Characterization of genes modulated during pheomelanogenesis using differential display

Molecular and biochemical mechanisms that modulate the production of eumelanin or pheomelanin pigments involve the opposing effects of two intercellular signaling molecules, α-melanocyte stimulating hormone (MSH) and agouti signal protein (ASP). ASP is an antagonist of MSH signaling through the melanocyte-specific MSH receptor, although its mechanism(s) of action is controversial. We previously have reported significant down-regulation of all known melanogenic genes during the eumelanin to pheomelanin switch in murine hair follicle melanocytes and in cultured melanocytes treated with recombinant ASP. To identify factors that might be involved in the switch to pheomelanogenesis, we screened ASP-treated melanocytes by using differential display and identified three up-regulated genes: a DNA replication control protein, a basic helix–loop–helix transcription factor, and a novel gene. We have simultaneously identified six down-regulated genes in ASP-treated melanocytes; two of those encode tyrosinase and TRP2, melanogenic genes known to be down-regulated during pheomelanogenesis, which provide good internal controls for this approach. These results suggest that there are complex mechanisms involved in the switch to pheomelanin production, and that these modulated genes might be involved in the pleiotropic changes seen in yellow mice, including the change in coat color.

ETS target genes: Identification of Egr1 as a target by RNA differential display and whole genome PCR techniques

ETS transcription factors play important roles in hematopoiesis, angiogenesis, and organogenesis during murine development. The ETS genes also have a role in neoplasia, for example in Ewing’s sarcomas and retrovirally induced cancers. The ETS genes encode transcription factors that bind to specific DNA sequences and activate transcription of various cellular and viral genes. To isolate novel ETS target genes, we used two approaches. In the first approach, we isolated genes by the RNA differential display technique. Previously, we have shown that the overexpression of ETS1 and ETS2 genes effects transformation of NIH 3T3 cells and specific transformants produce high levels of the ETS proteins. To isolate ETS1 and ETS2 responsive genes in these transformed cells, we prepared RNA from ETS1, ETS2 transformants, and normal NIH 3T3 cell lines and converted it into cDNA. This cDNA was amplified by PCR and displayed on sequencing gels. The differentially displayed bands were subcloned into plasmid vectors. By Northern blot analysis, several clones showed differential patterns of mRNA expression in the NIH 3T3-, ETS1-, and ETS2-expressing cell lines. Sixteen clones were analyzed by DNA sequence analysis, and 13 of them appeared to be unique because their DNA sequences did not match with any of the known genes present in the gene bank. Three known genes were found to be identical to the CArG box binding factor, phospholipase A2-activating protein, and early growth response 1 (Egr1) genes. In the second approach, to isolate ETS target promoters directly, we performed ETS1 binding with MboI-cleaved genomic DNA in the presence of a specific mAb followed by whole genome PCR. The immune complex-bound ETS binding sites containing DNA fragments were amplified and subcloned into pBluescript and subjected to DNA sequence and computer analysis. We found that, of a large number of clones isolated, 43 represented unique sequences not previously identified. Three clones turned out to contain regulatory sequences derived from human serglycin, preproapolipoprotein C II, and Egr1 genes. The ETS binding sites derived from these three regulatory sequences showed specific binding with recombinant ETS proteins. Of interest, Egr1 was identified by both of these techniques, suggesting strongly that it is indeed an ETS target gene.

A model-based approach for assessing in vivo combination therapy interactions

We present an approach for evaluating the efficacy of combination antitumor agent schedules that accounts for order and timing of drug administration. Our model-based approach compares in vivo tumor volume data over a time course and offers a quantitative definition for additivity of drug effects, relative to which synergism and antagonism are interpreted. We begin by fitting data from individual mice receiving at most one drug to a differential equation tumor growth/drug effect model and combine individual parameter estimates to obtain population statistics. Using two null hypotheses: (i) combination therapy is consistent with additivity or (ii) combination therapy is equivalent to treating with the more effective single agent alone, we compute predicted tumor growth trajectories and their distribution for combination treated animals. We illustrate this approach by comparing entire observed and expected tumor volume trajectories for a data set in which HER-2/neu-overexpressing MCF-7 human breast cancer xenografts are treated with a humanized, anti-HER-2 monoclonal antibody (rhuMAb HER-2), doxorubicin, or one of five proposed combination therapy schedules.

Reciprocal subtraction differential RNA display: An efficient and rapid procedure for isolating differentially expressed gene sequences

A reciprocal subtraction differential RNA display (RSDD) approach has been developed that permits the rapid and efficient identification and cloning of both abundant and rare differentially expressed genes. RSDD comprises reciprocal subtraction of cDNA libraries followed by differential RNA display. The RSDD strategy was applied to analyze the gene expression alterations resulting during cancer progression as adenovirus-transformed rodent cells developed an aggressive transformed state, as documented by elevated anchorage-independence and enhanced in vivo oncogenesis in nude mice. This approach resulted in the identification and cloning of both known and a high proportion (>65%) of unknown sequences, including cDNAs displaying elevated expression as a function of progression (progression-elevated gene) and cDNAs displaying suppressed expression as a function of progression (progression-suppressed gene). Sixteen differentially expressed genes, including five unknown progression-elevated genes and six unknown progression-suppressed genes, have been characterized. The RSDD scheme should find wide application for the effective detection and isolation of differentially expressed genes.

Molecular mechanisms underlying differential odor responses of a mouse olfactory receptor

The prevailing paradigm for G protein-coupled receptors is that each receptor is narrowly tuned to its ligand and closely related agonists. An outstanding problem is whether this paradigm applies to olfactory receptor (ORs), which is the largest gene family in the genome, in which each of 1,000 different G protein-coupled receptors is believed to interact with a range of different odor molecules from the many thousands that comprise “odor space.” Insights into how these interactions occur are essential for understanding the sense of smell. Key questions are: (i) Is there a binding pocket? (ii) Which amino acid residues in the binding pocket contribute to peak affinities? (iii) How do affinities change with changes in agonist structure? To approach these questions, we have combined single-cell PCR results [Malnic, B., Hirono, J., Sato, T. & Buck, L. B. (1999) Cell 96, 713–723] and well-established molecular dynamics methods to model the structure of a specific OR (OR S25) and its interactions with 24 odor compounds. This receptor structure not only points to a likely odor-binding site but also independently predicts the two compounds that experimentally best activate OR S25. The results provide a mechanistic model for olfactory transduction at the molecular level and show how the basic G protein-coupled receptor template is adapted for encoding the enormous odor space. This combined approach can significantly enhance the identification of ligands for the many members of the OR family and also may shed light on other protein families that exhibit broad specificities, such as chemokine receptors and P450 oxidases.

Coordinating DNA replication initiation with cell growth: differential roles for DnaA and SeqA proteins.

We describe here the development of a new approach to the analysis of Escherichia coli replication control. Cells were grown at low growth rates, in which case the bacterial cell cycle approximates that of eukaryotic cells with G1, S, and G2 phases: cell division is followed sequentially by a gap period without DNA replication, replication of the single chromosome, another gap period, and finally the next cell division. Flow cytometry of such slowly growing cells reveals the timing of replication initiation as a function of cell mass. The data show that initiation is normally coupled to cell physiology extremely tightly: the distribution of individual cell masses at the time of initiation in wild-type cells is very narrow, with a coefficient of variation of less than 9%. Furthermore, a comparison between wild-type and seqA mutant cells shows that initiation occurs at a 10-20% lower mass in the seqA mutant, providing direct evidence that SeqA is a bona fide negative regulator of replication initiation. In dnaA (Ts) mutants the opposite is found: the mass at initiation is dramatically increased and the variability in cell mass at initiation is much higher than that for wild-type cells. In contrast to wild-type and dnaA(Ts) cells, seqA mutant cells frequently go through two initiation events per cell division cycle, and all the origins present in each cell are not initiated in synchrony. The implications for the complex interplay amongst growth, cell division, and DNA replication are discussed.

Analysis of differential gene expression by display of 3' end restriction fragments of cDNAs.

We have developed an approach to study changes in gene expression by selective PCR amplification and display of 3' end restriction fragments of double-stranded cDNAs. This method produces highly consistent and reproducible patterns, can detect almost all mRNAs in a sample, and can resolve hidden differences such as bands that differ in their sequence but comigrate on a gel. Bands corresponding to known cDNAs move to predictable positions on the gel, making this a powerful approach to correlate gel patterns with cDNA data bases. Applying this method, we have examined differences in gene expression patterns during T-cell activation. Of a total of 700 bands that were evaluated in this study, as many as 3-4% represented mRNAs that are upregulated, while approximately 2% were down-regulated within 4 hr of activation of Jurkat T cells. These and other results suggest that this approach is suitable for the systematic, expeditious, and nearly exhaustive elucidation of subtle changes in the patterns of gene expression in cells with altered physiologic states.

Comparative expressed-sequence-tag analysis of differential gene expression profiles in PC-12 cells before and after nerve growth factor treatment.

Nerve growth factor-induced differentiation of adrenal chromaffin PC-12 cells to a neuronal phenotype involves alterations in gene expression and represents a model system to study neuronal differentiation. We have used the expressed-sequence-tag approach to identify approximately 600 differentially expressed mRNAs in untreated and nerve growth factor-treated PC-12 cells that encode proteins with diverse structural and biochemical functions. Many of these mRNAs encode proteins belonging to cellular pathways not previously known to be regulated by nerve growth factor. Comparative expressed-sequence-tag analysis provides a basis for surveying global changes in gene-expression patterns in response to biological signals at an unprecedented scale, is a powerful tool for identifying potential interactions between different cellular pathways, and allows the gene-expression profiles of individual genes belonging to a particular pathway to be followed.