Insufficient kinetic stability of exoinulinase (EI) restricts its application in many areas including enzymatic transformation of inulin for production of ultra-high fructose syrup and oligofructan, as well as fermentation of inulin into bioethanol. The conventional method for enzyme stabilization involves mutagenesis and therefore risks alteration of an enzyme’s desired properties, such as activity. Here, we report a novel method for stabilization of EI without any modification of its primary sequence. Our method employs domain insertion of an entire EI domain into a thermophilic scaffold protein. Insertion of EI into a loop of a thermophilic maltodextrin-binding protein from Pyrococcus furiosus (PfMBP) resulted in improvement of kinetic stability (the duration over which an enzyme remains active) at 37 degrees C without any compromise in EI activity. Our analysis suggests that the improved kinetic stability at 37 degrees C might originate from a raised kinetic barrier for irreversible conversion of unfolded intermediates to completely inactivated species, rather than an increased energy difference between the folded and unfolded forms.

Although the vinegar fly, Drosophila melanogaster, has been a biological model organism for over a century, its emergence as a model system for the study of neurophysiology is comparatively recent. The primary reason for this is that the vinegar fly and its neurons are tiny; up until 5 years ago, it was prohibitively difficult to record intracellularly from individual neurons in the intact Drosophila brain (Wilson et al., 2004). Today, fly electrophysiologists can genetically label neurons with GFP and reliably record from many (but not all) neurons in the fruit fly brain. Using genetic tools to drive expression of fluorescent calcium indicators, light-sensitive ion channels, or cell activity suppressors, we are beginning to understand how the external environment is represented with electrical potentials in Drosophila neurons (for review, see Olsen and Wilson, 2008).

We built a digital nuclear atlas of the newly hatched, first larval stage (L1) of the wild-type hermaphrodite of Caenorhabditis elegans at single-cell resolution from confocal image stacks of 15 individual worms. The atlas quantifies the stereotypy of nuclear locations and provides other statistics on the spatial patterns of the 357 nuclei that could be faithfully segmented and annotated out of the 558 present at this developmental stage. We then developed an automated approach to assign cell names to each nucleus in a three-dimensional image of an L1 worm. We achieved 86% accuracy in identifying the 357 nuclei automatically. This computational method will allow high-throughput single-cell analyses of the post-embryonic worm, such as gene expression analysis, or ablation or stimulation of cells under computer control in a high-throughput functional screen.

Synaptic plasticity in adult neural circuits may involve the strengthening or weakening of existing synapses as well as structural plasticity, including synapse formation and elimination. Indeed, long-term in vivo imaging studies are beginning to reveal the structural dynamics of neocortical neurons in the normal and injured adult brain. Although the overall cell-specific morphology of axons and dendrites, as well as of a subpopulation of small synaptic structures, are remarkably stable, there is increasing evidence that experience-dependent plasticity of specific circuits in the somatosensory and visual cortex involves cell type-specific structural plasticity: some boutons and dendritic spines appear and disappear, accompanied by synapse formation and elimination, respectively. This Review focuses on recent evidence for such structural forms of synaptic plasticity in the mammalian cortex and outlines open questions.

Conditional expression of hairpin constructs in Drosophila is a powerful method to disrupt the activity of single genes with a spatial and temporal resolution that is impossible, or exceedingly difficult, using classical genetic methods. We previously described a method (Ni et al. 2008) whereby RNAi constructs are targeted into the genome by the phiC31-mediated integration approach using Vermilion-AttB-Loxp-Intron-UAS-MCS (VALIUM), a vector that contains vermilion as a selectable marker, an attB sequence to allow for phiC31-targeted integration at genomic attP landing sites, two pentamers of UAS, the hsp70 core promoter, a multiple cloning site, and two introns. As the level of gene activity knockdown associated with transgenic RNAi depends on the level of expression of the hairpin constructs, we generated a number of derivatives of our initial vector, called the "VALIUM" series, to improve the efficiency of the method. Here, we report the results from the systematic analysis of these derivatives and characterize VALIUM10 as the most optimal vector of this series. A critical feature of VALIUM10 is the presence of gypsy insulator sequences that boost dramatically the level of knockdown. We document the efficacy of VALIUM as a vector to analyze the phenotype of genes expressed in the nervous system and have generated a library of 2282 constructs targeting 2043 genes that will be particularly useful for studies of the nervous system as they target, in particular, transcription factors, ion channels, and transporters.

Real-time lineage tracing in flies gets a boost with three techniques to specifically label a progenitor’s daughter cells.

The shapes of dendritic arbors are fascinating and important, yet the principles underlying these complex and diverse structures remain unclear. Here, we analyzed basal dendritic arbors of 2,171 pyramidal neurons sampled from mammalian brains and discovered 3 statistical properties: the dendritic arbor size scales with the total dendritic length, the spatial correlation of dendritic branches within an arbor has a universal functional form, and small parts of an arbor are self-similar. We proposed that these properties result from maximizing the repertoire of possible connectivity patterns between dendrites and surrounding axons while keeping the cost of dendrites low. We solved this optimization problem by drawing an analogy with maximization of the entropy for a given energy in statistical physics. The solution is consistent with the above observations and predicts scaling relations that can be tested experimentally. In addition, our theory explains why dendritic branches of pyramidal cells are distributed more sparsely than those of Purkinje cells. Our results represent a step toward a unifying view of the relationship between neuronal morphology and function.