Minx, P., Sasaki, N., Anderson, R.W., Bénard, M., Biggar, K.K., Buchler, N.E., Bundschuh, R., Chen, X. and Fronick, C., 2015. The Physarum polycephalum genome reveals extensive use of prokaryotic two-component and metazoan-type tyrosine kinase signaling. Genome biology and evolution, 8(1), pp.109-125