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

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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.