Honors & Awards
Hanna Gray Fellow, Howard Hughes Medical Institute (2022-ongoing)
Postdoctoral NRSA Fellowship awardee, F32 NIAID Ruth L. Kirschstein Postdoctoral Individual National Research Service Award (2021-2024)
Postdoctoral Fellowship awardee, School of Medicine Dean's Postdoctoral Fellowship (2021)
Postdoctoral Fellow, Stanford Center for Computational, Evolutionary and Human Genomics (CEHG) (2020-2021)
Postdoctoral Training Grant awardee, Stanford Genomics Training Program NHGRI training grant (2020-2021)
Graduate Research Fellowship Program (GRFP) awardee, National Science Foundation (2015-2019)
Predoctoral Training Grant awardee, University of Washington NHGRI Genome Training Grant (2014-2015)
Gavin Sherlock, Postdoctoral Faculty Sponsor
A natural variant of the essential host gene MMS21 restricts the parasitic 2-micron plasmid in Saccharomyces cerevisiae.
Antagonistic coevolution with selfish genetic elements (SGEs) can drive evolution of host resistance. Here, we investigated host suppression of 2-micron (2m) plasmids, multicopy nuclear parasites that have co-evolved with budding yeasts. We developed SCAMPR (Single-Cell Assay for Measuring Plasmid Retention) to measure copy number heterogeneity and 2m plasmid loss in live cells. We identified three S. cerevisiae strains that lack endogenous 2m plasmids and reproducibly inhibit mitotic plasmid stability. Focusing on the Y9 ragi strain, we determined that plasmid restriction is heritable and dominant. Using bulk segregant analysis, we identified a high-confidence Quantitative Trait Locus (QTL) with a single variant of MMS21 associated with increased 2m instability. MMS21 encodes a SUMO E3 ligase and an essential component of the Smc5/6 complex, involved in sister chromatid cohesion, chromosome segregation, and DNA repair. Our analyses leverage natural variation to uncover a novel means by which budding yeasts can overcome highly successful genetic parasites.
View details for DOI 10.7554/eLife.62337
View details for PubMedID 33063663
Independent Origins of Yeast Associated with Coffee and Cacao Fermentation.
Current biology : CB
2016; 26 (7): 965-71
Modern transportation networks have facilitated the migration and mingling of previously isolated populations of plants, animals, and insects. Human activities can also influence the global distribution of microorganisms. The best-understood example is yeasts associated with winemaking. Humans began making wine in the Middle East over 9,000 years ago [1, 2]. Selecting favorable fermentation products created specialized strains of Saccharomyces cerevisiae [3, 4] that were transported along with grapevines. Today, S. cerevisiae strains residing in vineyards around the world are genetically similar, and their population structure suggests a common origin that followed the path of human migration [3-7]. Like wine, coffee and cacao depend on microbial fermentation [8, 9] and have been globally dispersed by humans. Theobroma cacao originated in the Amazon and Orinoco basins of Colombia and Venezuela , was cultivated in Central America by Mesoamerican peoples, and was introduced to Europeans by Hernán Cortés in 1530 . Coffea, native to Ethiopia, was disseminated by Arab traders throughout the Middle East and North Africa in the 6(th) century and was introduced to European consumers in the 17(th) century . Here, we tested whether the yeasts associated with coffee and cacao are genetically similar, crop-specific populations or genetically diverse, geography-specific populations. Our results uncovered populations that, while defined by niche and geography, also bear signatures of admixture between major populations in events independent of the transport of the plants. Thus, human-associated fermentation and migration may have affected the distribution of yeast involved in the production of coffee and chocolate.
View details for DOI 10.1016/j.cub.2016.02.012
View details for PubMedID 27020745
View details for PubMedCentralID PMC4821677
Aneuploidy underlies a multicellular phenotypic switch.
Proceedings of the National Academy of Sciences of the United States of America
2013; 110 (30): 12367-72
Although microorganisms are traditionally used to investigate unicellular processes, the yeast Saccharomyces cerevisiae has the ability to form colonies with highly complex, multicellular structures. Colonies with the "fluffy" morphology have properties reminiscent of bacterial biofilms and are easily distinguished from the "smooth" colonies typically formed by laboratory strains. We have identified strains that are able to reversibly toggle between the fluffy and smooth colony-forming states. Using a combination of flow cytometry and high-throughput restriction-site associated DNA tag sequencing, we show that this switch is correlated with a change in chromosomal copy number. Furthermore, the gain of a single chromosome is sufficient to switch a strain from the fluffy to the smooth state, and its subsequent loss to revert the strain back to the fluffy state. Because copy number imbalance of six of the 16 S. cerevisiae chromosomes and even a single gene can modulate the switch, our results support the hypothesis that the state switch is produced by dosage-sensitive genes, rather than a general response to altered DNA content. These findings add a complex, multicellular phenotype to the list of molecular and cellular traits known to be altered by aneuploidy and suggest that chromosome missegregation can provide a quick, heritable, and reversible mechanism by which organisms can toggle between phenotypes.
View details for DOI 10.1073/pnas.1301047110
View details for PubMedID 23812752
View details for PubMedCentralID PMC3725063
Use of pleiotropy to model genetic interactions in a population.
2012; 8 (10): e1003010
Systems-level genetic studies in humans and model systems increasingly involve both high-resolution genotyping and multi-dimensional quantitative phenotyping. We present a novel method to infer and interpret genetic interactions that exploits the complementary information in multiple phenotypes. We applied this approach to a population of yeast strains with randomly assorted perturbations of five genes involved in mating. We quantified pheromone response at the molecular level and overall mating efficiency. These phenotypes were jointly analyzed to derive a network of genetic interactions that mapped mating-pathway relationships. To determine the distinct biological processes driving the phenotypic complementarity, we analyzed patterns of gene expression to find that the pheromone response phenotype is specific to cellular fusion, whereas mating efficiency was a combined measure of cellular fusion, cell cycle arrest, and modifications in cellular metabolism. We applied our novel method to global gene expression patterns to derive an expression-specific interaction network and demonstrate applicability to global transcript data. Our approach provides a basis for interpretation of genetic interactions and the generation of specific hypotheses from populations assayed for multiple phenotypes.
View details for DOI 10.1371/journal.pgen.1003010
View details for PubMedID 23071457
View details for PubMedCentralID PMC3469415
Data-driven multiscale modeling reveals the role of metabolic coupling for the spatio-temporal growth dynamics of yeast colonies.
BMC molecular and cell biology
2019; 20 (1): 59
Multicellular entities like mammalian tissues or microbial biofilms typically exhibit complex spatial arrangements that are adapted to their specific functions or environments. These structures result from intercellular signaling as well as from the interaction with the environment that allow cells of the same genotype to differentiate into well-organized communities of diversified cells. Despite its importance, our understanding how this cell-cell and metabolic coupling lead to functionally optimized structures is still limited.Here, we present a data-driven spatial framework to computationally investigate the development of yeast colonies as such a multicellular structure in dependence on metabolic capacity. For this purpose, we first developed and parameterized a dynamic cell state and growth model for yeast based on on experimental data from homogeneous liquid media conditions. The inferred model is subsequently used in a spatially coarse-grained model for colony development to investigate the effect of metabolic coupling by calibrating spatial parameters from experimental time-course data of colony growth using state-of-the-art statistical techniques for model uncertainty and parameter estimations. The model is finally validated by independent experimental data of an alternative yeast strain with distinct metabolic characteristics and illustrates the impact of metabolic coupling for structure formation.We introduce a novel model for yeast colony formation, present a statistical methodology for model calibration in a data-driven manner, and demonstrate how the established model can be used to generate predictions across scales by validation against independent measurements of genetically distinct yeast strains.
View details for DOI 10.1186/s12860-019-0234-z
View details for PubMedID 31856706
View details for PubMedCentralID PMC6923950
Transcriptional Profiling of Biofilm Regulators Identified by an Overexpression Screen in Saccharomyces cerevisiae
G3-GENES GENOMES GENETICS
2017; 7 (8): 2845–54
Biofilm formation by microorganisms is a major cause of recurring infections and removal of biofilms has proven to be extremely difficult given their inherent drug resistance . Understanding the biological processes that underlie biofilm formation is thus extremely important and could lead to the development of more effective drug therapies, resulting in better infection outcomes. Using the yeast Saccharomyces cerevisiae as a biofilm model, overexpression screens identified DIG1, SFL1, HEK2, TOS8, SAN1, and ROF1/YHR177W as regulators of biofilm formation. Subsequent RNA-seq analysis of biofilm and nonbiofilm-forming strains revealed that all of the overexpression strains, other than DIG1 and TOS8, were adopting a single differential expression profile, although induced to varying degrees. TOS8 adopted a separate profile, while the expression profile of DIG1 reflected the common pattern seen in most of the strains, plus substantial DIG1-specific expression changes. We interpret the existence of the common transcriptional pattern seen across multiple, unrelated overexpression strains as reflecting a transcriptional state, that the yeast cell can access through regulatory signaling mechanisms, allowing an adaptive morphological change between biofilm-forming and nonbiofilm states.
View details for DOI 10.1534/g3.117.042440
View details for Web of Science ID 000407314000038
View details for PubMedID 28673928
View details for PubMedCentralID PMC5555487
Dissecting Gene Expression Changes Accompanying a Ploidy-Based Phenotypic Switch.
G3 (Bethesda, Md.)
2017; 7 (1): 233-246
Aneuploidy, a state in which the chromosome number deviates from a multiple of the haploid count, significantly impacts human health. The phenotypic consequences of aneuploidy are believed to arise from gene expression changes associated with the altered copy number of genes on the aneuploid chromosomes. To dissect the mechanisms underlying altered gene expression in aneuploids, we used RNA-seq to measure transcript abundance in colonies of the haploid Saccharomyces cerevisiae strain F45 and two aneuploid derivatives harboring disomies of chromosomes XV and XVI. F45 colonies display complex "fluffy" morphologies, while the disomic colonies are smooth, resembling laboratory strains. Our two disomes displayed similar transcriptional profiles, a phenomenon not driven by their shared smooth colony morphology nor simply by their karyotype. Surprisingly, the environmental stress response (ESR) was induced in F45, relative to the two disomes. We also identified genes whose expression reflected a nonlinear interaction between the copy number of a transcriptional regulatory gene on chromosome XVI, DIG1, and the copy number of other chromosome XVI genes. DIG1 and the remaining chromosome XVI genes also demonstrated distinct contributions to the effect of the chromosome XVI disome on ESR gene expression. Expression changes in aneuploids appear to reflect a mixture of effects shared between different aneuploidies and effects unique to perturbing the copy number of particular chromosomes, including nonlinear copy number interactions between genes. The balance between these two phenomena is likely to be genotype- and environment-specific.
View details for DOI 10.1534/g3.116.036160
View details for PubMedID 27836908
View details for PubMedCentralID PMC5217112
Identification and characterization of a drug-sensitive strain enables puromycin-based translational assays in Saccharomyces cerevisiae.
Yeast (Chichester, England)
2014; 31 (5): 167-78
Puromycin is an aminonucleoside antibiotic with structural similarity to aminoacyl tRNA. This structure allows the drug to bind the ribosomal A site and incorporate into nascent polypeptides, causing chain termination, ribosomal subunit dissociation and widespread translational arrest at high concentrations. In contrast, at sufficiently low concentrations, puromycin incorporates primarily at the C-terminus of proteins. While a number of techniques utilize puromycin incorporation as a tool for probing translational activity in vivo, these methods cannot be applied in yeasts that are insensitive to puromycin. Here, we describe a mutant strain of the yeast Saccharomyces cerevisiae that is sensitive to puromycin and characterize the cellular response to the drug. Puromycin inhibits the growth of yeast cells mutant for erg6∆, pdr1∆ and pdr3∆ (EPP) on both solid and liquid media. Puromycin also induces the aggregation of the cytoplasmic processing body component Edc3 in the mutant strain. We establish that puromycin is rapidly incorporated into yeast proteins and test the effects of puromycin on translation in vivo. This study establishes the EPP strain as a valuable tool for implementing puromycin-based assays in yeast, which will enable new avenues of inquiry into protein production and maturation.
View details for DOI 10.1002/yea.3007
View details for PubMedID 24610064
View details for PubMedCentralID PMC4013229
High-throughput tetrad analysis.
2013; 10 (7): 671-5
Tetrad analysis has been a gold-standard genetic technique for several decades. Unfortunately, the need to manually isolate, disrupt and space tetrads has relegated its application to small-scale studies and limited its integration with high-throughput DNA sequencing technologies. We have developed a rapid, high-throughput method, called barcode-enabled sequencing of tetrads (BEST), that uses (i) a meiosis-specific GFP fusion protein to isolate tetrads by FACS and (ii) molecular barcodes that are read during genotyping to identify spores derived from the same tetrad. Maintaining tetrad information allows accurate inference of missing genetic markers and full genotypes of missing (and presumably nonviable) individuals. An individual researcher was able to isolate over 3,000 yeast tetrads in 3 h, an output equivalent to that of almost 1 month of manual dissection. BEST is transferable to other microorganisms for which meiotic mapping is significantly more laborious.
View details for DOI 10.1038/nmeth.2479
View details for PubMedID 23666411
View details for PubMedCentralID PMC3696418
Predicting the effects of copy-number variation in double and triple mutant combinations.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
The study of genetic interactions is a powerful tool in inferring structure and function of biological networks. To date, genetic interaction studies have been dominated by pair-wise gene deletion screens. However, classical genetic analysis and natural genetic variation involve diverse gene forms ranging from null alleles to copy number variants. Moreover, genetic variation is typically multifactorial. Addressing multiple combinatorial genetic variations ranging in gene activity is therefore of critical value. We approach this problem using genetic network modeling that quantitatively encodes how genes influence the activity of one another and phenotype outcomes. A network model was initially inferred from linear decomposition of gene expression data. We used this network to predict the effects of combining multi-copy and deletion mutations of specific gene pairs and a gene triplet. Predicted expression patterns across hundreds of genes were experimentally validated. Prediction success was critically dependent on how a multi-copy gene interacted with other genes in the network model. This strategy provides a template for the inference, prediction, and testing of genetically complex hypotheses involving diverse genetic variation.
View details for PubMedID 22174259
View details for PubMedCentralID PMC3334851