A new study published in the journal Proceedings of the National Academy of Sciences details an exhaustive compilation of the potential toxicities to the human genome from rare-earth heavy metals called lanthanides. Because of their magnetic properties and ability to emit light, lanthanides are commonly used in organic light-emitting displays, medical MRIs and hybrid vehicles, and yet new observations put into question the health risks they could pose. To explore these risks, researchers from the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) and UC Berkeley employed an unlikely test subject: baker’s yeast.
"Yeast is the smallest eukaryote -- but their thousands of genes represent a great approximation to the gene variants in humans," explained senior author Assistant Professor Rebecca Abergel, who leads the BioActinide Chemistry Group. "What's cool about this study is that it was done with a library of yeast genes, and we could screen the whole genome of the yeast and compare how a normal gene strain versus a gene-deletion strain was actually affected by lanthanide exposure."
This exploration with Saccharomyces cerevisiae and lanthanides lasted almost ten years as Abergel’s team worked within the Yeast Deletion Project to test over 4,000 genes against 13 of the 15 lanthanide metals with the goal of illuminating the relationships between genes and chemical exposures. Their analysis showed that lanthanides disrupt the cell-signaling pathways that control skeletal and neurological processes.
"This study could point us to understanding which lanthanide metals are more toxic than others, and whether someone is more genetically predisposed to lanthanide toxicity," Abergel said, referring to the disturbing finding that some MRI patients experience side effects such as long-term kidney damage linked to their exposure to the MRI contrast agent lanthanide gadolinium.
"This was a massive study showing all the potential pathways affected by lanthanide metal exposure -- but we're just scratching the surface of a huge dataset," she concluded.