UT Austin Predicts Genes for 35 Mendelian Disorders From 1.8-Billion-Year-Old Protein Networks
Updated
Updated · The Brighter Side of News · May 27
UT Austin Predicts Genes for 35 Mendelian Disorders From 1.8-Billion-Year-Old Protein Networks
6 articles · Updated · The Brighter Side of News · May 27
Using an ancient protein-interaction map, a University of Texas at Austin team predicted new disease-gene candidates for about one-third of 109 disorders—roughly 35 Mendelian diseases.
The method reconstructed the last eukaryotic common ancestor’s network from 31 species, roughly 26,000 mass-spectrometry experiments, and 109,466 protein interactions, then applied a guilt-by-association model to known disease genes.
Three follow-up cases backed the approach: EFHC2 was tied to a ciliopathy-like kidney disorder, ATP6V1A to osteopetrosis, and GLG1 to short-rib thoracic dysplasia through animal and patient data.
The map also traced 13,571 human genes to 4,777 ancestral groups, suggesting deeply conserved cellular machinery can help explain rare disease mechanisms and interpret unexplained patient variants.
Half our genes are nearly two billion years old. Does this ancient inheritance mean some human diseases can never truly be cured?
With our ancestor's protein network mapped, can powerful new AI now design therapies based on life's original biological code?
The LECA Interactome: Tracing 2 Billion Years of Protein Networks to Accelerate Human Disease Gene Discovery
Overview
On May 27, 2026, a University of Texas at Austin-led team published a landmark study unveiling the most detailed map yet of the protein networks from the Last Eukaryotic Common Ancestor (LECA), the ancient progenitor of all complex life. By comparing proteins across 156 modern eukaryotic species and using advanced computational methods, the researchers identified over 10,000 gene families likely present in LECA. This breakthrough reveals how ancient molecular systems have shaped life today and opens new paths for understanding the genetic roots of human diseases by tracing their origins back to these conserved protein networks.