SummaryOf the 13 two-component signal transduction systems (TCS) identified in Streptococcus pneumoniae , two, ComDE and CiaRH, are known to affect competence for natural genetic transformation. ComD and ComE act together with the comC-encoded competence-stimulating peptide (CSP) and with ComAB, the CSP-dedicated exporter, to co-ordinate activation of genes required for differentiation to competence. Several lines of evidence suggest that the CiaRH TCS and competence regulation are interconnected, including the observation that inactivation of the CiaR response regulator derepresses competence. However, the nature of the interconnection remains poorly understood. Interpretation of previous transcriptome analyses of ciaR mutants was complicated by competence derepression in the mutants. To circumvent this problem, we have used microarray analysis to investigate the transition from noncompetence to competence in a comC -null wild-type strain and its ciaR derivative after the addition of CSP. This study increased the number of known CSPinduced genes from ª ª ª ª 47 to 105 and revealed ª ª ª ª 42 genes with reduced expression in competent cells. Induction of the CiaR regulon, as well as the entire HrcA and part of the CtsR stress response regulons, was observed in wild-type competent cells. Enhanced induction of stress response genes was detected in ciaR competent cells. In line with these observations, CSP was demonstrated to trigger growth arrest and stationary phase autolysis in ciaR cells. Taken together, these data strongly suggest that differentiation to competence imposes a temporary stress on cells, and that the CiaRH TCS is required for the cells to exit normally from the competent state.

External information propagates in the cell mainly through signaling cascades and transcriptional activation, allowing it to react to a wide spectrum of environmental changes. High-throughput experiments identify numerous molecular components of such cascades that may, however, interact through unknown partners. Some of them may be detected using data coming from the integration of a protein-protein interaction network and mRNA expression profiles. This inference problem can be mapped onto the problem of finding appropriate optimal connected subgraphs of a network defined by these datasets. The optimization procedure turns out to be computationally intractable in general. Here we present a new distributed algorithm for this task, inspired from statistical physics, and apply this scheme to alpha factor and drug perturbations data in yeast. We identify the role of the COS8 protein, a member of a gene family of previously unknown function, and validate the results by genetic experiments. The algorithm we present is specially suited for very large datasets, can run in parallel, and can be adapted to other problems in systems biology. On renowned benchmarks it outperforms other algorithms in the field.computational biology | minimum Steiner tree | prize-collecting Steiner tree S ignaling cascades, an exemplar of which is the phosphorylation MAPK kinase pathways, consist of sequential reactions starting at receptor proteins and transmitted through protein interactions to effector proteins. Activation of these effectors leads to cellular changes, notably at the transcriptional level, and results in the adaptation of the cell to its surroundings (1). Identifying signaling pathways is particularly important for medical studies because their malfunction is responsible for many diseases, such as cancer (2) or Alzheimer's disease (3).From an engineering point of view, signaling cascades present interesting properties: They provide signal filtering (4) and amplification (5). Their global interconnected organization equips the cell with an integrated sensor network where pathways can modulate one another through crosstalk and retroactions. In this complex system, signal specificity is maintained by scaffold proteins (6, 7) acting as connectors of particular reactions. Finally, the output of the information carried by the transduction network allows for another layer of regulation-namely combinatorial control in gene expression (8). A purely experimental approach to the identification of all components of a pathway, or all components of a functional gene module, would need long and costly experiments. Such a process would greatly benefit from the extraction of indirect information about pathways from producible large-scale data, such as expression, sequencing, and protein interaction data. Indeed, even if the correlation between signaling pathway activity and expression data may be weak (although this may be case-dependent; see ref. 9as an interesting example), expression data are available in shear quantity. By introducing a ...

Cell-wall damage caused by mutations of cell-wall-related genes triggers a compensatory mechanism which eventually results in hyperaccumulation of chitin reaching 20% of the cell-wall dry mass. We show that activation of chitin synthesis is accompanied by a rise, from 1.3-fold to 3.5-fold according to the gene mutation, in the expression of most of the genes encoding enzymes of the chitin metabolic pathways. Evidence that GFA1, which encodes glutamine-fructose-6-Phosphate amidotransferase (Gfa1p), the first committed enzyme of this pathway, plays a major role in this process was as follows. Activation of chitin synthesis in the cell-wall mutants correlated with activation of GFA1 and with a proportional increase in Gfa1p activity. Overexpression of GFA1 caused an approximately threefold increase in chitin in the transformed cells, whereas chitin content was barely affected by the joint overexpression of CHS3 and CHS7. Introduction of a gfa1-97 allele mutation in the cellwall-defective gas1D mutant or cultivation of this mutant in a hyperosmotic medium resulted in reduction in chitin synthesis that was proportional to the decrease in Gfa1p activity. Finally, the stimulation of chitin production was also accompanied by an increase in pools of fructose 6-Phosphate, a substrate of Gfa1p. In quantitative terms, we estimated the flux-coefficient control of Gfa1p to be in the range of 0.90, and found that regulation of the chitin metabolic pathway was mainly hierarchical, i.e. dominated by regulation of the amount of newly synthesized GFA1 protein. In the search for the mechanism by which GFA1 is activated in response to cell-wall perturbations, we could only show that neither MCM1 nor RLM1, which encode two transcriptional factors of the MADS box family that are required for expression of cell-cycle and cell-wall-related genes, was involved in this process.