# Developing a predictive understanding of harmful cyanbacteria growth, toxins production and comparative toxicity across environmentally important gradients of n:p and salinity

> **NIH NIH P01** · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · 2022 · $173,151

## Abstract

PROJECT SUMMARY
This project will specifically support achieving the overarching goal of the University of South Carolina Center
(USC Center) and its overall Specific Aim, which includes assessing the effects of climate change (through
alterations in temperature, salinity, pH and biogeochemical cycling of trace metals and microplastics) on the
antibiotic resistance and/virulence of Vibrio bacteria and the growth and toxins production by cyanobacteria
that adversely affect drinking water, contact recreation and seafood safety exposure to humans, which may
lead to increases in Vibrio infections, increased inflammation and disease (e.g., Non Alcoholic Liver Disease)
in humans. Though we have known for decades that nutrient enrichment of surface waters can lead to
excessive algal growth, including the development of harmful algal blooms (HABs), the causes and
consequences of toxins produced by these blooms has recently received heightened attention from
environmental public health practitioners. Nutrient enrichment, primarily from phosphorus (P) and nitrogen (N),
increases the frequency and magnitude of blooms along the freshwater to marine continuum. However, less is
known about how the stoichiometric interactions between N and P across environmentally relevant gradients,
particularly in combination with salinity, may influence the growth, toxins production and comparative toxicity of
cyanobacteria HABs. Climate change can affect incidents of HABs and salinity, which can be altered by both
changes in precipitation (droughts or floods) and sea level rise. Whereas ecological studies and monitoring
activities have previously examined “toxicity,” these efforts are routinely limited by absence of robust analytical
quantitation of diverse toxins produced by specific HAB species and comparative toxicity exerted through
multiple mechanisms of action including major alterations in water quality conditions resulting in differential
risks to human health and ecosystems. This represents a critical consideration for management of water
resources and protection of human health because algae growth does not necessarily predict toxins
production, yet routine monitoring and surveillance activities, an essential environmental public health service,
when these efforts do exist, use microscopic methods for cyanobacteria and thus do not quantify the presence
of toxins. If toxins analysis occurs, it most commonly uses ELISA techniques to check for presence of
microcystins. Further, commonly used water quality models lack inputs for toxins production, which inherently
limits predictive capacity of HAB events. Some species of cyanobacteria have evolved unique adaptations to
promote their growth under N-deficient conditions, but it remains unknown whether or not these traits actively
exist simultaneously with toxins production. Developing predictive growth, toxins production and comparative
toxicity models, proposed through the Specific Aims of this project, for cyanobacter...

## Key facts

- **NIH application ID:** 10443653
- **Project number:** 5P01ES028942-05
- **Recipient organization:** UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA
- **Principal Investigator:** BRYAN WILLIAM BROOKS
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $173,151
- **Award type:** 5
- **Project period:** 2018-09-30 → 2024-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10443653

## Citation

> US National Institutes of Health, RePORTER application 10443653, Developing a predictive understanding of harmful cyanbacteria growth, toxins production and comparative toxicity across environmentally important gradients of n:p and salinity (5P01ES028942-05). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10443653. Licensed CC0.

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