The long-term objective of the PI's research program is to understand the molecular genetic mechanisms and driving forces of phenotypic variation and evolution. Pleiotropy is one of the most common yet least understood phenomena in genetics. It refers to the observation that one mutation impacts multiple phenotypic traits. Pleiotropy may be concordant or antagonistic, depending on whether the mutational effects on multiple traits are in the same or opposite directions (when the directions are alignable). Pleiotropy, especially antagonistic pleiotropy, is widely invoked in explanations and models of senescence, cancer, genetic disease, sexual conflict, cooperation, evolutionary constraint, adaptation, neofunctionalization, and speciation, among other things. This project addresses three key gaps in our understanding of pleiotropy: patterns, mechanisms, and evolutionary consequences. First, while the environmental pleiotropy of null mutations has been extensively studied, the same is not true for non-null mutations. This project will use a high-throughput method to determine the in vivo fitness landscapes of one yeast RNA gene and four protein genes in 12 environments. Each landscape will include >20,000 genotypes, providing unprecedentedly large data for inducing general principles of environmental pleiotropy. More importantly, these data will allow inferring fitness effects of mutations in one environment from those in another, which will be instrumental in explaining and predicting evolution in nature. Second, while pleiotropy is typically studied from the perspective of mutations, the other side of the coin is the relationship between phenotypic traits that are often impacted by the same mutations. Maximum growth rate r and carrying capacity K of density-dependent population growth are key life-history traits fundamental to many ecological and evolutionary theories and are directly relevant to combating pathogens and tumors. Although r and K are generally thought to be negatively correlated, both r-K tradeoffs and tradeups have been observed. However, neither the conditions under which each of these relationships occur nor the causes of these relationships are well understood. These questions will be addressed in yeast by mapping quantitative trait loci influencing r and K and estimating the r and K of 500 single-gene deletion strains in multiple environments, followed by modeling of biological processes impacting r and K. Third, if mutations with large benefits in one environment are generally deleterious in other environments, a population adapting to a changing environment may have few adaptive substitutions, despite continuous and strong selections. This project will test the above hypothesis using experimental evolution of yeast in constant vs. changing environments. If supported, this hypothesis will profoundly alter our interpretation of the nonsynonymous/synonymous substitution rate ratio estimated from intra and interspecific comparisons, impacting the assessment of the relative roles of genetic drift and positive selection in molecular evolution.

Public Health Relevance

Although pleiotropy, especially antagonistic pleiotropy, is commonly invoked in explanations of aging, cancer, and genetic disease, empirical knowledge about pleiotropy is limited. The proposed work characterizes general patterns of pleiotropy of a large number of mutations, dissects the mechanistic basis of varying relationships between two key life-history traits frequently co-influenced by the same mutations, and study the impact of pleiotropy on molecular evolution during environmental adaptations. The knowledge gained will likely provide important information for understanding the proximate and ultimate causes of diseases and biomedical phenomena and help devise better strategies to combat pathogens and cancer.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM103232-06
Application #
9993534
Study Section
Genetic Variation and Evolution Study Section (GVE)
Program Officer
Krasnewich, Donna M
Project Start
2013-09-01
Project End
2023-07-31
Budget Start
2020-08-01
Budget End
2021-07-31
Support Year
6
Fiscal Year
2020
Total Cost
Indirect Cost
Name
University of Michigan Ann Arbor
Department
Biology
Type
Schools of Arts and Sciences
DUNS #
073133571
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109
Li, Chuan; Zhang, Jianzhi (2018) Multi-environment fitness landscapes of a tRNA gene. Nat Ecol Evol 2:1025-1032
Yang, Jian-Rong; Maclean, Calum J; Park, Chungoo et al. (2017) Intra and Interspecific Variations of Gene Expression Levels in Yeast Are Largely Neutral: (Nei Lecture, SMBE 2016, Gold Coast). Mol Biol Evol 34:2125-2139
Wei, Xinzhu; Zhang, Jianzhi (2017) The Genomic Architecture of Interactions Between Natural Genetic Polymorphisms and Environments in Yeast Growth. Genetics 205:925-937
Zou, Zhengting; Zhang, Jianzhi (2017) Gene Tree Discordance Does Not Explain Away the Temporal Decline of Convergence in Mammalian Protein Sequence Evolution. Mol Biol Evol 34:1682-1688
Ho, Wei-Chin; Ohya, Yoshikazu; Zhang, Jianzhi (2017) Testing the neutral hypothesis of phenotypic evolution. Proc Natl Acad Sci U S A 114:12219-12224
Wei, Xinzhu; Zhang, Jianzhi (2017) Why Phenotype Robustness Promotes Phenotype Evolvability. Genome Biol Evol 9:3509-3515
Zhang, Jianzhi (2017) Epistasis Analysis Goes Genome-Wide. PLoS Genet 13:e1006558
Xu, Jinrui; Zhang, Jianzhi (2016) Impact of structure space continuity on protein fold classification. Sci Rep 6:23263
Moyers, Bryan A; Zhang, Jianzhi (2016) Evaluating Phylostratigraphic Evidence for Widespread De Novo Gene Birth in Genome Evolution. Mol Biol Evol 33:1245-56
Chen, Xiaoshu; Zhang, Jianzhi (2016) The Genomic Landscape of Position Effects on Protein Expression Level and Noise in Yeast. Cell Syst 2:347-54

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