Naturally occurring metabolite identified that converts “bad” fat into “good” fat

Scientists from Scripps Research and Calibr worked together to discover possible therapeutic treatments for metabolic diseases.

“Metabolism” describes the chemical changes in the body that create the ingredients necessary for growth and overall health. Metabolites are the substances that are made and used during these metabolic processes — or, as a new discovery from Scripps Research and its drug development arm Calibr shows, they could also be powerful molecules to treat serious diseases.

Researchers used cutting-edge drug discovery technologies to uncover a metabolite that converts white fat cells (“bad” fat) into brown fat cells (“good” fat). This revelation offers a potential avenue to address obesity, type 2 diabetes, cardiovascular disease and other metabolic disorders. Additionally, it speaks to the promise of using this creative drug discovery method to identify myriad other potential therapeutics. The study was recently published in the journal metabolites.

Zafirlukast induces brown adipocyte tissue production. In zafirlukast-treated preadipocytes (right image), brown adipocytes (shown in red) were much more common than preadipocytes cultured with the DMSO control (left image). Image credits: Scripps Research and Calibr

“The reason many types of molecules don’t make it to market is their toxicity,” says co-senior author Gary Siuzdak, PhD. “With our technology, we can pull out endogenous metabolites — those that the body makes itself — which can have the same effect as a drug with fewer side effects. The potential of this approach is even demonstrated by the FDA’s recent approval of Relyvrio, the combination of two endogenous metabolites for the treatment of amyotrophic lateral sclerosis (ALS). Computational Biology at Scripps Research.

Metabolic diseases are often caused by an imbalance in energy homeostasis – when the body takes in more energy than it uses. Because of this, certain therapeutic approaches have focused on converting white fat cells (called adipocytes) into brown fat cells. White adipocytes store excess energy and can eventually lead to metabolic diseases like obesity, while brown adipocytes convert this stored energy into heat — ultimately increasing the body’s energy expenditure and helping to rebalance it.

DIAM uses technologies such as liquid chromatography and mass spectrometry to pool thousands of molecules and identify specific metabolites. In this case, the researchers first reduced 30,000 metabolic markers to just 17 metabolites and then found myristoylglycine – an active endogenous metabolite that could convert white fat cells into brown fat cells, similar to the drug zafirlukast. Image credits: Scripps Research and Calibr

To uncover a therapy that could stimulate brown adipocyte production, the researchers searched Calibr’s ReFRAME drug reuse collection — a library of 14,000 known drug compounds that the FDA has approved for other diseases or extensively evaluated for their safety in humans have been tested. Using high-throughput screening – an automated drug discovery method for sifting through large pools of information – scientists searched ReFRAME for a drug with these specific abilities.

This is how they discovered zafirlukast, an FDA-approved drug to treat asthma. Through a series of cell culture experiments, they found that zafirlukast could convert adipocyte progenitor cells (known as preadipocytes) into predominantly brown adipocytes, as well as convert white adipocytes into brown adipocytes.

While zafirlukast is an encouraging finding, it is toxic when administered in higher doses, and it wasn’t entirely clear how zafirlukast metabolized fat cells. At this point, the researchers were working with Siuzdak and his team of metabolic experts.

“We had to use additional tools to break down the chemicals in the mechanism of zafirlukast,” says Kristen Johnson, PhD, co-senior author of the paper and director of translational drug discovery research at Calibr. “Put another way, can we find a metabolite that has the same functional effect as zafirlukast but without the side effects?”

Siuzdak and his team designed a novel set of experiments known as drug-initiated activity metabolomics (DIAM) screening to answer Johnson’s question. DIAM uses technologies such as liquid chromatography (a tool that separates components in a mixture) and mass spectrometry (an analytical technique that separates particles by weight and charge) to cluster thousands of molecules and identify specific metabolites. In this case, the researchers looked for metabolites in the adipose tissue that could lead to the production of brown fat cells.

After reducing 30,000 metabolic traits to just 17 metabolites, they found myristoylglycine – an endogenous metabolite that stimulated brown adipocyte formation without damaging the cell. Of the thousands of metabolic properties measured in the analysis, only myristoylglycine had this unique property, even among nearly structurally identical metabolites.

“The identification of myristoylglycine among thousands of other molecules speaks to the strength of Siuzdak’s approach and these technologies,” adds Johnson. “Our results illustrate what happens when an analytical chemistry team and a drug discovery group work closely together.”

Reference: “Drug-Initiated Activity Metabolomics Identists Myristoylglycine as a Potent Endogenous Metabolite for Human Brown Fat Differentiation” by Carlos Guijas, Andrew To, J. Rafael Montenegro-Burke, Xavier Domingo-Almenara, Zaida Alipio-Gloria, Bernard P. Kok, Enrique Saez, Nicole H. Alvarez, Kristen A. Johnson and Gary Siuzdak, August 16, 2022, metabolites.
DOI: 10.3390/metabo12080749

Authors of the study “Drug-Initiated Activity Metabolomics Identify Myristoylglycine as a Potent Endogenous Metabolite for Human Brown Fat Differentiation” include Siuzdak and Johnson, Carolos Guijas and J. Rafael Montenegro-Burke, Xavier Domingo-Almenara, Bernard P. Kok, and Enrique Saez of Scripps Research; and Andrew To, Zaida Alipio-Gloria, and Nicole H. Alvarez of Calibr.

This research was funded in part by the National Institutes of Health and the NIH Cloud Credits Model Pilot.