Gene expression profiling of mouse Sertoli cell lines

The proliferation and differentiation of Sertoli cells is regulated by follicle-stimulating hormone (FSH). The molecular events following FSH stimulation are only partially known. To investigate FSH action in Sertoli cells, we established two novel FSH-responsive mouse Sertoli-cell-derived lines expressing human wild-type (WT) FSH receptor (FSHR) or overexpressing mutated (Asp567Gly) constitutively active FSHR (MUT). Gene expression profiling with commercially available cDNA arrays, including 588 mouse genes, revealed 146 genes expressed in both cell lines. Compared with the expression pattern of WT cells, 20 genes were identified as being either up- or down-regulated (>two-fold) in the MUT cells. We observed a strong differential expression of factors involved in cellular proliferation, e.g. cyclin D2 (repressed to nearly undetectable levels), proliferating cell nuclear antigen (2.5-fold repression) and Eps-8 (six-fold repression), and in genes involved in cellular differentiation, e.g. cytokeratin-18 (13-fold induction). The cDNA array results for six representative genes were confirmed by Northern blotting, which also included the parental SK-11 cell line devoid of FSHR expression. We found no further acute FSH- or forskolin-induced change in expression levels after 3-h stimulations, suggesting that the observed differences between the two cell lines is a consequence of mild, chronically increased, cAMP production in MUT cells. These results provide a platform for the further investigation of selected candidate genes in primary cultures and/or in vivo.Electronic Supplementary Material Supplementary material is available in the online version of this article at This work was supported by grants from the Deutsche Forschungsgemeinschaft (Confocal Research Group The Male Gamete: Production, Maturation, Function, grant FOR 197-3) and from the German Academic Exchange Service (DAAD) to P. Mathur     

Quality prediction of cell substrate using gene expression profiling

Changes in cell culture conditions influence the metabolism of cells, which consequently affects the quality of the products that they produce, such as viral vectors, recombinant proteins, or vaccines. Currently there is no effective technique available to monitor global quality of cells in cell culture. Here we describe a new method using gene expression profiling by microarray to predict the quality of cell substrates. Human embryonic kidney 293 cells are a commonly used cell substrate in the production of biological products. We demonstrate that the yield of adenoviral vectors was lower in over-confluent 293 cells, compared to 40 or 90% confluent cells. Total RNA derived from these cells of different confluence states was reverse transcribed, labeled, and used to hybridize 10K cDNA arrays to determine biomarkers for confluence states. Phenotype scatter-plot analysis and cluster analysis were used for class discovery. Based on this approach, we identified genes that were either up-regulated or down-modulated in response to different cell confluence states. By multivariate predictive models we identified a set of 37 genes that were either down-regulated or up-regulated compared to 90% confluent cells as a predictor of cell confluence and quality of 293 cell cultures. The predictive accuracy of these models was assessed by the leave-one-out cross-validation method. The expression of selected gene predictors was validated by quantitative PCR analysis. Our results demonstrate that gene expression profiling can assess the quality of cell substrates prior to large-scale production of a biological product.     

Gene expression profiling of mouse embryonic stem cell subpopulations

We previously demonstrated that mouse embryonic stem (ES) cells show a wide variation in the expression of platelet endothelial cell adhesion molecule 1 (PECAM1) and that the level of expression is positively correlated with the pluripotency of ES cells. We also found that PECAM1-positive ES cells could be divided into two subpopulations according to the expression of stage-specific embryonic antigen (SSEA)-1. ES cells that showed both PECAM1 and SSEA-1 predominantly differentiated into epiblast after the blastocyst stage. In the present study, we performed pairwise oligo microarray analysis to characterize gene expression profiles in PECAM1-positive and -negative subpopulations of ES cells. The microarray analysis identified 2034 genes with a more than 2-fold difference in expression levels between the PECAM1-positive and -negative cells. Of these genes, 803 were more highly expressed in PECAM1-positive cells and 1231 were more highly expressed in PECAM1-negative cells. As expected, genes known to function in ES cells, such as Pou5f1(Oct3/4)and Nanog, were found to be upregulated in PECAM1-positive cells. We also isolated 23 previously uncharacterized genes. A comparison of gene expression profiles in PECAM1-positive cells that were either positive or negative for SSEA-1 expression identified only 53 genes that showed a more than 2-fold greater difference in expression levels between these subpopulations. However, many genes that are under epigenetic regulation, such as globins, Igf2, Igf2r, andH19, showed differential expression. Our results suggest that in addition to differences in gene expression profiles, epigenetic status was altered in the three cell subpopulations.     

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.     

Cloning and expression profiling of small RNAs expressed in the mouse ovary

Small noncoding RNAs have been suggested to play important roles in the regulation of gene expression across all species from plants to humans. To identify small RNAs expressed by the ovary, we generated mouse ovarian small RNA complementary DNA (srcDNA) libraries and sequenced 800 srcDNA clones. We identified 236 small RNAs including 122 microRNAs (miRNAs), 79 piwi-interacting RNAs (piRNAs), and 35 small nucleolar RNAs (snoRNAs). Among these small RNAs, 15 miRNAs, 74 piRNAs, and 21 snoRNAs are novel. Approximately 70% of the ovarian piRNAs are encoded by multicopy genes located within the repetitive regions, resembling previously identified repeat-associated small interference RNAs (rasiRNAs), whereas the remaining approximately 30% of piRNA genes are located in nonrepetitive regions of the genome with characteristics similar to the majority of piRNAs originally cloned from the testis. Since these two types of piRNAs display different structural features, we categorized them into two classes: repeat-associated piRNAs (rapiRNAs, equivalent of the rasiRNAs) and non-repeat-associated piRNAs (napiRNAs). Expression profiling analyses revealed that ovarian miRNAs were either ubiquitously expressed in multiple tissues or preferentially expressed in a few tissues including the ovary. Ovaries appear to express more rapiRNAs than napiRNAs, and sequence analyses support that both may be generated through the "ping-pong" mechanism. Unique expression and structural features of these ovarian small noncoding RNAs suggest that they may play important roles in the control of folliculogenesis and female fertility.     

The cell cycle- and insulin-signaling-inhibiting miRNA expression pattern of very small embryonic-like stem cells contributes to their quiescent state

Murine Oct4⁺, very small embryonic-like stem cells (VSELs), are a quiescent stem cell population that requires a supportive co-culture layer to proliferate and/or to differentiate in vitro. Gene expression studies have revealed that the quiescence of these cells is due to changes in expression of parentally imprinted genes, including genes involved in cell cycle regulation and insulin and insulin-like growth factor signaling (IIS). To investigate the role of microRNAs (miRNAs) in VSEL quiescence, we performed miRNA studies in highly purified VSELs and observed a unique miRNA expression pattern in these cells. Specifically, we observed significant differences in the expression of certain miRNA species (relative to a reference cell population), including (i) miRNA-25_1 and miRNA-19 b, whose downregulation has the effect of upregulating cell cycle checkpoint genes and (ii) miRNA-675-3 p and miRNA-675-5 p, miRNA-292-5 p, miRNA-184, and miRNA-125 b, whose upregulation attenuates IIS. These observations are important for understanding the biology of these cells and for developing efficient ex vivo expansion strategies for VSELs isolated from adult tissues.     

Accurate detection of very sparse sequence motifs.

Protein sequence alignments are more reliable the shorter the evolutionary distance. Here, we align distantly related proteins using many closely spaced intermediate sequences as stepping stones. Such transitive alignments can be generated between any two proteins in a connected set, whether they are direct or indirect sequence neighbors in the underlying library of pairwise alignments. We have implemented a greedy algorithm, MaxFlow, using a novel consistency score to estimate the relative likelihood of alternative paths of transitive alignment. In contrast to traditional profile models of amino acid preferences, MaxFlow models the probability that two positions are structurally equivalent and retains high information content across large distances in sequence space. Thus, MaxFlow is able to identify sparse and narrow active-site sequence signatures which are embedded in high-entropy sequence segments in the structure based multiple alignment of large diverse enzyme superfamilies. In a challenging benchmark based on the urease superfamily, MaxFlow yields better reliability and double coverage compared to available sequence alignment software. This promises to increase information returns from functional and structural genomics, where reliable sequence alignment is a bottleneck to transferring the functional or structural characterization of model proteins to entire protein superfamilies.     

Serum protein profiling to identify biomarkers for small renal cell carcinoma

Diagnostic biomarkers for early detection of renal cell carcinoma (RCC) are in great need. In the present study, we compared the serum protein profiles of patients with small RCC to those of healthy individuals to identify the differentially expressed proteins with potential to serve as biomarkers. Serum samples were collected from 10 patients with small RCC and 10 healthy individuals. The serum protein expression profiles were analyzed by two-dimensional (2-D) gel electrophoresis. Twenty-seven proteins with differences in expression levels between RCC patients and healthy volunteers were identified. Of these, 19 were expressed at different levels and eight were expressed in serum from the RCC group, but not from the control group. Six differentially expressed proteins identified by using mass spectrometry included coagulation factor XIII B, complement C3 and its precursor, misato homolog 1 (isoform CRA_b), hemopexin, and alpha-1-B-glycoprotein. Some of these serum proteins are known regulators of tumor progression in human malignancies. In conclusion, we successfully applied 2-D gel electrophoresis and identified six serum proteins differentially expressed between patients with small RCC and healthy volunteers. These proteins may provide novel biomarkers for early detection and diagnosis of human RCC.     

Gene expression profiling of cell lines derived from T-cell malignancies

Alterations in the pentose ring of ATP have a major impact on cystic fibrosis transmembrane conductance regulator (CFTR) function. Both 2'- and 3'-deoxy-ATP (dATP) accelerate ion channel openings and stabilize open channel structure better than ATP. Purified wild-type CFTR hydrolyzes dATP. The apparent first-order rate constants for hydrolysis at low substrate concentration are the same for dATP and ATP. This suggests that product release and/or relaxation of the enzyme structure to the initial ligand free state is the rate-limiting step in the CFTR hydrolytic cycle. Circumvention of the normal requirement for protein kinase A phosphorylation of the R-domain for channel activation implies that the impact of the deoxyribonucleotide interaction with the nucleotide binding domains is transmitted to the channel-forming elements of the protein more readily than that of the ribonucleotide.     

Rapid quantitative profiling of complex microbial populations

Diverse and complex microbial ecosystems are found in virtually every environment on earth, yet we know very little about their composition and ecology. Comprehensive identification and quantification of the constituents of these microbial communities—a ‘census’—is an essential foundation for understanding their biology. To address this problem, we developed, tested and optimized a DNA oligonucleotide microarray composed of 10 462 small subunit (SSU) ribosomal DNA (rDNA) probes (7167 unique sequences) selected to provide quantitative information on the taxonomic composition of diverse microbial populations. Using our optimized experimental approach, this microarray enabled detection and quantification of individual bacterial species present at fractional abundances of <0.1% in complex synthetic mixtures. The estimates of bacterial species abundance obtained using this microarray are similar to those obtained by phylogenetic analysis of SSU rDNA sequences from the same samples—the current ‘gold standard’ method for profiling microbial communities. Furthermore, probes designed to represent higher order taxonomic groups of bacterial species reliably detected microbes for which there were no species-specific probes. This simple, rapid microarray procedure can be used to explore and systematically characterize complex microbial communities, such as those found within the human body.     

The kinetics of villus cell populations in the mouse small intestine

Abstract. The cell population kinetics of the villus epithelium of the mouse have been analysed with respect to the size, flux and time. Microdissection methods were employed to measure the villus cell population size and yielded reproducible, precise results. There was a proximodistal negative size gradient in villus cell population and, in those villi of normal morphology, there was a good correlation with the usual morphometric estimators such as height and row count, although correlation was improved by a product variable consisting of a height multiplied by a width parameter.
Flux onto the villus is the product of the crypt cell production rate, which was measured by a metaphase arrest method using vincristine and crypt microdissection, and the crypt:villus ratio; net villus influx was maximum proximally in the bowel, where the largest villi were found, and decreased distally. The distribution of transit times of labelled cells to the crypt: villus junction and to the villus tip was measured, allowing the measurement of the median villus transit time.
Comparison of the measured villus transit time with the theoretical transit time calculated from the villus influx and population size gave results consistent with a steady state hypothesis. It was found, at each level of the small intestine studied, that the number of epithelial cells on the villus was equivalent to the total number of crypt cells associated with the villus.     

Accurate cancer classification using expressions of very few genes

We aim at finding the smallest set of genes that can ensure highly accurate classification of cancers from microarray data by using supervised machine learning algorithms. The significance of finding the minimum gene subsets is three-fold: 1) it greatly reduces the computational burden and "noise" arising from irrelevant genes. In the examples studied in this paper, finding the minimum gene subsets even allows for extraction of simple diagnostic rules which lead to accurate diagnosis without the need for any classifiers, 2) it simplifies gene expression tests to include only a very small number of genes rather than thousands of genes, which can bring down the cost for cancer testing significantly, 3) it calls for further investigation into the possible biological relationship between these small numbers of genes and cancer development and treatment. Our simple yet very effective method involves two steps. In the first step, we choose some important genes using a feature importance ranking scheme. In the second step, we test the classification capability of all simple combinations of those important genes by using a good classifier. For three "small" and "simple" data sets with two, three, and four cancer (sub)types, our approach obtained very high accuracy with only two or three genes. For a "large" and "complex" data set with 14 cancer types, we divided the whole problem into a group of binary classification problems and applied the 2-step approach to each of these binary classification problems. Through this "divide-and-conquer" approach, we obtained accuracy comparable to previously reported results but with only 28 genes rather than 16,063 genes. In general, our method can significantly reduce the number of genes required for highly reliable diagnosis