Recent research suggests that common chemical byproducts produced by car tire failure can interfere with the molecular machinery of human brain cells and increase the risk of Alzheimer’s disease. Scientists used advanced computational models to map how this environmental pollutant interacts with specific genes and proteins associated with neurodegeneration. The research results were published in a magazine open medicine.
To make tires more durable and prevent cracking, manufacturers add a chemical known as 6PPD. When the tiny particles of tire rubber wear down on the road and react with ozone gas in the air, they transform into a new chemical called 6PPD-quinone. This secondary pollutant washes into waterways, is deposited in road dust, and is frequently detected in human urine and blood samples.
Previous studies have shown that 6PPD-quinone is highly toxic to certain aquatic organisms such as salmon. It also has the ability to cross the blood-brain barrier in mice. The blood-brain barrier is a highly selective membrane that protects the brain from harmful substances in the bloodstream. The ability of a pollutant to evade this shield provides evidence that it can directly impact human brain health.
The molecular structure of 6PPD-quinone suggests that 6PPD-quinone can cause oxidative stress, a condition in which unstable oxygen molecules damage cellular structures. It also tends to cause neuroinflammation, a condition of chronic swelling of the brain and overactivity of the immune system. Oxidative stress and neuroinflammation are both hallmarks of Alzheimer’s disease, the most common form of dementia worldwide.
This chemical can reach the brain and cause cell damage similar to that that occurs in dementia, so researchers Chun Zhang from Chongqing Three Gorges Medical University and Jingqi Zhang from Chengdu University of Traditional Chinese Medicine wanted to map the exact molecular pathway that connects the two. They designed a study to systematically investigate how human genetics and protein function change due to exposure to this particular tire pollutant.
The researchers started by predicting which human proteins 6PPD-quinone might interact with in the body. They used three different pharmacological databases to gather a list of potential chemical targets. Through this software, they identified more than 100 potential interaction points within human biology.
Next, they assembled a vast catalog of genes associated with Alzheimer’s disease from clinical and genetic databases. By comparing the list of chemical targets with the list of disease genes, the authors identified 92 overlapping targets. This overlap indicates a common biological network between environmental pollutants and neurodegenerative diseases.
To narrow down this list, scientists built a protein-protein interaction network. This type of analysis maps how different proteins communicate and work together within a cell. By filtering out less active proteins, they isolated 23 core target genes that act as central communication hubs.
The authors then analyzed where these 23 core genes were most active in the human body. They found that gene expression was enriched in specific brain regions, such as the cerebral cortex and basal ganglia. These are areas deeply involved in memory and movement, and are notoriously vulnerable to Alzheimer’s disease.
To see how these specific genes function in real patients, the researchers examined two genetic datasets containing postmortem brain tissue samples. The initial dataset included 12 Alzheimer’s disease patients, 10 elderly healthy controls, and 8 young healthy controls. The second dataset included 44 patients with the disease and 46 healthy controls.
They found that the expression levels of core genes were significantly altered in diseased brains. Genes involved in managing inflammation and cell damage were abnormally activated or suppressed compared to healthy brain samples. This provided real-world biological support for the computer-generated predictions.
The researchers then trained an artificial intelligence model using genetic data from 90 individuals in the second dataset. They wanted to identify specific genes that could best predict whether a brain sample came from an Alzheimer’s patient or a healthy person. The machine learning model identified five specific genes as the strongest diagnostic predictors.
Among these top predictors were NFKB1, which manages the body’s inflammatory response, and a gene known as NFE2L2, which normally protects cells from oxidative damage. The model also focused on kinase genes, which produce enzymes that act like control switches for cell behavior. In neurodegenerative conditions, defects in kinase switches can lead to dangerous tangles of proteins within brain cells.
To investigate causal relationships, the scientists analyzed genetic data from 488,285 people using a technique called Mendelian randomization. This method uses natural genetic variation to determine whether a particular biological factor directly causes disease. This analysis suggested that genetic mutations that alter the activity of the NFKB1 gene in the brain may directly impact a person’s risk of developing Alzheimer’s disease.
The authors also used a computational technique called molecular docking to simulate how 6PPD-quinone molecules physically fit into these specific human proteins. Proteins have complex three-dimensional shapes with specific pockets, and molecular docking tests how well foreign molecules fit into those pockets. Computer simulations have shown that tire pollutants can bind tightly to some core proteins, preventing them from performing their normal functions.
Finally, the scientists ran computer simulations on 163,824 individual brain cells to see what would happen if these important genes were disrupted. They focused on microglia, the central nervous system’s main immune cells. These cells act as the brain’s garbage collectors, cleaning out damaged cells and managing local immune responses.
The simulations predicted that interfering with these core genes disrupts the way microglia generate energy and respond to cell damage. This predicted disruption was particularly pronounced in cells that simulated the Alzheimer’s disease environment. The findings suggest that pollutants can paralyze the brain’s natural cleansing ability and worsen existing neurological problems.
This study provides a detailed theoretical framework for how common environmental pollutants negatively impact the brain, but relies primarily on computational predictions and existing datasets. The researchers point out that computer simulations of chemical bonds do not guarantee that the exact same biological reactions will occur in a living human body. Experimental tests are required to confirm physical interactions.
Another limitation is the reliance on brain tissue samples from patients with late-stage Alzheimer’s disease. These samples represent the final stage of a long disease process and may mask earlier molecular changes caused by initial exposure to pollutants. In real-world scenarios, it is difficult to determine exactly when a chemical starts to change brain chemistry.
The authors suggest that future studies should include laboratory experiments using living cells and animal models. Scientists need to expose animals to low doses of 6PPD quinone over long periods of time to see if the chemical consistently crosses the blood-brain barrier and causes these specific genetic changes. This would provide concrete evidence of the contaminant’s neurotoxicity.
Epidemiological studies are also needed to track human exposure to tire pollution over long periods of time. Tracking populations heavily exposed to road pollution could help determine whether daily contact with this chemical actually leads to increased rates of dementia. Such studies will help public health officials understand the true extent of the risk.
The study, “6PPD-quinone exposure and Alzheimer’s disease: Insights from integrated network pharmacology, transcriptomics, machine learning, and molecular docking,” was authored by Chun Zhang and Jingqi Zhang.
