What determines protein abundance in plants?

蛋白质是细胞的主力：它们促进化学反应，充当基因开关，并具有结构作用。为了让细胞高效工作，蛋白质需要在正确的地点、正确的时间和正确的数量产生。当不再需要时，也需要将其移除。克里克的《中央道格玛》指出，DNA的编码序列被转录成mRNAs，mRNAs再被翻译成蛋白质。这一过程在许多层面上受到监管，但我们的知识仍有许多空白。我们希望个体之间的遗传和环境差异在蛋白质的控制中发挥重要作用。这个项目试图使用模式植物拟南芥来回答关于蛋白质表达控制的基本问题，包括哪些机制是重要的，以及它们如何在复杂的多细胞有机体中相互作用。我们还旨在确定给定细胞、组织或器官的蛋白质含量在多大程度上预测植物的可观察特征(表型)。为了解决这些问题，我们围绕一个遗传可变的拟南芥种群(称为魔法种群)，设计了一个综合的实验和复杂的数学分析方案。这是一个强大的遗传资源，用于定位与性状变异相关的DNA片段(称为数量性状基因座，QTL)，以识别因果变异并剖析基因组表达的调节。我们将描述和比较以下可能影响魔术线中蛋白质表达的不同过程：1.基因组内的结构变异(包括小范围的变异和大规模的结构重排)2.染色质的可及性，这是对给定DNA区域可用于转录的衡量标准。对DNA的化学修饰，不涉及DNA序列的改变，称为表观遗传标记，通常表明环境干扰。信使核糖核酸丰度。蛋白质丰度。采取整体的方法是很重要的，因为个体中任何给定蛋白质的量都是由这些过程的平衡决定的。人们花了很多精力来研究基因转录，因为在全基因组范围内测量基因转录相对容易。然而，证据表明，转录不能很好地预测蛋白质的丰度，因为翻译和蛋白质降解的控制是重要的，特别是在植物中。在测量翻译、蛋白质含量和蛋白质分解方面所做的研究较少，但现在技术的进步使我们能够做到这一点。尽管测量基因组结构变异和DNA甲基化等表观遗传标记相对简单，但它们对蛋白质表达的影响尚不清楚。因此，我们处于一个令人兴奋的位置，可以为蛋白质调控提供巨大的洞察力。这个项目的力量来自创新的计算分析，它将使我们能够分配基因、转录、蛋白质合成和蛋白质降解的相对贡献，并确定控制蛋白质表达的网络。由于从许多样本中收集基因组规模的数据既昂贵又耗时，我们将使用新的统计方法来获得更多信息，而不会大幅增加样本量，包括合并不同的信息层。这将是这种规模的第一次此类研究。除了将我们的数据存储在公共存储库之外，我们的发现还将通过用户友好的知识发现和基因挖掘资源提供给学术界。在这个项目中开发的方法将提供有价值的基本见解，这些见解将适用于其他生物，并将为未来作物改良铺平道路。

Proteins are the workhorses of the cell: they facilitate chemical reactions, act as gene switches and have structural roles. For cells to work efficiently, proteins need to be produced in the right place, at the right time and in the right amount. They also need to be removed when no longer needed. Crick's Central Dogma states that coding sequences of DNA are transcribed into mRNAs, which in turn are translated into proteins. There are many levels at which this process is regulated and there are still many gaps in our knowledge. We expect both inherited and environmental differences between individuals to play important roles in the control of proteins.This project seeks to use the model plant, Arabidopsis thaliana, to answer fundamental questions about the control of protein expression, including which mechanisms are important and how they interact in a complex multi-cellular organism. We also aim to determine to what extent the protein content of a given cell, tissue or organ predicts observable traits (the phenotype) of the plant. To address these questions, we have designed an integrated programme of experiments and sophisticated mathematical analysis around a genetically variable population of Arabidopsis (known as the MAGIC population). This is a powerful genetic resource for mapping sections of DNA that correlate with variation in a trait (known as quantitative trait loci, QTL), to identify causal variants and dissect the regulation of genome expression. We will characterise and compare the following different processes that potentially influence protein expression in the MAGIC lines:1. Structural variation within the genome (including small-scale variation and large-scale structural rearrangements)2. Chromatin accessibility, a measure of the availability of a given region of DNA for transcription.3. Chemical modifications to DNA that do not involve a change in DNA sequence, known as epigenetic marks, which often indicate environmental perturbation.3. mRNA abundance.4. Protein abundance.It is important to take an holistic approach, because the amount of any given protein in an individual is determined by the balance of these processes. Much effort has been spent studying gene transcription, because it is relatively easy to measure on a genome-wide scale. However, evidence suggests that transcription is a poor predictor of protein abundance, because the control of translation and protein degradation are important, particularly in plants. Less research has been done on measuring translation, protein amount and protein breakdown but advances in technology now let us do so. Although it is relatively straightforward to measure genomic structural variation and epigenetic marks such as DNA methylation, their impact on protein expression is unclear. Therefore, we are in an exciting position to provide enormous insight into protein regulation. The power of this project derives from innovative computational analysis that will enable us to apportion the relative contributions of genotype, transcription, protein synthesis and protein degradation and identify networks controlling protein expression. Because collecting genome-scale data from many samples is expensive and time-consuming, we will use novel statistical methods to get more information without significantly increasing sample size, including combining different layers of information. This will be the first study of this kind on this scale. As well as depositing our data in public repositories, our findings will be made available to the academic community via a user-friendly knowledge discovery and gene mining resource. The approaches developed in this project will provide valuable fundamental insights that will be applicable to other organisms and which will also pave the way to future crop improvement.