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

NIEHS-funded researchers 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 building block of DNA and its complement are added, deleted, or changed during replication. Linked to a variety of diseases, including cancer, point mutations have been difficult to study because they can be unique for 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 strung together. These copies are independent replicas of the original DNA fragment, reducing potential for errors to spread. Then, the long single-stranded DNA 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 polymorphisms database. They carried out proof-of-principle tests to detect both age-associated mutations and those following low-dose exposure to a compound known to cause mutations.

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

Citation: 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, prevent disease

NIEHS-funded researchers developed a new approach to leverage 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 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 an abdominal age predictor by training a sophisticated machine learning method on 45,552 liver MRIs and 36,784 pancreas MRIs collected from UK Biobank participants aged 37-82 years old. Then they looked to see 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, predictions were driven by anatomical features in both liver and pancreas as well as their surrounding organs and tissues. They also identified that the gene EFEMP1, markers related to poor liver and metabolic function, and poor general health were associated with increased abdominal aging, as were sedentary behavior, 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 rejuvenating therapies. They suggested that the genes they identified may point to new therapeutic gene targets and new instruments to study causality.

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

Novel genetic sensor traces environmental genotoxic stress associated with Parkinson’s disease

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

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

The sensor leverages the properties of a virus often used in gene therapy. Host cells fight against 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 the researchers to explore DNA damage repair in cells.

The team tested the effectiveness and sensitivity of the sensor to detect genetic toxicity 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 PD; and the brains of patients with PD.

Exposure to paraquat heightened genetic toxicity in neurons. Neurons involved with dopamine transmission in the brain were most affected in cells, mice, and patients with PD. Loss of dopamine is a hallmark of PD. Neurons had subtle structural and cellular changes that may increase their vulnerability and affect function before cell death.

According to the researchers, PRISM successfully labeled genetic stress in neurons and may offer a useful tool for further understanding the underlying mechanisms by which environmental factors lead to neurodegeneration and exploring new therapies.

Citation: 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 keep information in one’s mind and mentally manipulate it. Although prenatal exposure to individual chemicals may adversely affect working memory among children, few studies have explored the association of co-exposure to multiple chemicals with this outcome in adolescence, a time when working memory develops substantially.

The researchers evaluated prenatal exposure to individual chemicals and their mixture in relation to working memory among 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 males and females and between groups with higher or lower social disadvantage.  

The team found worse verbal working memory among adolescents with higher exposure to manganese and the chemical mixture. There were no significant differences between males and females, 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 deficiencies may be associated with psychiatric and behavioral disorders, further research should examine the effect of environmental exposures on working memory in this age group, as well as social and economic stressors that may alter susceptibility, according to the team.

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