Many genes are regulated by multiple enhancers that often simultaneously activate their target gene. Yet, how individual enhancers collaborate to activate transcription is not well understood. Here, we dissect the functions and interdependencies of five enhancer elements that form a previously identified enhancer cluster and activate the Fgf5 locus during exit from naïve murine pluripotency. Four elements are located downstream of the Fgf5 gene and form a super-enhancer. Each of these elements contributes to Fgf5 induction at a distinct time point of differentiation. The fifth element is located in the first intron of the Fgf5 gene and contributes to Fgf5 expression at every time point by amplifying overall Fgf5 expression levels. This amplifier element strongly accumulates paused RNA Polymerase II but does not give rise to a mature Fgf5 mRNA. By transplanting the amplifier to a different genomic position, we demonstrate that it enriches for high levels of paused RNA Polymerase II autonomously. Based on our data, we propose a model for a mechanism by which RNA Polymerase II accumulation at a novel type of enhancer element, the amplifier, contributes to enhancer collaboration..

We used the 4C-Seq technique to characterize the genome-wide patterns of spatial contacts of several CpG islands located on chromosome 14 in cultured chicken lymphoid and erythroid cells. We observed a clear tendency for the spatial clustering of CpG islands present on the same and different chromosomes, regardless of the presence or absence of promoters within these CpG islands. Accordingly, we observed preferential spatial contacts between Sp1 binding motifs and other GC-rich genomic elements, including the DNA sequence motifs capable of forming G-quadruplexes. However, an anchor placed in a gene/CpG island-poor area formed spatial contacts with other gene/CpG island-poor areas on chromosome 14 and other chromosomes. These results corroborate the two-compartment model of the spatial organization of interphase chromosomes and suggest that the clustering of CpG islands constitutes an important determinant of the 3D organization of the eukaryotic genome in the cell nucleus. Using the ChIP-Seq technique, we mapped the genome-wide CTCF deposition sites in the chicken lymphoid and erythroid cells that were used for the 4C analysis. We observed a good correlation between the density of CTCF deposition sites and the level of 4C signals for the anchors located in CpG islands but not for an anchor located in a gene desert. It is thus possible that CTCF contributes to the clustering of CpG islands observed in our experiments.

Purpose Preclinical evaluation of PCA3 and AMACR transcript simultaneous detection in urine to diagnose clinical significant prostate cancer (prostate cancer with Gleason score ≥7) in a Russian cohort. Patients and Methods We analyzed urine samples of patients with a total serum PSA ≥2 ng/mL: 31 men with prostate cancer scheduled for radical prostatectomy, 128 men scheduled for first diagnostic biopsy (prebiopsy cohort). PCA3 , AMACR , PSA and GPI transcripts were detected by multiplex reverse transcription quantitative polymerase chain reaction, and the results were used for scores for calculation and statistical analysis. Results There was no significant difference between clinically significant and nonsignificant prostate cancer PCA3 scores. However, there was a significant difference in the AMACR score (patients scheduled for radical prostatectomy p =0.0088, prebiopsy cohort p =0.029). We estimated AUCs, optimal cutoffs, sensitivities and specificities for PCa and csPCa detection in the prebiopsy cohort by tPSA, PCA3 score, PCPT Risk Calculator and classification models based on tPSA, PCA3 score and AMACR score. In the clinically significant prostate cancer ROC analysis, the PCA3 score AUC was 0.632 (95%CI: 0.511–0.752), the AMACR score AUC was 0.711 (95%CI: 0.617–0.806) and AUC of classification model based on the PCA3 score, the AMACR score and total PSA was 0.72 (95%CI: 0.58–0.83). In addition, the correlation of the AMACR score with the ratio of total RNA and RNA of prostate cells in urine was shown (tau=0.347, p =6.542e–09). Significant amounts of nonprostate RNA in urine may be a limitation for the AMACR score use. Conclusion The AMACR score is a good predictor of clinically significant prostate cancer. Significant amounts of nonprostate RNA in urine may be a limitation for the AMACR score use. Evaluation of the AMACR score and classification models based on it for clinically significant prostate cancer detection with larger samples and a follow-up analysis is promising.