Method found to forecast ocean’s harmful blooms of algae

Researchers have discovered a way to predict harmful algal blooms in oceans up to three days in advance. Such forecasts could be made by checking bacterial communities in the water for early biochemical warning signs.  

The study is the first to show that microbial proteins can be examined to foresee a harmful algal bloom. The findings were published in the journal Nature Communications. 

"We found that the ocean's bacteria can detect small changes in the ecosystem which can serve as warning signals, if we know how to interpret them,” said the project’s senior scientist, Brook Nunn, a professor of genome sciences at the University of Washington School of Medicine in Seattle.  

Harmful algal blooms refer to the rapid and excessive growth of algae in aquatic environments.  They can pose a threat to people, fish, shellfish, marine mammals and the environment. While their cause is still unknown, these blooms are becoming more frequent and are appearing in more bodies of water. 

These overgrowths become a hazard by producing toxins that can cause neurotoxic shellfish poisoning, and by creating so much biomass that, when the bloom dies and bacteria break it down, the resulting oxygen consumption leaves large zones in the water where oxygen is absent. 

"In the past,” Nunn said, “people were informed about a harmful algal bloom only after toxins were already in the water or detected in the shellfish. Often this was too late. This research flips the script by offering a way to see the bloom coming before it plagues the coastline." 

In studying bloom initiation, Nunn’s team ignored the harmful algae collected and instead focused examinations on marine bacteria that surrounded the algae. The researchers collected ocean water samples every 4 hours for 22 days off the Washington coast, generating over 4,000 samples.   

In addition to water-chemistry studies, the team combined a variety of advanced biotechnologies to understand gene expression and regulation, protein and peptide production, and byproducts released into the environment. The scientists were able to track ecosystem changes in real time.  

Because two naturally occurring Chaetocerous algal-bloom events began while they were onsite, the scientists discovered and validated pre-bloom biomarkers. After identifying peptide biomarker candidates that aligned with the first bloom, they went back to their sample set and tested those peptides in the second bloom event.   

The study’s lead investigator, Miranda Mudge, a recently minted Ph.D. student in genome sciences, determined that 12 peptide biomarkers forecast both bloom events. 

“These biomarkers we found can inform us when a harmful algal bloom is coming, 24 to 72 hours in advance of the standard approaches now used,” Mudge explained. Instead of waiting until toxins are found in shellfish or water mid-bloom, scientists could identify microbiome signals before the bloom becomes dangerous, she said.  

The findings have the potential to lead to the first tool to protect public health and coastal economies from harmful algal blooms by anticipating their occurrence. Such a timely alert could help fisheries, hatcheries, state fish and wildlife agencies, and health departments take precautions before people get sick or seafood harvests need to be discarded.  

Conducting research in a region where fishing, tourism and seafood processing are vital, Nunn hopes to invigorate the stagnant field of harmful algal blooms and improve ecosystem resilience. 

The team includes marine molecular biologist Emma Timmins-Schiffman, biostatistician and software developer Michael Riffle, computational biologist and machine learning specialist Bill Noble, and phytoplankton ecologists Julia Kubanek and Gabi Chebli.  

The work was funded by the NIH’s National Institute of Environmental Health Sciences (R21ESO34337, F31 ES032733-01A1), the UW Royalty Research Foundations, the National Science Foundation (OCE 2401644OCE 2401645, OCE 2401646) and the UW Proteomics Resource (UWPR95794). 

Written by Leila Gray

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