Sequencing a Soil Sentinel (SeqaWorm)

利用当代生态毒理基因组学技术（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，至少在两个基本方面努力模仿人类医学中的分子诊断原理。首先，人们越来越强调用分子术语描述化学中毒导致从发育、生存、生长到繁殖的生理过程中断的机制。其次，技术正在被利用来逐步拆除传统的“基因”方法，以支持包含复杂的，模块化的基因-蛋白质网络的系统观点。毫无疑问，环境诊断学正在接近一个令人兴奋的阶段，它正在演变成一门强大的、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，很明显，在无脊椎动物的非模式物种中缺乏充分注释的序列数据必须立即得到纠正。为了应对这一挑战，我们建议在这项研究中，进一步集中在一个无处不在的蚯蚓物种（红蚯蚓），在其分类群的其他成员一样，是一个关键的生态工程有机体，其特定的生理生态特性，使其成为一个开拓性的殖民者的压力负荷布朗菲尔德和废弃的工业土壤，以及清洁的酸性高地和白垩质土壤的居民。令人惊讶的是，人们对L. Rubellus或任何其他植物。在之前的NERC资助的（EcoWorm）计划中，我们生成了第一个大型环节动物DNA序列数据集，现在总计超过20，000个EST序列，聚类为8，000个“基因对象”。这个数据集（存放在一个名为LumbriBASE的公共访问数据库，www.earthworms.org）。估计仅代表这种环境哨兵生物表达的基因的~40%，并且已经促进了在不相关分类群中保守的关键反应途径的鉴定。此外，这些信息有望将土壤污染的基因型、表型和生态后果联系起来。目前提出的测序计划打算利用EcoWorm中收集的战略投资和资源作为跳板，扩大L.红色。L. Rubellus估计具有430 Mb的基因组（http：//www.genomesize.com/annelids.htm），分布在18对染色体上。我们将筛选一个BAC文库或约50，000个平均大小为100 Kb的克隆，这代表了约15倍的Escherichia coli基因组覆盖率，以寻找具有已知环境响应特征的靶标。然而，我们的建议是，远远超过遗传积累的练习，它承诺在我们的能力，以描述抵抗或脆弱性的功能基础上的量子增强环境变化和影响，在一个显着重要的土壤居住的大型无脊椎动物。通过利用EcoWorm中生成的信息学工具，转录数据可以转换为支持理解环境扰动与转录组和蛋白质组之间相互作用的资源。此外，我们的目标是揭示基于非编码序列的基因组表达改变的功能途径，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.