WDR5 Expression Is Prognostic of Breast Cancer Outcome

WDR5 is a core component of the human mixed lineage leukemia-2 complex, which plays central roles in ER positive tumour cells and is a major driver of androgen-dependent prostate cancer cell proliferation. Given the similarities between breast and prostate cancers, we explore the potential prognostic value of WDR5 gene expression on breast cancer survival. Our findings reveal that WDR5 over-expression is associated with poor breast cancer clinical outcome in three gene expression data sets and BreastMark. The eQTL analysis reveals 130 trans-eQTL SNPs whose genes mapped with statistical significance are significantly associated with patient survival. These genes together with WDR5 are enriched with “cellular development, gene expression, cell cycle” signallings. Knocking down WDR5 in MCF7 dramatically decreases cell viability, but does not alter tumour cell response to doxorubicin. Our study reveals the prognostic value of WDR5 expression in breast cancer which is under long-range regulation of genes involved in cell cycle, and anthracycline could be coupled with treatments targeting WDR5 once such a regimen is available.     

Coordination between cell proliferation and apoptosis after DNA damage in Drosophila

Exposure to genotoxic stress promotes cell cycle arrest and DNA repair or apoptosis. These “life” or “death” cell fate decisions often rely on the activity of the tumor suppressor gene p53. Therefore, the precise regulation of p53 is essential to maintain tissue homeostasis and to prevent cancer development. However, how cell cycle progression has an impact on p53 cell fate decision-making is mostly unknown. In this work, we demonstrate that Drosophila p53 proapoptotic activity can be impacted by the G2/M kinase Cdk1. We find that cell cycle arrested or endocycle-induced cells are refractory to ionizing radiation-induced apoptosis. We show that p53 binding to the regulatory elements of the proapoptotic genes and its ability to activate their expression is compromised in experimentally arrested cells. Our results indicate that p53 genetically and physically interacts with Cdk1 and that p53 proapoptotic role is regulated by the cell cycle status of the cell. We propose a model in which cell cycle progression and p53 proapoptotic activity are molecularly connected to coordinate the appropriate response after DNA damage.Subject terms: Cell biology, Development, Gene regulation, Molecular biology     

Expression Quantitative Trait Loci Are Highly Sensitive to Cellular Differentiation State

Genetical genomics is a strategy for mapping gene expression variation to expression quantitative trait loci (eQTLs). We performed a genetical genomics experiment in four functionally distinct but developmentally closely related hematopoietic cell populations isolated from the BXD panel of recombinant inbred mouse strains. This analysis allowed us to analyze eQTL robustness/sensitivity across different cellular differentiation states. Although we identified a large number (365) of “static” eQTLs that were consistently active in all four cell types, we found a much larger number (1,283) of “dynamic” eQTLs showing cell-type–dependence. Of these, 140, 45, 531, and 295 were preferentially active in stem, progenitor, erythroid, and myeloid cells, respectively. A detailed investigation of those dynamic eQTLs showed that in many cases the eQTL specificity was associated with expression changes in the target gene. We found no evidence for target genes that were regulated by distinct eQTLs in different cell types, suggesting that large-scale changes within functional regulatory networks are uncommon. Our results demonstrate that heritable differences in gene expression are highly sensitive to the developmental stage of the cell population under study. Therefore, future genetical genomics studies should aim at studying multiple well-defined and highly purified cell types in order to construct as comprehensive a picture of the changing functional regulatory relationships as possible.     

Impact of Natural Genetic Variation on Gene Expression Dynamics

DNA sequence variation causes changes in gene expression, which in turn has profound effects on cellular states. These variations affect tissue development and may ultimately lead to pathological phenotypes. A genetic locus containing a sequence variation that affects gene expression is called an “expression quantitative trait locus” (eQTL). Whereas the impact of cellular context on expression levels in general is well established, a lot less is known about the cell-state specificity of eQTL. Previous studies differed with respect to how “dynamic eQTL” were defined. Here, we propose a unified framework distinguishing static, conditional and dynamic eQTL and suggest strategies for mapping these eQTL classes. Further, we introduce a new approach to simultaneously infer eQTL from different cell types. By using murine mRNA expression data from four stages of hematopoiesis and 14 related cellular traits, we demonstrate that static, conditional and dynamic eQTL, although derived from the same expression data, represent functionally distinct types of eQTL. While static eQTL affect generic cellular processes, non-static eQTL are more often involved in hematopoiesis and immune response. Our analysis revealed substantial effects of individual genetic variation on cell type-specific expression regulation. Among a total number of 3,941 eQTL we detected 2,729 static eQTL, 1,187 eQTL were conditionally active in one or several cell types, and 70 eQTL affected expression changes during cell type transitions. We also found evidence for feedback control mechanisms reverting the effect of an eQTL specifically in certain cell types. Loci correlated with hematological traits were enriched for conditional eQTL, thus, demonstrating the importance of conditional eQTL for understanding molecular mechanisms underlying physiological trait variation. The classification proposed here has the potential to streamline and unify future analysis of conditional and dynamic eQTL as well as many other kinds of QTL data.     

Robust Inference of Cell-to-Cell Expression Variations from Single- and K-Cell Profiling

Quantifying heterogeneity in gene expression among single cells can reveal information inaccessible to cell-population averaged measurements. However, the expression level of many genes in single cells fall below the detection limit of even the most sensitive technologies currently available. One proposed approach to overcome this challenge is to measure random pools of k cells (e.g., 10) to increase sensitivity, followed by computational “deconvolution” of cellular heterogeneity parameters (CHPs), such as the biological variance of single-cell expression levels. Existing approaches infer CHPs using either single-cell or k-cell data alone, and typically within a single population of cells. However, integrating both single- and k-cell data may reap additional benefits, and quantifying differences in CHPs across cell populations or conditions could reveal novel biological information. Here we present a Bayesian approach that can utilize single-cell, k-cell, or both simultaneously to infer CHPs within a single condition or their differences across two conditions. Using simulated as well as experimentally generated single- and k-cell data, we found situations where each data type would offer advantages, but using both together can improve precision and better reconcile CHP information contained in single- and k-cell data. We illustrate the utility of our approach by applying it to jointly generated single- and k-cell data to reveal CHP differences in several key inflammatory genes between resting and inflammatory cytokine-activated human macrophages, delineating differences in the distribution of ‘ON’ versus ‘OFF’ cells and in continuous variation of expression level among cells. Our approach thus offers a practical and robust framework to assess and compare cellular heterogeneity within and across biological conditions using modern multiplexed technologies.     

Tyrosine Phosphorylation of Dbl Regulates GTPase Signaling

Rho GTPases are molecular “switches” that cycle between “on” (GTP-bound) and “off” (GDP-bound) states and regulate numerous cellular activities such as gene expression, protein synthesis, cytoskeletal rearrangements, and metabolic responses. Dysregulation of GTPases is a key feature of many diseases, especially cancers. Guanine nucleotide exchange factors (GEFs) of the Dbl family are activated by mitogenic cell surface receptors and activate the Rho family GTPases Cdc42, Rac1, and RhoA. The molecular mechanisms that regulate GEFs from the Dbl family are poorly understood. Our studies reveal that Dbl is phosphorylated on tyrosine residues upon stimulation by growth factors and that this event is critical for the regulated activation of the GEF. These findings uncover a novel layer of complexity in the physiological regulation of this protein.     

Temporal Expression-based Analysis of Metabolism

Metabolic flux is frequently rerouted through cellular metabolism in response to dynamic changes in the intra- and extra-cellular environment. Capturing the mechanisms underlying these metabolic transitions in quantitative and predictive models is a prominent challenge in systems biology. Progress in this regard has been made by integrating high-throughput gene expression data into genome-scale stoichiometric models of metabolism. Here, we extend previous approaches to perform a Temporal Expression-based Analysis of Metabolism (TEAM). We apply TEAM to understanding the complex metabolic dynamics of the respiratorily versatile bacterium Shewanella oneidensis grown under aerobic, lactate-limited conditions. TEAM predicts temporal metabolic flux distributions using time-series gene expression data. Increased predictive power is achieved by supplementing these data with a large reference compendium of gene expression, which allows us to take into account the unique character of the distribution of expression of each individual gene. We further propose a straightforward method for studying the sensitivity of TEAM to changes in its fundamental free threshold parameter θ, and reveal that discrete zones of distinct metabolic behavior arise as this parameter is changed. By comparing the qualitative characteristics of these zones to additional experimental data, we are able to constrain the range of θ to a small, well-defined interval. In parallel, the sensitivity analysis reveals the inherently difficult nature of dynamic metabolic flux modeling: small errors early in the simulation propagate to relatively large changes later in the simulation. We expect that handling such “history-dependent” sensitivities will be a major challenge in the future development of dynamic metabolic-modeling techniques.     

Overexpression of a novel osteopetrosis-related gene CCDC154 suppresses cell proliferation by inducing G2/M arrest

Osteopetrosis, a disorder of skeletal bone, can cause death during childhood. We previously described a new spontaneous autosomal recessive osteopetrosis mouse mutant, “new toothless” (ntl). In this study, we reported for the first time the identification, cloning and characterization of the coiled-coil domain-containing 154 (CCDC154), a novel gene whose deletion of ~5 kb sequence including exons 1–6 was completely linked to the ntl mutant. The CCDC154 was conserved between mouse and human and is wildly expressed in mouse tissues. The cellular localization of CCDC154 was in the early endosomes. Overexpression of CCDC154 inhibited cell proliferation of HEK293 cells by inducing G₂/M arrest. CCDC154 also inhibited tumor cell growth, and the soft agar assay revealed a significant decrease of the colony size of Hela cells upon transfection of CCDC154. Our results indicate that CCDC154 is a novel osteopetrosis-related gene involved in cell cycle regulation and tumor suppression growth.     

A Novel TetR-Regulating Peptide Turns off rtTA-Mediated Activation of Gene Expression

Conditional regulation of gene expression is a powerful and indispensable method for analyzing gene function. The “Tet-On” system is a tool widely used for that purpose. Here, the transregulator rtTA mediates expression of a gene of interest after addition of the small molecule effector doxycycline. Although very effective in rapidly turning on gene expression, the system is hampered by the long half-life of doxycycline which makes shutting down gene expression rapidly very difficult to achieve. We isolated an rtTA-binding peptide by in vivo selection that acts as a doxycycline antagonist and leads to rapid and efficient shut down of rtTA-mediated reporter gene expression in a human cell line. This peptide represents the basis for novel effector molecules which complement the “Tet-system” by enabling the investigator to rapidly turn gene expression not just on at will, but now also off.     

O-GlcNAc and the Epigenetic Regulation of Gene Expression

O-GlcNAcylation is an abundant nutrient-driven modification linked to cellular signaling and regulation of gene expression. Utilizing precursors derived from metabolic flux, O-GlcNAc functions as a homeostatic regulator. The enzymes of O-GlcNAc cycling, OGT and O-GlcNAcase, act in mitochondria, the cytoplasm, and the nucleus in association with epigenetic “writers” and “erasers” of the histone code. Both O-GlcNAc and O-phosphate modify repeats within the RNA polymerase II C-terminal domain (CTD). By communicating with the histone and CTD codes, O-GlcNAc cycling provides a link between cellular metabolic status and the epigenetic machinery. Thus, O-GlcNAcylation is poised to influence trans-generational epigenetic inheritance.     

In vivo robustness analysis of cell division cycle genes in Saccharomyces cerevisiae

Intracellular biochemical parameters, such as the expression level of gene products, are considered to be optimized so that a biological system, including the parameters, works effectively. Those parameters should have some permissible range so that the systems have robustness against perturbations, such as noise in gene expression. However, little is known about the permissible range in real cells because there has been no experimental technique to test it. In this study, we developed a genetic screening method, named “genetic tug-of-war” (gTOW) that evaluates upper limit copy numbers of genes in a model eukaryote Saccharomyces cerevisiae, and we applied it for 30 cell-cycle related genes (CDC genes). The experiment provided unique quantitative data that could be used to argue the system-level properties of the cell cycle such as robustness and fragility. The data were used to evaluate the current computational model, and refinements to the model were suggested.