BurstDECONV: a signal deconvolution method to uncover mechanisms of transcriptional bursting in live cells.

Monitoring transcription in living cells gives access to the dynamics of this complex fundamental process. It reveals that transcription is discontinuous, whereby active periods (bursts) are separated by one or several types of inactive periods of distinct lifetimes. However, decoding temporal fluctuations arising from live imaging and inferring the distinct transcriptional steps eliciting them is a challenge. We present BurstDECONV, a novel statistical inference method that deconvolves signal traces into individual transcription initiation events. We use the distribution of waiting times between successive polymerase initiation events to identify mechanistic features of transcription such as the number of rate-limiting steps and their kinetics. Comparison of our method to alternative methods emphasizes its advantages in terms of precision and flexibility. Unique features such as the direct determination of the number of promoter states and the simultaneous analysis of several potential transcription models make BurstDECONV an ideal analytic framework for live cell transcription imaging experiments. Using simulated realistic data, we found that our method is robust with regards to noise or suboptimal experimental designs. To show its generality, we applied it to different biological contexts such as Drosophila embryos or human cells.

Integrative genome wide analysis of protein tyrosine phosphatases identifies CDC25C as prognostic and predictive marker for chemoresistance in breast cancer.

BACKGROUND: The breast cancer subtype deficient in estrogen receptor and human epidermal growth factor receptor-2 (ER-/HER2-) displays enhanced aggressiveness, metastasis and disease relapse due to chemoresistance. ER-/HER2- patients lack molecularly targeted treatment hence, new therapeutic and prognostic biomarkers are required for better patient management. OBJECTIVES: To investigate the prognostic role of protein tyrosine phosphatase genes in Breast Cancer and their relevance as predictive markers for chemoresistance. METHODS: We examined the expression of 114 protein tyrosine phosphatase (PTP) genes in 1700 breast cancer patient's tumor samples with respect to ER-/HER2- subtype. Correlation of relevant candidates with chemoresistance was analyzed in breast cancer cells resistant to taxane/anthracycline based drugs. The prognostic value of key candidates was assessed using Kaplan Meier plots and Nottingham prognostic index and expression pattern was confirmed using qRT-PCR. The epigenetic regulation was analyzed using ChIP-Seq datasets. By plotting ROC plots, clinical outcome after treatment with taxane and anthracycline was established. RESULTS: Overexpression of CDC25A and CDC25C and under-expression of DUSP16 was observed in tumor samples of ER-/HER2- patients and breast cancer cells. Similar expression patterns of these candidate genes were observed in MCF7 cells resistant to paclitaxel and adriamycin and also correlated with poor prognosis of breast cancer patients. Increased CDC25A and CDC25C in ER-/HER2- cells was found to be regulated epigenetically by histone H3K4 methylation. Overall, the present study establishes increased expression of protein tyrosine phosphatase CDC25C as a poor prognostic marker for breast cancer. CONCLUSION: Our study highlights the role of CDC25C in chemoresistance to taxane and anthracycline based therapy and proposes CDC25C as a potential predictive marker for these cancer therapies.

Stochastic pausing at latent HIV-1 promoters generates transcriptional bursting.

Promoter-proximal pausing of RNA polymerase II is a key process regulating gene expression. In latent HIV-1 cells, it prevents viral transcription and is essential for latency maintenance, while in acutely infected cells the viral factor Tat releases paused polymerase to induce viral expression. Pausing is fundamental for HIV-1, but how it contributes to bursting and stochastic viral reactivation is unclear. Here, we performed single molecule imaging of HIV-1 transcription. We developed a quantitative analysis method that manages multiple time scales from seconds to days and that rapidly fits many models of promoter dynamics. We found that RNA polymerases enter a long-lived pause at latent HIV-1 promoters (>20 minutes), thereby effectively limiting viral transcription. Surprisingly and in contrast to current models, pausing appears stochastic and not obligatory, with only a small fraction of the polymerases undergoing long-lived pausing in absence of Tat. One consequence of stochastic pausing is that HIV-1 transcription occurs in bursts in latent cells, thereby facilitating latency exit and providing a rationale for the stochasticity of viral rebounds.

Integrated bioinformatic analysis identifies UBE2Q1 as a potential prognostic marker for high grade serous ovarian cancer.

BACKGROUND: High grade serous ovarian cancer (HGSOC) accounts for nearly 60% of total cases of epithelial ovarian cancer. It is the most aggressive subtype, which shows poor prognosis and low patient survival. For better management of HGSOC patients, new prognostic biomarkers are required to facilitate improved treatment strategies and ensure suitable healthcare decisions. METHODS: We performed genome wide expression analysis of HGSOC patient samples to identify differentially expressed genes (DEGs) using R based Limma package, Clust and other statistical tools. The identified DEGs were subjected to weighted gene co-expression network analysis (WGCNA) to identify co-expression patterns of relevant genes. Module trait and gene ontology analyses were performed to establish important gene co-expression networks and their biological functions. Overlapping the most relevant DEG cluster 4 with prominent WGCNA cyan module identified strongest correlation of UBE2Q1 with ovarian cancer and its prognostic significance on survival probability of ovarian cancer patients was investigated. The predictive value of UBE2Q1 as a potential biomarker was analysed by correlating its expression with 12-months relapse free survival of patients in response to platin/taxane, the standard first-line chemotherapy for ovarian cancer, and analysing area under the ROC curve. RESULTS: An integrated gene expression analysis and WGCNA, identified UBE2Q1 as a potential prognostic marker associated with poor relapse-free survival and response outcome to platin/taxane treatment of patients with high grade serous ovarian cancer. CONCLUSIONS: Our study identifies a potential UBE2Q1 - B4GALT3 functional axis in ovarian cancer, where only the E2 conjugating enzyme showed a poor prognostic impact on the disease.