Mechanisms of Cdc42 Polarization in Yeast

Polarization is important for the function and morphology of many different cell types. The keys regulators of polarity in eukaryotes are the Rho-family GTPases. In the budding yeast Saccharomyces cerevisiae, which must polarize in order to bud and to mate, the master regulator is the highly conserved Rho GTPase, Cdc42. During polarity establishment, active Cdc42 accumulates at a site on the plasma membrane characterizing the “front” of the cell where the bud will emerge. The orientation of polarization is guided by upstream cues that dictate the site of Cdc42 clustering. However, in the absence of upstream cues, yeast can still polarize in a random direction during symmetry breaking. Symmetry breaking suggests cells possess an autocatalytic polarization mechanism that can amplify stochastic fluctuations of polarity proteins through a positive feedback mechanism.

Two different positive feedback mechanisms have been proposed to polarize Cdc42 in budding yeast. One model posits that Cdc42 activation must be localized to a site at the plasma membrane. Another model posits that Cdc42 delivery must be localized to a particular site at the plasma membrane. Although both mechanisms could work in parallel to polarize Cdc42, it is unclear which mechanism is critical to polarity establishment. We directly tested the predictions of the two positive feedback models using genetics and live microscopy. We found that localized Cdc42 activation is necessary for polarity establishment.

While this explains how active Cdc42 localizes to a particular site at the plasma membrane, it does not address how Cdc42 concentrates at that site. Several different mechanisms have been proposed to concentrate Cdc42. The GDI can extract Cdc42 from membranes and selective mobilize GDP-Cdc42 in the cytoplasm. It was proposed that selectively mobilizing GDP-Cdc42 in combination with local activation could locally concentrate total Cdc42 at the polarity site. Although the GDI is important for rapid Cdc42 accumulation at the polarity site, it is not essential to Cdc42 concentration. It was proposed that delivery of Cdc42 by actin-mediated vesicle can act as a backup pathway to concentrate Cdc42. However, we found no evidence for an actin-dependent concentrating pathway. Live microscopy experiments reveal that prenylated proteins are not restricted to membranes, and can enter the cytoplasm. We found that the GDI-independent concentrating pathway still requires Cdc42 to exchange between the plasma membrane and the cytoplasm, which is supported by computational modeling. In the absence of the GDI, we found that Cdc42 GAP became essential for polarization. We propose that the GAP limits GTP-Cdc42 leak into the cytoplasm, which would be prohibitive to Cdc42 polarization.

Topoisomerase 1 (Top1)-associated Genome Instability in Yeast: Effects of Persistent Cleavage Complexes or Increased Top1 Levels

Topoisomerase 1 (Top1), a Type IB topoisomerase, functions to relieve transcription- and replication-associated torsional stress in DNA. Top1 cleaves one strand of DNA, covalently associates with the 3’ end of the nick to form a Top1-cleavage complex (Top1cc), passes the intact strand through the nick and finally re-ligates the broken strand. The chemotherapeutic drug, Camptothecin, intercalates at a Top1cc and prevents the crucial re-ligation reaction that is mediated by Top1, resulting in the conversion of a nick to a toxic double-strand break during DNA replication or the accumulation of Top1cc. This mechanism of action preferentially targets rapidly dividing tumor cells, but can also affect non-tumor cells when patients undergo treatment. Additionally, Top1 is found to be elevated in numerous tumor tissues making it an attractive target for anticancer therapies. We investigated the effects of Top1 on genome stability, effects of persistent Top1-cleavage complexes and elevated Top1 levels, in Saccharomyces cerevisiae. We found that increased levels of the Top1cc resulted in a five- to ten-fold increase in reciprocal crossovers, three- to fifteen fold increase in mutagenesis and greatly increased instability within the rDNA and CUP1 tandem arrays. Increased Top1 levels resulted in a fifteen- to twenty-two fold increase in mutagenesis and increased instability in rDNA locus. These results have important implications for understanding the effects of CPT and elevated Top1 levels as a chemotherapeutic agent.

Predicting Transcript Production Rates in Yeast With Sparse Linear Models

To provide biological insights into transcriptional regulation, a couple of groups have recently presented models relating the promoter DNA-bound transcription factors (TFs) to downstream gene’s mean transcript level or transcript production rates over time. However, transcript production is dynamic in response to changes of TF concentrations over time. Also, TFs are not the only factors binding to promoters; other DNA binding factors (DBFs) bind as well, especially nucleosomes, resulting in competition between DBFs for binding at same genomic location. Additionally, not only TFs, but also some other elements regulate transcription. Within core promoter, various regulatory elements influence RNAPII recruitment, PIC formation, RNAPII searching for TSS, and RNAPII initiating transcription. Moreover, it is proposed that downstream from TSS, nucleosomes resist RNAPII elongation.

Here, we provide a machine learning framework to predict transcript production rates from DNA sequences. We applied this framework in the S. cerevisiae yeast for two scenarios: a) to predict the dynamic transcript production rate during the cell cycle for native promoters; b) to predict the mean transcript production rate over time for synthetic promoters. As far as we know, our framework is the first successful attempt to have a model that can predict dynamic transcript production rates from DNA sequences only: with cell cycle data set, we got Pearson correlation coefficient Cp = 0.751 and coefficient of determination r2 = 0.564 on test set for predicting dynamic transcript production rate over time. Also, for DREAM6 Gene Promoter Expression Prediction challenge, our fitted model outperformed all participant teams, best of all teams, and a model combining best team’s k-mer based sequence features and another paper’s biologically mechanistic features, in terms of all scoring metrics.

Moreover, our framework shows its capability of identifying generalizable fea- tures by interpreting the highly predictive models, and thereby provide support for associated hypothesized mechanisms about transcriptional regulation. With the learned sparse linear models, we got results supporting the following biological insights: a) TFs govern the probability of RNAPII recruitment and initiation possibly through interactions with PIC components and transcription cofactors; b) the core promoter amplifies the transcript production probably by influencing PIC formation, RNAPII recruitment, DNA melting, RNAPII searching for and selecting TSS, releasing RNAPII from general transcription factors, and thereby initiation; c) there is strong transcriptional synergy between TFs and core promoter elements; d) the regulatory elements within core promoter region are more than TATA box and nucleosome free region, suggesting the existence of still unidentified TAF-dependent and cofactor-dependent core promoter elements in yeast S. cerevisiae; e) nucleosome occupancy is helpful for representing +1 and -1 nucleosomes’ regulatory roles on transcription.

Physical and Genetic Analysis of the CUP1 Tandem Array in the Yeast Saccharomyces cerevisiae

The genomes of many strains of baker’s yeast, Saccharomyces cerevisiae, contain multiple repeats of the copper-binding protein Cup1. Cup1 is a member of the metallothionein family, and is found in a tandem array on chromosome VIII. In this thesis, I describe studies that characterized these tandem arrays and their mechanism of formation across diverse strains of yeast. I show that CUP1 arrays are an illuminating model system for observing recombination in eukaryotes, and describe insights derived from these observations.

In our first study, we analyzed 101 natural isolates of S. cerevisiae in order to examine the diversity of CUP1-containing repeats across different strains. We identified five distinct classes of repeats that contain CUP1. We also showed that some strains have only a single copy of CUP1. By comparing the sequences of all the strains, we were able to elucidate the mechanism of formation of the CUP1 tandem arrays, which involved unequal non-homologous recombination events starting from a strain that had only a single CUP1 gene. Our observation of CUP1 repeat formation allows more general insights about the formation of tandem repeats from single-copy genes in eukaryotes, which is one of the most important mechanisms by which organisms evolve.

In our second study, we delved deeper into our mechanistic investigations by measuring the relative rates of inter-homolog and intra-/inter-sister chromatid recombination in CUP1 tandem arrays. We used a diploid strain that is heterozygous both for insertion of a selectable marker (URA3) inside the tandem array, and also for markers at either end of the array. The intra-/inter-sister chromatid recombination rate turned out to be more than ten-fold greater than the inter-homolog rate. Moreover, we found that loss of the proteins Rad51 and Rad52, which are required for most inter-homolog recombination, did not greatly reduce recombination in the CUP1 tandem repeats. Additionally, we investigated the effects of elevated copper levels on the rate of each type of recombination at the CUP1 locus. Both types of recombination are increased at high concentrations of copper (as is known to be the case for CUP1 transcription). Furthermore, the inter-homolog recombination rate at the CUP1 locus is higher than the average over the genome during mitosis, but is lower than the average during meiosis.

The research described in Chapter 2 is published in 2014.