Xylella fastidiosa gene expression analysis by DNA microarrays

Xylella fastidiosa genome sequencing has generated valuable data by identifying genes acting either on metabolic pathways or in associated pathogenicity and virulence. Based on available information on these genes, new strategies for studying their expression patterns, such as microarray technology, were employed. A total of 2,600 primer pairs were synthesized and then used to generate fragments using the PCR technique. The arrays were hybridized against cDNAs labeled during reverse transcription reactions and which were obtained from bacteria grown under two different conditions (liquid XDM2 and liquid BCYE). All data were statistically analyzed to verify which genes were differentially expressed. In addition to exploring conditions for X. fastidiosa genome-wide transcriptome analysis, the present work observed the differential expression of several classes of genes (energy, protein, amino acid and nucleotide metabolism, transport, degradation of substances, toxins and hypothetical proteins, among others). The understanding of expressed genes in these two different media will be useful in comprehending the metabolic characteristics of X. fastidiosa, and in evaluating how important certain genes are for the functioning and survival of these bacteria in plants.

LATENT RANK CHANGE DETECTION FOR ANALYSIS OF SPLICE-JUNCTION MICROARRAYS WITH NONLINEAR EFFECTS

Alternative splicing of gene transcripts greatly expands the functional capacity of the genome, and certain splice isoforms may indicate specific disease states such as cancer. Splice junction microarrays interrogate thousands of splice junctions, but data analysis is difficult and error prone because of the increased complexity compared to differential gene expression analysis. We present Rank Change Detection (RCD) as a method to identify differential splicing events based upon a straightforward probabilistic model comparing the over-or underrepresentation of two or more competing isoforms. RCD has advantages over commonly used methods because it is robust to false positive errors due to nonlinear trends in microarray measurements. Further, RCD does not depend on prior knowledge of splice isoforms, yet it takes advantage of the inherent structure of mutually exclusive junctions, and it is conceptually generalizable to other types of splicing arrays or RNA-Seq. RCD specifically identifies the biologically important cases when a splice junction becomes more or less prevalent compared to other mutually exclusive junctions. The example data is from different cell lines of glioblastoma tumors assayed with Agilent microarrays.

Effect of the immunosuppressants on hepatocyte proliferation and apoptosis in a young animal model of liver regeneration: An immunohistochemical study using tissue microarrays

Hepatocyte proliferation and apoptosis (programmed cell death) occur during the liver parenchyma regeneration and the liver size modeling is mainly controlled by hepatocyte apoptosis. The purpose of the present study was to verify the influence of immunosuppressant drugs on these phenomena by utilizing tissue microarray techniques. Thirty-six weaning rats (age 21-23 days, weight 30-50 g) were divided into six groups: control, sham, hepatectomy, hepatectomy plus solumedrol, hepatectomy plus CsA, and hepatectomy plus Tac. The animals were killed one day after hepatectomy, and the remnant livers were weighed and harvested for tissue microarray sections. Liver cell proliferation was evaluated by staining for PCNA and apoptosis was detected by the TUNEL method. It was verified that CsA promoted a decrease in the liver weight, Tac and CsA decreased the proliferation index of hepatocytes, and glucocorticoid had no significant effects. The apoptosis index was not altered by hepatectomy or immunosuppressants. Our data indicate that, in the growing rat, CsA and Tac have negative effects on hepatocyte proliferation and have no effect on the hepatocyte apoptosis.

Optical encoding of microbeads for gene screening: alternatives to microarrays

Rapid access to genetic information is central to the revolution presently occurring in the pharmaceutical industry, particularly In relation to novel drug target identification and drug development. Genetic variation, gene expression, gene function and gene structure are just some of the important research areas requiring efficient methods of DNA screening. Here, we highlight state-of-the-art techniques and devices for gene screening that promise cheaper and higher-throughput yields than currently achieved with DNA microarrays. We include an overview of existing and proposed bead-based strategies designed to dramatically increase the number of probes that can be interrogated in one assay. We focus, in particular, on the issue of encoding and/or decoding (bar-coding) large bead-based libraries for HTS.

Application of DNA microarrays to study human alcoholism

An emerging idea is that long-term alcohol abuse results in changes in gene expression in the brain and that these changes are responsible at least partly for alcohol tolerance, dependence and neurotoxicity, The overall goal of our research is to identify genes which are differentia[ly expressed in the brains of well-characterized human alcoholics as compared with non-alcoholics. This should identify as-yet-unknown alcohol-responsive genes, and may well confirm changes in the expression of genes which have been delineated in animal models of alcohol abuse. Cases were carefully selected and samples pooled on the basis of relevant criteria; differential expression was monitored by microarray hybridization. The inherent diversity of human alcoholics can be exploited to identify genes associated with specific pathological processes, as well as to assess the effects of concomitant disease, severity of brain damage, drinking behavior, and factors such as gender and smoking history. initial results show selective changes in gene expression in alcoholics; of particular importance is a coordinated reduction in genes coding for myelin components, Copyright (C) 2001 National Science Council, ROC and S. Karger AG, Basel.

Model-based clustering in gene expression microarrays: An application to breast cancer data

In microarray studies, the application of clustering techniques is often used to derive meaningful insights into the data. In the past, hierarchical methods have been the primary clustering tool employed to perform this task. The hierarchical algorithms have been mainly applied heuristically to these cluster analysis problems. Further, a major limitation of these methods is their inability to determine the number of clusters. Thus there is a need for a model-based approach to these. clustering problems. To this end, McLachlan et al. [7] developed a mixture model-based algorithm (EMMIX-GENE) for the clustering of tissue samples. To further investigate the EMMIX-GENE procedure as a model-based -approach, we present a case study involving the application of EMMIX-GENE to the breast cancer data as studied recently in van 't Veer et al. [10]. Our analysis considers the problem of clustering the tissue samples on the basis of the genes which is a non-standard problem because the number of genes greatly exceed the number of tissue samples. We demonstrate how EMMIX-GENE can be useful in reducing the initial set of genes down to a more computationally manageable size. The results from this analysis also emphasise the difficulty associated with the task of separating two tissue groups on the basis of a particular subset of genes. These results also shed light on why supervised methods have such a high misallocation error rate for the breast cancer data.

DNA microarrays and plant defence

Recent progress in understanding plant defence has highlighted a complex, interacting network of signalling pathways leading to the induction of numerous genes. The advent of new technologies for the global analysis of gene expression is fundamentally affecting research in biology, and studies on plant defence should benefit from these new approaches. Genome-wide microarrays will provide a powerful tool for the discovery of all defence-related genes and should help in elucidating their function. The association of a particular signalling pathway with a defence response can be tested with microarrays and defined mutants. Comparison of transcript profiles after biotic and abiotic stresses reveals overlapping activation of defence-related genes and defines new concepts on how plants cope with multiple aggressions. The combination of expression data with other biochemical or metabolite measurements seems another promising approach. Finally, small-scale, dedicated microarrays containing sets of well-characterised genes might prove to be a very useful complement to more expensive, less accessible, large-scale arrays.

Rpe65 microarrays, from genes to phosphorylated proteins

Purpose:To describe a novel in silico method to gather and analyze data from high-throughput heterogeneous experimental procedures, i.e. gene and protein expression arrays. Methods:Each microarray is assigned to a database which handles common data (names, symbols, antibody codes, probe IDs, etc.). Links between informations are automatically generated from knowledge obtained in freely accessible databases (NCBI, Swissprot, etc). Requests can be made from any point of entry and the displayed result is fully customizable. Results:The initial database has been loaded with two sets of data: a first set of data originating from an Affymetrix-based retinal profiling performed in an RPE65 knock-out mouse model of Leber's congenital amaurosis. A second set of data generated from a Kinexus microarray experiment done on the retinas from the same mouse model has been added. Queries display wild type versus knock out expressions at several time points for both genes and proteins. Conclusions:This freely accessible database allows for easy consultation of data and facilitates data mining by integrating experimental data and biological pathways.

Protein-binding microarrays: probing disease markers at the interface of proteomics and genomics.

DNA-binding proteins mediate a variety of crucial molecular functions, such as transcriptional regulation and chromosome maintenance, replication and repair, which in turn control cell division and differentiation. The roles of these proteins in disease are currently being investigated using microarray-based approaches. However, these assays can be difficult to adapt to routine diagnosis of complex diseases such as cancer. Here, we review promising alternative approaches involving protein-binding microarrays (PBMs) that probe the interaction of proteins from crude cell or tissue extracts with large collections of synthetic or natural DNA sequences. Recent studies have demonstrated the use of these novel PBM approaches to provide rapid and unbiased characterization of DNA-binding proteins as molecular markers of disease, for example cancer progression or infectious diseases.

Concordance among digital gene expression, microarrays, and qPCR when measuring differential expression of microRNAs.

Profiling microRNA (miRNA) expression is of widespread interest given the critical role of miRNAs in many cellular functions. Profiling can be achieved via hybridization-based (microarrays), sequencing-based, or amplification-based (quantitative reverse transcription-PCR, qPCR) technologies. Among these, microarrays face the significant challenge of accurately distinguishing between mature and immature miRNA forms, and different vendors have developed different methods to meet this challenge. Here we measure differential miRNA expression using the Affymetrix, Agilent, and Illumina microarray platforms, as well as qPCR (Applied Biosystems) and ultra high-throughput sequencing (Illumina). We show that the differential expression measurements are more divergent when the three types of microarrays are compared than when the Agilent microarray, qPCR, and sequencing technology measurements are compared, which exhibit a good overall concordance.

Gene Expression Profiling of Desmoid Tumors by cDNA Microarrays and Correlation with Progression-Free Survival.

PURPOSE: Because desmoid tumors exhibit an unpredictable clinical course, translational research is crucial to identify the predictive factors of progression in addition to the clinical parameters. The main issue is to detect patients who are at a higher risk of progression. The aim of this work was to identify molecular markers that can predict progression-free survival (PFS). EXPERIMENTAL DESIGN: Gene-expression screening was conducted on 115 available independent untreated primary desmoid tumors using cDNA microarray. We established a prognostic gene-expression signature composed of 36 genes. To test robustness, we randomly generated 1,000 36-gene signatures and compared their outcome association to our define 36-genes molecular signature and we calculated positive predictive value (PPV) and negative predictive value (NPV). RESULTS: Multivariate analysis showed that our molecular signature had a significant impact on PFS while no clinical factor had any prognostic value. Among the 1,000 random signatures generated, 56.7% were significant and none was more significant than our 36-gene molecular signature. PPV and NPV were high (75.58% and 81.82%, respectively). Finally, the top two genes downregulated in no-recurrence were FECH and STOML2 and the top gene upregulated in no-recurrence was TRIP6. CONCLUSIONS: By analyzing expression profiles, we have identified a gene-expression signature that is able to predict PFS. This tool may be useful for prospective clinical studies. Clin Cancer Res; 21(18); 4194-200. ©2015 AACR.

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Identification of prebiotic fructooligosaccharide metabolism in Lactobacillus plantarum WCFS1 through microarrays

Short-chain fructooligosaccharides (scFOS) and other prebiotics are used to selectively stimulate the growth and activity of lactobacilli and bifidobacteria in the colon. However, there is little information on the mechanisms whereby prebiotics exert their specific effects upon such microorganisms. To study the genomic basis of scFOS metabolism in Lactobacillus plantarum WCFS1, two-color microarrays were used to screen for differentially expressed genes when grown on scFOS compared to glucose (control). A significant up-regulation (8- to 60-fold) was observed with a set of only five genes located in a single locus and predicted to encode a sucrose phosphoenolpyruvate transport system (PTS), a beta-fructofuranosidase, a fructokinase, an alpha-glucosidase, and a sucrose operon repressor. Several other genes were slightly overexpressed, including pyruvate dehydrogenase. For the latter, no detectable activity in L. plantarum under various growth conditions has been previously reported. A mannose-PTS likely to encode glucose uptake was 50-fold down-regulated as well as, to a lower extent, other PTSs. Chemical analysis of the different moieties of scFOS that were depleted in the growth medium revealed that the trisaccharide 1-kestose present in scFOS was preferentially utilized, in comparison with the tetrasaccharide nystose and the pentasaccharide fructofuranosylnystose. The main end products of scFOS fermentation were lactate and acetate. This is the first example in lactobacilli of the association of a sucrose PTS and a beta-fructofuranosidase that could be used for scFOS degradation.

Pathotyping Escherichia coli by using miniaturized DNA microarrays

The detection of virulence determinants harbored by pathogenic Escherichia coli is important for establishing the pathotype responsible for infection. A sensitive and specific miniaturized virulence microarray containing 60 oligonucleotide probes was developed. It detected six E. coli pathotypes and will be suitable in the future for high-throughput use.