A sequence-oriented comparison of gene expression measurements across different hybridization-based technologies

VIQoR: a web service for visually supervised protein inference and protein quantification

MOTIVATION

In quantitative bottom-up mass spectrometry (MS)-based proteomics, the reliable estimation of protein concentration changes from peptide quantifications between different biological samples is essential. This estimation is not a single task but comprises the two processes of protein inference and protein abundance summarization. Furthermore, due to the high complexity of proteomics data and associated uncertainty about the performance of these processes, there is a demand for comprehensive visualization methods able to integrate protein with peptide quantitative data including their post-translational modifications. Hence, there is a lack of a suitable tool that provides post-identification quantitative analysis of proteins with simultaneous interactive visualization.

RESULTS

In this article, we present VIQoR, a user-friendly web service that accepts peptide quantitative data of both labeled and label-free experiments and accomplishes the crucial components protein inference and summarization and interactive visualization modules, including the novel VIQoR plot. We implemented two different parsimonious algorithms to solve the protein inference problem, while protein summarization is facilitated by a well-established factor analysis algorithm called fast-FARMS followed by a weighted average summarization function that minimizes the effect of missing values. In addition, summarization is optimized by the so-called Global Correlation Indicator (GCI). We test the tool on three publicly available ground truth datasets and demonstrate the ability of the protein inference algorithms to handle shared peptides. We furthermore show that GCI increases the accuracy of the quantitative analysis in datasets with replicated design.

AVAILABILITY AND IMPLEMENTATION

VIQoR is accessible at: http://computproteomics.bmb.sdu.dk/Apps/VIQoR/. The source code is available at: https://bitbucket.org/veitveit/viqor/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A blood-based gene expression and signaling pathway analysis to differentiate between high and low grade gliomas

The aims of the present study were to undertake gene expression profiling of the blood of glioma patients to determine key genetic components of signaling pathways and to develop a panel of genes that could be used as a potential blood-based biomarker to differentiate between high and low grade gliomas, non-gliomas and control samples. In this study, blood samples were obtained from glioma patients, non-glioma and control subjects. Ten samples each were obtained from patients with high and low grade tumours, respectively, ten samples from non-glioma patients and twenty samples from control subjects. Total RNA was isolated from each sample after which first and second strand synthesis was performed. The resulting cRNA was then hybridized with the Agilent Whole Human Genome (4×44K) microarray chip according to the manufacturer's instructions. Universal Human Reference RNA and samples were labeled with Cy3 CTP and Cy5 CTP, respectively. Microarray data were analyzed by the Agilent Gene Spring 12.1V software using stringent criteria which included at least a 2-fold difference in gene expression between samples. Statistical analysis was performed using the unpaired Student's t-test with a P<0.01. Pathway enrichment was also performed, with key genes selected for validation using droplet digital polymerase chain reaction (ddPCR). The gene expression profiling indicated that were a substantial number of genes that were differentially expressed with more than a 2-fold change (P<0.01) between each of the four different conditions. We selected key genes within significant pathways that were analyzed through pathway enrichment. These key genes included regulators of cell proliferation, transcription factors, cytokines and tumour suppressor genes. In the present study, we showed that key genes involved in significant and well established pathways, could possibly be used as a potential blood-based biomarker to differentiate between high and low grade gliomas, non-gliomas and control samples.

Challenges for MicroRNA Microarray Data Analysis

Microarray is a high throughput discovery tool that has been broadly used for genomic research. Probe-target hybridization is the central concept of this technology to determine the relative abundance of nucleic acid sequences through fluorescence-based detection. In microarray experiments, variations of expression measurements can be attributed to many different sources that influence the stability and reproducibility of microarray platforms. Normalization is an essential step to reduce non-biological errors and to convert raw image data from multiple arrays (channels) to quality data for further analysis. In general, for the traditional microarray analysis, most established normalization methods are based on two assumptions: (1) the total number of target genes is large enough (>10,000); and (2) the expression level of the majority of genes is kept constant. However, microRNA (miRNA) arrays are usually spotted in low density, due to the fact that the total number of miRNAs is less than 2,000 and the majority of miRNAs are weakly or not expressed. As a result, normalization methods based on the above two assumptions are not applicable to miRNA profiling studies. In this review, we discuss a few representative microarray platforms on the market for miRNA profiling and compare the traditional methods with a few novel strategies specific for miRNA microarrays.

Systematic Review of Micro-RNA Expression in Pre-Eclampsia Identifies a Number of Common Pathways Associated with the Disease

Background

Pre-eclampsia (PE) is a complex, multi-systemic condition of pregnancy which greatly impacts maternal and perinatal morbidity and mortality. MicroRNAs (miRs) are differentially expressed in PE and may be important in helping to understand the condition and its pathogenesis.

Methods

Case-control studies investigating expression of miRs in PE were collected through a systematic literature search. Data was extracted and compared from 58 studies to identify the most promising miRs associated with PE pathogenesis and identify areas of methodology which could account for often conflicting results.

Results

Some of the most frequently differentially expressed miRs in PE include miR-210, miR-223 and miR-126/126* which associate strongly with the etiological domains of hypoxia, immunology and angiogenesis. Members of the miR-515 family belonging to the imprinted chromosome 19 miR cluster with putative roles in trophoblast invasion were also found to be differentially expressed. Certain miRs appear to associate with more severe forms of PE such as miR-210 and the immune-related miR-181a and miR-15 families. Patterns of miR expression may help pinpoint key pathways (e.g. IL-6/miR-223/STAT3) and aid in untangling the heterogeneous nature of PE. The detectable presence of many PE-associated miRs in antenatal circulatory samples suggests their usefulness as predictive biomarkers. Further progress in ascertaining the clinical value of miRs and in understanding how they might contribute to pathogenesis is predicated upon resolving current methodological challenges in studies. These include differences in diagnostic criteria, cohort characteristics, sampling technique, RNA isolation and platform-dependent variation in miR profiling.

Conclusion

Reviewing studies of PE-associated miRs has revealed their potential as informants of underlying target genes and pathways relating to PE pathogenesis. However, the incongruity in results across current studies hampers their capacity to be useful biomarkers of the condition.

Integrated analysis of the Plasmodium species transcriptome

The genome sequence available for different Plasmodium species is a valuable resource for understanding malaria parasite biology. However, comparative genomics on its own cannot fully explain all the species-specific differences which suggests that other genomic aspects such as regulation of gene expression play an important role in defining species-specific characteristics. Here, we developed a comprehensive approach to measure transcriptional changes of the evolutionary conserved syntenic orthologs during the intraerythrocytic developmental cycle across six Plasmodium species. We show significant transcriptional constraint at the mid-developmental stage of Plasmodium species while the earliest stages of parasite development display the greatest transcriptional variation associated with critical functional processes. Modeling of the evolutionary relationship based on changes in transcriptional profile reveal a phylogeny pattern of the Plasmodium species that strictly follows its mammalian hosts. In addition, the work shows that transcriptional conserved orthologs represent potential future targets for anti-malaria intervention as they would be expected to carry out key essential functions within the parasites. This work provides an integrated analysis of orthologous transcriptome, which aims to provide insights into the Plasmodium evolution thereby establishing a framework to explore complex pathways and drug discovery in Plasmodium species with broad host range.

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Comparison of variations in mRNA abundance across six different Plasmodium species.

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Transcriptional conservation and divergence of Plasmodium syntenic orthologs.

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Pattern of Plasmodium transcriptome evolution are established.

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Transcriptionally conserved orthologs represent attractive intervention targets.

Malaria remains a major public health concern despite global efforts in the fight against this disease. The intraerythrocytic stage of the malaria parasites is currently in the spotlight for anti-malarial intervention and vaccine targets. The primary goal of this study is to generate a comprehensive and directly comparable transcriptome dataset across multiple Plasmodium species originating from different hosts. We establish that specific pathways and intraerythrocytic stages are more transcriptionally diverged than others, reflecting transcriptional evolutionary diversity. We further propose a panel of transcriptionally conserved genes as potential drug targets.

MetaKTSP: a meta-analytic top scoring pair method for robust cross-study validation of omics prediction analysis

MOTIVATION

Supervised machine learning is widely applied to transcriptomic data to predict disease diagnosis, prognosis or survival. Robust and interpretable classifiers with high accuracy are usually favored for their clinical and translational potential. The top scoring pair (TSP) algorithm is an example that applies a simple rank-based algorithm to identify rank-altered gene pairs for classifier construction. Although many classification methods perform well in cross-validation of single expression profile, the performance usually greatly reduces in cross-study validation (i.e. the prediction model is established in the training study and applied to an independent test study) for all machine learning methods, including TSP. The failure of cross-study validation has largely diminished the potential translational and clinical values of the models. The purpose of this article is to develop a meta-analytic top scoring pair (MetaKTSP) framework that combines multiple transcriptomic studies and generates a robust prediction model applicable to independent test studies.

RESULTS

We proposed two frameworks, by averaging TSP scores or by combining P-values from individual studies, to select the top gene pairs for model construction. We applied the proposed methods in simulated data sets and three large-scale real applications in breast cancer, idiopathic pulmonary fibrosis and pan-cancer methylation. The result showed superior performance of cross-study validation accuracy and biomarker selection for the new meta-analytic framework. In conclusion, combining multiple omics data sets in the public domain increases robustness and accuracy of the classification model that will ultimately improve disease understanding and clinical treatment decisions to benefit patients.

AVAILABILITY AND IMPLEMENTATION

An R package MetaKTSP is available online. (http://tsenglab.biostat.pitt.edu/software.htm).

CONTACT

ctseng@pitt.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

aRrayLasso: a network-based approach to microarray interconversion

Summary: Robust conversion between microarray platforms is needed to leverage the wide variety of microarray expression studies that have been conducted to date. Currently available conversion methods rely on manufacturer annotations, which are often incomplete, or on direct alignment of probes from different platforms, which often fail to yield acceptable genewise correlation. Here, we describe aRrayLasso, which uses the Lasso-penalized generalized linear model to model the relationships between individual probes in different probe sets. We have implemented aRrayLasso in a set of five open-source R functions that allow the user to acquire data from public sources such as Gene Expression Omnibus, train a set of Lasso models on that data and directly map one microarray platform to another. aRrayLasso significantly predicts expression levels with similar fidelity to technical replicates of the same RNA pool, demonstrating its utility in the integration of datasets from different platforms.

Availability and implementation: All functions are available, along with descriptions, at https://github.com/adam-sam-brown/aRrayLasso.

Contact:chirag_patel@hms.harvard.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

Genomic-Wide Analysis with Microarrays in Human Oncology

DNA microarray technologies have advanced rapidly and had a profound impact on examining gene expression on a genomic scale in research. This review discusses the history and development of microarray and DNA chip devices, and specific microarrays are described along with their methods and applications. In particular, microarrays have detected many novel cancer-related genes by comparing cancer tissues and non-cancerous tissues in oncological research. Recently, new methods have been in development, such as the double-combination array and triple-combination array, which allow more effective analysis of gene expression and epigenetic changes. Analysis of gene expression alterations in precancerous regions compared with normal regions and array analysis in drug-resistance cancer tissues are also successfully performed. Compared with next-generation sequencing, a similar method of genome analysis, several important differences distinguish these techniques and their applications. Development of novel microarray technologies is expected to contribute to further cancer research.

Sensory ability in the narwhal tooth organ system

The erupted tusk of the narwhal exhibits sensory ability. The hypothesized sensory pathway begins with ocean water entering through cementum channels to a network of patent dentinal tubules extending from the dentinocementum junction to the inner pulpal wall. Circumpulpal sensory structures then signal pulpal nerves terminating near the base of the tusk. The maxillary division of the fifth cranial nerve then transmits this sensory information to the brain. This sensory pathway was first described in published results of patent dentinal tubules, and evidence from dissection of tusk nerve connection via the maxillary division of the fifth cranial nerve to the brain. New evidence presented here indicates that the patent dentinal tubules communicate with open channels through a porous cementum from the ocean environment. The ability of pulpal tissue to react to external stimuli is supported by immunohistochemical detection of neuronal markers in the pulp and gene expression of pulpal sensory nerve tissue. Final confirmation of sensory ability is demonstrated by significant changes in heart rate when alternating solutions of high-salt and fresh water are exposed to the external tusk surface. Additional supporting information for function includes new observations of dentinal tubule networks evident in unerupted tusks, female erupted tusks, and vestigial teeth. New findings of sexual foraging divergence documented by stable isotope and fatty acid results add to the discussion of the functional significance of the narwhal tusk. The combined evidence suggests multiple tusk functions may have driven the tooth organ system's evolutionary development and persistence.

Microarray-based RNA profiling of breast cancer: batch effect removal improves cross-platform consistency

Microarray is a powerful technique used extensively for gene expression analysis. Different technologies are available, but lack of standardization makes it challenging to compare and integrate data. Furthermore, batch-related biases within datasets are common but often not tackled. We have analyzed the same 234 breast cancers on two different microarray platforms. One dataset contained known batch-effects associated with the fabrication procedure used. The aim was to assess the significance of correcting for systematic batch-effects when integrating data from different platforms. We here demonstrate the importance of detecting batch-effects and how tools, such as ComBat, can be used to successfully overcome such systematic variations in order to unmask essential biological signals. Batch adjustment was found to be particularly valuable in the detection of more delicate differences in gene expression. Furthermore, our results show that prober adjustment is essential for integration of gene expression data obtained from multiple sources. We show that high-variance genes are highly reproducibly expressed across platforms making them particularly well suited as biomarkers and for building gene signatures, exemplified by prediction of estrogen-receptor status and molecular subtypes. In conclusion, the study emphasizes the importance of utilizing proper batch adjustment methods when integrating data across different batches and platforms.

Evaluation of high throughput gene expression platforms using a genomic biomarker signature for prediction of skin sensitization

Background

Allergic contact dermatitis (ACD) develops upon exposure to certain chemical compounds termed skin sensitizers. To reduce the occurrence of skin sensitizers, chemicals are regularly screened for their capacity to induce sensitization. The recently developed Genomic Allergen Rapid Detection (GARD) assay is an in vitro alternative to animal testing for identification of skin sensitizers, classifying chemicals by evaluating transcriptional levels of a genomic biomarker signature. During assay development and biomarker identification, genome-wide expression analysis was applied using microarrays covering approximately 30,000 transcripts. However, the microarray platform suffers from drawbacks in terms of low sample throughput, high cost per sample and time consuming protocols and is a limiting factor for adaption of GARD into a routine assay for screening of potential sensitizers. With the purpose to simplify assay procedures, improve technical parameters and increase sample throughput, we assessed the performance of three high throughput gene expression platforms - nCounter®, BioMark HD™ and OpenArray® - and correlated their performance metrics against our previously generated microarray data. We measured the levels of 30 transcripts from the GARD biomarker signature across 48 samples. Detection sensitivity, reproducibility, correlations and overall structure of gene expression measurements were compared across platforms.

Results

Gene expression data from all of the evaluated platforms could be used to classify most of the sensitizers from non-sensitizers in the GARD assay. Results also showed high data quality and acceptable reproducibility for all platforms but only medium to poor correlations of expression measurements across platforms. In addition, evaluated platforms were superior to the microarray platform in terms of cost efficiency, simplicity of protocols and sample throughput.

Conclusions

We evaluated the performance of three non-array based platforms using a limited set of transcripts from the GARD biomarker signature. We demonstrated that it was possible to achieve acceptable discriminatory power in terms of separation between sensitizers and non-sensitizers in the GARD assay while reducing assay costs, simplify assay procedures and increase sample throughput by using an alternative platform, providing a first step towards the goal to prepare GARD for formal validation and adaption of the assay for industrial screening of potential sensitizers.

Emerging technologies in extracellular vesicle-based molecular diagnostics

Extracellular vesicles (EVs), including exosomes and microvesicles, have been shown to carry a variety of biomacromolecules including mRNA, microRNA and other non-coding RNAs. Within the past 5 years, EVs have emerged as a promising minimally invasive novel source of material for molecular diagnostics. Although EVs can be easily identified and collected from biological fluids, further research and proper validation is needed in order for them to be useful in the clinical setting. In addition, innovative and more efficient means of nucleic acid profiling are needed to facilitate investigations into the cellular and molecular mechanisms of EV function and to establish their potential as useful clinical biomarkers and therapeutic tools. In this article, we provide an overview of recent technological improvements in both upstream EV isolation and downstream analytical technologies, including digital PCR and next generation sequencing, highlighting future prospects for EV-based molecular diagnostics.

Assessing the validity and reproducibility of genome-scale predictions

MOTIVATION

Validation and reproducibility of results is a central and pressing issue in genomics. Several recent embarrassing incidents involving the irreproducibility of high-profile studies have illustrated the importance of this issue and the need for rigorous methods for the assessment of reproducibility.

RESULTS

Here, we describe an existing statistical model that is very well suited to this problem. We explain its utility for assessing the reproducibility of validation experiments, and apply it to a genome-scale study of adenosine deaminase acting on RNA (ADAR)-mediated RNA editing in Drosophila. We also introduce a statistical method for planning validation experiments that will obtain the tightest reproducibility confidence limits, which, for a fixed total number of experiments, returns the optimal number of replicates for the study.

AVAILABILITY

Downloadable software and a web service for both the analysis of data from a reproducibility study and for the optimal design of these studies is provided at http://ccmbweb.ccv.brown.edu/reproducibility.html .

Hepatic gene expression in herbivores on diets with natural and novel plant secondary compounds

Herbivores are predicted to evolve appropriate mechanisms to process the plant secondary compounds (PSCs) in their diet, and these mechanisms are likely specific to particular suites of PSCs. Changes in diet composition over evolutionary time should select for appropriate alterations in metabolism of the more recent dietary components. We investigated differences in gene expression profiles in the liver with respect to prior ecological and evolutionary experience with PSCs in the desert woodrat, Neotoma lepida. This woodrat species has populations in the Mojave Desert that have switched from feeding on juniper to feeding on creosote at the end of the Holocene as well as populations in the Great Basin Desert that still feed on the ancestral diet of juniper and are naïve to creosote. Juniper and creosote have notable differences in secondary chemistry. Woodrats from the Mojave and Great Basin Deserts were subjected to a fully crossed feeding trial on diets of juniper and creosote after which their livers were analyzed for gene expression. Hybridization of hepatic mRNAs to laboratory rat microarrays resulted in a total of 20,031 genes that met quality control standards. We analyzed differences in large-scale patterns of liver gene expression with respect to GO term enrichment. Diet had a larger effect on gene expression than population membership. However, woodrats with no prior evolutionary experience to the diet upregulated a greater proportion of genes indicative of physiological stress compared with those on their natural diet. This pattern may be the result of a naïve animal's attempting to mitigate physiological damage caused by novel PSCs.

A novel method for cross-species gene expression analysis

Background

Analysis of gene expression from different species is a powerful way to identify evolutionarily conserved transcriptional responses. However, due to evolutionary events such as gene duplication, there is no one-to-one correspondence between genes from different species which makes comparison of their expression profiles complex.

Results

In this paper we describe a new method for cross-species meta-analysis of gene expression. The method takes the homology structure between compared species into account and can therefore compare expression data from genes with any number of orthologs and paralogs. A simulation study shows that the proposed method results in a substantial increase in statistical power compared to previously suggested procedures. As a proof of concept, we analyzed microarray data from heat stress experiments performed in eight species and identified several well-known evolutionarily conserved transcriptional responses. The method was also applied to gene expression profiles from five studies of estrogen exposed fish and both known and potentially novel responses were identified.

Conclusions

The method described in this paper will further increase the potential and reliability of meta-analysis of gene expression profiles from evolutionarily distant species. The method has been implemented in R and is freely available at http://bioinformatics.math.chalmers.se/Xspecies/.

Transcription profiling during the cell cycle shows that a subset of Polycomb-targeted genes is upregulated during DNA replication

Genome-wide gene expression analyses of the human somatic cell cycle have indicated that the set of cycling genes differ between primary and cancer cells. By identifying genes that have cell cycle dependent expression in HaCaT human keratinocytes and comparing these with previously identified cell cycle genes, we have identified three distinct groups of cell cycle genes. First, housekeeping genes enriched for known cell cycle functions; second, cell type-specific genes enriched for HaCaT-specific functions; and third, Polycomb-regulated genes. These Polycomb-regulated genes are specifically upregulated during DNA replication, and consistent with being epigenetically silenced in other cell cycle phases, these genes have lower expression than other cell cycle genes. We also find similar patterns in foreskin fibroblasts, indicating that replication-dependent expression of Polycomb-silenced genes is a prevalent but unrecognized regulatory mechanism.

Where statistics and molecular microarray experiments biology meet

This review chapter presents a statistical point of view to microarray experiments with the purpose of understanding the apparent contradictions that often appear in relation to their results. We give a brief introduction of molecular biology for nonspecialists. We describe microarray experiments from their construction and the biological principles the experiments rely on, to data acquisition and analysis. The role of epidemiological approaches and sample size considerations are also discussed.

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.

Volcano plots in analyzing differential expressions with mRNA microarrays

A volcano plot displays unstandardized signal (e.g. log-fold-change) against noise-adjusted/standardized signal (e.g. t-statistic or -log(10)(p-value) from the t-test). We review the basic and interactive use of the volcano plot and its crucial role in understanding the regularized t-statistic. The joint filtering gene selection criterion based on regularized statistics has a curved discriminant line in the volcano plot, as compared to the two perpendicular lines for the "double filtering" criterion. This review attempts to provide a unifying framework for discussions on alternative measures of differential expression, improved methods for estimating variance, and visual display of a microarray analysis result. We also discuss the possibility of applying volcano plots to other fields beyond microarray.

A systems biology view of responses to lignin biosynthesis perturbations in Arabidopsis

Lignin engineering is an attractive strategy to improve lignocellulosic biomass quality for processing to biofuels and other bio-based products. However, lignin engineering also results in profound metabolic consequences in the plant. We used a systems biology approach to study the plant's response to lignin perturbations. To this end, inflorescence stems of 20 Arabidopsis thaliana mutants, each mutated in a single gene of the lignin biosynthetic pathway (phenylalanine ammonia-lyase1 [PAL1], PAL2, cinnamate 4-hydroxylase [C4H], 4-coumarate:CoA ligase1 [4CL1], 4CL2, caffeoyl-CoA O-methyltransferase1 [CCoAOMT1], cinnamoyl-CoA reductase1 [CCR1], ferulate 5-hydroxylase [F5H1], caffeic acid O-methyltransferase [COMT], and cinnamyl alcohol dehydrogenase6 [CAD6], two mutant alleles each), were analyzed by transcriptomics and metabolomics. A total of 566 compounds were detected, of which 187 could be tentatively identified based on mass spectrometry fragmentation and many were new for Arabidopsis. Up to 675 genes were differentially expressed in mutants that did not have any obvious visible phenotypes. Comparing the responses of all mutants indicated that c4h, 4cl1, ccoaomt1, and ccr1, mutants that produced less lignin, upregulated the shikimate, methyl-donor, and phenylpropanoid pathways (i.e., the pathways supplying the monolignols). By contrast, f5h1 and comt, mutants that provoked lignin compositional shifts, downregulated the very same pathways. Reductions in the flux to lignin were associated with the accumulation of various classes of 4-O- and 9-O-hexosylated phenylpropanoids. By combining metabolomic and transcriptomic data in a correlation network, system-wide consequences of the perturbations were revealed and genes with a putative role in phenolic metabolism were identified. Together, our data provide insight into lignin biosynthesis and the metabolic network it is embedded in and provide a systems view of the plant's response to pathway perturbations.

Reliable and sample saving gene expression analysis approach for diagnostic tool development

This work answers the question of whether it is necessary to hybridize individual instead of pooled RNA samples on microarrays for screening gene targets suitable as diagnostic tools for radiation exposure scenarios, while at the same time meeting comparable microarray quality criteria. For developing new clinical diagnostic tools, a two-stage study design was employed in five projects. At first, pooled and not individual RNA samples were hybridized on microarrays for screening purposes. Potential gene candidates were selected based on their fold-change only. This was followed by a validation/quantification step using individual RNA samples and quantitative RT-PCR. Quality criteria from the screening approach with pooled RNA samples were compared with published data from the MicroArray Quality Control (MAQC) consortium that hybridized each reference RNA sample separately and established quality criteria for microarrays. When comparing both approaches, only insignificant differences for quality criteria such as false positives, sensitivity, specificity, and overall agreement were found. However, material, costs, and time were drastically reduced when hybridizing pooled RNA and gene targets applicable for clinical diagnostic purposes could be successfully selected. In search of new diagnostic tools for radiation exposure scenarios, the two stage study design using either pooled or individual RNA samples on microarrays shows comparable quality criteria, but the RNA pooling approach saves unique material, costs, and efforts and successfully selects gene targets that can be used for the desired diagnostic purposes.

A primer on the current state of microarray technologies

DNA microarray technology has been used for genome-wide gene expression studies that incorporate molecular genetics and computer science analyses on massive levels. The availability of microarrays permit the simultaneous analysis of tens of thousands of genes for the purposes of gene discovery, disease diagnosis, improved drug development, and therapeutics tailored to specific disease processes. In this chapter, we provide an overview on the current state of common microarray technologies and platforms. Since many genes contribute to normal functioning, research efforts are moving from the search for a disease-specific gene to the understanding of the biochemical and molecular functioning of a variety of genes whose disrupted interaction in complicated networks can lead to a disease state. The field of microarrays has evolved over the past decade and is now standardized with a high level of quality control, while providing a relatively inexpensive and reliable alternative to studying various aspects of gene expression.

Performance comparison of multiple microarray platforms for gene expression profiling

With genome-wide gene expression microarrays being increasingly applied in various areas of biomedical research, the diversity of platforms and analytical methods has made comparison of data from multiple platforms very challenging. In this chapter, we describe a generalized framework for systematic comparisons across gene expression profiling platforms, which could accommodate both the available commercial arrays and "in-house" platforms, with both one-dye and two-dye platforms. It includes experimental design, data preprocessing protocols, cross-platform gene matching approaches, measures of data consistency comparisons, and considerations in biological validation. In the design of this framework, we considered the variety of platforms available, the need for uniform quality control procedures, real-world practical limitations, statistical validity, and the need for flexibility and extensibility of the framework. Using this framework, we studied ten diverse microarray platforms, and we conclude that using probe sequences matched at the exon level is important to improve cross-platform data consistency compared to annotation-based matches. Generally, consistency was good for highly expressed genes, and variable for genes with lower expression values, as confirmed by QRT-PCR. After stringent preprocessing, commercial arrays were more consistent than "in-house" arrays, and by most measures, one-dye platforms were more consistent than two-dye platforms.

Multiscale integration of -omic, imaging, and clinical data in biomedical informatics

This paper reviews challenges and opportunities in multiscale data integration for biomedical informatics. Biomedical data can come from different biological origins, data acquisition technologies, and clinical applications. Integrating such data across multiple scales (e.g., molecular, cellular/tissue, and patient) can lead to more informed decisions for personalized, predictive, and preventive medicine. However, data heterogeneity, community standards in data acquisition, and computational complexity are big challenges for such decision making. This review describes genomic and proteomic (i.e., molecular), histopathological imaging (i.e., cellular/tissue), and clinical (i.e., patient) data; it includes case studies for single-scale (e.g., combining genomic or histopathological image data), multiscale (e.g., combining histopathological image and clinical data), and multiscale and multiplatform (e.g., the Human Protein Atlas and The Cancer Genome Atlas) data integration. Numerous opportunities exist in biomedical informatics research focusing on integration of multiscale and multiplatform data.

Empirical comparison of cross-platform normalization methods for gene expression data

Background

Simultaneous measurement of gene expression on a genomic scale can be accomplished using microarray technology or by sequencing based methods. Researchers who perform high throughput gene expression assays often deposit their data in public databases, but heterogeneity of measurement platforms leads to challenges for the combination and comparison of data sets. Researchers wishing to perform cross platform normalization face two major obstacles. First, a choice must be made about which method or methods to employ. Nine are currently available, and no rigorous comparison exists. Second, software for the selected method must be obtained and incorporated into a data analysis workflow.

Results

Using two publicly available cross-platform testing data sets, cross-platform normalization methods are compared based on inter-platform concordance and on the consistency of gene lists obtained with transformed data. Scatter and ROC-like plots are produced and new statistics based on those plots are introduced to measure the effectiveness of each method. Bootstrapping is employed to obtain distributions for those statistics. The consistency of platform effects across studies is explored theoretically and with respect to the testing data sets.

Conclusions

Our comparisons indicate that four methods, DWD, EB, GQ, and XPN, are generally effective, while the remaining methods do not adequately correct for platform effects. Of the four successful methods, XPN generally shows the highest inter-platform concordance when treatment groups are equally sized, while DWD is most robust to differently sized treatment groups and consistently shows the smallest loss in gene detection. We provide an R package, CONOR, capable of performing the nine cross-platform normalization methods considered. The package can be downloaded at http://alborz.sdsu.edu/conor and is available from CRAN.

Prerequisites, performance and profits of transcriptional profiling the abiotic stress response

During the last decade, microarrays became a routine tool for the analysis of transcripts in the model plant Arabidopsis thaliana and the crop plant species rice, poplar or barley. The overwhelming amount of data generated by gene expression studies is a valuable resource for every scientist. Here, we summarize the most important findings about the abiotic stress responses in plants. Interestingly, conserved patterns of gene expression responses have been found that are common between different abiotic stresses or that are conserved between different plant species. However, the individual histories of each plant affect the inter-comparability between experiments already before the onset of the actual stress treatment. This review outlines multiple aspects of microarray technology and highlights some of the benefits, limitations and also pitfalls of the technique. This article is part of a Special Issue entitled: Plant gene regulation in response to abiotic stress.

Image defocus and altered retinal gene expression in chick: clues to the pathogenesis of ametropia

PURPOSE

Because of the retina's role in refractive development, this study was conducted to analyze the retinal transcriptome in chicks wearing a spectacle lens, a well-established means of inducing refractive errors, to identify gene expression alterations and to develop novel mechanistic hypotheses about refractive development.

METHODS

One-week-old white Leghorn chicks wore a unilateral spectacle lens of +15 or -15 D for 6 hours or 3 days. With total RNA from the retina/(retinal pigment epithelium, RPE), chicken gene microarrays were used to compare gene expression levels between lens-wearing and contralateral control eyes (n = 6 chicks for each condition). Normalized microarray signal intensities were evaluated by analysis of variance, using a false discovery rate of <10% as the statistical criterion. Selected differentially expressed genes were validated by qPCR.

RESULTS

Very few retina/RPE transcripts were differentially expressed after plus lens wear. In contrast, approximately 1300 transcripts were differentially expressed under each of the minus lens conditions, with minimal overlap. For each condition, low fold-changes typified the altered transcriptome. Differentially regulated genes under the minus lens conditions included many potentially informative signaling molecules and genes whose protein products have roles in intrinsic retinal circadian rhythms.

CONCLUSIONS

Plus or minus lens wear induce markedly different, not opposite, alterations in retina/RPE gene expression. The initial retinal responses to defocus are quite different from those when the eye growth patterns are well established, suggesting that different mechanisms govern the initiation and persistence or progression of refractive errors. The gene lists identify promising signaling candidates and regulatory pathways for future study, including a potential role for circadian rhythms in refractive development.

Measurement of ER and PR status in breast cancer using the QuantiGene2.0 assay

AIM

The QuantiGene2.0 assay has low interlaboratory variability and can directly measure RNA levels without a reverse transcription step or a polymerase chain reaction process. To evaluate the utility of the QuantiGene2.0 assay for the assessment of oestrogen receptor (ER) and progesterone receptor (PR) status as an alternative to immunohistochemistry (IHC), we compared disease-free survival according to quantitative ER and PR measurements when using IHC and the QuantiGene2.0 assay.

METHODS

Samples were collected from 171 patients who underwent breast cancer surgery between January 2003 and December 2006. Cox proportional hazard analysis was performed and concordance between IHC and the QuantiGene2.0 assay for the assessment of ER and PR status was evaluated.

RESULTS

ER and PR assessments were well correlated between IHC and the QuantiGene2.0 assay. The difference in disease-free survival was statistically significant according to expression of PR, but not ER, between the two groups.

CONCLUSIONS

The QuantiGene2.0 assay showed results similar to those of IHC for the assessment of ER and PR in response to treatment. Therefore, our data suggest that the QuantiGene2.0 assay can be used to determine ER and PR status and corroborate IHC findings, which at present are considered as the standard for the evaluation of ER and PR status.

Meta Analysis of Gene Expression Data within and Across Species

Since the second half of the 1990s, a large number of genome-wide analyses have been described that study gene expression at the transcript level. To this end, two major strategies have been adopted, a first one relying on hybridization techniques such as microarrays, and a second one based on sequencing techniques such as serial analysis of gene expression (SAGE), cDNA-AFLP, and analysis based on expressed sequence tags (ESTs). Despite both types of profiling experiments becoming routine techniques in many research groups, their application remains costly and laborious. As a result, the number of conditions profiled in individual studies is still relatively small and usually varies from only two to few hundreds of samples for the largest experiments. More and more, scientific journals require the deposit of these high throughput experiments in public databases upon publication. Mining the information present in these databases offers molecular biologists the possibility to view their own small-scale analysis in the light of what is already available. However, so far, the richness of the public information remains largely unexploited. Several obstacles such as the correct association between ESTs and microarray probes with the corresponding gene transcript, the incompleteness and inconsistency in the annotation of experimental conditions, and the lack of standardized experimental protocols to generate gene expression data, all impede the successful mining of these data. Here, we review the potential and difficulties of combining publicly available expression data from respectively EST analyses and microarray experiments. With examples from literature, we show how meta-analysis of expression profiling experiments can be used to study expression behavior in a single organism or between organisms, across a wide range of experimental conditions. We also provide an overview of the methods and tools that can aid molecular biologists in exploiting these public data.

COLOMBOS: access port for cross-platform bacterial expression compendia

Background

Microarrays are the main technology for large-scale transcriptional gene expression profiling, but the large bodies of data available in public databases are not useful due to the large heterogeneity. There are several initiatives that attempt to bundle these data into expression compendia, but such resources for bacterial organisms are scarce and limited to integration of experiments from the same platform or to indirect integration of per experiment analysis results.

Methodology/Principal Findings

We have constructed comprehensive organism-specific cross-platform expression compendia for three bacterial model organisms (Escherichia coli, Bacillus subtilis, and Salmonella enterica serovar Typhimurium) together with an access portal, dubbed COLOMBOS, that not only provides easy access to the compendia, but also includes a suite of tools for exploring, analyzing, and visualizing the data within these compendia. It is freely available at http://bioi.biw.kuleuven.be/colombos. The compendia are unique in directly combining expression information from different microarray platforms and experiments, and we illustrate the potential benefits of this direct integration with a case study: extending the known regulon of the Fur transcription factor of E. coli. The compendia also incorporate extensive annotations for both genes and experimental conditions; these heterogeneous data are functionally integrated in the COLOMBOS analysis tools to interactively browse and query the compendia not only for specific genes or experiments, but also metabolic pathways, transcriptional regulation mechanisms, experimental conditions, biological processes, etc.

Conclusions/Significance

We have created cross-platform expression compendia for several bacterial organisms and developed a complementary access port COLOMBOS, that also serves as a convenient expression analysis tool to extract useful biological information. This work is relevant to a large community of microbiologists by facilitating the use of publicly available microarray experiments to support their research.

A tissue-specific landscape of sense/antisense transcription in the mouse intestine

Background

The intestinal mucosa is characterized by complex metabolic and immunological processes driven highly dynamic gene expression programs. With the advent of next generation sequencing and its utilization for the analysis of the RNA sequence space, the level of detail on the global architecture of the transcriptome reached a new order of magnitude compared to microarrays.

Results

We report the ultra-deep characterization of the polyadenylated transcriptome in two closely related, yet distinct regions of the mouse intestinal tract (small intestine and colon). We assessed tissue-specific transcriptomal architecture and the presence of novel transcriptionally active regions (nTARs). In the first step, signatures of 20,541 NCBI RefSeq transcripts could be identified in the intestine (74.1% of annotated genes), thereof 16,742 are common in both tissues. Although the majority of reads could be linked to annotated genes, 27,543 nTARs not consistent with current gene annotations in RefSeq or ENSEMBL were identified. By use of a second independent strand-specific RNA-Seq protocol, 20,966 of these nTARs were confirmed, most of them in vicinity of known genes. We further categorized our findings by their relative adjacency to described exonic elements and investigated regional differences of novel transcribed elements in small intestine and colon.

Conclusions

The current study demonstrates the complexity of an archetypal mammalian intestinal mRNA transcriptome in high resolution and identifies novel transcriptionally active regions at strand-specific, single base resolution. Our analysis for the first time shows a strand-specific comparative picture of nTARs in two tissues and represents a resource for further investigating the transcriptional processes that contribute to tissue identity.

AnyExpress: integrated toolkit for analysis of cross-platform gene expression data using a fast interval matching algorithm

Background

Cross-platform analysis of gene express data requires multiple, intricate processes at different layers with various platforms. However, existing tools handle only a single platform and are not flexible enough to support custom changes, which arise from the new statistical methods, updated versions of reference data, and better platforms released every month or year. Current tools are so tightly coupled with reference information, such as reference genome, transcriptome database, and SNP, which are often erroneous or outdated, that the output results are incorrect and misleading.

Results

We developed AnyExpress, a software package that combines cross-platform gene expression data using a fast interval-matching algorithm. Supported platforms include next-generation-sequencing technology, microarray, SAGE, MPSS, and more. Users can define custom target transcriptome database references for probe/read mapping in any species, as well as criteria to remove undesirable probes/reads.

AnyExpress offers scalable processing features such as binding, normalization, and summarization that are not present in existing software tools.

As a case study, we applied AnyExpress to published Affymetrix microarray and Illumina NGS RNA-Seq data from human kidney and liver. The mean of within-platform correlation coefficient was 0.98 for within-platform samples in kidney and liver, respectively. The mean of cross-platform correlation coefficients was 0.73. These results confirmed those of the original and secondary studies. Applying filtering produced higher agreement between microarray and NGS, according to an agreement index calculated from differentially expressed genes.

Conclusion

AnyExpress can combine cross-platform gene expression data, process data from both open- and closed-platforms, select a custom target reference, filter out undesirable probes or reads based on custom-defined biological features, and perform quantile-normalization with a large number of microarray samples. AnyExpress is fast, comprehensive, flexible, and freely available at http://anyexpress.sourceforge.net.

Improved sensitivity of DNA microarrays using photonic crystal enhanced fluorescence

DNA microarrays are used to profile changes in gene expression between samples in a high-throughput manner, but measurements of genes with low expression levels can be problematic with standard microarray substrates. In this work, we expand the detection capabilities of a standard microarray experiment using a photonic crystal (PC) surface that enhances fluorescence observed from microarray spots. This PC is inexpensively and uniformly fabricated using a nanoreplica molding technique, with very little variation in its optical properties within- and between-devices. By using standard protocols to process glass microarray substrates in parallel with PCs, we evaluated the impact of this substrate on a one-color microarray experiment comparing gene expression in two developmental stages of Glycine max. The PCs enhanced the signal-to-noise ratio observed from microarray spots by 1 order of magnitude, significantly increasing the number of genes detected above substrate fluorescence noise. PC substrates more than double the number of genes classified as differentially expressed, detecting changes in expression even for low expression genes. This approach increases the dynamic range of a surface-bound fluorescence-based assay to reliably quantify small quantities of DNA that would be impossible with standard substrates.

Consistency of predictive signature genes and classifiers generated using different microarray platforms

Microarray-based classifiers and associated signature genes generated from various platforms are abundantly reported in the literature; however, the utility of the classifiers and signature genes in cross-platform prediction applications remains largely uncertain. As part of the MicroArray Quality Control Phase II (MAQC-II) project, we show in this study 80-90% cross-platform prediction consistency using a large toxicogenomics data set by illustrating that: (1) the signature genes of a classifier generated from one platform can be directly applied to another platform to develop a predictive classifier; (2) a classifier developed using data generated from one platform can accurately predict samples that were profiled using a different platform. The results suggest the potential utility of using published signature genes in cross-platform applications and the possible adoption of the published classifiers for a variety of applications. The study reveals an opportunity for possible translation of biomarkers identified using microarrays to clinically validated non-array gene expression assays.

Interlaboratory and interplatform comparison of microarray gene expression analysis of HepG2 cells exposed to benzo(a)pyrene

Microarray technology is being used increasingly to study gene expression of biological systems on a large scale. Both interlaboratory and interplatform differences are known to contribute to variability in microarray data. In this study we have investigated data from different platforms and laboratories on the transcriptomic profile of HepG2 cells exposed to benzo(a)pyrene (BaP). RNA samples generated in two different laboratories were analyzed using both Agilent oligonucleotide microarrays and Cancer Research UK (CR-UK) cDNA microarrays. Comparability of the expression profiles was assessed at various levels including correlation and overlap between the data, clustering of the data and affected biological processes. Overlap and correlation occurred, but it was not possible to deduce whether choice of platform or interlaboratory differences contributed more to the data variation. Principal component analysis (PCA) and hierarchical clustering of the expression profiles indicated that the data were most clearly defined by duration of exposure to BaP, suggesting that laboratory and platform variability does not mask the biological effects. Real-time quantitative PCR was used to validate the two array platforms and indicated that false negatives, rather than false positives, are obtained with both systems. All together these results suggest that data from similar biological experiments analyzed on different microarray platforms can be combined to give a more complete transcriptomic profile. Each platform gives a slight variation in the BaP-gene expression response and, although it cannot be stated which is more correct, combining the two data sets is more informative than considering them individually.

Power of deep sequencing and agilent microarray for gene expression profiling study

Next-generation sequencing-based Digital Gene Expression tag profiling (DGE) has been used to study the changes in gene expression profiling. To compare the quality of the data generated by microarray and DGE, we examined the gene expression profiles of an in vitro cell model with these platforms. In this study, 17,362 and 15,938 genes were detected by microarray and DGE, respectively, with 13,221 overlapping genes. The correlation coefficients between the technical replicates were >0.99 and the detection variance was <9% for both platforms. The dynamic range of microarray was fixed with four orders of magnitude, whereas that of DGE was extendable. The consistency of the two platforms was high, especially for those abundant genes. It was more difficult for the microarray to distinguish the expression variation of less abundant genes. Although microarrays might be eventually replaced by DGE or transcriptome sequencing (RNA-seq) in the near future, microarrays are still stable, practical, and feasible, which may be useful for most biological researchers.

A Review of Methods to Monitor the Modulation of mRNA Stability

Posttranscriptional regulation of gene expression is an elaborate and intricate process, constituting an important mechanism for the control of protein expression. During its existence, mRNA is escorted by proteins and other RNAs, which control the maturation, transportation, localization, translational efficiency, and ultimately its degradation. Without changes at the transcription level, mRNA steady-state levels can vary dramatically by just small changes in mRNA stability. By influencing the metabolism of specific mRNAs, the abundance of specific mRNAs can be controlled in organisms from bacteria to mammals. In eukaryotic cells, the control of mRNA stability is exerted through specific cis-acting elements (sequence-specific control elements) and trans-acting factors (mRNA binding proteins and some miRNAs). mRNA stability appears to be a key regulator in controlling the expression of many proteins. Dysregulation of mRNA stability has been associated with human diseases, including cancer, inflammatory disease, and Alzheimer’s. These observations suggest that modulating the stability of specific mRNAs may represent a viable strategy for pharmaceutical intervention. The literature already describes several compounds that influence mRNA stability. Measuring mRNA stability by conventional methods is labor intensive and time-consuming. However, several systems have been described that can be used to screen for modulators of mRNA levels in a high-throughput format. Thus, these assay systems offer a novel approach for screening targets that at present appear to be poorly “drugable.” This review describes the utility of mRNA stability as a novel approach to drug discovery, focusing on assay methods and tool compounds available to monitor mRNA stability. The authors describe mRNA stability assays and issues related to this approach.

Expression quantitative trait loci analysis in plants

An expression Quantitative Trait Locus or eQTL is a chromosomal region that accounts for a proportion of the variation in abundance of a mRNA transcript observed between individuals in a genetic mapping population. A single gene can have one or multiple eQTLs. Large scale mRNA profiling technologies advanced genome-wide eQTL mapping in a diverse range of organisms allowing thousands of eQTLs to be detected in a single experiment. When combined with classical or trait QTLs, correlation analyses can directly suggest candidates for genes underlying these traits. Furthermore, eQTL mapping data enables genetic regulatory networks to be modelled and potentially provide a better understanding of the underlying phenotypic variation. The mRNA profiling data sets can also be used to infer the chromosomal positions of thousands of genes, an outcome that is particularly valuable for species with unsequenced genomes where the chromosomal location of the majority of genes remains unknown. In this review we focus on eQTL studies in plants, addressing conceptual and technical aspects that include experimental design, genetic polymorphism prediction and candidate gene identification.

ChIP-Seq of transcription factors predicts absolute and differential gene expression in embryonic stem cells

Next-generation sequencing has greatly increased the scope and the resolution of transcriptional regulation study. RNA sequencing (RNA-Seq) and ChIP-Seq experiments are now generating comprehensive data on transcript abundance and on regulator-DNA interactions. We propose an approach for an integrated analysis of these data based on feature extraction of ChIP-Seq signals, principal component analysis, and regression-based component selection. Compared with traditional methods, our approach not only offers higher power in predicting gene expression from ChIP-Seq data but also provides a way to capture cooperation among regulators. In mouse embryonic stem cells (ESCs), we find that a remarkably high proportion of variation in gene expression (65%) can be explained by the binding signals of 12 transcription factors (TFs). Two groups of TFs are identified. Whereas the first group (E2f1, Myc, Mycn, and Zfx) act as activators in general, the second group (Oct4, Nanog, Sox2, Smad1, Stat3, Tcfcp2l1, and Esrrb) may serve as either activator or repressor depending on the target. The two groups of TFs cooperate tightly to activate genes that are differentially up-regulated in ESCs. In the absence of binding by the first group, the binding of the second group is associated with genes that are repressed in ESCs and derepressed upon early differentiation.

Essential role for Argonaute2 protein in mouse oogenesis

Background

Argonaute2 protein (Ago2) is a key component of RNA-induced gene silencing complex, which is crucial for microRNA-mediated repression of target genes. The function of Ago2 in the mouse oocyte and early embryonic development is less well characterized but it is likely to have an important role in regulating maternally inherited mRNA. We have examined the role of Ago2 by conditional deletion of the gene in developing oocytes.

Results

Ago2 was deleted specifically in the growing oocytes. Although the Ago2-deficient oocytes are able to develop to mature oocytes, they have abnormal spindles and chromosomes that are unable to cluster together properly. This phenotype is very similar to the phenotype of Dicer-deficient oocytes. We examined the microRNA expression profile in the Ago2-deficient oocyte and found that the expression of most microRNAs was reduced by more than 80%. To determine the downstream genes that are regulated by Ago2, we used microarray analysis on Ago2-deficient oocytes and found that 512 genes were upregulated and 1,073 genes were downregulated (FC > 2, P < 0.05).

Conclusion

Our study shows that Ago2 has a key function in the mouse oocyte through global regulation of microRNA stability, and through this mechanism it affects gene expression in developing oocytes.