Modeling Microbiome Peptides Using Metaproteomics for the Prediction of Harmful Algal Blooms

项目摘要 有害赤潮是一种通过污染而威胁公众健康的反复发生的有毒事件 世界各地的水质问题。各种有毒浮游植物物种经常在两个沿海地区经历水华事件 和内陆水体，对水处理设施，渔业和娱乐业造成严重破坏， 每年积累约110亿美元的与人类接触有关的医疗费用。随着气候和气候的变化 农业继续改变水化学，观察到水华事件发生得更频繁，过去 更长的时间，并释放更广泛的有毒化学物质。目前，还没有预测水华的方法。 发病，使公众容易受到一系列可能可以避免的有害毒素的影响。 共享生态系统的悠久历史和共生进化建立了 形成赤潮的浮游植物及其微生物群。细菌已被证明对光合作用的 藻类的昼夜节律，模仿代谢所需蛋白质表达的昼夜节律。 生态系统的显著变化可能会导致蛋白质表达模式的反应性变化， 可被检测为具有相似分类来源或功能类别的单个多肽或多肽群。 如果在HAB启动前24小时丢失了一个或一组多肽的已建立的昼夜节律性， 它可以作为预测即将到来的水华毒性的一个指标。我假设追踪量化的 HAB相关微生物组的表达多肽将使我能够检测节律性和 这些多肽的节律性；这些多肽或多肽基团可以作为生物标记物 开发用于预测赤潮的生物测定或探针，以更好地警告公众。 在这个项目中，我将收集已知微生物群周围随时间变化的水样。 华盛顿州普吉特湾一年两次形成赤潮的浮游植物伪菱形藻。我的实验性设计包括 与华盛顿的声音毒素计划合作对浮游植物进行高分辨率采样 从预测的水华事件发生前2周开始，每4小时采样一次微生物组，直到HAB毒素 匹克。然后，我将使用与数据无关的定量采集量来分析微生物样本 光谱分析方法建立与时间相关的多肽丰度。这些多肽将被分组并 将使用MetaGOmics和时间进程数据注释到所有潜在的分类和功能组中 使用结合非参数方法的节律性分析进行分析。这将使我能够检测到 开花前单肽(AIM 1)和按类群或功能分组的多肽(AIM 2)的节律性 事件。在开花前表现出显著的节律性变化或失去节律性的多肽或多肽 代表了未来发展基于快速分子多肽的检测或探针的潜在生物标志物 预测HAB事件。该项目使用代谢蛋白质组学方法的进展来预防对人类有害的 通过使用微生物组生物标记物多肽组预测水华发生，暴露于赤潮毒素。

Project Abstract Harmful algal blooms (HABs) are a reoccurring toxic event threatening public health through the contamination of water quality worldwide. Various toxic phytoplankton species regularly undergo bloom events in both coastal and inland water bodies, wreaking havoc for water treatment facilities, fishing, and recreational industries, amassing ~$11 billion annually in healthcare costs related to human exposure. As changes in climate and agriculture continue to alter water chemistry, bloom events have been observed to occur more frequently, last longer, and release a wider range of toxic chemicals. Currently, there exists no method for predicting bloom onset, leaving the public vulnerable to a spectrum of potentially avoidable harmful toxins. A long history of shared ecosystems and co-occurring evolution has established a close relationship between HAB-forming phytoplankton and their microbiome. Bacteria have been shown to respond to the photosynthetic circadian rhythm of the algae, mimicking circadian patterns in the expression of metabolically necessary proteins. A significant change in the ecosystem is likely to cause reactionary changes in patterns of protein expression, detectable as either individual peptides or peptide-groups sharing similar taxonomic origin or functional category. If the established circadian rhythmicity of a peptide or group of peptides is lost >24 hours prior to HAB initiation, it could be used as an indicator to predict impending bloom toxicity. I hypothesize that tracking the quantified expressed peptides of the HAB-associated microbiome will allow me to detect rhythmicity and the loss of rhythmicity of those peptides; these peptides, or groups of peptides, can serve as biomarkers to be developed as bioassays or probes for forecasting HABs to better warn the public. For this project, I will be collecting time-dependent water samples of the microbiome surrounding the known HAB-forming phytoplankton Pseudo-nitzschia biannually in Puget Sound, WA. My experimental design includes working with Washington’s Sound Toxins Program to conduct high-resolution sampling of the phytoplankton microbiome every 4 hours beginning 2 weeks prior to a predicted bloom event and sampling until HAB-toxins peak. I will then analyze the microbiome samples using quantitative data-independent acquisition mass spectrometry methods to establish time-dependent peptide abundances. These peptides will be grouped and annotated into all potential taxonomic and functional groups using MetaGOmics and time-course data will be analyzed using Rhythmicity Analysis Incorporating Non-parametric methods. This will allow me to detect rhythmicity from individual peptides (AIM 1) and peptides grouped by taxa or function (AIM 2) prior to the bloom event. Peptides or peptide groups exhibiting significant changes in or loss of rhythmicity prior to bloom onset represent potential biomarkers for the future development of a rapid molecular peptide-based assay or probe for predicting HAB events. This project uses advances in metaproteomic methods to prevent harmful human exposure to HAB toxins by predicting bloom onset using microbiome biomarker peptide groups.