SEARCH · PubMed Health

Results for “Biological sequence analysis”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 721 records · Page 40Linked to original sources

PubMed2006

Global gene expression analysis in liver of obese diabetic db/db mice treated with metformin.

AIMS/HYPOTHESIS: Metformin is widely used as a hypoglycaemic reagent for type 2 diabetes. While the reduction of hepatic gluconeogenesis is thought to be a key effect, the detailed molecular mechanism of action of metformin remains to be elucidated. To gain insight into this, we performed a global gene expression profiling study. MATERIALS AND METHODS: We performed DNA microarray analysis to study global gene expression in the livers of obese diabetic db/db mice 2 h after a single administration of metformin (400 mg/kg). RESULTS: This analysis identified 14 genes that showed at least a 1.5-fold difference in expression following metformin treatment, including a reduction of glucose-6-phosphatase gene expression. The mRNA levels of glucose-6-phosphatase showed one of the best correlations with blood glucose levels among 12,000 genes. Enzymatic activity of glucose-6-phosphatase was also reduced in metformin-treated liver. Moreover, intensive analysis of the expression profile revealed that metformin effected significant alterations in gene expression across at least ten metabolic pathways, including those involved in glycolysis-gluconeogenesis, fatty acid metabolism and amino acid metabolism. CONCLUSIONS/INTERPRETATION: These results suggest that reduction of glucose-6-phosphatase activity, as well as suppression of mRNA expression levels of this gene, in liver is of prime importance for controlling blood glucose levels in vivo, at least at early time points after metformin treatment. Our results also suggest that metformin not only affects expression of specific genes, but also alters the expression level of multiple genes linked to the metabolic pathways involved in glucose and lipid metabolism in the liver.

Animals↗

PubMed2003

Covariation of larval gene expression and adult body size in natural populations of Drosophila melanogaster.

Understanding adaptive phenotypic variation is one of the most fundamental problems in evolutionary biology. Genes involved in adaptation are most likely those that affect traits most intimately connected to fitness: life-history traits. The genetics of quantitative trait variation (including life histories) is still poorly understood, but several studies suggest that (1) quantitative variation might be the result of variation in gene expression, rather than protein evolution, and (2) natural variation in gene expression underlies adaptation. The next step in studying the genetics of adaptive phenotypic variation is therefore an analysis of naturally occuring covariation of global gene expression and a life-history trait. Here, we report a microarray study addressing the covariation in larval gene expression and adult body weight, a life-history trait involved in adaptation. Natural populations of Drosophila melanogaster show adaptive geographic variation in adult body size, with larger animals at higher latitudes. Conditions during larval development also affect adult size with larger flies emerging at lower temperatures. We found statistically significant differences in normalized larval gene expression between geographic populations at one temperature (genetic variation) and within geographic populations between temperatures (developmental plasticity). Moreover, larval gene expression correlated highly with adult weight, explaining 81% of its natural variation. Of the genes that show a correlation of gene expression with adult weight, most are involved in cell growth or cell maintenance or are associated with growth pathways.

Animals↗

PubMed2006

Genome-wide analysis of re-replication reveals inhibitory controls that target multiple stages of replication initiation.

DNA replication must be tightly controlled during each cell cycle to prevent unscheduled replication and ensure proper genome maintenance. The currently known controls that prevent re-replication act redundantly to inhibit pre-replicative complex (pre-RC) assembly outside of the G1-phase of the cell cycle. The yeast Saccharomyces cerevisiae has been a useful model organism to study how eukaryotic cells prevent replication origins from reinitiating during a single cell cycle. Using a re-replication-sensitive strain and DNA microarrays, we map sites across the S. cerevisiae genome that are re-replicated as well as sites of pre-RC formation during re-replication. Only a fraction of the genome is re-replicated by a subset of origins, some of which are capable of multiple reinitiation events. Translocation experiments demonstrate that origin-proximal sequences are sufficient to predispose an origin to re-replication. Origins that reinitiate are largely limited to those that can recruit Mcm2-7 under re-replicating conditions; however, the formation of a pre-RC is not sufficient for reinitiation. Our findings allow us to categorize origins with respect to their propensity to reinitiate and demonstrate that pre-RC formation is not the only target for the mechanisms that prevent genomic re-replication.

Cell Cycle↗

PubMed2005

Transcriptome analysis of frontal cortex in alcohol-preferring and nonpreferring rats.

Although it is widely accepted that alcohol abuse and alcoholism have a significant genetic component of risk, the identities of the genes themselves remain obscure. To illuminate such potential genetic contributions, DNA macroarrays were used to probe for differences in normative cortical gene expression between rat strains genetically selected for alcohol self-administration preference, AA (Alko, alcohol) and P (Indiana, preferring), or avoidance, ANA (Alko, nonalcohol) and NP (Indiana, nonpreferring). Among 1,176 genes studied, six demonstrated confirmable, differential expression following comparison of ethanol-naive AA and ANA rats. Specifically, the mRNA level for metabotropic glutamate receptor 3 (mGluR3) was down-regulated in the AA vs. ANA lines. In contrast, calcium channel subunit alpha2delta1 (cacna2d1), vesicle-associated membrane protein 2 (VAMP2), syntaxin 1 (both syntaxin 1a and 1b; STX1a and STX1b), and syntaxin binding protein (MUNC-18) mRNAs were found to be increased in frontal cortex following comparison of AA with ANA animals. Bioinformatic analysis of these molecular targets showed that mGluR3 and cacna2d1 fall within chromosomal locations reported to be alcohol-related by the Collaborative Study on the Genetics of Alcoholism (COGA) as well as quantitative trait loci (QTL) studies. To determine further whether these differences were strain specific, the above-mentioned genes were compared in ethanol-preferring (P) and -nonpreferring (NP) selected lines. VAMP2 was the only gene that displayed statistically different mRNA levels in a comparison of P and NP rats. In conclusion, the altered cortical gene expression illuminated here would have the effect of altering neurotransmitter release in AA rats (compared with ANA rats). Such alterations, however, might not be a universal characteristic of all animal models of alcohol abuse and will also require further investigation in post-mortem human samples.

Alcohol Drinking↗

PubMed2004

Elucidation of gene interaction networks through time-lagged correlation analysis of transcriptional data.

The photosynthetic cyanobacterium Synechocystis sp. strain PCC 6803 uses a complex genetic program to control its physiological response to alternating light conditions. To study this regulatory program time-series experiments were conducted by exposing Synechocystis sp. to serial perturbations in light intensity. In each experiment whole-genome DNA microarrays were used to monitor gene transcription in 20-min intervals over 8- and 16-h periods. The data was analyzed using time-lagged correlation analysis, which identifies genetic interaction networks by constructing correlations between time-shifted transcription profiles with different levels of statistical confidence. These networks allow inference of putative cause-effect relationships among the organism's genes. Using light intensity as our initial input signal, we identified six groups of genes whose time-lagged profiles possessed significant correlation, or anti-correlation, with the light intensity. We expanded this network by using the average profile from each group of genes as a seed, and searching for other genes whose time-lagged profiles possessed significant correlation, or anti-correlation, with the group's average profile. The final network comprised 50 different groups containing 259 genes. Several of these gene groups possess known light-stimulated gene clusters, such as Synechocystis sp. photosystems I and II and carbon dioxide fixation pathways, while others represent novel findings in this work.

Cyanobacteria↗

PubMed2007

Transcriptional analysis of L-methionine catabolism in Brevibacterium linens ATCC9175.

The expression of genes possibly involved in L-methionine and lactate catabolic pathways were performed in Brevibacterium linens (ATCC9175) in the presence or absence of added L-methionine. The expression of 27 genes of 39 selected genes differed significantly in L-methionine-enriched cultures. The expression of the gene encoding L-methionine gamma-lyase (MGL) is high in L-methionine-enriched cultures and is accompanied by a dramatic increase in volatile sulfur compounds (VSC) biosynthesis. Several genes encoding alpha-ketoacid dehydrogenase and one gene encoding an acetolactate synthase were also up-regulated by L-methionine, and are probably involved in the catabolism of alpha-ketobutyrate, the primary degradation product of L-methionine to methanethiol. Gene expression profiles together with biochemical data were used to propose catabolic pathways for L-methionine in B. linens and their possible regulation by L-methionine.

3-Methyl-2-Oxobutanoate Dehydrogenase (Lipoamide)↗

PubMed2006

Large-scale transcriptional analysis of bovine embryo biopsies in relation to pregnancy success after transfer to recipients.

The purpose of this work is to address the relationship between transcriptional profile of embryos and the pregnancy success based on gene expression analysis of blastocyst biopsies taken prior to transfer to recipients. Biopsies (30-40% of the intact embryo) were taken from in vitro-produced day 7 blastocysts (n = 118), and 60-70% were transferred to recipients after reexpansion. Based on the success of pregnancy, biopsies were pooled in three groups (each 10 biopsies) namely: those resulting in no pregnancy (G1), resorbed embryos (G2), and those resulting in calf delivery (G3). Gene expression analysis of these groups was performed using home-made bovine preimplantation-specific cDNA array (219 clones) and BlueChip (with approximately 2,000 clones). Microarray data analysis results revealed a total of 52 and 58 genes were differentially regulated during comparison between G1 vs. G3 and G2 vs. G3. Biopsies resulted in calf delivery were enriched with genes necessary for implantation (COX2 and CDX2), carbohydrate metabolism (ALOX15), growth factor (BMP15), signal transduction (PLAU), and placenta-specific 8 (PLAC8). Biopsies from embryos resulting in resorption are enriched with transcripts involved protein phosphorylation (KRT8), plasma membrane (OCLN), and glucose metabolism (PGK1 and AKR1B1). Biopsies from embryos resulting in no pregnancy are enriched with transcripts involved inflammatory cytokines (TNF), protein amino acid binding (EEF1A1), transcription factors (MSX1, PTTG1), glucose metabolism (PGK1, AKR1B1), and CD9, which is an inhibitor of implantation. In conclusion, we generated direct candidates of blastocyst-specific genes which may play an important role in determining the fate of the embryo after transfer.

Animals↗

PubMed2002

Transcriptional adaptation to cystic fibrosis transmembrane conductance regulator deficiency.

Cystic fibrosis, the most commonly inherited lethal pulmonary disorder in Caucasians, is caused by mutations in the cystic fibrosis transmembrane conductance regulator gene (CFTR). To identify genomic responses to the presence or absence of CFTR in pulmonary tissues in vivo, microarray analyses of lung mRNAs were performed on whole lung tissue from mice lacking (CFTR(-)) or expressing mouse CFTR (CFTR(+)). Whereas the histology of lungs from CFTR(-) and CFTR(+) mice was indistinguishable, statistically significant increases in the relative abundance of 29 and decreases in 25 RNAs were identified by RNA microarray analysis. Of RNAs whose expression was consistently altered by the absence of CFTR, functional classes of genes influencing gene transcription, inflammation, intracellular trafficking, signal transduction, and ion transport were identified. RNAs encoding the transcription factor CCAAT enhancer-binding protein (CEBP) delta and interleukin (IL) 1beta, both known to regulate CFTR expression, were induced, perhaps indicating adaptation to the lack of CFTR. RNAs mediating lung inflammation including calgranulin-S100 family members, IL-1beta and IL-4, were increased. Likewise, expression of several membrane transport proteins that interact directly with CFTR were increased, suggesting that CFTR-protein complexes initiate genomic responses. Absence of CFTR influenced the expression of genes modulating diverse pulmonary cell functions that may ameliorate or contribute to the pathogenesis of CF.

Animals↗

PubMed2005

Induction of apoptosis in mouse liver by microcystin-LR: a combined transcriptomic, proteomic, and simulation strategy.

Microcystins (MCs) are a family of cyclic heptapeptide hepatotoxins produced by freshwater species of cyanobacteria that have been implicated in the development of liver cancer, necrosis, and even deadly intrahepatic bleeding. MC-LR, the most toxic MC variant, is also the most commonly encountered in a contaminated aquatic system. This study presents the first data in the toxicological research of MCs that combines the use of standard apoptotic assays with transcriptomics, proteomic technologies, and computer simulations. By using histochemistry, DNA fragmentation assays, and flow cytometry analysis, we determined that MC-LR causes rapid, dose-dependent apoptosis in mouse liver when BALB/c mice are treated with MC-LR for 24 h at doses of either 50, 60, or 70 microg/kg of body weight. We then used gene expression profiling to demonstrate differential expressions (>2-fold) of 61 apoptosis-related genes in cells treated with MC-LR. Further proteomic analysis identified a total of 383 proteins of which 35 proteins were up-regulated and 30 proteins were down-regulated more than 2.5-fold when compared with controls. Combining computer simulations with the transcriptomic and proteomic data, we found that low doses (50 microg/kg) of MC-LR lead to apoptosis primarily through the BID-BAX-BCL-2 pathway, whereas high doses of MC-LR (70 microg/kg) caused apoptosis via a reactive oxygen species pathway. These results indicated that MC-LR exposure can cause apoptosis in mouse liver and revealed two independent pathways playing a major regulatory role in MC-LR-induced apoptosis, thereby contributing to a better understanding of the hepatotoxicity and the tumor-promoting mechanisms of MCs.

Animals↗

PubMed2005

Mining time-dependent gene features.

This paper presents an application of the Independent Component Analysis (ICA) method to genomic data. In particular, experimentally produced perturbation effects over the E.coli bacterium are monitored through the changes of gene expression values observed at regular times, and until steady state has been reached. The aim is to control the response of the SOS system to DNA damage. We might assume that only part of the genetic regulatory network is affected directly by the perturbation conditions, as indirect cascade effects might also be present, and some genes may change just because of randomness. ICA decomposes the gene matrix and identifies groups of genes belonging to a certain estimated component by virtue of co-expression; it is of course of interest to establish co-regulation dynamics, which might underlie the captured correlation. Stronger forms of dependence, like Mutual Information, are thus computed and compared with linear correlation in order to validate the results and establish the role of the identified components in determining the network dynamics.

Algorithms↗

PubMed2005

Comparative analysis of cell cycle regulated genes in eukaryotes.

We compared microarray experiments on cell cycle of three model eukaryotes: budding and fission yeast and human cells. Only 112 orthologous groups were cyclic in the three model organisms. The common set of cyclic orthologs includes many taking part in the cell cycle progression, like cyclin B homologs, CDC5, SCH9, DSK2, ZPR1. Proteins involved in DNA replication included histones, some checkpoint kinases and some proteins regulating DNA damage and repair. Conserved cyclic proteins involved in cytokinesis included myosins and kinesins. Many groups of genes related to translation and other metabolic processes were also cyclic in all three organisms. This reflects rebuilding of cellular components after the replication and changes of metabolism during the cell cycle. Many genes important in cell cycle control are not cyclic or not conserved. This includes transcription factors implicated in the regulation of budding yeast cell cycle. The partially overlapping roles of regulatory proteins might allow the evolutionary substitution of components of cell cycle.

Computational Biology↗

PubMed2006

Discovery of Time-Delayed Gene Regulatory Networks based on temporal gene expression profiling.

BACKGROUND: It is one of the ultimate goals for modern biological research to fully elucidate the intricate interplays and the regulations of the molecular determinants that propel and characterize the progression of versatile life phenomena, to name a few, cell cycling, developmental biology, aging, and the progressive and recurrent pathogenesis of complex diseases. The vast amount of large-scale and genome-wide time-resolved data is becoming increasing available, which provides the golden opportunity to unravel the challenging reverse-engineering problem of time-delayed gene regulatory networks. RESULTS: In particular, this methodological paper aims to reconstruct regulatory networks from temporal gene expression data by using delayed correlations between genes, i.e., pairwise overlaps of expression levels shifted in time relative each other. We have thus developed a novel model-free computational toolbox termed TdGRN (Time-delayed Gene Regulatory Network) to address the underlying regulations of genes that can span any unit(s) of time intervals. This bioinformatics toolbox has provided a unified approach to uncovering time trends of gene regulations through decision analysis of the newly designed time-delayed gene expression matrix. We have applied the proposed method to yeast cell cycling and human HeLa cell cycling and have discovered most of the underlying time-delayed regulations that are supported by multiple lines of experimental evidence and that are remarkably consistent with the current knowledge on phase characteristics for the cell cyclings. CONCLUSION: We established a usable and powerful model-free approach to dissecting high-order dynamic trends of gene-gene interactions. We have carefully validated the proposed algorithm by applying it to two publicly available cell cycling datasets. In addition to uncovering the time trends of gene regulations for cell cycling, this unified approach can also be used to study the complex gene regulations related to the development, aging and progressive pathogenesis of a complex disease where potential dependences between different experiment units might occurs.

Algorithms↗

PubMed2006

Quantifying gene network connectivity in silico: scalability and accuracy of a modular approach.

Large, complex data sets that are generated from microarray experiments, create a need for systematic analysis techniques to unravel the underlying connectivity of gene regulatory networks. A modular approach, previously proposed by Kholodenko and co-workers, helps to scale down the network complexity into more computationally manageable entities called modules. A functional module includes a gene's mRNA, promoter and resulting products, thus encompassing a large set of interacting states. The essential elements of this approach are described in detail for a three-gene model network and later extended to a ten-gene model network, demonstrating scalability. The network architecture is identified by analysing in silico steady-state changes in the activities of only the module outputs, communicating intermediates, that result from specific perturbations applied to the network modules one at a time. These steady-state changes form the system response matrix, which is used to compute the network connectivity or network interaction map. By employing a known biochemical network, the accuracy of the modular approach and its sensitivity to key assumptions are evaluated.

Algorithms↗

PubMed2002

Tree-based analysis of microarray data for classifying breast cancer.

DNA microarray data have provided us with the opportunity to assess the expression levels for thousands of genes simultaneously. One of the uses of this information is to classify cancer tumors. A noted challenge in using microarray information is analytical. Following the work of Zhang et al. (1), we further pursue the use of recursive partitioning in analyses of microarray data for cancer classification. Not only does the recursive partitioning technique create intuitive classification rules, but also it is most flexible as to the handling of a massive number of genes, missing expressions, and multi-class tissues. Using a published data set (2), we demonstrate that the recursive partitioning technique creates a more precise and simpler classification rule than other commonly used approaches. In particular, we introduce the concept of A-tree and propose a procedure to assess a large number of A-trees. One of the identified genes (ERBB2) is in the close region of BRCA1 (17q21.1) and has been shown by others to have altered expression levels in breast cancer. Nonetheless, our identified genes warrant further investigation as to whether they play a role in the etiology of breast cancer.

Breast Neoplasms↗

PubMed2005

Coordinated activation of metabolic pathways for antioxidants and defence compounds by jasmonates and their roles in stress tolerance in Arabidopsis.

Jasmonic acid (JA) and methyl jasmonate (MeJA), collectively termed jasmonates, are ubiquitous plant signalling compounds. Several types of stress conditions, such as wounding and pathogen infection, cause endogenous JA accumulation and the expression of jasmonate-responsive genes. Although jasmonates are important signalling components for the stress response in plants, the mechanism by which jasmonate signalling contributes to stress tolerance has not been clearly defined. A comprehensive analysis of jasmonate-regulated metabolic pathways in Arabidopsis was performed using cDNA macroarrays containing 13516 expressed sequence tags (ESTs) covering 8384 loci. The results showed that jasmonates activate the coordinated gene expression of factors involved in nine metabolic pathways belonging to two functionally related groups: (i) ascorbate and glutathione metabolic pathways, which are important in defence responses to oxidative stress, and (ii) biosynthesis of indole glucosinolate, which is a defence compound occurring in the Brassicaceae family. We confirmed that JA induces the accumulation of ascorbate, glutathione and cysteine and increases the activity of dehydroascorbate reductase, an enzyme in the ascorbate recycling pathway. These antioxidant metabolic pathways are known to be activated under oxidative stress conditions. Ozone (O3) exposure, a representative oxidative stress, is known to cause activation of antioxidant metabolism. We showed that O3 exposure caused the induction of several genes involved in antioxidant metabolism in the wild type. However, in jasmonate-deficient Arabidopsis 12-oxophytodienoate reductase 3 (opr3) mutants, the induction of antioxidant genes was abolished. Compared with the wild type, opr3 mutants were more sensitive to O3 exposure. These results suggest that the coordinated activation of the metabolic pathways mediated by jasmonates provides resistance to environmental stresses.

Acetates↗

PubMed2006

A hybrid two-component system protein of a prominent human gut symbiont couples glycan sensing in vivo to carbohydrate metabolism.

Bacteroides thetaiotaomicron is a prominent member of our normal adult intestinal microbial community and a useful model for studying the foundations of human-bacterial mutualism in our densely populated distal gut microbiota. A central question is how members of this microbiota sense nutrients and implement an appropriate metabolic response. B. thetaiotaomicron contains a large number of glycoside hydrolases not represented in our own proteome, plus a markedly expanded collection of hybrid two-component system (HTCS) proteins that incorporate all domains found in classical two-component environmental sensors into one polypeptide. To understand the role of HTCS in nutrient sensing, we used B. thetaiotaomicron GeneChips to characterize their expression in gnotobiotic mice consuming polysaccharide-rich or -deficient diets. One HTCS, BT3172, was selected for further analysis because it is induced in vivo by polysaccharides, and its absence reduces B. thetaiotaomicron fitness in polysaccharide-rich diet-fed mice. Functional genomic and biochemical analyses of WT and BT3172-deficient strains in vivo and in vitro disclosed that alpha-mannosides induce BT3172 expression, which in turn induces expression of secreted alpha-mannosidases. Yeast two-hybrid screens revealed that the cytoplasmic portion of BT3172's sensor domain serves as a scaffold for recruiting glucose-6-phosphate isomerase and dehydrogenase. These interactions are a unique feature of BT3172 and specific for the cytoplasmic face of its sensor domain. Loss of BT3172 reduces glycolytic pathway activity in vitro and in vivo. Thus, this HTCS functions as a metabolic reaction center, coupling nutrient sensing to dynamic regulation of monosaccharide metabolism. An expanded repertoire of HTCS proteins with diversified sensor domains may be one reason for B. thetaiotaomicron's success in our intestinal ecosystem.

Animals↗

PubMed2005

Rank-based methods as a non-parametric alternative of the T-statistic for the analysis of biological microarray data.

We have recently introduced a rank-based test statistic, RankProducts (RP), as a new non-parametric method for detecting differentially expressed genes in microarray experiments. It has been shown to generate surprisingly good results with biological datasets. The basis for this performance and the limits of the method are, however, little understood. Here we explore the performance of such rank-based approaches under a variety of conditions using simulated microarray data, and compare it with classical Wilcoxon rank sums and t-statistics, which form the basis of most alternative differential gene expression detection techniques. We show that for realistic simulated microarray datasets, RP is more powerful and accurate for sorting genes by differential expression than t-statistics or Wilcoxon rank sums - in particular for replicate numbers below 10, which are most commonly used in biological experiments. Its relative performance is particularly strong when the data are contaminated by non-normal random noise or when the samples are very inhomogenous, e.g. because they come from different time points or contain a mixture of affected and unaffected cells. However, RP assumes equal measurement variance for all genes and tends to give overly optimistic p-values when this assumption is violated. It is therefore essential that proper variance stabilizing normalization is performed on the data before calculating the RP values. Where this is impossible, another rank-based variant of RP (average ranks) provides a useful alternative with very similar overall performance. The Perl scripts implementing the simulation and evaluation are available upon request. Implementations of the RP method are available for download from the authors website (http://www.brc.dcs.gla.ac.uk/glama).

Algorithms↗

PubMed2003

Gene expression profiling of malignant mesothelioma.

PURPOSE: Malignant mesothelioma is a uniformly fatal cancer of the pleural and peritoneal spaces. Several challenging clinical problems include poor understanding of the pathophysiology, inaccurate diagnosis from tissue samples, and unsuccessful treatment strategies. The purpose of this study was to use microarray analysis to identify specific gene expression changes in mesothelioma compared with normal mesothelium. EXPERIMENTAL DESIGN: We performed gene expression analysis on mesothelioma tissue specimens from 16 patients and compared these to 4 control pleural tissue samples using cDNA microarray filters with 4132 clones. Multiple normalization and analysis approaches were used. Quantitative reverse transcription-PCR and immunohistochemistry were used to validate results. RESULTS: Genes (166) were significantly up-regulated, and 26 were down-regulated. Validation of 18 genes using real-time PCR confirmed array predictions in every case. Analysis revealed activation of several key pathways including genes involved in glucose metabolism, mRNA translation, and cytoskeletal remodeling. Expression profiling identified processes likely responsible for 18-fluoro-2-deoxy-glucose uptake and tumor localization by positron emission tomography, and a role for hypoxia-inducible factor-1 was suggested. Potentially important up-regulated genes included gp96, lung resistance-related protein, galectin-3 binding protein, the M(r) 67,000 laminin receptor (on tumor vessels), and voltage-dependent anion channels. Prospective testing using reverse transcription-PCR confirmed up-regulation of these novel markers. CONCLUSIONS: Expression profiling revealed marked up-regulation of energy, protein translation, and cytoskeletal remodeling pathways in mesothelioma. Additional genes that could be important in our understanding of the pathogenesis of mesothelioma, aiding in diagnosis, or improving targets for therapy were also identified.

Aged↗