RNA-Seq reveals household chemicals that may be making you fat

Chemicals in your furniture, plastic housewares and pesticides used in your yard may be making you fat, according to Boston University Schools of Medicine (BUSM) and Public Health (BUSPH)  researchers.

A growing number of environmental pollutants (organotins in pesticides, phthalates in plastics, flame retardants in furniture) activate fat-forming pathways and enhance weight gain through white-fat accumulation.

In a new study, researchers developed a novel experimental and computational framework for the identification of so-called metabolism-disrupting chemicals (MDCs), also known as obesogens, which are environmental chemicals that increase the risk of metabolic diseases (such as obesity, diabetes and cardiovascular disease) in subjects exposed to them.

“Our study developed machine learning methods to accurately identify and characterize new metabolism-disrupting chemicals and applied these methods to the classification of a set of as-yet uncharacterized chemicals suspected to be obesogens,” explained corresponding author Stefano Monti, PhD, associate professor of medicine at BUSM.

Using a high-throughput chemical screening approach combined with a machine learning approach, the researchers “profiled” a set of more than 60 chemicals with known effects (known to be either obesogens or non-obesogens) and used them to “train” a computer model to predict their metabolism-disrupting potential. These RNA-sequencing profiles, together with the known chemical labels, were fed to a computer model that was trained to distinguish between the two classes, and then applied to the classification of unlabeled chemicals.

According to the researchers, the rapid increases in obesity and metabolic diseases over the last few decades correlate with substantial increases in environmental chemical production and exposures.

“The prevalence of obesity has reached epidemic proportions, and changes in diet and the modern lifestyle cannot fully account for it. Thus, the accurate prediction of the adverse effects of chemical exposure is an urgent goal,” said co-corresponding author Jennifer J. Schlezinger, PhD, associate professor of environmental health at BUSPH.

Chemical taxonomy of adipogens based on K2 clustering of the 3′ DGE data

Figure 3 is a horizontal dendrogram graph, plotting Weak Peroxisome proliferator-activated receptor lowercase gamma agonists, including B A D G E, ProPara, 15dP G J, S R 1664, M E T B P, D I N P, BluPara, Fenth; Vehicle, T 007, Honok; Peroxisome proliferator-activated receptor lowercase gamma Modifiers, including Prote, Resol, Rosco; Peroxisome proliferator-activated receptor lowercase gamma activity repressors, including 9c R A, D B T, L G 754, A T R A; Naïve; Strong Peroxisome proliferator-activated receptor lowercase gamma Agonists, including n T Z D pa, Allet, Tessag, Quino, T B u P, Tonal, TPhT; R X R agonists, including T B T and L G 268; Phithalates, including M Bup, M E H P, B B z p P; F M , Trifl, Magno, Cande, Thiazolidinediones, including S 26948, Rosig, M C C 555, Trogl, and Flame Retardants, including T B B P A and T P h P (y-axis) across the proportion of gene-level bootstraps (x-axis) for uppercase a, including Adipogenesis, Lipid Metabolism, and Extracellular Comp.; uppercase b, including Adipogenesis and Lipid Metabolism; uppercase c, including cellular respiration, lipid metabolism, electron transport chain, extracellular comp., and vascularization; uppercase d, including Adipogenesis; uppercase e, including extracellular comp.; uppercase f, including Adipogenesis, extracellular comp., and vascularization; uppercase h, including lipid metabolism and Peroxisome proliferator-activated receptor lowercase gamma Phosphorylation; uppercase i, including cellular respiration and lipid metabolism; uppercase j, including beta oxidation; and uppercase k, including Peroxisome proliferator-activated receptor lowercase gamma Phosphorylation.

The dendrogram shows the taxonomy-driven hierarchical grouping of test chemical exposures of 3T3-L1 cells or naïve preadipocytes. Each split is labeled with a letter (A–K), and the proportion of gene-level bootstraps which produced the resulting split is shown. Highlights of hyper-enrichment of Gene Ontology (GO) biological processes are shown. 

The researchers believe this study goes beyond simply predicting whether a chemical may or may not adversely affect a person’s metabolism, as it also points to the possible biological mechanisms through which it may exercise that effect. “In our panel of profiled chemicals, we also included known drugs used in the treatment of metabolic diseases, such as type 2 diabetes, which allowed us to compare and contrast the positive and negative effects of chemicals targeting our metabolism. This understanding will in turn be instrumental to the design of more effective and targeted drugs with minimal side-effects,” said Monti.

“By improving our ability to identify potentially harmful chemicals, we would be able to limit their use, or adopt safer procedures for their use, and thus prevent their adverse effects on human health,” said Schlezinger.

SourceBoston University School of Medicine

Kim S, Reed E, Monti S, Schlezinger JJ. (2021) A Data-Driven Transcriptional Taxonomy of Adipogenic Chemicals to Identify White and Brite Adipogens. Environ Health Perspect 129(7):77006. [article]

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