The crystal structure of a bacterial Class II ketol-acid reductolsomerase: Domain conservation and evolution

Ketol-acid reductoisomerase (KARI; EC 1.1.1.86) catalyzes two steps in the biosynthesis of branched-chain amino acids. Amino acid sequence comparisons across species reveal that there are two types of this enzyme: a short form (Class 1) found in fungi and most bacteria, and a long form (Class 11) typical of plants. Crystal structures of each have been reported previously. However, some bacteria such as Escherichia coli possess a long form, where the amino acid sequence differs appreciably from that found in plants. Here, we report the crystal structure of the E. coli enzyme at 2.6 A resolution, the first three-dimensional structure of any bacterial Class 11 KARI. The enzyme consists of two domains, one with mixed alpha/beta structure, which is similar to that found in other pyridine nucleotide-dependent dehydrogenases. The second domain is mainly alpha-helical and shows strong evidence of internal duplication. Comparison of the active sites between KARI of E. coli, Pseudomonas aeruginosa, and spinach shows that most residues occupy conserved positions in the active site. E. coli KARI was crystallized as a tetramer, the likely biologically active unit. This contrasts with P. aeruginosa KARI, which forms a dodecamer, and spinach KARI, a dimer. In the E. coli KARI tetramer, a novel subunit-to-subunit interacting surface is formed by a symmetrical pair of bulbous protrusions.

Fractal analysis as a tool for studying specialization in neuronal structure: The study of the evolution of the primate cerebral cortex and human intellect

We review recent findings that, using fractal analysis, have demonstrated systematic regional and species differences in the branching complexity of neocortical pyramidal neurons. In particular, attention is focused on how fractal analysis is being applied to the study of specialization in pyramidal cell structure during the evolution of the primate cerebral cortex. These studies reveal variation in pyramidal cell phenotype that cannot be attributed solely to increasing brain volume. Moreover, the results of these studies suggest that the primate cerebral cortex is composed of neurons of different structural complexity. There is growing evidence to suggest that regional and species differences in neuronal structure influence function at both the cellular and circuit levels. These data challenge the prevailing dogma for cortical uniformity.

Rapid evolution of noncoding RNAs: lack of conservation does not mean lack of function

The mammalian transcriptome contains many nonprotein-coding RNAs (ncRNAs), but most of these are of unclear significance and lack strong sequence conservation, prompting suggestions that they might be non-functional. However, certain long functional ncRNAs such as Air and Xist are also poorly conserved. In this article, we systematically analyzed the conservation of several groups of functional ncRNAs, including miRNAs, snoRNAs and longer ncRNAs whose function has been either documented or confidently predicted. As expected, miRNAs and snoRNAs were highly conserved. By contrast, the longer functional non-micro, non-sno ncRNAs were much less conserved with many displaying rapid sequence evolution. Our findings suggest that longer ncRNAs are under the influence of different evolutionary constraints and that the lack of conservation displayed by the thousands of candidate ncRNAs does not necessarily signify an absence of function.

Divergent selection and the evolution of signal traits and mating preferences

Mating preferences are common in natural populations, and their divergence among populations is considered an important source of reproductive isolation during speciation. Although mechanisms for the divergence of mating preferences have received substantial theoretical treatment, complementary experimental tests are lacking. We conducted a laboratory evolution experiment, using the fruit fly Drosophila serrata, to explore the role of divergent selection between environments in the evolution of female mating preferences. Replicate populations of D. serrata were derived from a common ancestor and propagated in one of three resource environments: two novel environments and the ancestral laboratory environment. Adaptation to both novel environments involved changes in cuticular hydrocarbons, traits that predict mating success in these populations. Furthermore, female mating preferences for these cuticular hydrocarbons also diverged among populations. A component of this divergence occurred among treatment environments, accounting for at least 17.4% of the among- population divergence in linear mating preferences and 17.2% of the among-population divergence in nonlinear mating preferences. The divergence of mating preferences in correlation with environment is consistent with the classic by- product model of speciation in which premating isolation evolves as a side effect of divergent selection adapting populations to their different environments.

Network topology and the evolution of dynamics in an artificial genetic regulatory network model created by whole genome duplication and divergence

Topological measures of large-scale complex networks are applied to a specific artificial regulatory network model created through a whole genome duplication and divergence mechanism. This class of networks share topological features with natural transcriptional regulatory networks. Specifically, these networks display scale-free and small-world topology and possess subgraph distributions similar to those of natural networks. Thus, the topologies inherent in natural networks may be in part due to their method of creation rather than being exclusively shaped by subsequent evolution under selection. The evolvability of the dynamics of these networks is also examined by evolving networks in simulation to obtain three simple types of output dynamics. The networks obtained from this process show a wide variety of topologies and numbers of genes indicating that it is relatively easy to evolve these classes of dynamics in this model. (c) 2006 Elsevier Ireland Ltd. All rights reserved.

Structural evolution in a hydrothermal reaction between Nb2O5 and NaOH solution: From Nb2O5 grains to microporous Na2Nb2O6 center dot(2)/3H2O fibers and NaNbO3 cubes

Niobium pentoxide reacts actively with concentrate NaOH solution under hydrothermal conditions at as low as 120 degrees C. The reaction ruptures the corner-sharing of NbO7 decahedra and NbO6 octahedra in the reactant Nb2O5, yielding various niobates, and the structure and composition of the niobates depend on the reaction temperature and time. The morphological evolution of the solid products in the reaction at 180 degrees C is monitored via SEM: the fine Nb2O5 powder aggregates first to irregular bars, and then niobate fibers with an aspect ratio of hundreds form. The fibers are microporous molecular sieve with a monoclinic lattice, Na2Nb2O6 center dot(2)/3H2O. The fibers are a metastable intermediate of this reaction, and they completely convert to the final product NaNbO3 Cubes in the prolonged reaction of 1 h. This study demonstrates that by carefully optimizing the reaction condition, we can selectively fabricate niobate structures of high purity, including the delicate microporous fibers, through a direct reaction between concentrated NaOH solution and Nb2O5. This synthesis route is simple and suitable for the large-scale production of the fibers. The reaction first yields poorly crystallized niobates consisting of edge-sharing NbO6 octahedra, and then the microporous fibers crystallize and grow by assembling NbO6 octahedra or clusters of NbO6 octahedra and NaO6 units. Thus, the selection of the fibril or cubic product is achieved by control of reaction kinetics. Finally, niobates with different structures exhibit remarkable differences in light absorption and photoluminescence properties. Therefore, this study is of importance for developing new functional materials by the wet-chemistry process.

Studying phenotypic evolution using multivariate quantitative genetics

Quantitative genetics provides a powerful framework for studying phenotypic evolution and the evolution of adaptive genetic variation. Central to the approach is G, the matrix of additive genetic variances and covariances. G summarizes the genetic basis of the traits and can be used to predict the phenotypic response to multivariate selection or to drift. Recent analytical and computational advances have improved both the power and the accessibility of the necessary multivariate statistics. It is now possible to study the relationships between G and other evolutionary parameters, such as those describing the mutational input, the shape and orientation of the adaptive landscape, and the phenotypic divergence among populations. At the same time, we are moving towards a greater understanding of how the genetic variation summarized by G evolves. Computer simulations of the evolution of G, innovations in matrix comparison methods, and rapid development of powerful molecular genetic tools have all opened the way for dissecting the interaction between allelic variation and evolutionary process. Here I discuss some current uses of G, problems with the application of these approaches, and identify avenues for future research.