Biology subjects

Ginalski, K.

Publications and source records attributed to Ginalski, K..

3 recordsLinked to original sources

bioRxiv2017

Integrated analysis of DSB patterns reveals precisely DSB formation mechanisms following replication fork collapse

DNA double-strand breaks (DSBs) can be detected by label-based sequencing or pulsed-field gel electrophoresis (PFGE). Sequencing yields population-average DSB frequencies genome-wide, while PFGE reveals percentages of broken chromosomes. We constructed a mathematical framework to combine advantages of both: high-resolution DSB locations and their population distribution. We also use sequencing read patterns to identify replication-induced DSBs and active replication origins. We describe changes in spatiotemporal replication program upon hydroxyurea-induced replication stress. We found that one-ended DSBs, resulting from collapsed replication forks, are population-representative, while majority of two-ended DSBs (79-100%) are not. To study replication fork collapse, we used strains lacking the checkpoint protein Mec1 and the endonuclease Mus81 and quantified that 19% and 13% of hydroxyurea-induced one-ended DSBs are Mec1-and Mus81-dependent, respectively. We also clarified that Mus81-induced one-ended DSBs are Mec1-dependent.

genomics↗

bioRxiv2017

Quantitative DSB sequencing (qDSB-Seq): a method for genome-wide accurate estimation of absolute DNA double-strand break frequencies per cell

Sequencing-based methods for mapping DNA double-strand breaks (DSBs) allow measurement only of relative frequencies of DSBs between loci, which limits our understanding of the physiological relevance of detected DSBs. We propose quantitative DSB sequencing (qDSB-Seq), a method providing both DSB frequencies per cell and their precise genomic coordinates. We induced spike-in DSBs by a site-specific endonuclease and used them to quantify labeled DSBs (e.g. using i-BLESS). Utilizing qDSB-Seq, we determined numbers of DSBs induced by a radiomimetic drug and various forms of replication stress, and revealed several orders of magnitude differences in DSB frequencies. We also measured for the first time Top1-dependent absolute DSB frequencies at replication fork barriers. qDSB-Seq is compatible with various DSB labeling methods in different organisms and allows accurate comparisons of absolute DSB frequencies across samples.

genomics↗

bioRxiv2017

Inferring proteome dynamics during yeast cell cycle using gene expression data

Protein levels are most relevant physiologically, but measuring them genome-wide remains a challenge. In contrast, mRNA levels are much easier and less expensive to measure globally. Therefore, RNA levels are typically used to infer the corresponding protein levels. The steady-state condition (assumption that protein levels remain constant) is typically used to calculate protein abundances, as it is mathematically very convenient, even though it is often clear that it is not satisfied for proteins of interest. Here, we propose a simple, yet very effective, method to estimate genome wide protein abundances, which does not require the assumption that protein levels remain constant, and thus allows us to also predict proteome dynamics. Instead, we assume that the system returns to the baseline at the end of experiments; such an assumption is satisfied in many time-course experiments and in all periodic conditions (e.g. cell cycle). The approach only requires availability of gene expression and protein half-life data. As proof-of-concept, we calculated the predicted proteome dynamics for the budding yeast proteome during the cell cycle, which can be conveniently browsed online. The approach was validated experimentally by verifying that the predicted protein concentration changes were consistent with measurements for all proteins tested. Additionally, if proteomic data are also available, our approach can be used to predict how half-lives change in response to posttranslational regulation. We illustrated this application of our method with de novo prediction of changes in the degradation rate of Clb2 in response to post-translational modifications. The predicted changes were consistent with earlier observations in the literature.

bioinformatics↗