The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models.

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

Functional analysis: evaluation of response intensities--tailoring ANOVA for lists of expression subsets.

Background: Microarray data is frequently used to characterize the expression profile of a whole genome and to compare the characteristics of that genome under several conditions. Geneset analysis methods have been described previously to analyze the expression values of several genes related by known biological criteria (metabolic pathway, pathology signature, co-regulation by a common factor, etc.) at the same time and the cost of these methods allows for the use of more values to help discover the underlying biological mechanisms. Results: As several methods assume different null hypotheses, we propose to reformulate the main question that biologists seek to answer. To determine which genesets are associated with expression values that differ between two experiments, we focused on three ad hoc criteria: expression levels, the direction of individual gene expression changes (up or down regulation), and correlations between genes. We introduce the FAERI methodology, tailored from a two-way ANOVA to examine these criteria. The significance of the results was evaluated according to the self-contained null hypothesis, using label sampling or by inferring the null distribution from normally distributed random data. Evaluations performed on simulated data revealed that FAERI outperforms currently available methods for each type of set tested. We then applied the FAERI method to analyze three real-world datasets on hypoxia response. FAERI was able to detect more genesets than other methodologies, and the genesets selected were coherent with current knowledge of cellular response to hypoxia. Moreover, the genesets selected by FAERI were confirmed when the analysis was repeated on two additional related datasets. Conclusions: The expression values of genesets are associated with several biological effects. The underlying mathematical structure of the genesets allows for analysis of data from several genes at the same time. Focusing on expression levels, the direction of the expression changes, and correlations, we showed that two-step data reduction allowed us to significantly improve the performance of geneset analysis using a modified two-way ANOVA procedure, and to detect genesets that current methods fail to detect.

Risk assessment of recurrence in sporadic retinoblastoma using a molecular-based algorithm.

PURPOSE: Most RB1 mutations are unique and distributed throughout the RB1 gene. Their detection can be time-consuming and the yield especially low in cases of conservatively-treated sporadic unilateral retinoblastoma (Rb) patients. In order to identify patients with true risk of developing Rb, and to reduce the number of unnecessary examinations under anesthesia in all other cases, we developed a universal sensitive, efficient and cost-effective strategy based on intragenic haplotype analysis. METHODS: This algorithm allows the calculation of the a posteriori risk of developing Rb and takes into account (a) RB1 loss of heterozygosity in tumors, (b) preferential paternal origin of new germline mutations, (c) a priori risk derived from empirical data by Vogel, and (d) disease penetrance of 90% in most cases. We report the occurrence of Rb in first degree relatives of patients with sporadic Rb who visited the Jules Gonin Eye Hospital, Lausanne, Switzerland, from January 1994 to December 2006 compared to expected new cases of Rb using our algorithm. RESULTS: A total of 134 families with sporadic Rb were enrolled; testing was performed in 570 individuals and 99 patients younger than 4 years old were identified. We observed one new case of Rb. Using our algorithm, the cumulated total a posteriori risk of recurrence was 1.77. CONCLUSIONS: This is the first time that linkage analysis has been validated to monitor the risk of recurrence in sporadic Rb. This should be a useful tool in genetic counseling, especially when direct RB1 screening for mutations leaves a negative result or is unavailable.

Cross-species and cross-platform gene expression studies with the Bioconductor-compliant R package 'annotationTools'.

Background: The variety of DNA microarray formats and datasets presently available offers an unprecedented opportunity to perform insightful comparisons of heterogeneous data. Cross-species studies, in particular, have the power of identifying conserved, functionally important molecular processes. Validation of discoveries can now often be performed in readily available public data which frequently requires cross-platform studies.Cross-platform and cross-species analyses require matching probes on different microarray formats. This can be achieved using the information in microarray annotations and additional molecular biology databases, such as orthology databases. Although annotations and other biological information are stored using modern database models ( e. g. relational), they are very often distributed and shared as tables in text files, i.e. flat file databases. This common flat database format thus provides a simple and robust solution to flexibly integrate various sources of information and a basis for the combined analysis of heterogeneous gene expression profiles.Results: We provide annotationTools, a Bioconductor-compliant R package to annotate microarray experiments and integrate heterogeneous gene expression profiles using annotation and other molecular biology information available as flat file databases. First, annotationTools contains a specialized set of functions for mining this widely used database format in a systematic manner. It thus offers a straightforward solution for annotating microarray experiments. Second, building on these basic functions and relying on the combination of information from several databases, it provides tools to easily perform cross-species analyses of gene expression data.Here, we present two example applications of annotationTools that are of direct relevance for the analysis of heterogeneous gene expression profiles, namely a cross-platform mapping of probes and a cross-species mapping of orthologous probes using different orthology databases. We also show how to perform an explorative comparison of disease-related transcriptional changes in human patients and in a genetic mouse model.Conclusion: The R package annotationTools provides a simple solution to handle microarray annotation and orthology tables, as well as other flat molecular biology databases. Thereby, it allows easy integration and analysis of heterogeneous microarray experiments across different technological platforms or species.

The peroxisome proliferator-activated receptor alpha regulates amino acid metabolism.

The peroxisome proliferator-activated receptor alpha is a ligand-activated transcription factor that plays an important role in the regulation of lipid homeostasis. PPARalpha mediates the effects of fibrates, which are potent hypolipidemic drugs, on gene expression. To better understand the biological effects of fibrates and PPARalpha, we searched for genes regulated by PPARalpha using oligonucleotide microarray and subtractive hybridization. By comparing liver RNA from wild-type and PPARalpha null mice, it was found that PPARalpha decreases the mRNA expression of enzymes involved in the metabolism of amino acids. Further analysis by Northern blot revealed that PPARalpha influences the expression of several genes involved in trans- and deamination of amino acids, and urea synthesis. Direct activation of PPARalpha using the synthetic PPARalpha ligand WY14643 decreased mRNA levels of these genes, suggesting that PPARalpha is directly implicated in the regulation of their expression. Consistent with these data, plasma urea concentrations are modulated by PPARalpha in vivo. It is concluded that in addition to oxidation of fatty acids, PPARalpha also regulates metabolism of amino acids in liver, indicating that PPARalpha is a key controller of intermediary metabolism during fasting.

Improving the sensitivity of the sequence profile method.

The sequence profile method (Gribskov M, McLachlan AD, Eisenberg D, 1987, Proc Natl Acad Sci USA 84:4355-4358) is a powerful tool to detect distant relationships between amino acid sequences. A profile is a table of position-specific scores and gap penalties, providing a generalized description of a protein motif, which can be used for sequence alignments and database searches instead of an individual sequence. A sequence profile is derived from a multiple sequence alignment. We have found 2 ways to improve the sensitivity of sequence profiles: (1) Sequence weights: Usage of individual weights for each sequence avoids bias toward closely related sequences. These weights are automatically assigned based on the distance of the sequences using a published procedure (Sibbald PR, Argos P, 1990, J Mol Biol 216:813-818). (2) Amino acid substitution table: In addition to the alignment, the construction of a profile also needs an amino acid substitution table. We have found that in some cases a new table, the BLOSUM45 table (Henikoff S, Henikoff JG, 1992, Proc Natl Acad Sci USA 89:10915-10919), is more sensitive than the original Dayhoff table or the modified Dayhoff table used in the current implementation. Profiles derived by the improved method are more sensitive and selective in a number of cases where previous methods have failed to completely separate true members from false positives.

Test of four colon cancer risk-scores in formalin fixed paraffin embedded microarray gene expression data.

BACKGROUND: Prognosis prediction for resected primary colon cancer is based on the T-stage Node Metastasis (TNM) staging system. We investigated if four well-documented gene expression risk scores can improve patient stratification. METHODS: Microarray-based versions of risk-scores were applied to a large independent cohort of 688 stage II/III tumors from the PETACC-3 trial. Prognostic value for relapse-free survival (RFS), survival after relapse (SAR), and overall survival (OS) was assessed by regression analysis. To assess improvement over a reference, prognostic model was assessed with the area under curve (AUC) of receiver operating characteristic (ROC) curves. All statistical tests were two-sided, except the AUC increase. RESULTS: All four risk scores (RSs) showed a statistically significant association (single-test, P < .0167) with OS or RFS in univariate models, but with HRs below 1.38 per interquartile range. Three scores were predictors of shorter RFS, one of shorter SAR. Each RS could only marginally improve an RFS or OS model with the known factors T-stage, N-stage, and microsatellite instability (MSI) status (AUC gains < 0.025 units). The pairwise interscore discordance was never high (maximal Spearman correlation = 0.563) A combined score showed a trend to higher prognostic value and higher AUC increase for OS (HR = 1.74, 95% confidence interval [CI] = 1.44 to 2.10, P < .001, AUC from 0.6918 to 0.7321) and RFS (HR = 1.56, 95% CI = 1.33 to 1.84, P < .001, AUC from 0.6723 to 0.6945) than any single score. CONCLUSIONS: The four tested gene expression-based risk scores provide prognostic information but contribute only marginally to improving models based on established risk factors. A combination of the risk scores might provide more robust information. Predictors of RFS and SAR might need to be different.

Causal relationship between obesity and vitamin D status: bi-directional Mendelian randomization analysis of multiple cohorts.

BACKGROUND: Obesity is associated with vitamin D deficiency, and both are areas of active public health concern. We explored the causality and direction of the relationship between body mass index (BMI) and 25-hydroxyvitamin D [25(OH)D] using genetic markers as instrumental variables (IVs) in bi-directional Mendelian randomization (MR) analysis. METHODS AND FINDINGS: We used information from 21 adult cohorts (up to 42,024 participants) with 12 BMI-related SNPs (combined in an allelic score) to produce an instrument for BMI and four SNPs associated with 25(OH)D (combined in two allelic scores, separately for genes encoding its synthesis or metabolism) as an instrument for vitamin D. Regression estimates for the IVs (allele scores) were generated within-study and pooled by meta-analysis to generate summary effects. Associations between vitamin D scores and BMI were confirmed in the Genetic Investigation of Anthropometric Traits (GIANT) consortium (n = 123,864). Each 1 kg/m(2) higher BMI was associated with 1.15% lower 25(OH)D (p = 6.52×10⁻²⁷). The BMI allele score was associated both with BMI (p = 6.30×10⁻⁶²) and 25(OH)D (-0.06% [95% CI -0.10 to -0.02], p = 0.004) in the cohorts that underwent meta-analysis. The two vitamin D allele scores were strongly associated with 25(OH)D (p≤8.07×10⁻⁵⁷ for both scores) but not with BMI (synthesis score, p = 0.88; metabolism score, p = 0.08) in the meta-analysis. A 10% higher genetically instrumented BMI was associated with 4.2% lower 25(OH)D concentrations (IV ratio: -4.2 [95% CI -7.1 to -1.3], p = 0.005). No association was seen for genetically instrumented 25(OH)D with BMI, a finding that was confirmed using data from the GIANT consortium (p≥0.57 for both vitamin D scores). CONCLUSIONS: On the basis of a bi-directional genetic approach that limits confounding, our study suggests that a higher BMI leads to lower 25(OH)D, while any effects of lower 25(OH)D increasing BMI are likely to be small. Population level interventions to reduce BMI are expected to decrease the prevalence of vitamin D deficiency.

Growth Arrest-Specific Gene 6 product modulates innate immunity in sepsis

Background: Growth Arrest-Specific Gene 6 product (Gas6) is, like anticoagulant protein C, a vitamin K-dependent protein. Our aim was to determine whether Gas6 plays a role in sepsis. Materials and methods: We submitted mice lacking Gas6 (Gas6)/)) or one of its receptors (Axl)/), Tyro3)/) or Mertk)/)) to LPS-induced endotoxemia and peritonitis (cecal ligation and puncture (CLP) and inoculation of E. coli). In addition, we measured Gas6 or its soluble receptors in plasma of eight volunteers that received LPS, 13 healthy subjects, 28 patients with severe sepsis, and 18 patients with non-infectious inflammatory diseases. Results: Gas6 and its soluble receptor sAxl raised in mice models and TNF-a was more elevated in Gas6)/) mice than in wild-type (WT). Protein array showed that before and after LPS injection, titers of 62 cytokines were more elevated in plasma of Gas6)/) than WT mice. Endotoxemia-induced mortality was higher in Gas6)/), Axl)/), Tyro3)/) and Mertk)/) compared to WT mice and mortality subsequent to CLP was amplified in Gas6)/) mice. LPS-stimulated Gas6)/) macrophages produced more cytokines than WT macrophages. This production was dampened by recombinant Gas6. Phosphorylation of Akt in Gas6)/) macrophages was reduced, but p38 phosphorylation and NF-jB translocation were increased. In human, Gas6 raised in plasma after LPS (2 ng/kg). Gas6 and sAxl were higher in patients with severe sepsis than in healthy subjects or control patients, and there was a non-significant trend for higher Gas6 in the survival group. Conclusions: Our data point to Gas6 as a major modulator of innate immunity and provide thereby novel insights into the mechanism of sepsis. Thus Gas6 and its receptors might constitute potential therapeutic targets for the development of new immunomodulating drugs.

Comparative protein profiling identifies elongation factor-1beta and tryparedoxin peroxidase as factors associated with metastasis in Leishmania guyanensis.

Parasites of the Leishmania Viannia subgenus are major causative agents of mucocutaneous leishmaniasis (MCL), a disease characterised by parasite dissemination (metastasis) from the original cutaneous lesion to form debilitating secondary lesions in the nasopharyngeal mucosa. We employed a protein profiling approach to identify potential metastasis factors in laboratory clones of L. (V.) guyanensis with stable phenotypes ranging from highly metastatic (M+) through infrequently metastatic (M+/M-) to non-metastatic (M-). Comparison of the soluble proteomes of promastigotes by two-dimensional electrophoresis revealed two abundant protein spots specifically associated with M+ and M+/M- clones (Met2 and Met3) and two others exclusively expressed in M- parasites (Met1 and Met4). The association between clinical disease phenotype and differential expression of Met1-Met4 was less clear in L. Viannia strains from mucosal (M+) or cutaneous (M-) lesions of patients. Identification of Met1-Met4 by biological mass spectrometry (LC-ES-MS/MS) and bioinformatics revealed that M+ and M- clones express distinct acidic and neutral isoforms of both elongation factor-1 subunit beta (EF-1beta) and cytosolic tryparedoxin peroxidase (TXNPx). This interchange of isoforms may relate to the mechanisms by which the activities of EF-1beta and TXNPx are modulated, and/or differential post-translational modification of the gene product(s). The multiple metabolic functions of EF-1 and TXNPx support the plausibility of their participation in parasite survival and persistence and thereby, metastatic disease. Both polypeptides are active in resistance to chemical and oxidant stress, providing a basis for further elucidation of the importance of antioxidant defence in the pathogenesis underlying MCL.

Identification of CT46/HORMAD1, an immunogenic cancer/testis antigen encoding a putative meiosis-related protein.

Transcripts with ESTs derived exclusively or predominantly from testis, and not from other normal tissues, are likely to be products of genes with testis-restricted expression, and are thus potential cancer/testis (CT) antigen genes. A list of 371 genes with such characteristics was compiled by analyzing publicly available EST databases. RT-PCR analysis of normal and tumor tissues was performed to validate an initial selection of 20 of these genes. Several new CT and CT-like genes were identified. One of these, CT46/HORMAD1, is expressed strongly in testis and weakly in placenta; the highest level of expression in other tissues is &lt;1% of testicular expression. The CT46/HORMAD1 gene was expressed in 31% (34/109) of the carcinomas examined, with 11% (12/109) showing expression levels &gt;10% of the testicular level of expression. CT46/HORMAD1 is a single-copy gene on chromosome 1q21.3, encoding a putative protein of 394 aa. Conserved protein domain analysis identified a HORMA domain involved in chromatin binding. The CT46/HORMAD1 protein was found to be homologous to the prototype HORMA domain-containing protein, Hop1, a yeast meiosis-specific protein, as well as to asy1, a meiotic synaptic mutant protein in Arabidopsis thaliana.

Genome-wide arrays in routine diagnostics of hematological malignancies.

Over the last three decades, cytogenetic analysis of malignancies has become an integral part of disease evaluation and prediction of prognosis or responsiveness to therapy. In most diagnostic laboratories, conventional karyotyping, in conjunction with targeted fluorescence in situ hybridization analysis, is routinely performed to detect recurrent aberrations with prognostic implications. However, the genetic complexity of cancer cells requires a sensitive genome-wide analysis, enabling the detection of small genomic changes in a mixed cell population, as well as of regions of homozygosity. The advent of comprehensive high-resolution genomic tools, such as molecular karyotyping using comparative genomic hybridization or single-nucleotide polymorphism microarrays, has overcome many of the limitations of traditional cytogenetic techniques and has been used to study complex genomic lesions in, for example, leukemia. The clinical impact of the genomic copy-number and copy-neutral alterations identified by microarray technologies is growing rapidly and genome-wide array analysis is evolving into a diagnostic tool, to better identify high-risk patients and predict patients' outcomes from their genomic profiles. Here, we review the added clinical value of an array-based genome-wide screen in leukemia, and discuss the technical challenges and an interpretation workflow in applying arrays in the acquired cytogenetic diagnostic setting.

Associations of serum EBV DNA and gammopathy with post-transplant lymphoproliferative disease.

BACKGROUND: Post-transplant lymphoproliferative disease (PTLD) is a life-threatening complication of immunosuppression following transplantation. Epstein-Barr virus (EBV) and gammopathy in serum are associated with PTLD, but these two parameters have not been evaluated in parallel for their association with PTLD. METHODS: We evaluated the incidence of EBV load positivity, gammopathy, and protein expression in sera from all PTLD patients diagnosed at our hospital during the past seven yr. Results were compared with those of a control group including matched transplanted patients who did not develop PTLD. RESULTS: Seven of 10 PTLD patients presented EBV(+) PTLD, for which five patients had detectable serum EBV DNA levels compared with none of 38 controls (RR between two groups =121, p &lt; 0.0001). Five out of 10 patients had gammopathy at PTLD diagnosis compared with 5/38 controls (RR between two groups = 6.6, p = 0.022). Additionally, protein serum analysis by high-resolution two-dimensional gel electrophoresis and image examination failed to evidence specific abnormality in patients with PTLD compared with controls. CONCLUSIONS: Our results confirm an association between EBV in sera and gammopathy with PTLD, and highlight the high specificity of the former analysis. Whether a combination of both analyses will improve the clinical detection of PTLD remains to be evaluated in a larger prospective cohort study.

Bioinformatics-driven identification and examination of candidate genes for non-alcoholic fatty liver disease.

ObjectiveCandidate genes for non-alcoholic fatty liver disease (NAFLD) identified by a bioinformatics approach were examined for variant associations to quantitative traits of NAFLD-related phenotypes.Research Design and MethodsBy integrating public database text mining, trans-organism protein-protein interaction transferal, and information on liver protein expression a protein-protein interaction network was constructed and from this a smaller isolated interactome was identified. Five genes from this interactome were selected for genetic analysis. Twenty-one tag single-nucleotide polymorphisms (SNPs) which captured all common variation in these genes were genotyped in 10,196 Danes, and analyzed for association with NAFLD-related quantitative traits, type 2 diabetes (T2D), central obesity, and WHO-defined metabolic syndrome (MetS).Results273 genes were included in the protein-protein interaction analysis and EHHADH, ECHS1, HADHA, HADHB, and ACADL were selected for further examination. A total of 10 nominal statistical significant associations (P<0.05) to quantitative metabolic traits were identified. Also, the case-control study showed associations between variation in the five genes and T2D, central obesity, and MetS, respectively. Bonferroni adjustments for multiple testing negated all associations.ConclusionsUsing a bioinformatics approach we identified five candidate genes for NAFLD. However, we failed to provide evidence of associations with major effects between SNPs in these five genes and NAFLD-related quantitative traits, T2D, central obesity, and MetS.