Bernard P. Duncker

The meiosis-specific kinase Mek1 regulates key steps in meiotic recombination in the budding yeast, Saccharomyces cerevisiae. MEK1 limits resection at double strand break (DSB) ends and is required for preferential strand invasion into homologs, a process known as interhomolog bias. After strand invasion, MEK1 promotes phosphorylation of the synaptonemal complex protein Zip1 that is necessary for DSB repair mediated by a crossover specific pathway that enables chromosome synapsis. In addition, Mek1 phosphorylation of the meiosis-specific transcription factor, Ndt80, regulates the meiotic recombination checkpoint that prevents exit from pachytene when DSBs are present. Mek1 interacts with Ndt80 through a five amino acid sequence, RPSKR, located between the DNA binding and activation domains of Ndt80. AlphaFold Multimer modeling of a fragment of Ndt80 containing the RPSKR motif and full length Mek1 indicated that RPSKR binds to an acidic loop located in the Mek1 FHA domain, a non-canonical interaction with this motif. A second protein, the 5’-3’ helicase Rrm3, similarly interacts with Mek1 through an RPAKR motif and is an in vitro substrate of Mek1. Genetic analysis using various mutants in the MEK1 acidic loop validated the AlphaFold model, in that they specifically disrupt two-hybrid interactions with Ndt80 and Rrm3. Phenotypic analyses further showed that the acidic loop mutants are defective in the meiotic recombination checkpoint, and in certain circumstances exhibit more severe phenotypes compared to the NDT80 mutant with the RPSKR sequence deleted, suggesting that additional, as yet unknown, substrates of Mek1 also bind to Mek1 using an RPXKR motif.

The budding yeast Dbf4-dependent kinase (DDK) complex—comprised of cell division cycle (Cdc7) kinase and its regulatory subunit dumbbell former 4 (Dbf4)—is required to trigger the initiation of DNA replication through the phosphorylation of multiple minichromosome maintenance complex subunits 2-7 (Mcm2-7). DDK is also a target of the radiation sensitive 53 (Rad53) checkpoint kinase in response to replication stress. Numerous investigations have determined mechanistic details, including the regions of Mcm2, Mcm4, and Mcm6 phosphorylated by DDK, and a number of DDK docking sites. Similarly, the way in which the Rad53 forkhead-associated 1 (FHA1) domain binds to DDK—involving both canonical and non-canonical interactions—has been elucidated. Recent work has revealed mutual promotion of DDK and synthetic lethal with dpb11-1 3 (Sld3) roles. While DDK phosphorylation of Mcm2-7 subunits facilitates their interaction with Sld3 at origins, Sld3 in turn stimulates DDK phosphorylation of Mcm2. Details of a mutually antagonistic relationship between DDK and Rap1-interacting factor 1 (Rif1) have also recently come to light. While Rif1 is able to reverse DDK-mediated Mcm2-7 complex phosphorylation by targeting the protein phosphatase glycogen 7 (Glc7) to origins, there is evidence to suggest that DDK can counteract this activity by binding to and phosphorylating Rif1.

: There are multiple sources of biological and technical variation in a typical ecotoxicology study that may not be revealed by traditional endpoints but that become apparent in an omics dataset. As researchers increasingly apply omics technologies to environmental studies, it will be necessary to understand and control the main source(s) of variability to facilitate meaningful interpretation of such data. For instance, can variability in omics studies be addressed by changing the approach to study design and data analysis? Are there statistical methods that can be employed to correctly interpret omics data and make use of unattributed, inherent variability? The present study presents a review of experimental design and statistical considerations applicable to the use of omics methods in systems toxicology studies. In addition to highlighting potential sources that contribute to experimental variability, this review suggests strategies with which to reduce and/or control such variability so as to improve reliability, reproducibility, and ultimately the application of omics data for systems toxicology. Environ Toxicol Chem 2015;34:1693–1704. © 2015 SETAC