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 有害藻华（HAB）是一种重复发生的有毒事件，通过污染威胁公共健康 全球水质。各种有毒浮游植物物种定期在沿海地区发生水华事件 和内陆水体，对水处理设施、渔业和娱乐业造成严重破坏， 每年与人类接触相关的医疗费用约为110亿美元。随着气候的变化， 农业继续改变水化学，水华事件已被观察到更频繁地发生， 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潜在可避免的有害毒素的影响。 共享的生态系统和共同发生的进化的悠久历史已经建立了密切的关系， 形成赤潮的浮游植物及其微生物组。细菌已经被证明能对光合作用产生反应， 藻类的昼夜节律，模仿代谢必需蛋白质表达的昼夜模式。 生态系统的重大变化可能会导致蛋白质表达模式的反应性变化， 可检测为共享相似分类学来源或功能类别的单个肽或肽组。 如果一种肽或一组肽的已建立的昼夜节律性在HAB开始前>24小时丧失， 它可以作为一个指标来预测即将发生的水华毒性。我假设追踪量化的 HAB相关微生物组的表达肽将使我能够检测到节律性和 这些肽的节律性;这些肽，或肽组，可以作为生物标志物， 作为生物测定或探针，用于预测有害藻华，以更好地警告公众。 在这个项目中，我将收集已知环境周围微生物组的随时间变化的水样。 HAB形成浮游植物Pseudo-nitzschia每年两次在普吉特湾，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，对浮游植物进行高分辨率采样 在预测的水华事件之前2周开始，每4小时进行一次微生物组采样，直到产生赤潮毒素 峰然后，我将使用定量数据独立采集质量分析微生物组样本， 光谱分析方法以建立时间依赖性肽丰度。这些肽将被分组， 将使用MetaGO和时间过程数据注释到所有潜在的分类和功能组中， 使用节奏性分析非参数方法进行分析。这样我就能检测出 开花前来自单个肽（AIM 1）和按分类群或功能分组的肽（AIM 2）的节律性 活动在水华发生之前表现出节律性的显著变化或丧失的肽或肽组 代表了未来开发基于分子肽的快速测定或探针的潜在生物标志物， 预测赤潮事件该项目利用元蛋白质组学方法的进步，以防止有害的人类 通过使用微生物组生物标志物肽组预测水华开始，暴露于HAB毒素。