Daughter-Specific Transcription Factors Regulate Cell Size Control in Budding Yeast

Stefano Di Talia, Hongyin Wang, Jan M. Skotheim,Adam P. Rosebrock, Bruce Futcher, Frederick R. Cross

Abstract | In budding yeast, asymmetric cell division yields a larger mother and a smaller daughter cell, which transcribe different genes due to the daughter-specific transcription factors Ace2 and Ash1. Cell size control at the Start checkpoint has long been considered to be a main regulator of the length of the G1 phase of the cell cycle, resulting in longer G1 in the smaller daughter cells. Our recent data confirmed this concept using quantitative time-lapse microscopy. However, it has been proposed that daughter-specific, Ace2-dependent repression of expression of the G1 cyclin CLN3 had a dominant role in delaying daughters in G1. We wanted to reconcile these two divergent perspectives on the origin of long daughter G1 times. We quantified size control using single-cell time-lapse imaging of fluorescently labeled budding yeast, in the presence or absence of the daughter-specific transcriptional regulators Ace2 and Ash1. Ace2 and Ash1 are not required for efficient size control, but they shift the domain of efficient size control to larger cell size, thus increasing cell size requirement for Start in daughters. Microarray and chromatin immunoprecipitation experiments show that Ace2 and Ash1 are direct transcriptional regulators of the G1 cyclin gene CLN3. Quantification of cell size control in cells expressing titrated levels of Cln3 from ectopic promoters, and from cells with mutated Ace2 and Ash1 sites in the CLN3promoter, showed that regulation of CLN3 expression by Ace2 and Ash1 can account for the differential regulation of Start in response to cell size in mothers and daughters. We show how daughter-specific transcriptional programs can interact with intrinsic cell size control to differentially regulate Start in mother and daughter cells. This work demonstrates mechanistically how asymmetric localization of cell fate determinants results in cell-type-specific regulation of the cell cycle.

http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1000221

How to make primers for direct integration into markers of BY4741

Tutorial | I have been captivated by the idea of directly integrating a PCR product into the yeast genome. The integration usually is done at one of the auxotrophic marker loci in the lab strain. These regions contain the remains of the original gene or a crippled version of the gene. Since integration there requires homology, and the genes have been deleted using different strategies, I did set to make a set of primer fragments or overhangs that I could use to directly target to the deleted regions of, in my case, BY4741. You can find a notebook containing the annotated genomic sequences for a few markers, a Word file with the fragment themselves and the relevant literature here.

Harnessing gene expression to identify the genetic basis of drug resistance

Bo-Juen Chen, Helen C Causton, Denesy Mancenido, Noel L Goddard, Ethan O Perlstein and Dana Pe’er

Abstract | The advent of cost-effective genotyping and sequencing methods have recently made it possible to ask questions that address the genetic basis of phenotypic diversity and how natural variants

interact with the environment.We developed Camelot (CAusal Modelling with Expression Linkage for cOmplex Traits), a statistical method that integrates genotype, gene expression and phenotype data to automatically build models that both predict complex quantitative phenotypes and identify genes that actively influence these traits. Camelot integrates genotype and gene expression data, both generated under a reference condition, to predict the response to entirely different conditions. We systematically applied our algorithm to data generated from a collection of yeast segregants, using genotype and gene expression data generated under drug-free conditions to predict the response to 94 drugs and experimentally confirmed 14 novel gene–drug interactions. Our approach is robust, applicable to other phenotypes and species, and has potential for applications in personalized medicine, for example, in predicting how an individual will respond to a previously unseen drug.

http://www.nature.com/msb/journal/v5/n1/pdf/msb200969.pdf