Performance comparison of multiple microarray platforms for gene expression profiling

Identification of a SNP cluster associated with taxane-induced peripheral neuropathy risk in patients being treated for breast cancer using GWAS data derived from a large cooperative group trial

BACKGROUND

Chemotherapy-induced peripheral neuropathy (CIPN) is a common toxicity of taxanes for which there is no effective intervention. Genomic CIPN risk determination has yielded promising, but inconsistent results. The present study assessed the utility of a collective SNP cluster identified using novel analytics to describe taxane-associated CIPN risk.

METHODS

We analyzed GWAS data derived from ECOG-5103, first identifying SNPs that were most strongly associated with CIPN using Fisher's ratio (FR). We then ranked ordered those SNPs which discriminated CIPN-positive (CIPN +) from CIPN-negative phenotypes based on their discriminatory power and developed the cluster of SNPs which provided the highest predictive accuracy using leave-one-out cross-validation (LOOCV).

RESULTS

Using aggregated genotype data obtained from the previously reported ECOG-5103 clinical trial (in which two different arrays were used, HumanOmniExpress (727,227 SNPs) and HumanOmni1-Quad1 (1,131,857 SNPs)), we identified a 267 SNP cluster which was associated with a CIPN + phenotype with an accuracy of 96.1%.

CONCLUSIONS

A cluster of SNPs was identified which prospectively discriminated patients most likely to develop symptomatic CIPN following taxane exposure as part of a breast cancer chemotherapy regimen. Validation using an independent patient cohort should be performed.

Network analysis in aged C. elegans reveals candidate regulatory genes of ageing

Ageing is a biological process guided by genetic and environmental factors that ultimately lead to adverse outcomes for organismal lifespan and healthspan. Determination of molecular pathways that are affected with age and increase disease susceptibility is crucial. The gene expression profile of the ideal ageing model, namely the nematode Caenorhabditis elegans mapped with the microarray technology initially led to the identification of age-dependent gene expression alterations that characterize the nematode's ageing process. The list of differentially expressed genes was then utilized to construct a network of molecular interactions with their first neighbors/interactors using the interactions listed in the WormBase database. The subsequent network analysis resulted in the unbiased selection of 110 candidate genes, among which well-known ageing regulators appeared. More importantly, our approach revealed candidates that have never been linked to ageing before, thus suggesting promising potential targets/ageing regulators.

'Omic' technologies as a helpful tool in radioecological research

This article presents a brief review of the modern 'omic' technologies, namely genomics, epigenomics, transcriptomics, proteomics, and metabolomics, as well as the examples of their possible use in radioecology. For each technology, a short description of advances, limitations, and instrumental applications is given. In addition, the review contains examples of successful use of 'omic' technologies in the assessment of biological effects of pollutants in the field conditions.

Cross-platform comparison of independent datasets identifies an immune signature associated with improved survival in metastatic melanoma

Platform and study differences in prognostic signatures from metastatic melanoma (MM) gene expression reports often hinder consensus arrival. We performed survival/outcome-based pairwise comparisons of three independent MM gene expression profiles using the threshold-free algorithm rank-rank hypergeometric overlap analysis (RRHO). We found statistically significant overlap for genes overexpressed in favorable outcome (FO) groups, but no overlap for poor outcome (PO) groups. This “favorable outcome signature” (FOS) of 228 genes coinciding on all three overlapping gene lists showed immune function predominated in FO MM. Surprisingly, specific cell signature-enrichment analysis showed B cell-associated genes enriched in FO MM, along with T cell-associated genes. Higher levels of B and T cells (p<0.05) and their relative proximity (p<0.05) were detected in FO-to-PO tumor comparisons from an independent MM patients cohort. Finally, expression of FOS in two independent Stage III MM tumor datasets correctly predicted clinical outcome in 12/14 and 44/70 patients using a weighted gene voting classifier (area under the curve values 0.96 and 0.75, respectively). This RRHO-based, cross-study analysis emphasizes the RRHO approach power, confirms T cells relevance for prolonged MM survival, supports a favorable role for B cells in anti-melanoma immunity, and suggests B cells potential as means of intervention in melanoma treatment.

Metabolic parameters linked by phenotype microarray to acid resistance profiles of poultry-associated Salmonella enterica

Phenotype microarrays were analyzed for 51 datasets derived from Salmonella enterica. The top 4 serotypes associated with poultry products and one associated with turkey, respectively Typhimurium, Enteritidis, Heidelberg, Infantis and Senftenberg, were represented. Datasets were partitioned initially into two clusters based on ranking by values at pH 4.5 (PM10 A03). Negative control wells were used to establish 90 respiratory units as the point differentiating acid resistance from sensitive strains. Thus, 24 isolates that appeared most acid-resistant were compared initially to 27 that appeared most acid-sensitive (24 × 27 format). Paired cluster analysis was also done and it included the 7 most acid-resistant and -sensitive datasets (7 × 7 format). Statistical analyses of ranked data were then calculated in order of standard deviation, probability value by the Student's t-test and a measure of the magnitude of difference called effect size. Data were reported as significant if, by order of filtering, the following parameters were calculated: i) a standard deviation of 24 respiratory units or greater from all datasets for each chemical, ii) a probability value of less than or equal to 0.03 between clusters and iii) an effect size of at least 0.50 or greater between clusters. Results suggest that between 7.89% and 23.16% of 950 chemicals differentiated acid-resistant isolates from sensitive ones, depending on the format applied. Differences were more evident at the extremes of phenotype using the subset of data in the paired 7 × 7 format. Results thus provide a strategy for selecting compounds for additional research, which may impede the emergence of acid-resistant Salmonella enterica in food.

Development and validation of a custom microarray for global transcriptome profiling of the fungus Aspergillus nidulans

Transcriptome profiling is a powerful tool for identifying gene networks from whole genome expression analysis in many living species. Here is described the first extensively characterized platform using Agilent microarray technology for transcriptome analysis in the filamentous fungus Aspergillus (Emericella) nidulans. We developed and validated a reliable gene expression microarray in 8 × 15 K format, with predictive and experimental data establishing its specificity and sensitivity. Either one or two 60-mer oligonucleotide probes were selected for each of 10,550 nuclear as well as 20 mitochondrial coding sequences. More than 99 % of probes were predicted to hybridize with 100 % identity to their aimed specific A. nidulans target only. Probe sensitivity was supported by a highly narrow distribution of melting temperatures together with thermodynamic features, which strongly favored probe-target perfect match hybridization, in comparison with predicted secondary structures. Array quality was evaluated through transcriptome comparison of two A. nidulans strains, differing by the presence or not of Escherichia coli LacZ transgene. High signal-to-noise ratios were measured, and signal reproducibility was established at intra-probe and inter-probe levels. Reproducibility of microarray performances was assessed by high correlation between two-color dye signals and between technical replicates. Results were confirmed by RT-qPCR analysis on five genes. Though it covers 100 % of the A. nidulans targeted coding sequences, this low density array allows limited experimental costs and simplified data analysis process, making it suitable for studying gene expression in this model organism through large numbers of experimental conditions, in basic, biomedical or industrial microbiology research fields.

Analyzing the gene expression profile of anaplastic histology Wilms' tumor with real-time polymerase chain reaction arrays

Background

Wilms’ tumor (WT) is one of the most common malignant neoplasms of the urinary tract in children. Anaplastic histology (unfavorable histology) accounts for about 10% of whole WTs, and it is the single most important histologic predictor of treatment response and survival in patients with WT; however, until now the molecular basis of this phenotype is not very clearly.

Methods

A real-time polymerase chain reaction (PCR) array was designed and tested. Next, the gene expression profile of pediatric anaplastic histology WT and normal adjacent tissues were analyzed. These expression data were anlyzed with Multi Experiment View (MEV) cluster software further. Datasets representing genes with altered expression profiles derived from cluster analyses were imported into the Ingenuity Pathway Analysis Tool (IPA).

Results

88 real-time PCR primer pairs for quantitative gene expression analysis of key genes involved in pediatric anaplastic histology WT were designed and tested. The gene expression profile of pediatric anaplastic histology WT is significantly different from adjacent normal controls; we identified 15 genes that are up-regulated and 16 genes that are down-regulated in the former. To investigate biological interactions of these differently regulated genes, datasets representing genes with altered expression profiles were imported into the IPA for further analysis, which revealed three significant networks: Cancer, Hematological Disease, and Gene Expression, which included 27 focus molecules and a significance score of 43. The IPA analysis also grouped the differentially expressed genes into biological mechanisms related to Cell Death and Survival 1.15E⁻¹², Cellular Development 2.84E⁻¹¹, Cellular Growth and Proliferation 2.84E^-11, Gene Expression 4.43E⁻¹⁰, and DNA Replication, Recombination, and Repair 1.39E⁻⁰⁷. The important upstream regulators of pediatric anaplastic histology WT were TP53 and TGFβ1 signaling (P = 1.15E⁻¹⁴ and 3.79E⁻¹³, respectively).

Conclusions

Our study demonstrates that the gene expression profile of pediatric anaplastic histology WT is significantly different from adjacent normal tissues with real-time PCR array. We identified some genes that are dysregulated in pediatric anaplastic histology WT for the first time, such as HDAC7, and IPA analysis showed the most important pathways for pediatric anaplastic histology WT are TP53 and TGFβ1 signaling. This work may provide new clues into the molecular mechanisms behind pediatric anaplastic histology WT.

Electronic supplementary material

The online version of this article (doi:10.1186/s12935-015-0197-x) contains supplementary material, which is available to authorized users.

Inferring Boolean network states from partial information

Networks of molecular interactions regulate key processes in living cells. Therefore, understanding their functionality is a high priority in advancing biological knowledge. Boolean networks are often used to describe cellular networks mathematically and are fitted to experimental datasets. The fitting often results in ambiguities since the interpretation of the measurements is not straightforward and since the data contain noise. In order to facilitate a more reliable mapping between datasets and Boolean networks, we develop an algorithm that infers network trajectories from a dataset distorted by noise. We analyze our algorithm theoretically and demonstrate its accuracy using simulation and microarray expression data.

A novel gene expression index (GEI) with software support for comparing microarray gene signatures

This study was aimed to examine the validity of commonly used statistical tests for comparison of expression data from simulated and real gene signatures as well as pathway-characterized gene sets. A novel algorithm based on 10 sub-gradations (5 for up- and 5 for down-regulation) of fold-changes has been designed and testified using an Excel add-in software support. Our findings showed the limitations of conventional statistics for comparing the microarray gene expression data. However, the newly introduced Gene Expression Index (GEI) appeared to be more robust and straightforward for two-group comparison of normalized data. The software automation simplifies the task and the results are displayed in a comprehensive format including a color-coded bar showing the intensity of cumulative gene expression.

Experimental approaches for gene regulatory network construction: the chick as a model system

Setting up the body plan during embryonic development requires the coordinated action of many signals and transcriptional regulators in a precise temporal sequence and spatial pattern. The last decades have seen an explosion of information describing the molecular control of many developmental processes. The next challenge is to integrate this information into logic "wiring diagrams" that visualize gene actions and outputs, have predictive power and point to key control nodes. Here, we provide an experimental workflow on how to construct gene regulatory networks using the chick as model system.

Comparability and reproducibility of biomedical data

With the development of novel assay technologies, biomedical experiments and analyses have gone through substantial evolution. Today, a typical experiment can simultaneously measure hundreds to thousands of individual features (e.g. genes) in dozens of biological conditions, resulting in gigabytes of data that need to be processed and analyzed. Because of the multiple steps involved in the data generation and analysis and the lack of details provided, it can be difficult for independent researchers to try to reproduce a published study. With the recent outrage following the halt of a cancer clinical trial due to the lack of reproducibility of the published study, researchers are now facing heavy pressure to ensure that their results are reproducible. Despite the global demand, too many published studies remain non-reproducible mainly due to the lack of availability of experimental protocol, data and/or computer code. Scientific discovery is an iterative process, where a published study generates new knowledge and data, resulting in new follow-up studies or clinical trials based on these results. As such, it is important for the results of a study to be quickly confirmed or discarded to avoid wasting time and money on novel projects. The availability of high-quality, reproducible data will also lead to more powerful analyses (or meta-analyses) where multiple data sets are combined to generate new knowledge. In this article, we review some of the recent developments regarding biomedical reproducibility and comparability and discuss some of the areas where the overall field could be improved.