Supported Au nanoclusters are well-known for their unusual properties in catalysis. We describe here that nanostructured porous Au made via dealloying represents a new class of unsupported catalysts with extraordinary activities in important reactions such as CO oxidation. Although nanoporous Au may contain some oxides on the surface, our results demonstrate that it is metallic Au that plays the main role in this catalytic reaction. Furthermore, this material has good low-temperature catalytic stability and is extremely CO tolerant.

Invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC) are the two major histological types of breast cancer worldwide. Whereas IDC incidence has remained stable, ILC is the most rapidly increasing breast cancer phenotype in the United States and Western Europe. It is not clear whether IDC and ILC represent molecularly distinct entities and what genes might be involved in the development of these two phenotypes. We conducted comprehensive gene expression profiling studies to address these questions. Total RNA from 21 ILCs, 38 IDCs, two lymph node metastases, and three normal tissues were amplified and hybridized to ϳ42,000 clone cDNA microarrays. Data were analyzed using hierarchical clustering algorithms and statistical analyses that identify differentially expressed genes (significance analysis of microarrays) and minimal subsets of genes (prediction analysis for microarrays) that succinctly distinguish ILCs and IDCs. Eleven of 21 (52%) of the ILCs ("typical" ILCs) clustered together and displayed different gene expression profiles from IDCs, whereas the other ILCs ("ductal-like" ILCs) were distributed between different IDC subtypes. Many of the differentially expressed genes between ILCs and IDCs code for proteins involved in cell adhesion/motility, lipid/fatty acid transport and metabolism, immune/defense response, and electron transport. Many genes that distinguish typical and ductal-like ILCs are involved in regulation of cell growth and immune response. Our data strongly suggest that over half the ILCs differ from IDCs not only in histological and clinical features but also in global transcription programs. The remaining ILCs closely resemble IDCs in their transcription patterns. Further studies are needed to explore the differences between ILC molecular subtypes and to determine whether they require different therapeutic strategies.

Clear cell renal cell carcinoma (ccRCC) is the predominant RCC subtype, but even within this classification, the natural history is heterogeneous and difficult to predict. A sophisticated understanding of the molecular features most discriminatory for the underlying tumor heterogeneity should be predicated on identifiable and biologically meaningful patterns of gene expression. Gene expression microarray data were analyzed using software that implements iterative unsupervised consensus clustering algorithms to identify the optimal molecular subclasses, without clinical or other classifying information. ConsensusCluster analysis identified two distinct subtypes of ccRCC within the training set, designated clear cell type A (ccA) and B (ccB). Based on the core tumors, or most well-defined arrays, in each subtype, logical analysis of data (LAD) defined a small, highly predictive gene set that could then be used to classify additional tumors individually. The subclasses were corroborated in a validation data set of 177 tumors and analyzed for clinical outcome. Based on individual tumor assignment, tumors designated ccA have markedly improved disease-specific survival compared to ccB (median survival of 8.6 vs 2.0 years, P = 0.002). Analyzed by both univariate and multivariate analysis, the classification schema was independently associated with survival. Using patterns of gene expression based on a defined gene set, ccRCC was classified into two robust subclasses based on inherent molecular features that ultimately correspond to marked differences in clinical outcome. This classification schema thus provides a molecular stratification applicable to individual tumors that has implications to influence treatment decisions, define biological mechanisms involved in ccRCC tumor progression, and direct future drug discovery.

Synaptobrevins are vesicle-associated proteins implicated in neurotransmitter release by both biochemical studies and perturbation experiments that use botulinum toxins. To test these models in vivo, we have isolated and characterized the first synaptobrevin mutants in metazoans and show that neurotransmission is severely disrupted in mutant animals. Mutants lacking snb-1 die just after completing embryogenesis. The dying animals retain some capability for movement, although they are extremely uncoordinated and incapable of feeding. We also have isolated and characterized several hypomorphic snb-1 mutants. Although fully viable, these mutants exhibit a variety of behavioral abnormalities that are consistent with a general defect in the efficacy of synaptic transmission. The viable mutants are resistant to the acetylcholinesterase inhibitor aldicarb, indicating that cholinergic transmission is impaired. Extracellular recordings from pharyngeal muscle also demonstrate severe defects in synaptic transmission in the mutants. The molecular lesions in the hypomorphic alleles reside on the hydrophobic face of a proposed amphipathic-helical region implicated biochemically in interacting with the t-SNAREs syntaxin and SNAP-25. Finally, we demonstrate that double mutants lacking both the v-SNAREs synaptotagmin and snb-1 are phenotypically similar to snb-1 mutants and less severe than syntaxin mutants. Our work demonstrates that synaptobrevin is essential for viability and is required for functional synaptic transmission. However, our analysis also suggests that transmitter release is not completely eliminated by removal of either one or both v-SNAREs.

BackgroundConventional renal cell carcinoma (cRCC) accounts for most of the deaths due to kidney cancer. Tumor stage, grade, and patient performance status are used currently to predict survival after surgery. Our goal was to identify gene expression features, using comprehensive gene expression profiling, that correlate with survival.Methods and FindingsGene expression profiles were determined in 177 primary cRCCs using DNA microarrays. Unsupervised hierarchical clustering analysis segregated cRCC into five gene expression subgroups. Expression subgroup was correlated with survival in long-term follow-up and was independent of grade, stage, and performance status. The tumors were then divided evenly into training and test sets that were balanced for grade, stage, performance status, and length of follow-up. A semisupervised learning algorithm (supervised principal components analysis) was applied to identify transcripts whose expression was associated with survival in the training set, and the performance of this gene expression-based survival predictor was assessed using the test set. With this method, we identified 259 genes that accurately predicted disease-specific survival among patients in the independent validation group (p < 0.001). In multivariate analysis, the gene expression predictor was a strong predictor of survival independent of tumor stage, grade, and performance status (p < 0.001).ConclusionscRCC displays molecular heterogeneity and can be separated into gene expression subgroups that correlate with survival after surgery. We have identified a set of 259 genes that predict survival after surgery independent of clinical prognostic factors.