Getting to the genetic basis of cardiovascular disease
If Edwin G. Peña Martínez were speaking to high school students, he’d explain the science behind his work this way: “Spiderman got his superpowers from mutations. But for us, mutations cause diseases and are not necessarily a superpower.”
Peña Martínez is a fifth-year doctoral student in biochemistry at the University of Puerto Rico, Río Piedras Campus. His research focuses on understanding how mutations in noncoding sequences contribute to cardiovascular disease, or CVD. He uses machine learning to predict how these mutations can alter binding of transcription factors involved in heart development. To apply computational approaches to his research, he had to train preexisting software with publicly available genomic data sets for two to three years.
This work clears up a common misconception, Peña Martínez said. “Noncoding mutations are thought to be functionally silent and evolutionarily neutral, whereas coding mutations alter the structure and function of proteins.”
The paper reporting this research earned him a 2024 JBC Herbert Tabor Early Career Investigator Award.
“It is unusual to study mutations that occur in non-coding regions of the genome,” Peña Martínez said, “but more than 90% of the mutations associated with CVD are located in regulatory sequences like promoter or enhancer sequences, and not in coding sequences.”
Working in Puerto Rico, Peña Martínez said he must make a greater effort to plan experiments than his counterparts on the mainland. “It takes a longer time to receive reagents and equipment when you’re not in the U.S. mainland, and there are delivery issues,” he said. “You need patience because experiments will fail and it takes time to troubleshoot but, it does get better and you shouldn’t get discouraged.”
Peña Martínez trained as a certified life and leadership coach, perhaps in an effort to pay it forward, he said. He regularly gives talks and hosts workshops for students enrolled in public education. He said this is his way of motivating them to pursue a graduate degree in STEM. His goal is to become a professor at a research institution and continue mentoring students, specifically underrepresented minorities.
“As long as you’re curious and passionate about what you do and you’re ready to fail and to learn from failure,” he said, “it is possible to do this, and you will achieve your goal.”
Genes at the heart of a killer
Cardiovascular disease, or CVD, has a genetic component. Genetic mutations, including single nucleotide polymorphisms, or SNPs, can alter the function of regulatory proteins, such as transcription factors, or TFs.
A recent study by Edwin G. Peña Martínez and a team in Puerto Rico identified noncoding variants associated with CVDs and CV traits using data from several public repositories. They found SNPs in two cardiac transcription factors in human-induced pluripotent stem cell–derived heart cells; the computational predictive model was trained using data from the cardiac transcription factors in this type of cell. The team identified five SNPs that alter the binding of these two transcription factors.
Expression quantitative trait loci, or eQTL, analysis tests the level of association of specific SNPs with gene expression levels in a given tissue and is typically measured in hundreds of individuals. For these researchers, this meant adopting a linear support vector machine classifier trained to distinguish TF binding sites from background sequences in their data set.
According to the researchers’ computational predictions, the location of the five SNPs, the extent of the change in TF binding resulting from these SNPs, and eQTLs in heart tissue would be positive indicators of association with CVD. They identified specific eQTLs in heart tissue that they consider to be these positive indicators.
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