Bayesian model accounting for within-class biological variability in Serial Analysis of Gene Expression (SAGE)

Abstract Background An important challenge for transcript counting methods such as Serial Analysis of Gene Expression (SAGE), "Digital Northern" or Massively Parallel Signature Sequencing (MPSS), is to carry out statistical analyses that account for the within-class variability, i.e., variability due to the intrinsic biological differences among sampled individuals of the same class, and not only variability due to technical sampling error. Results We introduce a Bayesian model that accounts for the within-class variability by means of mixture distribution. We show that the previously available approaches of aggregation in pools ("pseudo-libraries") and the Beta-Binomial model, are particular cases of the mixture model. We illustrate our method with a brain tumor vs. normal comparison using SAGE data from public databases. We show examples of tags regarded as differentially expressed with high significance if the within-class variability is ignored, but clearly not so significant if one accounts for it. Conclusion Using available information about biological replicates, one can transform a list of candidate transcripts showing differential expression to a more reliable one. Our method is freely available, under GPL/GNU copyleft, through a user friendly web-based on-line tool or as R language scripts at supplemental web-site.

Serial analysis of the malapposed and uncovered struts of the new generation of everolimus-eluting bioresorbable scaffold with optical coherence tomography

The aim of this study is to assess the serial changes in strut apposition and coverage of the bioresorbable vascular scaffolds (BVS) and to relate this with the presence of intraluminal masses at 6 months with optical coherence tomography (OCT).

Dynamics of Gene Expression Revealed by Comparison of Serial Analysis of Gene Expression Transcript Profiles from Yeast Grown on Two Different Carbon SourcesD⃞

We describe a genome-wide characterization of mRNA transcript levels in yeast grown on the fatty acid oleate, determined using Serial Analysis of Gene Expression (SAGE). Comparison of this SAGE library with that reported for glucose grown cells revealed the dramatic adaptive response of yeast to a change in carbon source. A major fraction (>20%) of the 15,000 mRNA molecules in a yeast cell comprised differentially expressed transcripts, which were derived from only 2% of the total number of ∼6300 yeast genes. Most of the mRNAs that were differentially expressed code for enzymes or for other proteins participating in metabolism (e.g., metabolite transporters). In oleate-grown cells, this was exemplified by the huge increase of mRNAs encoding the peroxisomal β-oxidation enzymes required for degradation of fatty acids. The data provide evidence for the existence of redox shuttles across organellar membranes that involve peroxisomal, cytoplasmic, and mitochondrial enzymes. We also analyzed the mRNA profile of a mutant strain with deletions of the PIP2 and OAF1 genes, encoding transcription factors required for induction of genes encoding peroxisomal proteins. Induction of genes under the immediate control of these factors was abolished; other genes were up-regulated, indicating an adaptive response to the changed metabolism imposed by the genetic impairment. We describe a statistical method for analysis of data obtained by SAGE.

Generation of longer cDNA fragments from serial analysis of gene expression tags for gene identification

We have developed a technique called the generation of longer cDNA fragments from serial analysis of gene expression (SAGE) tags for gene identification (GLGI), to convert SAGE tags of 10 bases into their corresponding 3′ cDNA fragments covering hundred bases. A primer containing the 10-base SAGE tag is used as the sense primer, and a single base anchored oligo(dT) primer is used as an antisense primer in PCR, together with Pfu DNA polymerase. By using this approach, a cDNA fragment extending from the SAGE tag toward the 3′ end of the corresponding sequence can be generated. Application of the GLGI technique can solve two critical issues in applying the SAGE technique: one is that a longer fragment corresponding to a SAGE tag, which has no match in databases, can be generated for further studies; the other is that the specific fragment corresponding to a SAGE tag can be identified from multiple sequences that match the same SAGE tag. The development of the GLGI method provides several potential applications. First, it provides a strategy for even wider application of the SAGE technique for quantitative analysis of global gene expression. Second, a combined application of SAGE/GLGI can be used to complete the catalogue of the expressed genes in human and in other eukaryotic species. Third, it can be used to identify the 3′ cDNA sequence from any exon within a gene. It can also be used to confirm the reality of exons predicted by bioinformatic tools in genomic sequences. Fourth, a combined application of SAGE/GLGI can be applied to define the 3′ boundary of expressed genes in the genomic sequences in human and in other eukaryotic genomes.

Identification of diverse nerve growth factor-regulated genes by serial analysis of gene expression (SAGE) profiling

Neurotrophic factors such as nerve growth factor (NGF) promote a wide variety of responses in neurons, including differentiation, survival, plasticity, and repair. Such actions often require changes in gene expression. To identify the regulated genes and thereby to more fully understand the NGF mechanism, we carried out serial analysis of gene expression (SAGE) profiling of transcripts derived from rat PC12 cells before and after NGF-promoted neuronal differentiation. Multiple criteria supported the reliability of the profile. Approximately 157,000 SAGE tags were analyzed, representing at least 21,000 unique transcripts. Of these, nearly 800 were regulated by 6-fold or more in response to NGF. Approximately 150 of the regulated transcripts have been matched to named genes, the majority of which were not previously known to be NGF-responsive. Functional categorization of the regulated genes provides insight into the complex, integrated mechanism by which NGF promotes its multiple actions. It is anticipated that as genomic sequence information accrues the data derived here will continue to provide information about neurotrophic factor mechanisms.

Analysis of wheat SAGE tags reveals evidence for widespread antisense transcription

BACKGROUND: Serial Analysis of Gene Expression (SAGE) is a powerful tool for genome-wide transcription studies. Unlike microarrays, it has the ability to detect novel forms of RNA such as alternatively spliced and antisense transcripts, without the need for prior knowledge of their existence. One limitation of using SAGE on an organism with a complex genome and lacking detailed sequence information, such as the hexaploid bread wheat Triticum aestivum, is accurate annotation of the tags generated. Without accurate annotation it is impossible to fully understand the dynamic processes involved in such complex polyploid organisms. Hence we have developed and utilised novel procedures to characterise, in detail, SAGE tags generated from the whole grain transcriptome of hexaploid wheat. RESULTS: Examination of 71,930 Long SAGE tags generated from six libraries derived from two wheat genotypes grown under two different conditions suggested that SAGE is a reliable and reproducible technique for use in studying the hexaploid wheat transcriptome. However, our results also showed that in poorly annotated and/or poorly sequenced genomes, such as hexaploid wheat, considerably more information can be extracted from SAGE data by carrying out a systematic analysis of both perfect and "fuzzy" (partially matched) tags. This detailed analysis of the SAGE data shows first that while there is evidence of alternative polyadenylation this appears to occur exclusively within the 3' untranslated regions. Secondly, we found no strong evidence for widespread alternative splicing in the developing wheat grain transcriptome. However, analysis of our SAGE data shows that antisense transcripts are probably widespread within the transcriptome and appear to be derived from numerous locations within the genome. Examination of antisense transcripts showing sequence similarity to the Puroindoline a and Puroindoline b genes suggests that such antisense transcripts might have a role in the regulation of gene expression. CONCLUSION: Our results indicate that the detailed analysis of transcriptome data, such as SAGE tags, is essential to understand fully the factors that regulate gene expression and that such analysis of the wheat grain transcriptome reveals that antisense transcripts maybe widespread and hence probably play a significant role in the regulation of gene expression during grain development.

Global transcript analysis of rice leaf and seed using SAGE technology.

We have compiled two comprehensive gene expression profiles from mature leaf and immature seed tissue of rice (Oryza sativa ssp. japonica cultivar Nipponbare) using Serial Analysis of Gene Expression (SAGE) technology. Analysis revealed a total of 50 519 SAGE tags, corresponding to 15 131 unique transcripts. Of these, the large majority (approximately 70%) occur only once in both libraries. Unexpectedly, the most abundant transcript (approximately 3% of the total) in the leaf library was derived from a type 3 metallothionein gene. The overall frequency profiles of the abundant tag species from both tissues differ greatly and reveal seed tissue as exhibiting a non-typical pattern of gene expression characterized by an over abundance of a small number of transcripts coding for storage proteins. A high proportion ( approximately 80%) of the abundant tags (> or = 9) matched entries in our reference rice EST database, with many fewer matches for low abundant tags. Singleton transcripts that are common to both tissues were collated to generate a summary of low abundant transcripts that are expressed constitutively in rice tissues. Finally and most surprisingly, a significant number of tags were found to code for antisense transcripts, a finding that suggests a novel mechanism of gene regulation, and may have implications for the use of antisense constructs in transgenic technology.

Differential gene expression analysis of iodide-treated rat thyroid follicular cell line PCCl3

The inhibitory effect of supraphysiological iodide concentrations on thyroid hormone synthesis (Wolff - Chaikoff effect) and on thyrocyte proliferation is largely known as iodine autoregulation. However, the molecular mechanisms by which iodide modulates thyroid function remain unclear. In this paper, we analyze the transcriptome profile of the rat follicular cell lineage PCCl3 under untreated and treated conditions with 10 (- 3) M sodium iodide (NaI). Serial analysis of gene expression (SAGE) revealed 84 transcripts differentially expressed in response to iodide (p <= 0.001). We also showed that iodide excess inhibits the expression of essential genes for thyroid differentiation: Tshr, Nis, Tg, and Tpo. Relative expression of 14 of 20 transcripts selected by SAGE was confirmed by real-time PCR. Considering the key role of iodide organification in thyroid physiology, we also observed that both the oxidized form of iodide and iodide per se are responsible for gene expression modulation in response to iodide excess. (c) 2008 Elsevier Inc. All rights reserved.

Searching for molecular markers in head and neck squamous cell carcinomas (HNSCC) by statistical and bioinformatic analysis of larynx-derived SAGE libraries

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

SAGE analysis highlights the putative role of underexpression of ribosomal proteins in GH-secreting pituitary adenomas

Background: Although the molecular pathogenesis of pituitary adenomas has been assessed by several different techniques, it still remains partially unclear. Ribosomal proteins (RPs) have been recently related to human tumorigenesis, but they have not yet been evaluated in pituitary tumorigenesis. Objective: The aim of this study was to introduce serial analysis of gene expression (SAGE), a high-throughput method, in pituitary research in order to compare differential gene expression. Methods: Two SAGE cDNA libraries were constructed, one using a pool of mRNA obtained from five GH-secreting pituitary tumors and another from three normal pituitaries. Genes differentially expressed between the libraries were further validated by real-time PCR in 22 GH-secreting pituitary tumors and in 15 normal pituitaries. Results: Computer-generated genomic analysis tools identified 13 722 and 14 993 exclusive genes in normal and adenoma libraries respectively. Both shared 6497 genes, 2188 were underexpressed and 4309 overexpressed in tumoral library. In adenoma library, 33 genes encoding RPs were underexpressed. Among these, RPSA, RPS3, RPS14, and RPS29 were validated by real-time PCR. Conclusion: We report the first SAGE library from normal pituitary tissue and GH-secreting pituitary tumor, which provide quantitative assessment of cellular transcriptome. We also validated some downregulated genes encoding RPs. Altogether, the present data suggest that the underexpression of the studied RP genes possibly collaborates directly or indirectly with other genes to modify cell cycle arrest, DNA repair, and apoptosis, leading to an environment that might have a putative role in the tumorigenesis, introducing new perspectives for further studies on molecular genesis of somatotrophinomas.

Serial microanalysis of renal transcriptomes

Large-scale gene expression studies can now be routinely performed on macroamounts of cells, but it is unclear to which extent current methods are valuable for analyzing complex tissues. In the present study, we used the method of serial analysis of gene expression (SAGE) for quantitative mRNA profiling in the mouse kidney. We first performed SAGE at the whole-kidney level by sequencing 12,000 mRNA tags. Most abundant tags corresponded to transcripts widely distributed or enriched in the predominant kidney epithelial cells (proximal tubular cells), whereas transcripts specific for minor cell types were barely evidenced. To better explore such cells, we set up a SAGE adaptation for downsized extracts, enabling a 1,000-fold reduction of the amount of starting material. The potential of this approach was evaluated by studying gene expression in microdissected kidney tubules (50,000 cells). Specific gene expression profiles were obtained, and known markers (e.g., uromodulin in the thick ascending limb of Henle's loop and aquaporin-2 in the collecting duct) were found appropriately enriched. In addition, several enriched tags had no databank match, suggesting that they correspond to unknown or poorly characterized transcripts with specific tissue distribution. It is concluded that SAGE adaptation for downsized extracts makes possible large-scale quantitative gene expression measurements in small biological samples and will help to study the tissue expression and function of genes not evidenced with other high-throughput methods.

Heterogeneity in polyadenylation cleavage sites in mammalian mRNA sequences: implications for SAGE analysis

The analysis of a human thyroid serial analysis of gene expression (SAGE) library shows the presence of an abundant SAGE tag corresponding to the mRNA of thyroglobulin (TG). Additional, less abundant tags are present that can not be linked to any other known gene, but show considerable homology to the wild-type TG tag. To determine whether these tags represent TG mRNA molecules with alternative cleavage, 3′-RACE clones were sequenced. The results show that the three putative TG SAGE tags can be attributed to TG transcripts and reflect the use of alternative polyadenylation cleavage sites downstream of a single polyadenylation signal in vivo. By screening more than 300 000 sequences corresponding to human, mouse and rat transcripts for this phenomenon we show that a considerable percentage of mRNA transcripts (44% human, 22% mouse and 22% rat) show cleavage site heterogeneity. When analyzing SAGE-generated expression data, this phenomenon should be considered, since, according to our calculations, 2.8% of human transcripts show two or more different SAGE tags corresponding to a single gene because of alternative cleavage site selection. Both experimental and in silico data show that the selection of the specific cleavage site for poly(A) addition using a given polyadenylation signal is more variable than was previously thought.

Olfactomedin-4 regulation by estrogen in the human endometrium requires epidermal growth factor signaling

Olfactomedin-4 (OLFM-4) is an extracellular matrix protein that is highly expressed in human endometrium. We have examined the regulation and function of OLFM-4 in normal endometrium and in cases of endometriosis and endometrial cancer. OLFM-4 expression levels are highest in proliferative-phase endometrium, and 17 beta-estradiol up-regulates OLFM-4 mRNA in endometrial explant cultures. Using the luciferase reporter under control of the OLFM-4 promoter, it was shown that both 17 beta-estradiol and OH-tamoxifen induce luciferase activity, and epidermal growth factor receptor-1 is required for this estrogenic response. In turn, EGF activates the OLFM-4 promoter, and estrogen receptor-alpha is needed for the complete EGF response. The cellular functions of OLFM-4 were examined by its expression in OLFM-4-negative HEK-293 cells, which resulted in decreased vimentin expression and cell adherence as well as increased apoptosis resistance. In cases of endometriosis and endometrial cancer, OLFM-4 expression correlated with the presence of epidermal growth factor receptor-1 and estrogen receptor-alpha (or estrogen signaling). An increase of OLFM-4 mRNA was observed in the endometrium of endometriosis patients. No change in OLFM-4 expression levels were observed in patients with endometrial cancer relative with controts. In conclusion, cross-talk between estrogen and EGF signaling regulates OLFM-4 expression. The role of OLFM-4 in endometrial tissue remodeling before the secretory phase and during the predisposition and early events in endometriosis can be postulated but requires additional investigation. (Am J Pathol 2010, 177:2495-2508: DOI: 10.2353/ajpath.2010.100026

MADS-box基因在水稻和太行花中的相关研究& SAGE 在水稻上应用的生物信息学分析

本论文主要涉及两部分内容：第一部分是水稻和太行花中MADS-box基因的相关研究，第二部分是水稻中SAGE技术的生物信息学分析。 MADS-box基因家族在花发育过程中起着重要的作用。水稻是单子叶植物的模式植物，其基因组序列的公布为在基因组水平上鉴定MADS-box基因提供了条件。根据已发表的水稻MADS-box基因序列，利用关键词搜索、HMMER分析、同源比较、系统进化树分析等手段，对GeneBank和TIGR水稻基因组注释中所有已知和未知的水稻MADS-box基因进行了分析。结果表明，水稻中有64个MADS-box基因编码不同氨基酸，其中已知cDNA序列的为43个。在64个基因中，有23个属于ABCDE类基因，并且其cDNA序列已知，其中的10个已有功能方面的研究。通过3’-RACE和筛选水稻花器官的cDNA文库，得到了一个A类基因片断（A5B2），两个E类基因片断（MW1，MW2）。这三个基因分别与OsMADS14，OsMADS7和OsMADS5相似或相同。禾本科植物花器官的起源，尤其是内外稃的起源仍存在疑问，A功能基因是否以及如何参与其形成成为问题的关键。初步原位杂交分析表明，OsMADS14在花发育的早期开始表达。在小穗原基发育过程中，OsMADS14在内外稃原基中有较强的表达。随着小穗的发育，OsMADS14在整个花序中都有表达。在小穗发育的后期，OsMADS14在胚珠中有较强的表达。OsMADS14的表达模式与FUL类基因一致，并且证明了其在花发育的后期仍然表达强烈，暗示其可能与胚珠发育有重要关系。 SEPALLATA基因被认为是花的特异因子，参与了花的四轮花器官的决定过程。在本文中，通过3’-，5’-RACE，从太行花花芽中克隆了一个MADS-box基因。该基因推导的的氨基酸序列含有典型的M，I，K和C四个结构域，与FBP2和SEP3的相似性较高，系统进化树分析表明该基因属于SEP3亚家族，于是将其命名为TrSEP3。TrSEP3首先在花分生组织中表达，然后在雌雄蕊原基及花瓣中表达;在成熟花中，TrSEP3仅在花瓣和雌蕊中表达。这种表达模式与其它的SEP-like基因有稍不同。TrSEP3拟南芥中过量表达后并未导致表型的改变，暗示其功能与拟南芥的SEP基因可能存在差异。选择压力分析显示TrSEP3受到了不显著的负选择压力。这些结果暗示TrSEP3的功能可能与其它的SEP-like基因有差别，值得进一步研究。 SAGE(Serial Analysis of Gene Expression)是对比样品间转录谱的差异、发现新基因的有效的方法。tag mapping是将SAGE-tag与其转录本匹配的过程，其效率直接影响对转录谱的解释，该过程受多种因素影响。目前，对水稻tag mapping过程缺少详细研究，导致其效率不高。为了确定参考数据库和其它合适的条件，我们利用EST序列和基因组序列构建了不同的参考图谱，从全长cDNA数据库中提取虚拟的SAGE-tag，研究了参考图谱、锚定酶、tag长度以及匹配方法对tag mapping效率的影响，并比较了tag mapping的准确率。结果表明，用EST序列构建的参考图谱能够匹配大多数的SAGE-tag，并具有较高的准确率;利用迭代的方法，可以充分利用基因组序列。各种锚定酶之间的差别不明显，其中NlaIII， HpyCH4V 和 AluI 表现较好;17bp的tag比较适合水稻;而用双锚定酶和17bp的tag则可以显著提高tag mapping的效率和准确率。

Event models for tumor classification with SAGE gene expression data

Serial Analysis of Gene Expression (SAGE) is a relatively new method for monitoring gene expression levels and is expected to contribute significantly to the progress in cancer treatment by enabling a precise and early diagnosis. A promising application of SAGE gene expression data is classification of tumors. In this paper, we build three event models (the multivariate Bernoulli model, the multinomial model and the normalized multinomial model) for SAGE data classification. Both binary classification and multicategory classification are investigated. Experiments on two SAGE datasets show that the multivariate Bernoulli model performs well with small feature sizes, but the multinomial performs better at large feature sizes, while the normalized multinomial performs well with medium feature sizes. The multinomial achieves the highest overall accuracy.