Project description:Primary objectives: The primary objective is to investigate circulating tumor DNA (ctDNA) via deep sequencing for mutation detection and by whole genome sequencing for copy number analyses before start (baseline) with regorafenib and at defined time points during administration of regorafenib for treatment efficacy in colorectal cancer patients in terms of overall survival (OS).
Primary endpoints: circulating tumor DNA (ctDNA) via deep sequencing for mutation detection and by whole genome sequencing for copy number analyses before start (baseline) with regorafenib and at defined time points during administration of regorafenib for treatment efficacy in colorectal cancer patients in terms of overall survival (OS).
Project description:We used RNA sequencing to measure genome-wide gene expression in the cyanobacterium Synechococcus elongatus PCC 7942 grown under dynamic light regimes that mimic the variation in light intensity seen on a Clear Day in nature, or the rapid changes in light intensity that accompany changes in shading We compare these gene expression dynamics to those of a culture grown under a Low Light condition that mimics the standard laboratory conditions used for study of cyanobacteria. Our analysis reveals that naturally relevant light conditions drastically modify gene expression dynamics in cyanobacteria Notably, the expression of circadian clock-controlled genes is responsive to changes in light intensity, showing modulated dynamics that can allow cyanobacteria to adapt their metabolism to changing environmental conditions
Project description:The aim of this study was to find the cause for the previously reported inconsistency between oscillating transcription and constant protein levels under day-night growth conditions in cyanobacteria. To determine whether translational regulation counteracts transcriptional changes, Synechocystis sp. PCC 6803 was cultivated in an artificial day-night setting and the level of transcription, translation and protein was measured across the genome at different time points using mRNA sequencing, ribosome profiling and quantitative proteomics. This proteomics data set represents one layer of a larger data set covering three 'omics' levels.