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Environmental Factor

Environmental Factor

Your Online Source for NIEHS News

April 2022

Papers of the month

DNTP uses large-scale approach to assess chemical cardiovascular risk

High-throughput screening coupled with computational models predicts the likelihood that a compound will inhibit the human ether-a-go-go-related gene (hERG) potassium channel, according to researchers from the NIEHS Division of the National Toxicology Program (DNTP).

Disrupting hERG activity can trigger abnormal heart rhythms and lead to sudden death. Many drugs have been withdrawn from the market due to severe heart disease from hERG inhibition. However, toxicological screening methods using animal models are slow and costly, and such methods can present ethical issues and produce results that may not translate to humans.

To overcome these shortcomings, the researchers used a quantitative high-throughput screening approach to evaluate nearly 10,000 diverse drugs and environmental chemicals for their ability to alter hERG functioning in human cells. By applying several machine-learning techniques to their robust and reliable dataset, the researchers built statistical models for predicting the probability that compounds will inhibit the hERG channel.

The resulting data and algorithms are provided in an open-access format to facilitate their widespread application to drug development and environmental chemical screening. This large-scale approach could rapidly and efficiently yield critical information about the potential of more than 100,000 undertested compounds to pose a cardiovascular risk to public health, helping to prioritize chemicals for extensive toxicological evaluation.

CitationKrishna S, Borrel A, Huang R, Zhao J, Xia M, Kleinstreuer N. 2022. High-throughput chemical screening and structure-based models to predict hERG inhibition. Biology (Basel) 11(2):209.

Oil-spill cleanup may raise blood pressure

Cleanup work in response to the Deepwater Horizon oil spill was associated with an increased risk of later developing high blood pressure, according to NIEHS researchers and their collaborators.

The Deepwater Horizon explosion on April 20, 2010, caused the largest marine oil spill in U.S. history. Cleanup workers were exposed to total petroleum hydrocarbons (THC) and inhalable fine particulate matter (PM 2.5), which can cause cardiovascular problems. However, little is known about the health effects of exposure to THC and PM 2.5 from oil spills. Few studies have investigated how oil spill cleanup may harm cardiovascular health, and none have examined the effect on blood pressure.

The researchers conducted telephone interviews and in-person home exams of 8,351 participants, which included 6,846 adults involved in oil spill cleanup. None of the participants had high blood pressure at the time of the oil spill. Their blood pressure was measured up to three years after the disaster.

High blood pressure was more prevalent among workers exposed to higher cumulative levels of THC or PM 2.5 from burning and flaring of oil and gas. These results spotlight the potential long-term health hazards of oil spill cleanup and suggest that blood pressure screening should be considered for workers exposed to hydrocarbons.

CitationKwok RK, Jackson WB 2nd, Curry MD, Stewart PA, McGrath JA, Stenzel M, Huynh TB, Groth CP, Ramachandran G, Banerjee S, Pratt GC, Miller AK, Zhang X, Engel LS, Sandler DP. 2022. Association of Deepwater Horizon oil spill response and cleanup work with risk of developing hypertension. JAMA Netw Open 5(2):e220108. Story

Gene variants lead to congenital malformation or muscular dystrophy

Molecular master switches may explain how mutations in the SMCHD1 gene can give rise to strikingly distinct disorders, according to NIEHS researchers and their collaborators.

SMCHD1 genetic variants cause two different diseases called facioscapulohumeral muscular dystrophy type 2 (FSHD2) and arhinia. FSHD2 is marked by muscle weakness and wasting, and arhinia is characterized by the lack of an external nose. The mechanisms by which variants affecting the same gene lead to these divergent conditions are unclear.

To address this knowledge gap, the researchers performed detailed clinical evaluation and muscle imaging of 11 individuals with congenital arhinia or related conditions, and the scientists performed genetic testing to determine risk for FSHD2. Three individuals were at genetic risk for FSHD2 and displayed a molecular hallmark of the disorder — abnormal expression of the DUX4 gene in skeletal muscle cells. Surprisingly, those individuals did not show typical clinical signs or symptoms of FSHD2 — muscle weakness or abnormal muscle imaging.

The results suggest that unknown molecular factors may act as master switches, resulting in either arhinia or FSHD2, by affecting DUX4 expression during development. In other words, molecular factors leading to arhinia may protect against FSHD2 and vice versa. Further investigation of these master switches may provide valuable therapeutic insights into both disorders.

CitationMohassel P, Chang N, Inoue K, Delaney A, Hu Y, Donkervoort S, Saade D, Billioux BJ, Meader B, Volochayev R, Konersman CG, Kaindl AM, Cho CH, Russell B, Rodriguez A, Foster KW, Foley AR, Moore SA, Jones PL, Bonnemann CG, Jones T, Shaw ND. 2022. Cross-sectional, neuromuscular phenotyping study of arhinia patients with SMCHD1 variants. Neurology; doi:10.1212/WNL.0000000000200032 [Online 4 February 2022].

Diabetes during pregnancy poses lifetime risk of type 2 diabetes

Developing diabetes during pregnancy, especially multiple pregnancies, increases the risk of subsequent type 2 diabetes for decades, according to NIEHS researchers and their collaborators.

Gestational diabetes mellitus is characterized by high blood sugar levels during pregnancy in women who never had diabetes when they were not pregnant. Approximately 6% of pregnancies are complicated by gestational diabetes mellitus, which strongly predicts the later development of type 2 diabetes. It has not been clear how long this risk lasts, or whether it depends on the number of affected pregnancies.

To address these questions, the researchers analyzed the reproductive and medical histories of a nationwide sample of 50,884 women participating in the Sister Study. One or more past pregnancies affected by gestational diabetes mellitus predicted an elevated risk of type 2 diabetes. Although this risk declined somewhat with time, it increased steeply with multiple affected pregnancies, and remained elevated for more than 35 years.

The results suggest that women with a history of gestational diabetes mellitus should be screened regularly for type 2 diabetes, even late in life. Personalized lifestyle interventions targeting these individuals may be effective at preventing or delaying the development of type 2 diabetes.

CitationDiaz-Santana MV, O'Brien KM, Park YM, Sandler DP, Weinberg CR. 2022. Persistence of risk for type 2 diabetes after gestational diabetes mellitus. Diabetes Care; doi:10.2337/dc21-1430 [Online 1 February 2022].

Seeing the cell-state forest for the trees

A new computational approach takes inspiration from forests to visualize both discrete and continuous cell transitions, according to NIEHS researchers and their collaborators.

A major controversy in developmental biology is whether cell types and cell states are continuous, discrete, or a mixture of the two. Unfortunately, models for studying cellular progression are not optimized for visualizing both disjointed and continuous transitions between states.

To address this challenge, the researchers outlined a novel data-driven framework called dynamic spanning forest mixtures (DSFMix). This powerful tool uses decision trees to build a forest for visualizing and characterizing complex developmental processes that unfold over time. DSFMix input consists of single-cell data collected at different time points, representing distinct stages of development, and its output is a mixture of discrete and continuous cell lineages. This approach can also be used to investigate relationships between the components of larger biological systems, such as the human body.

The researchers applied DSFMix to a range of biological processes, including the development of sperm and stem cells, the effects of hormones on gene activity, and immune responses to coronavirus disease. According to the authors, further refinement of decision tree–based algorithms could improve our mechanistic understanding of developmental biology at single-cell resolution.

CitationAnchang B, Mendez-Giraldez R, Xu X, Archer TK, Chen Q, Hu G, Plevritis SK, Motsinger-Reif AA, Li JL. 2022. Visualization, benchmarking and characterization of nested single-cell heterogeneity as dynamic forest mixtures. Brief Bioinform; doi:10.1093/bib/bbac017 [Online 22 February 2022].

(Janelle Weaver, Ph.D., is a contract writer for the NIEHS Office of Communications and Public Liaison.)

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