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Paclitaxel is widely used in the combination chemotherapy for many cancers including esophageal squamous cell carcinoma (ESCC). However, the paclitaxel resistance occurs frequently in treating ESCC and the mechanism is not fully understood yet. The heterogeneity of gene expression within the drug-resistant cancer cells may be one of the major factors contributing to its resistance. In the present study, we successfully induced paclitaxel resistance in ESCC cell line KYSE-30 through low dose and long-term treatment of paclitaxel. Gene expression profiles were measured utilizing population RNA-seq and single-cell RNA-seq (scRNA-seq). 37 single cells from KYSE-30 cells and 73 single cells from paclitaxel resistant KYSE-30 cells (Taxol-R) were subjected to scRNA-seq. Weighted gene co-expression network analysis (WGCNA) of scRNA-seq data revealed two major subpopulations in both KYSE-30 and Taxol-R cancer cells. Two subpopulations based on the KRT19 expression levels in KYSE-30 cells exhibited different paclitaxel sensitivity, suggesting the existence of an intrinsic paclitaxel resistance in KYSE-30 cells. In addition, the Taxol-R cells that acquired the resistance to paclitaxel through induction were characterized with higher expressions of proteasomes but a lower expression of HIF-1 signaling genes. Furthermore, we showed that carfilzomib (CFZ), a proteasome inhibitor, could attenuate the paclitaxel resistance in Taxol-R cancer cells through activating the HIF-1 signaling. Our new finding may pave a way leading to an improvement in the treatment on cancers including ESCC by combining CFZ with paclitaxel as a novel approach for cancer therapy., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Background: Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model., Results: We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models., Conclusions: We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.

There is a critical need for standard approaches to assess, report and compare the technical performance of genome-scale differential gene expression experiments. Here we assess technical performance with a proposed standard 'dashboard' of metrics derived from analysis of external spike-in RNA control ratio mixtures. These control ratio mixtures with defined abundance ratios enable assessment of diagnostic performance of differentially expressed transcript lists, limit of detection of ratio (LODR) estimates and expression ratio variability and measurement bias. The performance metrics suite is applicable to analysis of a typical experiment, and here we also apply these metrics to evaluate technical performance among laboratories. An interlaboratory study using identical samples shared among 12 laboratories with three different measurement processes demonstrates generally consistent diagnostic power across 11 laboratories. Ratio measurement variability and bias are also comparable among laboratories for the same measurement process. We observe different biases for measurement processes using different mRNA-enrichment protocols.

RNA-Seq provides the capability to characterize the entire transcriptome in multiple levels including gene expression, allele specific expression, alternative splicing, fusion gene detection, and etc. The US FDA-led SEQC (i.e., MAQC-III) project conducted a comprehensive study focused on the transcriptome profiling of rat liver samples treated with 27 chemicals to evaluate the utility of RNA-Seq in safety assessment and toxicity mechanism elucidation. The chemicals represented multiple chemogenomic modes of action (MOA) and exhibited varying degrees of transcriptional response. The paired-end 100 bp sequencing data were generated using Illumina HiScanSQ and/or HiSeq 2000. In addition to the core study, six animals (i.e., three aflatoxin B1 treated rats and three vehicle control rats) were sequenced three times, with two separate library preparations on two sequencing machines. This large toxicogenomics dataset can serve as a resource to characterize various aspects of transcriptomic changes (e.g., alternative splicing) that are byproduct of chemical perturbation.

The rat is used extensively by the pharmaceutical, regulatory, and academic communities for safety assessment of drugs and chemicals and for studying human diseases; however, its transcriptome has not been well studied. As part of the SEQC (i.e., MAQC-III) consortium efforts, a comprehensive RNA-Seq data set was constructed using 320 RNA samples isolated from 10 organs (adrenal gland, brain, heart, kidney, liver, lung, muscle, spleen, thymus, and testes or uterus) from both sexes of Fischer 344 rats across four ages (2-, 6-, 21-, and 104-week-old) with four biological replicates for each of the 80 sample groups (organ-sex-age). With the Ribo-Zero rRNA removal and Illumina RNA-Seq protocols, 41 million 50 bp single-end reads were generated per sample, yielding a total of 13.4 billion reads. This data set could be used to identify and validate new rat genes and transcripts, develop a more comprehensive rat transcriptome annotation system, identify novel gene regulatory networks related to tissue specific gene expression and development, and discover genes responsible for disease and drug toxicity and efficacy.

Background: Pre-exposure chemoprophylaxis (PrECP) using antiretroviral agents is a promising strategy for the prevention of sexual HIV transmission in women. Molecular transporters in the human vaginal tract (VT) may play a pivotal role in determining drug disposition and, consequently, pharmacodynamic outcomes in these efforts. Little is known, however, on the expression of these transporters in vaginal tissues, representing a critical knowledge gap., Methodology/principal Findings: Our study analyzed the genome-wide transcriptome in 44 vaginal tissue samples from 6 reproductive-age women undergoing gynecologic surgeries. The analysis revealed that, unexpectedly, a large number (43%) of gene isoforms corresponding to membrane transporters were over-expressed (above the median expression level) in all samples. A subset of 12 highly expressed membrane transporters was identified and contained 10 members (83%) of the solute carrier superfamily. The largest difference in membrane transporter gene expression was observed across subjects, but more subtle differential expression also was found along the anterior-posterior axis of the VT. Cross-validation of the microarray analyses with measurements RT-qPCR demonstrated high concordance between these data sets. Immunofluorescence labeling of membrane transporter proteins in vaginal tissues was highly dependent on tissue/cell types., Conclusions/significance: Antiretroviral PrECP drugs currently under evaluation are substrates for molecular transporters that were commonly expressed, but fell into both over- or under-expressed categories in all samples, suggesting a complex role for carrier-mediated processes in determining the disposition of these xenobiotics in vaginal tissues. These findings hold important implications for the successful development of products, either oral or intravaginal, for female-controlled HIV PrECP.

The human reference genome is still incomplete and a number of gene sequences are missing from it. The approaches to uncover them, the reasons causing their absence and their functions are less explored. Here, we comprehensively identified and characterized the missing genes of human reference genome with RNA-Seq data from 16 different human tissues. By using a combined approach of genome-guided transcriptome reconstruction coupled with genome-wide comparison, we uncovered 3.78 and 2.37 Mb transcribed regions in the human genome assemblies of Celera and HuRef either missed from their homologous chromosomes of NCBI human reference genome build 37.2 or partially or entirely absent from the reference. We further identified a significant number of novel transcript contigs in each tissue from de novo transcriptome assembly that are unalignable to NCBI build 37.2 but can be aligned to at least one of the genomes from Celera, HuRef, chimpanzee, macaca or mouse. Our analyses indicate that the missing genes could result from genome misassembly, transposition, copy number variation, translocation and other structural variations. Moreover, our results further suggest that a large portion of these missing genes are conserved between human and other mammals, implying their important biological functions. Totally, 1,233 functional protein domains were detected in these missing genes. Collectively, our study not only provides approaches for uncovering the missing genes of a genome, but also proposes the potential reasons causing genes missed from the genome and highlights the importance of uncovering the missing genes of incomplete genomes.

The molecular mechanism underlying tumor-induced epileptogenesis is poorly understood. Alterations in the peritumoral microenvironment are believed to play a significant role in inducing epileptogenesis. We hypothesize that the change of gene expression in brain peritumoral tissues may contribute to the increased neuronal excitability and epileptogenesis. To identify the genes possibly involved in tumor-induced epilepsy, a genome-wide gene expression profiling was conducted using Affymetrix HG U133 plus 2.0 arrays and RNAs derived from formalin-fixed paraffin embedded (FFPE) peritumoral cortex tissue slides from 5-seizure vs. 5-non-seizure low grade brain tumor patients. We identified many differentially expressed genes (DEGs). Seven dysregulated genes (i.e., C1QB, CALCRL, CCR1, KAL1, SLC1A2, SSTR1 and TYRO3) were validated by qRT-PCR, which showed a high concordance. Principal Component Analysis (PCA) showed that epilepsy subjects were clustered together tightly (except one sample) and were clearly separated from the non-epilepsy subjects. Molecular functional categorization showed that significant portions of the DEGs functioned as receptor activity, molecular binding including enzyme binding and transcription factor binding. Pathway analysis showed these DEGs were mainly enriched in focal adhesion, ECM-receptor interaction, and cell adhesion molecules pathways. In conclusion, our study showed that dysregulation of gene expression in the peritumoral tissues may be one of the major mechanisms of brain tumor induced-epilepsy. However, due to the small sample size of the present study, further validation study is needed. A deeper characterization on the dysregulated genes involved in brain tumor-induced epilepsy may shed some light on the management of epilepsy due to brain tumors.

Background: Heliconius butterfly wing pattern diversity offers a unique opportunity to investigate how natural genetic variation can drive the evolution of complex adaptive phenotypes. Positional cloning and candidate gene studies have identified a handful of regulatory and pigmentation genes implicated in Heliconius wing pattern variation, but little is known about the greater developmental networks within which these genes interact to pattern a wing. Here we took a large-scale transcriptomic approach to identify the network of genes involved in Heliconius wing pattern development and variation. This included applying over 140 transcriptome microarrays to assay gene expression in dissected wing pattern elements across a range of developmental stages and wing pattern morphs of Heliconius erato., Results: We identified a number of putative early prepattern genes with color-pattern related expression domains. We also identified 51 genes differentially expressed in association with natural color pattern variation. Of these, the previously identified color pattern "switch gene" optix was recovered as the first transcript to show color-specific differential expression. Most differentially expressed genes were transcribed late in pupal development and have roles in cuticle formation or pigment synthesis. These include previously undescribed transporter genes associated with ommochrome pigmentation. Furthermore, we observed upregulation of melanin-repressing genes such as ebony and Dat1 in non-melanic patterns., Conclusions: This study identifies many new genes implicated in butterfly wing pattern development and provides a glimpse into the number and types of genes affected by variation in genes that drive color pattern evolution.

Background: The Affymetrix GeneChip® system is a commonly used platform for microarray analysis but the technology is inherently expensive. Unfortunately, changes in experimental planning and execution, such as the unavailability of previously anticipated samples or a shift in research focus, may render significant numbers of pre-purchased GeneChip® microarrays unprocessed before their manufacturer's expiration dates. Researchers and microarray core facilities wonder whether expired microarrays are still useful for gene expression analysis. In addition, it was not clear whether the two human reference RNA samples established by the MAQC project in 2005 still maintained their transcriptome integrity over a period of four years. Experiments were conducted to answer these questions., Results: Microarray data were generated in 2009 in three replicates for each of the two MAQC samples with either expired Affymetrix U133A or unexpired U133Plus2 microarrays. These results were compared with data obtained in 2005 on the U133Plus2 microarray. The percentage of overlap between the lists of differentially expressed genes (DEGs) from U133Plus2 microarray data generated in 2009 and in 2005 was 97.44%. While there was some degree of fold change compression in the expired U133A microarrays, the percentage of overlap between the lists of DEGs from the expired and unexpired microarrays was as high as 96.99%. Moreover, the microarray data generated using the expired U133A microarrays in 2009 were highly concordant with microarray and TaqMan® data generated by the MAQC project in 2005., Conclusions: Our results demonstrated that microarray data generated using U133A microarrays, which were more than four years past the manufacturer's expiration date, were highly specific and consistent with those from unexpired microarrays in identifying DEGs despite some appreciable fold change compression and decrease in sensitivity. Our data also suggested that the MAQC reference RNA samples, stored at -80°C, were stable over a time frame of at least four years.

Microarray technology has great potential for improving our understanding of biological processes, medical conditions, and diseases. Often, microarray datasets are collected using different microarray platforms (provided by different companies) under different conditions in different laboratories. The cross-platform and cross-laboratory concordance of the microarray technology needs to be evaluated before it can be successfully and reliably applied in biological/clinical practice. New measures and techniques are proposed for comparing and evaluating the quality of microarray datasets generated from different platforms/laboratories. These measures and techniques are based on the following philosophy: the practical usefulness of the microarray technology may be confirmed if discriminating genes and classifiers, which are the focus of most, if not all, comparative investigations, discovered/trained from data collected in one lab/platform combination can be transferred to another lab/platform combination. The rationale is that the nondiscriminating genes might not be as strongly regulated as the discriminating genes, by the biological process of the tissue cells under study, and hence they may behave more randomly than the discriminating genes. Our experiment results, on microarray datasets generated from different platforms/laboratories using the reference mRNA samples in the Microarray Quality Control (MAQC) project, showed that DNA microarrays can produce highly repeatable data in a cross-platform cross-lab manner, when one focuses on the discriminating genes and classifiers. In our comparative study, we compare samples of one type against samples of another type; the methodology can be applied to situations where one compares one arbitrary class of data against another. Other findings include: (1) using three discriminating-gene/classifier-based methods to test the concordance between microarray datasets gave consistent results; (2) when noisy (nondiscriminating) genes were removed, the microarray datasets from different laboratories using common platform were found to be highly concordant, and the data generated using most of the commercial platforms studied here were also found to be concordant with each other; (3) several series of artificial datasets with known degree of difference were created, to establish a bridge between consistency rate and P-value, allowing us to estimate P-value if consistency rate between two datasets is known.

Background: Reproducibility is a fundamental requirement in scientific experiments. Some recent publications have claimed that microarrays are unreliable because lists of differentially expressed genes (DEGs) are not reproducible in similar experiments. Meanwhile, new statistical methods for identifying DEGs continue to appear in the scientific literature. The resultant variety of existing and emerging methods exacerbates confusion and continuing debate in the microarray community on the appropriate choice of methods for identifying reliable DEG lists., Results: Using the data sets generated by the MicroArray Quality Control (MAQC) project, we investigated the impact on the reproducibility of DEG lists of a few widely used gene selection procedures. We present comprehensive results from inter-site comparisons using the same microarray platform, cross-platform comparisons using multiple microarray platforms, and comparisons between microarray results and those from TaqMan - the widely regarded "standard" gene expression platform. Our results demonstrate that (1) previously reported discordance between DEG lists could simply result from ranking and selecting DEGs solely by statistical significance (P) derived from widely used simple t-tests; (2) when fold change (FC) is used as the ranking criterion with a non-stringent P-value cutoff filtering, the DEG lists become much more reproducible, especially when fewer genes are selected as differentially expressed, as is the case in most microarray studies; and (3) the instability of short DEG lists solely based on P-value ranking is an expected mathematical consequence of the high variability of the t-values; the more stringent the P-value threshold, the less reproducible the DEG list is. These observations are also consistent with results from extensive simulation calculations., Conclusion: We recommend the use of FC-ranking plus a non-stringent P cutoff as a straightforward and baseline practice in order to generate more reproducible DEG lists. Specifically, the P-value cutoff should not be stringent (too small) and FC should be as large as possible. Our results provide practical guidance to choose the appropriate FC and P-value cutoffs when selecting a given number of DEGs. The FC criterion enhances reproducibility, whereas the P criterion balances sensitivity and specificity.

Background: In DNA microarray gene expression profiling studies, a fundamental task is to extract statistically significant genes that meet certain research hypothesis. Currently, Venn diagram is a frequently used method for identifying overlapping genes that meet the investigator's research hypotheses. However this simple operation of intersecting multiple gene lists, known as the Intersection-Union Tests (IUTs), is performed without knowing the incurred changes in Type 1 error rate and can lead to loss of discovery power., Results: We developed an IUT adjustment procedure, called Relaxed IUT (RIUT), which is proved to be less conservative and more powerful for intersecting independent tests than the traditional Venn diagram approach. The advantage of the RIUT procedure over traditional IUT is demonstrated by empirical Monte-Carlo simulation and two real toxicogenomic gene expression case studies. Notably, the enhanced power of RIUT enables it to identify overlapping gene sets leading to identification of certain known related pathways which were not detected using the traditional IUT method., Conclusion: We showed that traditional IUT via a Venn diagram is generally conservative, which may lead to loss discovery power in DNA microarray studies. RIUT is proved to be a more powerful alternative for performing IUTs in identifying overlapping genes from multiple gene lists derived from microarray gene expression profiling.

Over the last decade, the introduction of microarray technology has had a profound impact on gene expression research. The publication of studies with dissimilar or altogether contradictory results, obtained using different microarray platforms to analyze identical RNA samples, has raised concerns about the reliability of this technology. The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and data analysis issues. Expression data on four titration pools from two distinct reference RNA samples were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. Here we describe the experimental design and probe mapping efforts behind the MAQC project. We show intraplatform consistency across test sites as well as a high level of interplatform concordance in terms of genes identified as differentially expressed. This study provides a resource that represents an important first step toward establishing a framework for the use of microarrays in clinical and regulatory settings.

To validate and extend the findings of the MicroArray Quality Control (MAQC) project, a biologically relevant toxicogenomics data set was generated using 36 RNA samples from rats treated with three chemicals (aristolochic acid, riddelliine and comfrey) and each sample was hybridized to four microarray platforms. The MAQC project assessed concordance in intersite and cross-platform comparisons and the impact of gene selection methods on the reproducibility of profiling data in terms of differentially expressed genes using distinct reference RNA samples. The real-world toxicogenomic data set reported here showed high concordance in intersite and cross-platform comparisons. Further, gene lists generated by fold-change ranking were more reproducible than those obtained by t-test P value or Significance Analysis of Microarrays. Finally, gene lists generated by fold-change ranking with a nonstringent P-value cutoff showed increased consistency in Gene Ontology terms and pathways, and hence the biological impact of chemical exposure could be reliably deduced from all platforms analyzed.

Motivation: DNA microarrays can provide information about the expression levels of thousands of genes simultaneously at the transcriptomic level, while conventional cell viability and cytotoxicity measurement methods provide information about the biological functions at the cellular level. Integrating these data at different levels provides a promising approach for evaluating or predicting how cells respond to chemical exposure. It is important to investigate the multi-scale biological system in a systematic way to better understand the gene regulation networks and signal transduction pathways involved in the cellular responses to environmental factors., Results: Primary rat hepatocytes were exposed to cadmium acetate at 0, 1.25 and 2 microM. mRNA expression profiles at 0, 3, 6, 12 and 24 h were measured using the Affymetrix RatTox U34 GeneChip arrays. Simultaneously, cytotoxicity was assessed by lactase dehydrogenase leakage assay. Gene expression profiles at different time points were used to evaluate cytotoxicity at subsequent time points using partial least squares, and it was found that gene expression profiles at 0 h had the best prediction accuracy for the cytotoxicity observed at 12 h. Some biomarkers whose expression profiles showed strong relationship with cytotoxicity were identified and the underlying pathways were reconstructed to illustrate how hepatocytes respond to cadmium exposure. Permutation studies were also applied to assess the reliability of the predictive models., Availability: Matlab source code is available upon request and DNA microarray data are available at GEO (http://www.ncbi.nlm.nih.gov/geo).

BALB/c mice are susceptible and C57BL/6 mice are resistant to Graves' hyperthyroidism induced by immunization with adenovirus encoding the thyrotropin receptor (TSHR) A-subunit. Both strains develop comparable levels of TSHR antibodies, but potent TSH blocking antibody activity in C57BL/6 mice likely blocks development of hyperthyroidism. We used microarrays to compare gene expression in spleens of mice immunized with A-subunit adenovirus (TSHR-Ad) or control adenovirus (Con-Ad). To preclude the effects of variable thyroxine (T(4)) levels, mice were studied when euthyroid as follows: BALB/c mice immunized three times with TSHR-Ad or Con-Ad and C57BL/6 mice immunized three times with TSHR-Ad or Con-Ad. Among the 14,000 expressed probe sets, there were no statistically significant differences in gene expression in BALB/c mice immunized with TSHR-Ad versus Con-Ad. In contrast, expression of 57 transcripts (representing 40 genes) changed in response to TSHR-Ad in C57BL/6 mice. Diverse genes were identified, including proteins involved in immune responses, inflammation, and cell cycling as well as heat-shock proteins and proteases. Down-regulation of chitinase 3- and-4 gene expression likely reflects cytokines produced by T-helper 2 (Th2) type cells. Indeed, the immunoglobulin (IgG) subclass for TSHR antibodies reflects a deviation away from Th2 cytokines and toward Th1 in C57BL/6 mice. In conclusion, TSHR-Ad immunization altered gene expression profiles in C57BL/6, but not in BALB/c, mice. This response primarily involved reduced gene expression. In C57BL/6 mice, decreased expression of genes such as cathelicidin, calgranulins, and lipocalin following TSHR A-subunit adenovirus immunization suggests the importance of innate immunity in this response.

The aim of the present study was to determine if the bone marrow (BM) beta2m-/Thy-1+ stem cells isolated from common bile duct ligated (CBDL) rats possess hepatocyte-like characteristics in their global gene expression profiles. The Affymetrix RG U34A arrays were used to conduct transcriptomic profiling on BM beta2m-/Thy-1+ stem cells isolated from CBDL and control rats as well as primary hepatocytes. Forty-one probe sets were up-regulated more than 2-fold in CBDL-derived beta2m-/Thy-1+ BM stem cells compared to control BM stem cells. Twenty-seven probe sets were present in both CBDL-derived beta2m-/Thy-1+ BM stem cells and control hepatocytes but absent in control beta2m-/Thy-1+ BM stem cells, including Tcf1 and Dbp. Compared to the control beta2m-/Thy-1+ BM stem cells, CBDL-derived beta2m-/Thy-1+ BM stem cells shared more commonly expressed genes with hepatocytes. Overall, CBDL-derived beta2m-/Thy-1+ stem cells displayed a different transcriptomic fingerprint compared with beta2m-/Thy-1+ BM stem cells isolated from control rats; and CBDL-derived beta2m-/Thy-1+ stem cells started to express some hepatocyte-like genes.

DNA microarray technology provides a promising approach to the diagnosis and prognosis of tumors on a genome-wide scale by monitoring the expression levels of thousands of genes simultaneously. One problem arising from the use of microarray data is the difficulty to analyze the high-dimensional gene expression data, typically with thousands of variables (genes) and much fewer observations (samples), in which severe collinearity is often observed. This makes it difficult to apply directly the classical statistical methods to investigate microarray data. In this paper, total principal component regression (TPCR) was proposed to classify human tumors by extracting the latent variable structure underlying microarray data from the augmented subspace of both independent variables and dependent variables. One of the salient features of our method is that it takes into account not only the latent variable structure but also the errors in the microarray gene expression profiles (independent variables). The prediction performance of TPCR was evaluated by both leave-one-out and leave-half-out cross-validation using four well-known microarray datasets. The stabilities and reliabilities of the classification models were further assessed by re-randomization and permutation studies. A fast kernel algorithm was applied to decrease the computation time dramatically. (MATLAB source code is available upon request.).

The expression of functional and regulatory genes by islet cells is a key determinant for the success of islet transplantation. The aim of this study is twofold: first, to characterize the cluster of genes expressed in human islet isolations; and second, to validate the capability of gene array technology to assess with accuracy the expression of various transcripts. RNA from isolated islet preparations obtained from three independent donors was converted to cDNA and then transcribed to cRNA. Individual cRNA preparations were then hybridized to U133A microarrays carrying approximately 23,000 genes, and analyzed using GeneSpring (SiliconGenetics, Redwood City, CA) software. Real-time reverse transcription-polymerase chain reaction was performed to validate results obtained by microarray analysis. Microarray analysis identified the expression of about 7,000 genes transcribed in cultured human islet preparations. Enzymes represented the most abundant class of genes identified, followed by nuclear binding proteins, signal transduction molecules, transport proteins, and growth factor receptors and their ligands. Real-time polymerase chain reaction confirmed the identification of various islet-specific genes detected by microarray analysis, but also showed that such genes as pancreatic duodenal homeobox 1 protein and glucagon-like peptide 1 receptor, which were not detected by gene array, can be readily identified and quantified. In addition, gene array produced a suboptimal quantification of genes expressed in large amounts by islet cells. Indeed, the abundance of mRNA for insulin when compared with the level of somatostatin mRNA was not as different as one would have predicated based on the classic knowledge of islet physiology. Gene array analysis appears to be a valuable tool to obtain preliminary information of genes expressed by a given tissue. The expression levels of transcripts expressed in very low or very high quantities need to be confirmed by an independent technique.

Accurate annotation of protein-coding genes is one of the primary tasks upon the completion of whole genome sequencing of any organism. In this study, we used an integrated transcriptomic and proteomic strategy to validate and improve the existing zebrafish genome annotation. We undertook high-resolution mass-spectrometry-based proteomic profiling of 10 adult organs, whole adult fish body, and two developmental stages of zebrafish (SAT line), in addition to transcriptomic profiling of six organs. More than 7,000 proteins were identified from proteomic analyses, and ∼ 69,000 high-confidence transcripts were assembled from the RNA sequencing data. Approximately 15% of the transcripts mapped to intergenic regions, the majority of which are likely long non-coding RNAs. These high-quality transcriptomic and proteomic data were used to manually reannotate the zebrafish genome. We report the identification of 157 novel protein-coding genes. In addition, our data led to modification of existing gene structures including novel exons, changes in exon coordinates, changes in frame of translation, translation in annotated UTRs, and joining of genes. Finally, we discovered four instances of genome assembly errors that were supported by both proteomic and transcriptomic data. Our study shows how an integrative analysis of the transcriptome and the proteome can extend our understanding of even well-annotated genomes.

BackgroundSeveral dynamic models of a gene regulatory network of the light-induced floral transition process in Arabidopsis have been developed to capture the behavior of gene transcription and infer predictions based on experimental observations. It has been proven that the models can make accurate and novel predictions, which generate testable hypotheses.Two major issues were addressed in this study. First, construction of dynamic models for gene regulatory networks requires the use of mathematic modeling that comprises equations of a large number of parameters. Second, the binding mechanism of the transcription factor with DNA is another factor that requires detailed modeling. The first issue was tackled by adopting an optimization algorithm, and the second was addressed by comparing the performance of three alternative modeling approaches, namely the S-system, the Michaelis-Menten model and the Mass-action model. The efficiencies of parameter estimation and modeling performance were calculated based on least square error (O(p)), mean relative error (MRE) and Akaike Information Criterion (AIC).ResultsWe compared three models to describe gene regulation of the flowering transition process in Arabidopsis. The Mass-action model is the simplest and has the least parameters. It is therefore less computation-intensive with the smallest AIC value. The disadvantage, however, is that it assumes the system is simply a second order reaction which is not the case in our study. The Michaelis-Menten model also assumes the system is homogeneous and ignores the intracellular protein transport process. The S-system model has the best performance and it does describe the diffusion effects. A disadvantage of the S-system is that it involves the most parameters. The largest AIC value also implies an over-fitting may occur in parameter estimation.ConclusionsThree dynamic models were adopted to describe the dynamics of the gene regulatory network of the flowering transition process in Arabidopsis. Based on MRE, the least square error and global sensitivity analysis, the S-system has the best performance. However, the fact that it has the highest AIC suggests an over-fitting may occur in parameter estimation. The result of this study may need to be applied carefully when modeling complex gene regulatory networks.

Background: Si-Wu-Tang (SWT), comprising the combination of four herbs, Paeoniae, Angelicae, Chuanxiong and Rehmanniae, is one of the most popular traditional oriental medicines for women’s diseases. In our previous study, the microarray gene expression profiles of SWT on breast cancer cell line MCF-7 were found similar to the effect of β-estradiol (E2) on MCF-7 cells in the Connectivity Map database. Methods: Further data analysis was conducted to find the main similarities and differences between the effects of SWT and E2 on MCF-7 gene expression. The cell proliferation assay on MCF-7 (ER-positive) and MDA-MB-231 (ER-negative) cells were used to examine such estrogenic activity. The estrogenic potency of SWT was further confirmed by estrogen-responsive element (ERE) luciferase reporter assay in MCF-7 cells. Results: Many estrogen regulated genes strongly up-regulated by E2 were similarly up-regulated by SWT, e.g., GREB1, PGR and EGR3. Of interest with regard to safety of SWT, the oncogenes MYBL1 and RET were strongly induced by E2 but not by SWT. Quantitative RT-PCR analysis revealed a highly concordant expression change in selected genes with data obtained by microarrays. Further supporting SWT’s estrogenic activity, in MCF-7 but not in MDA-MB-231 cells, SWT stimulated cell growth at lower concentrations (< 3.0 mg/ml), while at high concentrations, it inhibits the growth of both cell lines. The growth inhibitory potency of SWT was significantly higher in MDA-MB-231 than in MCF-7 cells. The SWT-induced cell growth of MCF-7 could be blocked by addition of the estrogen receptor antagonist tamoxifen. In addition, SWT was able to activate the ERE activity at lower concentrations. The herbal components Angelicae, Chuanxiong and Rehmanniae at lower concentrations (< 3.0 mg/ml) also showed growth-inducing and ERE-activating activity in MCF-7 cells. Conclusions: These results revealed a new mechanism to support the clinical use of SWT for estrogen related diseases and possibly for cancer prevention. This study also demonstrated the feasibility of using microarray transcriptional profiling to discover phytoestrogenic components that are present in natural products. [ABSTRACT FROM AUTHOR]

The aim of this study is to identify potential biomarkers for early diagnosis of gynecologic cancer in order to improve survival. Cervical cancer (CC) and endometrial cancer (EC) are the most common malignant tumors of gynecologic cancer among women in the world. As the underlying molecular mechanisms in both cervical and endometrial cancer remain unclear, a comprehensive and systematic bioinformatics analysis is required. In our study, gene expression profiles of GSE9750, GES7803, GES63514, GES17025, GES115810, and GES36389 downloaded from Gene Expression Omnibus (GEO) were utilized to analyze differential gene expression between cancer and normal tissues. A total of 78 differentially expressed genes (DEGs) common to CC and EC were identified to perform the functional enrichment analyses, including gene ontology and pathway analysis. KEGG pathway analysis of 78 DEGs indicated that three main types of pathway participate in the mechanism of gynecologic cancer such as drug metabolism, signal transduction, and tumorigenesis and development. Furthermore, 20 diagnostic signatures were confirmed using the least absolute shrink and selection operator (LASSO) regression with 10-fold cross validation. Finally, we used the GEPIA2 online tool to verify the expression of 20 genes selected by the LASSO regression model. Among them, the expression of PAMR1 and SLC24A3 in tumor tissues was downregulated significantly compared to the normal tissue, and found to be statistically significant in survival rates between the CC and EC of patients (p < 0.05). The two genes have their function: (1.) PAMR1 is a tumor suppressor gene, and many studies have proven that overexpression of the gene markedly suppresses cell growth, especially in breast cancer and polycystic ovary syndrome; (2.) SLC24A3 is a sodium–calcium regulator of cells, and high SLC24A3 levels are associated with poor prognosis. In our study, the gene signatures can be used to predict CC and EC prognosis, which could provide novel clinical evidence to serve as a potential biomarker for future diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Bladder cancer is a common malignancy with mechanisms of pathogenesis and progression. This study aimed to identify the prognostic hub genes, which are the central modulators to regulate the progression and proliferation in the specific subtype of bladder cancer. The identification of the candidate hub gene was performed by weighted gene co-expression network analysis to construct a free-scale gene co-expression network. The gene expression profile of GSE97768 from the Gene Expression Omnibus database was used. The association between prognosis and hub gene was evaluated by The Cancer Genome Atlas database. Four gene-expression modules were significantly related to bladder cancer disease: the red module (human adenocarcinoma lymph node metastasis), the darkturquioise module (grade 2 carcinoma), the lightgreen module (grade 3 carcinoma), and the royalblue module (transitional cell carcinoma lymphatic metastasis). Based on betweenness centrality and survival analysis, we identified laminin subunit gamma-2 (LAMC2) in the grade 2 carcinoma, gelsolin (GSN) in the grade 3 carcinoma, and homeodomain-interacting protein kinase 2 (HIPK2) in the transitional cell carcinoma lymphatic metastasis. Subsequently, the protein levels of LAMC2 and GSN were respectively down-regulated and up-regulated in tumor tissue with the Human Protein Atlas (HPA) database. Our results suggested that LAMC2 and GSN are the central modulators to transfer information in the specific subtype of the disease. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most common malignancies. However, the molecular mechanisms underlying its pathogenesis remain to be elucidated. The present study aimed to identify the potential prognostic marker genes associated with the progression of breast cancer. Weighted gene coexpression network analysis was used to construct free-scale gene coexpression networks, evaluate the associations between the gene sets and clinical features, and identify candidate biomarkers. The gene expression profiles of GSE48213 were selected from the Gene Expression Omnibus database. RNA-seq data and clinical information on breast cancer from The Cancer Genome Atlas were used for validation. Four modules were identified from the gene coexpression network, one of which was found to be significantly associated with patient survival time. The expression status of 28 genes formed the black module (basal); 18 genes, dark red module (claudin-low); nine genes, brown module (luminal), and seven genes, midnight blue module (nonmalignant). These modules were clustered into two groups according to significant difference in survival time between the groups. Therefore, based on betweenness centrality, we identified TXN and ANXA2 in the nonmalignant module, TPM4 and LOXL2 in the luminal module, TPRN and ADCY6 in the claudin-low module, and TUBA1C and CMIP in the basal module as the genes with the highest betweenness, suggesting that they play a central role in information transfer in the network. In the present study, eight candidate biomarkers were identified for further basic and advanced understanding of the molecular pathogenesis of breast cancer by using co-expression network analysis. [ABSTRACT FROM AUTHOR]