Replication Fork Collapse and Genome Instability in a Deoxycytidylate Deaminase Mutant

Ribonucleotide reductase (RNR) and deoxycytidylate deaminase (dCMP deaminase) are pivotal allosteric enzymes required to maintain adequate pools of deoxyribonucleoside triphosphates (dNTPs) for DNA synthesis and repair. Whereas RNR inhibition slows DNA replication and activates checkpoint responses, the effect of dCMP deaminase deficiency is largely unknown. Here, we report that deleting the Schizosaccharomyces pombe dcd1⁺ dCMP deaminase gene (SPBC2G2.13c) increases dCTP ∼30-fold and decreases dTTP ∼4-fold. In contrast to the robust growth of a Saccharomyces cerevisiae dcd1Δ mutant, fission yeast dcd1Δ cells delay cell cycle progression in early S phase and are sensitive to multiple DNA-damaging agents, indicating impaired DNA replication and repair. DNA content profiling of dcd1Δ cells differs from an RNR-deficient mutant. Dcd1 deficiency activates genome integrity checkpoints enforced by Rad3 (ATR), Cds1 (Chk2), and Chk1 and creates critical requirements for proteins involved in recovery from replication fork collapse, including the γH2AX-binding protein Brc1 and Mus81 Holliday junction resolvase. These effects correlate with increased nuclear foci of the single-stranded DNA binding protein RPA and the homologous recombination repair protein Rad52. Moreover, Brc1 suppresses spontaneous mutagenesis in dcd1Δ cells. We propose that replication forks stall and collapse in dcd1Δ cells, burdening DNA damage and checkpoint responses to maintain genome integrity.     

Learning a Prior on Regulatory Potential from eQTL Data

Genome-wide RNA expression data provide a detailed view of an organism's biological state; hence, a dataset measuring expression variation between genetically diverse individuals (eQTL data) may provide important insights into the genetics of complex traits. However, with data from a relatively small number of individuals, it is difficult to distinguish true causal polymorphisms from the large number of possibilities. The problem is particularly challenging in populations with significant linkage disequilibrium, where traits are often linked to large chromosomal regions containing many genes. Here, we present a novel method, Lirnet, that automatically learns a regulatory potential for each sequence polymorphism, estimating how likely it is to have a significant effect on gene expression. This regulatory potential is defined in terms of “regulatory features”—including the function of the gene and the conservation, type, and position of genetic polymorphisms—that are available for any organism. The extent to which the different features influence the regulatory potential is learned automatically, making Lirnet readily applicable to different datasets, organisms, and feature sets. We apply Lirnet both to the human HapMap eQTL dataset and to a yeast eQTL dataset and provide statistical and biological results demonstrating that Lirnet produces significantly better regulatory programs than other recent approaches. We demonstrate in the yeast data that Lirnet can correctly suggest a specific causal sequence variation within a large, linked chromosomal region. In one example, Lirnet uncovered a novel, experimentally validated connection between Puf3—a sequence-specific RNA binding protein—and P-bodies—cytoplasmic structures that regulate translation and RNA stability—as well as the particular causative polymorphism, a SNP in Mkt1, that induces the variation in the pathway.     

Signals and systems

A report on the 2006 Keystone Conference on Signaling Networks, Vancouver, Canada, 30 January-4 February 2006.     

Functional genomics of the yeast DNA-damage response

High-throughput approaches are beginning to have an impact on many areas of yeast biology. Two recent studies, using different experimental platforms, provide insight into new pathways involved in the response of yeast to DNA damage.     

Quantitative genetic analysis in Saccharomyces cerevisiae using epistatic miniarray profiles (E-MAPs) and its application to chromatin functions

The use of the budding yeast Saccharomyces cerevisiae as a simple eukaryotic model system for the study of chromatin assembly and regulation has allowed rapid discovery of genes that influence this complex process. The functions of many of the proteins encoded by these genes have not yet been fully characterized. Here, we describe a high-throughput methodology that can be used to illuminate gene function and discuss its application to a set of genes involved in the creation, maintenance and remodeling of chromatin structure. Our technique, termed E-MAPs, involves the generation of quantitative genetic interaction maps that reveal the function and organization of cellular proteins and networks.