Serial Analysis Of Gene Expression Research Articles

Comparative Transcriptome Analysis of Different Subsets of CD34+ Normal and Chronic Myeloid Leukemia Cells Identifies Novel Perturbations in the CML Stem Cell Population.

Chronic myeloid leukemia (CML) arises from a hematopoietic stem cell that acquires a BCR-ABL fusion gene. During the chronic phase of the disease, this cell produces an expanding multi-lineage clone that usually comes to dominate all terminal stages of myelopoiesis. However, eventually, further mutations are acquired which cause progression to a rapidly fatal acute leukemia. To obtain new insights into the molecular perturbations that cause CML stem cells to initiate chronic phase disease, we generated Long Serial Analysis of Gene Expression (LongSAGE) libraries (∼200,000 tags/library) from extracts of highly purified lin−CD34+CD45RA−CD36−CD71−CD7−CD38+ and lin−CD34+CD45RA−CD36−CD71−CD7−CD38− normal bone marrow (BM) and G-CSF-mobilized peripheral blood (G-mPB) cells as well as cells from 3 chronic phase CML patients with predominantly Ph+/BCR-ABL+ cells in both of these very primitive cell subsets. Long term culture-initiating cell (LTC-IC) and direct colony-forming cell (CFC) assays performed on an aliquot of each of these cell populations showed the frequency of LTC-IC were 4 to 130-fold higher in the 34+38− cells than in the matching 34+38+ cells with the opposite trend for the CFCs. Comparison of the tags present in the pooled CML and pooled normal BM and G-mPB libraries revealed many differentially expressed genes. Real-time RT-PCR analysis of lin−CD34+ cells from 14 chronic phase CML patients and 3 normal BMs confirmed the differential expression of 14 candidate transcripts identified by SAGE (changes ranging from 3-fold lower to 80-fold higher in the CML cells, p<0.05). The altered levels of expression of 5 of these genes (i.e., beta-catenin, MLLT3, IL1R1, LY6E and GAS2) were highly correlated with the relative levels of BCR-ABL transcripts in the same cells (r ≥0.6). 5 of the 14 genes (IL1R1, vWF, SOX4, SELL and RHOB) were found to be differentially expressed in the 3-day post-transduction progeny of CD34+ cord blood cells exposed to a BCR/ABL-IRES-GFP vs a control GFP- lentivirus preparation (n=2). 3 (GAS2, DUSP1 and TP53BP2) were upregulated (5 to 11-fold) in imatinib-treated K562 cells (as compared to untreated K562 cells) but their expression remained unchanged in similarly treated KG1 cells (a primitive BCR-ABL-negative human AML cell line) providing further evidence that their deregulated expression is secondary to the kinase activity of p210BCR-ABL. In addition, from a comparison of the meta-library for the 34+38− CML cells with the meta-libraries for both the normal 34+38− and 34+38+ cells, the meta-library for the 34+38+ CML cells and most publicly accessible SAGE data, we were able to identify 65 novel tags in the 34+38− CML cells. 32 of these unique tags were located within conserved genomic regions and >2 kb away from known transcripts, and of these 32, 3 were confirmed to represent novel transcripts using a PCR approach. These results illustrate the potential of SAGE to reveal novel as well as known components in the transcriptomes of rare normal and cancer stem cell populations. Investigation of their roles in primitive human cells transduced with BCR-ABL and BCR-ABL+cell lines indicates the utility of these models for further delineation of the complex effects of BCR-ABL expression in chronic phase CML stem cells.

Serial analysis of gene expression reveals identifiable patterns in transcriptome profiles of Palaemonetes pugio exposed to three common environmental stressors

Serial analysis of gene expression (SAGE) is a transcriptome-profiling technique that allows accurate gene expression profiles to be generated without any need for prior sequence knowledge, making it ideal for assaying toxicant-specific genetic responses in nonmodel species. We used SAGE to perform a large-scale survey of the Palaemonetes pugio transcriptome under different chemical xenobiotic stresses. Four libraries were constructed from fipronil-exposed, endosulfan-exposed, and cadmium-exposed shrimp, as well as from carrier-control-exposed shrimp. A total of 37,152 tags were sequenced from the four libraries, including 13,754 unique tags. Statistical analysis revealed that 117 tags were differentially expressed significantly in at least one of the three exposures. Cluster analyses of the altered tags showed that fipronil and endosulfan elicit transcriptome profiles that are more similar to each other than either one was to cadmium. Quantitative polymerase chain reaction (QPCR) was performed on a subset of 46 differentially expressed tags, and largely confirmed the SAGE results. The tags were filtered to identify tags for which both SAGE and QPCR agreed in regard to the direction and magnitude of affect, and which were informative about the exposure experienced. A subset of 16 tags was identified that represents a putative biomarker suite that is indicative of exposures to the three toxicants.

Comparison between the Gene Expression Profile of Human Müller Cells and Two Spontaneous Müller Cell Lines

Müller cells are the principal glial cells in the retina. They play important functions in this tissue. Alterations in Müller cell behavior were observed in the retinal tissue of patients with proliferative diabetic retinopathy. The purpose of this study was to determine the gene expression profile of normal human Müller cells (NHMCs) and to compare it with that of two spontaneously generated human Müller cell lines (HMCLs) from type 1 and type 2 diabetic donors by using serial analysis of gene expression (SAGE). Approximatively 50,000 tags were sequenced for each SAGE library. Identification of the transcripts was obtained by matching the 15-bp tags with the UniGene and GenBank databases. Classification of the genes was based on the updated information of the genome directory found at the National Center for Biotechnology Information (NCBI) Web site. SAGE was used to characterize the entire transcriptome of human Müller cells and to compare these data with those from Müller cell lines generated from type 1 and type 2 diabetic donors. The transcriptome of NHMCs and HMCLs demonstrated that "metabolism" and "protein synthesis" are the two main categories of genes expressed by human Müller cells. Only 106 genes are differentially expressed between NHMCs and HMCLs. The SAGE libraries reported in this article provide the gene expression profile of NHMCs and HMLCs. It thus represents a valuable source of information regarding the function of Müller cells as well as their role in the development of diabetic retinopathy.

Upregulated expression of S100A6 in human gastric cancer

The expression of S100A6 (calcyclin), a member of the S100 calcium binding protein family, is elevated in a number of malignant tumors, but there have been few reports about its expression in gastric cancer. The aim of this study was to investigate its expression regulations in human gastric cancer and noncancerous mucosa, and the response to chemotherapeutic drugs in the gastric cancer cell line. In one matched gastric cancer sample pair, the serial analysis of gene expression (SAGE) experiment was conducted to compare the gene expression profiles between cancerous and adjacent tissues. To detect the expression regulations among more cancerous tissues, microarrays were carried out and real-time RT-PCR was conducted to validate the results. At the protein level, Western blot and tissue microarray (TMA) examination were further used to verify S100A6 expression. The regulation detection of S100A6 with flurouracil and doxorubicin at the mRNA and protein level was performed in the SGC7901 cell line. With the SAGE strategy, five times more S100A6 tags were identified in cancer tissues than in normal tissues. With the cDNA microarray, S100A6 was found to be significantly upregulated in 21 of 42 (50%) nonselective gastric cancers. In 10 other paired samples, the upregulation of S100A6 was consolidated with RT-PCR and Western blot analysis as well. A total of 14 endoscopy-sectioned gastric noncancerous lesions and corresponding normal gastric mucosa were also applied to profile the gene expression; both cDNA microarray and RT-PCR demonstrated no significant alterations of S100A6 at the mRNA level. TMA examination showed that 34 of 52 (65.4%) cancer samples were positively stained, while only 17 of 80 (21.3%) noncancerous lesions were positively detected and all nine normal mucosae were detected to be negative. An in vitro experiment showed that in the gastric cell line SGC-7901, S100A6 mRNA was detected to be upregulated from 24 to 72 h after treatment with 5 mg/L 5-flurouracil or 0.3 mg/L doxorubicin, and there were two wave upregulations of the S100A6 protein. The observed regulated expression of S100A6 suggests that it is associated with gastric cancer tumorigenesis and quantitation of S100A6 is a promising tool for diagnosis of gastric cancer.

The effect of the fungal metabolite radicicol analog A on mRNA degradation

The AU-rich element (ARE) is a stability determinant found in the 3′ UTR of a number of short-lived mRNAs. The best characterized ARE is the Shaw–Kamen (SK) box or AUUUA motif. Previously, a fungal metabolite, radicicol analog A (RAA), was shown to destabilize SK box-containing mRNAs based on 16 mRNAs examined [T. Kastelic et al., Cytokine 8 (1996) 751–761]. Using serial analysis of gene expression (SAGE) to examine the global effect of RAA on mRNA expression in interferon-γ/lipopolysaccharide-stimulated THP-1 human monocytes, we observed that the expression level of greater than 99% of the SAGE tags was unchanged by RAA treatment and only 34 of the 17,608 unique tags annotated were reduced ( p ≤ 0.0001). RAA destabilized approximately half of the down-regulated transcripts. Whereas all the destabilized mRNAs possessed at least one SK box, for transcripts not destabilized but nonetheless down-regulated, RAA appears to function by a SK box-independent mechanism not currently understood.

Serial Analysis of Gene Expression (SAGE): Experimental Method and Data Analysis

Serial analysis of gene expression (SAGE) involves the generation of short fragments of DNA, or tags, from a defined point in the sequence of all cDNAs in the sample analyzed. This short tag, because of its presence in a defined point in the sequence, is typically sufficient to uniquely identify every transcript in the sample. SAGE allows one to generate a comprehensive profile of gene expression in any sample desired from as little as 100,000 cells or 1 microg of total RNA. SAGE generates absolute, rather than relative, measurements of RNA abundance levels, and this fact allows an investigator to readily and reliably compare data to those produced by other laboratories, making the SAGE data set increasingly useful as more data is generated and shared. Software tools have also been specifically adapted for SAGE tags to allow cluster analysis of both public and user-generated data.

Differential gene expression in an elite hybrid rice cultivar (Oryza sativa, L) and its parental lines based on SAGE data

BackgroundIt was proposed that differentially-expressed genes, aside from genetic variations affecting protein processing and functioning, between hybrid and its parents provide essential candidates for studying heterosis or hybrid vigor. Based our serial analysis of gene expression (SAGE) data from an elite Chinese super-hybrid rice (LYP9) and its parental cultivars (93-11 and PA64s) in three major tissue types (leaves, roots and panicles) at different developmental stages, we analyzed the transcriptome and looked for candidate genes related to rice heterosis.ResultsBy using an improved strategy of tag-to-gene mapping and two recently annotated genome assemblies (93-11 and PA64s), we identified 10,268 additional high-quality tags, reaching a grand total of 20,595 together with our previous result. We further detected 8.5% and 5.9% physically-mapped genes that are differentially-expressed among the triad (in at least one of the three stages) with P-values less than 0.05 and 0.01, respectively. These genes distributed in 12 major gene expression patterns; among them, 406 up-regulated and 469 down-regulated genes (P < 0.05) were observed. Functional annotations on the identified genes highlighted the conclusion that up-regulated genes (some of them are known enzymes) in hybrid are mostly related to enhancing carbon assimilation in leaves and roots. In addition, we detected a group of up-regulated genes related to male sterility and 442 down-regulated genes related to signal transduction and protein processing, which may be responsible for rice heterosis.ConclusionWe improved tag-to-gene mapping strategy by combining information from transcript sequences and rice genome annotation, and obtained a more comprehensive view on genes that related to rice heterosis. The candidates for heterosis-related genes among different genotypes provided new avenue for exploring the molecular mechanism underlying heterosis.

Breast Cancer Molecular Signatures as Determined by SAGE: Correlation with Lymph Node Status

Global gene expression measured by DNA microarray platforms have been extensively used to classify breast carcinomas correlating with clinical characteristics, including outcome. We generated a breast cancer Serial Analysis of Gene Expression (SAGE) high-resolution database of approximately 2.7 million tags to perform unsupervised statistical analyses to obtain the molecular classification of breast-invasive ductal carcinomas in correlation with clinicopathologic features. Unsupervised statistical analysis by means of a random forest approach identified two main clusters of breast carcinomas, which differed in their lymph node status (P=0.01); this suggested that lymph node status leads to globally distinct expression profiles. A total of 245 (55 up-modulated and 190 down-modulated) transcripts were differentially expressed between lymph node (+) and lymph node (-) primary breast tumors (fold change, >or=2; P<0.05). Various lymph node (+) up-modulated transcripts were validated in independent sets of human breast tumors by means of real-time reverse transcription-PCR (RT-PCR). We validated significant overexpression of transcripts for HOXC10 (P=0.001), TPD52L1 (P=0.007), ZFP36L1 (P=0.011), PLINP1 (P=0.013), DCTN3 (P=0.025), DEK (P=0.031), and CSNK1D (P=0.04) in lymph node (+) breast carcinomas. Moreover, the DCTN3 (P=0.022) and RHBDD2 (P=0.002) transcripts were confirmed to be overexpressed in tumors that recurred within 6 years of follow-up by real-time RT-PCR. In addition, meta-analysis was used to compare SAGE data associated with lymph node (+) status with publicly available breast cancer DNA microarray data sets. We have generated evidence indicating that the pattern of gene expression in primary breast cancers at the time of surgical removal could discriminate those tumors with lymph node metastatic involvement using SAGE to identify specific transcripts that behave as predictors of recurrence as well.

Nuclear Labeling of Coccidioides posadasii with Green Fluorescent Protein

Coccidioidomycosis is a mild to life-threatening disease in otherwise healthy humans and other mammals caused by the fungus Coccidioides spp. Understanding the development of the unique dimorphic life cycle of Coccidioides spp. and its role in pathogenesis has been an area of research focus. However, nuclear behavior during the saprobic and parasitic life cycle has not been studied intensively. In this study, green fluorescent protein (GFP) was fused to histone H1 and introduced into Coccidioides posadasii (C. posadasii) strain Silveira to monitor the nuclear behavior of the fungus during the saprobic and parasitic stages of the life cycle. We constructed an Agrobacterium tumefaciens-mediated transformation (ATMT) vector that had in its T-DNA region a hygromycin-resistance gene as well as the fused histone H1-GFP gene under the control of the histone H3 promoter of C. posadasii. More than 30 hygromycin-resistant transformants were obtained and 23 were purified to homozygosity through multiple passages of the original transformants on hygromycin-containing media. One strain (VFC1420) transformed with a single copy of the fusion histone H1-GFP gene was selected for cytological studies. Strong nuclear-localized GFP signals were observed in arthroconidia, hyphae, as well as in spherules and endospores developed in vitro. Thus GFP can be used to study the expression pattern of potential virulence genes identified in serial analysis of gene expression (SAGE) or expressed sequence tags (EST) libraries, and could be a useful tool to monitor disease development in the murine model.

Cancer immunomics and application of ‘omics’ for cancer management

Several ‘omics’ sciences have been launched. The outstanding diversity of immune system components, together with the complexity of the regulatory pathways and network-type interactions, makes immunology a combinatorial science. Cancer immonomics is a branch of immunomics. The present cancer immunomics project aims at diagnostics and therapy. Comparative and structural genomics can be applied for understanding the underlying tumorogenesis process. For diagnostic purposes, in order to develop a new marker, gene expression analysis technologies, such as DNA microarray, differential display, cDNA subtraction, serial analysis of gene expression and serological proteome analysis, which enable investigators to obtain comprehensive data with respect to gene expression profiles, are progressing rapidly. For therapeutic purposes, vaccine candidate finding, gene therapy and drug–tumor interaction modeling can be clarified by the advance immunomics techniques. In this article, the author will present the basic concepts on cancer immunomics and application for basic study of the pathogenesis, diagnosis and treatment of cancer.

NAC-1 Controls Cell Growth and Survival by Repressing Transcription of Gadd45GIP1, a Candidate Tumor Suppressor

Cancer mortality and morbidity are primarily related to recurrent tumors, and characterization of recurrence-associated genes should illuminate fundamental properties of tumor progression and provide new therapeutic targets. We have previously identified NAC-1, a member of the BTB/POZ gene family and a transcription repressor, as a gene associated with recurrent ovarian carcinomas after chemotherapy. We further showed that homodimerization of NAC-1 proteins is essential for tumor growth and survival. In this study, we applied serial analysis of gene expression and identified growth arrest and DNA-damage-inducible 45-gamma interacting protein (Gadd45GIP1) as one of the downstream genes negatively regulated by NAC-1. NAC-1 knockdown in both SKOV3 and HeLa cells that expressed abundant endogenous NAC-1 induced Gadd45GIP1 expression transcriptionally; on the other hand, engineered expression of NAC-1 in NAC-1-negative RK3E and HEK293 cells suppressed endogenous Gadd45GIP1 expression. In NAC-1-expressing tumor cells, induction of dominant negative NAC-1 conferred a growth-inhibitory effect that can be partially reversed by Gadd45GIP1 knockdown. Induced Gadd45GIP1 expression resulted in growth arrest in SKOV3 and HeLa cells both in vitro and in vivo. In summary, NAC-1 contributes to tumor growth and survival by at least inhibiting Gadd45GIP1 expression, which has a tumor suppressor effect in cancer cells.

SAGETTARIUS: a program to reduce the number of tags mapped to multiple transcripts and to plan SAGE sequencing stages

SAGE (Serial Analysis of Gene Expression) experiments generate short nucleotide sequences called ‘tags’ which are assumed to map unambiguously to their original transcripts (1 tag to 1 transcript mapping). Nevertheless, many tags are generated that do not map to any transcript or map to multiple transcripts. Current bioinformatics resources, such as SAGEmap and TAGmapper, have focused on reducing the number of unmapped tags. Here, we describe SAGETTARIUS, a new high-throughput program that performs successive precise Nla3 and Sau3A tag to transcript mapping, based on specifically designed Virtual Tag (VT) libraries. First, SAGETTARIUS decreases the number of tags mapped to multiple transcripts. Among the various mapping resources compared, SAGETTARIUS performed the best in this respect by decreasing up to 11% the number of multiply mapped tags. Second, SAGETTARIUS allows the establishment of a guideline for SAGE experiment sequencing efforts through efficient mapping of the CRT (Cytoplasmic Ribosomal protein Transcripts)-specific tags. Using all publicly available human and mouse Nla3 SAGE experiments, we show that sequencing 100 000 tags is sufficient to map almost all CRT-specific tags and that four sequencing stages can be identified when carrying out a human or mouse SAGE project. SAGETTARIUS is web interfaced and freely accessible to academic users.

Transcriptome annotation using tandem SAGE tags

Analysis of several million expressed gene signatures (tags) revealed an increasing number of different sequences, largely exceeding that of annotated genes in mammalian genomes. Serial analysis of gene expression (SAGE) can reveal new Poly(A) RNAs transcribed from previously unrecognized chromosomal regions. However, conventional SAGE tags are too short to identify unambiguously unique sites in large genomes. Here, we design a novel strategy with tags anchored on two different restrictions sites of cDNAs. New transcripts are then tentatively defined by the two SAGE tags in tandem and by the spanning sequence read on the genome between these tagged sites. Having developed a new algorithm to locate these tag-delimited genomic sequences (TDGS), we first validated its capacity to recognize known genes and its ability to reveal new transcripts with two SAGE libraries built in parallel from a single RNA sample. Our algorithm proves fast enough to experiment this strategy at a large scale. We then collected and processed the complete sets of human SAGE tags to predict yet unknown transcripts. A cross-validation with tiling arrays data shows that 47% of these TDGS overlap transcriptional active regions. Our method provides a new and complementary approach for complex transcriptome annotation.

Molecular Profiling of Matched Samples Identifies Biomarkers of Papillary Thyroid Carcinoma Lymph Node Metastasis

Biomarkers of papillary thyroid carcinoma (PTC) metastasis can accurately identify metastatic cells and aggressive tumor behavior. To find new markers, serial analysis of gene expression (SAGE) was done on three samples from the same patient: normal thyroid tissue, primary PTC, and a PTC lymph node metastasis. This genomewide expression analysis identified 31 genes expressed in lymph node metastasis, but not in the primary tumor. Eleven genes were evaluated by quantitative real-time reverse transcription-PCR (qPCR) on independent sets of matched samples to find genes that were consistently different between the tumor and metastatic samples. LIMD2 and PTPRC (CD45) showed a statistically significant difference in expression between tumor and metastatic samples (P < 0.0045), and an additional gene (LTB) had borderline significance. PTPRC and LTB were tested by immunohistochemistry in an independent set of paired samples, with both markers showing a difference in protein expression. All 20 metastases from 6 patients showed expression in both markers, with little or no expression in primary tumor. Some of these markers could provide an improved means to detect metastatic PTC cells during initial staging of a newly diagnosed carcinoma and/or to rule out recurrence. The functional role of these genes may also provide insight into mechanisms of thyroid cancer metastasis.

In silico gene expression analysis – an overview

Efforts aimed at deciphering the molecular basis of complex disease are underpinned by the availability of high throughput strategies for the identification of biomolecules that drive the disease process. The completion of the human genome-sequencing project, coupled to major technological developments, has afforded investigators myriad opportunities for multidimensional analysis of biological systems. Nowhere has this research explosion been more evident than in the field of transcriptomics. Affordable access and availability to the technology that supports such investigations has led to a significant increase in the amount of data generated. As most biological distinctions are now observed at a genomic level, a large amount of expression information is now openly available via public databases. Furthermore, numerous computational based methods have been developed to harness the power of these data. In this review we provide a brief overview of in silico methodologies for the analysis of differential gene expression such as Serial Analysis of Gene Expression and Digital Differential Display. The performance of these strategies, at both an operational and result/output level is assessed and compared. The key considerations that must be made when completing an in silico expression analysis are also presented as a roadmap to facilitate biologists. Furthermore, to highlight the importance of these in silico methodologies in contemporary biomedical research, examples of current studies using these approaches are discussed. The overriding goal of this review is to present the scientific community with a critical overview of these strategies, so that they can be effectively added to the tool box of biomedical researchers focused on identifying the molecular mechanisms of disease.

Statistical analysis and significance testing of serial analysis of gene expression data using a Poisson mixture model

BackgroundSerial analysis of gene expression (SAGE) is used to obtain quantitative snapshots of the transcriptome. These profiles are count-based and are assumed to follow a Binomial or Poisson distribution. However, tag counts observed across multiple libraries (for example, one or more groups of biological replicates) have additional variance that cannot be accommodated by this assumption alone. Several models have been proposed to account for this effect, all of which utilize a continuous prior distribution to explain the excess variance. Here, a Poisson mixture model, which assumes excess variability arises from sampling a mixture of distinct components, is proposed and the merits of this model are discussed and evaluated.ResultsThe goodness of fit of the Poisson mixture model on 15 sets of biological SAGE replicates is compared to the previously proposed hierarchical gamma-Poisson (negative binomial) model, and a substantial improvement is seen. In further support of the mixture model, there is observed: 1) an increase in the number of mixture components needed to fit the expression of tags representing more than one transcript; and 2) a tendency for components to cluster libraries into the same groups. A confidence score is presented that can identify tags that are differentially expressed between groups of SAGE libraries. Several examples where this test outperforms those previously proposed are highlighted.ConclusionThe Poisson mixture model performs well as a) a method to represent SAGE data from biological replicates, and b) a basis to assign significance when testing for differential expression between multiple groups of replicates. Code for the R statistical software package is included to assist investigators in applying this model to their own data.

A systematic screen for genes expressed in definitive endoderm by Serial Analysis of Gene Expression (SAGE)

BackgroundThe embryonic definitive endoderm (DE) gives rise to organs of the gastrointestinal and respiratory tract including the liver, pancreas and epithelia of the lung and colon. Understanding how DE progenitor cells generate these tissues is critical to understanding the cause of visceral organ disorders and cancers, and will ultimately lead to novel therapies including tissue and organ regeneration. However, investigation into the molecular mechanisms of DE differentiation has been hindered by the lack of early DE-specific markers.ResultsWe describe the identification of novel as well as known genes that are expressed in DE using Serial Analysis of Gene Expression (SAGE). We generated and analyzed three longSAGE libraries from early DE of murine embryos: early whole definitive endoderm (0–6 somite stage), foregut (8–12 somite stage), and hindgut (8–12 somite stage). A list of candidate genes enriched for expression in endoderm was compiled through comparisons within these three endoderm libraries and against 133 mouse longSAGE libraries generated by the Mouse Atlas of Gene Expression Project encompassing multiple embryonic tissues and stages. Using whole mount in situ hybridization, we confirmed that 22/32 (69%) genes showed previously uncharacterized expression in the DE. Importantly, two genes identified, Pyy and 5730521E12Rik, showed exclusive DE expression at early stages of endoderm patterning.ConclusionThe high efficiency of this endoderm screen indicates that our approach can be successfully used to analyze and validate the vast amount of data obtained by the Mouse Atlas of Gene Expression Project. Importantly, these novel early endoderm-expressing genes will be valuable for further investigation into the molecular mechanisms that regulate endoderm development.

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.

A modified GLGI method for identification of novel porcine genes from long serial analysis of gene expression tags

AbstractCombining the long serial analysis of gene expression (LongSAGE) and the generation of longer cDNA fragments from serial analysis of gene expression tags for gene identification (GLGI) technique, a new strategy called modified GLGI (M-GLGI) was developed to isolate unknown 3′ expressed sequence tags (ESTs) and discover novel genes. A 17 bp LongSAGE tag was used as sense primer instead of a 10-base SAGE tag; PCR reaction was performed under an appropriate annealing temperature for each tag; universal DNA polymerase was used in PCR amplification instead of Pfu enzyme; a common cloning strategy using pMD-18T vector and Escherichia coli DH5α cells were used instead of a special vector and competent cells. Moreover, ESTs isolated by M-GLGI had 3′ ends with the polyadenylation signals and poly(dA) tails. This method is more sensitive for identifying genes expressed in low abundance than conventional EST sequencing.

Gender difference of androgen actions on skeletal muscle transcriptome

Sarcopenia is related to metabolic syndrome in postmenopausal women. Hormone replacement therapies with androgens improve muscle functions by molecular mechanisms that are still unknown, at least partly because the skeletal muscle transcriptome has been less characterized in females. We performed the serial analysis of gene expression method in six experimental groups, intact (male and female), ovariectomy (OVX), OVX+dihydrotestosterone (DHT) injection 1, 3, or 24 h before kill in mice. The 438 transcript species differentially expressed between gender showed that females had higher expression levels of mRNA related to cytoskeleton/contractile apparatus and mitochondrial processes as well as protein, lipid, and amino acid metabolisms. In females, OVX and DHT modulated 109 and 128 transcript species respectively. OVX repressed transcripts of fast/glycolytic fiber, glycolysis, and glucose transport, whereas all these effects were reversed 3 h after the DHT injection. Moreover, DHT treatment induced transcripts which reduce intracellular Ca(2+) level at early time points. These results may suggest that DHT treatment in OVX mice increases muscle contractility by affecting fiber distribution and intracellular Ca(2+) concentration as well as improving glucose metabolism. On the other hand, transcripts of fast/oxidative fiber, oxidative phosphorylation, and ATP production were repressed 24 h after DHT administration. In our previous study using male mice, transcripts in oxidative phosphorylation and ATP production were induced 24 h after DHT injection (Yoshioka M, Boivin A, Ye P, Labrie F & St-Amand J 2006 Effects of dihydrotestosterone on skeletal muscle transcriptome in mice measured by serial analysis of gene expression. Journal of Molecular Endocrinology 36 247-259 ). These results demonstrate gender differences in DHT actions on skeletal muscle, and contribute to a precise understanding of the molecular mechanisms of androgen actions in the female skeletal muscle.