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.     

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.     

A novel harmony search-K means hybrid algorithm for clustering gene expression data

Recent progress in bioinformatics research has led to the accumulation of huge quantities of biological data at various data sources. The DNA microarray technology makes it possible to simultaneously analyze large number of genes across different samples. Clustering of microarray data can reveal the hidden gene expression patterns from large quantities of expression data that in turn offers tremendous possibilities in functional genomics, comparative genomics, disease diagnosis and drug development. The k- ¬means clustering algorithm is widely used for many practical applications. But the original k-¬means algorithm has several drawbacks. It is computationally expensive and generates locally optimal solutions based on the random choice of the initial centroids. Several methods have been proposed in the literature for improving the performance of the k-¬means algorithm. A meta-heuristic optimization algorithm named harmony search helps find out near-global optimal solutions by searching the entire solution space. Low clustering accuracy of the existing algorithms limits their use in many crucial applications of life sciences. In this paper we propose a novel Harmony Search-K means Hybrid (HSKH) algorithm for clustering the gene expression data. Experimental results show that the proposed algorithm produces clusters with better accuracy in comparison with the existing algorithms.