Discriminating lymphomas and reactive lymphadenopathy in lymph node biopsies by gene expression profiling

Background

Diagnostic accuracy of lymphoma, a heterogeneous cancer, is essential for patient management. Several ancillary tests including immunophenotyping, and sometimes cytogenetics and PCR are required to aid histological diagnosis. In this proof of principle study, gene expression microarray was evaluated as a single platform test in the differential diagnosis of common lymphoma subtypes and reactive lymphadenopathy (RL) in lymph node biopsies.

Methods

116 lymph node biopsies diagnosed as RL, classical Hodgkin lymphoma (cHL), diffuse large B cell lymphoma (DLBCL) or follicular lymphoma (FL) were assayed by mRNA microarray. Three supervised classification strategies (global multi-class, local binary-class and global binary-class classifications) using diagonal linear discriminant analysis was performed on training sets of array data and the classification error rates calculated by leave one out cross-validation. The independent error rate was then evaluated by testing the identified gene classifiers on an independent (test) set of array data.

Results

The binary classifications provided prediction accuracies, between a subtype of interest and the remaining samples, of 88.5%, 82.8%, 82.8% and 80.0% for FL, cHL, DLBCL, and RL respectively. Identified gene classifiers include LIM domain only-2 (LMO2), Chemokine (C-C motif) ligand 22 (CCL22) and Cyclin-dependent kinase inhibitor-3 (CDK3) specifically for FL, cHL and DLBCL subtypes respectively.

Conclusions

This study highlights the ability of gene expression profiling to distinguish lymphoma from reactive conditions and classify the major subtypes of lymphoma in a diagnostic setting. A cost-effective single platform "mini-chip" assay could, in principle, be developed to aid the quick diagnosis of lymph node biopsies with the potential to incorporate other pathological entities into such an assay.

     

Genome-wide identification and expression profiling of DNA methyltransferase gene family in maize

Key message

In this study, we identified eight DNA MTase genes in maize and the diversity of expression patterns of them was presented by EST mining, microarray and semi-quantitative expression profile analyses.

Abstract

DNA methylation plays a pivotal role in promoting genomic stability through diverse biological processes including regulation of gene expression during development and chromatin organization. Although this important biological process is mainly regulated by several conserved Cytosine-5 DNA methyltransferases encoded by a smaller multigene family in plants, investigation of the plant C5-MTase-encoding gene family will serve to elucidate the epigenetic mechanism diversity in plants. Recently, genome-wide identification and evolutionary analyses of the C5-MTase-encoding gene family have been characterized in multiple plant species including Arabidopsis, rice, carrot and wheat. However, little is known regarding the C5-MTase-encoding genes in the entire maize genome. Here, genome-wide identification and expression profile analyses of maize C5-MTase-encoding genes (ZmMETs) were performed from the latest version of the maize (B73) genome. Phylogenetic analysis indicated that the orthologs from the three species (maize, Arabidopsis and rice) were categorized into four classes. Chromosomal location of these genes revealed that they are unevenly distributed on 6 of all 10 chromosomes with three chromosomal/segmental duplication events, suggesting that gene duplication played a key role in expansion of the maize C5-MTase-encoding gene family. Furthermore, EST expression data mining, microarray data and semi-quantitative expression profile analyses detected in the leaves by two different abiotic stress treatments have demonstrated that these genes had temporal and spatial expression pattern and exhibited different expression levels in stress treatments, suggesting that functional diversification of ZmMET genes family. Overall, our study will serve to present signification insights to explore the plant C5-MTase-encoding gene expression and function and also be beneficial for future experimental research to further unravel the mechanisms of epigenetic regulation in plants.     

DNA array profiling of gene expression changes during maize embryo development

We are using DNA microarray-based gene expression profiling to classify temporal patterns of gene expression during the development of maize embryos, to understand mRNA-level control of embryogenesis and to dissect metabolic pathways and their interactions in the maize embryo. Genes involved in carbohydrate, fatty acid, and amino acid metabolism, the tricarboxylic acid (TCA) cycle, glycolysis, the pentose phosphate pathway, embryogenesis, membrane transport, signal transduction, cofactor biosynthesis, photosynthesis, oxidative phosphorylation and electron transfer, as well as 600 random complementary DNA (cDNA) clones from maize embryos, were arrayed on glass slides. DNA arrays were hybridized with fluorescent dye-labeled cDNA probes synthesized from kernel and embryo poly(A)⁺RNA from different stages of maize seed development. Several characteristic developmental patterns of expression were identified and correlated with gene function. Patterns of coordinated gene expression in the TCA cycle and glycolysis were analyzed in detail. The steady state level of poly(A)⁺ RNA for many genes varies dramatically during maize embryo development. Expression patterns of genes coding for enzymes of fatty acid biosynthesis and glycolysis are coordinately regulated during development. Genes of unknown function may by assigned a hypothetical role based on their patterns of expression resembling well characterized genes. Electronic supplementary material to this paper can be obtained by using the Springer LINK server located at http://dx.doi.org/10.1007/s10142-002-0046-6. Electronic Publication     

On predicting medulloblastoma metastasis by gene expression profiling

Accurately predicting clinical outcome or metastatic status from gene expression profiles remains one of the biggest hurdles facing the adoption of predictive medicine. Recently, MacDonald et al. (Nat. Genet. 2001, 29, 143-152) used gene expression profiles, from samples taken at diagnosis, to distinguish between clinically designated metastatic and nonmetastatic primary medulloblastomas, helping to elucidate the genetic mechanisms underlying metastasis and suggesting novel therapeutic targets. The obtained accuracy of predicting metastatic status does not, however, reach statistical significance on Fisher's exact test, although 22 training samples were used to make each prediction via leave-one-out testing. This paper introduces readily implemented nonlinear filters to transform sequences of gene expression levels into output signals that are significantly easier to classify and predict metastasis. It is shown that when only 3 exemplars each from the metastatic and nonmetastatic classes were assumed known, a predictor was constructed whose accuracy is statistically significant over the remaining profiles set aside as a test set. The predictor was as effective in recognizing metastatic as nonmetastatic medulloblastomas, and may be helpful in deciding which patients require more aggressive therapy. The same predictor was similarly effective on an independent set of 5 nonmetastatic tumors and 3 metastatic cell lines also used by MacDonald et al.     

Comparative gene expression profiling by oligonucleotide fingerprinting.

The use of hybridisation of synthetic oligonucleotides to cDNAs under high stringency to characterise gene sequences has been demonstrated by a number of groups. We have used two cDNA libraries of 9 and 12 day mouse embryos (24 133 and 34 783 clones respectively) in a pilot study to characterise expressed genes by hybridisation with 110 hybridisation probes. We have identified 33 369 clusters of cDNA clones, that ranged in representation from 1 to 487 copies (0.7%). 737 were assigned to known rodent genes, and a further 13 845 showed significant homologies. A total of 404 clusters were identified as significantly differentially represented (P < 0.01) between the two cDNA libraries. This study demonstrates the utility of the fingerprinting approach for the generation of comparative gene expression profiles through the analysis of cDNAs derived from different biological materials.     

Cutting-edge technology. I. Global gene expression profiling using DNA microarrays

Having the complete human genomic sequence poses a new challenge: to use genomic structural information to display and analyze biological processes on a genome-wide scale to assign gene function. DNA microarrays are a miniaturized, ordered arrangement of nucleic acid fragments from individual genes located at defined positions on a solid support, enabling the analysis of thousands of genes in parallel by specific hybridization. This review describes technical aspects, discusses relevant applications, and suggests factors affecting the use of this technology and how it fits in the grand scheme of meeting the needs of the postgenomic era.     

Promotion of DNA renaturation by a non-DNA factor in crown call tumor DNA preparation.

A 5400-fold excess of tobacco crown gall tumor DNA increased the renaturation rate of $\text{Agrobacterium tumefaciens}$ DNA, whereas, the same excess of healthy plant DNA had no effect on the rate or kinetics of renaturation. Since deoxyribonuclease treatment of the tumor DNA did not remove its ability to accelerate renaturation, the tumor tissue contains a non-DNA factor that increases the rate of renaturation of $\text{A. tumefaciens}$ DNA.     

类胡萝卜素的抗癌作用与基因表达的联系

类胡萝卜素是通过干扰癌细胞生长或细胞死亡的有关途径显示其抗癌作用的,包括细胞增殖、生长因子的信号传输、细胞间隙连接通讯、细胞分化及凋亡。类胡萝卜素引起参与这些过程的调节蛋白质表达发生改变。已发现几个转录系统在其抗癌活性中起作用,如类视黄素受体、过氧化物酶体激活受体(PPAR)、抗氧化剂应答元件(ARE)、异生素受体及激活剂蛋白-1(AP-1),它们构成各种类胡萝卜素与其它微量营养素协同抗癌作用的基础。     

Gene expression profiling of human diseases by serial analysis of gene expression

Until recently, the approach to understanding the molecular basis of complex syndromes such as cancer, coronary artery disease, and diabetes was to study the behavior of individual genes. However, it is generally recognized that expression of a number of genes is coordinated both spatially and temporally and that this coordination changes during the development and progression of diseases. Newly developed functional genomic approaches, such as serial analysis of gene expression (SAGE) and DNA microarrays have enabled researchers to determine the expression pattern of thousands of genes simultaneously. One attractive feature of SAGE compared to microarrays is its ability to quantify gene expression without prior sequence information or information about genes that are thought to be expressed. SAGE has been successfully applied to the gene expression profiling of a number of human diseases. In this review, we will first discuss SAGE technique and contrast it to microarray. We will then highlight new biological insights that have emerged from its application to the study of human diseases.     

Using mRNA expression profiling to determine anticancer drug efficacy.

Pharmacogenomics is a fast-growing field of investigations that aims to further elucidate the inherited nature of interindividual differences in drug disposition and effects, with the ultimate goal of providing a stronger scientific basis for selecting the optimal drug therapy. Providing the right drug for the right patient is an important problem in the treatment of cancer. This is mainly due to the lack of information about the sensitivity of the tumor for a specific treatment modality, such as either chemotherapy or radiation treatment. This presentation highlights two approaches to identify responsiveness to treatment. Both approaches are based on the identification of expression profiles. The first approach concentrates on drug resistance and the second on the signaling pathways leading up to the death of the cell. Both approaches provide expression profiles; however, the more dynamic expression profiling as used to determine the signaling in damage cells promises to be a better determinant for the pharmacogenomic changes in expression profiles and, consequently, a potential better determinant for drug efficacy.     

Characterization and expression profiling of a novel cereal cyst nematode resistance gene analog in wheat

Based on the conserved regions of known resistance genes, an NBS-LRR-type CCN resistance gene analog was isolated from the CCN resistant E-10 near isogenic lines (NILs) of wheat, designated as CreZ (GenBank accession number: EU327996). It contained a complete ORF that was 2775 bp in length and encoded 924 amino acids. Sequence comparison indicated that it shared 92% nucleotide and 87% amino acid identity with those of the known CCN-resistance gene Cre3 and had similar characteristic conserved motifs to those in other established NBS-LRR disease resistance genes. The expression profiling of CreZ indicated that it was specifically expressed in the roots of resistant plants and real-time PCR analysis demonstrated that expression levels drastically increased when the plants were inoculated with cereal cyst nematodes. It could be inferred, then, that CreZ belongs to the NBS-LRR resistance gene family and is a candidate gene for potential resistance to the cereal cyst nematode. Published in Russian in Molekulyarnaya Biologiya, 2008, Vol. 42, No. 6, pp. 1070–1077. The text was submitted by the authors in English.     

Global functional profiling of gene expression

The typical result of a microarray experiment is a list of tens or hundreds of genes found to be differentially regulated in the condition under study. Independent of the methods used to select these genes, the common task faced by any researcher is to translate these lists of genes into a better understanding of the biological phenomena involved. Currently, this is done through a tedious combination of searches through the literature and a number of public databases. We developed Onto-Express (OE) as a novel tool able to automatically translate such lists of differentially regulated genes into functional profiles characterizing the impact of the condition studied. OE constructs functional profiles (using Gene Ontology terms) for the following categories: biochemical function, biological process, cellular role, cellular component, molecular function, and chromosome location. Statistical significance values are calculated for each category. We demonstrate the validity and the utility of this comprehensive global analysis of gene function by analyzing two breast cancer datasets from two separate laboratories. OE was able to identify correctly all biological processes postulated by the original authors, as well as discover novel relevant mechanisms.     

TOX4 and its binding partners recognize DNA adducts generated by platinum anticancer drugs

Platinating agents are commonly prescribed anticancer drugs damaging DNA. Induced lesions are recognized by a wide range of proteins. These are involved in cellular mechanisms such as DNA repair, mediation of cytotoxicity or chromatin remodeling. They therefore constitute crucial actors to understand pharmacology of these drugs. To expand our knowledge about this subproteome, we developed a ligand fishing trap coupled to high throughput proteomic tools. This trap is made of damaged plasmids attached to magnetic beads, and was exposed to cell nuclear extracts. Retained proteins were identified by nanoHPLC coupled to tandem mass spectrometry. This approach allowed us to establish a list of 38 proteins interacting with DNA adducts generated by cisplatin, oxaliplatin and satraplatin. Some of them were already known interactome members like high mobility group protein 1 (HMGB1) or the human upstream binding factor (hUBF), but we also succeeded in identifying unexpected proteins such as TOX HMG box family member 4 (TOX4), phosphatase 1 nuclear targeting subunit (PNUTS), and WD repeat-containing protein 82 (WDR82), members of a recently discovered complex. Interaction between TOX4 and platinated DNA was subsequently validated by surface plasmon resonance imaging (SPRi). These interactions highlight new cellular responses to DNA damage induced by chemotherapeutic agents.