Chromatin occupancy patterns of the ETS repressor Yan: a mechanism for buffering gene expression against noise?

Dynamic BTB-domain filaments promote clustering of ZBTB proteins

The formation of dynamic protein filaments contributes to various biological functions by clustering individual molecules together and enhancing their binding to ligands. We report such a propensity for the BTB domains of certain proteins from the ZBTB family, a large eukaryotic transcription factor family implicated in differentiation and cancer. Working with Xenopus laevis and human proteins, we solved the crystal structures of filaments formed by dimers of the BTB domains of ZBTB8A and ZBTB18 and demonstrated concentration-dependent higher-order assemblies of these dimers in solution. In cells, the BTB-domain filamentation supports clustering of full-length human ZBTB8A and ZBTB18 into dynamic nuclear foci and contributes to the ZBTB18-mediated repression of a reporter gene. The BTB domains of up to 21 human ZBTB family members and two related proteins, NACC1 and NACC2, are predicted to behave in a similar manner. Our results suggest that filamentation is a more common feature of transcription factors than is currently appreciated.

Gene expression variability in clonal populations: Causes and consequences

During the last decade it has been shown that among cell variation in gene expression plays an important role within clonal populations. Here, we provide an overview of the different mechanisms contributing to gene expression variability in clonal populations. These are ranging from inherent variations in the biochemical process of gene expression itself, such as intrinsic noise, extrinsic noise and bistability to individual responses to variations in the local micro-environment, a phenomenon called phenotypic plasticity. Also genotypic variations caused by clonal evolution and phase variation can contribute to gene expression variability. Consequently, gene expression studies need to take these fluctuations in expression into account. However, frequently used techniques for expression quantification, such as microarrays, RNA sequencing, quantitative PCR and gene reporter fusions classically determine the population average of gene expression. Here, we discuss how these techniques can be adapted towards single cell analysis by integration with single cell isolation, RNA amplification and microscopy. Alternatively more qualitative selection-based techniques, such as mutant screenings, in vivo expression technology (IVET) and recombination-based IVET (RIVET) can be applied for detection of genes expressed only within a subpopulation. Finally, differential fluorescence induction (DFI), a protocol specially designed for single cell expression is discussed.