Obesity is currently considered as an epidemic in the western world, and it represents a major risk factor for life-threatening diseases such as heart attack, stroke, diabetes, and cancer. Taking advantage of DNA microarray technology, we tried to identify the molecules explaining the relationship between obesity and vascular disorders, comparing mRNA expression of about 12,000 genes in white adipose tissue between normal, high fat diet-induced obesity (DIO) and D-Trp 34 neuropeptide Yinduced obesity in mice. Expression of monocyte chemoattractant protein-1 (MCP-1) mRNA displayed a 7.2-fold increase in obese mice as compared with normal mice, leading to substantially elevated MCP-1 protein levels in adipocytes. MCP-1 levels in plasma were also increased in DIO mice, and a strong correlation between plasma MCP-1 levels and body weight was identified. We also showed that elevated MCP-1 protein levels in plasma increased the CD11b-positive monocyte/macrophage population in DIO mice. Furthermore, infusion of MCP-1 into lean mice increased the CD11b-positive monocyte population without inducing changes in body weight. Given the importance of MCP-1 in activation of monocytes and subsequent atherosclerotic development, these results suggest a novel role of adiposity in the development of vascular disorders.

During female germline development oocytes become a highly specialized cell type, and form a maternal cytoplasmic store of crucial factors during oocyte growth. Oocyte growth is triggered at the primordial-primary follicle transition accompanied with dynamic changes in gene expression 1 , yet the gene regulatory network underpinning oocyte growth remains elusive. Here we identified a set of transcription factors sufficient to trigger oocyte growth. By dissection of the change in gene expression and functional screening using an in vitro oocyte development system, we identified 8 transcription factors, each of which was essential for the primordial-primary follicle transition. Surprisingly, enforced expression of these transcription factors swiftly converted pluripotent stem cells to oocyte-like cells that were competent for fertilization and subsequent cleavage. These transcription factor-induced oocyte-like cells were formed without PGC specification, epigenetic reprogramming or meiosis, establishing that oocyte growth and lineage-specific de novo DNA methylation is separable from the preceding epigenetic reprogramming in PGCs. This study identifies a core set of transcription factors for orchestrating oocyte growth, and also provides an alternative source of ooplasm, which is a unique material for reproductive biology and medicine.

Interleukin (IL)-17-producing T helper (Th17) cells are crucial for host defense against extracellular microbes and pathogenesis of autoimmune diseases. Here we show that the AP-1 transcription factor JunB is required for Th17 cell development. Junb-deficient CD4+ T cells are able to develop in vitro into various helper T subsets except Th17. The RNA-seq transcriptome analysis reveals that JunB is crucial for the Th17-specific gene expression program. Junb-deficient mice are completely resistant to experimental autoimmune encephalomyelitis, a Th17-mediated inflammatory disease, and naive T helper cells from such mice fail to differentiate into Th17 cells. JunB appears to activate Th17 signature genes by forming a heterodimer with BATF, another AP-1 factor essential for Th17 differentiation. The mechanism whereby JunB controls Th17 cell development likely involves activation of the genes for the Th17 lineage-specifying orphan receptors RORγt and RORα and reduced expression of Foxp3, a transcription factor known to antagonize RORγt function.

Gene regulatory networks inferred from RNA abundance data have generated significant interest, but despite this, gene network approaches are used infrequently and often require input from bioinformaticians. We have assembled a suite of tools for analysing regulatory networks, and we illustrate their use with microarray datasets generated in human endothelial cells. We infer a range of regulatory networks, and based on this analysis discuss the strengths and limitations of network inference from RNA abundance data. We welcome contact from researchers interested in using our inference and visualization tools to answer biological questions.