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23 Publications visible to you, out of a total of 23
Gene position within a long transcript as a determinant for stochastic switching in bacteria

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How cultures of genetically identical cells bifurcate into distinct phenotypic subpopulations under uniform growth conditions is an important question in developmental biology of relevance even to … relatively simple developmental systems, such as spore formation in bacteria. A growing Bacillus subtilis culture consists of either cells that are motile and can swim or cells that are non-motile and are chained together. In this issue of Molecular Microbiology, Cozy and Kearns show that the probability of a cell to become motile depends on the position of the sigD gene within the long (27 kb) motility operon. sigD encodes the alternative sigma factor sigma(D) that, together with RNA polymerase, drives expression of genes required for cell separation and the assembly of flagella. sigD is the penultimate gene of the B. subtilis motility operon and, in the control strain approximately, 70% of the cells are motile. When sigD was moved upstream within the operon, a larger fraction of cells became motile (up to 100%). This study highlights that the position of a gene within an operon can have a large impact on the control of gene expression. Furthermore, it suggests that RNA polymerase processivity or mRNA turnover can play important roles as sources of noise in bacterial development, and that gene position might be an unrecognized and possibly widespread mechanism to regulate phenotypic variation.

Editor:

Date Published: 10th Mar 2010

Publication Type: Not specified

GraphAlignment: Bayesian pairwise alignment of biological networks

Abstract (Expand)

ABSTRACT: BACKGROUND: With increased experimental availability and accuracy of bio-molecular networks, tools for their comparative and evolutionary analysis are needed. A key component for such studies … is the alignment of networks. RESULTS: We introduce the Bioconductor package GraphAlignment for pairwise alignment of bio-molecular networks. The alignment incorporates information both from network vertices and network edges and is based on an explicit evolutionary model, allowing inference of all scoring parameters directly from empirical data. We compare the performance of our algorithm to an alternative algorithm, Graemlin 2.0.On simulated data, GraphAlignment outperforms Graemlin 2.0 in several benchmarks except for computational complexity. When there is little or no noise in the data, GraphAlignment is slower than Graemlin 2.0. It is faster than Graemlin 2.0 when processing noisy data containing spurious vertex associations. Its typical case complexity grows approximately as O(N^2.6). On empirical bacterial protein-protein interaction networks (PIN) and gene co-expression networks, GraphAlignment outperforms Graemlin 2.0 with respect to coverage and specificity, albeit by a small margin. On large eukaryotic PIN, Graemlin 2.0 outperforms GraphAlignment. CONCLUSIONS: The GraphAlignment algorithm is robust to spurious vertex associations, correctly resolves paralogs, and shows very good performance in identification of homologous vertices defined by high vertex and/or interaction similarity.

Authors: Michal Kolar, Jörn Meier, Ville Mustonen, Michael Lässig,

Date Published: 21st Nov 2012

Publication Type: Not specified

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