Looking ultra deep: short identical sequences and transcriptional slippage

Studying transcriptomes by ultra deep sequencing provides an in-depth picture of transcriptional regulation and it facilitates the detection of rare transcriptional events. Using ultra deep sequencing of amplicons we identified known isoforms and also various new low frequency variants. Most of these variants likely involve the splicing machinery except for two events that we named variations affecting multiple exons, which are mainly deletions affecting parts of adjacent exons and intra-exonic deletions. Both events involve short identical sequences of 1 to 8 nucleotides at the junction and canonical splice sites are missing. They were identified in different genes and species at very low frequencies. We excluded that they are an artifact of PCR, sequencing, or reverse transcription. We propose that these variants represent intramolecular slippage events that require short identical sequences for reannealing of dissociated transcript

Streptococcus pneumoniae arginine synthesis genes promote growth and virulence in pneumococcal meningitis

Streptococcus pneumoniae (pneumococcus) is a major human pathogen causing pneumonia, sepsis and bacterial meningitis. Using a clinical phenotype based approach with bacterial whole-genome sequencing we identified pneumococcal arginine biosynthesis genes to be associated with outcome in patients with pneumococcal meningitis. Pneumococci harboring these genes show increased growth in human blood and cerebrospinal fluid (CSF). Mouse models of meningitis and pneumonia showed that pneumococcal strains without arginine biosynthesis genes were attenuated in growth or cleared, from lung, blood and CSF. Thus, S. pneumoniae arginine synthesis genes promote growth and virulence in invasive pneumococcal diseas

SGCE isoform characterization and expression in human brain: implications for myoclonus–dystonia pathogenesis?

Myoclonus–dystonia (M–D) is a neurological movement disorder with involuntary jerky and dystonic movements as major symptoms. About 50% of M–D patients have a mutation in ɛ-sarcoglycan (SGCE), a maternally imprinted gene that is widely expressed. As little is known about SGCE function, one can only speculate about the pathomechanisms of the exclusively neurological phenotype in M–D. We characterized different SGCE isoforms in the human brain using ultra-deep sequencing. We show that a major brain-specific isoform is differentially expressed in the human brain with a notably high expression in the cerebellum, namely in the Purkinje cells and neurons of the dentate nucleus. Its expression was low in the globus pallidus and moderate to low in caudate nucleus, putamen and substantia nigra. Our data are compatible with a model in which dysfunction of the cerebellum is involved in the pathogenesis of M–D

Computational study of noise in a large signal transduction network

<p>Abstract</p> <p>Background</p> <p>Biochemical systems are inherently noisy due to the discrete reaction events that occur in a random manner. Although noise is often perceived as a disturbing factor, the system might actually benefit from it. In order to understand the role of noise better, its quality must be studied in a quantitative manner. Computational analysis and modeling play an essential role in this demanding endeavor.</p> <p>Results</p> <p>We implemented a large nonlinear signal transduction network combining protein kinase C, mitogen-activated protein kinase, phospholipase A2, and <it>β </it>isoform of phospholipase C networks. We simulated the network in 300 different cellular volumes using the exact Gillespie stochastic simulation algorithm and analyzed the results in both the time and frequency domain. In order to perform simulations in a reasonable time, we used modern parallel computing techniques. The analysis revealed that time and frequency domain characteristics depend on the system volume. The simulation results also indicated that there are several kinds of noise processes in the network, all of them representing different kinds of low-frequency fluctuations. In the simulations, the power of noise decreased on all frequencies when the system volume was increased.</p> <p>Conclusions</p> <p>We concluded that basic frequency domain techniques can be applied to the analysis of simulation results produced by the Gillespie stochastic simulation algorithm. This approach is suited not only to the study of fluctuations but also to the study of pure noise processes. Noise seems to have an important role in biochemical systems and its properties can be numerically studied by simulating the reacting system in different cellular volumes. Parallel computing techniques make it possible to run massive simulations in hundreds of volumes and, as a result, accurate statistics can be obtained from computational studies.</p