Mapping the Synthetic Dosage Lethality Network of CDK1/CDC28

Cyclin-dependent kinases-based synthetic lethality: Evidence, concept, and strategy

Synthetic lethality is a proven effective antitumor strategy that has attracted great attention. Large-scale screening has revealed many synthetic lethal genetic phenotypes, and relevant small-molecule drugs have also been implemented in clinical practice. Increasing evidence suggests that CDKs, constituting a kinase family predominantly involved in cell cycle control, are synthetic lethal factors when combined with certain oncogenes, such as MYC, TP53, and RAS, which facilitate numerous antitumor treatment options based on CDK-related synthetic lethality. In this review, we focus on the synthetic lethal phenotype and mechanism related to CDKs and summarize the preclinical and clinical discoveries of CDK inhibitors to explore the prospect of CDK inhibitors as antitumor compounds for strategic synthesis lethality in the future.

LI Detector: a framework for sensitive colony-based screens regardless of the distribution of fitness effects

Microbial growth characteristics have long been used to investigate fundamental questions of biology. Colony-based high-throughput screens enable parallel fitness estimation of thousands of individual strains using colony growth as a proxy for fitness. However, fitness estimation is complicated by spatial biases affecting colony growth, including uneven nutrient distribution, agar surface irregularities, and batch effects. Analytical methods that have been developed to correct for these spatial biases rely on the following assumptions: (1) that fitness effects are normally distributed, and (2) that most genetic perturbations lead to minor changes in fitness. Although reasonable for many applications, these assumptions are not always warranted and can limit the ability to detect small fitness effects. Beneficial fitness effects, in particular, are notoriously difficult to detect under these assumptions. Here, we developed the linear interpolation-based detector (LI Detector) framework to enable sensitive colony-based screening without making prior assumptions about the underlying distribution of fitness effects. The LI Detector uses a grid of reference colonies to assign a relative fitness value to every colony on the plate. We show that the LI Detector is effective in correcting for spatial biases and equally sensitive toward increase and decrease in fitness. LI Detector offers a tunable system that allows the user to identify small fitness effects with unprecedented sensitivity and specificity. LI Detector can be utilized to develop and refine gene-gene and gene-environment interaction networks of colony-forming organisms, including yeast, by increasing the range of fitness effects that can be reliably detected.

Cdk1 gates cell cycle-dependent tRNA synthesis by regulating RNA polymerase III activity

tRNA genes are transcribed by RNA polymerase III (RNAPIII). During recent years it has become clear that RNAPIII activity is strictly regulated by the cell in response to environmental cues and the homeostatic status of the cell. However, the molecular mechanisms that control RNAPIII activity to regulate the amplitude of tDNA transcription in normally cycling cells are not well understood. Here, we show that tRNA levels fluctuate during the cell cycle and reveal an underlying molecular mechanism. The cyclin Clb5 recruits the cyclin dependent kinase Cdk1 to tRNA genes to boost tDNA transcription during late S phase. At tDNA genes, Cdk1 promotes the recruitment of TFIIIC, stimulates the interaction between TFIIIB and TFIIIC, and increases the dynamics of RNA polymerase III in vivo. Furthermore, we identified Bdp1 as a putative Cdk1 substrate in this process. Preventing Bdp1 phosphorylation prevented cell cycle-dependent recruitment of TFIIIC and abolished the cell cycle-dependent increase in tDNA transcription. Our findings demonstrate that under optimal growth conditions Cdk1 gates tRNA synthesis in S phase by regulating the RNAPIII machinery, revealing a direct link between the cell cycle and RNAPIII activity.

Yeast genetic interaction screens in the age of CRISPR/Cas

The ease of performing both forward and reverse genetics in Saccharomyces cerevisiae, along with its stable haploid state and short generation times, has made this budding yeast the consummate model eukaryote for genetics. The major advantage of using budding yeast for reverse genetics is this organism's highly efficient homology-directed repair, allowing for precise genome editing simply by introducing DNA with homology to the chromosomal target. Although plasmid- and PCR-based genome editing tools are quite efficient, they depend on rare spontaneous DNA breaks near the target sequence. Consequently, they can generate only one genomic edit at a time, and the edit must be associated with a selectable marker. However, CRISPR/Cas technology is efficient enough to permit markerless and multiplexed edits in a single step. These features have made CRISPR/Cas popular for yeast strain engineering in synthetic biology and metabolic engineering applications, but it has not been widely employed for genetic screens. In this review, we critically examine different methods to generate multi-mutant strains in systematic genetic interaction screens and discuss the potential of CRISPR/Cas to supplement or improve on these methods.

Synthetic Lethal Networks for Precision Oncology: Promises and Pitfalls

Synthetic lethal interactions, in which the simultaneous loss of function of two genes produces a lethal phenotype, are being explored as a means to therapeutically exploit cancer-specific vulnerabilities and expand the scope of precision oncology. Currently, three Food and Drug Administration-approved drugs work by targeting the synthetic lethal interaction between BRCA1/2 and PARP. This review examines additional efforts to discover networks of synthetic lethal interactions and discusses both challenges and opportunities regarding the translation of new synthetic lethal interactions into the clinic.

Mapping a diversity of genetic interactions in yeast

Genetic interactions occur when the combination of multiple mutations yields an unexpected phenotype, and they may confound our ability to fully understand the genetic mechanisms underlying complex diseases. Genetic interactions are challenging to study because there are millions of possible different variant combinations within a given genome. Consequently, they have primarily been systematically explored in unicellular model organisms, such as yeast, with a focus on pairwise genetic interactions between loss-of-function alleles. However, there are many different types of genetic interactions, such as those occurring between gain-of-function or heterozygous mutations. Here, we review recent advances made in the systematic analysis of such diverse genetic interactions in yeast, and briefly discuss how similar studies could be undertaken in human cells.