Correlation between citric acid and nitrate metabolisms during CAM cycle in the atmospheric bromeliad Tillandsia pohliana

Crassulacean acid metabolism (CAM) confers crucial adaptations for plants living under frequent environmental stresses. A wide metabolic plasticity can be found among CAM species regarding the type of storage carbohydrate, organic acid accumulated at night and decarboxylating system. Consequently, many aspects of the CAM pathway control are still elusive while the impact of this photosynthetic adaptation on nitrogen metabolism has remained largely unexplored. In this study, we investigated a possible link between the CAM cycle and the nitrogen assimilation in the atmospheric bromeliad Tillandsia pohliana by simultaneously characterizing the diel changes in key enzyme activities and metabolite levels of both organic acid and nitrate metabolisms. The results revealed that T. pohliana performed a typical CAM cycle in which phosphoenolpyruvate carboxylase and phosphoenolpyruvate carboxykinase phosphorylation seemed to play a crucial role to avoid futile cycles of carboxylation and decarboxylation. Unlike all other bromeliads previously investigated, almost equimolar concentrations of malate and citrate were accumulated at night. Moreover, a marked nocturnal depletion in the starch reservoirs and an atypical pattern of nitrate reduction restricted to the nighttime were also observed. Since reduction and assimilation of nitrate requires a massive supply of reducing power and energy and considering that T. pohliana lives overexposed to the sunlight, we hypothesize that citrate decarboxylation might be an accessory mechanism to increase internal CO(2) concentration during the day while its biosynthesis could provide NADH and ATP for nocturnal assimilation of nitrate. Therefore, besides delivering photoprotection during the day, citrate might represent a key component connecting both CAM pathway and nitrogen metabolism in T. pohliana: a scenario that certainly deserves further study not only in this species but also in other CAM plants that nocturnally accumulate citrate. (C) 2010 Elsevier GmbH. All rights reserved.

Covariance structure in the skull of Catarrhini: a case of pattern stasis and magnitude evolution

The study of the genetic variance/covariance matrix (G-matrix) is a recent and fruitful approach in evolutionary biology, providing a window of investigating for the evolution of complex characters. Although G-matrix studies were originally conducted for microevolutionary timescales, they could be extrapolated to macroevolution as long as the G-matrix remains relatively constant, or proportional, along the period of interest. A promising approach to investigating the constancy of G-matrices is to compare their phenotypic counterparts (P-matrices) in a large group of related species; if significant similarity is found among several taxa, it is very likely that the underlying G-matrices are also equivalent. Here we study the similarity of covariance and correlation structure in a broad sample of Old World monkeys and apes (Catarrhini). We made phylogenetically structured comparisons of correlation and covariance matrices derived from 39 skull traits, ranging from between species to the superfamily level. We also compared the overall magnitude of integration between skull traits (r(2)) for all Catarrhim genera. Our results show that P-matrices were not strictly constant among catarrhines, but the amount of divergence observed among taxa was generally low. There was significant and positive correlation between the amount of divergence in correlation and covariance patterns among the 30 genera and their phylogenetic distances derived from a recently proposed phylogenetic hypothesis. Our data demonstrate that the P-matrices remained relatively similar along the evolutionary history of catarrhines, and comparisons with the G-matrix available for a New World monkey genus (Saguinus) suggests that the same holds for all anthropoids. The magnitude of integration, in contrast, varied considerably among genera, indicating that evolution of the magnitude, rather than the pattern of inter-trait correlations, might have played an important role in the diversification of the catarrhine skull. (C) 2009 Elsevier Ltd. All rights reserved.

Functional and morphologic evaluation of kidney proximal tubuli and correlation with renal allograft prognosis

P>Renal transplant patients with stable graft function and proximal tubular dysfunction (PTD) have an increased risk for chronic allograft nephropathy (CAN). In this study, we investigated the histologic pattern associated with PTD and its correlation with graft outcome. Forty-nine transplant patients with stable graft function were submitted to a biopsy. Simultaneously, urinary retinol-binding protein (uRBP) was measured and creatinine clearance was also determined. Banff`s score and semi-quantitative histologic analyses were performed to assess tubulointerstitial alterations. Patients were followed for 24.0 +/- 7.8 months. At biopsy time, mean serum creatinine was 1.43 +/- 0.33 mg/dl. Twelve patients (24.5%) had uRBP >= 1 mg/l, indicating PTD and 67% of biopsies had some degree of tubulointerstitial injury. At the end of the study period, 18 (36.7%) patients had lost renal function. uRBP levels were not associated with morphologic findings of interstitial fibrosis and tubular atrophy (IF/TA), interstitial fibrosis measured by Sirius red or tubulointerstitial damage. However, in multivariate analysis, the only variable associated with the loss of renal function was uRBP level >= 1 mg/l, determining a risk of 5.290 of loss of renal function (P = 0.003). Renal transplant patients who present PTD have functional alteration, which is not associated with morphologic alteration. This functional alteration is associated to progressive decrease in renal function.

A pattern recognition approach to complex networks

Complex networks exist in many areas of science such as biology, neuroscience, engineering, and sociology. The growing development of this area has led to the introduction of several topological and dynamical measurements, which describe and quantify the structure of networks. Such characterization is essential not only for the modeling of real systems but also for the study of dynamic processes that may take place in them. However, it is not easy to use several measurements for the analysis of complex networks, due to the correlation between them and the difficulty of their visualization. To overcome these limitations, we propose an effective and comprehensive approach for the analysis of complex networks, which allows the visualization of several measurements in a few projections that contain the largest data variance and the classification of networks into three levels of detail, vertices, communities, and the global topology. We also demonstrate the efficiency and the universality of the proposed methods in a series of real-world networks in the three levels.