Genetic diversity patterns in Curcuma reflect differences in genome size

The relationships between genome size and the systematic and evolutionary patterns in vascular plants are equivocal, although a close relationship between genome size and evolutionary patterns has been previously reported. However, several studies have also revealed the dynamic nature of genome size evolution and its considerable ‘ups’ and ‘downs’. Thus, in this study, the phylogenetic relationships among three previously revealed genome size groups and among species of the highly polyploid genus Curcuma were evaluated using AFLP. Our results suggest two main lineages within Indian Curcuma reflecting evolution of genome size. The first one includes hexaploids and higher polyploids of the previously recognized genome size group I, and the second one includes mainly hexaploids of genome size groups II and III. Within genome size group I, relationships among species seem to be influenced by reticulate evolution and higher polyploids are likely to be of allopolyploid origin. Reproductive systems in Indian Curcuma vary considerably among ploidy levels and these differences considerably affect morphological and genetic variation. In general, clonally reproducing species are expected to exhibit low genotypic diversity, but, at the same time, species of allopolyploid origin are expected to maintain higher levels of heterozygosity compared with their progenitors. We investigated intra‐populational genetic variability in Curcuma spp. to evaluate whether mode of reproduction or ploidy represent the main factor influencing the degree of genetic diversity. We found that hexaploid species exhibited significantly higher genetic diversity than higher polyploids (9x, 15x). Our results suggest that this genetic diversity pattern is largely influenced by the mode of reproduction, as higher polyploids reproduce exclusively vegetatively, whereas hexaploids reproduce mainly sexually. © 2011 The Linnean Society of London, Botanical Journal of the Linnean Society, 2011, 165 , 388–401.     

Acute toxicity and adverse effects of aqueous and ethanol extracts of Parkia biglobosa pods on biochemical parameters of Clarias gariepinus

The acute toxicity of the aqueous and ethanol extracts of Parkia biglobosa pods against Clarias gariepinus was investigated under laboratory conditions. Agitated behaviours and respiratory distress were also observed during the exposure period. The adverse effects on biochemical parameters were assessed using semi-static bioassays for 28 days. The ethanol extract of P. biglobosa pods was found to be more acutely toxic with a 96 h LC₅₀ value of 13.96 mg l^?1 than the aqueous extracts, with a 96 h LC₅₀ value of 19.95 mg l^?1 against C. gariepinus. Both extracts induced agitated behaviours and respiratory distress in exposed organisms. The activities of superoxide dismutase (SOD), catalase (CAT) and the concentration of malondialdehyde (MDA) were significantly lower (p < 0.05) in groups of organisms exposed to extracts of P. biglobosa when compared with the control group after 14 days. The activities of aspartate aminotransferase (AST), alanine aminotransferase (ALT) and alkaline phosphatase (ALP) were also significantly (p < 0.05) lower compared with activities of the enzymes in the control group after 28 days. The current study has shown that the introduction of P. biglobosa pods into aquatic ecosystems is acutely toxic to fish and would possibly be to other non-target aquatic organisms especially invertebrates.     

Neuronal firing sensitivity to morphologic and active membrane parameters

Both the excitability of a neuron's membrane, driven by active ion channels, and dendritic morphology contribute to neuronal firing dynamics, but the relative importance and interactions between these features remain poorly understood. Recent modeling studies have shown that different combinations of active conductances can evoke similar firing patterns, but have neglected how morphology might contribute to homeostasis. Parameterizing the morphology of a cylindrical dendrite, we introduce a novel application of mathematical sensitivity analysis that quantifies how dendritic length, diameter, and surface area influence neuronal firing, and compares these effects directly against those of active parameters. The method was applied to a model of neurons from goldfish Area II. These neurons exhibit, and likely contribute to, persistent activity in eye velocity storage, a simple model of working memory. We introduce sensitivity landscapes, defined by local sensitivity analyses of firing rate and gain to each parameter, performed globally across the parameter space. Principal directions over which sensitivity to all parameters varied most revealed intrinsic currents that most controlled model output. We found domains where different groups of parameters had the highest sensitivities, suggesting that interactions within each group shaped firing behaviors within each specific domain. Application of our method, and its characterization of which models were sensitive to general morphologic features, will lead to advances in understanding how realistic morphology participates in functional homeostasis. Significantly, we can predict which active conductances, and how many of them, will compensate for a given age- or development-related structural change, or will offset a morphologic perturbation resulting from trauma or neurodegenerative disorder, to restore normal function. Our method can be adapted to analyze any computational model. Thus, sensitivity landscapes, and the quantitative predictions they provide, can give new insight into mechanisms of homeostasis in any biological system.     

Automated three-dimensional detection and shape classification of dendritic spines from fluorescence microscopy images

A fundamental challenge in understanding how dendritic spine morphology controls learning and memory has been quantifying three-dimensional (3D) spine shapes with sufficient precision to distinguish morphologic types, and sufficient throughput for robust statistical analysis. The necessity to analyze large volumetric data sets accurately, efficiently, and in true 3D has been a major bottleneck in deriving reliable relationships between altered neuronal function and changes in spine morphology. We introduce a novel system for automated detection, shape analysis and classification of dendritic spines from laser scanning microscopy (LSM) images that directly addresses these limitations. The system is more accurate, and at least an order of magnitude faster, than existing technologies. By operating fully in 3D the algorithm resolves spines that are undetectable with standard two-dimensional (2D) tools. Adaptive local thresholding, voxel clustering and Rayburst Sampling generate a profile of diameter estimates used to classify spines into morphologic types, while minimizing optical smear and quantization artifacts. The technique opens new horizons on the objective evaluation of spine changes with synaptic plasticity, normal development and aging, and with neurodegenerative disorders that impair cognitive function.     

Disentangling genetic and epigenetic determinants of ultrafast adaptation

A major rationale for the advocacy of epigenetically mediated adaptive responses is that they facilitate faster adaptation to environmental challenges. This motivated us to develop a theoretical–experimental framework for disclosing the presence of such adaptation‐speeding mechanisms in an experimental evolution setting circumventing the need for pursuing costly mutation–accumulation experiments. To this end, we exposed clonal populations of budding yeast to a whole range of stressors. By growth phenotyping, we found that almost complete adaptation to arsenic emerged after a few mitotic cell divisions without involving any phenotypic plasticity. Causative mutations were identified by deep sequencing of the arsenic‐adapted populations and reconstructed for validation. Mutation effects on growth phenotypes, and the associated mutational target sizes were quantified and embedded in data‐driven individual‐based evolutionary population models. We found that the experimentally observed homogeneity of adaptation speed and heterogeneity of molecular solutions could only be accounted for if the mutation rate had been near estimates of the basal mutation rate. The ultrafast adaptation could be fully explained by extensive positive pleiotropy such that all beneficial mutations dramatically enhanced multiple fitness components in concert. As our approach can be exploited across a range of model organisms exposed to a variety of environmental challenges, it may be used for determining the importance of epigenetic adaptation‐speeding mechanisms in general.