Indian Institute of Technology Madras (IIT Madras) and Danish researchers have shown how interactions between genetic variants can act like “switches” to unlock hidden cellular pathways.

By revealing how these gene–gene interactions rewire metabolism over time, the study provides vital insights into the genetic basis of complex diseases such as cancer, diabetes, and neurodegenerative disorders.

The findings have far-reaching implications for human health, open up new possibilities for personalised medicine, including the identification of biomarkers and potential drug targets.

The study demonstrates how specific genetic variants in yeast can work together to activate a previously dormant metabolic pathway. This discovery provides a framework for decoding how multiple genes interact to shape health and disease in higher organisms, including humans.

The research was led by Mr. Srijith Sasikumar, a PhD student, and Prof. Himanshu Sinha from the Department of Biotechnology, IIT Madras, in collaboration with Dr. Shannara Taylor Parkins and Dr. Suresh Sudarsan from the Technical University of Denmark.

The findings were published in a research paper, “Interaction of genetic variants activates latent metabolic pathways in yeast”, on 27th August 2025, in Nature Communications journal (DOI: 10.1038/s41467-025-63306-4.), an open-access journal also published by Springer Nature.

Elaborating on the importance of this research, Prof. Himanshu Sinha, Department of Biotechnology, IIT Madras, said, “The implications of this discovery extend well beyond yeast. Many complex human diseases—including cancer, diabetes, and neurodegenerative disorders—arise from the interplay of multiple genes rather than single mutations. The IIT Madras study provides a mechanistic framework for studying these interactions systematically.”

Prof. Himanshu Sinha added, “By combining multi-omics with temporal analysis, we could see not just which pathways were affected, but when and how these changes unfolded. This is especially important for developmental and progressive diseases, where timing of gene interactions can be as critical as the interactions themselves. Our research also highlights how combinations of genetic changes can reprogram metabolic networks, creating new vulnerabilities that could be targeted with therapies.”

Further, Mr. Srijith Sasikumar, PhD student, Department of Biotechnology, IIT Madras, added, “It is like flipping two switches at once suddenly, a hidden backup circuit turns on, and the whole system behaves differently. This shows us that genes don’t just act alone, but their interaction can create new outcomes that we would never see otherwise. Observing these changes over time was crucial, because many dynamic effects only emerge at specific stages, which is directly relevant for understanding developmental diseases like cancer or neurodegeneration.”

The practical applications of this research include

O Development of biomarkers and identification of drug targets that capture the combined effects of genetic variants, enabling more accurate disease diagnosis, prognosis, and the design of personalized treatment strategies tailored to an individual’s unique genetic background.

O Application in synthetic biology and biotechnology, where engineered gene interactions could be used to activate or suppress specific pathways for improved production of metabolites, biofuels, or pharmaceuticals.

O Providing a framework for precision agriculture and microbial engineering, by leveraging gene–gene interactions to design stress-tolerant crops or industrial microbes with optimized metabolic traits.

The Key Findings of the Study include

O Two genetic variants in yeast—MKT1(89G) and TAO3(4477C)—were found to activate a hidden arginine biosynthesis pathway only when both were present together.

O This gene–gene interaction revealed a new biological rule, showing how variant combinations can create entirely new molecular outcomes not seen when acting alone.

O Using a temporal multi-omics approach—combining transcriptomics, proteomics, and metabolomics—the team captured how these genetic switches reprogrammed cellular activity.

O The double-variant combination uniquely activated arginine biosynthesis and suppressed ribosome production, creating a “metabolic trade-off” that boosted sporulation efficiency in yeast.

O Importantly, the arginine pathway was shown to be essential for mitochondrial activity only in the double-variant background, proving that genetic interactions can generate new dependencies in cells.

Such insights will be crucial in developing personalised medicine approaches, where treatments are tailored to an individual’s unique genetic background.