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Introduction:The aim of this study was to screen for cell surface markers that could discriminate CLLtype MBL from CLL or identify CLL cases likely to have stable disease.Methods:Six color flow cytometry was performed on CLLtype MBL n 94 and CLL n 387 at diagnosis or relapse; 39 cases had poorrisk chromosomal abnormalities 17p andor 11q deletion. Expression of 30 markers was analysed: CCR6, CD10, CD103, CD11c, CD138, CD200, CD22, CD23, CD24, CD25, CD27, CD31, CD38, CD39, CD43, CD49d, CD5, CD52, CD62L, CD63, CD79b, CD81, CD86, CD95, CXCR5, HLADR, IgD, IgG, IgM, LAIR1.Results:There was no difference in expression between CLLtype MBL and CLL for the majority of markers. Differential expression was observed for several markers, mainly between MBL and CLL cases with adverserisk chromosomal abnormalities. These differences included lower expression of CD38 9.4fold lower, P 0.007 and CD49d 3.2fold lower, P 0.008 and higher expression of LAIR1 3.7fold higher, P 0.003, CXCR5 1.25fold higher, P 0.002, and CCR6 1.9fold higher P< 0.001 on CLLtype MBL compared to CLL with adverse chromosomal abnormalities. CD62L Lselectin which mediates lymphocyte adhesion to endothelial venules of lymphoid tissue, was expressed at a significantly different level between CLLtype MBL and both CLL subgroups, with 1.3fold lower P 0.04 expression levels on the MBL cases. However, there was broad overlap in expression levels.Conclusions:CLLtype MBL is phenotypically identical to CLL for a very broad range of markers. Differential expression is predominantly related to known prognostic markers and proteins involved in homing to lymphoid tissue. © 2010 International Clinical Cytometry Society

Background Transcription regulatory networks are composed of interactions between transcription factors and their target genes. Whereas unicellular networks have been studied extensively, metazoan transcription regulatory networks remain largely unexplored. Caenorhabditis elegans provides a powerful model to study such metazoan networks because its genome is completely sequenced and many functional genomic tools are available. While C. elegans gene predictions have undergone continuous refinement, this is not true for the annotation of functional transcription factors. The comprehensive identification of transcription factors is essential for the systematic mapping of transcription regulatory networks because it enables the creation of physical transcription factor resources that can be used in assays to map interactions between transcription factors and their target genes.Results By computational searches and extensive manual curation, we have identified a compendium of 934 transcription factor genes (referred to as wTF2.0). We find that manual curation drastically reduces the number of both false positive and false negative transcription factor predictions. We discuss how transcription factor splice variants and dimer formation may affect the total number of functional transcription factors. In contrast to mouse transcription factor genes, we find that C. elegans transcription factor genes do not undergo significantly more splicing than other genes. This difference may contribute to differences in organism complexity. We identify candidate redundant worm transcription factor genes and orthologous worm and human transcription factor pairs. Finally, we discuss how wTF2.0 can be used together with physical transcription factor clone resources to facilitate the systematic mapping of C. elegans transcription regulatory networks.Conclusion wTF2.0 provides a starting point to decipher the transcription regulatory networks that control metazoan development and function.