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

使用宏蛋白质组学对微生物组肽进行建模以预测有害藻华

• 批准号: 10689674
• 负责人: Miranda Mudge
• 金额: $ 4.24万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10689674
• 关键词: Agriculture; Algae; Algal Blooms; Asthma; Bacteria; Biological Assay; Biological Markers; Categories; Cessation of life; Chemistry; Circadian Rhythms; Classification; Climate; Communities; Data; Data Set; Dermatitis; Detection; Development; Disasters; Ecosystem; Event; Evolution; Exhibits; Experimental Designs; Exposure to; Family; Fishes; Foundations; Future; Goals; Government; Grouping; Health; Health Care Costs; Hour; Human; Individual; Industry; Investigation; Iron; Lead; Link; Mass Spectrum Analysis; Metabolic; Metals; Methods; Mission; Modeling; Molecular; National Institute of Environmental Health Sciences; Nervous System Trauma; Pattern; Peptides; Periodicity; Personal Satisfaction; Phytoplankton; Poison; Protein Dynamics; Proteins; Public Health; Recording of previous events; Recreation; Research; Resolution; Risk; Safety; Sampling; Science; Scientist; Seasons; Site; Sum; Taxonomy; Testing; Time; Toxic effect; Toxicity Tests; Toxin; Washington; Water; Work; biomarker development; biomarker identification; candidate marker; circadian; contaminated drinking water; contaminated water; exposed human population; functional group; harmful algal blooms; improved; innovation; insight; instrumentation; metagenome; metaproteomics; microbial community; microbiome; microbiome analysis; microorganism; potential biomarker; predictive marker; predictive test; prevent; programs; protein expression; response; sound; success; tool; undergraduate student; water quality; water sampling; water treatment

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.

项目摘要 有害的藻类血液（HAB）是通过污染威胁公共卫生的有毒事件 全球水质的水质。各种有毒的浮游植物物种定期在这两个沿海地区发生开花事件 和内陆水体，造成水处理设施的破坏，钓鱼和休闲行业， 与人类暴露有关的医疗费用每年收集约110亿美元。随着气候变化和 农业继续改变水化学，已经观察到开花事件发生更频繁，最后 更长，并释放更广泛的有毒化学物质。目前，没有预测盛开的方法 发作，让公众容易受到一系列可能避免的有害毒素的影响。 共享生态系统和共同发生的发展的悠久历史已经建立了密切的关系 HAB形成的浮游植物及其微生物组。细菌已被证明可以对光合作用反应 藻类的昼夜节律，模仿了代谢必要蛋白的表达中的昼夜节律。 生态系统的重大变化可能会导致蛋白质表达模式的反动变化， 可检测为单个Petides或Pepper-groups共享类似的分类学起源或功能类别。 如果在HAB开始前24小时丢失肽或一组肽的既定昼夜节律，否则 它可以用作预测即将来临的开花毒性的指标。我假设跟踪量化的 与HAB相关的微生物组的表达肽将允许我检测节奏和丧失 那些胡椒的节奏性；这些辣椒或辣椒群可以用作生物标志物 作为生物测定或预测HAB的问题开发，以更好地警告公众。 对于这个项目，我将收集围绕已知的微生物组的时间相关水样 hab形成的浮游植物伪nitzschia bian bianly in Puget Sound，华盛顿州。我的实验设计包括 与华盛顿的声音毒素计划合作进行浮游植物的高分辨率抽样 在预测的盛开事件之前2周开始每4小时的微生物组，并进行采样直至Hab-toxins 顶峰。然后，我将使用定量数据独立的采集质量分析微生物组样品 建立时间依赖性肽丰度的光谱法。这些宠物将分组 使用元高统和时间表数据注释到所有潜在的分类学和官能团中 使用纳入非参数方法的节奏分析分析。这将使我发现 来自单个胡椒的节奏性（AIM 1）和由分类单元或功能分组（AIM 2）在开花之前 事件。肽或胡椒组在开放前表现出明显变化或丧失节奏性的变化或丧失 代表潜在的生物标志物，用于未来开发基于分子肽的快速测定或探针 预测HAB事件。该项目使用元蛋白质组学方法的进步来预防有害人类 通过使用微生物组生物标志物辣椒组预测开花发作，暴露于HAB毒素。