Tumor-specific gene expression patterns with gene expression profiles

Gene expression profiles of 14 common tumors and their counterpart normal tissues were analyzed with machine learning methods to address the problem of selection of tumor-specific genes and analysis of their differential expressions in tumor tissues. First, a variation of the Relief algorithm, “RFE_Relief algorithm” was proposed to learn the relations between genes and tissue types. Then, a support vector machine was employed to find the gene subset with the best classification performance for distinguishing cancerous tissues and their counterparts. After tissue-specific genes were removed, cross validation experiments were employed to demonstrate the common deregulated expressions of the selected gene in tumor tissues. The results indicate the existence of a specific expression fingerprint of these genes that is shared in different tumor tissues, and the hallmarks of the expression patterns of these genes in cancerous tissues are summarized at the end of this paper.     

Tumor-specific gene expression patterns with gene expression profiles

Gene expression profiles of 14 common tumors and their counterpart normal tissues were analyzed with machine learning methods to address the problem of selection of tumor-specific genes and analysis of their differential expressions in tumor tissues. First, a variation of the Relief algorithm, "RFE_Relief algorithm" was proposed to learn the relations between genes and tissue types. Then, a support vector machine was employed to find the gene subset with the best classification performance for distinguishing cancerous tissues and their counterparts. After tissue-specific genes were removed, cross validation experiments were employed to demonstrate the common deregulated expressions of the selected gene in tumor tissues. The results indicate the existence of a specific expression fingerprint of these genes that is shared in different tumor tissues, and the hallmarks of the expression patterns of these genes in cancerous tissues are summarized at the end of this paper.     

High-level expression of full-length antibodies using trans-complementing expression vectors

Recombinant antibodies are increasingly used as therapeutics for a wide variety of diseases. Generation of cell lines expressing high levels of recombinant antibody typically requires labor-intensive cloning and screening steps. We describe a mammalian expression system for the high-level production of full-length antibody molecules. It has been shown that the dihydrofolate reductase (DHFR) selectable marker can be divided into two fragments that, with the aid of a leucine zipper, can re-associate to form an active molecule. Using bicistronic vectors, we linked the expression of each antibody chain to the expression of a DHFR fragment. Survival in selective media requires expression of both DHFR fragments that, by virtue of these vectors, also selects for the expression of both antibody chains. Initial pools produced 5 microg of Ab/10(6) cells/d (qP = microg/10(6) cells/d). Expression of each antibody chain in conjunction with a portion of DHFR also leads to concurrent amplification of both antibody chains in the presence of methotrexate, a DHFR inhibitor, and results in a two- to fivefold increase in antibody production with basal qPs ranging from 10-25 ug/10(6) cells/d. Shake-flask cultures of amplified pools produced up to 600 mg/L of antibody in 7 days. This system allows for rapid generation of antibodies without cloning and greatly simplifies selection of cell lines for the production of potential antibody therapeutics.     

Normalization and quantification of differential expression in gene expression microarrays

Array-based gene expression studies frequently serve to identify genes that are expressed differently under two or more conditions. The actual analysis of the data, however, may be hampered by a number of technical and statistical problems. Possible remedies on the level of computational analysis lie in appropriate preprocessing steps, proper normalization of the data and application of statistical testing procedures in the derivation of differentially expressed genes. This review summarizes methods that are available for these purposes and provides a brief overview of the available software tools.     

Mining gene expression profiles: expression signatures as cancer phenotypes

Many examples highlight the power of gene expression profiles, or signatures, to inform an understanding of biological phenotypes. This is perhaps best seen in the context of cancer, where expression signatures have tremendous power to identify new subtypes and to predict clinical outcomes. Although the ability to interpret the meaning of the individual genes in these signatures remains a challenge, this does not diminish the power of the signature to characterize biological states. The use of these signatures as surrogate phenotypes has been particularly important, linking diverse experimental systems that dissect the complexity of biological systems with the in vivo setting in a way that was not previously feasible.     

Auxin-regulated gene expression

During the 1960s a wide range of studies provided an information base that led to the suggestion that auxin-regulated cell processes--especially cell elongation--may be mediated by auxin-regulated gene expression. Indirect evidence from our work, based on the influence of inhibitors of RNA synthesis (e.g. actinomycin D) and of protein synthesis (e.g. cycloheximide) on auxin-induced cell elongation, coupled with correlations of the influence of auxin on RNA synthesis and cell elongation, provided the basis for this suggestion. With the availability of techniques for DNA-DNA and DNA-RNA hybridization, mRNA isolation-translation, in vitro 2D gel analysis of the translation products, and ultimately the cloning by recombinant DNA technologies of genomic DNA and copy DNAs (cDNAs) made to poly(A)+ mRNAs, we and others have provided direct evidence for the influence of auxin on the expression of a few genes (i.e. poly(A)+ RNA levels). Our laboratory has provided evidence for auxin's both down-regulating and up-regulating the level of a few poly(A)+ mRNAs out of a population of about 4 X 10(4) sequences that are not significantly affected by auxin. In our studies on auxin-regulated cell elongation, two cDNA clones (pJCW1 and pJCW2) were isolated which corresponded to poly(A)+ mRNAs that responded during growth transitions in a way consistent with a potential role of their protein products in cell elongation. These mRNAs are most abundant in the elongating zone of the soybean hypocotyl. Upon excision and incubation in the absence of auxin, these mRNAs deplete in concert with a decreasing rate of cell elongation. Addition of auxin to the medium results in both increased levels of these mRNAs and enhanced rates of cell elongation. These mRNAs do not deplete if auxin is added to the medium at the onset of excised incubation, and cell elongation rates remain high. We have isolated and sequenced genomic clones that are homologous to these cDNAs. Of the two genes sequenced, both genes are members of small multigene families. There are regions of high amino acid homology even though the nucleotide sequences are sufficiently different in these regions for cross-hybridization of the clones not to be observed. More recently others, especially Guilfoyle's laboratory, have shown that auxin selectively and rapidly influences the level of certain mRNAs and proteins. We have worked on other gene systems such as ribosomal proteins and possible cell wall proteins that are responsive to auxin; again the nature of regulation of expression of these genes is not known.(ABSTRACT TRUNCATED AT 400 WORDS)     

Dimerizer-regulated gene expression

Control of gene expression using small molecules is a powerful research tool and has clinical utility in the context of regulated gene therapy. Use of chemical inducers of dimerization, or dimerizers, for this purpose has several advantages, including tight regulation, modularity to facilitate iterative improvements, and assembly from human proteins to minimize immune responses in clinical applications. Recent developments include the use of the rapamycin-based dimerizer system to regulate the expression of endogenous genes, the generation of new chemical dimerizers based on FK506, dexamethasone and methotrexate, and progress towards the clinical use of adeno-associated virus and adenovirus vectors regulated by rapamycin analogs.