A previously unknown microprotein hidden within the human mitochondrial genome may help explain certain forms of Type 2 diabetes and could point toward a new, precision-medicine approach to treating it, according to a new USC study.

Obesity and Type 2 diabetes are among the fastest-growing threats to human health, yet their genetic underpinnings remain only partly understood. While most disease-gene research focuses on the larger set of DNA found in the nucleus within cells, the much smaller genome found in mitochondria — cells’ energy factories — is now known to encode a family of microproteins with wide-ranging biological effects.

The new study adds a striking example to that list, said Pinchas Cohen, the study’s senior author, USC Distinguished Professor and dean of the USC Leonard Davis School of Gerontology. The findings were published July 20 in the journal Theranostics.

A genetic clue in a vulnerable population

In a mitochondrial genomewide interaction study using health and genetic data from more than 15,000 adults, the team identified a single-nucleotide polymorphism (SNP, or “snip”) associated with Type 2 diabetes. A SNP is a common type of genetic variation representing a difference in a single nucleotide, or individual “building block” of DNA. SNPs can play a role in individuals’ different responses to certain medications, environmental factors or pathogens.

This genetic variant sits within the gene for a newly identified mitochondrial-derived microprotein, which the researchers have named MENTSH (MDP Encoded in the ND-Two Subunit of Humans).

Notably, this common variant is found most frequently in populations indigenous to the Americas and in 20% of Mexican and Mexican American individuals. The SNP disables MENTSH’s “start codon,” the portion of the gene that would normally signal the cell to start producing the microprotein. The discovery suggests this SNP may be a genetic contributor to metabolic disease in a population that bears a disproportionate burden of diabetes.

From discovery to therapeutic candidate

The researchers confirmed that MENTSH is a genuine, biologically active microprotein using cell-culture experiments and detected it directly using mass spectrometry. They then tested MENTSH and more potent engineered analogues in mouse models of diabetes and obesity.

In these preclinical studies, administering MENTSH improved insulin signaling, while MENTSH analogues potently blocked weight gain in mice fed a high-fat diet. Analyses pointed to a tissue-specific mechanism: MENTSH activates signaling for an enzyme called AKT in muscle but simultaneously reduces AKT signaling in fat, a pattern consistent with improved metabolic health.

“What’s exciting is that this molecule appears to act differently in muscle versus fat, which is exactly the kind of targeted effect you’d want in a metabolic therapy,” said USC Leonard Davis School Research Associate Professor of Gerontology Kelvin Yen, the study’s first author.

Toward precision medicine

Together, the results identify a new biological cause of metabolic dysfunction and suggest that MENTSH-based therapies could one day offer a precision-medicine approach to Type 2 diabetes, particularly for individuals who carry this SNP. The presence of this genetic variant can be easily screened for and could serve as a test for diabetes risk.

The authors emphasize that these findings are preclinical. Further research, including additional safety and efficacy studies, will be required before MENTSH or its analogues can be evaluated in humans.

“For the first time, we’ve connected a mitochondrial microprotein to diabetes risk in a specific population, which opens the door to treatments tailored to the people who need them most,” said co-author Jerome Rotter, a professor at the Lundquist Institute for Biomedical Innovation.

MENTSH could represent an exciting new therapeutic target for diabetes and obesity: a muscle-sparing weight loss peptide, Cohen said.

“This discovery not only represents a potential novel therapeutic for diabetes and obesity, which are major problems around the world, but it also unravels a new cause of diabetes in Hispanics, who are known to be disproportionately affected by these conditions,” he said.

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About the study: Additional authors included Ricardo Ramirez, Hiroshi Kumagai, Ana Silverstein, Roberto Vicinanza, Melanie Flores, Zeferino Reyna, Su-Jeong Kim, Noel Guerrero, Hemal H. Mehta, Junxiang Wan, Zhongzheng Niu, Carrie V. Breton, Thalida Em Arpawong and Eileen Crimmins of USC; Brendan Miller of the Salk Institute for Biological Studies; Jie Yao, Xiuqing Guo and Kent D. Taylor of the Lundquist Institute; Morgan Levine of Altos Labs; Jihui Sha and James Wohlschlegel of UCLA; and Maria C. Kenney of the University of California, Irvine.

The study was supported by grants from the National Institutes of Health (P30AG068345, R35 GM153408, R01AG068405, R01AG069698 and P30AG094848); the Navigage Foundation; the Ella Fitzgerald Charitable Foundation; the Hanson-Thorell Family Research Award; the Hinrich Foundation; the Hevolution Foundation (HF-AGE-23-1273964-51); and the Hinrich Endowment for Mitochondrial Genetics at the USC Leonard Davis School.

Across USC’s schools and campuses, researchers, faculty and practitioners are developing innovative solutions for societal issues, imagining new approaches to making art and finding ways for creators to prepare for a shifting landscape as AI increasingly intersects with different fields.

Under USC President Beong-Soo Kim’s leadership, advancing ethical AI that puts people first has become a university priority — building on USC’s legacy of innovation while preparing the next generation of researchers, scientists, engineers, entrepreneurs, artists and health professionals. 

This story is part of Trojan Family Magazine’s AI series highlighting the people and ideas driving USC’s vision forward. 

AI is helping USC researchers answer a question with life-or-death consequences: Why do medications work well for some patients but not others?

Gauri Rao, associate professor of clinical pharmacy at the USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, is the director of the Center for Quantitative Drug and Disease Modeling. At her lab, Rao and her team are using machine learning to find the answer, analyzing vast amounts of bacterial genomic and metabolomic data to better understand the mechanisms that drive antimicrobial resistance.

By identifying patterns in bacterial genomes and metabolic pathways, the team aims to predict how bacteria develop resistance to antibiotics and uncover strategies to preserve the effectiveness of existing therapies. Their work combines advanced computational approaches with experimental research to address one of the world’s most pressing public health challenges.

“Developing a new antibiotic is incredibly costly and can take more than a decade,” Rao says. “Given the urgent rise in antimicrobial resistance, we are focused on using machine learning and quantitative modeling to maximize the effectiveness of the antibiotics we already have and help slow the emergence of resistance.”

Given the growing threat of drug-resistant infections, a large portion of Rao’s research program is dedicated to bacteriophage (phage) therapy, an emerging approach that uses naturally occurring viruses to target and kill bacteria. Rao says machine learning is critical for helping researchers rapidly identify phages that are most likely to be effective against specific bacterial pathogens.

Rao is part of the National Institutes of Health’s first coordinated U.S. research network for phage therapeutics. Through this collaborative effort, computational approaches, including machine learning, are integrated with laboratory (in vitro) and animal model (in vivo) testing to identify promising phage candidates and accelerate their translation into clinical practice. The goal is to develop more precise and effective treatments for multidrug-resistant infections while preserving the effectiveness of current antibiotics.

Prashant Dogra, associate director of the center and assistant professor of clinical pharmacy at USC Mann, also integrates mechanistic mathematical modeling with AI to better understand disease mechanisms and drug delivery systems.

One of Dogra’s current projects, supported by funding from the NIH, studies how nanomedicines move through the body to design safer and more effective drug delivery systems to transform treatment for diseases ranging from cancer to chronic illnesses.

Using machine learning, he analyzes a large body of published studies to identify design principles for safer and more effective nanoparticles, potentially leading to better clinical outcomes for patients.

Dogra, who joined the USC Mann faculty last year, says he can’t imagine doing the projects he’s currently working on at any other university.

“I think USC has a very interdisciplinary environment that I find is really unmatchable,” Dogra says. “Having access to colleagues who are very strong in complementary domains facilitates newer ways of thinking and allows us to develop these novel methods of pursuing our scientific questions.”

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Read the entire series: USC at the Leading Edge of AI

Part 1: The Business Students | Part 2: The Pharmaceutical Developers | Part 3: The Creatives | Part 4: The Filmmaker | Part 5: The Urban Planners