Characterization and regulation of a bacterial sugar phosphatase of the haloalkanoate dehalogenase superfamily, AraL, from Bacillus subtilis

FEBS journal, Volume 278, Issue 14, pages 2511-2524, July 2011

Characterization and regulation of a bacterial sugar phosphatase of the HAD superfamily, AraL, from Bacillus subtilis.

AraL from Bacillus subtilis is a member of the ubiquitous haloalkanoate dehalogenase, HAD, superfamily. The araL gene has been cloned, over-expressed in Escherichia coli and its product purified to homogeneity. The enzyme displays phosphatase activity, which is optimal at neutral pH (7.0) and 65 °C. Substrate screening and kinetic analysis showed AraL to have low specificity and catalytic activity towards several sugar phosphates, which are metabolic intermediates of the glycolytic and pentose phosphate pathways. Based on substrate specificity and gene context within the arabinose metabolic operon, a putative physiological role of AraL in detoxification of accidental accumulation of phosphorylated metabolites has been proposed. The ability of AraL to catabolise several related secondary metabolites requires regulation at the genetic level. Here, by site- directed mutagenesis, we show that AraL production is regulated by a structure in the translation initiation region of the mRNA, which most probably blocks access to the ribosome-binding site, preventing protein synthesis. Members of HAD subfamily IIA and IIB are characterised by a broad-range and overlapping specificity that anticipated the need for regulation at the genetic level. In this study we provide evidence for the existence of a genetic regulatory mechanism controlling AraL production.

Statistical learning theory for geospatial data. Case study: Aral sea

In recent years there has been an explosive growth in the development of adaptive and data driven methods. One of the efficient and data-driven approaches is based on statistical learning theory (Vapnik 1998). The theory is based on Structural Risk Minimisation (SRM) principle and has a solid statistical background. When applying SRM we are trying not only to reduce training error ? to fit the available data with a model, but also to reduce the complexity of the model and to reduce generalisation error. Many nonlinear learning procedures recently developed in neural networks and statistics can be understood and interpreted in terms of the structural risk minimisation inductive principle. A recent methodology based on SRM is called Support Vector Machines (SVM). At present SLT is still under intensive development and SVM find new areas of application (www.kernel-machines.org). SVM develop robust and non linear data models with excellent generalisation abilities that is very important both for monitoring and forecasting. SVM are extremely good when input space is high dimensional and training data set i not big enough to develop corresponding nonlinear model. Moreover, SVM use only support vectors to derive decision boundaries. It opens a way to sampling optimization, estimation of noise in data, quantification of data redundancy etc. Presentation of SVM for spatially distributed data is given in (Kanevski and Maignan 2004).

Aral : un mar sin agua.

Programa emitido el 18 de marzo de 1996

On the relationship between the Indian summer monsoon and river flow in the Aral Sea basin

We study the contemporaneous relationship between the intensity of the Indian Summer Monsoon (ISM) and runoff in the major rivers of the Aral Sea basin (Amudarya, Syrdarya) and some of their subcatchments. To this end, we use All-India rainfall (AIR) data, CRU surface observations of precipitation and temperature, ERA40 atmospheric data, and natural discharge data corrected for human interference. We show that there is a highly significant positive correlation between ISM intensity and Amudarya runoff. This finding cannot be explained by the spill-over of ISM precipitation over the Hindu Kush into the Amudarya basin. Instead, we suggest that the observed co-variability is mediated by tropospheric temperature variations due to fluctuations in the ISM intensity. These variations are known to be due to Rossby-wave propagation in response to condensational heating during monsoon precipitation. We hypothesise that the corresponding anomalies in surface temperatures imply anomalies in meltwater formation.

Phytoplankton parameters in the Large Aral Sea in June 2008

Species composition, abundance, and biomass of phytoplankton in the surface water layer were determined at 10 stations in the central part of the Western Basin (WB) and at one station in the Eastern Basin (EB) of the Large Aral Sea. 42 algal species were found. Diatoms had the highest number of species. Similarity of phytoplankton composition in the WB was high, whereas phytoplankton composition in the WB and EB differed significantly. In WB abundance and biomass of phytoplankton varied from 826x10**3 to 6312x10**3 cells/l (aver. 1877x10**3 cells/l) and from 53 to 241 ?g C/l (aver. 95 ?g C/l). In EB the phytoplankton abundance was 915x10**3 cells/l and 93 ?g C/l. Vertical distribution of phytoplankton in upper 35 m was investigated at one station in WB. Maximum values of phytoplankton abundance and biomass were recorded under the thermocline at 20 m depth. Integrated biomass of phytoplankton was 14 g C/m**2.