Bruce M. Spiegelman

Bio: Bruce M. Spiegelman is an academic researcher from Harvard University. The author has contributed to research in topics: Adipose tissue & Peroxisome proliferator-activated receptor. The author has an hindex of 179, co-authored 434 publications receiving 158009 citations. Previous affiliations of Bruce M. Spiegelman include University of California, San Francisco & Vassar College.

Boström et al. reply

Abstract: Replying to J. A. Timmons, K. Baar, P. K. Davidsen & P. J. Atherton , 10.1038/nature11364 (2012)

Compositions, kits, and methods for identification, assessment, prevention, and therapy of metabolic disorders

Abstract: The invention provides methods and compositions for selectively promoting anti- metabolic disorder activity over classical PPAR gamma activation through modulation of PPAR gamma phosphorylation (e.g., Ser-273 phosphorylation of murine peroxisome proliferator activated receptor gamma (PPAR gamma) 2 or a corresponding serine residue in a murine PPAR gamma 2 homolog, including a human). Also provided are methods for preventing, treating, or predictiving responsiveness of therapies for metabolic disorders in a subject through selective inhibition of such PPAR gamma phosphorylation. Further provided are methods for identifying compounds that are capable of modulating such PPAR gamma phosphorylation.

Publisher Correction: Innervation of thermogenic adipose tissue via a calsyntenin 3β-S100b axis.

Abstract: In Fig. 6a of this Article, the two dots corresponding to Cidea and S100b were erroneously moved to the top left of the volcano plot; this figure has been corrected online. An amendment to this paper has been published and can be accessed via a link at the top of the paper

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

Abstract: Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

limma powers differential expression analyses for RNA-sequencing and microarray studies

Abstract: limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Obesity is associated with macrophage accumulation in adipose tissue

Abstract: Obesity alters adipose tissue metabolic and endocrine function and leads to an increased release of fatty acids, hormones, and proinflammatory molecules that contribute to obesity associated complications. To further characterize the changes that occur in adipose tissue with increasing adiposity, we profiled transcript expression in perigonadal adipose tissue from groups of mice in which adiposity varied due to sex, diet, and the obesity-related mutations agouti (Ay) and obese (Lepob). We found that the expression of 1,304 transcripts correlated significantly with body mass. Of the 100 most significantly correlated genes, 30% encoded proteins that are characteristic of macrophages and are positively correlated with body mass. Immunohistochemical analysis of perigonadal, perirenal, mesenteric, and subcutaneous adipose tissue revealed that the percentage of cells expressing the macrophage marker F4/80 (F4/80+) was significantly and positively correlated with both adipocyte size and body mass. Similar relationships were found in human subcutaneous adipose tissue stained for the macrophage antigen CD68. Bone marrow transplant studies and quantitation of macrophage number in adipose tissue from macrophage-deficient (Csf1op/op) mice suggest that these F4/80+ cells are CSF-1 dependent, bone marrow-derived adipose tissue macrophages. Expression analysis of macrophage and nonmacrophage cell populations isolated from adipose tissue demonstrates that adipose tissue macrophages are responsible for almost all adipose tissue TNF-alpha expression and significant amounts of iNOS and IL-6 expression. Adipose tissue macrophage numbers increase in obesity and participate in inflammatory pathways that are activated in adipose tissues of obese individuals.