Podocalyxin enhances the adherence of cells to platelets

Podocalyxin (PODXL) is a mucin protein of the CD34 family expressed in kidney glomerular podocytes, vascular endothelium, progenitor bone marrow and tumor cells. It is assumed that PODXL plays an anti-adherent role in kidney podocytes. CHO cells stably expressing human PODXL (CHO-PODXL) or human tumor cells (Tera-1) inherently expressing PODXL showed increased adherence to platelets. The adherence of cells was inhibited (70%) by blockers of platelet P-selectin, prevented by the soluble ectodomain of human PODXL (PODXL-Δ) or by the arginine-glycine-aspartate (RGDS) peptide and partially impeded by inhibition of integrin αVβ3/αVβ5, suggesting a coordinated action of P-selectin and integrins. Colocalization of platelet P-selectin and PODXL expressed on CHO cells was demonstrated by confocal immunofluorescence. No adherence to platelets was observed when PODXL was expressed in glycomutant CHO cells deficient in sialic acid. Received 14 August 2007; received after revision 12 September 2007; accepted 13 September 2007     

From endoderm to pancreas: a multistep journey

The formation of the vertebrate pancreas is a complex process that typifies the basic steps of embryonic development. It involves the establishment of competence, specification, signaling from neighboring tissues, morphogenesis, and the elaboration of tissue-specific genetic networks. A full analysis of this multistep process will help us to understand classic principles of embryonic development. Furthermore, this will provide the blueprint for experimental programming of pancreas formation from embryonic stem cells in the context of diabetes cell-therapy. Although in the past decade many studies have contributed to a solid foundation for understanding pancreatogenesis, important gaps persist in our knowledge of early pancreas formation. This review will summarize the current understanding of the early mechanisms coming into play to pattern the "pre-pancreatic" region within the endoderm and, gradually, specify the pancreatic tissue.     

Genetic studies of diseases

Pancreatitis is usually inflammation of the pancreas without infection. Our understanding of pancreatitis has been built on autopsy studies, surgical biopsies and surrogate markers of inflammation and fibroses, including abdominal imaging techniques and pancreatic functional studies. However, the discovery that a number of different environmental factors and various genetic abnormalities are seen in patients with similar appearing pancreatitis phenotypes teaches us that end-stage pathology is not the disorder. Understanding complex associations and interactions requires that the components and their interactions be organized, stratified and functionally defined. Systems biology, in the broad sense, provides the approach and tools to define the complex mechanisms driving pathology. As the mathematics behind these pathways and mechanisms are defined and calibrated, the potential pathology of patients with early signs of disease can be predicted, and a number of patient-specific targets for intervention can be defined.     

Genetic studies of diseases

Genomic alterations lead to cancer complexity and form a major hurdle for comprehensive understanding of the molecular mechanisms underlying oncogenesis. In this review, we describe recent advances in studying cancer-associated genes from a systems biology point of view. The integration of known cancer genes onto protein and signaling networks reveals the characteristics of cancer genes within networks. This approach shows that cancer genes often function as network hub proteins which are involved in many cellular processes and form focal nodes in information exchange between many signaling pathways. Literature mining allows constructing gene-gene networks, in which new cancer genes can be identified. The gene expression profiles of cancer cells are used for reconstructing gene regulatory networks. By doing so, genes which are involved in the regulation of cancer progression can be picked up from these networks, after which their functions can be further confirmed in the laboratory.     

Computational protein function prediction: Are we making progress?

The computational prediction of gene and protein function is rapidly gaining ground as a central undertaking in computational biology. Making sense of the flood of genomic data requires fast and reliable annotation. Many ingenious algorithms have been devised to infer a protein's function from its amino acid sequence, 3D structure and chromosomal location of the encoding genes. However, there are significant challenges in assessing how well these programs perform. In this article we explore those challenges and review our own attempt at assessing the performance of those programs. We conclude that the task is far from complete and that a critical assessment of the performance of function prediction programs is necessary to make true progress in computational function prediction.