The S (or RpoS) subunit of RNA polymerase is the master regulator of the general stress response in Escherichia coli. While nearly absent in rapidly growing cells, S is strongly induced during entry into stationary phase and/or many other stress conditions and is essential for the expression of multiple stress resistances. Genome-wide expression profiling data presented here indicate that up to 10% of the E. coli genes are under direct or indirect control of S and that S should be considered a second vegetative sigma factor with a major impact not only on stress tolerance but on the entire cell physiology under nonoptimal growth conditions. This large data set allowed us to unequivocally identify a S consensus promoter in silico. Moreover, our results suggest that S -dependent genes represent a regulatory network with complex internal control (as exemplified by the acid resistance genes). This network also exhibits extensive regulatory overlaps with other global regulons (e.g., the cyclic AMP receptor protein regulon). In addition, the global regulatory protein Lrp was found to affect S and/or 70 selectivity of many promoters. These observations indicate that certain modules of the S -dependent general stress response can be temporarily recruited by stress-specific regulons, which are controlled by other stress-responsive regulators that act together with 70 RNA polymerase. Thus, not only the expression of genes within a regulatory network but also the architecture of the network itself can be subject to regulation.The general stress sigma factor S (or RpoS) is strongly induced when Escherichia coli cells are exposed to various stress conditions, which include starvation, hyperosmolarity, pH downshift, or nonoptimal high or low temperature (for a review of S regulation, see reference 24). By standard genetic and molecular biology methods, more than 80 S -controlled genes have been identified to date, indicating that S is the master regulator of a rather large regulon which represents the genetic basis of the E. coli general stress response (for summaries, see references 23 and 41).In their regulatory patterns, many S -controlled genes just follow the cellular S level; i.e., they are activated whenever S and therefore S -containing RNA polymerase (E S ) accumulate in the cell. Other S -dependent genes, however, exhibit highly specific regulation, with a narrow window of expression only under some sort of stress condition. The best-studied example of this type of S -controlled gene is the csiD gene, which is mainly induced by carbon starvation because the cyclic AMP (cAMP)-cAMP receptor protein (CRP) acts as an essential activator for S -containing RNA polymerase at the csiD promoter (21,46,49). Also, the leucine-responsive regulatory protein (Lrp) is involved in the regulation of certain S -dependent genes (9,13,33,64). These findings indicate that the S -containing RNA polymerase holoenzyme has the ability to cooperate with additional regulatory factors, just as the vegetative RNA polymerase containing 70 does. ...

SummaryBis-(3Ј-5Ј)-cyclic-di-guanosine monophosphate (c-di-GMP) is a bacterial signalling molecule produced by diguanylate cyclases (DGC, carrying GGDEF domains) and degraded by specific phosphodiesterases (PDE, carrying EAL domains). Neither its full physiological impact nor its effector mechanisms are currently understood. Also, the existence of multiple GGDEF/EAL genes in the genomes of most species raises questions about output specificity and robustness of c-di-GMP signalling. Using microarray and gene fusion analyses, we demonstrate that at least five of the 29 GGDEF/EAL genes in Escherichia coli are not only stationary phase-induced under the control of the general stress response master regulator s S (RpoS), but also exhibit differential control by additional environmental and temporal signals. Two of the corresponding proteins, YdaM (GGDEF only) and YciR (GGDEF + EAL), which in vitro show DGC and PDE activity, respectively, play an antagonistic role in the expression of the biofilm-associated curli fimbriae. This control occurs at the level of transcription of the curli and cellulose regulator CsgD. Moreover, we show that H-NS positively affects curli expression by inversely controlling the expression of ydaM and yciR. Furthermore, we demonstrate a temporally fine-tuned GGDEF cascade in which YdaM controls the expression of another GGDEF protein, YaiC. By genome-wide microarray analysis, evidence is provided that YdaM and YciR strongly and nearly exclusively control CsgD-regulated genes. We conclude that specific GGDEF/EAL proteins have very distinct expression patterns, and when present in physiological amounts, can act in a highly precise, non-global and perhaps microcompartmented manner on a few or even a single specific target(s).

Aim We provide the first European‐scale geospatial training set relating the charcoal signal in surface lake sediments to fire parameters (number, intensity and area) recorded by satellite moderate resolution imaging spectroradiometer (MODIS) sensors. Our calibration is intended for quantitative reconstructions of key fire‐regime parameters by using sediment sequences of microscopic (MIC from pollen slides, particles 10–500 µm) and macroscopic charcoal (MAC from sieves, particles > 100 µm). Location North–south and east–west transects across Europe, covering the mediterranean, temperate, alpine, boreal and steppe biomes. Time period Lake sediments and MODIS active fire and burned area products were collected for the years 2012–2015. Methods Cylinder sediment traps were installed in lakes to annually collect charcoal particles in sediments. We quantitatively assessed the relationships between MIC and MAC influx (particles/cm2/year) and the MODIS‐derived products to identify source areas of charcoal and the extent to which lake‐sediment charcoal is linked to fire parameters across the continent. Results Source area of sedimentary charcoal was estimated to a 40‐km radius around sites for both MIC and MAC particles. Fires occurred in grasslands and in forests, with grass morphotypes of MAC accurately reflecting the burned fuel‐type. Despite the lack of local fires around the sites, MAC influx levels reached those reported for local fires. Both MIC and MAC showed strong and highly significant relationships with the MODIS‐derived fire parameters, as well as with climatic variation along a latitudinal temperature gradient. Main conclusions MIC and MAC are suited to quantitatively reconstructing fire number and fire intensity on a regional scale. However, burned area may only be estimated using MAC. Local fires may be identified by using several lines of evidence, e.g. analysis of large particles (> 600 µm), magnetic susceptibility and sedimentological data. Our results offer new insights and applications to quantitatively reconstruct fires and to interpret available sedimentary charcoal records.

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