Wastewater surveillance tracks trends in COVID-19

Monitoring wastewater can be useful for tracking COVID-19 disease trends, according to researchers funded in part by NIEHS. Current strategies to monitor infections have limitations, such as lack of data on asymptomatic cases. The authors’ goal was to assess whether wastewater surveillance can complement clinical testing to track disease trends more accurately.

The team aimed to detect and quantify genetic material called RNA from SARS-CoV-2 — the virus that causes COVID-19 — in wastewater. For their analysis, they used wastewater samples collected in 2020 from two North Carolina wastewater treatment plants: one serving a larger population in Orange County and one serving a smaller population in Chatham County.

The researchers found evidence of SARS-CoV-2 RNA in all wastewater samples from the plant serving a larger population and in 79% of samples from the smaller plant. They also compared their results to COVID-19 cases in the respective counties and found that wastewater data from the larger plant generally correlated with and even predicted the trends in reported COVID-19 cases. Using data from the larger plant, they identified a notable spike in viral RNA preceding a spike in cases when students returned to a college campus in Orange County.

Wastewater data from the smaller plant did not match COVID-19 case data as accurately as the larger plant. Given that this plant only serves a subset of the county population, it may provide an inadequate geographic resolution to reliably predict county-level cases, the scientists noted.

According to the authors, findings from this study show that wastewater surveillance may be instrumental for identifying outbreaks, tracking disease trends, allocating resources, and evaluating policy during pandemics.

Citation: Grube AM, Coleman CK, LaMontagne CD, Miller ME, Kothegal NP, Holcomb DA, Blackwood AD, Clerkin TJ, Serre ML, Engel LS, Guidry VT, Noble RT, Stewart JR. 2023. Detection of SARS-CoV-2 RNA in wastewater and comparison to COVID-19 cases in two sewersheds, North Carolina, USA. Sci Total Environ 858(Pt 3):159996.

Geospatial analysis shows disproportionate exposure to arsenic and uranium across the U.S.

NIEHS-funded scientists found that higher proportions of people belonging to racial and ethnic minorities in the U.S. are associated with significantly higher arsenic and uranium concentrations in their drinking water compared with non-Hispanic White residents.

The team used geospatial models to analyze how the composition of racial and ethnic groups in various communities relates to concentrations of arsenic and uranium in respective water systems. For their analysis, the researchers examined county-level data across the U.S. from 2000 to 2011.

They found that nationwide, communities with more Hispanic, Latino, American Indian, and Alaskan Native residents were associated with significantly higher water arsenic and uranium concentrations at the county level. Communities with higher proportions of non-Hispanic White residents were associated with lower arsenic and uranium concentrations.

The researchers noted that inequalities in public water exposures may be more likely in geographic regions with both a high percentage of racial and ethnic minority groups and high concentrations of the contaminants. For example, in urban areas in the southwestern U.S, where arsenic and uranium concentrations are high, a higher proportion of non-Hispanic Black residents was associated with higher concentrations of these contaminants in community water systems.

Findings from this study can inform federal and state infrastructure investments in public water systems that serve communities disproportionately exposed to drinking water contaminants, potentially reducing exposures and preventing associated health outcomes, the authors noted.

Citation: Martinez-Morata I, Bostick BC, Conroy-Ben O, Duncan DT, Jones MR, Spaur M, Patterson KP, Prins SJ, Navas-Acien A, Nigra AE. 2022. Nationwide geospatial analysis of county racial and ethnic composition and public drinking water arsenic and uranium. Nature Communications 13(1):7461.

Grouping wildfire exposures for improved health risk assessments

NIEHS-funded researchers developed a computer-based approach to group wildfire exposure conditions based on their effect on genetic expression and potential health risks.

Beyond destroying property and livelihoods, wildfires can also affect human health. Characterizing health risks can be challenging because wildfire conditions vary widely. For instance, different types of organic matter, or biomass, can fuel fires, and biomass can burn at different temperatures. For their study, the team wanted to see how different fuel and combustion conditions affect gene activity in the lung.

The scientists exposed mice to smoke produced from burning biomass, including eucalyptus, peat, pine, pine needles, or red oak species under high-temperature combustion, or flaming, and low-temperature combustion, or smoldering. To find which genes were altered during combustion, they used a technique called transcriptomics, which measures gene activity, in lung tissues. Then, they used computer modeling to see which wildfire scenarios led to similar changes in gene expression.

The team identified four exposure clusters based on genetic activity. For example, flaming peat caused the greatest changes in gene expression. By contrast, smoldering red oak and peat caused the fewest.

They also compared their groupings to changes in biological signs, or biomarkers, associated with lung injury and found that genetic changes in each grouping matched changes in biomarkers. For example, the flaming peat cluster was associated with lipopolysaccharide, a molecule known to induce lung inflammation.

This new approach, according to the authors, provides a framework that can help inform which wildfire exposure conditions may be grouped for risk assessment strategies, to ultimately protect public health.

Citation: Koval LE, Carberry CK, Kim YH, Mcdermott E, Hartwell H, Jaspers I, Gilmour MI and Rager JE. 2022. Wildfire variable toxicity: identifying biomass smoke exposure groupings through transcriptomic similarity scoring. Environ Sci Technol 56(23):17131–17142.

Validating cell-based approaches for analyzing risk of exposure to industrial chemicals TCE and PCE

Cell-based experiments can provide relevant estimates of trichloroethylene (TCE) and tetrachloroethylene (PCE) metabolism in humans and associated health risks, found NIEHS-funded researchers. Their goal was to resolve uncertainties in data used by the U.S. Environmental Protection Agency (EPA) for health risk assessments.

TCE and PCE are frequently used to dissolve grease in industrial operations. Exposure to these chemicals is associated with numerous health risks, including cancer. How the body metabolizes these chemicals plays a critical role in eliciting such health effects. Indeed, most organ-specific toxicity has been attributed to the substances produced as the contaminants break down in the body, which are called metabolites.

In this study, the team characterized TCE and PCE metabolism using several liver cell culture models, including a new model called MPCC, which consists of human, rat, and mouse liver cells. They aimed to assess which model was most effective at quantifying S-(1,2-dichlorovinyl)glutathione and S-(1,2,2-trichlorovinyl)glutathione, which are metabolites of TCE and PCE respectively. Formed in the kidneys, these metabolites are known for their ability to damage cells’ genetic information.

Compared with all liver cell models studied, data derived from MPCC were most consistent with estimates from animal studies, the authors found. For TCE, the new data provided more information about its effects on the kidney, which is critical for EPA’s toxicity assessments. For PCE, the data helped explain the extent of PCE metabolism in humans and update several EPA kidney-specific toxicity thresholds.

These results suggest that MPCCs can provide relevant estimates of TCE and PCE metabolism, thereby improving how EPA characterizes exposure risk, according to the authors.

Citation: Valdiviezo A, Brown GE, Michell AR, Trinconi CM, Bodke VV, Khetani SR, Luo YS, Chiu WA and Rusyn I. 2022. Reanalysis of trichloroethylene and tetrachloroethylene metabolism to glutathione conjugates using human, rat, and mouse liver in vitro models to improve precision in risk characterization. Environ Health Perspect 130:117009.