Hypoxia induces angiogenesis and glycolysis for cell growth and survival, and also leads to growth arrest and apoptosis. HIF‐1α, a basic helix–loop–helix PAS transcription factor, acts as a master regulator of oxygen homeostasis by upregulating various genes under low oxygen tension. Although genetic studies have indicated the requirement of HIF‐1α for hypoxia‐induced growth arrest and activation of p21cip1, a key cyclin‐dependent kinase inhibitor controlling cell cycle checkpoint, the mechanism underlying p21cip1 activation has been elusive. Here we demonstrate that HIF‐1α, even in the absence of hypoxic signal, induces cell cycle arrest by functionally counteracting Myc, thereby derepressing p21cip1. The HIF‐1α antagonism is mediated by displacing Myc binding from p21cip1 promoter. Neither HIF‐1α transcriptional activity nor its DNA binding is essential for cell cycle arrest, indicating a divergent role for HIF‐1α. In keeping with its antagonism of Myc, HIF‐1α also downregulates Myc‐activated genes such as hTERT and BRCA1. Hence, we propose that Myc is an integral part of a novel HIF‐1α pathway, which regulates a distinct group of Myc target genes in response to hypoxia.

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Calorie restriction (CR) is the most effective and reproducible intervention for increasing lifespan in a variety of animal species, including mammals. CR is also the most potent, broadly acting cancer-prevention regimen in experimental carcinogenesis models. Translation of the knowledge gained from CR research to human chronic disease prevention and the promotion of healthy aging is critical, especially because obesity, which is an important risk factor for several chronic diseases, including many cancers, is alarmingly increasing in the Western world. This review synthesizes the key biological mechanisms underlying many of the beneficial effects of CR, with a particular focus on the insulin-like growth factor-1 pathway. We also describe some of the opportunities now available for investigations, including gene expression profiling studies, the development of pharmacological mimetics of CR, and the integration of CR regimens with targeted, mechanism-based interventions. These approaches will facilitate the translation of CR research into strategies for effective human chronic disease prevention.

We have developed GoMiner, a program package that organizes lists of 'interesting' genes (for example, under-and overexpressed genes from a microarray experiment) for biological interpretation in the context of the Gene Ontology. GoMiner provides quantitative and statistical output files and two useful visualizations. The first is a tree-like structure analogous to that in the AmiGO browser and the second is a compact, dynamically interactive 'directed acyclic graph'. Genes displayed in GoMiner are linked to major public bioinformatics resources. RationaleGene-expression profiling and other forms of high-throughput genomic and proteomic studies are revolutionizing biology. That much is universally agreed. But the new technologies pose new challenges. The first is the experiment itself, the second is statistical analysis of results, the third is biological interpretation. That third challenge is often the most vexing and time-consuming. In gene-expression microarray studies, for example, one generally obtains a list of dozens or hundreds of genes that differ in expression between samples and then asks: 'What does all of this mean biologically?' The work of the Gene Ontology (GO) Consortium [1] provides a way to address that question. GO organizes genes into hierarchical categories based on biological process, molecular function and subcellular localization. In the past, this GO information was queried one gene at a time. Recently, batch processing has been introduced [2], but with a flat-format output that does not communicate the richness of GO's hierarchical structure.We have developed, and present here, the program package GoMiner as a freely available computer resource that fully incorporates the hierarchical structure of the Gene Ontology to automate the functional categorization of gene lists of any length. GoMiner is downloadable free of charge from [3] or [4]. GoMiner was developed particularly for biological interpretation of microarray data; one can input a list of underand overexpressed genes and a list of all genes on the array, and then calculate enrichment or depletion of categories with genes that have changed expression. GoMiner thus facilitates analysis and organization of the results for rapid interpretation of 'omic' [5,6] data. For concreteness, the descriptions in