Biophysical genetics of collective feeding in C. elegans

线虫集体进食的生物物理遗传学

• 批准号: BB/N00065X/1
• 负责人: Andre Brown
• 金额: $ 77.49万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FN00065X%2F1
• 关键词: Biophysical; genetics; collective; feeding; C.

The goal of behavioural genetics is to understand what aspects of behaviour are inherited and which DNA sequence differences are responsible. Geneticists have long dismissed the notion that there are genes 'for' particular behaviours and recognise that reality is more complicated. DNA sequence differences change how proteins work inside of cells and these changes at the molecular level influence the cells that make up organs and ultimately a whole animal. A given animal will typically contain multiple sequence differences that can interact with each other, further complicating prediction. That is why it is often useful to study the simplest organism that displays a behaviour of interest.In this proposal, we are trying to understand the genetics of collective behaviour--that is, the behaviour of groups of animals. Think starling flocks, ant colonies, and traffic jams. This is a daunting task, but fortunately, the nematode worm C. elegans, a small animal with only 302 neurons, is also capable of a simple kind of collective behaviour: some C. elegans strains feed in large groups while others feed alone. Over the last fifteen years, geneticists have identified several genes that disrupt worm collective feeding when they are mutated, but to understand which aspects of the behaviour these genes affect and how they work together in an intact animal, we need a model of collective feeding.Studies of other kinds of collective behaviour have shown that animals following relatively simple rules, which depend only on what their neighbours are doing, can give rise to complex group behaviours. In this approach, we write down a set of rules that capture what we think we know about the system and then simulate the results on a computer. If the simulated animals behave like the real animals, then we gain confidence that we have identified the right rules. One of the keys to good modelling is to have good data and so the first step is to record movies of worms as they form groups and to quantify exactly how they move once the groups have formed. To see how worms move in tight groups we will use a technique called fluorescence microscopy that allows us to see particular parts inside of worms, which makes them easier to identify. We will then use our knowledge of how worms move to design a tracking algorithm that can automatically identify individual worms in the group. Tracking real animals will give us the same kind of information that we will derive from the simulated results enabling us to precisely compare the simulated and real animals. If we need to, we will update the rules in the simulation to achieve better agreement between the simulations and experiments and learn how worms interact in the process. By repeating the cycle of experiment and simulation with mutant worms, we will also begin to understand which aspects of the behaviour are under genetic control.Despite years of study, we actually still do not know why some worm strains form groups. Is there some advantage to feeding collectively instead of alone? It could be that these animals with very limited computational capacity are able to take advantage of the wisdom of the crowds to find high quality food patches more efficiently. We will use our computational model to predict food patch arrangements that favour aggregating or solitary animals and then test those predictions experimentally. If we find environments where collective feeding is advantageous this might tell us why collective behaviour has evolved in worms.This work will advance our understanding of how sets of genetic mutations can give rise to changes in complex behaviours. It will also give some insight into how nematode worms forage as groups which may have implications in agriculture because some plant parasitic nematodes, which cause more than $100 billion in crop damages annually, can form swarms.

行为遗传学的目标是了解行为的哪些方面是遗传的，哪些DNA序列差异是负责的。遗传学家长期以来一直不认为存在“特定行为”的基因，并认识到现实更为复杂。DNA序列的差异改变了蛋白质在细胞内的工作方式，这些分子水平的变化影响了构成器官的细胞，最终影响了整个动物。给定的动物通常包含多个序列差异，这些差异可以相互作用，从而使预测进一步复杂化。这就是为什么研究最简单的生物体表现出感兴趣的行为往往是有用的，在这个提议中，我们试图了解集体行为的遗传学-即动物群体的行为。想想椋鸟群、蚂蚁群和交通堵塞。这是一项艰巨的任务，但幸运的是，线虫C。秀丽线虫是一种只有302个神经元的小动物，它也有一种简单的集体行为：一些C.秀丽线虫种群以大群进食而其他种群单独进食。在过去的15年里，遗传学家已经发现了几个基因，当它们发生突变时，它们会破坏蠕虫的集体进食，但是为了了解这些基因影响行为的哪些方面，以及它们如何在完整的动物中共同工作，我们需要一个集体进食的模型。对其他类型的集体行为的研究表明，动物遵循相对简单的规则，这些规则只取决于它们的邻居在做什么，可能会引发复杂的群体行为。在这种方法中，我们写下一组规则，这些规则捕获了我们认为我们对系统的了解，然后在计算机上模拟结果。如果模拟动物的行为像真实的动物，那么我们就有信心确定了正确的规则。良好建模的关键之一是拥有良好的数据，因此第一步是记录蠕虫形成群体时的电影，并准确量化它们在群体形成后的移动方式。为了观察蠕虫如何在紧密的群体中移动，我们将使用一种称为荧光显微镜的技术，这种技术使我们能够看到蠕虫内部的特定部分，这使得它们更容易识别。然后，我们将利用我们对蠕虫如何移动的了解来设计一种跟踪算法，该算法可以自动识别组中的单个蠕虫。跟踪真实的动物将给我们提供与我们从模拟结果中获得的信息相同的信息，使我们能够精确地比较模拟和真实的动物。如果需要，我们将更新模拟中的规则，以实现模拟和实验之间更好的一致性，并了解蠕虫在此过程中如何相互作用。通过对变异蠕虫重复实验和模拟的循环，我们也将开始了解哪些行为是受遗传控制的。尽管经过多年的研究，我们实际上仍然不知道为什么有些蠕虫品系会形成群体。集体喂食比单独喂食有什么好处吗？这可能是因为这些计算能力非常有限的动物能够利用群体的智慧更有效地找到高质量的食物。我们将使用我们的计算模型来预测食物斑块的安排，有利于聚集或孤独的动物，然后测试这些预测实验。如果我们找到有利于集体进食的环境，这可能会告诉我们为什么蠕虫会进化出集体行为。这项工作将促进我们对基因突变如何引起复杂行为变化的理解。它还将深入了解蠕虫如何作为可能对农业产生影响的群体觅食，因为一些植物寄生线虫每年造成超过1000亿美元的作物损失，可以形成成群。

项目成果

Shared behavioral mechanisms underlie C. elegans aggregation and swarming

• DOI: 10.7554/elife.43318
• 发表时间: 2019-04-25
• 期刊: ELIFE
• 影响因子: 7.7
• 作者: Ding, Siyu Serena;Schumacher, Linus J.;Brown, Andre E. X.
• 通讯作者: Brown, Andre E. X.

Measuring Caenorhabditis elegans Spatial Foraging and Food Intake Using Bioluminescent Bacteria

• DOI: 10.1534/genetics.119.302804
• 发表时间: 2020-03-01
• 期刊: GENETICS
• 影响因子: 3.3
• 作者: Ding, Siyu Serena;Romenskyy, Maksym;Brown, Andre E. X.
• 通讯作者: Brown, Andre E. X.

Thermodynamics of switching in multistable non-equilibrium systems

多稳态非平衡系统中切换的热力学

• DOI: 10.48550/arxiv.1908.07405
• 发表时间: 2019
• 作者: Cook J

Measuring C. elegans spatial foraging and food intake using bioluminescent bacteria

使用生物发光细菌测量线虫空间觅食和食物摄入量

• DOI: 10.1101/759928
• 发表时间: 2019
• 作者: Ding S

A critical-like collective state leads to long-range cell communication in Dictyostelium discoideum aggregation.

• DOI: 10.1371/journal.pbio.1002602
• 发表时间: 2017-04
• 期刊: PLoS biology
• 影响因子: 9.8
• 作者: De Palo G;Yi D;Endres RG
• 通讯作者: Endres RG