Model-Based Deconvolution of Cell Cycle Time-Series Data Reveals Gene Expression Details at High Resolution

In both prokaryotic and eukaryotic cells, gene expression is regulated across the cell cycle to ensure “just-in-time” assembly of select cellular structures and molecular machines. However, present in all time-series gene expression measurements is variability that arises from both systematic error in the cell synchrony process and variance in the timing of cell division at the level of the single cell. Thus, gene or protein expression data collected from a population of synchronized cells is an inaccurate measure of what occurs in the average single-cell across a cell cycle. Here, we present a general computational method to extract “single-cell”-like information from population-level time-series expression data. This method removes the effects of 1) variance in growth rate and 2) variance in the physiological and developmental state of the cell. Moreover, this method represents an advance in the deconvolution of molecular expression data in its flexibility, minimal assumptions, and the use of a cross-validation analysis to determine the appropriate level of regularization. Applying our deconvolution algorithm to cell cycle gene expression data from the dimorphic bacterium Caulobacter crescentus, we recovered critical features of cell cycle regulation in essential genes, including ctrA and ftsZ, that were obscured in population-based measurements. In doing so, we highlight the problem with using population data alone to decipher cellular regulatory mechanisms and demonstrate how our deconvolution algorithm can be applied to produce a more realistic picture of temporal regulation in a cell.     

Collective dynamics of gene expression in cell populations

The phenotypic state of the cell is commonly thought to be determined by the set of expressed genes. However, given the apparent complexity of genetic networks, it remains open what processes stabilize a particular phenotypic state. Moreover, it is not clear how unique is the mapping between the vector of expressed genes and the cell's phenotypic state. To gain insight on these issues, we study here the expression dynamics of metabolically essential genes in twin cell populations. We show that two yeast cell populations derived from a single steady-state mother population and exhibiting a similar growth phenotype in response to an environmental challenge, displayed diverse expression patterns of essential genes. The observed diversity in the mean expression between populations could not result from stochastic cell-to-cell variability, which would be averaged out in our large cell populations. Remarkably, within a population, sets of expressed genes exhibited coherent dynamics over many generations. Thus, the emerging gene expression patterns resulted from collective population dynamics. It suggests that in a wide range of biological contexts, gene expression reflects a self-organization process coupled to population-environment dynamics.     

The role of chromatin structure in regulating the expression of clustered genes

Much of what we know about the chromatin-based mechanisms that regulate gene expression in mammals has come from the study of what are, paradoxically, atypical genes. These are clusters of structurally and/or functionally related genes that are coordinately regulated during development, or between different cell types. Can unravelling the mechanisms of gene regulation at these gene clusters help us to understand how other genes are controlled? Moreover, can it explain why there is clustering of apparently unrelated genes in mammalian genomes?     

Stage-specific gene expression in erythroid progenitor cells (CFU-E)

In erythropoietic differentiation, mature red blood cells are generated from specific progenitor cells through the action of specific growth regulatory molecules. To know the mechanism of differentiation, it is important to examine the control of gene expression in these progenitor cells in combination with growth regulatory molecules. We have cloned two genes expressing at a maximal level in the CFU-E (colony forming unit-erythroid), one of the erythroid progenitor cells from novel murine erythroleukemia (MEL) cell line (TSA8) which can be induced to CFU-E in vitro. The expression of these genes is well correlated with the appearance of CFU-E during induction of TSA8 cells, and is higher in the CFU-E-cells enriched from mouse fetal livers than in the more differentiated erythroid cells. Combining these with our previous results, it is suggested that in the erythropoiesis the progenitor cells have distinct patterns of gene expression. This expression is replaced through each progenitor cell rather than by the continuous increase in the expression of a set of genes specific to the mature erythroid cell following the commitment process.     

Bistability in bacteria

Gene expression in bacteria is traditionally studied from the average behaviour of cells in a population, which has led to the assumption that under a particular set of conditions all cells express genes in an approximately uniform manner. The advent of methods for visualizing gene expression in individual cells reveals, however, that populations of genetically identical bacteria are sometimes heterogeneous, with certain genes being expressed in a non-uniform manner across the population. In some cases, heterogeneity is manifested by the bifurcation into distinct subpopulations, and we adopt the common usage, referring to this phenomenon as bistability. Here we consider four cases of bistability, three from Bacillus subtilis and one from Escherichia coli, with an emphasis on random switching mechanisms that generate alternative cell states and the biological significance of phenotypic heterogeneity. A review describing additional examples of bistability in bacteria has been published recently.     

Mapping the environmental fitness landscape of a synthetic gene circuit

Gene expression actualizes the organismal phenotypes encoded within the genome in an environment-dependent manner. Among all encoded phenotypes, cell population growth rate (fitness) is perhaps the most important, since it determines how well-adapted a genotype is in various environments. Traditional biological measurement techniques have revealed the connection between the environment and fitness based on the gene expression mean. Yet, recently it became clear that cells with identical genomes exposed to the same environment can differ dramatically from the population average in their gene expression and division rate (individual fitness). For cell populations with bimodal gene expression, this difference is particularly pronounced, and may involve stochastic transitions between two cellular states that form distinct sub-populations. Currently it remains unclear how a cell population's growth rate and its subpopulation fractions emerge from the molecular-level kinetics of gene networks and the division rates of single cells. To address this question we developed and quantitatively characterized an inducible, bistable synthetic gene circuit controlling the expression of a bifunctional antibiotic resistance gene in Saccharomyces cerevisiae. Following fitness and fluorescence measurements in two distinct environments (inducer alone and antibiotic alone), we applied a computational approach to predict cell population fitness and subpopulation fractions in the combination of these environments based on stochastic cellular movement in gene expression space and fitness space. We found that knowing the fitness and nongenetic (cellular) memory associated with specific gene expression states were necessary for predicting the overall fitness of cell populations in combined environments. We validated these predictions experimentally and identified environmental conditions that defined a "sweet spot" of drug resistance. These findings may provide a roadmap for connecting the molecular-level kinetics of gene networks to cell population fitness in well-defined environments, and may have important implications for phenotypic variability of drug resistance in natural settings.     

Global analysis of hematopoietic and vascular endothelial gene expression by tissue specific microarray profiling in zebrafish

In this study, we utilize fluorescent activated cell sorting (FACS) of cells from transgenic zebrafish coupled with microarray analysis to globally analyze expression of cell type specific genes. We find that it is possible to isolate cell populations from Tg(fli1:egfp)(y1) zebrafish embryos that are enriched in vascular, hematopoietic and pharyngeal arch cell types. Microarray analysis of GFP+ versus GFP- cells isolated from Tg(fli1:egfp)(y1) embryos identifies genes expressed in hematopoietic, vascular and pharyngeal arch tissue, consistent with the expression of the fli1:egfp transgene in these cell types. Comparison of expression profiles from GFP+ cells isolated from embryos at two different time points reveals that genes expressed in different fli1+ cell types display distinct temporal expression profiles. We also demonstrate the utility of this approach for gene discovery by identifying numerous previously uncharacterized genes that we find are expressed in fli1:egfp-positive cells, including new markers of blood, endothelial and pharyngeal arch cell types. In parallel, we have developed a database to allow easy access to both our microarray and in situ results. Our results demonstrate that this is a robust approach for identification of cell type specific genes as well as for global analysis of cell type specific gene expression in zebrafish embryos.     

Regulatory control and the costs and benefits of biochemical noise

Experiments in recent years have vividly demonstrated that gene expression can be highly stochastic. How protein concentration fluctuations affect the growth rate of a population of cells is, however, a wide-open question. We present a mathematical model that makes it possible to quantify the effect of protein concentration fluctuations on the growth rate of a population of genetically identical cells. The model predicts that the population's growth rate depends on how the growth rate of a single cell varies with protein concentration, the variance of the protein concentration fluctuations, and the correlation time of these fluctuations. The model also predicts that when the average concentration of a protein is close to the value that maximizes the growth rate, fluctuations in its concentration always reduce the growth rate. However, when the average protein concentration deviates sufficiently from the optimal level, fluctuations can enhance the growth rate of the population, even when the growth rate of a cell depends linearly on the protein concentration. The model also shows that the ensemble or population average of a quantity, such as the average protein expression level or its variance, is in general not equal to its time average as obtained from tracing a single cell and its descendants. We apply our model to perform a cost-benefit analysis of gene regulatory control. Our analysis predicts that the optimal expression level of a gene regulatory protein is determined by the trade-off between the cost of synthesizing the regulatory protein and the benefit of minimizing the fluctuations in the expression of its target gene. We discuss possible experiments that could test our predictions.     

Whole Genome Expression Profiling Reveals a Significant Role for the Cell Junction and Apoptosis Pathways in Breast Cancer Stem Cells

Side population (SP) cells in primary tumors and cell lines are a small cell population, but they are known to enrich cancer stem cells (CSCs). In this study, we isolated SP cells from the human breast cancer cell line MCF7 as a model for studying CSCs. Compared with non-SP cells, MCF7 SP cells had higher mammosphere-formation efficiency (MFE) in vitro and greater tumorigenicity in vivo, suggesting that MCF7 SP cells enrich CSCs. We first directly compared the gene expression profile of SP and non-SP cells from MCF7 cell line. Comparing the expression signature of SP to non-SP cells, we identified 753 differentially expressed genes (DEGs). Using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, we identified multiple pathways that were aberrantly regulated in SP compared with non-SP cells. Several pathways, including cell junction and apoptosis, play important roles in breast CSC function. This study demonstrates that combining global gene expression analysis with detailed annotated pathway resources can enhance our understanding of the critical pathways that regulate breast CSCs.     

A Novel Five Gene Signature Derived from Stem-Like Side Population Cells Predicts Overall and Recurrence-Free Survival in NSCLC

Gene expression profiling has been used to characterize prognosis in various cancers. Earlier studies had shown that side population cells isolated from Non-Small Cell Lung Cancer (NSCLC) cell lines exhibit cancer stem cell properties. In this study we apply a systems biology approach to gene expression profiling data from cancer stem like cells isolated from lung cancer cell lines to identify novel gene signatures that could predict prognosis. Microarray data from side population (SP) and main population (MP) cells isolated from 4 NSCLC lines (A549, H1650, H460, H1975) were used to examine gene expression profiles associated with stem like properties. Differentially expressed genes that were over or under-expressed at least two fold commonly in all 4 cell lines were identified. We found 354 were upregulated and 126 were downregulated in SP cells compared to MP cells; of these, 89 up and 62 downregulated genes (average 2 fold changes) were used for Principle Component Analysis (PCA) and MetaCore™ pathway analysis. The pathway analysis demonstrated representation of 4 up regulated genes (TOP2A, AURKB, BRRN1, CDK1) in chromosome condensation pathway and 1 down regulated gene FUS in chromosomal translocation. Microarray data was validated using qRT-PCR on the 5 selected genes and all showed robust correlation between microarray and qRT-PCR. Further, we analyzed two independent gene expression datasets that included 360 lung adenocarcinoma patients from NCI Director''s Challenge Set for overall survival and 63 samples from Sungkyunkwan University (SKKU) for recurrence free survival. Kaplan-Meier and log-rank test analysis predicted poor survival of patients in both data sets. Our results suggest that genes involved in chromosome condensation are likely related with stem-like properties and might predict survival in lung adenocarcinoma. Our findings highlight a gene signature for effective identification of lung adenocarcinoma patients with poor prognosis and designing more aggressive therapies for such patients.     

Characterization of the Drosophila segment determination morphome

Here we characterize the expression of the full system of genes which control the segmentation morphogenetic field of Drosophila at the protein level in one dimension. The data used for this characterization are quantitative with cellular resolution in space and about 6 min in time. We present the full quantitative profiles of all 14 segmentation genes which act before the onset of gastrulation. The expression patterns of these genes are first characterized in terms of their average or typical behavior. At this level, the expression of all of the genes has been integrated into a single atlas of gene expression in which the expression levels of all genes in each cell are specified. We show that expression domains do not arise synchronously, but rather each domain has its own specific dynamics of formation. Moreover, we show that the expression domains shift position in the direction of the cephalic furrow, such that domains in the anlage of the segmented germ band shift anteriorly while those in the presumptive head shift posteriorly. The expression atlas of integrated data is very close to the expression profiles of individual embryos during the latter part of the blastoderm stage. At earlier times gap gene domains show considerable variation in amplitude, and significant positional variability. Nevertheless, an average early gap domain is close to that of a median individual. In contrast, we show that there is a diversity of developmental trajectories among pair-rule genes at a variety of levels, including the order of domain formation and positional accuracy. We further show that this variation is dynamically reduced, or canalized, over time. As the first quantitatively characterized morphogenetic field, this system and its behavior constitute an extraordinarily rich set of materials for the study of canalization and embryonic regulation at the molecular level.     

DQB: A novel dynamic quantitive classification model using artificial bee colony algorithm with application on gene expression profiles

In the medical domain, it is very significant to develop a rule-based classification model. This is because it has the ability to produce a comprehensible and understandable model that accounts for the predictions. Moreover, it is desirable to know not only the classification decisions but also what leads to these decisions. In this paper, we propose a novel dynamic quantitative rule-based classification model, namely DQB, which integrates quantitative association rule mining and the Artificial Bee Colony (ABC) algorithm to provide users with more convenience in terms of understandability and interpretability via an accurate class quantitative association rule-based classifier model. As far as we know, this is the first attempt to apply the ABC algorithm in mining for quantitative rule-based classifier models. In addition, this is the first attempt to use quantitative rule-based classification models for classifying microarray gene expression profiles. Also, in this research we developed a new dynamic local search strategy named DLS, which is improved the local search for artificial bee colony (ABC) algorithm. The performance of the proposed model has been compared with well-known quantitative-based classification methods and bio-inspired meta-heuristic classification algorithms, using six gene expression profiles for binary and multi-class cancer datasets. From the results, it can be concludes that a considerable increase in classification accuracy is obtained for the DQB when compared to other available algorithms in the literature, and it is able to provide an interpretable model for biologists. This confirms the significance of the proposed algorithm in the constructing a classifier rule-based model, and accordingly proofs that these rules obtain a highly qualified and meaningful knowledge extracted from the training set, where all subset of quantitive rules report close to 100% classification accuracy with a minimum number of genes. It is remarkable that apparently (to the best of our knowledge) several new genes were discovered that have not been seen in any past studies. For the applicability demand, based on the results acqured from microarray gene expression analysis, we can conclude that DQB can be adopted in a different real world applications with some modifications.     

Impact of vitrification on granulosa cell survival and gene expression

IntroductionCryopreservation of ovarian tissue is an essential step in Ovarian Tissue Banking. In order to prevent the formation of ice crystals, typically the tissue is slowly frozen using a cryoprotectant. As an alternative the method of ultra-fast freezing by vitrification becomes more attention for freezing ovarian tissue because it has successfully been used for oocytes, embryos and sperm. However the impact of vitrification on granulosa cells, which are an essential part of ovarian tissue is uncertain.AimIn this study, we have therefore analysed the influence of vitrification on the survival rates of granulosa cells, the impact of DMSO or ethylenglycol containing vitrification protocols and investigated to what extent the gene expression of apoptosis- and temperature-sensitive genes changes.Material and methodsWe used the human granulosa cell line KGN as a model for human granulosa cells and determined the survival rate and cell cycle stages by FACS analyses. The change in gene expression was determined by quantitative PCR analyses.ResultsOur results show that vitrification is possible in granulosa cells but it reduces cell viability and leads to fluctuations in the cell cycle. The DMSO containing protocol results in a lower amount of dead cells than the ethylenglycol containing protocol. Gene expression analysis reveals that TNF-alpha expression is strongly increased after vitrification, while other apoptosis or temperature-related genes seem to stay unaffected.ConclusionWe conclude that vitrification influences the viability of human granulosa cells. Furthermore, our results suggest that this could be mediated by a change in TNF-alpha gene expression.