Probe set filtering increases correlation between Affymetrix GeneChip and qRT-PCR expression measurements

The role of lateral pterygoid muscle in the traumatic temporomandibular joint ankylosis: A gene chip based analysis

Traumatic temporomandibular joint ankylosis (TMJA) is a common disease and disorder of the temporomandibular joint (TMJ); however, its pathogenesis has yet to be completely elucidated. In the authors' previous studies, the lateral pterygoid muscle (LPM) was confirmed to exert a function in distraction osteogenesis (DO) during the healing of a condylar fracture, which resulted in the formation of excess bone. The aim of the present study was to investigate alterations in the expression of any associated genes via an Affymetrix GeneChip method. The traumatic TMJA model was fabricated by a condylar fracture in the TMJ area of sheep with either a dissected LPM (LPD) or normal (LPN). The untreated sheep served as a control. At 4- and 12 weeks post-surgery, the condylar zone was isolated to perform the gene chip analysis, which was performed according to a standard Affymetrix protocol. The validated genes were further evaluated by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). The gene chip analysis indicated that the LPN gene expression pattern was similar compared with the DO process, while LPD was similar to that of normal bone fracture healing. The validated genes were collagen type II α1 chain, C-type lectin domain family 3 member A, interleukin 1A, cartilage oligomeric matrix protein, chondromodulin (LECT1), calcitonin receptor (CALCR), transforming growth factor (TGF)-β1, Fos proto-oncogene (FOS), bone γ-carboxyglutamate protein and bone morphogenic protein (BMP)7, among which, BMP7, LECT1, CALCR and FOS were confirmed by RT-qPCR. In conclusion, the present study demonstrated that LPM exerts a DO effect during the pathogenesis of traumatic TMJA, which may provide a novel target for preventing TMJA.

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.

Down-regulation of IKKβ expression in glioma-infiltrating microglia/macrophages is associated with defective inflammatory/immune gene responses in glioblastoma

Glioblastoma (GBM) is an aggressive malignancy associated with profound host immunosuppression. Microglia and macrophages infiltrating GBM acquire the pro-tumorigenic, M2 phenotype and support tumor invasion, proliferation, survival, angiogenesis and block immune responses both locally and systematically. Mechanisms responsible for immunological deficits in GBM patients are poorly understood. We analyzed immune/inflammatory gene expression in five datasets of low and high grade gliomas, and performed Gene Ontology and signaling pathway analyses to identify defective transcriptional responses. The expression of many immune/inflammatory response and TLR signaling pathway genes was reduced in high grade gliomas compared to low grade gliomas. In particular, we found the reduced expression of the IKBKB, a gene coding for IKKβ, which phosphorylates IκB proteins and represents a convergence point for most signal transduction pathways leading to NFκB activation. The reduced IKBKB expression and IKKβ levels in GBM tissues were demonstrated by qPCR, Western blotting and immunohistochemistry. The IKKβ expression was down-regulated in microglia/macrophages infiltrating glioblastoma. NFκB activation, prominent in microglia/macrophages infiltrating low grade gliomas, was reduced in microglia/macrophages in glioblastoma tissues. Down-regulation of IKBKB expression and NFκB signaling in microglia/macrophages infiltrating glioblastoma correlates with defective expression of immune/inflammatory genes and M2 polarization that may result in the global impairment of anti-tumor immune responses in glioblastoma.

Integration of genome-wide of Stat3 binding and epigenetic modification mapping with transcriptome reveals novel Stat3 target genes in glioma cells

BACKGROUND

Signal transducer and activator of transcription 3 (STAT3) is constitutively activated in many human tumors, including gliomas, and regulates the expression of genes implicated in proliferation, survival, apoptosis, angiogenesis and immune regulation. Only a small fraction of those genes has been proven to be direct STAT3 targets. In gliomas, STAT3 can play tumor suppressive or oncogenic roles depending on the tumor genetic background with target genes being largely unknown.

RESULTS

We used chromatin immunoprecipitation, promoter microarrays and deep sequencing to assess the genome-wide occupancy of phospho (p)-Stat3 and epigenetic modifications of H3K4me3 and H3ac in C6 glioma cells. This combined assessment identified a list of 1200 genes whose promoters have both Stat3 binding sites and epigenetic marks characteristic for actively transcribed genes. The Stat3 and histone markings data were also intersected with a set of microarray data from C6 glioma cells after inhibition of Jak2/Stat3 signaling. Subsequently, we found 284 genes characterized by p-Stat3 occupancy, activating histone marks and transcriptional changes. Novel genes were screened for their potential involvement in oncogenesis, and the most interesting hits were verified by ChIP-PCR and STAT3 knockdown in human glioma cells.

CONCLUSIONS

Non-random association between silent genes, histone marks and p-Stat3 binding near transcription start sites was observed, consistent with its repressive role in transcriptional regulation of target genes in glioma cells with specific genetic background.

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.

mTORC1 controls the adaptive transition of quiescent stem cells from G0 to G(Alert)

A unique property of many adult stem cells is their ability to exist in a non-cycling, quiescent state¹. Although quiescence serves an essential role in preserving stem cell function until the stem cell is needed in tissue homeostasis or repair, defects in quiescence can lead to an impairment in tissue function², the extent to which stem cells can regulate quiescence is unknown. Here, we show that the stem cell quiescent state is composed of two distinct functional phases: G₀ and an “alert” phase we term G_Alert, and that stem cells actively and reversibly transition between these phases in response to injury-induced, systemic signals. Using genetic models specific to muscle stem cells (or satellite cells (SCs)), we show that mTORC1 activity is necessary and sufficient for the transition of SCs from G₀ into G_Alert and that signaling through the HGF receptor, cMet is also necessary. We also identify G₀-to-G_Alert transitions in several populations of quiescent stem cells. Quiescent stem cells that transition into G_Alert possess enhanced tissue regenerative function. We propose that the transition of quiescent stem cells into G_Alert functions as an ‘alerting’ mechanism, an adaptive response that positions stem cells to respond rapidly under conditions of injury and stress without requiring cell cycle entry or a cell fate commitment.

Longitudinal Analysis of Whole Blood Transcriptomes to Explore Molecular Signatures Associated with Acute Renal Allograft Rejection

In this study, we explored a time course of peripheral whole blood transcriptomes from kidney transplantation patients who either experienced an acute rejection episode or did not in order to better delineate the immunological and biological processes measureable in blood leukocytes that are associated with acute renal allograft rejection. Using microarrays, we generated gene expression data from 24 acute rejectors and 24 nonrejectors. We filtered the data to obtain the most unambiguous and robustly expressing probe sets and selected a subset of patients with the clearest phenotype. We then performed a data-driven exploratory analysis using data reduction and differential gene expression analysis tools in order to reveal gene expression signatures associated with acute allograft rejection. Using a template-matching algorithm, we then expanded our analysis to include time course data, identifying genes whose expression is modulated leading up to acute rejection. We have identified molecular phenotypes associated with acute renal allograft rejection, including a significantly upregulated signature of neutrophil activation and accumulation following transplant surgery that is common to both acute rejectors and nonrejectors. Our analysis shows that this expression signature appears to stabilize over time in nonrejectors but persists in patients who go on to reject the transplanted organ. In addition, we describe an expression signature characteristic of lymphocyte activity and proliferation. This lymphocyte signature is significantly downregulated in both acute rejectors and nonrejectors following surgery; however, patients who go on to reject the organ show a persistent downregulation of this signature relative to the neutrophil signature.

Longitudinal analysis of whole blood transcriptomes to explore molecular signatures associated with acute renal allograft rejection

In this study, we explored a time course of peripheral whole blood transcriptomes from kidney transplantation patients who either experienced an acute rejection episode or did not in order to better delineate the immunological and biological processes measureable in blood leukocytes that are associated with acute renal allograft rejection. Using microarrays, we generated gene expression data from 24 acute rejectors and 24 nonrejectors. We filtered the data to obtain the most unambiguous and robustly expressing probe sets and selected a subset of patients with the clearest phenotype. We then performed a data-driven exploratory analysis using data reduction and differential gene expression analysis tools in order to reveal gene expression signatures associated with acute allograft rejection. Using a template-matching algorithm, we then expanded our analysis to include time course data, identifying genes whose expression is modulated leading up to acute rejection. We have identified molecular phenotypes associated with acute renal allograft rejection, including a significantly upregulated signature of neutrophil activation and accumulation following transplant surgery that is common to both acute rejectors and nonrejectors. Our analysis shows that this expression signature appears to stabilize over time in nonrejectors but persists in patients who go on to reject the transplanted organ. In addition, we describe an expression signature characteristic of lymphocyte activity and proliferation. This lymphocyte signature is significantly downregulated in both acute rejectors and nonrejectors following surgery; however, patients who go on to reject the organ show a persistent downregulation of this signature relative to the neutrophil signature.

The signal transducers Stat1 and Stat3 and their novel target Jmjd3 drive the expression of inflammatory genes in microglia

Most neurological diseases are associated with chronic inflammation initiated by the activation of microglia, which produce cytotoxic and inflammatory factors. Signal transducers and activators of transcription (STATs) are potent regulators of gene expression but contribution of particular STAT to inflammatory gene expression and STAT-dependent transcriptional networks underlying brain inflammation need to be identified. In the present study, we investigated the genomic distribution of Stat binding sites and the role of Stats in the gene expression in lipopolysaccharide (LPS)-activated primary microglial cultures. Integration of chromatin immunoprecipitation-promoter microarray data and transcriptome data revealed novel Stat-target genes including Jmjd3, Ccl5, Ezr, Ifih1, Irf7, Uba7, and Pim1. While knockdown of individual Stat had little effect on the expression of tested genes, knockdown of both Stat1 and Stat3 inhibited the expression of Jmjd3 and inflammatory genes. Transcriptional regulation of Jmjd3 by Stat1 and Stat3 is a novel mechanism crucial for launching inflammatory responses in microglia. The effects of Jmjd3 on inflammatory gene expression were independent of its H3K27me3 demethylase activity. Forced expression of constitutively activated Stat1 and Stat3 induced the expression of Jmjd3, inflammation-related genes, and the production of pro-inflammatory cytokines as potently as lipopolysacharide. Gene set enrichment and gene function analysis revealed categories linked to the inflammatory response in LPS and Stat1C + Stat3C groups. We defined upstream pathways that activate STATs in response to LPS and demonstrated contribution of Tlr4 and Il-6 and interferon-γ signaling. Our findings define novel direct transcriptional targets of Stat1 and Stat3 and highlight their contribution to inflammatory gene expression.

KEY MESSAGE

Combined analysis of genomic Stat occupancy and transcriptome revealed novel Stat target genes in LPS-induced microglia. Jmjd3 transcription factor is a novel transcriptional target of Stat1 and Stat3. Stat1 and Stat3 cooperate with Jmjd3 to induce the expression of pro-inflammatory genes. Constitutively active Stat1 and Stat3 fully mimic the LPS-induced upregulation of inflammatory genes and secretion of cytokines.

A decision theory paradigm for evaluating identifier mapping and filtering methods using data integration

Background

In bioinformatics, we pre-process raw data into a format ready for answering medical and biological questions. A key step in processing is labeling the measured features with the identities of the molecules purportedly assayed: “molecular identification” (MI). Biological meaning comes from identifying these molecular measurements correctly with actual molecular species. But MI can be incorrect. Identifier filtering (IDF) selects features with more trusted MI, leaving a smaller, but more correct dataset. Identifier mapping (IDM) is needed when an analyst is combining two high-throughput (HT) measurement platforms on the same samples. IDM produces ID pairs, one ID from each platform, where the mapping declares that the two analytes are associated through a causal path, direct or indirect (example: pairing an ID for an mRNA species with an ID for a protein species that is its putative translation). Many competing solutions for IDF and IDM exist. Analysts need a rigorous method for evaluating and comparing all these choices.

Results

We describe a paradigm for critically evaluating and comparing IDF and IDM methods, guided by data on biological samples. The requirements are: a large set of biological samples, measurements on those samples from at least two high-throughput platforms, a model family connecting features from the platforms, and an association measure. From these ingredients, one fits a mixture model coupled to a decision framework. We demonstrate this evaluation paradigm in three settings: comparing performance of several bioinformatics resources for IDM between transcripts and proteins, comparing several published microarray probeset IDF methods and their combinations, and selecting optimal quality thresholds for tandem mass spectrometry spectral events.

Conclusions

The paradigm outlined here provides a data-grounded approach for evaluating the quality not just of IDM and IDF, but of any pre-processing step or pipeline. The results will help researchers to semantically integrate or filter data optimally, and help bioinformatics database curators to track changes in quality over time and even to troubleshoot causes of MI errors.

Changes in mRNA stability associated with cold stress in Arabidopsis cells

Control of mRNA half-life is a powerful strategy to adjust individual mRNA levels to various stress conditions, because the mRNA degradation rate controls not only the steady-state mRNA level but also the transition speed of mRNA levels. Here, we analyzed mRNA half-life changes in response to cold stress in Arabidopsis cells using genome-wide analysis, in which mRNA half-life measurements and transcriptome analysis were combined. Half-lives of average transcripts were determined to be elongated under cold conditions. Taking this general shift into account, we identified more than a thousand transcripts that were classified as relatively stabilized or relatively destabilized. The relatively stabilized class was predominantly observed in functional categories that included various regulators involved in transcriptional, post-transcriptional and post-translational processes. On the other hand, the relatively destabilized class was enriched in categories related to stress and hormonal response proteins, supporting the idea that rapid decay of mRNA is advantageous for swift responses to stress. In addition, pentatricopeptide repeat, cyclin-like F-box and Myb transcription factor protein families were significantly over-represented in the relatively destabilized class. The global analysis presented here demonstrates not only the importance of mRNA turnover control in the cold stress response but also several structural characteristics that might be important in the control of mRNA stability.

The LO-BaFL method and ALS microarray expression analysis

BACKGROUND

Sporadic Amyotrophic Lateral Sclerosis (sALS) is a devastating, complex disease of unknown etiology. We studied this disease with microarray technology to capture as much biological complexity as possible. The Affymetrix-focused BaFL pipeline takes into account problems with probes that arise from physical and biological properties, so we adapted it to handle the long-oligonucleotide probes on our arrays (hence LO-BaFL). The revised method was tested against a validated array experiment and then used in a meta-analysis of peripheral white blood cells from healthy control samples in two experiments. We predicted differentially expressed (DE) genes in our sALS data, combining the results obtained using the TM4 suite of tools with those from the LO-BaFL method. Those predictions were tested using qRT-PCR assays.

RESULTS

LO-BaFL filtering and DE testing accurately predicted previously validated DE genes in a published experiment on coronary artery disease (CAD). Filtering healthy control data from the sALS and CAD studies with LO-BaFL resulted in highly correlated expression levels across many genes. After bioinformatics analysis, twelve genes from the sALS DE gene list were selected for independent testing using qRT-PCR assays. High-quality RNA from six healthy Control and six sALS samples yielded the predicted differential expression for 7 genes: TARDBP, SKIV2L2, C12orf35, DYNLT1, ACTG1, B2M, and ILKAP. Four of the seven have been previously described in sALS studies, while ACTG1, B2M and ILKAP appear in the context of this disease for the first time. Supplementary material can be accessed at: http://webpages.uncc.edu/~cbaciu/LO-BaFL/supplementary_data.html.

CONCLUSION

LO-BaFL predicts DE results that are broadly similar to those of other methods. The small healthy control cohort in the sALS study is a reasonable foundation for predicting DE genes. Modifying the BaFL pipeline allowed us to remove noise and systematic errors, improving the power of this study, which had a small sample size. Each bioinformatics approach revealed DE genes not predicted by the other; subsequent PCR assays confirmed seven of twelve candidates, a relatively high success rate.

The effects of MicroRNA transfections on global patterns of gene expression in ovarian cancer cells are functionally coordinated

Background

MicroRNAs (miRNAs) are a class of small RNAs that have been linked to a number of diseases including cancer. The potential application of miRNAs in the diagnostics and therapeutics of ovarian and other cancers is an area of intense interest. A current challenge is the inability to accurately predict the functional consequences of exogenous modulations in the levels of potentially therapeutic miRNAs.

Methods

In an initial effort to systematically address this issue, we conducted miRNA transfection experiments using two miRNAs (miR-7, miR-128). We monitored the consequent changes in global patterns of gene expression by microarray and quantitative (real-time) polymerase chain reaction. Network analysis of the expression data was used to predict the consequence of each transfection on cellular function and these predictions were experimentally tested.

Results

While ~20% of the changes in expression patterns of hundreds to thousands of genes could be attributed to direct miRNA-mRNA interactions, the majority of the changes are indirect, involving the downstream consequences of miRNA-mediated changes in regulatory gene expression. The changes in gene expression induced by individual miRNAs are functionally coordinated but distinct between the two miRNAs. MiR-7 transfection into ovarian cancer cells induces changes in cell adhesion and other developmental networks previously associated with epithelial-mesenchymal transitions (EMT) and other processes linked with metastasis. In contrast, miR-128 transfection induces changes in cell cycle control and other processes commonly linked with cellular replication.

Conclusions

The functionally coordinated patterns of gene expression displayed by different families of miRNAs have the potential to provide clinicians with a strategy to treat cancers from a systems rather than a single gene perspective.

Identification of pathway deregulation--gene expression based analysis of consistent signal transduction

Signaling pathways belong to a complex system of communication that governs cellular processes. They represent signal transduction from an extracellular stimulus via a receptor to intracellular mediators, as well as intracellular interactions. Perturbations in signaling cascade often lead to detrimental changes in cell function and cause many diseases, including cancer. Identification of deregulated pathways may advance the understanding of complex diseases and lead to improvement of therapeutic strategies. We propose Analysis of Consistent Signal Transduction (ACST), a novel method for analysis of signaling pathways. Our method incorporates information regarding pathway topology, as well as data on the position of every gene in each pathway. To preserve gene-gene interactions we use a subject-sampling permutation model to assess the significance of pathway perturbations. We applied our approach to nine independent datasets of global gene expression profiling. The results of ACST, as well as three other methods used to analyze signaling pathways, are presented in the context of biological significance and repeatability among similar, yet independent, datasets. We demonstrate the usefulness of using information of pathway structure as well as genes’ functions in the analysis of signaling pathways. We also show that ACST leads to biologically meaningful results and high repeatability.

Using genome-wide expression profiling to define gene networks relevant to the study of complex traits: from RNA integrity to network topology

Postgenomic studies of the function of genes and their role in disease have now become an area of intense study since efforts to define the raw sequence material of the genome have largely been completed. The use of whole-genome approaches such as microarray expression profiling and, more recently, RNA-sequence analysis of transcript abundance has allowed an unprecedented look at the workings of the genome. However, the accurate derivation of such high-throughput data and their analysis in terms of biological function has been critical to truly leveraging the postgenomic revolution. This chapter will describe an approach that focuses on the use of gene networks to both organize and interpret genomic expression data. Such networks, derived from statistical analysis of large genomic datasets and the application of multiple bioinformatics data resources, potentially allow the identification of key control elements for networks associated with human disease, and thus may lead to derivation of novel therapeutic approaches. However, as discussed in this chapter, the leveraging of such networks cannot occur without a thorough understanding of the technical and statistical factors influencing the derivation of genomic expression data. Thus, while the catch phrase may be "it's the network … stupid," the understanding of factors extending from RNA isolation to genomic profiling technique, multivariate statistics, and bioinformatics are all critical to defining fully useful gene networks for study of complex biology.

SCOREM: statistical consolidation of redundant expression measures

Many platforms for genome-wide analysis of gene expression contain ‘redundant’ measures for the same gene. For example, the most highly utilized platforms for gene expression microarrays, Affymetrix GeneChip® arrays, have as many as ten or more probe sets for some genes. Occasionally, individual probe sets for the same gene report different trends in expression across experimental conditions, a situation that must be resolved in order to accurately interpret the data. We developed an algorithm, SCOREM, for determining the level of agreement between such probe sets, utilizing a statistical test of concordance, Kendall's W coefficient of concordance, and a graph-searching algorithm for the identification of concordant probe sets. We also present methods for consolidating concordant groups into a single value for its corresponding gene and for post hoc analysis of discordant groups. By combining statistical consolidation with sequence analysis, SCOREM possesses the unique ability to identify biologically meaningful discordant behaviors, including differing behaviors in alternate RNA isoforms and tissue-specific patterns of expression. When consolidating concordant behaviors, SCOREM outperforms other methods in detecting both differential expression and overrepresented functional categories.

Chronic alcohol exposure alters gene expression in HepG2 cells

BACKGROUND

The liver is the primary site of alcohol metabolism and is highly vulnerable to injuries due to chronic alcohol abuse. Several molecular mechanisms, including oxidative stress and altered cellular metabolism, have been implicated in the development and progression of alcoholic liver disease. We sought to gain further insight into the molecular pathogenesis by studying the effects of ethanol exposure on the global gene expression in HepG2 cells.

METHODS

HepG2 cells were cultured in the presence or absence of 75 mM ethanol for 9 days, with fresh media daily. Global gene expression changes were studied using Affymetrix GeneChip(®) Human Exon 1.0 ST Arrays. Gene expression differences were validated for 13 genes by quantitative real-time RT-PCR. To identify biological pathways affected by ethanol treatment, differentially expressed genes were analyzed by Ingenuity Pathway Analysis software.

RESULTS

Long-term ethanol exposure altered the expression of 1,093 genes (false discovery rate ≤ 3%); many of these changes were modest. Long-term ethanol exposure affected several pathways, including acute phase response, amino acid metabolism, carbohydrate metabolism, and lipid metabolism.

CONCLUSIONS

Global measurements of gene expression show that a large number of genes are affected by chronic ethanol, although most show modest effect. These data provide insight into the molecular pathology resulting from extended alcohol exposure.

Evidence for the complexity of microRNA-mediated regulation in ovarian cancer: a systems approach

MicroRNAs (miRNAs) are short (∼22 nucleotides) regulatory RNAs that can modulate gene expression and are aberrantly expressed in many diseases including cancer. Previous studies have shown that miRNAs inhibit the translation and facilitate the degradation of their targeted messenger RNAs (mRNAs) making them attractive candidates for use in cancer therapy. However, the potential clinical utility of miRNAs in cancer therapy rests heavily upon our ability to understand and accurately predict the consequences of fluctuations in levels of miRNAs within the context of complex tumor cells. To evaluate the predictive power of current models, levels of miRNAs and their targeted mRNAs were measured in laser captured micro-dissected (LCM) ovarian cancer epithelial cells (CEPI) and compared with levels present in ovarian surface epithelial cells (OSE). We found that the predicted inverse correlation between changes in levels of miRNAs and levels of their mRNA targets held for only ∼11% of predicted target mRNAs. We demonstrate that this low inverse correlation between changes in levels of miRNAs and their target mRNAs in vivo is not merely an artifact of inaccurate miRNA target predictions but the likely consequence of indirect cellular processes that modulate the regulatory effects of miRNAs in vivo. Our findings underscore the complexities of miRNA-mediated regulation in vivo and the necessity of understanding the basis of these complexities in cancer cells before the therapeutic potential of miRNAs can be fully realized.

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.

A global insight into a cancer transcriptional space using pancreatic data: importance, findings and flaws

Despite the increasing wealth of available data, the structure of cancer transcriptional space remains largely unknown. Analysis of this space would provide novel insights into the complexity of cancer, assess relative implications in complex biological processes and responses, evaluate the effectiveness of cancer models and help uncover vital facets of cancer biology not apparent from current small-scale studies. We conducted a comprehensive analysis of pancreatic cancer-expression space by integrating data from otherwise disparate studies. We found (i) a clear separation of profiles based on experimental type, with patient tissue samples, cell lines and xenograft models forming distinct groups; (ii) three subgroups within the normal samples adjacent to cancer showing disruptions to biofunctions previously linked to cancer; and (iii) that ectopic subcutaneous xenografts and cell line models do not effectively represent changes occurring in pancreatic cancer. All findings are available from our online resource for independent interrogation. Currently, the most comprehensive analysis of pancreatic cancer to date, our study primarily serves to highlight limitations inherent with a lack of raw data availability, insufficient clinical/histopathological information and ambiguous data processing. It stresses the importance of a global-systems approach to assess and maximise findings from expression profiling of malignant and non-malignant diseases.

ArrayInitiative - a tool that simplifies creating custom Affymetrix CDFs

Background

Probes on a microarray represent a frozen view of a genome and are quickly outdated when new sequencing studies extend our knowledge, resulting in significant measurement error when analyzing any microarray experiment. There are several bioinformatics approaches to improve probe assignments, but without in-house programming expertise, standardizing these custom array specifications as a usable file (e.g. as Affymetrix CDFs) is difficult, owing mostly to the complexity of the specification file format. However, without correctly standardized files there is a significant barrier for testing competing analysis approaches since this file is one of the required inputs for many commonly used algorithms. The need to test combinations of probe assignments and analysis algorithms led us to develop ArrayInitiative, a tool for creating and managing custom array specifications.

Results

ArrayInitiative is a standalone, cross-platform, rich client desktop application for creating correctly formatted, custom versions of manufacturer-provided (default) array specifications, requiring only minimal knowledge of the array specification rules and file formats. Users can import default array specifications, import probe sequences for a default array specification, design and import a custom array specification, export any array specification to multiple output formats, export the probe sequences for any array specification and browse high-level information about the microarray, such as version and number of probes. The initial release of ArrayInitiative supports the Affymetrix 3' IVT expression arrays we currently analyze, but as an open source application, we hope that others will contribute modules for other platforms.

Conclusions

ArrayInitiative allows researchers to create new array specifications, in a standard format, based upon their own requirements. This makes it easier to test competing design and analysis strategies that depend on probe definitions. Since the custom array specifications are easily exported to the manufacturer's standard format, researchers can analyze these customized microarray experiments using established software tools, such as those available in Bioconductor.

Principal component analysis-based filtering improves detection for Affymetrix gene expression arrays

Gene expression array technology has reached the stage of being routinely used to study clinical samples in search of diagnostic and prognostic biomarkers. Due to the nature of array experiments, which examine the expression of tens of thousands of genes simultaneously, the number of null hypotheses is large. Hence, multiple testing correction is often necessary to control the number of false positives. However, multiple testing correction can lead to low statistical power in detecting genes that are truly differentially expressed. Filtering out non-informative genes allows for reduction in the number of null hypotheses. While several filtering methods have been suggested, the appropriate way to perform filtering is still debatable. We propose a new filtering strategy for Affymetrix GeneChips®, based on principal component analysis of probe-level gene expression data. Using a wholly defined spike-in data set and one from a diabetes study, we show that filtering by the proportion of variation accounted for by the first principal component (PVAC) provides increased sensitivity in detecting truly differentially expressed genes while controlling false discoveries. We demonstrate that PVAC exhibits equal or better performance than several widely used filtering methods. Furthermore, a data-driven approach that guides the selection of the filtering threshold value is also proposed.

Blood gene expression correlated with tic severity in medicated and unmedicated patients with Tourette Syndrome

BACKGROUND

Tourette Syndrome (TS) has been linked to both genetic and environmental factors. Gene-expression studies provide valuable insight into the causes of TS; however, many studies of gene expression in TS do not account for the effects of medication.

MATERIALS & METHODS

To investigate the effects of medication on gene expression in TS patients, RNA was isolated from the peripheral blood of 20 medicated TS subjects (MED) and 23 unmedicated TS subjects (UNMED), and quantified using whole-genome Affymetrix microarrays.

RESULTS

D2 dopamine receptor expression correlated positively with tic severity in MED but not UNMED. GABA(A) receptor ε subunit expression negatively correlated with tic severity in UNMED but not MED. Phenylethanolamine N-methyltransferase expression positively correlated with tic severity in UNMED but not MED.

CONCLUSION

Modulation of tics by TS medication is associated with changes in dopamine, norepinephrine and GABA pathways.

Identification of reference genes across physiological states for qRT-PCR through microarray meta-analysis

Background

The accuracy of quantitative real-time PCR (qRT-PCR) is highly dependent on reliable reference gene(s). Some housekeeping genes which are commonly used for normalization are widely recognized as inappropriate in many experimental conditions. This study aimed to identify reference genes for clinical studies through microarray meta-analysis of human clinical samples.

Methodology/Principal Findings

After uniform data preprocessing and data quality control, 4,804 Affymetrix HU-133A arrays performed by clinical samples were classified into four physiological states with 13 organ/tissue types. We identified a list of reference genes for each organ/tissue types which exhibited stable expression across physiological states. Furthermore, 102 genes identified as reference gene candidates in multiple organ/tissue types were selected for further analysis. These genes have been frequently identified as housekeeping genes in previous studies, and approximately 71% of them fall into Gene Expression (GO:0010467) category in Gene Ontology.

Conclusions/Significance

Based on microarray meta-analysis of human clinical sample arrays, we identified sets of reference gene candidates for various organ/tissue types and then examined the functions of these genes. Additionally, we found that many of the reference genes are functionally related to transcription, RNA processing and translation. According to our results, researchers could select single or multiple reference gene(s) for normalization of qRT-PCR in clinical studies.

Metabolomics-based identification of apoptosis-inducing metabolites in recombinant fed-batch CHO culture media

A liquid chromatography-mass spectrometry (LC-MS) based metabolomics platform was previously established to identify and profile extracellular metabolites in culture media of mammalian cells. This presented an opportunity to isolate novel apoptosis-inducing metabolites accumulating in the media of antibody-producing Chinese hamster ovary (CHO mAb) fed-batch bioreactor cultures. Media from triplicate cultures were collected daily for the metabolomics analysis. Concurrently, cell pellets were obtained for determination of intracellular caspase activity. Metabolite profiles from the LC-MS data were subsequently examined for their degree of correlation with the caspase activity. A panel of extracellular metabolites, the majority of which were nucleotides/nucleosides and amino acid derivatives, exhibited good (R² > 0.8) and reproducible correlation. Some of these metabolites, such as oxidized glutathione, AMP and GMP, were later shown to induce apoptosis when introduced to fresh CHO mAb cultures. Finally, metabolic engineering targets were proposed to potentially counter the harmful effects of these metabolites.

Hippocampal gene profiling: toward a systems biology of the hippocampus

Transcriptomics and proteomics approaches give a unique perspective for understanding brain and hippocampal functions but also pose unique challenges because of the singular complexity of the nervous system. The proliferation of genome-wide expression studies during the last decade has provided important insight into the molecular underpinnings of brain anatomy, neural plasticity, and neurological diseases. Microarray technology has dominated transcriptomics research, but this situation is rapidly changing with the recent technological advances in high-throughput sequencing. The full potential of transcriptomics in the neurosciences will be achieved as a result of its integration with other "-omics" disciplines as well as the development of novel analytical bioinformatics and systems biology tools for meta-analysis. Here, we review some of the most relevant advances in the gene profiling of the hippocampus, its relationship with proteomics approaches, and the promising perspectives for the future.