Transformation of metabolism with age and lifestyle in Antarctic seals: a case study of systems biology approach to cross-species microarray experiment

*_Background:_* The metabolic transformation that changes Weddell seal pups born on land into aquatic animals is not only interesting for the study of general biology, but it also provides a model for the acquired and congenital muscle disorders which are associated with oxygen metabolism in skeletal muscle. However, the analysis of gene expression in seals is hampered by the lack of specific microarrays and the very limited annotation of known Weddell seal (_Leptonychotes weddellii_) genes.

*_Results:_* Muscle samples from newborn, juvenile, and adult Weddell seals were collected during an Antarctic expedition. Extracted RNA was hybridized on Affymetrix Human Expression chips. Preliminary studies showed a detectable signal from at least 7000 probe sets present in all samples and replicates. Relative expression levels for these genes was used for further analysis of the biological pathways implicated in the metabolism transformation which occurs in the transition from newborn, to juvenile, to adult seals. Cytoskeletal remodeling, WNT signaling, FAK signaling, hypoxia-induced HIF1 activation, and insulin regulation were identified as being among the most important biological pathways involved in transformation. 

*_Conclusion:_* In spite of certain losses in specificity and sensitivity, the cross-species application of gene expression microarrays is capable of solving challenging puzzles in biology. A Systems Biology approach based on gene interaction patterns can compensate adequately for the lack of species-specific genomics information.
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THINK Back: KNowledge-based Interpretation of High Throughput data

Results of high throughput experiments can be challenging to interpret. Current approaches have relied on bulk processing the set of expression levels, in conjunction with easily obtained external evidence, such as co-occurrence. While such techniques can be used to reason probabilistically, they are not designed to shed light on what any individual gene, or a network of genes acting together, may be doing. Our belief is that today we have the information extraction ability and the computational power to perform more sophisticated analyses that consider the individual situation of each gene. The use of such techniques should lead to qualitatively superior results

Extending pathways based on gene lists using InterPro domain signatures

<p>Abstract</p> <p>Background</p> <p>High-throughput technologies like functional screens and gene expression analysis produce extended lists of candidate genes. Gene-Set Enrichment Analysis is a commonly used and well established technique to test for the statistically significant over-representation of particular pathways. A shortcoming of this method is however, that most genes that are investigated in the experiments have very sparse functional or pathway annotation and therefore cannot be the target of such an analysis. The approach presented here aims to assign lists of genes with limited annotation to previously described functional gene collections or pathways. This works by comparing InterPro domain signatures of the candidate gene lists with domain signatures of gene sets derived from known classifications, e.g. KEGG pathways.</p> <p>Results</p> <p>In order to validate our approach, we designed a simulation study. Based on all pathways available in the KEGG database, we create test gene lists by randomly selecting pathway genes, removing these genes from the known pathways and adding variable amounts of noise in the form of genes not annotated to the pathway. We show that we can recover pathway memberships based on the simulated gene lists with high accuracy. We further demonstrate the applicability of our approach on a biological example.</p> <p>Conclusion</p> <p>Results based on simulation and data analysis show that domain based pathway enrichment analysis is a very sensitive method to test for enrichment of pathways in sparsely annotated lists of genes. An R based software package <it>domainsignatures</it>, to routinely perform this analysis on the results of high-throughput screening, is available via Bioconductor.</p

Gene set enrichment meta-learning analysis: next- generation sequencing versus microarrays

Background Reproducibility of results can have a significant impact on the acceptance of new technologies in gene expression analysis. With the recent introduction of the so-called next-generation sequencing (NGS) technology and established microarrays, one is able to choose between two completely different platforms for gene expression measurements. This study introduces a novel methodology for gene-ranking stability analysis that is applied to the evaluation of gene-ranking reproducibility on NGS and microarray data. Results The same data used in a well-known MicroArray Quality Control (MAQC) study was also used in this study to compare ranked lists of genes from MAQC samples A and B, obtained from Affymetrix HG-U133 Plus 2.0 and Roche 454 Genome Sequencer FLX platforms. An initial evaluation, where the percentage ofoverlapping genes was observed, demonstrates higher reproducibility on microarray data in 10 out of 11 gene-ranking methods. A gene set enrichment analysis shows similar enrichment of top gene sets when NGS is compared with microarrays on a pathway level. Our novel approach demonstrates high accuracy of decision trees when used for knowledge extraction from multiple bootstrapped gene set enrichment analysis runs. A comparison of the two approaches in sample preparation for high-throughput sequencing shows that alternating decision trees represent the optimal knowledge representation method in comparison with classical decision trees. Conclusions Usual reproducibility measurements are mostly based on statistical techniques that offer very limited biological insights into the studied gene expression data sets. This paper introduces the meta-learning-based gene set enrichment analysis that can be used to complement the analysis of gene-ranking stabilityestimation techniques such as percentage of overlapping genes or classic gene set enrichment analysis. It is useful and practical when reproducibility of gene ranking results or different gene selection techniquesis observed. The proposed method reveals very accurate descriptive models that capture the co-enrichment of gene sets which are differently enriched in the compared data sets

ROAST: rotation gene set tests for complex microarray experiments

Motivation: A gene set test is a differential expression analysis in which a P-value is assigned to a set of genes as a unit. Gene set tests are valuable for increasing statistical power, organizing and interpreting results and for relating expression patterns across different experiments. Existing methods are based on permutation. Methods that rely on permutation of probes unrealistically assume independence of genes, while those that rely on permutation of sample are suitable only for two-group comparisons with a good number of replicates in each group

Intra- and Inter-Individual Variance of Gene Expression in Clinical Studies

BACKGROUND: Variance in microarray studies has been widely discussed as a critical topic on the identification of differentially expressed genes; however, few studies have addressed the influence of estimating variance. METHODOLOGY/PRINCIPAL FINDINGS: To break intra- and inter-individual variance in clinical studies down to three levels--technical, anatomic, and individual--we designed experiments and algorithms to investigate three forms of variances. As a case study, a group of "inter-individual variable genes" were identified to exemplify the influence of underestimated variance on the statistical and biological aspects in identification of differentially expressed genes. Our results showed that inadequate estimation of variance inevitably led to the inclusion of non-statistically significant genes into those listed as significant, thereby interfering with the correct prediction of biological functions. Applying a higher cutoff value of fold changes in the selection of significant genes reduces/eliminates the effects of underestimated variance. CONCLUSIONS/SIGNIFICANCE: Our data demonstrated that correct variance evaluation is critical in selecting significant genes. If the degree of variance is underestimated, "noisy" genes are falsely identified as differentially expressed genes. These genes are the noise associated with biological interpretation, reducing the biological significance of the gene set. Our results also indicate that applying a higher number of fold change as the selection criteria reduces/eliminates the differences between distinct estimations of variance

Transcriptional profiling of the ductus arteriosus: Comparison of rodent microarrays and human RNA sequencing

DA closure is crucial for the transition from fetal to neonatal life. This closure is supported by changes to the DA’s signaling and structural properties that distinguish it from neighboring vessels. Examining transcriptional differences between these vessels is key to identifying genes or pathways responsible for DA closure. Several microarray studies have explored the DA transcriptome in animal models but varied experimental designs have led to conflicting results. Thorough transcriptomic analysis of the human DA has yet to be performed. A clear picture of the DA transcriptome is key to guiding future research endeavors, both to allow more targeted treatments in the clinical setting, and to understand the basic biology of DA function. In this review, we use a cross-species cross-platform analysis to consider all available published rodent microarray data and novel human RNAseq data in order to provide high priority candidate genes for consideration in future DA studies

Gene-expression of metastasized versus non-metastasized primary head and neck squamous cell carcinomas: A pathway-based analysis

Background: Regional lymph node metastasis is an important prognostic factor in head and neck squamous cell carcinoma (HNSCC) and plays a decisive role in the choice of treatment. Here, we present an independent gene expression validation study of metastasized versus non-metastasized HNSCC. Methods: We used a dataset recently published by Roepman et al. as reference dataset and an independent gene expression dataset of 11 metastasized and 11 non-metastasized HNSCC tumors as validation dataset. Reference and validation studies were performed on different microarray platforms with different probe sets and probe content. In addition to a supervised gene-based analysis, a supervised pathway-based analysis was performed, evaluating differences in gene expression for predefined tumorigenesis- and metastasis related gene sets. Results: The gene-based analysis showed 26 significant differentially expressed genes in the reference dataset, 21 of which were present on the microarray platform used in the validation study. 7 of these genes appeared to be significantly expressed in the validation dataset, but failed to pass the correction for multiple testing. The pathway-based analysis revealed 23 significant differentially expressed gene sets, 7 of which were statistically validated. These gene sets are involved in extracellular matrix remodeling (MMPs, MMP regulating pathways and the uPA system), hypoxia and angiogenesis (HIF1α regulated angiogenic factors and HIF1α regulated invasion). Conclusion: Pathways that are differentially expressed between metastasized and non-metastasized HNSCC are involved in the processes of extracellular matrix remodeling, hypoxia and angiogenesis. A supervised pathway-based analysis enhances the understanding of the biological context of the results, the comparability of results across different microarray studies, and reduces multiple testing problems by focusing on a limited number of pathways of interest instead of analyzing the large number of probes available on the microarray