Bacteria and material made from corn kernels can clean up PCBs in aquatic environments
Researchers funded by NIEHS demonstrated a new method to clean up aquatic ecosystems using biochar — the carbon-rich byproduct of burning plant matter — and bacteria. Their cost-effective strategy has the potential to destroy polychlorinated biphenyls (PCBs), a group of harmful chemicals that persist in sediments.
Current strategies to remove PCBs from the environment, such as excavating sediments from the bottom of aquatic ecosystems, are costly and can result in water contamination. Remediation strategies that use bacteria to break down pollutants show promise, but bacteria are unable to fully degrade PCBs in the environment. To address this challenge, the team investigated whether adding biochar to solutions with bacteria and PCBs could enhance the performance of a type of PCB-degrading bacteria called Paraburkholderia xenovorans.
The scientists tested different types of biochar, including three natural biochars — made from burning corn kernels, bamboo, and wood — and activated carbon, which is commonly used in water treatment. Next, they measured the effects of each biochar on bacterial growth, bacterial attachment to biochar particles, and expression of bacterial genes that degrade PCBs.
Imaging analysis revealed that bacteria cells attached to the corn kernel biochar in greater numbers compared to the other types of biochar. Bacterial growth was also higher in the solution with the corn kernel material. In addition, there was increased expression of bacterial genes involved in PCB degradation in the corn kernel biochar solution compared with the other materials.
These findings suggest that combining biochar made from corn kernels and PCB-degrading bacteria may provide a cost-effective strategy to clean up contaminated sediments while protecting public and ecosystem health, according to the authors.
Citation:
- Dong Q, LeFevre GH, Mattes TE. 2024. Black carbon impacts on Paraburkholderia xenovorans strain LB400 cell enrichment and activity: implications toward lower-chlorinated polychlorinated biphenyls biodegradation potential. Environ Sci Technol 58(8):3895-907.
New lab model reveals the underlying mechanisms of PM2.5-induced lung disease
NIEHS-funded researchers developed a new model to study how fine particulate matter (PM2.5) exposure may lead to respiratory disease. The new multicellular model addresses the limitations of current methods, which use only one type of lung cell and are unable to capture the biological complexity of the respiratory system.
Upon breathing in PM2.5 air pollution, tiny particles enter the lung and are deposited in the alveolar capillary region (ACR), where gas exchange occurs. This exposure is linked to respiratory disease; however, the mechanisms are not well understood.
The scientists created a model using three types of lung cells and assembled them to mimic the structure of the ACR. The model included alveolar cells, which cover the surface of the ACR; fibroblasts, which support ACR connective tissue; and endothelial cells, which form the inner lining of blood vessels within the ACR. Then, they exposed the alveolar cells to a type of PM2.5 found in diesel exhaust for 24 hours and analyzed each cell’s response.
PM2.5 altered gene expression in both alveolar cells and endothelial cells. However, endothelial cells had more gene expression changes, despite having indirect contact with the particles. Endothelial cells also developed a type of biological stress, which led them to produce proteins that cause inflammation — an indicator of respiratory disease. Further analysis revealed that a cell signaling pathway in epithelial cells, known as mitogen activated protein kinase, played a key role in the changes observed in the endothelial cells.
The study shows that changes in endothelial cells may play an important role in how PM2.5 exposure leads to lung disease, according to the authors. They also noted that models that include multiple types of lung cells can help expand our understanding of how respiratory disease develops.
Citation:
- Vitucci ECM, Simmons AE, Martin EM, McCullough SD. 2024. Epithelial MAPK signaling directs endothelial NRF2 signaling and IL-8 secretion in a tri-culture model of the alveolar-microvascular interface following diesel exhaust particulate (DEP) exposure. Part Fibre Toxicol 21(1):15.
New strategy to prioritize PFAS for health risk assessments
An NIEHS-funded team developed a screening method that uses human-derived cells to evaluate how PFAS might affect health. The new approach might help prioritize different PFAS for further testing in efforts to improve health risk assessments.
PFAS are a large group of chemicals widely used in consumer products, but the majority lack toxicity data, making risk evaluation difficult. The most widely accepted approach to assess large numbers of PFAS organizes the chemicals based on structural similarities and then selects a few representative compounds for further testing.
In this study, the team explored a different approach using liver and heart cells grown in a lab and exposing them to 26 different PFAS. They looked at how the chemicals affected cell function and gene expression.
PFAS had minimal effect on liver cell function. In contrast, exposure to eight of the 26 compounds resulted in decreased beating frequency in heart cells. Genetic expression analysis of liver cells showed increased activity in genes that regulate stress and cellular structure, but decreased activity in genes that break down fats. In heart cells, PFAS exposure decreased the expression of genes related to how the heart contracts.
To compare their approach to the traditional structure-based grouping method, the team looked for associations between PFAS molecular weight or chemical structure and the observed biological effects. They found no structural similarities among compounds with similar biological effects.
These results suggest that grouping PFAS by structure alone might not adequately predict individual chemicals’ health effects, according to the authors. Their strategy could guide researchers and policymakers in determining which chemicals to prioritize for future evaluation.
Citation:
- Tsai HD, Ford LC, Chen Z, Dickey AN, Wright FA, Rusyn I. 2024. Risk-based prioritization of PFAS using phenotypic and transcriptomic data from human induced pluripotent stem cell-derived hepatocytes and cardiomyocytesSDGs, Targets, and Indicators
1. Which SDGs are addressed or connected to the issues highlighted in the article?
- SDG 3: Good Health and Well-being
- SDG 6: Clean Water and Sanitation
- SDG 11: Sustainable Cities and Communities
- SDG 12: Responsible Consumption and Production
2. What specific targets under those SDGs can be identified based on the article’s content?
- SDG 3.9: By 2030, substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water, and soil pollution and contamination.
- SDG 6.3: By 2030, improve water quality by reducing pollution, eliminating dumping, and minimizing release of hazardous chemicals and materials.
- SDG 11.6: By 2030, reduce the adverse per capita environmental impact of cities, including by paying special attention to air quality and municipal and other waste management.
- SDG 12.4: By 2020, achieve the environmentally sound management of chemicals and all wastes throughout their life cycle, in accordance with agreed international frameworks, and significantly reduce their release to air, water, and soil in order to minimize their adverse impacts on human health and the environment.
3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?
- Indicator for SDG 3.9: Number of deaths and illnesses attributed to hazardous chemicals and air, water, and soil pollution and contamination.
- Indicator for SDG 6.3: Proportion of bodies of water with good ambient water quality.
- Indicator for SDG 11.6: Annual mean levels of fine particulate matter (PM2.5) in cities.
- Indicator for SDG 12.4: Number of countries implementing the Globally Harmonized System of Classification and Labelling of Chemicals (GHS).
Table: SDGs, Targets, and Indicators
SDGs Targets Indicators SDG 3: Good Health and Well-being Target 3.9: By 2030, substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water, and soil pollution and contamination. Indicator: Number of deaths and illnesses attributed to hazardous chemicals and air, water, and soil pollution and contamination. SDG 6: Clean Water and Sanitation Target 6.3: By 2030, improve water quality by reducing pollution, eliminating dumping, and minimizing release of hazardous chemicals and materials. Indicator: Proportion of bodies of water with good ambient water quality. SDG 11: Sustainable Cities and Communities Target 11.6: By 2030, reduce the adverse per capita environmental impact of cities, including by paying special attention to air quality and municipal and other waste management. Indicator: Annual mean levels of fine particulate matter (PM2.5) in cities. SDG 12: Responsible Consumption and Production Target 12.4: By 2020, achieve the environmentally sound management of chemicals and all wastes throughout their life cycle, in accordance with agreed international frameworks, and significantly reduce their release to air, water, and soil in order to minimize their adverse impacts on human health and the environment. Indicator: Number of countries implementing the Globally Harmonized System of Classification and Labelling of Chemicals (GHS). Copyright: Dive into this article, curated with care by SDG Investors Inc. Our advanced AI technology searches through vast amounts of data to spotlight how we are all moving forward with the Sustainable Development Goals. While we own the rights to this content, we invite you to share it to help spread knowledge and spark action on the SDGs.
Fuente: factor.niehs.nih.gov
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