Direct comparison of GeneChip and SAGE on the quantitative accuracy in transcript profiling analysis

Expression patterns of HNF4α, TTF-1, and SMARCA4 in lung adenocarcinomas: impacts on clinicopathological and genetic features

INTRODUCTION

HNF4α expression and SMARCA4 loss were thought to be features of non-terminal respiratory unit (TRU)-type lung adenocarcinomas, but their relationships remained unclear.

MATERIALS AND METHODS

HNF4α-positive cases among 241 lung adenocarcinomas were stratified based on TTF-1 and SMARCA4 expressions, histological subtypes, and driver mutations. Immunohistochemical analysis was performed using xenograft tumors of lung adenocarcinoma cell lines with high HNF4A expression.

RESULT

HNF4α-positive adenocarcinomas(n = 33) were divided into two groups: the variant group(15 mucinous, 2 enteric, and 1 colloid), where SMARCA4 was retained in all cases, and the conventional non-mucinous group(6 papillary, 5 solid, and 4 acinar), where SMARCA4 was lost in 3/15 cases(20%). All variant cases were negative for TTF-1 and showed wild-type EGFR and frequent KRAS mutations(10/18, 56%). The non-mucinous group was further divided into two groups: TRU-type(n = 7), which was positive for TTF-1 and showed predominantly papillary histology(6/7, 86%) and EGFR mutations(3/7, 43%), and non-TRU-type(n = 8), which was negative for TTF-1, showed frequent loss of SMARCA4(2/8, 25%) and predominantly solid histology(4/8, 50%), and never harbored EGFR mutations. Survival analysis of 230 cases based on histological grading and HNF4α expression revealed that HNF4α-positive poorly differentiated (grade 3) adenocarcinoma showed the worst prognosis. Among 39 cell lines, A549 showed the highest level of HNF4A, immunohistochemically HNF4α expression positive and SMARCA4 lost, and exhibited non-mucinous, high-grade morphology in xenograft tumors.

CONCLUSION

HNF4α-positive non-mucinous adenocarcinomas included TRU-type and non-TRU-type cases; the latter tended to exhibit the high-grade phenotype with frequent loss of SMARCA4, and A549 was a representative cell line.

Genetic and phenotypic determinants of morphologies in 3D cultures and xenografts of lung tumor cell lines

We previously proposed the classification of lung adenocarcinoma into two groups: the bronchial epithelial phenotype (BE phenotype) with high-level expressions of bronchial epithelial markers and actionable genetic abnormalities of tyrosine kinase receptors and the non-BE phenotype with low-level expressions of bronchial Bronchial epithelial (BE) epithelial markers and no actionable genetic abnormalities of tyrosine kinase receptors. Here, we performed a comprehensive analysis of tumor morphologies in 3D cultures and xenografts across a panel of lung cancer cell lines. First, we demonstrated that 40 lung cancer cell lines (23 BE and 17 non-BE) can be classified into three groups based on morphologies in 3D cultures on Matrigel: round (n = 31), stellate (n = 5), and grape-like (n = 4). The latter two morphologies were significantly frequent in the non-BE phenotype (1/23 BE, 8/17 non-BE, p = 0.0014), and the stellate morphology was only found in the non-BE phenotype. SMARCA4 mutations were significantly frequent in stellate-shaped cells (4/4 stellate, 4/34 non-stellate, p = 0.0001). Next, from the 40 cell lines, we successfully established 28 xenograft tumors (18 BE and 10 non-BE) in NOD/SCID mice and classified histological patterns of the xenograft tumors into three groups: solid (n = 20), small nests in desmoplasia (n = 4), and acinar/papillary (n = 4). The latter two patterns were characteristically found in the BE phenotype. The non-BE phenotype exhibited a solid pattern with significantly less content of alpha-SMA-positive fibroblasts (p = 0.0004) and collagen (p = 0.0006) than the BE phenotype. Thus, the morphology of the tumors in 3D cultures and xenografts, including stroma genesis, reflects the intrinsic properties of the cancer cell lines. Furthermore, this study serves as an excellent resource for lung adenocarcinoma cell lines, with clinically relevant information on molecular and morphological characteristics and drug sensitivity.

Reciprocal expression of trefoil factor-1 and thyroid transcription factor-1 in lung adenocarcinomas

Molecular targeted therapies against EGFR and ALK have improved the quality of life of lung adenocarcinoma patients. However, targetable driver mutations are mainly found in thyroid transcription factor‐1 (TTF‐1)/NK2 homeobox 1 (NKX2‐1)‐positive terminal respiratory unit (TRU) types and rarely in non‐TRU types. To elucidate the molecular characteristics of the major subtypes of non‐TRU‐type adenocarcinomas, we analyzed 19 lung adenocarcinoma cell lines (11 TRU types and 8 non‐TRU types). A characteristic of non‐TRU‐type cell lines was the strong expression of TFF‐1 (trefoil factor‐1), a gastric mucosal protective factor. An immunohistochemical analysis of 238 primary lung adenocarcinomas resected at Jichi Medical University Hospital revealed that TFF‐1 was positive in 31 cases (13%). Expression of TFF‐1 was frequently detected in invasive mucinous (14/15, 93%), enteric (2/2, 100%), and colloid (1/1, 100%) adenocarcinomas, less frequent in acinar (5/24, 21%), papillary (7/120, 6%), and solid (2/43, 5%) adenocarcinomas, and negative in micropapillary (0/1, 0%), lepidic (0/23, 0%), and microinvasive adenocarcinomas or adenocarcinoma in situ (0/9, 0%). Expression of TFF‐1 correlated with the expression of HNF4‐α and MUC5AC (P < .0001, P < .0001, respectively) and inversely correlated with that of TTF‐1/NKX2‐1 (P < .0001). These results indicate that TFF‐1 is characteristically expressed in non‐TRU‐type adenocarcinomas with gastrointestinal features. The TFF‐1‐positive cases harbored KRAS mutations at a high frequency, but no EGFR or ALK mutations. Expression of TFF‐1 correlated with tumor spread through air spaces, and a poor prognosis in advanced stages. Moreover, the knockdown of TFF‐1 inhibited cell proliferation and soft‐agar colony formation and induced apoptosis in a TFF‐1‐high and KRAS‐mutated lung adenocarcinoma cell line. These results indicate that TFF‐1 is not only a biomarker, but also a potential molecular target for non‐TRU‐type lung adenocarcinomas.

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.

Identification of the principal transcriptional regulators for low-fat and high-fat meal responsive genes in small intestine

BACKGROUND

High-fat (HF) diet is a well-known cause of obesity. To identify principle transcriptional regulators that could be therapeutic targets of obesity, we investigated transcriptomic modulation in the duodenal mucosa following low-fat (LF) and HF meal ingestion.

METHODS

Whereas one group of mice was sacrificed after fasting, the others were fed ad libitum with LF or HF meal, and sacrificed 30 min, 1 h and 3 h after the beginning of the meal. A transcriptome analysis of the duodenal mucosa of the 7 groups was conducted using both microarray and serial analysis of gene expression (SAGE) method followed by an Ingenuity Pathways Analysis (IPA).

RESULTS

SAGE and microarray showed that the modulation of a total of 896 transcripts in the duodenal mucosa after LF and/or HF meal, compared to the fasting condition. The IPA identified lipid metabolism, molecular transport, and small molecule biochemistry as top three molecular and cellular functions for the HF-responsive, HF-specific, HF-delay, and LF-HF different genes. Moreover, the top transcriptional regulator for the HF-responsive and HF-specific genes was peroxisome proliferator-activated receptor alpha (PPARα). On the other hand, the LF-responsive and LF-specific genes were related to carbohydrate metabolism, cellular function and maintenance, and cell death/cellular growth and proliferation, and the top transcriptional regulators were forkhead box protein O1 (FOXO1) and cAMP response element binding protein 1 (CREB1), respectively.

CONCLUSIONS

These results will help to understand the molecular mechanisms of intestinal response after LF and HF ingestions, and contribute to identify therapeutic targets for obesity and obesity-related diseases.

Application of Toxicogenomics to Toxicology: Basic Concepts in the Analysis of Microarray Data

Toxicology and the practice of pathology are rapidly evolving in the postgenomic era. Observable treatment related changes have been the hallmark of toxicology studies. Toxicogenomics is a powerful new tool that may show gene and protein changes earlier and at treatment levels below the limits of detection of traditional measures of toxicity. It may also aid in the understanding of toxic mechanisms. It is important to remember that it is only a tool and will provide meaningful results only when properly applied. As is often the case with new experimental tools, the initial utilization is driven more by the technology than application to problem solving. Toxicogenomics is interdisciplinary in nature including at a minimum, pathology, toxicology, and genomics. Most studies will require the input from the disciplines of toxicology, pathology, molecular biology, bioinformatics, biochemistry, and others depending on the types of questions being asked.

A SAGE based approach to human glomerular endothelium: defining the transcriptome, finding a novel molecule and highlighting endothelial diversity

Background

Large scale transcript analysis of human glomerular microvascular endothelial cells (HGMEC) has never been accomplished. We designed this study to define the transcriptome of HGMEC and facilitate a better characterization of these endothelial cells with unique features. Serial analysis of gene expression (SAGE) was used for its unbiased approach to quantitative acquisition of transcripts.

Results

We generated a HGMEC SAGE library consisting of 68,987 transcript tags. Then taking advantage of large public databases and advanced bioinformatics we compared the HGMEC SAGE library with a SAGE library of non-cultured ex vivo human glomeruli (44,334 tags) which contained endothelial cells. The 823 tags common to both which would have the potential to be expressed in vivo were subsequently checked against 822,008 tags from 16 non-glomerular endothelial SAGE libraries. This resulted in 268 transcript tags differentially overexpressed in HGMEC compared to non-glomerular endothelia. These tags were filtered using a set of criteria: never before shown in kidney or any type of endothelial cell, absent in all nephron regions except the glomerulus, more highly expressed than statistically expected in HGMEC. Neurogranin, a direct target of thyroid hormone action which had been thought to be brain specific and never shown in endothelial cells before, fulfilled these criteria. Its expression in glomerular endothelium in vitro and in vivo was then verified by real-time-PCR, sequencing and immunohistochemistry.

Conclusions

Our results represent an extensive molecular characterization of HGMEC beyond a mere database, underline the endothelial heterogeneity, and propose neurogranin as a potential link in the kidney-thyroid axis.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-725) contains supplementary material, which is available to authorized users.

Methodologies to decipher the cell secretome

The cell secretome is a collection of proteins consisting of transmembrane proteins (TM) and proteins secreted by cells into the extracellular space. A significant portion (~13-20%) of the human proteome consists of secretory proteins. The secretory proteins play important roles in cell migration, cell signaling and communication. There is a plethora of methodologies available like Serial Analysis of Gene Expression (SAGE), DNA microarrays, antibody arrays and bead-based arrays, mass spectrometry, RNA sequencing and yeast, bacterial and mammalian secretion traps to identify the cell secretomes. There are many advantages and disadvantages in using any of the above methods. This review aims to discuss the methodologies available along with their potential advantages and disadvantages to identify secretory proteins. This review is a part of a Special issue on The Secretome. This article is part of a Special Issue entitled: An Updated Secretome.

Bridging Clinical Outcomes of Canakinumab Treatment in Patients With Rheumatoid Arthritis With a Population Model of IL-1β Kinetics

Canakinumab, an anti-interleukin-1β (IL-1β) monoclonal antibody, is approved for cryopyrin-associated periodic syndromes and is under investigation for the management of other inflammatory disorders. In this study, population-based pharmacokinetic–pharmacodynamic models were developed to understand responses to canakinumab in patients with rheumatoid arthritis (RA). Total canakinumab and total IL-1β concentrations were obtained from four clinical trials (n = 472). In contrast to traditional models, free IL-1β concentrations were calculated and used to link canakinumab to changes in C-reactive protein (CRP) concentrations and American College of Rheumatology (ACR) scores of 20, 50, and 70% improvement. Temporal patterns of total canakinumab, total IL-1β, CRP, and ACR scores were all well described. Simulations confirmed that 150 mg every 4 weeks improved ACR scores in patients with RA, but no additional benefit was provided by higher doses or more frequent administration. Integrating predicted endogenous free ligand concentrations with biomarkers and clinical outcomes could be extended to new therapies of anti-inflammatory diseases.

Computational Strategies for Analyzing Data in Gene Expression Microarray Experiments

Microarray analysis has become a widely used method for generating gene expression data on a genomic scale. Microarrays have been enthusiastically applied in many fields of biological research, even though several open questions remain about the analysis of such data. A wide range of approaches are available for computational analysis, but no general consensus exists as to standard for microarray data analysis protocol. Consequently, the choice of data analysis technique is a crucial element depending both on the data and on the goals of the experiment. Therefore, basic understanding of bioinformatics is required for optimal experimental design and meaningful interpretation of the results. This review summarizes some of the common themes in DNA microarray data analysis, including data normalization and detection of differential expression. Algorithms are demonstrated by analyzing cDNA microarray data from an experiment monitoring gene expression in T helper cells. Several computational biology strategies, along with their relative merits, are overviewed and potential areas for additional research discussed. The goal of the review is to provide a computational framework for applying and evaluating such bioinformatics strategies. Solid knowledge of microarray informatics contributes to the implementation of more efficient computational protocols for the given data obtained through microarray experiments.

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.

Co-activation of epidermal growth factor receptor and c-MET defines a distinct subset of lung adenocarcinomas

Epidermal growth factor receptor (EGFR) and MET are molecular targets for lung cancer treatment. The relationships between expression, activation, and gene abnormalities of these two targets are currently unclear. Here, we demonstrate that a panel of 40 lung cancer cell lines could be classified into two groups. Group I was characterized by (1) high phosphorylations of MET and EGFR, (2) frequent mutation or amplification of EGFR, MET, and human epidermal growth factor receptor-2 (HER2), (3) high expressions of bronchial epithelial markers (thyroid transcription factor-1 (TTF-1), MUC1, and Cytokeratin 7 (CK7)); and (4) high expressions of MET, human epidermal growth factor receptor-3, E-cadherin, cyclooxygenase-2, and laminin gamma2. In contrast, Group II exhibited little or no phosphorylation of MET and EGFR; no mutation or amplification of EGFR, MET, and HER2; were triple-negative for TTF-1, MUC1, and CK7; and showed high expressions of vimentin, fibroblast growth factor receptor-1, and transcription factor 8. Importantly, Group I was more sensitive to gefitinib and more resistant to cisplatin and paclitaxel than Group II. The clinical relevance was confirmed in publicly available data on 442 primary lung adenocarcinoma patients; survival benefits by postoperative chemotherapy were seen in only patients with tumors corresponding to Group II. Overall, co-activation of EGFR and MET defines a distinct subgroup of lung carcinoma with characteristic genetic abnormalities, gene expression pattern, and response to chemotherapeutic reagents.

A single dose of dihydrotestosterone induced a myogenic transcriptional program in female intra-abdominal adipose tissue

Sex steroids are key regulators of adipose tissue (AT) mass, determining gender-specific differences in fat distribution and accumulation. With the aim of exploring the relevance and peculiarities of androgen action in female intra-abdominal AT, we used the serial analysis of gene expression (SAGE) method to analyze the AT transcriptome in four groups of female mice: intact, ovariectomized (OVX), OVX plus dihydrotestosterone (DHT) injection at 3h or 24h before sacrifice (DHT3h, DHT24h). An average of 19555 transcript species was examined in retroperitoneal fat. We found a total of 321 transcripts differentially modulated by DHT and OVX, including 125 novel genes. Several genes involved in energy metabolism/ATP production were up-regulated by DHT, whereas important regulators of lipid metabolism were reduced. Transcripts involved in Ca(2+) uptake/release, cell signalling, cell defence and protein expression were differentially modulated by DHT. A surprising number of myogenic genes were up-regulated, including myosin light and heavy polypeptides, troponins, as well as several actin-binding proteins. These results suggest that DHT24h may have induced a myogenic-like transcriptional program in adipocytes. The present study sheds light on the distinctive female transcriptional pattern acutely induced by androgens in intra-abdominal fat, and may add new insights into the global understanding of menopausal endocrinology and its association to intra-abdominal obesity.

Minichromosome maintenance (MCM) protein 4 as a marker for proliferation and its clinical and clinicopathological significance in non-small cell lung cancer

BACKGROUND

Minichromosome maintenance (MCM) proteins 2-7 form a complex essential for the initiation of DNA replication. In the process to screen expression changes related to growth suppression of non-small cell lung cancer (NSCLC) cells by a cJun dominant-negative mutant, we found that reduced expression of MCM4 was correlated with this growth suppression.

METHOD

We determined the relevance of MCM4 in proliferation of NSCLC by downregulating its expression with small-interfering RNA in three NSCLC cell lines. We then immunohistochemically analyzed MCM4 expression in 156 surgically resected NSCLCs to correlate clinicopathologic characteristics.

RESULTS

MCM4 downregulation reduced proliferation in two cell lines. MCM4 expression was higher in cancer cells than in adjacent normal bronchial epithelial cells (p<0.001). High MCM4 expression was correlated with male gender, heavy smoking, poorer differentiation and non-adenocarcinoma histology (p<0.001, respectively). High MCM4 expression was also correlated with proliferation markers, Ki-67 and cyclin E expression (p<0.001, respectively). MCM4 expression was not associated with survival.

CONCLUSION

MCM4 may play an essential role in the proliferation of some NSCLC cells. Taken together with higher expression in NSCLCs and its correlation with clinicopathologic characteristics such as non-adenocarcinoma histology, MCM4 may have potential as a therapeutic target in certain population with NSCLCs.

Exploring the foundation of genomics: a northern blot reference set for the comparative analysis of transcript profiling technologies

In this paper we aim to create a reference data collection of Northern blot results and demonstrate how such a collection can enable a quantitative comparison of modern expression profiling techniques, a central component of functional genomics studies. Historically, Northern blots were the de facto standard for determining RNA transcript levels. However, driven by the demand for analysis of large sets of genes in parallel, high-throughput methods, such as microarrays, dominate modern profiling efforts. To facilitate assessment of these methods, in comparison to Northern blots, we created a database of published Northern results obtained with a standardized commercial multiple tissue blot (dbMTN). In order to demonstrate the utility of the dbMTN collection for technology comparison, we also generated expression profiles for genes across a set of human tissues, using multiple profiling techniques. No method produced profiles that were strongly correlated with the Northern blot data. The highest correlations to the Northern blot data were determined with microarrays for the subset of genes observed to be specifically expressed in a single tissue in the Northern analyses. The database and expression profiling data are available via the project website (http://www.cisreg.ca). We believe that emphasis on multitechnique validation of expression profiles is justified, as the correlation results between platforms are not encouraging on the whole. Supplementary material for this article can be found at: http://www.interscience.wiley.com/jpages/1531-6912/suppmat

A comparison of global gene expression measurement technologies in Arabidopsis thaliana

Microarrays and tag-based transcriptional profiling technologies represent diverse but complementary data types. We are currently conducting a comparison of high-density in situ synthesized microarrays and massively-parallel signature sequencing (MPSS) data in the model plant, Arabidopsis thaliana. The MPSS data (available at http://mpss.udel.edu/at) and the microarray data have been compiled using the same RNA source material. In this review, we outline the experimental strategy that we are using, and present preliminary data and interpretations from the transcriptional profiles of Arabidopsis leaves and roots. The preliminary data indicate that the log ratio differences of transcripts between leaves and roots measured by microarray data are in better agreement with the MPSS data than the absolute intensities measured for individual microarrays hybridized to only one of the cRNA populations. The correlation was substantially improved by focusing on a subset of genes excluding those with very low expression levels; this selection may have removed noisy data. Future reports will incorporate more than 10 tissues that have been sampled by MPSS.

Application of serial analysis of gene expression to the study of human genetic disease

Sequence tag analysis using serial analysis of gene expression (SAGE) is a powerful strategy for the quantitative analysis of gene expression in human genetic disorders. SAGE facilitates the measurement of mRNA transcripts and generates a non-biased gene expression profile of normal and pathological disease tissue. In addition, the SAGE technique has the capacity of detecting the expression of novel transcripts allowing for the identification of previously uncharacterised genes, thus providing a unique advantage over the traditional microarray-based approach for expression profiling. The technique has been successful in providing pathological gene expression profiles in a number of common genetic disorders including diabetes, cardiovascular disease, Parkinson disease and Down syndrome. When combined with next generation sequencing platforms, SAGE has the potential to become a more powerful and sensitive technique making it more amenable for diagnostic use. This review will therefore discuss the application of SAGE to several common genetic disorders and will further evaluate its potential use in diagnosing human genetic disease.

Cardiac 12/15 lipoxygenase-induced inflammation is involved in heart failure

To identify a novel target for the treatment of heart failure, we examined gene expression in the failing heart. Among the genes analyzed, Alox15 encoding the protein 12/15 lipoxygenase (LOX) was markedly up-regulated in heart failure. To determine whether increased expression of 12/15-LOX causes heart failure, we established transgenic mice that overexpressed 12/15-LOX in cardiomyocytes. Echocardiography showed that Alox15 transgenic mice developed systolic dysfunction. Cardiac fibrosis increased in Alox15 transgenic mice with advancing age and was associated with the infiltration of macrophages. Consistent with these observations, cardiac expression of monocyte chemoattractant protein 1 (MCP-1) was up-regulated in Alox15 transgenic mice compared with wild-type mice. Treatment with 12-hydroxy-eicosatetraenoic acid, a major metabolite of 12/15-LOX, increased MCP-1 expression in cardiac fibroblasts and endothelial cells but not in cardiomyocytes. Inhibition of MCP-1 reduced the infiltration of macrophages into the myocardium and prevented both systolic dysfunction and cardiac fibrosis in Alox15 transgenic mice. Likewise, disruption of 12/15-LOX significantly reduced cardiac MCP-1 expression and macrophage infiltration, thereby improving systolic dysfunction induced by chronic pressure overload. Our results suggest that cardiac 12/15-LOX is involved in the development of heart failure and that inhibition of 12/15-LOX could be a novel treatment for this condition.

Transcriptomic and proteomic profiling of two porcine tissues using high-throughput technologies

Background

The recent development within high-throughput technologies for expression profiling has allowed for parallel analysis of transcriptomes and proteomes in biological systems such as comparative analysis of transcript and protein levels of tissue regulated genes. Until now, such studies of have only included microarray or short length sequence tags for transcript profiling. Furthermore, most comparisons of transcript and protein levels have been based on absolute expression values from within the same tissue and not relative expression values based on tissue ratios.

Results

Presented here is a novel study of two porcine tissues based on integrative analysis of data from expression profiling of identical samples using cDNA microarray, 454-sequencing and iTRAQ-based proteomics. Sequence homology identified 2.541 unique transcripts that are detectable by both microarray hybridizations and 454-sequencing of 1.2 million cDNA tags. Both transcript-based technologies showed high reproducibility between sample replicates of the same tissue, but the correlation across these two technologies was modest. Thousands of genes being differentially expressed were identified with microarray. Out of the 306 differentially expressed genes, identified by 454-sequencing, 198 (65%) were also found by microarray. The relationship between the regulation of transcript and protein levels was analyzed by integrating iTRAQ-based proteomics data. Protein expression ratios were determined for 354 genes, of which 148 could be mapped to both microarray and 454-sequencing data. A comparison of the expression ratios from the three technologies revealed that differences in transcript and protein levels across heart and muscle tissues are positively correlated.

Conclusion

We show that the reproducibility within cDNA microarray and 454-sequencing is high, but that the agreement across these two technologies is modest. We demonstrate that the regulation of transcript and protein levels across identical tissue samples is positively correlated when the tissue expression ratios are used for comparison. The results presented are of interest in systems biology research in terms of integration and analysis of high-throughput expression data from mammalian tissues.

Chromosomal losses are associated with hypomethylation of the gene-control regions in the stomach with a low number of active genes

Transitional-CpG methylation between unmethylated promoters and nearby methylated retroelements plays a role in the establishment of tissue-specific transcription. This study examined whether chromosomal losses reducing the active genes in cancers can change transitional-CpG methylation and the transcription activity in a cancer-type-dependent manner. The transitional-CpG sites at the CpG-island margins of nine genes and the non-island-CpG sites round the transcription start sites of six genes lacking CpG islands were examined by methylation-specific polymerase chain reaction (PCR) analysis. The number of active genes in normal and cancerous tissues of the stomach, colon, breast, and nasopharynx were analyzed using the public data in silico. The CpG-island margins and non-island CpG sites tended to be hypermethylated and hypomethylated in all cancer types, respectively. The CpG-island margins were hypermethylated and a low number of genes were active in the normal stomach compared with other normal tissues. In gastric cancers, the CpG-island margins and non-island-CpG sites were hypomethylated in association with high-level chromosomal losses, and the number of active genes increased. Colon, breast, and nasopharyngeal cancers showed no significant association between the chromosomal losses and methylation changes. These findings suggest that chromosomal losses in gastric cancers are associated with the hypomethylation of the gene-control regions and the increased number of active genes.

Deep sequencing-based expression analysis shows major advances in robustness, resolution and inter-lab portability over five microarray platforms

The hippocampal expression profiles of wild-type mice and mice transgenic for δC-doublecortin-like kinase were compared with Solexa/Illumina deep sequencing technology and five different microarray platforms. With Illumina's digital gene expression assay, we obtained ∼2.4 million sequence tags per sample, their abundance spanning four orders of magnitude. Results were highly reproducible, even across laboratories. With a dedicated Bayesian model, we found differential expression of 3179 transcripts with an estimated false-discovery rate of 8.5%. This is a much higher figure than found for microarrays. The overlap in differentially expressed transcripts found with deep sequencing and microarrays was most significant for Affymetrix. The changes in expression observed by deep sequencing were larger than observed by microarrays or quantitative PCR. Relevant processes such as calmodulin-dependent protein kinase activity and vesicle transport along microtubules were found affected by deep sequencing but not by microarrays. While undetectable by microarrays, antisense transcription was found for 51% of all genes and alternative polyadenylation for 47%. We conclude that deep sequencing provides a major advance in robustness, comparability and richness of expression profiling data and is expected to boost collaborative, comparative and integrative genomics studies.

BMP2 regulates Osterix through Msx2 and Runx2 during osteoblast differentiation

Osterix/Sp7, a member of the Sp1 transcription factor family, plays an essential role in bone formation and osteoblastogenesis. Although Osterix has been shown to be induced by BMP2 in a mesenchymal cell line, the molecular basis of the regulation, expression and function of Osterix during osteoblast differentiation, is not fully understood. Thus we examined the role of BMP2 signaling in the regulation of Osterix using the mesenchymal cell lines C3H10T1/2 and C2C12. Osterix overexpression induced alkaline phosphatase activity and osteocalcin expression in C2C12 cells and stimulated calcification of murine primary osteoblasts. Considering that Runx2 overexpression induces Osterix, these results suggest that Osterix functions as downstream of Runx2. Surprisingly, BMP2 treatment induced Osterix expression and alkaline phosphatase activity in mesenchymal cells derived from Runx2-deficient mice. Furthermore, overexpression of Smad1 and Smad4 up-regulated Osterix expression, and an inhibitory Smad, Smad6, markedly suppressed BMP2-induced Osterix expression in the Runx2-deficient cells. Moreover, overexpression of a homeobox gene, Msx2, which is up-regulated by BMP2 and promotes osteoblastic differentiation, induced Osterix expression in the Runx2-deficient cells. Knockdown of Msx2 clearly inhibited induction of Osterix by BMP2 in the Runx2-deficient mesenchymal cells. Interestingly, microarray analyses using the Runx2-deficient cells revealed that the role of Osterix was distinct from that of Runx2. These findings suggest that Osterix is regulated via both Runx2-dependent and -independent mechanisms, and that Osterix controls osteoblast differentiation, at least in part, by regulating the expression of genes not controlled by Runx2.

High throughput techniques for characterizing the expression profile of Barrett's esophagus

Barrett's esophagus (BE) is the metaplastic change of the normal lined squamous epithelium of the distal esophagus to a columnar type of epithelium as a result of chronic long-standing gastroesophageal reflux disease. Patients with BE have a significantly increased risk of developing an esophageal adenocarcinoma, with an estimated annual incidence varying from 0.4 to 1.8%. Over the last 3 decades, the incidence of BE and its associated adenocarcinoma has increased in Western countries at a rate that exceeds that of any other malignancy. Despite all the research performed on BE, there is still an inadequate understanding of the biological basis of this mucosal transformation. With the upcoming modern high throughput technologies, important progression has been made in unraveling the expression profiles and gaining more insight in the biology of BE and esophageal adenocarcinoma. Several studies reported genome, transcriptome, proteome, and kinome investigations using high throughput techniques. These studies were conducted to find biomarkers that can be used to detect BE patients with increased risk for malignant progression or to obtain more insight in the mechanism underlying BE development. In the following review, we first discuss the different techniques that are currently available and summarize findings in this field, including several recent publications of our group.

Validation of oligoarrays for quantitative exploration of the transcriptome

BACKGROUND

Oligoarrays have become an accessible technique for exploring the transcriptome, but it is presently unclear how absolute transcript data from this technique compare to the data achieved with tag-based quantitative techniques, such as massively parallel signature sequencing (MPSS) and serial analysis of gene expression (SAGE). By use of the TransCount method we calculated absolute transcript concentrations from spotted oligoarray intensities, enabling direct comparisons with tag counts obtained with MPSS and SAGE. The tag counts were converted to number of transcripts per cell by assuming that the sum of all transcripts in a single cell was 5.105. Our aim was to investigate whether the less resource demanding and more widespread oligoarray technique could provide data that were correlated to and had the same absolute scale as those obtained with MPSS and SAGE.

RESULTS

A number of 1,777 unique transcripts were detected in common for the three technologies and served as the basis for our analyses. The correlations involving the oligoarray data were not weaker than, but, similar to the correlation between the MPSS and SAGE data, both when the entire concentration range was considered and at high concentrations. The data sets were more strongly correlated at high transcript concentrations than at low concentrations. On an absolute scale, the number of transcripts per cell and gene was generally higher based on oligoarrays than on MPSS and SAGE, and ranged from 1.6 to 9,705 for the 1,777 overlapping genes. The MPSS data were on same scale as the SAGE data, ranging from 0.5 to 3,180 (MPSS) and 9 to1,268 (SAGE) transcripts per cell and gene. The sum of all transcripts per cell for these genes was 3.8.105 (oligoarrays), 1.1.105 (MPSS) and 7.6.104 (SAGE), whereas the corresponding sum for all detected transcripts was 1.1.106 (oligoarrays), 2.8.105 (MPSS) and 3.8.105 (SAGE).

CONCLUSION

The oligoarrays and TransCount provide quantitative transcript concentrations that are correlated to MPSS and SAGE data, but, the absolute scale of the measurements differs across the technologies. The discrepancy questions whether the sum of all transcripts within a single cell might be higher than the number of 5.105 suggested in the literature and used to convert tag counts to transcripts per cell. If so, this may explain the apparent higher transcript detection efficiency of the oligoarrays, and has to be clarified before absolute transcript concentrations can be interchanged across the technologies. The ability to obtain transcript concentrations from oligoarrays opens up the possibility of efficient generation of universal transcript databases with low resource demands.

Gene expression profiling with microarray and SAGE identifies PLUNC as a marker for hepatoid adenocarcinoma of the stomach

Gastric cancer is one of the most common malignancies worldwide. In this study, we screened for genes upregulated in gastric cancer by comparing gene expression profiles from serial analysis of gene expression and microarray and identified the palate, lung, and nasal epithelium carcinoma-associated protein (PLUNC) gene. Immunostaining for PLUNC in 140 gastric cancer cases revealed strong and extensive staining of PLUNC in hepatoid adenocarcinoma of the stomach, whereas 7% of conventional gastric cancer cases showed focal immunostaining of PLUNC. Gastric hepatoid adenocarcinoma is an extrahepatic tumor characterized by morphologic similarities to hepatocellular carcinoma. To investigate the utility of PLUNC immunostaining in the diagnosis of gastric hepatoid adenocarcinoma, six cases of gastric hepatoid adenocarcinoma (six primary tumors and two associated liver metastases) were studied further. PLUNC staining was observed in all six primary hepatoid adenocarcinomas. PLUNC staining was observed in both the hepatoid adenocarcinoma and tubular/papillary adenocarcinoma components of primary tumors, although PLUNC staining was preferentially localized in tubular/papillary adenocarcinoma components. Staining of PLUNC was also detected in both liver metastases. PLUNC staining was not observed in 52 cases of primary hepatocellular carcinoma or in normal adult or fetal liver. These results indicate that PLUNC is a novel marker that distinguishes gastric hepatoid adenocarcinoma from primary hepatocellular carcinoma.

Microanalysis of gene expression in tissues using T7-SAGE: serial analysis of gene expression after high-fidelity T7-based RNA amplification

In this unit, the authors describe a new technique, T7-SAGE, in which a high-fidelity T7 amplification step is combined with SAGE analysis. In order to avoid extra PCR or other forms of amplification, the authors incorporate only two cycles of T7-based RNA amplification as the initial step. This T7-based amplification step has high accuracy. In addition, T7 RNA polymerase has high processivity and functions effectively even when broad stretches of nucleotides are being amplified. Although no protocol that includes an amplification step can claim to permit determination of absolute transcript number, since slight changes in estimated transcript frequency are always possible, T7 procedures appear to be the safest to date. This new T7-SAGE procedure should facilitate application of SAGE for gene-expression profiling using minimal quantities of starting material, such as from embryonic tissues and microdissected cells from histological sections of tissues.

Quantitative methods for genome-scale analysis of in situ hybridization and correlation with microarray data

This study introduces a novel method for standardized relative quantification of colorimetric in situ hybridization signal that enables a large-scale cross-platform expression level comparison of in situ hybridization with two publicly available microarray brain data sources.

With the emergence of genome-wide colorimetric in situ hybridization (ISH) data sets such as the Allen Brain Atlas, it is important to understand the relationship between this gene expression modality and those derived from more quantitative based technologies. This study introduces a novel method for standardized relative quantification of colorimetric ISH signal that enables a large-scale cross-platform expression level comparison of ISH with two publicly available microarray brain data sources.

Identification of Toll-like receptor 3 as a potential therapeutic target in clear cell renal cell carcinoma

PURPOSE

Renal cell carcinoma (RCC) is one of the most drug-refractory cancers. The aim of this study is to discover a novel therapeutic target molecule for clear cell RCC (CCRCC), which accounts for the majority of RCC.

EXPERIMENTAL DESIGN

Gene expression profiles of 27 CCRCCs and 9 normal kidney tissues as well as 15 various adult normal tissues were examined by Affymetrix U133 Plus 2.0 arrays. Among the 34 genes specifically up-regulated in CCRCC, overexpression of Toll-like receptor 3 (TLR3) mRNA and its protein was validated by quantitative reverse transcription-PCR, immunoblot, and immunohistochemistry. The effects of TLR3 signaling on in vitro cell growth were examined.

RESULTS

TLR3 gene was highly expressed in CCRCC, with only limited expression in a panel of normal tissues. On immunohistochemical analysis using a monoclonal antibody against TLR3, overexpression of TLR3 was observed in 139 of 189 (73.5%) cases of CCRCC as well as in lung metastatic CCRCC (6 of 8), whereas TLR3 expression was entirely absent in chromophobe RCC (0 of 8). Polyinosinic-polycytidilic acid, a TLR3 ligand, exerted a growth-inhibitory effect against RCC cells in a TLR3-dependent manner. Moreover, a combination of polyinosinic-polycytidilic acid and IFNalpha exerted a synergistic growth-inhibitory effect against Caki-1 RCC cells.

CONCLUSIONS

This is the first report that TLR3 is overexpressed in CCRCC. These observations suggest that TLR3 pathway may represent a novel therapeutic target in CCRCC.

A comparison of microarray and MPSS technology platforms for expression analysis of Arabidopsis

Background

Several high-throughput technologies can measure in parallel the abundance of many mRNA transcripts within a sample. These include the widely-used microarray as well as the more recently developed methods based on sequence tag abundances such as the Massively Parallel Signature Sequencing (MPSS) technology. A comparison of microarray and MPSS technologies can help to establish the metrics for data comparisons across these technology platforms and determine some of the factors affecting the measurement of mRNA abundances using different platforms.

Results

We compared transcript abundance (gene expression) measurement data obtained using Affymetrix and Agilent microarrays with MPSS data. All three technologies were used to analyze the same set of mRNA samples; these samples were extracted from various wild type Arabidopsis thaliana tissues and floral mutants. We calculated correlations and used clustering methodology to compare the normalized expression data and expression ratios across samples and technologies. Abundance expression measurements were more similar between different samples measured by the same technology than between the same sample measured by different technologies. However, when expression ratios were employed, samples measured by different technologies were found to cluster together more frequently than with abundance expression levels.

Furthermore, the two microarray technologies were more consistent with each other than with MPSS. We also investigated probe-position effects on Affymetrix data and tag-position effects in MPSS. We found a similar impact on Affymetrix and MPSS measurements, which suggests that these effects were more likely a characteristic of the RNA sample rather than technology-specific biases.

Conclusion

Comparisons of transcript expression ratios showed greater consistency across platforms than measurements of transcript abundance. In addition, for measurements based on abundances, technology differences can mask the impact of biological differences between samples and tissues.

The gene expression patterns of peripheral blood mononuclear cells in patients with systemic lupus erythematosus

This study examined the gene expression patterns of peripheral blood mononuclear cells (PBMCs) in patients with systemic lupus erythematosus (SLE) by using serial analysis of gene expression (SAGE) technology. Following the construction of serial analysis of gene expression (SAGE) library of PBMCs collected from 3 cases of familial SLE patients, a large scale of tag sequencing was performed. The data extracted from sequencing files was analyzed with SAGE 2000 V 4.5 software. The top 30 expressed genes of SLE patients were uploaded to http://david.niaid.nih.gov/david/ease.htm and the functional classification of genes was obtained. The differences among those expressed gene were analyzed by Chi-square tests. The results showed that a total of 1286 unique SAGE tags were identified from 1814 individual SAGE tags. Among the 1286 unique tags, 86.8% had single copy, and only 0.2% tags had more than 20 copies. And 68.4% of the tags matched known expressed sequences, 41.1% of which matched more than one known expressed sequence. About 31.6% of the tags had no match and could represent potentially novel genes. Approximately one third of the top 30 genes were ribosomal protein, and the rest were genes related to metabolism or with unknown functions. Eight tags were found to express differentially in SAGE library of SLE patients. This study draws a profile of gene expression patterns of PBMCs in patients with SLE. Comparison of SAGE database from PBMCs between normal individuals and SLE patients will help us to better understand the pathogenesis of SLE.

Comparison of hybridization-based and sequencing-based gene expression technologies on biological replicates

Background

High-throughput systems for gene expression profiling have been developed and have matured rapidly through the past decade. Broadly, these can be divided into two categories: hybridization-based and sequencing-based approaches. With data from different technologies being accumulated, concerns and challenges are raised about the level of agreement across technologies. As part of an ongoing large-scale cross-platform data comparison framework, we report here a comparison based on identical samples between one-dye DNA microarray platforms and MPSS (Massively Parallel Signature Sequencing).

Results

The DNA microarray platforms generally provided highly correlated data, while moderate correlations between microarrays and MPSS were obtained. Disagreements between the two types of technologies can be attributed to limitations inherent to both technologies. The variation found between pooled biological replicates underlines the importance of exercising caution in identification of differential expression, especially for the purposes of biomarker discovery.

Conclusion

Based on different principles, hybridization-based and sequencing-based technologies should be considered complementary to each other, rather than competitive alternatives for measuring gene expression, and currently, both are important tools for transcriptome profiling.

A feature selection approach for identification of signature genes from SAGE data

BACKGROUND

One goal of gene expression profiling is to identify signature genes that robustly distinguish different types or grades of tumors. Several tumor classifiers based on expression profiling have been proposed using microarray technique. Due to important differences in the probabilistic models of microarray and SAGE technologies, it is important to develop suitable techniques to select specific genes from SAGE measurements.

RESULTS

A new framework to select specific genes that distinguish different biological states based on the analysis of SAGE data is proposed. The new framework applies the bolstered error for the identification of strong genes that separate the biological states in a feature space defined by the gene expression of a training set. Credibility intervals defined from a probabilistic model of SAGE measurements are used to identify the genes that distinguish the different states with more reliability among all gene groups selected by the strong genes method. A score taking into account the credibility and the bolstered error values in order to rank the groups of considered genes is proposed. Results obtained using SAGE data from gliomas are presented, thus corroborating the introduced methodology.

CONCLUSION

The model representing counting data, such as SAGE, provides additional statistical information that allows a more robust analysis. The additional statistical information provided by the probabilistic model is incorporated in the methodology described in the paper. The introduced method is suitable to identify signature genes that lead to a good separation of the biological states using SAGE and may be adapted for other counting methods such as Massive Parallel Signature Sequencing (MPSS) or the recent Sequencing-By-Synthesis (SBS) technique. Some of such genes identified by the proposed method may be useful to generate classifiers.

Hypoxia increases the motility of lung adenocarcinoma cell line A549 via activation of the epidermal growth factor receptor pathway

Tumor hypoxia is associated with a malignant phenotype of cancer cells and poor patient prognosis. To investigate the role of hypoxia in tumor progression, we studied the effects of hypoxia in the A549 lung adenocarcinoma cell line. First, we showed that hypoxic treatment decreased cell-cell adhesion and induced a scattering of cancer cells. Concomitant with these morphological changes, the motility of cancer cells was increased, as demonstrated by the Boyden chamber assay. Then, we used oligonucleotide array analyses to identify the genes causally related to the hypoxia-induced motile phenotype. The results showed that the expression of approximately 100 genes was induced more than 5-fold by hypoxia. These included (among others) epidermal growth factor receptor (EGFR), as well as other well-known hypoxia-induced genes, such as vascular endothelial growth factor. Immunohistochemical analyses of primary lung adenocarcinomas confirmed the induction of EGFR in tumor cells in the vicinity of necrotic areas, a histological indicator of tumor hypoxia. Remarkably, the EGFR inhibitor AG1478 (10 microM) completely blocked the increased cell motility induced by hypoxia. Thus, the present study demonstrates the importance of the EGFR pathway in the increased motility of cancer cells that occurs in a hypoxic tumor environment.

Glucokinase and IRS-2 are required for compensatory beta cell hyperplasia in response to high-fat diet-induced insulin resistance

Glucokinase (Gck) functions as a glucose sensor for insulin secretion, and in mice fed standard chow, haploinsufficiency of beta cell-specific Gck (Gck(+/-)) causes impaired insulin secretion to glucose, although the animals have a normal beta cell mass. When fed a high-fat (HF) diet, wild-type mice showed marked beta cell hyperplasia, whereas Gck(+/-) mice demonstrated decreased beta cell replication and insufficient beta cell hyperplasia despite showing a similar degree of insulin resistance. DNA chip analysis revealed decreased insulin receptor substrate 2 (Irs2) expression in HF diet-fed Gck(+/-) mouse islets compared with wild-type islets. Western blot analyses confirmed upregulated Irs2 expression in the islets of HF diet-fed wild-type mice compared with those fed standard chow and reduced expression in HF diet-fed Gck(+/-) mice compared with those of HF diet-fed wild-type mice. HF diet-fed Irs2(+/-) mice failed to show a sufficient increase in beta cell mass, and overexpression of Irs2 in beta cells of HF diet-fed Gck(+/-) mice partially prevented diabetes by increasing beta cell mass. These results suggest that Gck and Irs2 are critical requirements for beta cell hyperplasia to occur in response to HF diet-induced insulin resistance.

Amelioration of hypertensive heart failure by amlodipine may occur via antioxidative effects

Although recent clinical studies have suggested that long-acting calcium channel blockers (CCBs) have beneficial effects on heart failure, the precise mechanism is unknown. In this study, Dahl salt-sensitive rats fed a high salt diet were treated with the long-acting CCB amlodipine, the low-molecular-weight membrane permeable superoxide dismutase mimetic 4-hydroxy-2,2,6,6-tetramethyl piperidinoxyl (Tempol), or saline from 11 weeks after birth. The cardiac geometry and function, and gene expression profiles were determined at 17 weeks. Dahl salt-sensitive rats fed a high salt diet followed by saline as a non-treatment control (HS group) showed a marked increase in blood pressure and developed concentric hypertrophy at 11 weeks, followed by left ventricular (LV) dilation and congestive heart failure by 17 weeks. The treatment with amlodipine (AMLO group) or Tempol (TEMP group) significantly inhibited the development of LV hypertrophy and cardiac dysfunction. Analysis using an Affymetrix GeneChip U34 revealed that the expression levels of 195 genes were changed by the treatment with amlodipine. Among these 195 genes, 110 genes were increased in HS rats and decreased in AMLO rats. And of these 110 genes, 54 genes were also decreased in TEMP rats. In contrast, 85 genes were decreased in HS rats and increased in AMLO rats. Of these 85 genes, 38 genes were also increased in TEMP rats. Approximately 48% of the genes were changed in similar fashion in AMLO and TEMP rats, suggesting that amlodipine shows beneficial effects on heart failure mainly via antioxidative mechanisms.

Gene-expression signatures in oncology diagnostics

The advent of DNA microarray technologies has enabled the development of gene expression signatures that can be used for prognostic and predictive purposes. This new information can change the paradigm of how medicine is practiced, coupling physical examination, pathology and clinical tests with new molecular information. However, many unanswered questions regarding sample acquisition, platform development, signature validation and clinical trial design will need to be addressed before this new medical content will have an impact on the clinical setting. This article will examine some of these issues in greater detail, focusing on tissue type, platform comparison, biospecimen collection and signature validation.

Characteristics of oligonucleotide tiling arrays measured by hybridizing full-length cDNA clones: causes of signal variation and false positive signals

An assessment of the hybridization characteristics of oligonucleotide tiling arrays was carried out using 162 full-length sequenced cDNA clones in spike-in experiments. The properties of array probes that influence signal intensity were investigated, and their capability in the detection of the cDNA exons was evaluated. The signal intensities detected in exonic and nonexonic genomic regions were examined by focusing on the features of probe sequences that raise or lower the level of intensity and on the causes of false positive signals found in nonexonic regions. The effectiveness of measures used in published protocols to improve the separation between signal and background intensity distributions, including the use of replicates and threshold parameterization of signal intensity, was assessed. Sensitivity and specificity in the detection of exons were measured using various sets of threshold parameters, and the effects of each parameter on the detection efficiency and the rate of false positives were evaluated. It was also demonstrated that hybridization of full-length cDNA clones is an excellent method to investigate the characteristics of oligonucleotide tiling arrays.

Expression and bioinformatic analysis of lymphoma-associated novel gene KIAA0372

The purpose of this study was to explore the differentially expressed genes in lymph-node cells (LNC) of lymphomas and reactive lymph node hyperplasia, and to perform an initial bioinformatic analysis on a novel gene, KIAA0372, which is highly expressed in the LNC of lymphomas. mRNA extracted from LNC of lymphomas and reactive lymph node hyperplasia were respectively marked with biotin and hybridized with Gene Expression Chips, resulting in differentially expressed genes. Initial bioinformatic analysis was then performed on a novel gene named KIAA0372, whose function has not yet been explored. Its structure and genomic location, its product's physical and chemical properties, subcellular localization and functional domains, were also predicted. Further, a systematic evolution analysis was performed on similar proteins from among several species. Using Gene Expression Chips, many differentially expressed genes were uncovered. Efficient bioinformatic analysis has fundamentally determined that KIAA0372 is an extracellular protein which may be involved in TGF-β signaling. Microarray is an efficient and high throughput strategy for detection of differentially expressed genes. And KIAA0372 is thought to be a potential target for tumor research using bioinformatic analysis.

Exploring host-pathogen interactions at the epithelial surface: application of transcriptomics in lung biology

The epithelial surface of the airways is the largest barrier-forming interface between the human body and the outside world. It is now well recognized that, at this strategic position, airway epithelial cells play an eminent role in host defense by recognizing and responding to microbial exposure. Conversely, inhaled microorganisms also respond to contact with epithelial cells. Our understanding of this cross talk is limited, requiring sophisticated experimental approaches to analyze these complex interactions. High-throughput technologies, such as DNA microarray analysis and serial analysis of gene expression (SAGE), have been developed to screen for gene expression levels at large scale within single experiments. Since their introduction, these hypothesis-generating technologies have been widely used in diverse areas such as oncology and brain research. Successful application of these genomics-based technologies has also revealed novel insights in host-pathogen interactions in both the host and pathogen. This review aims to provide an overview of the SAGE and microarray technology illustrated by their application in the analysis of host-pathogen interactions. In particular, the interactions between epithelial cells in the human lungs and clinically relevant microorganisms are the central focus of this review.

High-throughput genomic technology in research and clinical management of breast cancer. Molecular signatures of progression from benign epithelium to metastatic breast cancer

It is generally accepted that early detection of breast cancer has great impact on patient survival, emphasizing the importance of early diagnosis. In a widely recognized model of breast cancer development, tumor cells progress through chronological and well defined stages. However, the molecular basis of disease progression in breast cancer remains poorly understood. High-throughput molecular profiling techniques are excellent tools for the study of complex molecular alterations. By accurately mapping changes in the genome and subsequent biological/molecular pathways, the chances of finding potential novel treatment targets as well as intervention strategies are enhanced, and ultimately lives can be saved. This review provides a brief summary of recent progress in identifying molecular markers for invasiveness in early breast lesions.

New technologies used in the study of human melanoma

The amount of information on tumor biology has expanded enormously, essentially due to the completion of the human genome sequencing and to the application of new technologies that represent an exciting breakthrough in molecular analysis. Often these data spring from experimental procedures, such as a serial analysis of gene expression (SAGE) and DNA microarrays, which cannot be defined as hypothesis-driven: it may appear to be a "brute force" approach through which no information can be directly generated concerning the specific functions of selected genes in a definite context. However, interesting results are fruitfully generated, and thus it is important to consider the enormous potential these new technologies possess and to learn how to apply this novel form of knowledge in the emerging field of molecular medicine. This review, after a limited outline regarding several classic aspects of human cutaneous melanoma biology, genetics, and clinical approaches, will focus on the proficient use of up-to-date technologies in the study of the neoplastic disease and on their capability to provide effective support to conventional approaches in melanoma diagnosis, prognosis, and treatment.

Upregulation of Heat Shock Transcription Factor 1 Plays a Critical Role in Adaptive Cardiac Hypertrophy

Exercise-induced cardiac hypertrophy has been reported to have better prognosis than pressure overload-induced cardiac hypertrophy. Cardiac hypertrophy induced by exercise was associated with less cardiac fibrosis and better systolic function, suggesting that the adaptive mechanisms may exist in exercise-induced hypertrophy. Here, we showed a critical role of heat shock transcription factor 1 (HSF1), an important transcription factor for heat shock proteins, in the adaptive mechanism of cardiac hypertrophy. We examined expression of 8800 genes in the heart of exercise-induced hypertrophy model using DNA chip technique and compared with pressure overload-induced hypertrophy. Expression of HSF1 and its target molecule heat shock proteins was significantly upregulated in the heart by exercise but not by chronic pressure overload. Constitutive activation of HSF1 in the heart significantly ameliorated death of cardiomyocytes and cardiac fibrosis and thereby prevented cardiac dysfunction as well as hypertrophy induced by chronic pressure overload. Conversely, decreased activity of HSF1 in the heart promoted cardiac dysfunction in response to exercise, a load that normally leads to adaptive hypertrophy with preserved systolic function. Likewise, cardiac function was significantly impaired from the early phase of pressure overload, when HSF1 activation was inhibited. These results suggest that HSF1 plays a critical role in the transition between adaptive and maladaptive hypertrophy.

Identification of candidate mRNAs associated with gonadotropin-induced maturation of murine cumulus oocyte complexes using serial analysis of gene expression

In cultured cumulus oocyte complexes (COC), FSH induces gene transcription required for germinal vesicle breakdown (GVBD). Experiments were performed to determine the critical period when gene transcription is required for GVBD and to identify candidate mRNAs involved. Experiment I: murine COC were cultured 4 h in the presence of FSH with 5,6-dichloro-1-β-d-ribofuranosylbenzimidazole (DRB) added at different intervals after the start of culture. COC cultured with FSH underwent GVBD (82 ± 7%). When DRB was added at 0, 5, or 10 min after culture initiation, oocyte maturation was blocked (17 ± 7, 14 ± 6, and 21 ± 6% GVBD, respectively). When DRB was added after 15, 20, or 30 min, progressively more COC underwent GVBD (37 ± 6, 39 ± 6, and 66 ± 6%, respectively). The critical period of transcription required for GVBD occurred between 15 and 30 min after culture initiation. Experiment II: COC were cultured for 25 min in the presence (plusDRB) or absence (minusDRB) of DRB. SAGE libraries were generated from COC RNA of each treatment group. A total of 48,431 and 45,367 tags were sequenced for the plusDRB and minusDRB libraries, respectively. Criteria used to identify transcripts of interest included a total tag count of at least 10 across both libraries and a threefold or greater difference in expression between libraries. Using these criteria, 39 and 27 transcripts were identified as differentially expressed at the P ≤ 0.01 and P ≤ 0.001 levels, respectively. Differentially expressed transcripts were classed into major categories that included cell growth, development, and regulation of gene expression. Differentially expressed transcripts represent candidates potentially involved in regulating maturation of murine COC.

Understanding SAGE data

Serial analysis of gene expression (SAGE) is a method for identifying and quantifying transcripts from eukaryotic genomes. Since its invention, SAGE has been widely applied to analyzing gene expression in many biological and medical studies. Vast amounts of SAGE data have been collected and more than a thousand SAGE-related studies have been published since the mid-1990s. The principle of SAGE has been developed to address specific issues such as determination of normal gene structure and identification of abnormal genome structural changes. This review focuses on the general features of SAGE data, including the specificity of SAGE tags with respect to their original transcripts, the quantitative nature of SAGE data for differentially expressed genes, the reproducibility, the comparability of SAGE with microarray and the future potential of SAGE. Understanding these basic features should aid the proper interpretation of SAGE data to address biological and medical questions.

Too much data, but little inter-changeability: a lesson learned from mining public data on tissue specificity of gene expression

Background

The tissue expression pattern of a gene often provides an important clue to its potential role in a biological process. A vast amount of gene expression data have been and are being accumulated in public repository through different technology platforms. However, exploitations of these rich data sources remain limited in part due to issues of technology standardization. Our objective is to test the data comparability between SAGE and microarray technologies, through examining the expression pattern of genes under normal physiological states across variety of tissues.

Results

There are 42–54% of genes showing significant correlations in tissue expression patterns between SAGE and GeneChip, with 30–40% of genes whose expression patterns are positively correlated and 10–15% of genes whose expression patterns are negatively correlated at a statistically significant level (p = 0.05). Our analysis suggests that the discrepancy on the expression patterns derived from technology platforms is not likely from the heterogeneity of tissues used in these technologies, or other spurious correlations resulting from microarray probe design, abundance of genes, or gene function. The discrepancy can be partially explained by errors in the original assignment of SAGE tags to genes due to the evolution of sequence databases. In addition, sequence analysis has indicated that many SAGE tags and Affymetrix array probe sets are mapped to different splice variants or different sequence regions although they represent the same gene, which also contributes to the observed discrepancies between SAGE and array expression data.

Conclusion

To our knowledge, this is the first report attempting to mine gene expression patterns across tissues using public data from different technology platforms. Unlike previous similar studies that only demonstrated the discrepancies between the two gene expression platforms, we carried out in-depth analysis to further investigate the cause for such discrepancies. Our study shows that the exploitation of rich public expression resource requires extensive knowledge about the technologies, and experiment. Informatic methodologies for better interoperability among platforms still remain a gap. One of the areas that can be improved practically is the accurate sequence mapping of SAGE tags and array probes to full-length genes.

Reviewers

This article was reviewed by Dr. I. King Jordan, Dr. Joel Bader, and Dr. Arcady Mushegian.