Gene expression profiling in human esophageal cancers using cDNA microarray

Human esophageal cancer cell lines and human esophageal cancer tissues were profiled on cDNA microarrays. In esophageal cancer cell lines, KYAE and OE-33 (adenocarcinomas) were distinguished from KYSE series (squamous cell carcinomas). Although SK-GT-4 and TE7 were derived from adenocarcinomas, they had a comparatively similar expression profile to the KYSE series. A set of genes whose expression commonly either increased or decreased in cancer cell lines was identified. Genes that were characteristically expressed in KYAE and OE-33 were also identified. The gene expression profiles of cancer tissues (CTs) were remarkably different from those of the cancer cell lines (CCLs). Notable differences between CCLs and CTs were observed in matrix metalloproteinases, plasminogen activator, collagens, paxillin, and thrombospondin 2, etc., whose expression was not increased in CCLs but increased in CTs. Twenty-three genes were extracted to categorize patients according to their prognoses, and clustering analyses, using these genes, were performed successfully.     

Efficacy of RNA amplification is dependent on sequence characteristics: implications for gene expression profiling using a cDNA microarray

Minute tissue samples or single cells increasingly provide the starting material for gene expression profiling, which often requires RNA amplification. Although much effort has been put into optimizing amplification protocols, the relative abundance of RNA templates in the amplified product is frequently biased. We applied a T7 polymerase-based technique to amplify RNA from two tissues of a cichlid fish and compared expression levels of unamplified and amplified RNA on a cDNA microarray. Amplification bias was generally minor and comprised features that were lost (1.3%) or gained (2.5%) through amplification and features that were scored as regulated before but unregulated after amplification (4.2%) or vice versa (19.5%). We examined 10 sequence-specific properties and found that GC content, folding energy, hairpin length and number, and lengths of poly(A) and poly(T) stretches significantly affected RNA amplification. We conclude that, if RNA amplification is used in gene expression studies, preceding experiments controlling for amplification bias should be performed.     

Genome-wide profiling of gene expression in 29 normal human tissues with a cDNA microarray.

We have performed a comprehensive analysis of the expression profiles in 25 adult and 4 fetal human tissues by means of a cDNA microarray consisting of 23,040 human genes. This study revealed a number of genes that were expressed specifically in each of those tissues. Among the 29 tissues examined, 4,080 genes were highly expressed (at least a five-fold expression ratio) in one or only a few tissues and 1,163 of those were expressed exclusively (more than a ten-fold higher expression ratio) in a particular tissue. Expression of some of the genes in the latter category was confirmed by northern analysis. A hierarchical clustering analysis of gene-expression profiles in nerve tissues (adult brain, fetal brain, and spinal cord), lymphoid tissues (bone marrow, thymus, spleen, and lymph node), muscle tissues (heart and skeletal muscle), or adipose tissues (mesenteric adipose and mammary gland) identified a set of genes that were commonly expressed among related tissues. These data should provide useful information for medical research, especially for efforts to identify tissue-specific molecules as potential targets of novel drugs to treat human diseases.     

Combination of PCR subtraction and cDNA microarray for differential gene expression profiling.

PCR subtraction hybridization has been used effectively to enrich and single out differentially expressed genes. However identification of these genes by means of cloning and sequencing individual cDNAs is a tedious and lengthy process. In this report, an attempt has been made to combine the use of PCR select cDNA subtraction hybridization and cDNA microarrays to identify differentially expressed genes using a nonradioactive chemiluminescent detection method. mRNA from human prolactin (hPRL) or human prolactin antagonist (hPRL-G129R) treated and non-treated breast cancer cells was isolated, and cDNAs were synthesized and used for the PCR subtraction to enrich the differentially expressed genes in the treated cells. The PCR-amplified and subtracted cDNA pools were purified and labeled using the digoxigenin method. Labeled cDNAs were hybridized to a human apoptosis cDNA microarray membrane and identified by chemiluminescence. The results suggest that the strategy of combining all three methods will allow for a more efficient, nonradioactive way of identifying differentially expressed genes in target cells.     

Gene expression profile analysis in astaxanthin-induced Haematococcus pluvialis using a cDNA microarray

The unicellular green alga Haematococcus pluvialis (Volvocales) is known for the ketocarotenoid astaxanthin (3, 3′-dihydroxy-β, β-carotene-4, 4′-dione) accumulation, which is induced under unfavorable culture conditions. In this work, we used cDNA microarray analysis to screen differentially expressed genes in H. pluvialis under astaxanthin-inductive culture conditions, such as combination of cell exposure to high irradiance and nutrient deprivation. Among the 965 genes in the cDNA array, there are 144 genes exhibiting differential expression (twofold changes) under these conditions. A significant decrease in the expression of photosynthesis-related genes was shown in astaxanthin-accumulating cells (red cells). Defense- or stress-related genes and signal transduction genes were also induced in the red cells. A comparison of microarray and real-time PCR analysis showed good correlation between the differentially expressed genes by the two methods. Our results indicate that the cDNA microarray approach, as employed in this work, can be relied upon and used to monitor gene expression profiles in H. pluvialis. In addition, the genes that were differentially expressed during astaxanthin induction are suitable candidates for further study and can be used as tools for dissecting the molecular mechanism of this unique pigment accumulation process in the green alga H. pluvialis. Electronic Supplementary Material Supplementary material is available for this article at and is accessible for authorized users.     

Biologically meaningful scents: a framework for understanding predator–prey research across disciplines

Fear of predation is a universal motivator. Because predators hunt using stealth and surprise, there is a widespread ability among prey to assess risk from chemical information – scents – in their environment. Consequently, scents often act as particularly strong modulators of memory and emotions. Recent advances in ecological research and analytical technology are leading to novel ways to use this chemical information to create effective attractants, repellents and anti‐anxiolytic compounds for wildlife managers, conservation biologists and health practitioners. However, there is extensive variation in the design, results, and interpretation of studies of olfactory‐based risk discrimination. To understand the highly variable literature in this area, we adopt a multi‐disciplinary approach and synthesize the latest findings from neurobiology, chemical ecology, and ethology to propose a contemporary framework that accounts for such disparate factors as the time‐limited stability of chemicals, highly canalized mechanisms that influence prey responses, and the context within which these scents are detected (e.g. availability of alternative resources, perceived shelter, and ambient physical parameters). This framework helps to account for the wide range of reported responses by prey to predator scents, and explains, paradoxically, how the same individual predator scent can be interpreted as either safe or dangerous to a prey animal depending on how, when and where the cue was deposited. We provide a hypothetical example to illustrate the most common factors that influence how a predator scent (from dingoes, Canis dingo) may both attract and repel the same target organism (kangaroos, Macropus spp.). This framework identifies the catalysts that enable dynamic scents, odours or odorants to be used as attractants as well as deterrents. Because effective scent tools often relate to traumatic memories (fear and/or anxiety) that cause future avoidance, this information may also guide the development of appeasement, enrichment and anti‐anxiolytic compounds, and help explain the observed variation in post‐traumatic‐related behaviours (including post‐traumatic stress disorder, PTSD) among diverse terrestrial taxa, including humans.     

Factors influencing cDNA microarray hybridization on silylated glass slides

cDNA microarray technology is becoming the technique of choice for studying gene expression and gene expression patterns. Although experimental protocols are available, only limited methodological information on microarray manufacture, hybridization, and signal interpretation has been published. The aim of this paper is to provide more insight into the practical aspects of microarray construction and hybridization. The influence of the size, composition, and concentration of the spotted DNA fragments on the final hybridization signal and the effect of hybridization volume, sample concentration, and sample depletion have been tested and are discussed.     

Monitoring gene expression profile changes in ovarian carcinomas using cDNA microarray

The development of cancer is the result of a series of molecular changes occurring in the cell. These events lead to changes in the expression level of numerous genes that result in different phenotypic characteristics of tumors. In this report we describe the assembly and utilization of a 5766 member cDNA microarray to study the differences in gene expression between normal and neoplastic human ovarian tissues. Several genes that may have biological relevance in the process of ovarian carcinogenesis have been identified through this approach. Analyzing the results of microarray hybridizations may provides new leads for tumor diagnosis and intervention.     

Characterization of gene expression profiles in early bovine pregnancy using a custom cDNA microarray

Gene expression analysis comparing nonpregnant with pregnant bovine uteri, including placenta, was performed with a custom cDNA microarray containing 1,933 independent genes. These genes were classified into six categories according to biological function, as follows: cell and tissue structural dynamics (108 genes), intercellular communication (221), intracellular metabolism (265), cell cycle and apoptosis (26), regulation of gene expression (113), expressed sequence tag (EST) and function unknown (617), and uncomplemented genes (583 clones). This array possessed bovine placental/endometrial specificity, as it included many pregnancy-specific molecules, such as pregnancy-associated glycoprotein-1 (PAGs), placental lactogen (PLs), and prolactin-related protein-1 (PRPs). A total of 77 genes were induced and 12 repressed in the placenta/endometrium. Our results point to a fundamental role for bovine placental-specific genes such as PAGs, PLs, and PRPs, in implantation and placentogenesis, and document that cDNA microarray analysis from bovine placenta/endometrium is possible and is a specific tool for monitoring genome-wide gene expression during the establishment and maintenance of pregnancy.     

Classification across gene expression microarray studies

Background  

The increasing number of gene expression microarray studies represents an important resource in biomedical research. As a result, gene expression based diagnosis has entered clinical practice for patient stratification in breast cancer. However, the integration and combined analysis of microarray studies remains still a challenge. We assessed the potential benefit of data integration on the classification accuracy and systematically evaluated the generalization performance of selected methods on four breast cancer studies comprising almost 1000 independent samples. To this end, we introduced an evaluation framework which aims to establish good statistical practice and a graphical way to monitor differences. The classification goal was to correctly predict estrogen receptor status (negative/positive) and histological grade (low/high) of each tumor sample in an independent study which was not used for the training. For the classification we chose support vector machines (SVM), predictive analysis of microarrays (PAM), random forest (RF) and k-top scoring pairs (kTSP). Guided by considerations relevant for classification across studies we developed a generalization of kTSP which we evaluated in addition. Our derived version (DV) aims to improve the robustness of the intrinsic invariance of kTSP with respect to technologies and preprocessing.