How to decide? Different methods of calculating gene expression from short oligonucleotide array data will give different results

Prediction and expression analysis of deleterious nonsynonymous SNPs of Arabidopsis ACD11 gene by combining computational algorithms and molecular docking approach

Accelerated cell death 11 (ACD11) is an autoimmune gene that suppresses pathogen infection in plants by preventing plant cells from becoming infected by any pathogen. This gene is widely known for growth inhibition, premature leaf chlorosis, and defense-related programmed cell death (PCD) in seedlings before flowering in Arabidopsis plant. Specific amino acid changes in the ACD11 protein’s highly conserved domains are linked to autoimmune symptoms including constitutive defensive responses and necrosis without pathogen awareness. The molecular aspect of the aberrant activity of the ACD11 protein is difficult to ascertain. The purpose of our study was to find the most deleterious mutation position in the ACD11 protein and correlate them with their abnormal expression pattern. Using several computational methods, we discovered PCD vulnerable single nucleotide polymorphisms (SNPs) in ACD11. We analysed the RNA-Seq data, identified the detrimental nonsynonymous SNPs (nsSNP), built genetically mutated protein structures and used molecular docking to assess the impact of mutation. Our results demonstrated that the A15T and A39D mutations in the GLTP domain were likely to be extremely detrimental mutations that inhibit the expression of the ACD11 protein domain by destabilizing its composition, as well as disrupt its catalytic effectiveness. When compared to the A15T mutant, the A39D mutant was more likely to destabilize the protein structure. In conclusion, these mutants can aid in the better understanding of the vast pool of PCD susceptibilities connected to ACD11 gene GLTP domain activation.

Non synonymous single nucleotide polymorphism (nsSNP) is a process in which amino acid sequence of a protein is altered as a result of single nucleotide alteration in the coding region (mRNA) of any living organism. Therefore, the entire protein structure, interactions and stability are altered, which may have a negative impact on living organisms. Hence, to completely comprehend this biological process, we must first solve the unresolved mutational protein structure and mutated protein interactions. The major goal of our research is to identify the most harmful mutation in our target protein structure and how it interacts within cells. However, it was discovered that only a few alterations in residues had the largest negative impact on the protein’s internal structure and also on the protein-ligand interactions. We show that based on the amino acid sequence of a protein computationally, it is feasible to discover mutational positions in the sequence, generate mutation protein structure and interactions with related ligands. Our findings show that the essential mechanisms underlying protein mutations generated by this process are identical. The capacity to correctly detect mutations from sequence allows the annotation and study of protein-ligand interactions throughout a whole organism, which might aid function prediction and gene expression.

Transcriptomic Response of Huanglongbing-Infected Citrus sinensis Following Field Application of a Microbial Fermentation Product

Huanglongbing (HLB) is considered the most destructive disease in Citrus production and threatens the future of the industry. Microbial-derived defense elicitors have gained recognition for their role in plant defense priming. This work assessed a 5% (V/V) microbial fermentation application (MFA) and its role in the elicitation of defense responses in HLB-infected Citrus sinensis trees following a foliar application with a pump sprayer. Using a PCR detection method, HLB infection levels were monitored in healthy and infected trees for 20months. Nutrient analysis assessed N, P, K, Ca, Mg, Mn, Zn, Fe, B, and Cu concentrations in the trees. MFA significantly increased Cu concentrations in treated trees and resulted in the stabilization of disease index (DI) in infected trees. Initial real-time qPCR analysis of defense-associated genes showed a significant increase in pathogenesis-related protein 2 (PR2) and phenylalanine ammonia lyase (PAL) gene expression in healthy and HLB-infected trees in response to MFA. Gene expression of PR2 and PAL peaked 6h post-microbial fermentation application during an 8-h sampling period. A transcriptomic assessment using GeneChip microarray of the hour 6 samples revealed differential expression of 565 genes when MFA was applied to healthy trees and 909 genes when applied infected citrus trees when compared to their respective controls. There were 403 uniquely differentially expressed genes in response to MFA following an intersectional analysis of both healthy and infected citrus trees. The transcriptomic analysis revealed that several genes associated with plant development, growth, and defense were upregulated in response to MFA, including multiple PR genes, lignin formation genes, ROS-related genes, hormone synthases, and hormone regulators. This study provides further evidence that MFA may play an important role as a plant elicitor in an integrated pest management strategy in citrus and other agronomically important crops.

Relationship between insulin sensitivity and gene expression in human skeletal muscle

BACKGROUND

Insulin resistance (IR) in skeletal muscle is a key feature of the pre-diabetic state, hypertension, dyslipidemia, cardiovascular diseases and also predicts type 2 diabetes. However, the underlying molecular mechanisms are still poorly understood.

METHODS

To explore these mechanisms, we related global skeletal muscle gene expression profiling of 38 non-diabetic men to a surrogate measure of insulin sensitivity, i.e. homeostatic model assessment of insulin resistance (HOMA-IR).

RESULTS

We identified 70 genes positively and 110 genes inversely correlated with insulin sensitivity in human skeletal muscle, identifying autophagy-related genes as positively correlated with insulin sensitivity. Replication in an independent study of 9 non-diabetic men resulted in 10 overlapping genes that strongly correlated with insulin sensitivity, including SIRT2, involved in lipid metabolism, and FBXW5 that regulates mammalian target-of-rapamycin (mTOR) and autophagy. The expressions of SIRT2 and FBXW5 were also positively correlated with the expression of key genes promoting the phenotype of an insulin sensitive myocyte e.g. PPARGC1A.

CONCLUSIONS

The muscle expression of 180 genes were correlated with insulin sensitivity. These data suggest that activation of genes involved in lipid metabolism, e.g. SIRT2, and genes regulating autophagy and mTOR signaling, e.g. FBXW5, are associated with increased insulin sensitivity in human skeletal muscle, reflecting a highly flexible nutrient sensing.

The sensitivity of transcriptomics BMD modeling to the methods used for microarray data normalization

Whole-genome expression data generated by microarray studies have shown promise for quantitative human health risk assessment. While numerous approaches have been developed to determine benchmark doses (BMDs) from probeset-level dose responses, sensitivity of the results to methods used for normalization of the data has not yet been systematically investigated. Normalization of microarray data converts raw hybridization signals to expression estimates that are expected to be proportional to the amounts of transcripts in the profiled specimens. Different approaches to normalization have been shown to greatly influence the results of some downstream analyses, including biological interpretation. In this study we evaluate the influence of microarray normalization methods on the transcriptomic BMDs. We demonstrate using in vivo data that the use of alternative pipelines for normalization of Affymetrix microarray data can have a considerable impact on the number of detected differentially expressed genes and pathways (processes) determined to be treatment responsive, which may lead to alternative interpretations of the data. In addition, we found that normalization can have a considerable effect (as much as ~30-fold in this study) on estimation of the minimum biological potency (transcriptomic point of departure). We argue for consideration of alternative normalization methods and their data-informed selection to most effectively interpret microarray data for use in human health risk assessment.

A big data pipeline: Identifying dynamic gene regulatory networks from time-course Gene Expression Omnibus data with applications to influenza infection

A biological host response to an external stimulus or intervention such as a disease or infection is a dynamic process, which is regulated by an intricate network of many genes and their products. Understanding the dynamics of this gene regulatory network allows us to infer the mechanisms involved in a host response to an external stimulus, and hence aids the discovery of biomarkers of phenotype and biological function. In this article, we propose a modeling/analysis pipeline for dynamic gene expression data, called Pipeline4DGEData, which consists of a series of statistical modeling techniques to construct dynamic gene regulatory networks from the large volumes of high-dimensional time-course gene expression data that are freely available in the Gene Expression Omnibus repository. This pipeline has a consistent and scalable structure that allows it to simultaneously analyze a large number of time-course gene expression data sets, and then integrate the results across different studies. We apply the proposed pipeline to influenza infection data from nine studies and demonstrate that interesting biological findings can be discovered with its implementation.

Computational approach to understand molecular mechanism involved in BPH resistance in Bt- rice plant

In silico approach was utilised to identify differentially expressed key hub genes during BPH infestation on Bt rice plant, under laboratory conditions. Re-analysis of GSE74745 data with in-house R scripts and STRING database reveals that only 5 key hub genes, namely Os05g0176100, Os06g0683200, Os07g0208500, Os07g0252400 and Os07g0424400, belonging to cellulose synthase family, are differentially expressed and have confidence score ≥0.9 among themselves. Conserve domain analysis of all proteins encoded via these 5 key hub genes reveals that they have a common cellulose synthase domain, in which "Plant-Conserved Region" (PCR) is highly conserved. After binding with other domains of cellulose synthase proteins or other accessory proteins, like sucrose synthase, PCR serves as a metabolic channel to deliver UDP-Glucose, which is the main substrate for cellulose synthesis, into the active site of cellulose synthase and initiate cellulose synthesis. Simulation study of recently solved topological model of PCR [PDB ID: 5JNP] and molecular docking studies of PCR with UDP-glucose reveals that, during BPH infestation, in nearby phloem tissue where BPH suck sap, there is an increase interaction of UDP-glucose with PCR and other accessory proteins which in turn increases both the stability of PCR and the production of cellulose, finally causing callose deposition at that site and hence causing longer nymphal developmental period and lower fertility of BPH infested on Bt rice. In near future, these differentially identified 5 hub genes could be possible targets for controlling BPH infestation in rice plant under field conditions and increasing rice yield globally.

Identification and characterization of differentially expressed genes in Type 2 Diabetes using in silico approach

Diabetes mellitus is clinically characterized by hyperglycemia. Though many studies have been done to understand the mechanism of Type 2 Diabetes (T2D), however, the complete network of diabetes and its associated disorders through polygenic involvement is still under debate. The present study designed to re-analyze publicly available T2D related microarray raw datasets present in GEO database and T2D genes information present in GWAS catalog for screening out differentially expressed genes (DEGs) and identify key hub genes associated with T2D. T2D related microarray data downloaded from Gene Expression Omnibus (GEO) database and re-analysis performed with in house R packages scripts for background correction, normalization and identification of DEGs in T2D. Also retrieved T2D related DEGs information from GWAS catalog. Both DEGs lists were grouped after removal of overlapping genes. These screened DEGs were utilized further for identification and characterization of key hub genes in T2D and its associated diseases using STRING, WebGestalt and Panther databases. Computational analysis reveal that out of 99 identified key hub gene candidates from 348 DEGs, only four genes (CCL2, ELMO1, VEGFA and TCF7L2) along with FOS playing key role in causing T2D and its associated disorders, like nephropathy, neuropathy, rheumatoid arthritis and cancer via p53 or Wnt signaling pathways. MIR-29, and MAZ_Q6 are identified potential target microRNA and TF along with probable drugs alprostadil, collagenase and dinoprostone for the key hub gene candidates. The results suggest that identified key DEGs may play promising roles in prevention of diabetes.

Framework for Parallel Preprocessing of Microarray Data Using Hadoop

Nowadays, microarray technology has become one of the popular ways to study gene expression and diagnosis of disease. National Center for Biology Information (NCBI) hosts public databases containing large volumes of biological data required to be preprocessed, since they carry high levels of noise and bias. Robust Multiarray Average (RMA) is one of the standard and popular methods that is utilized to preprocess the data and remove the noises. Most of the preprocessing algorithms are time-consuming and not able to handle a large number of datasets with thousands of experiments. Parallel processing can be used to address the above-mentioned issues. Hadoop is a well-known and ideal distributed file system framework that provides a parallel environment to run the experiment. In this research, for the first time, the capability of Hadoop and statistical power of R have been leveraged to parallelize the available preprocessing algorithm called RMA to efficiently process microarray data. The experiment has been run on cluster containing 5 nodes, while each node has 16 cores and 16 GB memory. It compares efficiency and the performance of parallelized RMA using Hadoop with parallelized RMA using affyPara package as well as sequential RMA. The result shows the speed-up rate of the proposed approach outperforms the sequential approach and affyPara approach.

Pathway analysis of transcriptomic data shows immunometabolic effects of vitamin D

Unbiased genomic screening analyses have highlighted novel immunomodulatory properties of the active form of vitamin D, 1,25-dihydroxyvitamin D (1,25(OH)₂D). However, clearer interpretation of the resulting gene expression data is limited by cell model specificity. The aim of the current study was to provide a broader perspective on common gene regulatory pathways associated with innate immune responses to 1,25(OH)₂D, through systematic re-interrogation of existing gene expression databases from multiple related monocyte models (the THP-1 monocytic cell line (THP-1), monocyte-derived dendritic cells (DCs) and monocytes). Vitamin D receptor (VDR) expression is common to multiple immune cell types, and thus, pathway analysis of gene expression using data from multiple related models provides an inclusive perspective on the immunomodulatory impact of vitamin D. A bioinformatic workflow incorporating pathway analysis using PathVisio and WikiPathways was utilized to compare each set of gene expression data based on pathway-level context. Using this strategy, pathways related to the TCA cycle, oxidative phosphorylation and ATP synthesis and metabolism were shown to be significantly regulated by 1,25(OH)₂D in each of the repository models (Z-scores 3.52-8.22). Common regulation by 1,25(OH)₂D was also observed for pathways associated with apoptosis and the regulation of apoptosis (Z-scores 2.49-3.81). In contrast to the primary culture DC and monocyte models, the THP-1 myelomonocytic cell line showed strong regulation of pathways associated with cell proliferation and DNA replication (Z-scores 6.1-12.6). In short, data presented here support a fundamental role for active 1,25(OH)₂D as a pivotal regulator of immunometabolism.

Microarray-Based Gene Expression Analysis for Veterinary Pathologists: A Review

High-throughput, genome-wide transcriptome analysis is now commonly used in all fields of life science research and is on the cusp of medical and veterinary diagnostic application. Transcriptomic methods such as microarrays and next-generation sequencing generate enormous amounts of data. The pathogenetic expertise acquired from understanding of general pathology provides veterinary pathologists with a profound background, which is essential in translating transcriptomic data into meaningful biological knowledge, thereby leading to a better understanding of underlying disease mechanisms. The scientific literature concerning high-throughput data-mining techniques usually addresses mathematicians or computer scientists as the target audience. In contrast, the present review provides the reader with a clear and systematic basis from a veterinary pathologist's perspective. Therefore, the aims are (1) to introduce the reader to the necessary methodological background; (2) to introduce the sequential steps commonly performed in a microarray analysis including quality control, annotation, normalization, selection of differentially expressed genes, clustering, gene ontology and pathway analysis, analysis of manually selected genes, and biomarker discovery; and (3) to provide references to publically available and user-friendly software suites. In summary, the data analysis methods presented within this review will enable veterinary pathologists to analyze high-throughput transcriptome data obtained from their own experiments, supplemental data that accompany scientific publications, or public repositories in order to obtain a more in-depth insight into underlying disease mechanisms.

A Four-Biomarker Blood Signature Discriminates Systemic Inflammation Due to Viral Infection Versus Other Etiologies

The innate immune system of humans and other mammals responds to pathogen-associated molecular patterns (PAMPs) that are conserved across broad classes of infectious agents such as bacteria and viruses. We hypothesized that a blood-based transcriptional signature could be discovered indicating a host systemic response to viral infection. Previous work identified host transcriptional signatures to individual viruses including influenza, respiratory syncytial virus and dengue, but the generality of these signatures across all viral infection types has not been established. Based on 44 publicly available datasets and two clinical studies of our own design, we discovered and validated a four-gene expression signature in whole blood, indicative of a general host systemic response to many types of viral infection. The signature’s genes are: Interferon Stimulated Gene 15 (ISG15), Interleukin 16 (IL16), 2′,5′-Oligoadenylate Synthetase Like (OASL), and Adhesion G Protein Coupled Receptor E5 (ADGRE5). In each of 13 validation datasets encompassing human, macaque, chimpanzee, pig, mouse, rat and all seven Baltimore virus classification groups, the signature provides statistically significant (p < 0.05) discrimination between viral and non-viral conditions. The signature may have clinical utility for differentiating host systemic inflammation (SI) due to viral versus bacterial or non-infectious causes.

Accurate predictions of postmortem interval using linear regression analyses of gene meter expression data

In criminal and civil investigations, postmortem interval is used as evidence to help sort out circumstances at the time of human death. Many biological, chemical, and physical indicators can be used to determine the postmortem interval - but most are not accurate. Here, we sought to validate an experimental design to accurately predict the time of death by analyzing the expression of hundreds of upregulated genes in two model organisms, the zebrafish and mouse. In a previous study, the death of healthy adults was conducted under strictly controlled conditions to minimize the effects of confounding factors such as lifestyle and temperature. A total of 74,179 microarray probes were calibrated using the Gene Meter approach and the transcriptional profiles of 1063 genes that significantly increased in abundance were assembled into a time series spanning from life to 48 or 96h postmortem. In this study, the experimental design involved splitting the transcription profiles into training and testing datasets, randomly selecting groups of profiles, determining the modeling parameters of the genes to postmortem time using over- and/or perfectly-defined linear regression analyses, and calculating the fit (R²) and slope of predicted versus actual postmortem times. This design was repeated several thousand to million times to find the top predictive groups of gene transcription profiles. A group of eleven zebrafish genes yielded R² of 1 and a slope of 0.99, while a group of seven mouse liver genes yielded a R² of 0.98 and a slope of 0.97, and seven mouse brain genes yielded a R² of 0.95 and a slope of 0.87. In all cases, groups of gene transcripts yielded better postmortem time predictions than individual gene transcripts. The significance of this study is two-fold: selected groups of gene transcripts provide accurate prediction of postmortem time, and the successfully validated experimental design can now be used to accurately predict postmortem time in cadavers.

Tracing the dynamics of gene transcripts after organismal death

In life, genetic and epigenetic networks precisely coordinate the expression of genes-but in death, it is not known if gene expression diminishes gradually or abruptly stops or if specific genes and pathways are involved. We studied this by identifying mRNA transcripts that apparently increase in relative abundance after death, assessing their functions, and comparing their abundance profiles through postmortem time in two species, mouse and zebrafish. We found mRNA transcript profiles of 1063 genes became significantly more abundant after death of healthy adult animals in a time series spanning up to 96 h postmortem. Ordination plots revealed non-random patterns in the profiles by time. While most of these transcript levels increased within 0.5 h postmortem, some increased only at 24 and 48 h postmortem. Functional characterization of the most abundant transcripts revealed the following categories: stress, immunity, inflammation, apoptosis, transport, development, epigenetic regulation and cancer. The data suggest a step-wise shutdown occurs in organismal death that is manifested by the apparent increase of certain transcripts with various abundance maxima and durations.

Smoking induces overexpression of immediate early genes in active Graves' ophthalmopathy

BACKGROUND

Cigarette smoking is a risk factor for the development of Graves' ophthalmopathy (GO). In a previous study of gene expression in intraorbital fat, adipocyte-related immediate early genes (IEGs) were overexpressed in patients with GO compared to controls. We investigated whether IEGs are upregulated by smoking, and examined other pathways that may be affected by smoking.

METHODS

Gene expression in intraorbital fat was studied in smokers (n=8) and nonsmokers (n=8) with severe active GO, as well as in subcutaneous fat in thyroid-healthy smokers (n=5) and nonsmokers (n=5) using microarray and real-time polymerase chain reaction (PCR).

RESULTS

With microarray, eight IEGs were upregulated more than 1.5-fold in smokers compared to nonsmokers with GO. Five were chosen for confirmation and were also overexpressed with real-time PCR. Interleukin-1 beta/IL-1B/(2.3-fold) and interleukin-6/IL-6/(2.4-fold) were upregulated both with microarray and with real-time PCR in smokers with GO compared to nonsmokers. Major histocompatibility complex, class II, DR beta 1/HLA-DRB1/was upregulated with microarray (2.1-fold) and with borderline significance with real-time PCR. None of these genes were upregulated in smokers compared to nonsmokers in subcutaneous fat.

CONCLUSIONS

IEGs, IL-1B, and IL-6 were overexpressed in smokers with severe active GO compared to nonsmokers, suggesting that smoking activates pathways associated with adipogenesis and inflammation. This study underlines the importance of IEGs in the pathogenesis of GO, and provides evidence for possible novel therapeutic interventions in GO. The mechanisms activated by smoking may be shared with other conditions such as rheumatoid arthritis.

Comparison of gene expression microarray data with count-based RNA measurements informs microarray interpretation

BACKGROUND

Although numerous investigations have compared gene expression microarray platforms, preprocessing methods and batch correction algorithms using constructed spike-in or dilution datasets, there remains a paucity of studies examining the properties of microarray data using diverse biological samples. Most microarray experiments seek to identify subtle differences between samples with variable background noise, a scenario poorly represented by constructed datasets. Thus, microarray users lack important information regarding the complexities introduced in real-world experimental settings. The recent development of a multiplexed, digital technology for nucleic acid measurement enables counting of individual RNA molecules without amplification and, for the first time, permits such a study.

RESULTS

Using a set of human leukocyte subset RNA samples, we compared previously acquired microarray expression values with RNA molecule counts determined by the nCounter Analysis System (NanoString Technologies) in selected genes. We found that gene measurements across samples correlated well between the two platforms, particularly for high-variance genes, while genes deemed unexpressed by the nCounter generally had both low expression and low variance on the microarray. Confirming previous findings from spike-in and dilution datasets, this "gold-standard" comparison demonstrated signal compression that varied dramatically by expression level and, to a lesser extent, by dataset. Most importantly, examination of three different cell types revealed that noise levels differed across tissues.

CONCLUSIONS

Microarray measurements generally correlate with relative RNA molecule counts within optimal ranges but suffer from expression-dependent accuracy bias and precision that varies across datasets. We urge microarray users to consider expression-level effects in signal interpretation and to evaluate noise properties in each dataset independently.

A revised design for microarray experiments to account for experimental noise and uncertainty of probe response

Background

Although microarrays are analysis tools in biomedical research, they are known to yield noisy output that usually requires experimental confirmation. To tackle this problem, many studies have developed rules for optimizing probe design and devised complex statistical tools to analyze the output. However, less emphasis has been placed on systematically identifying the noise component as part of the experimental procedure. One source of noise is the variance in probe binding, which can be assessed by replicating array probes. The second source is poor probe performance, which can be assessed by calibrating the array based on a dilution series of target molecules. Using model experiments for copy number variation and gene expression measurements, we investigate here a revised design for microarray experiments that addresses both of these sources of variance.

Results

Two custom arrays were used to evaluate the revised design: one based on 25 mer probes from an Affymetrix design and the other based on 60 mer probes from an Agilent design. To assess experimental variance in probe binding, all probes were replicated ten times. To assess probe performance, the probes were calibrated using a dilution series of target molecules and the signal response was fitted to an adsorption model. We found that significant variance of the signal could be controlled by averaging across probes and removing probes that are nonresponsive or poorly responsive in the calibration experiment. Taking this into account, one can obtain a more reliable signal with the added option of obtaining absolute rather than relative measurements.

Conclusion

The assessment of technical variance within the experiments, combined with the calibration of probes allows to remove poorly responding probes and yields more reliable signals for the remaining ones. Once an array is properly calibrated, absolute quantification of signals becomes straight forward, alleviating the need for normalization and reference hybridizations.

Accurate data processing improves the reliability of Affymetrix gene expression profiles from FFPE samples

Formalin fixed paraffin-embedded (FFPE) tumor specimens are the conventionally archived material in clinical practice, representing an invaluable tissue source for biomarkers development, validation and routine implementation. For many prospective clinical trials, this material has been collected allowing for a prospective-retrospective study design which represents a successful strategy to define clinical utility for candidate markers. Gene expression data can be obtained even from FFPE specimens with the broadly used Affymetrix HG-U133 Plus 2.0 microarray platform. Nevertheless, important major discrepancies remain in expression data obtained from FFPE compared to fresh-frozen samples, prompting the need for appropriate data processing which could help to obtain more consistent results in downstream analyses. In a publicly available dataset of matched frozen and FFPE expression data, the performances of different normalization methods and specifically designed Chip Description Files (CDFs) were compared. The use of an alternative CDFs together with fRMA normalization significantly improved frozen-FFPE sample correlations, frozen-FFPE probeset correlations and agreement of differential analysis between different tumor subtypes. The relevance of our optimized data processing was assessed and validated using two independent datasets. In this study we demonstrated that an appropriate data processing can significantly improve the reliability of gene expression data derived from FFPE tissues using the standard Affymetrix platform. Tools for the implementation of our data processing algorithm are made publicly available at http://www.biocut.unito.it/cdf-ffpe/.

An effective approach for detection and segmentation of protein spots on 2-D gel images

Two-dimensional gel image analysis is widely recognized as a particularly challenging and arduous process in proteomics field. The detection and segmentation of protein spots are two significant stages of this process as they can considerably affect the final biological conclusions of a proteomic experiment. The available techniques and commercial software packages deal with the existing challenges of 2-D gel images in a different degree of success. Furthermore, they require extensive human intervention which not only limits the throughput but unavoidably questions the objectivity and reproducibility of results. This paper introduces a novel approach for the detection and segmentation of protein spots on 2-D gel images. The proposed approach is based on 2-D image histograms as well as on 3-D spots morphology. It is automatic and capable to deal with the most common deficiencies of existing software programs and techniques in an effective manner. Experimental evaluation includes tests on several real and synthetic 2-D gel images produced by different technology setups, containing a total of ∼ 21,400 spots. Furthermore, the proposed approach has been compared with two commercial software packages as well as with two state-of-the-art techniques. Results have demonstrated the effectiveness of the proposed approach and its superiority against compared software packages and techniques.

Identification of the IGF1/PI3K/NF κB/ERK gene signalling networks associated with chemotherapy resistance and treatment response in high-grade serous epithelial ovarian cancer

BACKGROUND

Resistance to platinum-based chemotherapy remains a major impediment in the treatment of serous epithelial ovarian cancer. The objective of this study was to use gene expression profiling to delineate major deregulated pathways and biomarkers associated with the development of intrinsic chemotherapy resistance upon exposure to standard first-line therapy for ovarian cancer.

METHODS

The study cohort comprised 28 patients divided into two groups based on their varying sensitivity to first-line chemotherapy using progression free survival (PFS) as a surrogate of response. All 28 patients had advanced stage, high-grade serous ovarian cancer, and were treated with standard platinum-based chemotherapy. Twelve patient tumours demonstrating relative resistance to platinum chemotherapy corresponding to shorter PFS (< eight months) were compared to sixteen tumours from platinum-sensitive patients (PFS > eighteen months). Whole transcriptome profiling was performed using an Affymetrix high-resolution microarray platform to permit global comparisons of gene expression profiles between tumours from the resistant group and the sensitive group.

RESULTS

Microarray data analysis revealed a set of 204 discriminating genes possessing expression levels which could influence differential chemotherapy response between the two groups. Robust statistical testing was then performed which eliminated a dependence on the normalization algorithm employed, producing a restricted list of differentially regulated genes, and which found IGF1 to be the most strongly differentially expressed gene. Pathway analysis, based on the list of 204 genes, revealed enrichment in genes primarily involved in the IGF1/PI3K/NF κB/ERK gene signalling networks.

CONCLUSIONS

This study has identified pathway specific prognostic biomarkers possibly underlying a differential chemotherapy response in patients undergoing standard platinum-based treatment of serous epithelial ovarian cancer. In addition, our results provide a pathway context for further experimental validations, and the findings are a significant step towards future therapeutic interventions.

Analysis of boutique arrays: a universal method for the selection of the optimal data normalization procedure

DNA microarrays, which are among the most popular genomic tools, are widely applied in biology and medicine. Boutique arrays, which are small, spotted, dedicated microarrays, constitute an inexpensive alternative to whole-genome screening methods. The data extracted from each microarray-based experiment must be transformed and processed prior to further analysis to eliminate any technical bias. The normalization of the data is the most crucial step of microarray data pre-processing and this process must be carefully considered as it has a profound effect on the results of the analysis. Several normalization algorithms have been developed and implemented in data analysis software packages. However, most of these methods were designed for whole-genome analysis. In this study, we tested 13 normalization strategies (ten for double-channel data and three for single-channel data) available on R Bioconductor and compared their effectiveness in the normalization of four boutique array datasets. The results revealed that boutique arrays can be successfully normalized using standard methods, but not every method is suitable for each dataset. We also suggest a universal seven-step workflow that can be applied for the selection of the optimal normalization procedure for any boutique array dataset. The described workflow enables the evaluation of the investigated normalization methods based on the bias and variance values for the control probes, a differential expression analysis and a receiver operating characteristic curve analysis. The analysis of each component results in a separate ranking of the normalization methods. A combination of the ranks obtained from all the normalization procedures facilitates the selection of the most appropriate normalization method for the studied dataset and determines which methods can be used interchangeably.

Genome-wide gene expression profiling, genotyping, and copy number analyses of acute myeloid leukemia using Affymetrix GeneChips

With novel genome-wide technologies it is nowadays possible to perform detailed molecular analyses of normal and malignant tissues. Acute myeloid leukemia (AML) is a heterogeneous group of diseases with variable response to therapy. Gene expression profiling and genome-wide genotyping have recently been successfully applied to unravel the heterogeneity of AML. This chapter gives instructions and recommendations for genome-wide gene expression analyses, genotyping, and copy number analyses, as performed for AML using Affymetrix GeneChips.

How cyanobacteria pose new problems to old methods: challenges in microarray time series analysis

Background

The transcriptomes of several cyanobacterial strains have been shown to exhibit diurnal oscillation patterns, reflecting the diurnal phototrophic lifestyle of the organisms. The analysis of such genome-wide transcriptional oscillations is often facilitated by the use of clustering algorithms in conjunction with a number of pre-processing steps. Biological interpretation is usually focussed on the time and phase of expression of the resulting groups of genes. However, the use of microarray technology in such studies requires the normalization of pre-processing data, with unclear impact on the qualitative and quantitative features of the derived information on the number of oscillating transcripts and their respective phases.

Results

A microarray based evaluation of diurnal expression in the cyanobacterium Synechocystis sp. PCC 6803 is presented. As expected, the temporal expression patterns reveal strong oscillations in transcript abundance. We compare the Fourier transformation-based expression phase before and after the application of quantile normalization, median polishing, cyclical LOESS, and least oscillating set (LOS) normalization. Whereas LOS normalization mostly preserves the phases of the raw data, the remaining methods introduce systematic biases. In particular, quantile-normalization is found to introduce a phase-shift of 180°, effectively changing night-expressed genes into day-expressed ones. Comparison of a large number of clustering results of differently normalized data shows that the normalization method determines the result. Subsequent steps, such as the choice of data transformation, similarity measure, and clustering algorithm, only play minor roles. We find that the standardization and the DTF transformation are favorable for the clustering of time series in contrast to the 12 m transformation. We use the cluster-wise functional enrichment of a clustering derived by LOS normalization, clustering using flowClust, and DFT transformation to derive the diurnal biological program of Synechocystis sp..

Conclusion

Application of quantile normalization, median polishing, and also cyclic LOESS normalization of the presented cyanobacterial dataset lead to increased numbers of oscillating genes and the systematic shift of the expression phase. The LOS normalization minimizes the observed detrimental effects. As previous analyses employed a variety of different normalization methods, a direct comparison of results must be treated with caution.

Stem cell-like gene expression in ovarian cancer predicts type II subtype and prognosis

Although ovarian cancer is often initially chemotherapy-sensitive, the vast majority of tumors eventually relapse and patients die of increasingly aggressive disease. Cancer stem cells are believed to have properties that allow them to survive therapy and may drive recurrent tumor growth. Cancer stem cells or cancer-initiating cells are a rare cell population and difficult to isolate experimentally. Genes that are expressed by stem cells may characterize a subset of less differentiated tumors and aid in prognostic classification of ovarian cancer. The purpose of this study was the genomic identification and characterization of a subtype of ovarian cancer that has stem cell-like gene expression. Using human and mouse gene signatures of embryonic, adult, or cancer stem cells, we performed an unsupervised bipartition class discovery on expression profiles from 145 serous ovarian tumors to identify a stem-like and more differentiated subgroup. Subtypes were reproducible and were further characterized in four independent, heterogeneous ovarian cancer datasets. We identified a stem-like subtype characterized by a 51-gene signature, which is significantly enriched in tumors with properties of Type II ovarian cancer; high grade, serous tumors, and poor survival. Conversely, the differentiated tumors share properties with Type I, including lower grade and mixed histological subtypes. The stem cell-like signature was prognostic within high-stage serous ovarian cancer, classifying a small subset of high-stage tumors with better prognosis, in the differentiated subtype. In multivariate models that adjusted for common clinical factors (including grade, stage, age), the subtype classification was still a significant predictor of relapse. The prognostic stem-like gene signature yields new insights into prognostic differences in ovarian cancer, provides a genomic context for defining Type I/II subtypes, and potential gene targets which following further validation may be valuable in the clinical management or treatment of ovarian cancer.

Genetical Genomics Reveals Large Scale Genotype-By-Environment Interactions in Arabidopsis thaliana

One of the major goals of quantitative genetics is to unravel the complex interactions between molecular genetic factors and the environment. The effects of these genotype-by-environment interactions also affect and cause variation in gene expression. The regulatory loci responsible for this variation can be found by genetical genomics that involves the mapping of quantitative trait loci (QTLs) for gene expression traits also called expression-QTL (eQTLs). Most genetical genomics experiments published so far, are performed in a single environment and hence do not allow investigation of the role of genotype-by-environment interactions. Furthermore, most studies have been done in a steady state environment leading to acclimated expression patterns. However a response to the environment or change therein can be highly plastic and possibly lead to more and larger differences between genotypes. Here we present a genetical genomics study on 120 Arabidopsis thaliana, Landsberg erecta × Cape Verde Islands, recombinant inbred lines (RILs) in active response to the environment by treating them with 3 h of shade. The results of this experiment are compared to a previous study on seedlings of the same RILs from a steady state environment. The combination of two highly different conditions but exactly the same RILs with a fixed genetic variation showed the large role of genotype-by-environment interactions on gene expression levels. We found environment-dependent hotspots of transcript regulation. The major hotspot was confirmed by the expression profile of a near isogenic line. Our combined analysis leads us to propose CSN5A, a COP9 signalosome component, as a candidate regulator for the gene expression response to shade.

Isolation and analysis of mRNAs from specific cell types of plants by ribosome immunopurification

Multiple ribosomes assemble onto an individual mRNA to form a polyribosome (polysome) complex. The epitope tagging of specific ribosomal proteins can enable the immunopurification of polysomes from crude cell extracts derived from cryopreserved tissue samples. Through expression of the epitope-tagged ribosomal protein in cell-type and regional specific domains of Arabidopsis thaliana and other organisms it is feasible to quantitatively assess the mRNAs that are associated with ribosomes with cell-specific resolution. Here we present detailed methods for development of transgenics that express a FLAG-tagged version of ribosomal protein L18 (RPL18) under the direction of individual promoters with specific domains of expression, the immunopurification of ribosomes, and bioinformatic analyses of the resultant datasets obtained by microarray profiling. This methodology provides researchers with the opportunity to assess rapid changes at the organ, tissue, regional or cell-type specific level of mRNAs that are associated with ribosomes and therefore engaged in translation.

Differential gene expression in adipose tissue from obese human subjects during weight loss and weight maintenance

BACKGROUND

Differential gene expression in adipose tissue during diet-induced weight loss followed by a weight stability period is poorly characterized. Markers of these processes may provide a deeper understanding of underlying mechanisms.

OBJECTIVE

We aimed to identify differentially expressed genes in human adipose tissue during weight loss and weight maintenance after weight loss.

DESIGN

RNA from subcutaneous abdominal adipose tissue from 9 obese subjects was analyzed by using a complementary DNA microarray at baseline after weight loss on a low-calorie diet and after weight maintenance.

RESULTS

Subjects lost 18.8 ± 1.8% of weight and maintained this loss during weight maintenance (1.1 ± 2.1%; range: -9.3 to 10.6%). Most differentially expressed genes exhibited a reciprocal regulation and returned to baseline after weight loss (2163 genes) and weight maintenance (3175 genes). CETP and ABCG1, both of which participate in the HDL-mediated reverse cholesterol transport (RCT), were among the most upregulated of the 750 genes that were differentially expressed after both processes. Several genes involved in inflammation were downregulated. The use of real-time polymerase chain reaction confirmed or partially confirmed the previously implicated genes TNMD and MMP9 (both downregulated), PNPLA3 (upregulated), and CIDEA and SCD (both reciprocally regulated).

CONCLUSIONS

The beneficial effects of weight loss should be investigated after long-term weight maintenance. The processes of weight loss and weight maintenance should be viewed as biologically distinct. CETP and ABCG1 may be important mediators of these effects through HDL-mediated RCT.

Cardiovascular genomics: a biomarker identification pipeline

Genomic biomarkers are essential for understanding the underlying molecular basis of human diseases such as cardiovascular disease. In this review, we describe a biomarker identification pipeline for cardiovascular disease, which includes 1) high-throughput genomic data acquisition, 2) preprocessing and normalization of data, 3) exploratory analysis, 4) feature selection, 5) classification, and 6) interpretation and validation of candidate biomarkers. We review each step in the pipeline, presenting current and widely used bioinformatics methods. Furthermore, we analyze several publicly available cardiovascular genomics datasets to illustrate the pipeline. Finally, we summarize the current challenges and opportunities for further research.

Shedding light on large-scale chromatin reorganization in Arabidopsis thaliana

Plants need to respond quickly and appropriately to various types of light signals from the environment to optimize growth and development. The immediate response to shading, reduced photon flux (low light), and changes in spectral quality involves changes in gene regulation. In the case of more persistent shade, the plant shows a dramatic change in the organization of chromatin. Both plant responses are controlled via photoreceptor signaling proteins. Recently, several studies have revealed similar features of chromatin reorganization in response to various abiotic and biotic signals, while others have unveiled intricate molecular networks of light signaling towards gene regulation. This opinion paper briefly describes the chromatin (de)compaction response from a light-signaling perspective to provide a link between chromatin and the molecular network of photoreceptors and E3 ubiquitin ligase complexes.

Ethylene-induced differential petiole growth in Arabidopsis thaliana involves local microtubule reorientation and cell expansion

• Hyponastic growth is an upward petiole movement induced by plants in response to various external stimuli. It is caused by unequal growth rates between adaxial and abaxial sides of the petiole, which bring rosette leaves to a more vertical position. The volatile hormone ethylene is a key regulator inducing hyponasty in Arabidopsis thaliana. Here, we studied whether ethylene-mediated hyponasty occurs through local stimulation of cell expansion and whether this involves the reorientation of cortical microtubules (CMTs). • To study cell size differences between the two sides of a petiole in ethylene and control conditions, we analyzed epidermal imprints. We studied the involvement of CMT orientation in epidermal cells using the tubulin marker line as well as genetic and pharmacological means of CMT manipulation. • Our results demonstrate that ethylene induces cell expansion at the abaxial side of the- petiole and that this can account for the observed differential growth. At the abaxial side, ethylene induces CMT reorientation from longitudinal to transverse, whereas, at the adaxial side, it has an opposite effect. The inhibition of CMTs disturbed ethylene-induced hyponastic growth. • This work provides evidence that ethylene stimulates cell expansion in a tissue-specific manner and that it is associated with tissue-specific changes in the arrangement of CMTs along the petiole.

Transcriptomics using next generation sequencing technologies

Next generation sequencing technologies may now be applied to the study of transcriptomics. RNA-Seq or RNA sequencing employs high-throughput sequencing of complementary DNA fragments delivering a transcriptional profile. In this chapter, we aim to provide a starting point for Xenopus researchers planning on starting an RNA-Seq transcriptomics study. We begin by providing a section on template isolation and library preparation. The next section comprises the main bioinformatics procedures that need to be performed for raw data processing, normalization, and differential gene expression. Finally, we have included a section on studying deep sequencing results in Xenopus, which offers general guidance as to what can be done in this model.

An integrative study ascertained SOD2 as a susceptibility gene for osteoporosis in Chinese

Osteoporosis is characterized by low BMD and has strong genetic determination. However, specific genetic variants influencing BMD and contributing to the pathogenesis of osteoporosis are largely uncharacterized. Current genetic studies in bone, which are aimed at identification of osteoporosis risk genes, are focused mostly on DNA, RNA, or the protein level individually, lacking integrative evidence from the three levels of genetic information flow to confidently ascertain the significance of genes for osteoporosis. Our previous proteomics study discovered that superoxide dismutase 2 (SOD2) in circulating monocytes (CMCs, ie, potential osteoclast precursors) was significantly upregulated at protein level in vivo in Chinese with low versus high hip BMD. Herein, at mRNA level, we found that SOD2 gene expression also was upregulated in CMCs (p < 0.05) in Chinese with low versus high hip BMD. At the DNA level, in 1627 unrelated Chinese subjects, we identified eight single-nucleotide polymorphisms (SNPs) at the SOD2 gene locus that were suggestively associated with hip BMD (peak signal at rs11968525, p = 0.048). Among the eight SNPs, three SNPs (rs7754103, rs7754295, and rs2053949) were associated with the SOD2 mRNA expression level (p < 0.05), suggesting that they are expression quantitative trait loci (eQTLs) regulating SOD2 gene expression. In conclusion, this integrative evidence from DNA, RNA, and protein levels support SOD2 as a susceptibility gene for osteoporosis.

Peripheral blood monocyte-expressed ANXA2 gene is involved in pathogenesis of osteoporosis in humans

Low bone mineral density (BMD) is a risk factor of osteoporosis and has strong genetic determination. Genes influencing BMD and fundamental mechanisms leading to osteoporosis have yet to be fully determined. Peripheral blood monocytes (PBM) are potential osteoclast precursors, which could access to bone resorption surfaces and differentiate into osteoclasts to resorb bone. Herein, we attempted to identify osteoporosis susceptibility gene(s) and characterize their function(s), through an initial proteomics discovery study on PBM in vivo, and multiscale validation studies in vivo and in vitro. Utilizing the quantitative proteomics methodology LC-nano-ESI-MS(E), we discovered that a novel protein, i.e. ANXA2, was up-regulated twofold in PBM in vivo in Caucasians with extremely low BMD (cases) versus those with extremely high BMD (controls) (n = 28, p < 0.05). ANXA2 gene up-regulation in low BMD subjects was replicated at the mRNA level in PBM in vivo in a second and independent case-control sample (n = 80, p < 0.05). At the DNA level, we found that SNPs in the ANXA2 gene were associated with BMD variation in a 3(rd) and independent case-control sample (n = 44, p < 0.05), as well as in a random population sample (n = 997, p < 0.05). The above integrative evidence strongly supports the concept that ANXA2 is involved in the pathogenesis of osteoporosis in humans. Through a follow-up cellular functional study, we found that ANXA2 protein significantly promoted monocyte migration across an endothelial barrier in vitro (p < 0.001). Thus, elevated ANXA2 protein expression level, as detected in low BMD subjects, probably stimulates more PBM migration through the blood vessel walls to bone resorption surfaces in vivo, where they differentiate into higher number of osteoclasts and resorb bone at higher rates, thereby decreasing BMD. In conclusion, this study identified a novel osteoporosis susceptibility gene ANXA2, and suggested a novel pathophysiological mechanism, mediated by ANXA2, for osteoporosis in humans.

Molecular markers and mechanisms of stroke: RNA studies of blood in animals and humans

Whole genome expression microarrays can be used to study gene expression in blood, which comes in part from leukocytes, immature platelets, and red blood cells. Since these cells are important in the pathogenesis of stroke, RNA provides an index of these cellular responses to stroke. Our studies in rats have shown specific gene expression changes 24  hours after ischemic stroke, hemorrhage, status epilepticus, hypoxia, hypoglycemia, global ischemia, and following brief focal ischemia that simulated transient ischemic attacks in humans. Human studies show gene expression changes following ischemic stroke. These gene profiles predict a second cohort with >90% sensitivity and specificity. Gene profiles for ischemic stroke caused by large-vessel atherosclerosis and cardioembolism have been described that predict a second cohort with >85% sensitivity and specificity. Atherosclerotic genes were associated with clotting, platelets, and monocytes, and cardioembolic genes were associated with inflammation, infection, and neutrophils. These gene profiles predicted the cause of stroke in 58% of cryptogenic patients. These studies will provide diagnostic, prognostic, and therapeutic markers, and will advance our understanding of stroke in humans. New techniques to measure all coding and noncoding RNAs along with alternatively spliced transcripts will markedly advance molecular studies of human stroke.

Gene expression in Graves' ophthalmopathy and arm lymphedema: similarities and differences

BACKGROUND

Graves' ophthalmopathy (GO) and lymphedema share some pathogenetic mechanisms, such as edema, inflammation, and adipogenesis. The aim of this study was to examine similarities and differences between chronic GO and chronic lymphedema.

METHODS

Intraorbital adipose tissue was collected from patients with active (n = 10) or chronic GO (n = 10) and thyroid-healthy controls (n = 10). Arm subcutaneous adipose tissue was obtained from patients with chronic arm lymphedema (n = 10), where the unaffected arm served as a control. Gene expression was studied using microarray and real-time polymerase chain reaction.

RESULTS

The following genes were significantly upregulated (p < 0.05) in lymphedema but not in GO and have functions in wound healing, fibrosis, fat metabolism, inflammation, differentiation, development, adhesion, and the cytoskeleton: ATP-binding cassette, sub-family G (WHITE), member 1 (ABCG1), actin, alpha 2, smooth muscle, aorta (ACTA2), secreted frizzled-related protein 2 (SFRP2), tenascin C (TNC), pentraxin-related gene, rapidly induced by IL-1 beta (PTX3), and carboxypeptidase X (M14 family), member 1 (CPMX1). In chronic GO, but not in lymphedema, adipocyte-related immediate early genes known to be overexpressed in patients with active GO were upregulated but at a lower level than previously shown for the active phase. Genes of the Wnt pathway, such as secreted frizzled-related protein 1, 2, and 3, were up- and downregulated in both chronic GO and lymphedema. Parathyroid hormone-like hormone (PTHLH) was downregulated (p = 0.01) and apolipoprotein L domain containing 1 (APOLD1) was upregulated (p = 0.05) in both active and chronic GO.

CONCLUSIONS

There are more differences than similarities between chronic ophthalmopathy and chronic lymphedema, but both conditions exhibit less inflammation and adipogenesis compared to the active phases. In lymphedema, fibrosis dominates. PTHLH, which can inhibit adipogenesis, is downregulated both in active and chronic ophthalmopathy, indicating the possibility of an increased risk of adipogenesis.

RefGenes: identification of reliable and condition specific reference genes for RT-qPCR data normalization

Background

RT-qPCR is a sensitive and increasingly used method for gene expression quantification. To normalize RT-qPCR measurements between samples, most laboratories use endogenous reference genes as internal controls. There is increasing evidence, however, that the expression of commonly used reference genes can vary significantly in certain contexts.

Results

Using the Genevestigator database of normalized and well-annotated microarray experiments, we describe the expression stability characteristics of the transciptomes of several organisms. The results show that a) no genes are universally stable, b) most commonly used reference genes yield very high transcript abundances as compared to the entire transcriptome, and c) for each biological context a subset of stable genes exists that has smaller variance than commonly used reference genes or genes that were selected for their stability across all conditions.

Conclusion

We therefore propose the normalization of RT-qPCR data using reference genes that are specifically chosen for the conditions under study. RefGenes is a community tool developed for that purpose. Validation RT-qPCR experiments across several organisms showed that the candidates proposed by RefGenes generally outperformed commonly used reference genes. RefGenes is available within Genevestigator at http://www.genevestigator.com.

Experimental design, preprocessing, normalization and differential expression analysis of small RNA sequencing experiments

Prior to the advent of new, deep sequencing methods, small RNA (sRNA) discovery was dependent on Sanger sequencing, which was time-consuming and limited knowledge to only the most abundant sRNA. The innovation of large-scale, next-generation sequencing has exponentially increased knowledge of the biology, diversity and abundance of sRNA populations. In this review, we discuss issues involved in the design of sRNA sequencing experiments, including choosing a sequencing platform, inherent biases that affect sRNA measurements and replication. We outline the steps involved in preprocessing sRNA sequencing data and review both the principles behind and the current options for normalization. Finally, we discuss differential expression analysis in the absence and presence of biological replicates. While our focus is on sRNA sequencing experiments, many of the principles discussed are applicable to the sequencing of other RNA populations.

Polymorphisms in predicted miRNA binding sites and osteoporosis

MicroRNAs (miRNAs) regulate posttranscriptional gene expression usually by binding to 3'-untranslated regions (3'-UTRs) of target message RNAs (mRNAs). Hence genetic polymorphisms on 3'-UTRs of mRNAs may alter binding affinity between miRNAs target 3'-UTRs, thereby altering translational regulation of target mRNAs and/or degradation of mRNAs, leading to differential protein expression of target genes. Based on a database that catalogues predicted polymorphisms in miRNA target sites (poly-miRTSs), we selected 568 polymorphisms within 3'-UTRs of target mRNAs and performed association analyses between these selected poly-miRTSs and osteoporosis in 997 white subjects who were genotyped by Affymetrix Human Mapping 500K arrays. Initial discovery (in the 997 subjects) and replication (in 1728 white subjects) association analyses identified three poly-miRTSs (rs6854081, rs1048201, and rs7683093) in the fibroblast growth factor 2 (FGF2) gene that were significantly associated with femoral neck bone mineral density (BMD). These three poly-miRTSs serve as potential binding sites for 9 miRNAs (eg, miR-146a and miR-146b). Further gene expression analyses demonstrated that the FGF2 gene was differentially expressed between subjects with high versus low BMD in three independent sample sets. Our initial and replicate association studies and subsequent gene expression analyses support the conclusion that these three polymorphisms of the FGF2 gene may contribute to susceptibility to osteoporosis, most likely through their effects on altered binding affinity for specific miRNAs.

Gene expression analysis of HUVEC in response to TF-binding

INTRODUCTION

Tissue factor (TF), the cofactor for factor VII/VIIa (FVII/FVIIa) and initiator of the extrinsic pathway, is transiently expressed on intravascular cells under control of cytokines and growth factors. In addition, endothelial cells express a binding site for external TF. In the present study, we investigated gene expression of endothelial cells derived from human umbilical veins (HUVEC) in response to TF-binding to identify differentially expressed genes.

MATERIALS AND METHODS

HUVEC were treated with recombinant relipidated TF (Innovin) versus nontreated cells, as well as TF/FVIIa versus FVIIa alone. TF binding was measured by ELISA. Gene expression profiles were examined using HG-U133 plus 2.0 arrays (Affymetrix).

RESULTS

Gene expression analysis of HUVEC showed 148 up-regulated and 29 down-regulated genes 4h after TF binding. Notably, the genes, which were significantly up- and down-regulated, either by TF alone or by the complex of TF/FVIIa, exhibited a complete overlap, indicating that activation of endothelial cells after binding of external added TF does not depend on FVIIa as has been demonstrated for TF-expressing cells. TF-mediated regulation of gene expression of several genes, involved in regulation of apoptosis, cell adhesion, cell motility, and angiogenesis, was confirmed by qPCR. Furthermore, in case of SELE, TGFB2, TNFAIP3, TNFSF4, TNFSF18, TAGLN, CXCL1, PCF11 antibodies directed to TF clearly inhibited TF-mediated regulation of gene expression.

CONCLUSIONS

The results demonstrate that interaction of TF with HUVEC via a binding site, independent from FVIIa, may result in regulation of a variety of genes involved in arteriosclerosis, cancer, and cardiovascular diseases.

Genes and biochemical pathways in human skeletal muscle affecting resting energy expenditure and fuel partitioning

Genes influencing resting energy expenditure (REE) and respiratory quotient (RQ) represent candidate genes for obesity and the metabolic syndrome because of the involvement of these traits in energy balance and substrate oxidation. We aim to explore the molecular basis for individual variation in REE and fuel partitioning as reflected by RQ. We performed microarray studies in human vastus lateralis muscle biopsies from 40 healthy subjects with measured REE and RQ values. We identified 2,392 and 1,115 genes significantly correlated with REE and RQ, respectively. Genes correlated with REE and RQ encompass a broad array of functions, including carbohydrate and lipid metabolism, gene expression, mitochondrial processes, and membrane transport. Microarray pathway analysis revealed that REE was positively correlated with upregulation of G protein-coupled receptor signaling (meet criteria/total genes: 65 of 283) involved in autonomic nervous system functions, including those receptors mediating adrenergic, dopamine, γ-aminobutyric acid (GABA), neuropeptide Y (NPY), and serotonin action (meet criteria/total genes: 46 of 176). Reduced REE was associated with an increase in genes participating in ubiquitin-proteasome-dependent proteolytic pathways (58 of 232). Serine-type peptidase activity (9 of 76) was positively correlated with RQ, while genes involved in the protein phosphatase type 2A complex (4 of 9), mitochondrial function and cellular respiration (38 of 315), and unfolded protein binding (19 of 97) were associated with reduced RQ values and a preference for lipid fuel metabolism. Individual variations in whole body REE and RQ are regulated by differential expressions of specific genes and pathways intrinsic to skeletal muscle.

Algorithm-driven artifacts in median polish summarization of microarray data

BACKGROUND

High-throughput measurement of transcript intensities using Affymetrix type oligonucleotide microarrays has produced a massive quantity of data during the last decade. Different preprocessing techniques exist to convert the raw signal intensities measured by these chips into gene expression estimates. Although these techniques have been widely benchmarked in the context of differential gene expression analysis, there are only few examples where their performance has been assessed in respect to coexpression-based studies such as sample classification.

RESULTS

In the present paper we benchmark the three most used normalization procedures (MAS5, RMA and GCRMA) in the context of inter-array correlation analysis, confirming and extending the finding that RMA and GCRMA consistently overestimate sample similarity upon normalization. We determine that median polish summarization is responsible for generating a large proportion of these over-similarity artifacts. Furthermore, we show that most affected probesets show also internal signal disagreement, and tend to be composed by individual probes hitting different gene transcripts. We finally provide a correction to the RMA/GCRMA summarization procedure that massively reduces inter-array correlation artifacts, without affecting the detection of differentially expressed genes.

CONCLUSIONS

We propose tRMA as a modification of RMA to normalize microarray experiments for correlation-based analysis.

A Brassica exon array for whole-transcript gene expression profiling

Affymetrix GeneChip® arrays are used widely to study transcriptional changes in response to developmental and environmental stimuli. GeneChip® arrays comprise multiple 25-mer oligonucleotide probes per gene and retain certain advantages over direct sequencing. For plants, there are several public GeneChip® arrays whose probes are localised primarily in 3' exons. Plant whole-transcript (WT) GeneChip® arrays are not yet publicly available, although WT resolution is needed to study complex crop genomes such as Brassica, which are typified by segmental duplications containing paralogous genes and/or allopolyploidy. Available sequence data were sampled from the Brassica A and C genomes, and 142,997 gene models identified. The assembled gene models were then used to establish a comprehensive public WT exon array for transcriptomics studies. The Affymetrix GeneChip® Brassica Exon 1.0 ST Array is a 5 µM feature size array, containing 2.4 million 25-base oligonucleotide probes representing 135,201 gene models, with 15 probes per gene distributed among exons. Discrimination of the gene models was based on an E-value cut-off of 1E(-5), with ≤98% sequence identity. The 135 k Brassica Exon Array was validated by quantifying transcriptome differences between leaf and root tissue from a reference Brassica rapa line (R-o-18), and categorisation by Gene Ontologies (GO) based on gene orthology with Arabidopsis thaliana. Technical validation involved comparison of the exon array with a 60-mer array platform using the same starting RNA samples. The 135 k Brassica Exon Array is a robust platform. All data relating to the array design and probe identities are available in the public domain and are curated within the BrassEnsembl genome viewer at http://www.brassica.info/BrassEnsembl/index.html.

Gene expression profiling of primary human articular chondrocytes in high-density micromasses reveals patterns of recovery, maintenance, re- and dedifferentiation

The high-density micromass culture has been widely applied to study chondrocyte cell physiology and pathophysiological mechanisms. Since an integrated image has not been established so far, we analyzed the phenotypic alterations of human articular chondrocytes in this model on the broad molecular level. Freshly isolated chondrocytes were assembled as micromasses and maintained up to 6 weeks in medium containing human serum. Formation of cartilaginous extracellular matrix (ECM) was evaluated by histological and immunohistochemical staining. At 0, 3 and 6 weeks, chondrocyte micromasses were subjected to gene expression analysis using oligonucleotide microarrays and real-time RT-PCR. Micromasses developed a cartilaginous ECM rich in proteoglycans and type II collagen. On gene expression level, time-dependent expression patterns was observed. The induction of genes associated with cartilage-specific ECM (COL2A1 and COL11A1) and developmental signaling (GDF5, GDF10, ID1, ID4 and FGFR1-3) indicated redifferentiation within the first 3 weeks. The repression of genes related to stress response (HSPA1A and HSPA4), apoptotic events (HYOU1, NFKBIA and TRAF1), and degradation (MMP1, MMP10 and MMP12) suggested a recovery of chondrocytes. Constant expression of other chondrogenic (ACAN, FN1 and MGP) and hypertrophic markers (COL10A1, ALPL, PTHR1 and PTHR2) indicated a pattern of phenotypic maintenance. Simultaneously, the expression of chondrogenic growth (BMP6, TGFA, FGF1 and FGF2) and transcription factors (SOX9, EGR1, HES1 and TGIF1), and other cartilage ECM-related genes (COMP and PRG4) was consistently repressed and expression of collagens related to dedifferentiation (COL1A1 and COL3A1) was steadily induced indicating a progressing loss of cartilage phenotype. Likewise, a steady increase of genes associated with proliferation (GAS6, SERPINF1, VEGFB and VEGFC) and apoptosis (DRAM, DPAK1, HSPB, GPX1, NGFRAP1 and TIA1) was observed. Sequence and interplay of identified expression patterns suggest that chondrocyte micromass cultures maintain a differentiated phenotype up to 3 weeks in vitro and might be useful for studying chondrocyte biology, pathophysiology and differentiation. Cultivation longer than 6 weeks leads to progressing dedifferentiation of chondrocytes that should be considered on long-term evaluations.

Dynamics of dendritic cell maturation are identified through a novel filtering strategy applied to biological time-course microarray replicates

Background

Dendritic cells (DC) play a central role in primary immune responses and become potent stimulators of the adaptive immune response after undergoing the critical process of maturation. Understanding the dynamics of DC maturation would provide key insights into this important process. Time course microarray experiments can provide unique insights into DC maturation dynamics. Replicate experiments are necessary to address the issues of experimental and biological variability. Statistical methods and averaging are often used to identify significant signals. Here a novel strategy for filtering of replicate time course microarray data, which identifies consistent signals between the replicates, is presented and applied to a DC time course microarray experiment.

Results

The temporal dynamics of DC maturation were studied by stimulating DC with poly(I:C) and following gene expression at 5 time points from 1 to 24 hours. The novel filtering strategy uses standard statistical and fold change techniques, along with the consistency of replicate temporal profiles, to identify those differentially expressed genes that were consistent in two biological replicate experiments. To address the issue of cluster reproducibility a consensus clustering method, which identifies clusters of genes whose expression varies consistently between replicates, was also developed and applied. Analysis of the resulting clusters revealed many known and novel characteristics of DC maturation, such as the up-regulation of specific immune response pathways. Intriguingly, more genes were down-regulated than up-regulated. Results identify a more comprehensive program of down-regulation, including many genes involved in protein synthesis, metabolism, and housekeeping needed for maintenance of cellular integrity and metabolism.

Conclusions

The new filtering strategy emphasizes the importance of consistent and reproducible results when analyzing microarray data and utilizes consistency between replicate experiments as a criterion in both feature selection and clustering, without averaging or otherwise combining replicate data. Observation of a significant down-regulation program during DC maturation indicates that DC are preparing for cell death and provides a path to better understand the process. This new filtering strategy can be adapted for use in analyzing other large-scale time course data sets with replicates.

Understanding prognostic gene expression signatures in lung cancer

In non-small-cell lung cancer (NSCLC), molecular profiling of tumors has led to the identification of gene expression patterns that are associated with specific phenotypes and prognosis. Such correlations could identify early-stage patients who are at increased risk of disease recurrence and death after complete surgical resection and who might benefit from adjuvant therapy. Profiling may also identify aberrant molecular pathways that might lead to specific molecularly targeted therapies. The technology behind the capturing and correlating of molecular profiles with clinical and biologic endpoints have evolved rapidly since microarrays were first developed a decade ago. In this review, we discuss multiple methods that have been used to derive prognostic gene expression signatures in NSCLC. Despite the diversity in the approaches used, 3 main steps are followed. First, the expression levels of several hundred to tens of thousands of genes are quantified by microarray or quantitative polymerase chain reaction techniques; the data are then preprocessed, normalized, and possibly filtered. In the second step, expression data are combined and grouped by clustering, risk score generation, or other means, to generate a gene signature that correlates with a clinical outcome, usually survival. Finally, the signature is validated in datasets of independent cohorts. This review discusses the concepts and methodologies involved in these analytical steps, primarily to facilitate the understanding of reports on large dataset gene expression studies that focus on prognostic signatures in NSCLC.

Downstream targets of HOXB4 in a cell line model of primitive hematopoietic progenitor cells

Enforced expression of the homeobox transcription factor HOXB4 has been shown to enhance hematopoietic stem cell self-renewal and expansion ex vivo and in vivo. To investigate the downstream targets of HOXB4 in hematopoietic progenitor cells, HOXB4 was constitutively overexpressed in the primitive hematopoietic progenitor cell line EML. Two genome-wide analytical techniques were used: RNA expression profiling using microarrays and chromatin immunoprecipitation (ChIP)-chip. RNA expression profiling revealed that 465 gene transcripts were differentially expressed in KLS (c-Kit(+), Lin(-), Sca-1(+))-EML cells that overexpressed HOXB4 (KLS-EML-HOXB4) compared with control KLS-EML cells that were transduced with vector alone. In particular, erythroid-specific gene transcripts were observed to be highly down-regulated in KLS-EML-HOXB4 cells. ChIP-chip analysis revealed that the promoter region for 1910 genes, such as CD34, Sox4, and B220, were occupied by HOXB4 in KLS-EML-HOXB4 cells. Side-by-side comparison of the ChIP-chip and RNA expression profiling datasets provided correlative information and identified Gp49a and Laptm4b as candidate "stemness-related" genes. Both genes were highly ranked in both dataset lists and have been previously shown to be preferentially expressed in hematopoietic stem cells and down-regulated in mature hematopoietic cells, thus making them attractive candidates for future functional studies in hematopoietic cells.

A robust method for estimating gene expression states using Affymetrix microarray probe level data

Background

Microarray technology is a high-throughput method for measuring the expression levels of thousand of genes simultaneously. The observed intensities combine a non-specific binding, which is a major disadvantage with microarray data. The Affymetrix GeneChip assigned a mismatch (MM) probe with the intention of measuring non-specific binding, but various opinions exist regarding usefulness of MM measures. It should be noted that not all observed intensities are associated with expressed genes and many of those are associated with unexpressed genes, of which measured values express mere noise due to non-specific binding, cross-hybridization, or stray signals. The implicit assumption that all genes are expressed leads to poor performance of microarray data analyses. We assume two functional states of a gene - expressed or unexpressed - and propose a robust method to estimate gene expression states using an order relationship between PM and MM measures.

Results

An indicator 'probability of a gene being expressed' was obtained using the number of probe pairs within a probe set where the PM measure exceeds the MM measure. We examined the validity of the proposed indicator using Human Genome U95 data sets provided by Affymetrix. The usefulness of 'probability of a gene being expressed' is illustrated through an exploration of candidate genes involved in neuroblastoma prognosis. We identified the candidate genes for which expression states differed (un-expressed or expressed) when compared between two outcomes. The validity of this result was subsequently confirmed by quantitative RT-PCR.

Conclusion

The proposed qualitative evaluation, 'probability of a gene being expressed', is a useful indicator for improving microarray data analysis. It is useful to reduce the number of false discoveries. Expression states - expressed or unexpressed - correspond to the most fundamental gene function 'On' and 'Off', which can lead to biologically meaningful results.