Environmental Factor – June 2022: Leaves of the Month Outside the Walls

New approach paves the way for large-scale genome safety studies

NIEHS-funded researchers have developed a high-throughput approach, called single-molecule mutation sequencing (SMM-seq), to characterize point mutations in normal cells. Point mutations occur when one building block of DNA and its complements are added, deleted, or changed during replication. Associated with a variety of diseases, including cancer, point mutations are difficult to study because they can be unique to each cell and occur at low frequencies.

SMM-seq includes a two-step library preparation protocol. First, the amplification process creates long, single-stranded DNA molecules that contain multiple copies of each of the DNA segments strung together. These copies are independent replicas of the original DNA fragment, thus reducing the possibility of errors being propagated. Then, single-stranded long DNA is amplified and converted into a sequencing library.

During this step, the team provided unique molecular identifiers for each end of the DNA. These identifiers allowed the team to identify matches to the original DNA fragment, filter out inherited mutations, and identify new mutations when results were compared against a single nucleotide polymorphism database. They performed proof-of-principle tests to detect age-related mutations and those following low-dose exposure to a compound known to cause mutations.

According to the authors, SMM-seq can detect naturally induced and acquired point mutations in normal cells and tissues with high accuracy while being significantly more cost-effective than conventional methods. Combined with the structural variant search assay, this method is well suited for comprehensively assessing genome integrity in large-scale human studies, according to the researchers.

the quote: Maslov AY, Makhortov S, Sun S, Heid J, Dong X, Lee M, Vijg J. 2022. Single Molecule, Quantitative Detection of Low-abundance Somatic Mutations by High-throughput Sequencing. Sci Adv 8 (14): eabm3259.

Leveraging deep learning to predict abdominal age and prevent disease

NIEHS-funded researchers have developed a new approach to take advantage of machine learning to predict biological abdominal age from magnetic resonance images (MRIs) of the liver and pancreas. Unlike chronological age, biological age can be altered by lifestyle habits and our environment. By predicting abdominal age and identifying risk factors for accelerating aging, the team hopes to uncover evidence for delaying the onset of age-related diseases, such as fatty liver disease and type 2 diabetes.

The team built a predictor of abdominal age by training a sophisticated machine learning method on 45,552 liver MRIs and 36,784 pancreatic MRIs collected from UK Biobank participants aged 37 to 82. Then they looked to see if certain genes, genetic variants, biomarkers, diseases, or environmental and socioeconomic variables were associated with accelerated abdominal aging.

The team reported that abdominal age is a complex trait that includes genes, clinical characteristics, disease, and environmental, social and economic factors. For example, predictions were driven by anatomical features in both the liver and pancreas as well as the surrounding organs and tissues. They also identified that the EFEMP1 gene, markers related to impaired liver and metabolic function, and poor general health were associated with increased abdominal aging, as well as sedentary behaviour, diet and smoking. The opposite was true for higher socioeconomic status.

According to the authors, their approach can be used to assess abdominal aging or the effectiveness of regenerative treatments. They suggested that the genes they identified may point to new therapeutic genetic targets and new tools for studying causation.

the quote: Le Goallec A, Diai S, Collin S, Prost JB, Vincent T, Patel CJ. 2022. Using deep learning to predict abdominal age from MRI images of the liver and pancreas. Nat Common 13 (1): 1979.

New genetic sensor tracks genotoxic environmental stress associated with Parkinson’s disease

NIEHS-funded researchers have designed a genetic sensor, called PRISM, to detect the DNA damage response in brain cells and visualize the neurodegeneration associated with Parkinson’s disease (PD).

The DNA damage response pathway allows brain cells to detect and repair DNA damage, but persistent genotoxic stress to brain cells leads to excessive activation of the pathway, leading to premature cell aging and cell death associated with neurodegeneration.

The sensor takes advantage of the properties of a virus that is often used in gene therapy. Host cells fight the viral genetic sensor using DNA damage response pathways, enabling the team to trace the fate of neurons exposed to genotoxic stress. It also uses a genetic marker with high mutation rates as an indicator of genetic instability, allowing researchers to explore the repair of DNA damage in cells.

The team tested the efficacy and sensitivity of the sensor to detect genotoxicity in mice treated with paraquat, an herbicide associated with PD risk; mice modified to overexpress a protein known to be involved in the onset and progression of Parkinson’s disease; The brains of Parkinson’s disease patients.

Exposure to paraquat increased genotoxicity in neurons. Neurons involved in dopamine transmission in the brain were the most damaged in cells, mice, and patients with Parkinson’s. Loss of dopamine is a hallmark of Parkinson’s disease. The neurons had subtle structural and cellular changes that might increase their vulnerability and affect function before cell death.

According to the researchers, PRISM successfully classifies genetic stress in neurons and may provide a useful tool for further understanding the underlying mechanisms by which environmental factors lead to neurodegeneration and to explore new treatments.

the quote: El-Saadi MW, Tian X, Grames M, Ren M, Keys K, Li H, Knott E, Yin H, Huang S, Lu XH. 2022. Tracing genotoxic stress in the brain in Parkinson’s disease using a novel single-cell genetic sensor. Sci Adv 8 (15): eabd1700.

Prenatal exposure to chemical mixtures worsens working memory in adolescents

Prenatal exposure to chemical mixtures worsens working memory in teens, NIEHS-funded researchers said. Working memory is the ability to hold information in one’s mind and mentally manipulate it. Although prenatal exposure to individual chemicals may negatively affect working memory among children, few studies have explored the association of co-exposure to multiple chemicals with this outcome in adolescence, which is the time when working memory is most highly developed.

Researchers evaluated prenatal exposure to individual chemicals and their combinations in relation to working memory among 373 adolescents living near the Superfund site in New Bedford, Massachusetts. Specifically, they compared dichlorodichloroethylene (DDE), hexachlorobenzene (HCB), and PCB-51 measured in cord serum, and Pb and Mn measured in cord blood with verbal and symbolic working memory. Their statistical analysis also looked for differences between males and females and between groups with the highest or lowest social harm.

The team found worse verbal working memory among teens who had greater exposure to manganese and the chemical mixture. There were no significant differences between males and females, but greater social deprivation during prenatal development as well as higher exposure to HCB and DDE worsened working memory scores.

Given that working memory undergoes significant development during adolescence and that deficiencies may be associated with psychological and behavioral disorders, further research should be conducted to study the impact of environmental exposure on working memory in this age group, as well as socioeconomic stresses that may alter susceptibility, according to the team.

the quote: Oppenheimer AV, Bellinger DC, Kol BA, Weiskopf MG, Korek SA. 2022. Prenatal exposure to chemical mixtures and working memory among adolescent girls. Environment Res 205: 112436.

(Adeline Lopez is a science writer at MDB Inc., a contractor with the NIEHS Division of Research and Training Beyond the Walls.)