The C. elegans cell lineage provides a unique opportunity to look at how cell lineage affects patterns of gene expression. We developed an automatic cell lineage analyzer that converts high-resolution images of worms into a data table showing fluorescence expression with single-cell resolution. We generated expression profiles of 93 genes in 363 specific cells from L1 stage larvae and found that cells with identical fates can be formed by different gene regulatory pathways. Molecular signatures identified repeating cell fate modules within the cell lineage and enabled the generation of a molecular differentiation map that reveals points in the cell lineage when developmental fates of daughter cells begin to diverge. These results demonstrate insights that become possible using computational approaches to analyze quantitative expression from many genes in parallel using a digital gene expression atlas.

Cortical information processing is under state-dependent control of subcortical neuromodulatory systems. Although this modulatory effect is thought to be mediated mainly by slow nonsynaptic metabotropic receptors, other mechanisms, such as direct synaptic transmission, are possible. Yet, it is currently unknown if any such form of subcortical control exists. Here, we present direct evidence of a strong, spatiotemporally precise excitatory input from an ascending neuromodulatory center. Selective stimulation of serotonergic median raphe neurons produced a rapid activation of hippocampal interneurons. At the network level, this subcortical drive was manifested as a pattern of effective disynaptic GABAergic inhibition that spread throughout the circuit. This form of subcortical network regulation should be incorporated into current concepts of normal and pathological cortical function.

Behaviour is governed by activity in highly structured neural circuits. Genetically targeted sensors and switches facilitate measurement and manipulation of activity in vivo, linking activity in defined nodes of neural circuits to behaviour. Because of access to specific cell types, these molecular tools will have the largest impact in genetic model systems such as the mouse. Emerging assays of mouse behaviour are beginning to rival those of behaving monkeys in terms of stimulus and behavioural control. We predict that the confluence of new behavioural and molecular tools in the mouse will reveal the logic of complex mammalian circuits.

Many theoretical advances have been made in applying probabilistic inference methods to improve the power of sequence homology searches, yet the BLAST suite of programs is still the workhorse for most of the field. The main reason for this is practical: BLAST’s programs are about 100-fold faster than the fastest competing implementations of probabilistic inference methods. I describe recent work on the HMMER software suite for protein sequence analysis, which implements probabilistic inference using profile hidden Markov models. Our aim in HMMER3 is to achieve BLAST’s speed while further improving the power of probabilistic inference based methods. HMMER3 implements a new probabilistic model of local sequence alignment and a new heuristic acceleration algorithm. Combined with efficient vector-parallel implementations on modern processors, these improvements synergize. HMMER3 uses more powerful log-odds likelihood scores (scores summed over alignment uncertainty, rather than scoring a single optimal alignment); it calculates accurate expectation values (E-values) for those scores without simulation using a generalization of Karlin/Altschul theory; it computes posterior distributions over the ensemble of possible alignments and returns posterior probabilities (confidences) in each aligned residue; and it does all this at an overall speed comparable to BLAST. The HMMER project aims to usher in a new generation of more powerful homology search tools based on probabilistic inference methods.

Hormones coordinate developmental, physiological, and behavioral processes within and between all living organisms. They orchestrate and shape organogenesis from early in development, regulate the acquisition, assimilation, and utilization of nutrients to support growth and metabolism, control gamete production and sexual behavior, mediate organismal responses to environmental change, and allow for communication of information between organisms. Genes that code for hormones; the enzymes that synthesize, metabolize, and transport hormones; and hormone receptors are important targets for natural selection, and variation in their expression and function is a major driving force for the evolution of morphology and life history. Hormones coordinate physiology and behavior of populations of organisms, and thus play key roles in determining the structure of populations, communities, and ecosystems. The field of endocrinology is concerned with the study of hormones and their actions. This field is rooted in the comparative study of hormones in diverse species, which has provided the foundation for the modern fields of evolutionary, environmental, and biomedical endocrinology. Comparative endocrinologists work at the cutting edge of the life sciences. They identify new hormones, hormone receptors and mechanisms of hormone action applicable to diverse species, including humans; study the impact of habitat destruction, pollution, and climatic change on populations of organisms; establish novel model systems for studying hormones and their functions; and develop new genetic strains and husbandry practices for efficient production of animal protein. While the model system approach has dominated biomedical research in recent years, and has provided extraordinary insight into many basic cellular and molecular processes, this approach is limited to investigating a small minority of organisms. Animals exhibit tremendous diversity in form and function, life-history strategies, and responses to the environment. A major challenge for life scientists in the 21st century is to understand how a changing environment impacts all life on earth. A full understanding of the capabilities of organisms to respond to environmental variation, and the resilience of organisms challenged by environmental changes and extremes, is necessary for understanding the impact of pollution and climatic change on the viability of populations. Comparative endocrinologists have a key role to play in these efforts.

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.