👤 Martijn A Huynen

During early mammalian development, transient pools of pluripotent cells emerge that can be immortalised upon stem cell derivation. The pluripotent state, 'naïve' or 'primed', depends on the embryonic stage and derivation conditions used. Here we analyse the temporal gene expression patterns of mouse, cattle and porcine embryos at stages that harbour different types of pluripotent cells. We document conserved and divergent traits in gene expression, and identify predictor genes shared across the species that are associated with pluripotent states Show less

Dendritic cells (DCs) are professional antigen presenting cells of the immune system that play a crucial role in initiating immune responses and maintaining self tolerance. Better understanding of the molecular basis of DC immunobiology is required to improve DC-based immunotherapies. We previously described the interaction of transcription factor LUMAN (also known as CREB3 or LZIP) with the DC-specific transmembrane protein DC-STAMP in DCs. Target genes of LUMAN and its role in DCs are currently unknown. In this study we set out to identify genes regulated by LUMAN in DCs using microarray analysis. Expression of a constitutively active form of LUMAN in mouse DC cell line D2SC/1 identified Apolipoprotein A4 (ApoA4) as its target gene. Subsequent validation experiments, bioinformatics-based promoter analysis, and silencing studies confirmed that ApoA4 is a true target gene of LUMAN in bone marrow-derived DCs (BMDCs). Show less

The responsible genes have not yet been identified for many genetically mapped disease loci. Physically interacting proteins tend to be involved in the same cellular process, and mutations in their genes may lead to similar disease phenotypes. To investigate whether protein-protein interactions can predict genes for genetically heterogeneous diseases. 72,940 protein-protein interactions between 10,894 human proteins were used to search 432 loci for candidate disease genes representing 383 genetically heterogeneous hereditary diseases. For each disease, the protein interaction partners of its known causative genes were compared with the disease associated loci lacking identified causative genes. Interaction partners located within such loci were considered candidate disease gene predictions. Prediction accuracy was tested using a benchmark set of known disease genes. Almost 300 candidate disease gene predictions were made. Some of these have since been confirmed. On average, 10% or more are expected to be genuine disease genes, representing a 10-fold enrichment compared with positional information only. Examples of interesting candidates are AKAP6 for arrythmogenic right ventricular dysplasia 3 and SYN3 for familial partial epilepsy with variable foci. Exploiting protein-protein interactions can greatly increase the likelihood of finding positional candidate disease genes. When applied on a large scale they can lead to novel candidate gene predictions. Show less