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.

Adipocytes as regulators of energy balance and glucose homeostasis

Abstract: Adipocytes have been studied with increasing intensity as a result of the emergence of obesity as a serious public health problem and the realization that adipose tissue serves as an integrator of various physiological pathways. In particular, their role in calorie storage makes adipocytes well suited to the regulation of energy balance. Adipose tissue also serves as a crucial integrator of glucose homeostasis. Knowledge of adipocyte biology is therefore crucial for understanding the pathophysiological basis of obesity and metabolic diseases such as type 2 diabetes. Furthermore, the rational manipulation of adipose physiology is a promising avenue for therapy of these conditions.

Fat and beyond: the diverse biology of PPARgamma.

Abstract: The nuclear receptor PPARgamma is a ligand-activated transcription factor that plays an important role in the control of gene expression linked to a variety of physiological processes. PPARgamma was initially characterized as the master regulator for the development of adipose cells. Ligands for PPARgamma include naturally occurring fatty acids and the thiazolidinedione (TZD) class of antidiabetic drugs. Activation of PPARgamma improves insulin sensitivity in rodents and humans through a combination of metabolic actions, including partitioning of lipid stores and the regulation of metabolic and inflammatory mediators termed adipokines. PPARgamma signaling has also been implicated in the control of cell proliferation, atherosclerosis, macrophage function, and immunity. Here, we review recent advances in our understanding of the diverse biological actions of PPARgamma with an eye toward the expanding therapeutic potential of PPARgamma agonist drugs.

Control of hepatic gluconeogenesis through the transcriptional coactivator PGC-1.

Abstract: Blood glucose levels are maintained by the balance between glucose uptake by peripheral tissues and glucose secretion by the liver. Gluconeogenesis is strongly stimulated during fasting and is aberrantly activated in diabetes mellitus. Here we show that the transcriptional coactivator PGC-1 is strongly induced in liver in fasting mice and in three mouse models of insulin action deficiency: streptozotocin-induced diabetes, ob/ob genotype and liver insulin-receptor knockout. PGC-1 is induced synergistically in primary liver cultures by cyclic AMP and glucocorticoids. Adenoviral-mediated expression of PGC-1 in hepatocytes in culture or in vivo strongly activates an entire programme of key gluconeogenic enzymes, including phosphoenolpyruvate carboxykinase (PEPCK) and glucose-6-phosphatase, leading to increased glucose output. Full transcriptional activation of the PEPCK promoter requires coactivation of the glucocorticoid receptor and the liver-enriched transcription factor HNF-4alpha (hepatic nuclear factor-4alpha) by PGC-1. These results implicate PGC-1 as a key modulator of hepatic gluconeogenesis and as a central target of the insulin-cAMP axis in liver.

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.