Microarray-based discovery of plant growth-regulatory genes

植物是人类所有食物的最终来源，(作为化石燃料)是现代文明所依赖的主要能源来源。因此，我们必须充分了解植物的生长是如何控制的，这一点至关重要。以前的工作已经确定了一系列蛋白质(称为Della蛋白质)，它们可以抑制植物的生长，以响应来自环境的信号。这些信号警告植物注意不利于生长的环境条件。然后，植物使用它们的Della蛋白来相应地适应它们的生长。然而，很明显，额外的蛋白质控制着植物的生长，以前对这些额外蛋白质的鉴定受到了阻碍，因为它们的影响被Della蛋白质的影响所掩盖。这一建议首先描述了如何利用遗传学来揭示这些以前未知的生长调节蛋白的影响，然后描述了如何利用植物基因组生物学和计算分析的最新进展来使我们能够识别编码这些生长调节蛋白的基因。调节生物体生长和发育的系统通常在生理上是“缓冲的”。考虑一种想象的情况，其中生长受到两种不同的机制A和B的调节。人们经常发现，影响机制B的基因突变只有在机制A不起作用的情况下才对生长产生可检测的影响。因此，当A发挥作用时，B在遗传上是“看不见的”，因为与B有关的基因突变对生长没有影响(没有可检测到的“表型”)。规避这些问题的方法是在缺乏A的遗传背景下进行突变筛选。因此，我们建议从缺少Della生长调节机制(在我们的想象中是A机制)的遗传背景中鉴定新的生长突变。新的突变体将使用快中子诱变产生。快中子是特别有用的诱变剂，因为它们往往会导致植物染色体上连续DNA片段的微小缺失。这些缺失通常足够小，可以包含在(或包含)单个基因中。最近，在分子水平上检测这种缺失已经成为可能。从缺失突变体到整个植物基因组的“芯片阵列”表示的DNA杂交(以及随后对结果数据的计算分析)可以精确地检测缺失突变的位置，从而识别受影响的基因。然而，虽然这些方法有效(参见支持案例)，但它们非常新，仍然相对原始。因此，我们建议进一步改进杂交反应和数据分析软件的方法，使突变基因的检测既可靠又常规。因此，该提案概述了突变检测“流水线”的发展，从最初识别影响生长的新突变，到基于微阵列检测这些突变的分子位置，到最终识别以前未知的生长调节基因(和蛋白质基因产品)。一旦这些以前未知的生长调节蛋白和基因被识别出来，我们将朝着了解植物生长是如何控制的方向迈进了一步。我们已经知道，生长是受来自环境和植物内部的不同信号组成的复杂网络的控制。不知何故，这个信号输入网络被整合到一个单一的增长产出中。这一关键的整合是如何实现的，目前还不完全清楚。这项建议中所描述的工作将能够识别执行这种整合作用的蛋白质(除了Della蛋白质之外)，因此将有助于全面了解植物生长控制是如何实现的最终目标。

Plants are the ultimate source of all human food and (as fossil fuels) the majority source of the energy upon which modern civilisation depends. It is therefore of crucial importance that we fully understand how the growth of plants is controlled. Previous work has identified a family of proteins (known as the DELLA proteins) that restrain the growth of plants in response to signals from the environment. These signals warn plants of environmental conditions that are not optimal for growth. Plants then use their DELLA proteins to adapt their growth accordingly. However, it is clear that additional proteins control plant growth, and that the identification of these additional proteins has previously been hindered because their effects are masked by the effects of the DELLA proteins. This proposal firstly describes how genetics can be used to unmask the effects of these previously unknown growth-regulatory proteins, and secondly describes how harnessing the latest advances in plant genome biology and computational analysis will enable us to identify the genes encoding those growth-regulatory proteins. The systems that regulate the growth and development of organisms are often physiologically 'buffered'. Consider an imaginery case where growth is regulated by two distinct mechanisms, A and B. It is frequently found that a gene mutation affecting mechanism B only has a detectable effect on growth when mechanism A is not functioning. Thus, when A is functioning, B is genetically 'invisible' because mutations in genes involved with B have no effect on growth (no detectable 'phenotype'). The way to circumvent these problems is to perform mutant screens in genetic backgrounds lacking A. Accordingly, we are proposing to identify novel growth mutants from a genetic background in which the DELLA growth-regulatory mechanism (mechanism A in our imaginery case) is missing. The new mutants will be generated using fast-neutron mutagenesis. Fast neutrons are particularly useful mutagens because they tend to generate small deletions of contiguous segments of DNA from plant chromosomes. These deletions are usually small enough to be be contained within (or encompass) single genes. It has recently become possible to detect such deletions at the molecular level. Hybridization of DNA from deletion mutants to 'chip-array' representations of the entire plant genome (and subsequent computational analysis of the resulting data) allows precise detection of the location of the deletion mutation, and hence identification of the gene affected. However, whilst these methods work (see Case for Support), they are very new and still relatively crude. We therefore propose further methods improvement, both of the hybridisation reaction and of the software that does the data-analysis, so as to make the detetion of mutated genes both reliable and routine. The proposal therefore outlines the development of a mutation detection 'pipeline' that quickly leads from initial identification of novel mutations affecting growth, through microarray-based detection of the molecular site of those mutations, to the eventual identification of previously unknown growth-regulatory genes (and protein gene products). Once these previously unknown growth-regulatory proteins and genes are identified we will have moved a step further towards understanding how plant growth is controlled. We already know that growth is controlled in response to a complex network of different signals from both the environment and from the plants interior. Somehow, this network of signalling inputs is integrated into a single growth output. How this crucial integration is achieved is currently only incompletely understood. The work described in this proposal will enable the identification of proteins (additional to the DELLA proteins) that perform this integrating role, and will thus contribute to the ultimate goal of a full understanding of how plant growth control is achieved.