High-resolution microarray analysis of chromosome 20q in human colon cancer metastasis model systems

Amplification of human chromosome 20q DNA is the most frequently occurring chromosomal abnormality detected in sporadic colorectal carcinomas and shows significant correlation with liver metastases. Through comprehensive high-resolution microarray comparative genomic hybridization and microarray gene expression profiling, we have characterized chromosome 20q amplicon genes associated with human colorectal cancer metastasis in two in vitro metastasis model systems. The results revealed increasing complexity of the 20q genomic profile from the primary tumor-derived cell lines to the lymph node and liver metastasis derived cell lines. Expression analysis of chromosome 20q revealed a subset of over expressed genes residing within the regions of genomic copy number gain in all the tumor cell lines, suggesting these are Chromosome 20q copy number responsive genes. Bases on their preferential expression levels in the model system cell lines and known biological function, four of the over expressed genes mapping to the common intervals of genomic copy gain were considered the most promising candidate colorectal metastasis-associated genes. Validation of genomic copy number and expression array data was carried out on these genes, with one gene, DNMT3B, standing out as expressed at a relatively higher levels in the metastasis-derived cell lines compared with their primary-derived counterparts in both the models systems analyzed. The data provide evidence for the role of chromosome 20q genes with low copy gain and elevated expression in the clonal evolution of metastatic cells and suggests that such genes may serve as early biomarkers of metastatic potential. The data also support the utility of the combined microarray comparative genomic hybridization and expression array analysis for identifying copy number responsive genes in areas of low DNA copy gain in cancer cells. ^

Knowledge-based analysis of microarray gene expression data by using support vector machines

We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of gene function to identify unknown genes of similar function from expression data. SVMs avoid several problems associated with unsupervised clustering methods, such as hierarchical clustering and self-organizing maps. SVMs have many mathematical features that make them attractive for gene expression analysis, including their flexibility in choosing a similarity function, sparseness of solution when dealing with large data sets, the ability to handle large feature spaces, and the ability to identify outliers. We test several SVMs that use different similarity metrics, as well as some other supervised learning methods, and find that the SVMs best identify sets of genes with a common function using expression data. Finally, we use SVMs to predict functional roles for uncharacterized yeast ORFs based on their expression data.

Transcriptomic Analysis of the Host Response and Innate Resilience to Enterotoxigenic Escherichia coli Infection in Humans.

BACKGROUND: Enterotoxigenic Escherichia coli (ETEC) is a globally prevalent cause of diarrhea. Though usually self-limited, it can be severe and debilitating. Little is known about the host transcriptional response to infection. We report the first gene expression analysis of the human host response to experimental challenge with ETEC. METHODS: We challenged 30 healthy adults with an unattenuated ETEC strain, and collected serial blood samples shortly after inoculation and daily for 8 days. We performed gene expression analysis on whole peripheral blood RNA samples from subjects in whom severe symptoms developed (n = 6) and a subset of those who remained asymptomatic (n = 6) despite shedding. RESULTS: Compared with baseline, symptomatic subjects demonstrated significantly different expression of 406 genes highlighting increased immune response and decreased protein synthesis. Compared with asymptomatic subjects, symptomatic subjects differentially expressed 254 genes primarily associated with immune response. This comparison also revealed 29 genes differentially expressed between groups at baseline, suggesting innate resilience to infection. Drug repositioning analysis identified several drug classes with potential utility in augmenting immune response or mitigating symptoms. CONCLUSIONS: There are statistically significant and biologically plausible differences in host gene expression induced by ETEC infection. Differential baseline expression of some genes may indicate resilience to infection.

Specific JAK2 mutation (JAK2R683) and multiple gene deletions in Down syndrome acute lymphoblastic leukemia

Children with Down syndrome (DS) have a greatly increased risk of acute megakaryoblastic leukemia (AMKL) and acute lymphoblastic leukemia (ALL). Both DS-AMKL and the related transient myeloproliferative disorder (TMD) have GATA1 mutations as obligatory, early events. To identify mutations contributing to leukemogenesis in DS-ALL, we undertook sequencing of candidate genes, including FLT3, RAS, PTPN11, BRAF, and JAK2. Sequencing of the JAK2 pseudokinase domain identified a specific, acquired mutation, JAK2R683, in 12 (28%) of 42 DS-ALL cases. Functional studies of the common JAK2R683G mutation in murine Ba/F3 cells showed growth factor independence and constitutive activation of the JAK/STAT signaling pathway. High-resolution SNP array analysis of 9 DS-ALL cases identified additional submicroscopic deletions in key genes, including ETV6, CDKN2A, and PAX5. These results infer a complex molecular pathogenesis for DS-ALL leukemogenesis, with trisomy 21 as an initiating or first hit and with chromosome aneuploidy, gene deletions, and activating JAK2 mutations as complementary genetic events. (Blood. 2009; 113: 646-648)

Phylogenetic Analysis Of The Microbial Community In Hypersaline Petroleum Produced Water From The Campos Basin.

In this work the archaea and eubacteria community of a hypersaline produced water from the Campos Basin that had been transported and discharged to an onshore storage facility was evaluated by 16S recombinant RNA (rRNA) gene sequence analysis. The produced water had a hypersaline salt content of 10 (w/v), had a carbon oxygen demand (COD) of 4,300 mg/l and contains phenol and other aromatic compounds. The high salt and COD content and the presence of toxic phenolic compounds present a problem for conventional discharge to open seawater. In previous studies, we demonstrated that the COD and phenolic content could be largely removed under aerobic conditions, without dilution, by either addition of phenol degrading Haloarchaea or the addition of nutrients alone. In this study our goal was to characterize the microbial community to gain further insight into the persistence of reservoir community members in the produced water and the potential for bioremediation of COD and toxic contaminants. Members of the archaea community were consistent with previously identified communities from mesothermic reservoirs. All identified archaea were located within the phylum Euryarchaeota, with 98 % being identified as methanogens while 2 % could not be affiliated with any known genus. Of the identified archaea, 37 % were identified as members of the strictly carbon-dioxide-reducing genus Methanoplanus and 59 % as members of the acetoclastic genus Methanosaeta. No Haloarchaea were detected, consistent with the need to add these organisms for COD and aromatic removal. Marinobacter and Halomonas dominated the eubacterial community. The presence of these genera is consistent with the ability to stimulate COD and aromatic removal with nutrient addition. In addition, anaerobic members of the phyla Thermotogae, Firmicutes, and unclassified eubacteria were identified and may represent reservoir organisms associated with the conversion hydrocarbons to methane.

Identification Of Target Genes Using Gene Expression Profile Of Granulocytes From Patients With Chronic Myeloid Leukemia Treated With Tyrosine Kinase Inhibitors.

Differential gene expression analysis by suppression subtractive hybridization with correlation to the metabolic pathways involved in chronic myeloid leukemia (CML) may provide a new insight into the pathogenesis of CML. Among the overexpressed genes found in CML at diagnosis are SEPT5, RUNX1, MIER1, KPNA6 and FLT3, while PAN3, TOB1 and ITCH were decreased when compared to healthy volunteers. Some genes were identified and involved in CML for the first time, including TOB1, which showed a low expression in patients with CML during tyrosine kinase inhibitor treatment with no complete cytogenetic response. In agreement, reduced expression of TOB1 was also observed in resistant patients with CML compared to responsive patients. This might be related to the deregulation of apoptosis and the signaling pathway leading to resistance. Most of the identified genes were related to the regulation of nuclear factor κB (NF-κB), AKT, interferon and interleukin-4 (IL-4) in healthy cells. The results of this study combined with literature data show specific gene pathways that might be explored as markers to assess the evolution and prognosis of CML as well as identify new therapeutic targets.

Tissue Microarray Is A Reliable Method For Immunohistochemical Analysis Of Pleomorphic Adenoma.

To determine the most adequate number and size of tissue microarray (TMA) cores for pleomorphic adenoma immunohistochemical studies. Eighty-two pleomorphic adenoma cases were distributed in 3 TMA blocks assembled in triplicate containing 1.0-, 2.0-, and 3.0-mm cores. Immunohistochemical analysis against cytokeratin 7, Ki67, p63, and CD34 were performed and subsequently evaluated with PixelCount, nuclear, and microvessel software applications. The 1.0-mm TMA presented lower results than 2.0- and 3.0-mm TMAs versus conventional whole section slides. Possibly because of an increased amount of stromal tissue, 3.0-mm cores presented a higher microvessel density. Comparing the results obtained with one, two, and three 2.0-mm cores, there was no difference between triplicate or duplicate TMAs and a single-core TMA. Considering the possible loss of cylinders during immunohistochemical reactions, 2.0-mm TMAs in duplicate are a more reliable approach for pleomorphic adenoma immunohistochemical study.

Pre-processing for noise detection in gene expression classification data

Due to the imprecise nature of biological experiments, biological data is often characterized by the presence of redundant and noisy data. This may be due to errors that occurred during data collection, such as contaminations in laboratorial samples. It is the case of gene expression data, where the equipments and tools currently used frequently produce noisy biological data. Machine Learning algorithms have been successfully used in gene expression data analysis. Although many Machine Learning algorithms can deal with noise, detecting and removing noisy instances from the training data set can help the induction of the target hypothesis. This paper evaluates the use of distance-based pre-processing techniques for noise detection in gene expression data classification problems. This evaluation analyzes the effectiveness of the techniques investigated in removing noisy data, measured by the accuracy obtained by different Machine Learning classifiers over the pre-processed data.

Gene expression profile of the plant pathogen Fusarium graminearum under the antagonistic effect of Pantoea agglomerans

The pathogenic fungus Fusarium graminearum is an ongoing threat to agriculture, causing losses in grain yield and quality in diverse crops. Substantial progress has been made in the identification of genes involved in the suppression of phytopathogens by antagonistic microorganisms; however, limited information regarding responses of plant pathogens to these biocontrol agents is available. Gene expression analysis was used to identify differentially expressed transcripts of the fungal plant pathogen F. graminearum under antagonistic effect of the bacterium Pantoea agglomerans. A macroarray was constructed, using 1014 transcripts from an F. graminearum cDNA library. Probes consisted of the cDNA of F. graminearum grown in the presence and in the absence of P. agglomerans. Twenty-nine genes were either up (19) or down (10) regulated during interaction with the antagonist bacterium. Genes encoding proteins associated with fungal defense and/or virulence or with nutritional and oxidative stress responses were induced. The repressed genes coded for a zinc finger protein associated with cell division, proteins containing cellular signaling domains, respiratory chain proteins, and chaperone-type proteins. These data give molecular and biochemical evidence of response of F. graminearum to an antagonist and could help develop effective biocontrol procedures for pathogenic plant fungi.

ProbFAST: Probabilistic Functional Analysis System Tool

Background: The post-genomic era has brought new challenges regarding the understanding of the organization and function of the human genome. Many of these challenges are centered on the meaning of differential gene regulation under distinct biological conditions and can be performed by analyzing the Multiple Differential Expression (MDE) of genes associated with normal and abnormal biological processes. Currently MDE analyses are limited to usual methods of differential expression initially designed for paired analysis. Results: We proposed a web platform named ProbFAST for MDE analysis which uses Bayesian inference to identify key genes that are intuitively prioritized by means of probabilities. A simulated study revealed that our method gives a better performance when compared to other approaches and when applied to public expression data, we demonstrated its flexibility to obtain relevant genes biologically associated with normal and abnormal biological processes. Conclusions: ProbFAST is a free accessible web-based application that enables MDE analysis on a global scale. It offers an efficient methodological approach for MDE analysis of a set of genes that are turned on and off related to functional information during the evolution of a tumor or tissue differentiation. ProbFAST server can be accessed at http://gdm.fmrp.usp.br/probfast.

Gene Expression Profiles in Radiation Workers Occupationally Exposed to Ionizing Radiation

Ionizing radiation OR) imposes risks to human health and the environment. IR at low doses and low (lose rates has the potency to initiate carcinogenesis. Genotoxic environmental agents such as IR trigger a cascade of signal transduction pathways for cellular protection. In this study, using cDNA microarray technique, we monitored the gene expression profiles in lymphocytes derived from radiation-ex posed individuals (radiation workers). Physical dosimetry records on these patients indicated that the absorbed dose ranged from 0.696 to 39.088 mSv. Gene expression analysis revealed statistically significant transcriptional changes in a total of 78 genes (21 up-regulated and 57 clown-regulated) involved in several biological processes such as ubiquitin cycle (UHRF2 and PIAS1), DNA repair (LIG3, XPA, ERCC5, RAD52, DCLRE1C), cell cycle regulation/proliferation (RHOA, CABLES2, TGFB2, IL16), and stress response (GSTP1, PPP2R5A, DUSP22). Some of the genes that showed altered expression profiles in this study call be used as biomarkers for monitoring the chronic low level exposure in humans. Additionally, alterations in gene expression patterns observed in chronically exposed radiation workers reinforces the need for defining the effective radiation dose that causes immediate genetic damage as well as the long-term effects on genomic instability, including cancer.

Genetic Background of Patients from a University Medical Center in Manhattan: Implications for Personalized Medicine

Background: The rapid progress currently being made in genomic science has created interest in potential clinical applications; however, formal translational research has been limited thus far. Studies of population genetics have demonstrated substantial variation in allele frequencies and haplotype structure at loci of medical relevance and the genetic background of patient cohorts may often be complex. Methods and Findings: To describe the heterogeneity in an unselected clinical sample we used the Affymetrix 6.0 gene array chip to genotype self-identified European Americans (N = 326), African Americans (N = 324) and Hispanics (N = 327) from the medical practice of Mount Sinai Medical Center in Manhattan, NY. Additional data from US minority groups and Brazil were used for external comparison. Substantial variation in ancestral origin was observed for both African Americans and Hispanics; data from the latter group overlapped with both Mexican Americans and Brazilians in the external data sets. A pooled analysis of the African Americans and Hispanics from NY demonstrated a broad continuum of ancestral origin making classification by race/ethnicity uninformative. Selected loci harboring variants associated with medical traits and drug response confirmed substantial within-and between-group heterogeneity. Conclusion: As a consequence of these complementary levels of heterogeneity group labels offered no guidance at the individual level. These findings demonstrate the complexity involved in clinical translation of the results from genome-wide association studies and suggest that in the genomic era conventional racial/ethnic labels are of little value.

A score system for quality evaluation of RNA sequence tags: an improvement for gene expression profiling

Background: High-throughput molecular approaches for gene expression profiling, such as Serial Analysis of Gene Expression (SAGE), Massively Parallel Signature Sequencing (MPSS) or Sequencing-by-Synthesis (SBS) represent powerful techniques that provide global transcription profiles of different cell types through sequencing of short fragments of transcripts, denominated sequence tags. These techniques have improved our understanding about the relationships between these expression profiles and cellular phenotypes. Despite this, more reliable datasets are still necessary. In this work, we present a web-based tool named S3T: Score System for Sequence Tags, to index sequenced tags in accordance with their reliability. This is made through a series of evaluations based on a defined rule set. S3T allows the identification/selection of tags, considered more reliable for further gene expression analysis. Results: This methodology was applied to a public SAGE dataset. In order to compare data before and after filtering, a hierarchical clustering analysis was performed in samples from the same type of tissue, in distinct biological conditions, using these two datasets. Our results provide evidences suggesting that it is possible to find more congruous clusters after using S3T scoring system. Conclusion: These results substantiate the proposed application to generate more reliable data. This is a significant contribution for determination of global gene expression profiles. The library analysis with S3T is freely available at http://gdm.fmrp.usp.br/s3t/.S3T source code and datasets can also be downloaded from the aforementioned website.

Selection of suitable housekeeping genes for expression analysis in glioblastoma using quantitative RT-PCR

Background: Considering the broad variation in the expression of housekeeping genes among tissues and experimental situations, studies using quantitative RT-PCR require strict definition of adequate endogenous controls. For glioblastoma, the most common type of tumor in the central nervous system, there was no previous report regarding this issue. Results: Here we show that amongst seven frequently used housekeeping genes TBP and HPRT1 are adequate references for glioblastoma gene expression analysis. Evaluation of the expression levels of 12 target genes utilizing different endogenous controls revealed that the normalization method applied might introduce errors in the estimation of relative quantities. Genes presenting expression levels which do not significantly differ between tumor and normal tissues can be considered either increased or decreased if unsuitable reference genes are applied. Most importantly, genes showing significant differences in expression levels between tumor and normal tissues can be missed. We also demonstrated that the Holliday Junction Recognizing Protein, a novel DNA repair protein over expressed in lung cancer, is extremely over-expressed in glioblastoma, with a median change of about 134 fold. Conclusion: Altogether, our data show the relevance of previous validation of candidate control genes for each experimental model and indicate TBP plus HPRT1 as suitable references for studies on glioblastoma gene expression.

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.