A joint study by the Indian Institute of Technology Madras (IIT Madras) and the Technical University of Denmark has shown how interactions between genetic variants can act as “switches” to activate hidden metabolic pathways. Researchers say the discovery could provide new insights into complex diseases such as cancer, diabetes and neurodegenerative disorders.
The findings, published on 27 August 2025 in Nature Communications, demonstrate how gene–gene interactions can reprogramme metabolism over time, creating outcomes not seen with single mutations. The work was carried out by PhD student Srijith Sasikumar and Professor Himanshu Sinha from the Department of Biotechnology, IIT Madras, in collaboration with Dr Shannara Taylor Parkins and Dr Suresh Sudarsan of the Technical University of Denmark.
The team reported that in yeast, specific variants—MKT1(89G) and TAO3(4477C)—activated a previously dormant arginine biosynthesis pathway, but only when both variants were present. This interaction created a “metabolic trade-off” that suppressed ribosome production while enhancing sporulation efficiency. The pathway was also shown to be essential for mitochondrial activity in the double-variant background, revealing how new cellular dependencies can emerge from genetic combinations.
Professor Himanshu Sinha said the implications extend well beyond yeast. He noted that many human diseases result from the interplay of multiple genes rather than single mutations and added that combining multi-omics with temporal analysis allowed the team to see not only which pathways were affected, but also when and how these changes took place.
He explained that such timing is especially relevant to developmental and progressive diseases. The research, he said, shows how combinations of genetic changes can reprogramme metabolic networks, creating vulnerabilities that may be targeted with therapies.
PhD researcher Srijith Sasikumar described the interaction as similar to flipping two switches at once, which activates a hidden circuit and alters the system’s behaviour. He said observing changes over time was crucial because many dynamic effects only appeared at specific stages, directly relevant for conditions such as cancer and neurodegeneration.
According to the researchers, the work has several potential applications. These include developing biomarkers and identifying drug targets that capture the combined effects of genetic variants, enabling personalised treatment strategies. It may also support biotechnology and synthetic biology by designing engineered gene interactions to regulate pathways for producing biofuels, pharmaceuticals and metabolites. In agriculture, similar approaches could be applied to create stress-tolerant crops or optimise microbial traits for industrial use.
Officials noted that the study provides a framework for understanding how multiple genes interact to influence health and disease, offering a basis for advances in personalised medicine.
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