Comparative analysis of multiple genomes in a phylogenetic framework dramatically improves the precision and sensitivity of evolutionary inference, producing more robust results than single-genome analyses can provide. The genomes of 12 Drosophila species, ten of which are presented here for the first time (sechellia, simulans, yakuba, erecta, ananassae, persimilis, willistoni, mojavensis, virilis and grimshawi), illustrate how rates and patterns of sequence divergence across taxa can illuminate evolutionary processes on a genomic scale. These genome sequences augment the formidable genetic tools that have made Drosophila melanogaster a pre-eminent model for animal genetics, and will further catalyse fundamental research on mechanisms of development, cell biology, genetics, disease, neurobiology, behaviour, physiology and evolution. Despite remarkable similarities among these Drosophila species, we identified many putatively non-neutral changes in protein-coding genes, non-coding RNA genes, and cis-regulatory regions. These may prove to underlie differences in the ecology and behaviour of these diverse species.

On August 1, 2006 the Howard Hughes Medical Institute's first stand-alone research campus opened at Janelia Farm, near Washington DC. Our mission at Janelia is to do exceptional fundamental research. Our two scientific foci are to understand the function of neural circuits and to develop synergistic imaging technologies. To achieve this we have changed many of the conventions of academic and/or industrial science. The founding director at Janelia is the well-known Drosophilist Gerry Rubin, who has been a central figure in fly molecular, developmental and genomic biology in recent decades. Not coincidentally, we at Janelia fully appreciate the potential of flies to contribute to an understanding of neuronal circuits. Our objectives are ambitious, and in the first ten months of operations at Janelia we have made some good beginnings.

Cell and tissue specific gene expression is a defining feature of embryonic development in multi-cellular organisms. However, the range of gene expression patterns, the extent of the correlation of expression with function, and the classes of genes whose spatial expression are tightly regulated have been unclear due to the lack of an unbiased, genome-wide survey of gene expression patterns.

Bacterial small non-coding RNAs (sRNAs) are being recognized as novel widespread regulators of gene expression in response to environmental signals. Here, we present the first search for sRNA-encoding genes in the nitrogen-fixing endosymbiont Sinorhizobium meliloti, performed by a genome-wide computational analysis of its intergenic regions. Comparative sequence data from eight related alpha-proteobacteria were obtained, and the interspecies pairwise alignments were scored with the programs eQRNA and RNAz as complementary predictive tools to identify conserved and stable secondary structures corresponding to putative non-coding RNAs. Northern experiments confirmed that eight of the predicted loci, selected among the original 32 candidates as most probable sRNA genes, expressed small transcripts. This result supports the combined use of eQRNA and RNAz as a robust strategy to identify novel sRNAs in bacteria. Furthermore, seven of the transcripts accumulated differentially in free-living and symbiotic conditions. Experimental mapping of the 5’-ends of the detected transcripts revealed that their encoding genes are organized in autonomous transcription units with recognizable promoter and, in most cases, termination signatures. These findings suggest novel regulatory functions for sRNAs related to the interactions of alpha-proteobacteria with their eukaryotic hosts.

Long-term potentiation (LTP) of synaptic transmission underlies aspects of learning and memory. LTP is input-specific at the level of individual synapses, but neural network models predict interactions between plasticity at nearby synapses. Here we show in mouse hippocampal pyramidal cells that LTP at individual synapses reduces the threshold for potentiation at neighbouring synapses. After input-specific LTP induction by two-photon glutamate uncaging or by synaptic stimulation, subthreshold stimuli, which by themselves were too weak to trigger LTP, caused robust LTP and spine enlargement at neighbouring spines. Furthermore, LTP induction broadened the presynaptic-postsynaptic spike interval for spike-timing-dependent LTP within a dendritic neighbourhood. The reduction in the threshold for LTP induction lasted approximately 10 min and spread over approximately 10 microm of dendrite. These local interactions between neighbouring synapses support clustered plasticity models of memory storage and could allow for the binding of behaviourally linked information on the same dendritic branch.

We present a model for olfactory coding based on spatial representation of glomerular responses. In this model distinct odorants activate specific subsets of glomeruli, dependent on the odorant’s chemical identity and concentration. The glomerular response specificities are understood statistically, based on experimentally measured distributions of activation thresholds. A simple version of the model, in which glomerular responses are binary (the all-or-nothing model), allows us to account quantitatively for the following results of human/rodent olfactory psychophysics: 1) just noticeable differences in the perceived concentration of a single odor (Weber ratios) are as low as dC/C approximately 0.04; 2) the number of simultaneously perceived odors can be as high as 12; and 3) extensive lesions of the olfactory bulb do not lead to significant changes in detection or discrimination thresholds. We conclude that a combinatorial code based on a binary glomerular response is sufficient to account for several important features of the discrimination capacity of the mammalian olfactory system.

When searching sequence databases for RNAs, it is desirable to score both primary sequence and RNA secondary structure similarity. Covariance models (CMs) are probabilistic models well-suited for RNA similarity search applications. However, the computational complexity of CM dynamic programming alignment algorithms has limited their practical application. Here we describe an acceleration method called query-dependent banding (QDB), which uses the probabilistic query CM to precalculate regions of the dynamic programming lattice that have negligible probability, independently of the target database. We have implemented QDB in the freely available Infernal software package. QDB reduces the average case time complexity of CM alignment from LN(2.4) to LN(1.3) for a query RNA of N residues and a target database of L residues, resulting in a 4-fold speedup for typical RNA queries. Combined with other improvements to Infernal, including informative mixture Dirichlet priors on model parameters, benchmarks also show increased sensitivity and specificity resulting from improved parameterization.

Inducible and reversible perturbation of the activity of selected neurons in vivo is critical to understanding the dynamics of brain circuits. Several genetically encoded systems for rapid inducible neuronal silencing have been developed in the past few years offering an arsenal of tools for in vivo experiments. Some systems are based on ion-channels or pumps, others on G protein coupled receptors, and yet others on modified presynaptic proteins. Inducers range from light to small molecules to peptides. This diversity results in differences in the various parameters that may determine the applicability of each tool to a particular biological question. Although further development would be beneficial, the current silencing tool kit already provides the ability to make specific perturbations of circuit function in behaving animals.

CA1 pyramidal neurons from animals that have acquired hippocampal tasks show increased neuronal excitability, as evidenced by a reduction in the postburst afterhyperpolarization (AHP). Studies of AHP plasticity require stable long-term recordings, which are affected by the intracellular solutions potassium methylsulphate (KMeth) or potassium gluconate (KGluc). Here we show immediate and gradual effects of these intracellular solutions on measurement of the AHP and basic membrane properties, and on the induction of AHP plasticity in CA1 pyramidal neurons from rat hippocampal slices. The AHP measured immediately after establishing whole-cell recordings was larger with KMeth than with KGluc. In general, the AHP in KMeth was comparable to the AHP measured in the perforated-patch configuration. However, KMeth induced time-dependent changes in the intrinsic membrane properties of CA1 pyramidal neurons. Specifically, input resistance progressively increased by 70% after 50 min; correspondingly, the current required to trigger an action potential and the fast afterdepolarization following action potentials gradually decreased by about 50%. Conversely, these measures were stable in KGluc. We also demonstrate that activity-dependent plasticity of the AHP occurs with physiologically relevant stimuli in KGluc. AHPs triggered with theta-burst firing every 30 s were progressively reduced, whereas AHPs elicited every 150 s were stable. Blockade of the apamin-sensitive AHP current (I(AHP)) was insufficient to block AHP plasticity, suggesting that plasticity is manifested through changes in the apamin-insensitive slow AHP current (sI(AHP)). These changes were observed in the presence of synaptic blockers, and therefore reflect changes in the intrinsic properties of the neurons. However, no AHP plasticity was observed using KMeth. In summary, these data show that KMeth produces time-dependent changes in basic membrane properties and prevents or obscures activity-dependent reduction of the AHP. In whole-cell recordings using KGluc, repetitive theta-burst firing induced AHP plasticity that mimics learning-related reduction in the AHP.

C. elegans, a roundworm in soil is widely used in studying animal development and aging, cell differentiation, etc. Recentlv, high-resolution fluorescence images of C. elegans have become available, introducing several new image analysis applications. One problem is that worm bodies usually curve greatly in images, thus it is highly desired to straighten worms so that they can be compared easily under the same canonical coordinate system. We develop a worm straightening algorithm (WSA) using a cutting-plane restacking method, which aggregates the linear rotation transforms of a continuous sequence of cutting lines/planes orthogonal to the "backbone" of a worm to best approximate the nonlinearly bended worm body. We formulate the backbone as a parametric form of cubic spline of a series of control points. We develop two minimum-spanning-tree based methods to automatically determine the locations of control points. Our experimental methods show that our approach can effectively straighten both 2D and 3D worm images.