In S. cerevisiae, mitochondrial DNA (mtDNA) molecules, in spite of their high copy number, segregate as if there were a small number of heritable units. The rapid segregation of mitochondrial genomes can be analyzed using mtDNA deletion variants. These small, amplified genomes segregate preferentially from mixed zygotes relative to wild-type mtDNA. This segregation advantage is abolished by mutations in a gene, MGT1, that encodes a recombination junction-resolving enzyme. We show here that resolvase deficiency causes a larger proportion of molecules to be linked together by recombination junctions, resulting in the aggregation of mtDNA into a small number of cytological structures. This change in mtDNA structure can account for the increased mitotic loss of mtDNA and the altered pattern of mtDNA segregation from zygotes. We propose that the level of unresolved recombination junctions influences the number of heritable units of mtDNA.
Silencing is a universal form of transcriptional regulation in which regions of the genome are reversibly inactivated by changes in chromatin structure. Sir2 (Silent Information Regulator) protein is unique among the silencing factors in Saccharomyces cerevisiae because it silences the rDNA as well as the silent mating-type loci and telomeres. Discovery of a gene family of Homologues of Sir Two (HSTs) in organisms from bacteria to humans suggests that SIR2's silencing mechanism might be conserved. The Sir2 and Hst proteins share a core domain, which includes two diagnostic sequence motifs of unknown function as well as four cysteines of a putative zinc finger. We demonstrate by mutational analyses that the conserved core and each of its motifs are essential for Sir2p silencing. Chimeras between Sir2p and a human Sir2 homologue (hSir2Ap) indicate that this human protein's core can substitute for that of Sir2p, implicating the core as a silencing domain. Immunofluorescence studies reveal partially disrupted localization, accounting for the yeast-human chimeras' ability to function at only a subset of Sir2p's target loci. Together, these results support a model for the involvement of distinct Sir2p-containing complexes in HM/telomeric and rDNA silencing and that HST family members, including the widely expressed hSir2A, may perform evolutionarily conserved functions.
Protein microarrays are similar to DNA microarrays; both enabling the parallel interrogation of thousands of probes immobilized on a surface. Consequently, they have benefited from technologies previously developed for DNA microarrays. However, assumptions for the analysis of DNA microarrays do not always translate to protein arrays, especially in the case of normalization. Hence, we have developed an experimental and computational framework to assess normalization procedures for protein microarrays. Specifically, we profiled two sera with markedly different autoantibody compositions. To analyze intra- and interarray variability, we compared a set of control proteins across subarrays and the corresponding spots across multiple arrays, respectively. To estimate the degree to which the normalization could help reveal true biological separability, we tested the difference in the signal between the sera relative to the variability within replicates. Next, by mixing the sera in different proportions (titrations), we correlated the reactivity of proteins with serum concentration. Finally, we analyzed the effect of normalization procedures on the list of reactive proteins. We compared global and quantile normalization, techniques that have traditionally been employed for DNA microarrays, with a novel normalization approach based on a robust linear model (RLM) making explicit use of control proteins. We show that RLM normalization is able to reduce both intra- and interarray technical variability while maintaining biological differences. Moreover, in titration experiments, RLM normalization enhances the correlation of protein signals with serum concentration. Conversely, while quantile and global normalization can reduce interarray technical variability, neither is as effective as RLM normalization in maintaining biological differences. Most importantly, both introduce artifacts that distort the signals and affect the correct identification of reactive proteins, impairing their use for biomarker discovery. Hence, we show RLM normalization is better suited to protein arrays than approaches used for DNA microarrays.
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