Project description:We used Progenika oligonucleotide arrays to monitore the gene expression of P. putida during carbon source stimulus experiments. After ensuring steady state conditions on benzoate, the stimuli were introduced by changing the carbon source from benzoate to glucose, from glucose to fructose and from fructose to benzoate, respectively. The single stimuli were monitored over a time period from 10 to 120 minutes after changing the carbon source by three representative timepoints. Moreover, the steady state conditions were compared among each other. Supplementary file: Individual normalized signal intensity VALUES for each channel of each array are provided in the FullNormalizedMatrix.txt file.
Project description:As a result of ancestral whole genome and small-scale duplication events, the genome of Saccharomyces cerevisiaeM-bM-^@M-^Ys, and of many eukaryotes, still contain a substantial fraction of duplicated genes. In all investigated organisms, metabolic pathways, and more particularly glycolysis, are specifically enriched for functionally redundant paralogs. In ancestors of the Saccharomyces lineage, the duplication of glycolytic genes is purported to have played an important role leading to S. cerevisiae current lifestyle favoring fermentative metabolism even in the presence of oxygen and characterized by a high glycolytic capacity. In modern S. cerevisiae, the 12 glycolytic reactions leading to the biochemical conversion from glucose to ethanol are encoded by 27 paralogs. In order to experimentally explore the physiological role of this genetic redundancy, a yeast strain with a minimal set of 14 paralogs was constructed (MG strain). Remarkably, a combination of quantitative, systems approach and of semi-quantitative analysis in a wide array of growth environments revealed the absence of phenotypic response to the cumulative deletion of 13 glycolytic paralogs. This observation indicates that duplication of glycolytic genes is not a prerequisite for achieving the high glycolytic fluxes and fermentative capacities that are characteristic for S. cerevisiae and essential for many of its industrial applications and argues against gene dosage effects as a means for fixing minor glycolytic paralogs in the yeast genome. MG was carefully designed and constructed to provide a robust, prototrophic platform for quantitative studies, and is made available to the scientific community. The goals of the present study are to experimentally explore genetic redundancy in yeast glycolysis by cumulative deletion of minor paralogs and to provide a new experimental platform for fundamental yeast research by constructing a yeast strain with a functional M-bM-^@M-^Xminimal glycolysisM-bM-^@M-^Y. To this end, we deleted 13 minor paralogs, leaving only the 14 major paralogs for the S. cerevisiae glycolytic pathway. The cumulative impact of deleting all minor paralogs was investigated by two complementary approaches. A first, quantitative analysis focused on the impact on glycolytic flux under a number of controlled cultivation conditions that, in wild-type strains, result in different glycolytic fluxes. These quantitative growth studies were combined with transcriptome, enzyme-activity and intracellular metabolite assays to capture potential small phenotypic effects. A second, semi-quantitative characterization explored the phenotype of the M-bM-^@M-^Xminimal glycolysisM-bM-^@M-^Y strain under a wide array of experimental conditions to identify potential context-dependent phenotypes
Project description:The field of epitranscriptomics is growing in importance, with chemical modification of RNA being associated with a wide variety of biological phenomena. Mass spectrometry (MS) enables the identification of modified RNA residues within their sequence contexts, by using analogous approaches to shotgun proteomics. We have developed a free and open-source database search engine for RNA MS data, called NucleicAcidSearchEngine (NASE), as part of the OpenMS software framework. NASE allows the reliable identification of (modified) RNA sequences from LC-MS/MS data in a high-throughput fashion. For this validation dataset, oligonucleotides with the sequence of mature Drosophila let-7 microRNA, 21 nt in length, were produced synthetically in unmodified and modified (2’-O-methylated at the 3’ uridine) forms. We characterised a 1:1 mixture of both forms of this RNA.
Project description:The field of epitranscriptomics is growing in importance, with chemical modification of RNA being associated with a wide variety of biological phenomena. Mass spectrometry (MS) enables the identification of modified RNA residues within their sequence contexts, by using analogous approaches to shotgun proteomics. We have developed a free and open-source database search engine for RNA MS data, called NucleicAcidSearchEngine (NASE), as part of the OpenMS software framework. NASE allows the reliable identification of (modified) RNA sequences from LC-MS/MS data in a high-throughput fashion. For this validation dataset, we prepared two samples of an in vitro-transcribed yeast lncRNA (NME1, 340 nt long), one of which was treated with an RNA methyltransferase (NCL1) catalyzing the 5-methylcytidine (m5C) modification. These samples were subsequently digested with an RNA endonuclease (RNase) to generate oligonucleotide sequences of a length amenable to mass spectrometry.
Project description:The field of epitranscriptomics is growing in importance, with chemical modification of RNA being associated with a wide variety of biological phenomena. Mass spectrometry (MS) enables the identification of modified RNA residues within their sequence contexts, by using analogous approaches to shotgun proteomics. We have developed a free and open-source database search engine for RNA MS data, called NucleicAcidSearchEngine (NASE), as part of the OpenMS software framework. NASE allows the reliable identification of (modified) RNA sequences from LC-MS/MS data in a high-throughput fashion. For this validation dataset, we generated samples of human long ribosomal RNA from a cellular extract. The samples were RNase-treated prior to nanoflow LC-MS/MS analysis.