[show abstract][hide abstract] ABSTRACT: The Black Sea, a land-locked deep basin with sulfide bearing waters below 150–200 m, has been subject to anthropogenic pressures since the 1970s. Large inputs of nutrients (nitrate — N, phosphate — P, silicate — Si) with high N/P but low Si/N ratios and subsequent development of intensive eutrophication over the basin have changed vertical distributions and inventories of nutrients and redox-sensitive metals in the oxic, suboxic and anoxic layers. Chemical data sets obtained between 1988 and 2010, and older data from before 1970 were evaluated to assess spatial/temporal variations of the dissolved oxygen (O2), nutrients and dissolved/particulate manganese (Mnd, Mnp) in the water column from the lower salinity, oxygenated surface waters through the SubOxic Layer (SOL; O2 < 20 μM; H2S < 1 μM) to the anoxic, sulfidic water interface. Correlations were observed between salinity and nutrients (nitrate, silicate) in the nearshore waters off the Danube delta and in the southwestern (SW) coastal waters which had low Si/N ratios. Surface waters from the western central gyre were consistently depleted in nitrate and phosphate with low N/P but higher Si/N molar ratios throughout the year. Chemical profiles obtained recently in the Rim Current and western central gyre displayed very similar vertical features through the halocline to the sulfidic water interface. However, in the SW coastal margin, lateral intrusion of O2 and nutrients by the Bosporus Plume resulted in formation of secondary maxima of nitrate, nitrite and O2 in the SOL, and local deepening of the first appearance of anoxic, sulfidic waters. Before the mid 1960s, nitrate-enriched major rivers fed the Black Sea with high N/P ratios (> 50). The surface waters over the basin were rich in silicate (25–70 μM), but poor in nitrate (< 0.1 μM) and phosphate (0.05–0.3 μM), resulting in very high Si/N (> 500) but very low N/P (< 1.0) ratios. After the mid 1970s, construction of dams, especially on the Danube River, resulted in lower Si concentrations. At this time the increased loads of anthropogenic nitrate and phosphate by the major rivers resulted in lower Si/N, but still high N/P molar ratios, which enhanced eutrophication (production of particulate organic matter, POM) drastically in the coastal waters. This led to reductions in the surface Si/N ratio by up to 500-fold in the western basin while the N/P ratio increased. The enhanced POM export increased the nitrate inventory and thus N/P ratios of the NW shelf waters spreading over the whole basin. The increased export of POM decreased the Si inventory of the upper layer down to the boundary of sulfidic waters. This export also increased O2 consumption and removal of nitrate to N2 form by denitrification in the oxic/suboxic interface, leading to seasonal/decadal changes in the boundaries of the nitracline and main oxycline and changes in the slopes of the nitrate-phosphate and Apparent Oxygen Utilization (AOU)-nitrate regressions in the steep oxycline down to the SOL. These slopes are much smaller than those observed in the lower layer of Marmara Sea fed by the Black Sea outflow. The enlargement of SOL by ~ 15–20 m after the 1970s modified the vertical features of nitrate, phosphate and manganese (Mnd, Mnp) species in the redox gradient zone.
[show abstract][hide abstract] ABSTRACT: ABSTRACT Nitrogen regulation in Escherichia coli is a model system for gene regulation in bacteria. Growth on glutamine as a sole nitrogen source is assumed to be nitrogen limiting, inferred from slow growth and strong NtrB/NtrC-dependent gene activation. However, we show that under these conditions, the intracellular glutamine concentration is not limiting but 5.6-fold higher than in ammonium-replete conditions; in addition, α-ketoglutarate concentrations are elevated. We address this glutamine paradox from a systems perspective. We show that the dominant role of NtrC is to regulate glnA transcription and its own expression, indicating that the glutamine paradox is not due to NtrC-independent gene regulation. The absolute intracellular NtrC and GS concentrations reveal molecular control parameters, where NtrC-specific activities were highest in nitrogen-starved cells, while under glutamine growth, NtrC showed intermediate specific activity. We propose an in vivo model in which α-ketoglutarate can derepress nitrogen regulation despite nitrogen sufficiency. IMPORTANCE Nitrogen is the most important nutrient for cell growth after carbon, and its metabolism is coordinated at the metabolic, transcriptional, and protein levels. We show that growth on glutamine as a sole nitrogen source, commonly assumed to be nitrogen limiting and used as such as a model system for nitrogen limitation, is in fact nitrogen replete. Our integrative quantitative analysis of key molecules involved in nitrogen assimilation and regulation reveal that glutamine is not necessarily the dominant molecule signaling nitrogen sufficiency and that α-ketoglutarate may play a more important role in signaling nitrogen status. NtrB/NtrC integrates α-ketoglutarate and glutamine signaling-sensed by the UTase (glnD) and PII (glnB), respectively-and regulates the nitrogen response through self-regulated expression and phosphorylation-dependent activation of the nitrogen (ntr) regulon. Our findings support α-ketoglutarate acting as a global regulatory metabolite.
[show abstract][hide abstract] ABSTRACT: Many biological networks have been labelled scale-free as their degree distribution can be approximately described by a powerlaw distribution. While the degree distribution does not summarize all aspects of a network it has often been suggested that its functional form contains important clues as to underlying evolutionary processes that have shaped the network. Generally determining the appropriate functional form for the degree distribution has been fitted in an ad-hoc fashion. Here we apply formal statistical model selection methods to determine which functional form best describes degree distributions of protein interaction and metabolic networks. We interpret the degree distribution as belonging to a class of probability models and determine which of these models provides the best description for the empirical data using maximum likelihood inference, composite likelihood methods, the Akaike information criterion and goodness-of-fit tests. The whole data is used in order to determine the parameter that best explains the data under a given model (e.g. scale-free or random graph). As we will show, present protein interaction and metabolic network data from different organisms suggests that simple scale-free models do not provide an adequate description of real network data.