Backup in gene regulatory networks explains differences between binding and knockout results

The complementarity of gene expression and protein–DNA interaction data led to several successful models of biological systems. However, recent studies in multiple species raise doubts about the relationship between these two datasets. These studies show that the overwhelming majority of genes bound by a particular transcription factor (TF) are not affected when that factor is knocked out. Here, we show that this surprising result can be partially explained by considering the broader cellular context in which TFs operate. Factors whose functions are not backed up by redundant paralogs show a fourfold increase in the agreement between their bound targets and the expression levels of those targets. In addition, we show that incorporating protein interaction networks provides physical explanations for knockout effects. New double knockout experiments support our conclusions. Our results highlight the robustness provided by redundant TFs and indicate that in the context of diverse cellular systems, binding is still largely functional

Age-Dependent in vitro Maturation Efficacy of Human Oocytes – Is There an Optimal Age?

In vitro maturation of oocytes from antral follicles seen during tissue harvesting is a fertility preservation technique with potential advantages over ovarian tissue cryopreservation (OTC), as mature frozen and later thawed oocyte used for fertilization poses decreased risk of malignant cells re-seeding, as compared to ovarian tissue implantation. We previously demonstrated that in vitro maturation (IVM) performed following OTC in fertility preservation patients, even in pre-menarche girls, yields a fair amount of oocytes available for IVM and freezing for future use. We conducted a retrospective cohort study, evaluating IVM outcomes in chemotherapy naïve patients referred for fertility preservation by OTC that had oocyte collected from the medium with attempted IVM. A total of 133 chemotherapy naïve patients aged 1–35 years were included in the study. The primary outcome was IVM rate in the different age groups – pre-menarche (1–5 and ≥6 years), post-menarche (menarche-17 years), young adults (18–24 years) and adults (25–29 and 30–35 years). We demonstrate a gradual increase in mean IVM rate in the age groups from 1 to 25 years [4.6% (1–5 years), 23.8% (6 years to menarche), and 28.4% (menarche to 17 years)], with a peak of 38.3% in the 18–24 years group, followed by a decrease in the 25–29 years group (19.3%), down to a very low IVM rate (8.9%) in the 30–35 years group. A significant difference in IVM rates was noted between the age extremes – the very young (1–5 years) and the oldest (30–35 years) groups, as compared with the 18–24-year group (p < 0.001). Importantly, number of oocytes matured, percent of patients with matured oocytes, and overall maturation rate differed significantly (p < 0.001). Our finding of extremely low success rates in those very young (under 6 years) and older (≥30 years) patients suggests that oocytes retrieved during OTC prior to chemotherapy have an optimal window of age that shows higher success rates, suggesting that oocytes may have an inherent tendency toward better maturation in those age groups

Four Linked Genes Participate in Controlling Sporulation Efficiency in Budding Yeast

Quantitative traits are conditioned by several genetic determinants. Since such genes influence many important complex traits in various organisms, the identification of quantitative trait loci (QTLs) is of major interest, but still encounters serious difficulties. We detected four linked genes within one QTL, which participate in controlling sporulation efficiency in Saccharomyces cerevisiae. Following the identification of single nucleotide polymorphisms by comparing the sequences of 145 genes between the parental strains SK1 and S288c, we analyzed the segregating progeny of the cross between them. Through reciprocal hemizygosity analysis, four genes, RAS2, PMS1, SWS2, and FKH2, located in a region of 60 kilobases on Chromosome 14, were found to be associated with sporulation efficiency. Three of the four “high” sporulation alleles are derived from the “low” sporulating strain. Two of these sporulation-related genes were verified through allele replacements. For RAS2, the causative variation was suggested to be a single nucleotide difference in the upstream region of the gene. This quantitative trait nucleotide accounts for sporulation variability among a set of ten closely related winery yeast strains. Our results provide a detailed view of genetic complexity in one “QTL region” that controls a quantitative trait and reports a single nucleotide polymorphism-trait association in wild strains. Moreover, these findings have implications on QTL identification in higher eukaryotes

Genetically defined elevated homocysteine levels do not result in widespread changes of DNA methylation in leukocytes

BACKGROUND:DNA methylation is affected by the activities of the key enzymes and intermediate metabolites of the one-carbon pathway, one of which involves homocysteine. We investigated the effect of the well-known genetic variant associated with mildly elevated homocysteine: MTHFR 677C>T independently and in combination with other homocysteine-associated variants, on genome-wide leukocyte DNA-methylation. METHODS:Methylation levels were assessed using Illumina 450k arrays on 9,894 individuals of European ancestry from 12 cohort studies. Linear-mixed-models were used to study the association of additive MTHFR 677C>T and genetic-risk score (GRS) based on 18 homocysteine-associated SNPs, with genome-wide methylation. RESULTS:Meta-analysis revealed that the MTHFR 677C>T variant was associated with 35 CpG sites in cis, and the GRS showed association with 113 CpG sites near the homocysteine-associated variants. Genome-wide analysis revealed that the MTHFR 677C>T variant was associated with 1 trans-CpG (nearest gene ZNF184), while the GRS model showed association with 5 significant trans-CpGs annotated to nearest genes PTF1A, MRPL55, CTDSP2, CRYM and FKBP5. CONCLUSIONS:Our results do not show widespread changes in DNA-methylation across the genome, and therefore do not support the hypothesis that mildly elevated homocysteine is associated with widespread methylation changes in leukocytes

Centromeres are dismantled by foundational meiotic proteins Spo11 and Rec8

Meiotic processes are potentially dangerous to genome stability and could be disastrous if activated in proliferative cells. Here we show that two key meiosis-defining proteins, the topoisomerase Spo11 (which forms double-strand breaks) and the meiotic cohesin Rec8, can dismantle centromeres. This dismantlement is normally observable only in mutant cells that lack the telomere bouquet, which provides a nuclear microdomain conducive to centromere reassembly(1); however, overexpression of Spo11 or Rec8 leads to levels of centromere dismantlement that cannot be countered by the bouquet. Specific nucleosome remodelling factors mediate centromere dismantlement by Spo11 and Rec8. Ectopic expression of either protein in proliferating cells leads to the loss of mitotic kinetochores in both fission yeast and human cells. Hence, while centromeric chromatin has been characterized as extraordinarily stable, Spo11 and Rec8 challenge this stability and may jeopardize kinetochores in cancers that express meiotic proteins.UPLI

Combination of genomic approaches with functional genetic experiments reveals two modes of repression of yeast middle-phase meiosis genes

BACKGROUND: Regulation of meiosis and sporulation in Saccharomyces cerevisiae is a model for a highly regulated developmental process. Meiosis middle phase transcriptional regulation is governed by two transcription factors: the activator Ndt80 and the repressor Sum1. It has been suggested that the competition between Ndt80 and Sum1 determines the temporal expression of their targets during middle meiosis. RESULTS: Using a combination of ChIP-on-chip and expression profiling, we characterized a middle phase transcriptional network and studied the relationship between Ndt80 and Sum1 during middle and late meiosis. While finding a group of genes regulated by both factors in a feed forward loop regulatory motif, our data also revealed a large group of genes regulated solely by Ndt80. Measuring the expression of all Ndt80 target genes in various genetic backgrounds (WT, sum1Δ and MK-ER-Ndt80 strains), allowed us to dissect the exact transcriptional network regulating each gene, which was frequently different than the one inferred from the binding data alone. CONCLUSION: These results highlight the need to perform detailed genetic experiments to determine the relative contribution of interactions in transcriptional regulatory networks