Dr. Jianzhi Zhang: Pioneering New Models in Evolutionary Genetics

When Dr. Jianzhi Zhang—known as George to many—walked into the University of Michigan 25 years ago, he arrived for his interview in the midst of a snowstorm and at the cusp of departmental change. Now, as one of the founding members of U-M’s Department of Ecology and Evolutionary Biology (EEB), Dr. Zhang leads a vibrant research group focused on unraveling the complexities of molecular evolution. With a team of thirteen, including graduate students, postdocs, and visiting scholars, Dr. Zhang's lab embodies both the spirit of collaboration and the thrill of scientific discovery.

Reimagining Evolution: The Adaptive Tracking Model

Perhaps the most exciting recent development from Dr. Zhang’s lab is a newly published paper introducing the “Adaptive Tracking with Antagonistic Pleiotropy” model—a fresh perspective on how populations evolve at the molecular level. This model challenges long-standing ideas in evolutionary biology.Traditionally, the neutral theory posits that most genetic polymorphisms and substitutions are neither beneficial nor harmful, while evolution by natural selection suggests that beneficial mutations sweep through populations. Dr. Zhang’s adaptive tracking model adds a dynamic twist: rather than a population reaching perfection, it’s locked in a perpetual chase of a rapidly changing environment. As populations adapt, new environmental shifts occur before beneficial mutations become fixed, causing those same mutations to rise and fall in frequency—never reaching full establishment.This “tracking” process could explain why long-term molecular evolution appears deceptively quiet, masking intense short-term adaptation beneath the surface. Crucially, Zhang believes this model more accurately reflects the realities faced by both yeast in a lab culture and humans living in modern society. The inability of populations to ever be completely “fit” for their environment may shed light on phenomena such as genetic mismatches that underlie diseases and defects.

From Yeast to Humans: Exploring the Genetic Basis of Adaptation

Dr. Zhang’s research path has traversed organisms big and small. Early in his tenure, he focused on primate and human evolution, later turning his attention to yeast as a powerhouse model organism. Yeast’s amenability to genetic manipulation allowed his group to conduct large-scale mutation studies—creating and characterizing as many as 8,000 individual mutations to measure their effects on fitness, a feat nearly impossible in most species.Recently, his lab has returned to human genetics, fueled by the explosion of big data and resources like the UK Biobank, which provides genomic and phenotypic data on about 500,000 individuals. With these datasets, Zhang’s team has tested longstanding hypotheses about aging, such as the idea that some mutations beneficial in youth are detrimental in old age—findings that have offered robust support for the antagonistic pleiotropy hypothesis.

The Next Frontier: High-Throughput Genome Editing and Machine Learning

Riding the wave of technological progress, Zhang’s future projects leverage advancements in CRISPR-Cas9 genome editing, now enhanced for high-throughput mutation studies. The lab plans to generate and analyze up to 10,000 mutations in yeast, across different genetic backgrounds and stages of adaptation. The goal is to study the changing landscape of the fitness effects of mutations during adaptation—findings that could unlock new understanding of evolutionary processes.On the computational side, Zhang’s “dry lab” utilizes machine learning to map the fitness landscape of DNA sequences. By combining experimental data and predictive algorithms, his team can simulate evolution and probe enduring questions: Why does full adaptation take so long, even in a stable environment? The famed long-term E. coli evolution experiment conducted by Rich Lenski, which after 80,000 generations still hasn’t plateaued in growth rate, exemplifies this puzzle.