Peroxisomes carry out various oxidative reactions that are tightly regulated to adapt to the changing needs of the cell and varying external environments. Accordingly, they are remarkably fluid and can change dramatically in abundance, size, shape and content in response to numerous cues. These dynamics are controlled by multiple aspects of peroxisome biogenesis that are coordinately regulated with each other and with other cellular processes. Ongoing studies are deciphering the diverse molecular mechanisms that underlie biogenesis and how they cooperate to dynamically control peroxisome utility. These important challenges should lead to an understanding of peroxisome dynamics that can be capitalized upon for bioengineering and the development of therapies to improve human health.

Water samples were collected in spring, summer, and winter from English rivers in urban/industrial (River Aire and River Calder, Yorkshire, UK) and rural environments (River Thames, Oxfordshire, UK) to study the biodegradation potential of the key steroid estrogen 17beta-estradiol (E2) and its synthetic derivate ethinylestradiol (EE2). Microorganisms in the river water samples were capable of transforming E2 to estrone (E1) with half-lives of 0.2 to 9 d when incubated at 20 degrees C. The E1 was then further degraded at similar rates. The most rapid biodegradation rates were associated with the downstream summer samples of the River Aire and River Calder. E2 degradation rates were similar for spiking concentrations throughout the range of 20 ng/L to 500 microg/L. Microbial cleavage of the steroid ring system was demonstrated by release of radiolabeled CO2 from the aromatic ring of E2 (position 4). When E2 was degraded, the loss of estrogenicity, measured by the yeast estrogen screen (YES) assay, closely followed the loss of the parent molecule. Thus, apart from the transient formation of E1, the degradation of E2 does not form other significantly estrogenic intermediates. The E2 could also be degraded when incubated with anaerobic bed sediments. Compared to E2, EE2 was much more resistant to biodegradation, but both E2 and EE2 were susceptible to photodegradation, with half-lives in the order of 10 d under ideal conditions.

Yeast cells were induced to proliferate peroxisomes, and microarray transcriptional profiling was used to identify PEX genes encoding peroxins involved in peroxisome assembly and genes involved in peroxisome function. Clustering algorithms identified 224 genes with expression profiles similar to those of genes encoding peroxisomal proteins and genes involved in peroxisome biogenesis. Several previously uncharacterized genes were identified, two of which, YPL112c and YOR084w, encode proteins of the peroxisomal membrane and matrix, respectively. Ypl112p, renamed Pex25p, is a novel peroxin required for the regulation of peroxisome size and maintenance. These studies demonstrate the utility of comparative gene profiling as an alternative to functional assays to identify genes with roles in peroxisome biogenesis.

Different experimental technologies measure different aspects of a system and to differing depth and breadth. High-throughput assays have inherently high false-positive and false-negative rates. Moreover, each technology includes systematic biases of a different nature. These differences make network reconstruction from multiple data sets difficult and error-prone. Additionally, because of the rapid rate of progress in biotechnology, there is usually no curated exemplar data set from which one might estimate data integration parameters. To address these concerns, we have developed data integration methods that can handle multiple data sets differing in statistical power, type, size, and network coverage without requiring a curated training data set. Our methodology is general in purpose and may be applied to integrate data from any existing and future technologies. Here we outline our methods and then demonstrate their performance by applying them to simulated data sets. The results show that these methods select truepositive data elements much more accurately than classical approaches. In an accompanying companion paper, we demonstrate the applicability of our approach to biological data. We have integrated our methodology into a free open source software package named POINTILLIST.Fisher's method ͉ mixture distribution models S ystems biology (1, 2) aims to understand cellular behavior in terms of the spatiotemporal interactions among cellular components, such as genes, proteins, metabolites, and organelles. In systems biology, one typically perturbs a system and, with highthroughput measurements to identify all pertinent elements and their interactions, integrates them into a biological network to understand the system's behavior. As such, systems biology is predicated on the integration of experimental data from an ever increasing number of technologies, such as gene expression arrays, proteomics, and chromatin immunoprecipitation on chip assays (3). Integration achieves one of the most important imperatives of systems biology, namely it reduces the dimensionality of global data to deliver useful information about the system of interest.A major challenge in systems biology is that technologies that globally interrogate biological systems have inherently high falsepositive and false-negative rates (4); thus, each data type alone has a limited utility. The integration of data from different sources provides an effective means to deal with this issue by reinforcing bona fide observations and reducing false negatives. Moreover, because different experimental technologies provide different insights into a system, the integration of multiple data types offers the greatest information about a particular cellular process. For example, gene perturbation experiments (e.g., knockouts or RNA interference) reveal relationships between genes that may imply direct physical interactions or indirect logical interactions. In contrast, chromatin immunoprecipitation chip data can reveal direct protein-DNA interactions or cofacto...

Animal toxins that modulate the activity of voltage-gated sodium (Na) channels are broadly divided into two categories-pore blockers and gating modifiers. The pore blockers tetrodotoxin (TTX) and saxitoxin (STX) are responsible for puffer fish and shellfish poisoning in humans, respectively. Here, we present structures of the insect Na channel NaPaS bound to a gating modifier toxin Dc1a at 2.8 angstrom-resolution and in the presence of TTX or STX at 2.6-Å and 3.2-Å resolution, respectively. Dc1a inserts into the cleft between VSD and the pore of NaPaS, making key contacts with both domains. The structures with bound TTX or STX reveal the molecular details for the specific blockade of Na access to the selectivity filter from the extracellular side by these guanidinium toxins. The structures shed light on structure-based development of Na channel drugs.

Human genetic studies have implicated the voltage-gated sodium channel NaV1.7 as a therapeutic target for the treatment of pain. A novel peptide, μ-theraphotoxin-Pn3a, isolated from venom of the tarantula Pamphobeteus nigricolor, potently inhibits NaV1.7 (IC50 0.9 nM) with at least 40–1000-fold selectivity over all other NaV subtypes. Despite on-target activity in small-diameter dorsal root ganglia, spinal slices, and in a mouse model of pain induced by NaV1.7 activation, Pn3a alone displayed no analgesic activity in formalin-, carrageenan- or FCA-induced pain in rodents when administered systemically. A broad lack of analgesic activity was also found for the selective NaV1.7 inhibitors PF-04856264 and phlotoxin 1. However, when administered with subtherapeutic doses of opioids or the enkephalinase inhibitor thiorphan, these subtype-selective NaV1.7 inhibitors produced profound analgesia. Our results suggest that in these inflammatory models, acute administration of peripherally restricted NaV1.7 inhibitors can only produce analgesia when administered in combination with an opioid.

SummaryIn shoots of the garden pea, the bioactive gibberellin (GA 1 ) is synthesised from GA 20 , and the enzyme which catalyses this step (a GA 3-oxidase Ð PsGA3ox1) is encoded by Mendel's LE gene. It has been reported previously that decapitation of the shoot (excision of the apical bud) dramatically reduces the conversion of [ ]GA 1 in stems, and here we show that endogenous GA 1 and PsGA3ox1 transcript levels are similarly reduced. We show also that these effects of decapitation are completely reversed by application of the auxin indole-3-acetic acid (IAA) to the`stump' of decapitated plants. Gibberellin A 20 is also converted to an inactive product, GA 29 , and this step is catalysed by a GA 2-oxidase, PsGA2ox1. In contrast to PsGA3ox1, PsGA2ox1 transcript levels were increased by decapitation and reduced by IAA application. Decapitation and IAA treatment did not markedly affect the level of GA 1 precursors. It is suggested that in intact pea plants, auxin from the apical bud moves into the elongating internodes where it (directly or indirectly) maintains PsGA3ox1 transcript levels and, consequently, GA 1 biosynthesis.

Cytoscape is a general network visualization, data integration, and analysis software package. Its development and use has been focused on the modeling requirements of systems biology, though it has been used in other fields. Cytoscape's flexibility has encouraged many users to adopt it and adapt it to their own research by using the plugin framework offered to specialize data analysis, data integration, or visualization. Plugins represent collections of community-contributed functionality and can be used to dynamically extend Cytoscape functionality. This community of users and developers has worked together since Cytoscape's initial release to improve the basic project through contributions to the core code and public offerings of plugin modules. This chapter discusses what Cytoscape does, why it was developed, and the extensions numerous groups have made available to the public. It also describes the development of a plugin used to investigate a particular research question in systems biology and walks through an example analysis using Cytoscape.