Signal transduction pathways in Synechocystis sp. PCC 6803 and biotechnological implications under abiotic stress

Cyanobacteria have developed various response mechanisms in long evolution to sense and adapt to external or internal changes under abiotic stresses. The signal transduction system of a model cyanobacterium Synechocystis sp. PCC 6803 includes mainly two-component signal transduction systems of eukaryotic-type serine/threonine kinases (STKs), on which most have been investigated at present. These two-component systems play a major role in regulating cell activities in cyanobacteria. More and more co-regulation and crosstalk regulations among signal transduction systems had been discovered due to increasing experimental data, and they are of great importance in corresponding to abiotic stresses. However, mechanisms of their functions remain unknown. Nevertheless, the two signal transduction systems function as an integral network for adaption in different abiotic stresses. This review summarizes available knowledge on the signal transduction network in Synechocystis sp. PCC 6803 and biotechnological implications under various stresses, with focuses on the co-regulation and crosstalk regulations among various stress-responding signal transduction systems.     

Selected anthropometric variables and aerobic fitness as predictors of cardiovascular disease risk in children

The aim of this study was to assess the suitability of body mass index, waist circumference, waist-to-height ratio and aerobic fitness as predictors of cardiovascular risk factor clustering in children. A cross-sectional study was conducted with 290 school boys and girls from 6 to 10 years old, randomly selected. Blood was collected after a 12-hour fasting period. Blood pressure, waist circumference (WC), height and weight were evaluated according to international standards. Aerobic fitness (AF) was assessed by the 20-metre shuttle-run test. Clustering was considered when three of these factors were present: high systolic or diastolic blood pressure, high low-density lipoprotein (LDL) cholesterol, high triglycerides, high plasma glucose, high insulin concentrations and low high-density lipoprotein (HDL) cholesterol. A ROC curve identified the cut-off points of body mass index (BMI), WC, waist-to-height ratio (WHtR) and AF as predictors of risk factor clustering. BMI, WC and WHR resulted in significant areas under the ROC curves, which was not observed for AF. The anthropometric variables were good predictors of cardiovascular risk factor clustering in both sexes, whereas aerobic fitness should not be used to identify cardiovascular risk factor clustering in these children.     

Evaluating the effects of preanalytical variables on the stability of the human plasma proteome

High quality clinical biospecimens are vital for biomarker discovery, verification, and validation. Variations in blood processing and handling can affect protein abundances and assay reliability. Using an untargeted LC-MS approach, we systematically measured the impact of preanalytical variables on the plasma proteome. Time prior to processing was the only variable that affected the plasma protein levels. LC-MS quantification showed that preprocessing times <6 h had minimal effects on the immunodepleted plasma proteome, but by 4 days significant changes were apparent. Elevated levels of many proteins were observed, suggesting that in addition to proteolytic degradation during the preanalytical phase, changes in protein structure are also important considerations for protocols using antibody depletion. As to processing variables, a comparison of single- vs double-spun plasma showed minimal differences. After processing, the impact ?3 freeze–thaw cycles was negligible regardless of whether freshly collected samples were processed in short succession or the cycles occurred during 14–17 years of frozen storage (−80 °C). Thus, clinical workflows that necessitate modest delays in blood processing times or employ different centrifugation steps can yield valuable samples for biomarker discovery and verification studies.     

Closely related species of birds differ more in body size when their ranges overlap—in warm,but not cool,climates

Differences in body size are widely thought to allow closely related species to coexist in sympatry, but body size also varies as an adaptive response to climate. Here, we use a sister lineage approach to test the prediction that body size differences between closely related species of birds worldwide are greater for species whose ranges are sympatric rather than allopatric. We further test if body size differences among sympatric versus allopatric species vary with geography, evolutionary distance, and environmental temperatures. We find greater differences in size among sympatric compared with allopatric lineages, but only in closely related species that live where mean annual temperatures are above 25°C. These size differences in warm environments declined with the evolutionary distance between sister lineages. In species living in cooler regions, closely related allopatric and sympatric species did not differ significantly in size, suggesting either that colder temperatures constrain the evolutionary divergence of size in sympatry, or that the biotic selective pressures favoring size differences in sympatry are weaker in colder environments. Our results are consistent with suggestions by Wallace, Darwin, and Dobzhansky that climatic selective pressures are more important in cooler environments (e.g., high elevations and latitudes) whereas biotic selective pressures dominate in warm environments (e.g., lowland tropics).     

Update of ichthyofauna diversity and ecological status of a coastal River Nero (Côte d’Ivoire – West Africa)

The general aim of this study is to update the inventory of the fish species and to specify distribution patterns in the Nero River ichthyofauna in order to establish some basis for the conservation of these fish communities and their habitat. From February 2009 to January 2010, thirty-three sites were sampled monthly with gill nets and a backpack electrofisher, and environmental variables were recorded. Overall, 46 species included in 33 genuses, 24 families and 9 orders were collected. Eleven families and 30 species were the first records for the Nero River. Including all species previously listed in the literature, the number of species presently known in the Nero River and its tributaries is revised to 59. Four families, Alestidae (21%), Schilbeidae (19%), Cyprinidae (17%) and Cichlidae (16%) that made up 73% of the total number of the catches, were the most dominant. The most dominant numerical species were Schilbe mandibularis and Brycinus longipinnis. Fish species and sampling sites along with eight environmental variables were ordinated with canonical correspondence analysis (CCA) coupled to the Monte Carlo test. Ecological status based on fish assemblage according to environmental variables and anthropogenic pressures showed that miss dead wood leaves and roots, electrical conductivity, total dissolved solids, mud, nitrite, basin width, dissolved oxygen and pH, were the primary factors influencing fish distribution. The environmental tolerance index (ETI), ecological tolerance (t_k) and optima (u_k) values of 10 species to 8 different environmental variables were analyzed. Six species (Hemichromis fasciatus, Epiplatys chaperi, Barbus ablabes, B. longipinnis, Hemichromis bimaculatus and Chromidotilapia guntheri) have high ETI and a cosmopolitan distribution in the Nero River. In the tributaries of the middle course, high concentrations of nitrite in the water, added to the presence of a lot of tolerant species in the ichthyofauna are indications of disturbance of these areas. Subsequent recommendations were formulated for efficient restoration and conservation management of this River.     

Statistical modelling and optimization of substrate composition for bacterial growth and cadmium removal using response surface methodology

In this study, substrate composition was optimized for the growth of Achromobacter xyloxidans and biosorption of Cd(II) from aqueous solution. Response surface methodology (RSM) was used to investigate the function of three independent operating variables, namely, peptone (2.5-10 g/L), beef extract (2.5-5.0 g/L) and incubation time (24-96 h), on dependent variables, i.e. sorption of Cd(II) ions, protein content and biomass growth of A. xyloxidans. The maximum Cd(II) removal efficiency of 69.2%, protein content 1.9 mg/L and growth 0.354 optical density was found at optimal conditions of peptone 10 g/L, incubation time 60 h and beef extract 2.5 g/L. The significance of independent variables and interactions between variables were tested by means of the analysis of variance (ANOVA) with 95% confidence limits and values of “Prob > F” less than 0.0500 indicate that model terms are significant. Fourier transfer infrared (FTIR) analysis was used to investigate sorption mechanism and involved functional groups in Cd(II) binding.     

A comparison of modeling techniques to predict juvenile 0+ fish species occurrences in a large river system

Even if European river management and restoration are largely supported by the use of reliable tools, these tools are most often “generalist” and provide only initial leads of alteration sources. Acknowledging that young-of-the-year (YOY) fish assemblages are highly dependent on riverine habitat conditions, the development of a YOY-based tool might be very useful or even essential in the design and implementation of conservation or restoration plan of large rivers, in measuring more straight-forward the losses and gains of hydro-ecological functionalities. In the past 20 years, new modeling techniques have emerged from a growing sophistication of statistical model applied to ecology. “Machine learning methods” (ML) are now recognized as holding great promise for the advancement of understanding and prediction of ecological phenomena. The aim of this work was to select the appropriate statistical technique to model YOY assemblages according to different meso-scale habitat variables that are meaningful to planners. To do this, two “Machine Learning” methods, Classification and Regression Trees (CART) and Boosted Regression Trees (BRT), were compared to Generalized Linear Models (GLM). We modeled the occurrence of 9 species from the Seine River basin (France) in order to compare models abilities to accurately predict the presence and absence of each species. BRT appeared to be the best technique for modeling 0+ fish occurrences in our dataset.