Environmental factor – June 2022: Extramural documents of the month

New approach paves the way for large-scale studies of genome integrity

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 a single DNA building block and its complement are added, deleted or changed during replication. Linked to various diseases, including cancer, point mutations have been 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, an amplification process creates long single-stranded DNA molecules that contain multiple copies of each DNA fragment bound together. These copies are independent replicas of the original DNA fragment, reducing the risk of error propagation. Then, the long single-stranded DNAs are individually amplified and converted into a sequencing library.

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

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

Quote: Maslov AY, Makhortov S, Sun S, Heid J, Dong X, Lee M, Vijg J. 2022. Quantitative single-molecule 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 leverage machine learning to predict the biological age of the abdomen from magnetic resonance images (MRIs) of the liver and pancreas. Unlike chronological age, biological age can be modified by lifestyle habits and our environment. By predicting abdominal age and identifying risk factors for accelerated aging, the team hoped to reveal clues to delay 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 MRIs of the liver and 36,784 MRIs of the pancreas collected from UK Biobank participants aged 37 to 82. Next, they investigated whether 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 involving genetics, clinical attributes, disease, and environmental and socioeconomic factors. For example, the predictions were based on anatomical features of the liver and pancreas as well as their surrounding organs and tissues. They also identified that the EFEMP1 gene, markers linked to poor liver and metabolic function, and poor general health were associated with increased abdominal aging, as were sedentary behavior, diet, and smoking. The reverse was true for higher socioeconomic status.

According to the authors, their approach can be used to assess abdominal aging or the effectiveness of rejuvenating therapies. They suggested that the genes they identified could point to new therapeutic gene targets and new tools to study causation.

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

New genetic sensor traces environmental genotoxic 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 neurodegeneration linked to Parkinson’s disease (PD).

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

The sensor exploits the properties of a virus often used in gene therapy. Host cells fight off the viral genetic sensor using DNA damage response pathways, allowing 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 DNA damage repair in cells.

The team tested the efficiency and sensitivity of the sensor to detect genetic toxicity in mice treated with paraquat, an herbicide associated with PD risk; mice engineered to overexpress a protein known to be involved in the onset and progression of PD; and the brain of patients with PD.

Paraquat exposure increased genetic toxicity of neurons. Neurons involved in dopamine transmission in the brain were most affected in cells, mice and patients with PD. Loss of dopamine is a hallmark of PD. Neurons have undergone subtle structural and cellular changes that may increase their vulnerability and affect their function prior to cell death.

According to the researchers, PRISM successfully tagged genetic stress in neurons and could offer a useful tool to better understand the underlying mechanisms by which environmental factors lead to neurodegeneration and explore new therapies.

Quote: El-Saadi MW, Tian X, Grames M, Ren M, Keys K, Li H, Knott E, Yin H, Huang S, Lu XH. 2022. Tracing brain genotoxic stress in Parkinson’s disease with 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 adolescents, according to NIEHS-funded researchers. Working memory is the ability to hold information in one’s mind and manipulate it mentally. Although prenatal exposure to individual chemicals may negatively affect working memory in children, few studies have explored the association of co-exposure to multiple chemicals with this outcome in adolescence, a period where working memory develops considerably.

Researchers assessed prenatal exposure to individual chemicals and their admixture in relation to working memory in 373 adolescents living near a Superfund site in New Bedford, Massachusetts. Specifically, they compared dichlorodiphenyldichloroethylene (DDE), hexachlorobenzene (HCB), and 51 polychlorinated biphenyls measured in cord serum, and lead and manganese measured in cord blood with verbal and symbolic working memory. Their statistical analysis also looked for differences between men and women and between groups with greater or lesser social disadvantage.

The team found poorer verbal working memory in adolescents most exposed to manganese and the chemical mixture. There were no significant differences between men and women, but greater social disadvantage during prenatal development combined with higher exposure to HCB and DDE worsened working memory scores.

Given that working memory undergoes considerable development during adolescence and that impairments may be associated with psychiatric and behavioral disorders, further research should examine the effect of environmental exposures on working memory in this group of children. age, as well as social and economic stressors that can alter susceptibility, according to the team.

Quote: Oppenheimer AV, Bellinger DC, Coull BA, Weisskopf MG, Korrick SA. 2022. Prenatal exposure to chemical mixtures and working memory in adolescents. Approximately Res 205:112436.

(Adeline Lopez is a science writer for MDB Inc., a contractor to the NIEHS Division of Extramural Research and Training.)

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