Chuan Li is a Postdoctoral Fellow in the Dmitri Lab (2017-now). Her research interests include epistasis and speciation. She received her Ph.D. in Ecology and Evolutionary Biology with a dual degree in Statistics from the University of Michigan in 2017. During her Ph.D. at Dr. Jianzhi Zhang’s lab, she worked in quantifying intergenic and intragenic epistasis at a large scale with both experimental and computational approaches using yeast as the model system. She published a highly influential paper on the empirical determination of the fitness landscape of the tRNA gene. In the Petrov lab, she studies the interaction between protein interfaces and antagonistic pleiotropy. She uses a high throughput Bar-seq methodology to systematically quantify intergenic epistasis between two interacting partners, GAL3 and GAL80, in a well-studied gene regulatory pathway metabolizing galactose in yeast, which will allow estimation of the ruggedness of fitness landscape and provide ample and in-depth information on these interaction interfaces.
Honors & Awards
CEHG Postdoctoral Fellowship, Stanford Center for Computational, Evolutionary and Human Genomics (05/04/2017)
Master of Science, University of Michigan Ann Arbor (2017)
Doctor of Philosophy, University of Michigan Ann Arbor (2017)
Bachelor of Science, Sun Yat-Sen University (2011)
Stop-codon read-through arises largely from molecular errors and is generally nonadaptive
2019; 15 (5): e1008141
Stop-codon read-through refers to the phenomenon that a ribosome goes past the stop codon and continues translating into the otherwise untranslated region (UTR) of a transcript. Recent ribosome-profiling experiments in eukaryotes uncovered widespread stop-codon read-through that also varies among tissues, prompting the adaptive hypothesis that stop-codon read-through is an important, regulated mechanism for generating proteome diversity. Here we propose and test a competing hypothesis that stop-codon read-through arises mostly from molecular errors and is largely nonadaptive. The error hypothesis makes distinct predictions about the probability of read-through, frequency of sequence motifs for read-through, and conservation of the read-through region, each of which is supported by genome-scale data from yeasts and fruit flies. Thus, except for the few cases with demonstrated functions, stop-codon read-through is generally nonadaptive. This finding, along with other molecular errors recently quantified, reveals a much less precise or orderly cellular life than is commonly thought.
View details for DOI 10.1371/journal.pgen.1008141
View details for Web of Science ID 000470208000029
View details for PubMedID 31120886
View details for PubMedCentralID PMC6550407
An Lkb1-Sik axis suppresses lung tumor growth and controls differentiation.
The kinase, LKB1, is a critical tumor suppressor in sporadic and familial human cancers, yet the mechanisms by which it suppresses tumor growth remain poorly understood. To investigate the tumor-suppressive capacity of four canonical families of Lkb1 substrates in vivo, we employed CRISPR/Cas9-mediated combinatorial genome editing in a mouse model of oncogenic Kras-driven lung adenocarcinoma. We demonstrate that members of the salt-inducible kinase (Sik) family are critical for constraining tumor development. Histological and gene expression similarities between Lkb1- and Sik-deficient tumors suggest that Siks and Lkb1 operate within the same axis. Furthermore, a gene expression signature reflecting Sik deficiency is enriched in LKB1 mutant human lung adenocarcinomas and is regulated by LKB1 in human cancer cell lines. Together, these findings reveal a key Lkb1-Sik tumor-suppressive axis and underscore the need to redirect the focus of efforts to elucidate the mechanisms through which LKB1 mediates tumor suppression.
View details for DOI 10.1158/2159-8290.CD-18-1237
View details for PubMedID 31350327
Multi-environment fitness landscapes of a tRNA gene.
Nature ecology & evolution
A fitness landscape (FL) describes the genotype-fitness relationship in a given environment. To explain and predict evolution, it is imperative to measure the FL in multiple environments because the natural environment changes frequently. Using a high-throughput method that combines precise gene replacement with next-generation sequencing, we determine the in vivo FL of a yeast tRNA gene comprising over 23,000 genotypes in four environments. Although genotype-by-environment interaction is abundantly detected, its pattern is so simple that we can transform an existing FL to that in a new environment with fitness measures of only a few genotypes in the new environment. Under each environment, we observe prevalent, negatively biased epistasis between mutations. Epistasis-by-environment interaction is also prevalent, but trends in epistasis difference between environments are predictable. Our study thus reveals simple rules underlying seemingly complex FLs, opening the door to understanding and predicting FLs in general.
View details for PubMedID 29686238
The fitness landscape of a tRNA gene
2016; 352 (6287): 837-840
Fitness landscapes describe the genotype-fitness relationship and represent major determinants of evolutionary trajectories. However, the vast genotype space, coupled with the difficulty of measuring fitness, has hindered the empirical determination of fitness landscapes. Combining precise gene replacement and next-generation sequencing, we quantified Darwinian fitness under a high-temperature challenge for more than 65,000 yeast strains, each carrying a unique variant of the single-copy tRNA(CCU)(Arg) gene at its native genomic location. Approximately 1% of single point mutations in the gene were beneficial and 42% were deleterious. Almost half of all mutation pairs exhibited statistically significant epistasis, which had a strong negative bias, except when the mutations occurred at Watson-Crick paired sites. Fitness was broadly correlated with the predicted fraction of correctly folded transfer RNA (tRNA) molecules, thereby revealing a biophysical basis of the fitness landscape.
View details for DOI 10.1126/science.aae0568
View details for Web of Science ID 000375663000044
View details for PubMedID 27080104
View details for PubMedCentralID PMC4894649
A truncated Danio rerio PKZ isoform functionally interacts with eIF2 alpha and inhibits protein synthesis
2013; 527 (1): 292-300
A protein kinase containing Z-DNA binding domains (PKZ), which resembles protein kinase R (PKR) in domain organization, was recently discovered to be a member of the eIF2α kinase family in fish. PKR has roles in antiviral immunity through inhibiting protein synthesis and activating NF-κB; therefore, it is thought that PKZ may have a similar role in fish antiviral immunity. In the present study, the roles of two Danio rerio PKZ isoforms (DrPKZ-A and DrPKZ-B) in eIF2α phosphorylation and protein synthesis regulation were explored. DrPKZ-A and DrPKZ-B possess N-terminal Z-DNA binding domains and a conserved eIF2α kinase domain; however, they have domains of differing lengths inserted between kinase subdomains IV and V. DrPKZ-A has an insert domain of 73 amino acids (aa), whereas DrPKZ-B has an insert sequence of only 10 aa, suggesting that DrPKZ-B could be a dysfunctional isoform or could interact with different substrates. Our results show that both DrPKZ-A and DrPKZ-B functionally interact with eIF2α and inhibit protein synthesis, although DrPKZ-B possesses attenuated kinase activity. Our results also show that deletion of the insert in either isoform results in the complete abrogation of kinase activity, suggesting that the insert is critical for PKZ kinase activity. Kinase activity appears to be independent of insert length but may depend on the presence of specific amino acids within the insert domain. Furthermore, the effects of the N-terminal regulatory domain on kinase activity were analyzed. Deletion of the N-terminus results in reduced kinase activity of these isoforms relative to the wild-type forms, indicating that the isolated kinase domain is sufficient for eIF2α phosphorylation and that DrPKZ-A and DrPKZ-B may be regulated in a similar manner. Overall, our results show that DrPKZ-B is a functional kinase in zebrafish and contribute to our understanding of the function of PKZ in fish.
View details for DOI 10.1016/j.gene.2013.05.043
View details for Web of Science ID 000323589100039
View details for PubMedID 23742890
Toward Genome-Wide Identification of Bateson-Dobzhansky-Muller Incompatibilities in Yeast: A Simulation Study
GENOME BIOLOGY AND EVOLUTION
2013; 5 (7): 1261-1272
The Bateson-Dobzhansky-Muller (BDM) model of reproductive isolation by genetic incompatibility is a widely accepted model of speciation. Because of the exceptionally rich biological information about the budding yeast Saccharomyces cerevisiae, the identification of BDM incompatibilities in yeast would greatly deepen our understanding of the molecular genetic basis of reproductive isolation and speciation. However, despite repeated efforts, BDM incompatibilities between nuclear genes have never been identified between S. cerevisiae and its sister species S. paradoxus. Such negative results have led to the belief that simple nuclear BDM incompatibilities do not exist between the two yeast species. Here, we explore an alternative explanation that such incompatibilities exist but were undetectable due to limited statistical power. We discover that previously employed statistical methods were not ideal and that a redesigned method improves the statistical power. We determine, under various sample sizes, the probabilities of identifying BDM incompatibilities that cause F1 spore inviability with incomplete penetrance, and confirm that the previously used samples were too small to detect such incompatibilities. Our findings call for an expanded experimental search for yeast BDM incompatibilities, which has become possible with the decreasing cost of genome sequencing. The improved methodology developed here is, in principle, applicable to other organisms and can help detect epistasis in general.
View details for DOI 10.1093/gbe/evt091
View details for Web of Science ID 000324594500002
View details for PubMedID 23742870
View details for PubMedCentralID PMC3730343