Sequencing a Soil Sentinel (SeqaWorm)

利用当代生态毒物基因组学技术(转录组学、蛋白质组学和代谢组学)通过监测自由生活受体生物体的性能指标来评估生态系统健康的努力正在努力至少在两个基本方面效仿人类医学中的分子诊断原理。首先，人们越来越重视从分子角度描述化学中毒导致从发育、生存和生长到生殖的生理过程中断的机制。第二，正在利用技术逐步废除传统的‘基因为’的方法，转而采用系统的观点，包括复杂的、模块化的基因-蛋白质网络。环境诊断学无疑正在接近一个令人兴奋的阶段，它正在演变为一门强大的、可预测的科学。然而，为了从现有的技术平台中获得最大的利益，显然必须紧急纠正非模式无脊椎动物物种中缺乏充分注释的序列数据。为了应对这一挑战，我们建议在这项研究中进一步关注一种普遍存在的蚯蚓物种(Lumbricus Ruellus)，它与其分类群的其他成员一样，是一个关键的生态工程有机体，其特殊的生态生理特性使其成为压力负荷沉重的棕地和废弃工业土壤的开拓性殖民者，以及清洁酸性旱地和白垩质土壤的居住者。令人惊讶的是，人们对红曲霉或任何其他蚯蚓的遗传学知之甚少。在之前由NERC资助的(EcoWorm)计划中，我们生成了第一个环节动物DNA序列的大型数据集，现在总共有20,000个EST序列，聚集成8,000个‘基因对象’。该数据集(存放在一个名为LumbriBASE的公共访问数据库中，网址为www.Worlworms.org。)据估计只占这种环境哨兵生物体表达的基因的40%，并且已经促进了跨无关类群的关键反应途径的识别。此外，这些信息有望将土壤污染的基因、表型和生态后果联系起来。目前拟议的测序方案打算利用在生态蠕虫中收集的战略投资和资源作为跳板，以扩大红曲霉的序列知识库。红曲霉基因组全长430Mb(http://www.genomesize.com/annelids.htm))，分布在18对染色体上。我们将筛选一个BAC文库或平均大小为100KB的~50,000个克隆，这代表了蚯蚓基因组覆盖范围的~15倍，以寻找具有已知环境响应特征的目标。然而，我们的建议远不止是基因增值的练习；它承诺我们描述对环境变化和影响的抵抗力或脆弱性的功能基础的能力将得到量子增强，这是一种非常重要的土壤栖息的大型无脊椎动物。通过利用EcoWorm中产生的信息工具，转录本数据可以被转换为支持努力理解环境扰动与转录组和蛋白质组之间的相互作用的资源。此外，我们的目标是揭示基于非编码序列的改变基因组表达的功能途径，从而为全基因组测序提供必要的前体数据。

Efforts to assess ecosystem health by monitoring performance indicators in free-living receptor organisms using contemporary ecotoxicogenomic technologies (transcriptomics, proteomics, and metabolomics) is striving in at least two fundamental ways to emulate the principles of molecular diagnostics in human medicine. First, there is increasing emphasis on describing in molecular terms the mechanisms of chemical toxicosis leading to disruptions in physiological processes, from development, survival, and growth, to reproduction. Second, technology is being harnessed to progressively dismantle the traditional 'gene for' approach in favour of a systems perspective that encompasses complex, modular, gene-protein networks. Environmental diagnostics is undoubtedly approaching an exciting phase in its evolution into a robust, predictive, science. However, in order to extract the maximum benefit from available technological platforms it is clear that the paucity of fully annotated sequence data in non-model species of invertebrates must be urgently corrected. To address this challenge, we propose in this study to focus further on a ubiquitous earthworm species (Lumbricus rubellus) which, in common with other members of its taxon, is a keystone ecological-engineering organism and whose particular ecophysiological traits enable it to be a pioneering colonizer of stress-laden brownfield and abandoned industrial soils, as well as a denizen of clean acidic upland and chalky soils. Surprisingly little is known of the genetics of L. rubellus or any other earthworm. In a previous NERC-funded (EcoWorm) programme we generated the first large dataset of annelid DNA sequences, now totalling > 20,000 EST sequences, clustered into 8,000 'gene objects'. This dataset (deposited in a public-access database called LumbriBASE, www.earthworms.org.) is estimated to represent only ~40% of the genes expressed by this environmental sentinel organism, and has already facilitated the identification of key response pathways conserved across unrelated taxa. Furthermore, the information promises to allow the genotypic, phenotypic and ecological consequences of soil pollution to be linked. The present proposed sequencing programme intends using the strategic investment and resources gleaned within EcoWorm as springboards to expand the sequence knowledge-base for L. rubellus. L. rubellus is estimated to possess a genome of 430 Mb (http://www.genomesize.com/annelids.htm) distributed over 18 chromosome pairs. We will screen a BAC library or ~50,000 clones with average size of 100 Kb, which represents ~15-fold coverage of the earthworm genome, for targets with known environmental response profiles. Our proposal is, however, far more than an exercise in genetic accretion; it promises a quantum enhancement in our ability to describe the functional basis of resistance or vulnerability to environmental change and impact in a conspicuously important soil-dwelling macroinvertebrate. By exploiting informatic tools generated within EcoWorm, transcript data can be converted into resources which support efforts to understand the interactions between environmental perturbations and both the transcriptome and proteome Furthermore, we aim to reveal the functional pathways that underlie altered genome expression based on non-coding sequences, thus providing the necessary precursor data for whole-genome sequencing.