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PubMed2004

Modified serial analysis of gene expression method for construction of gene expression profiles of microbial eukaryotic species.

Serial analysis of gene expression (SAGE) is a powerful approach for the identification of differentially expressed genes, providing comprehensive and quantitative gene expression profiles in the form of short tag sequences. Each tag represents a unique transcript, and the relative frequencies of tags in the SAGE library are equal to the relative proportions of the transcripts they represent. One of the major obstacles in the preparation of SAGE libraries from microorganisms is the requirement for large amounts of starting material (i.e., mRNA). Here, we present a novel approach for the construction of SAGE libraries from small quantities of total RNA by using Y linkers to selectively amplify 3' cDNA fragments. To validate this method, we constructed comprehensive gene expression profiles of the toxic dinoflagellate Pfiesteria shumwayae. SAGE libraries were constructed from an actively toxic fish-fed culture of P. shumwayae and from a recently toxic alga-fed culture. P. shumwayae-specific gene transcripts were identified by comparison of tag sequences in the two libraries. Representative tags with frequencies ranging from 0.026 to 3.3% of the total number of tags in the libraries were chosen for further analysis. Expression of each transcript was confirmed in separate control cultures of toxic P. shumwayae. The modified SAGE method described here produces gene expression profiles that appear to be both comprehensive and quantitative, and it is directly applicable to the study of gene expression in other environmentally relevant microbial species.

Animals↗

PubMed2001

Visualizing associations between genome sequences and gene expression data using genome-mean expression profiles.

The combination of genome-wide expression patterns and full genome sequences offers a great opportunity to further our understanding of the mechanisms and logic of transcriptional regulation. Many methods have been described that identify sequence motifs enriched in transcription control regions of genes that share similar gene expression patterns. Here we present an alternative approach that evaluates the transcriptional information contained by specific sequence motifs by computing for each motif the mean expression profile of all genes that contain the motif in their transcription control regions. These genome-mean expression profiles (GMEP's) are valuable for visualizing the relationship between genome sequences and gene expression data, and for characterizing the transcriptional importance of specific sequence motifs. Analysis of GMEP's calculated from a dataset of 519 whole-genome microarray experiments in Saccharomyces cerevisiae show a significant correlation between GMEP's of motifs that are reverse complements, a result that supports the relationship between GMEP's and transcriptional regulation. Hierarchical clustering of GMEP's identifies clusters of motifs that correspond to binding sites of well-characterized transcription factors. The GMEP's of these clustered motifs have patterns of variation across conditions that reflect the known activities of these transcription factors. Software that computed GMEP's from sequence and gene expression data is available under the terms of the Gnu Public License from http://rana.lbl.gov/.

Algorithms↗

PubMed2005

Gene expression profiling in seminoma and nonseminoma.

PURPOSE: Gene expression profiles of seminoma were compared with nonseminoma to get insights into tumorigenesis. MATERIALS AND METHODS: Eleven testicular tumor biopsies (five pure seminoma, six nonseminoma; pT1N0M0 to pT2N2M1) and biopsies from unaffected sites were analyzed once per patient using a macroarray (1,176 genes). On the same patients, six genes were validated using real-time quantitative (RTQ) polymerase chain reaction (PCR). Additionally, in a separate cohort of 19 patients, 24 genes selected from the macroarray were measured using RTQ-PCR. RESULTS: (1) The agreement in gene expression was 94% between the two methods and two different patient cohorts. (2) Two features in gene expression were independent of the tumor entity: Most changes of gene expression occurred in five functional groups like "cell cycle" and "apoptosis." Genes within these groups were almost similarly (> 80%) up- or downregulated. (3) Nonseminoma were characterized by downregulated genes (75%), but in seminoma, upregulated genes (64%) prevailed. Furthermore, 64.4% of those genes that were differentially expressed in both tumor entities were usually upregulated in seminoma but downregulated in nonseminoma. A reverse pattern was found in 24.4% of such genes. Eleven percent of these genes showed a similar up- or downregulation in gene expression in both tumor entities. CONCLUSION: Seminoma in this preliminary study can be differentiated from nonseminoma due to almost opposing gene expression profiles (89% of the significantly differentially expressed genes) and are in line with the histological discrimination of both tumor entities. Underlying mechanisms and implications regarding the origin and tumor progression of both entities are discussed.

Adolescent↗

PubMed2005

[Preliminary biomarker related to nasopharyngeal carcinoma filtered from the whole genome expression profiling involved in microdissection nasopharyngeal tissues].

OBJECTIVE: To filter biomarkers of nasopharyngeal carcinoma (NPC) by constructing the homogenesis tissue gene expression profiling with the whole human genome GeneChip. METHODS: The epithelium cells of the homogenesis NPC and the pure nasopharyngeal normal tissues microdissected from nasopharyngeal biopsy which was preserved in the RNAlater were used to isolate RNA and then to harvest the aRNA through in vitro transcription, and aRNA prober was labled to hybridize to HG-U133. plus 2.0, so the expression profiling of each homogenesis tissue could be constructed. RESULTS: Some candidate biomarker genes related to the tumorigenesis of NPC had been filtered by comparing the expression profiling of NPC samples with the expression profiling of normal nasopharyngeal epithelia samples. Any genes regarding the metastasis of NPC might have been selected by comparing the expression profiling of no-metastasis samples with those of the metastasis samples. CONCLUSION: Using the whole genome GeneChip to construct the expression profiling for the microdissected homogenesis tissue is effective to filter the candidate biomarker genes.

Adult↗

PubMed2004

GEPIS--quantitative gene expression profiling in normal and cancer tissues.

MOTIVATION: Expression profiling in diverse tissues is fundamental to understanding gene function as well as therapeutic target identification. The vast collection of expressed sequence tags (ESTs) and the associated tissue source information provides an attractive opportunity for studying gene expression. RESULTS: To facilitate EST-based expression analysis, we developed GEPIS (gene expression profiling in silico), a tool that integrates EST and tissue source information to compute gene expression patterns in a large panel of normal and tumor samples. We found EST-based expression patterns to be consistent with published papers as well as our own experimental results. We also built a GEPIS Regional Atlas that depicts expression characteristics of all genes in a selected genomic region. This program can be adapted for large-scale screening for genes with desirable expression patterns, as illustrated by our large-scale mining for tissue- and tumor-specific genes. AVAILABILITY: The email server version of the GEPIS application is freely available at http://share.gene.com/share/gepis. An interactive version of GEPIS will soon be freely available at http://www.cgl.ucsf.edu/Research/genentech/gepis/. The source code, modules, data and gene lists can be downloaded at http://share.gene.com/share/gepis.

Algorithms↗

PubMed2003

[Non small cell lung cancer gene expression profiles by cDNA microarrays].

OBJECTIVE: To study lung squamamouscarcinoma and lung adenocarcinoma gene expression profiles by cDNA microarrays, compare them with those of pulmonary inflammatory pseudoneoplasma, and screen the differential expression gene and the common expression gene in these types of lung cancer in order to further study early diagnosis and treatment of lung cancer. METHODS: cDNA microarray chips containing 4096 human target genes were used to identify gene expression profiles in lung squamous cell carcinoma, adenocarcinoma, and pulmonary inflammatory pseudoneoplasma. RESULTS: Among the target genes, 591 differentially expressed genes were identified in lung squamous cell carcinoma; among them, there were 476 genes unexpressed in pulmonary inflammatory pseudoneoplasma; 293 of 476 differentially expressed genes were up-regulated and the other 183 were down-regulated. There were 524 differentially expressed genes in adenocarcinoma; among them, 460 genes were not expressed in pulmonary inflammatory pseudoneoplasma; 214 of the 460 differentially expressed genes were up-regulated and 246 were down-regulated. However, there were a total of 113 genes differentially expressed the both lung squamous cell carcinoma and adenocarcinoma. CONCLUSION: There were distinct gene expression profiles among lung squamous cell carcinoma and adenocarcinoma. All those gene expression profiles and genes screened out provide important clues for further investigation of carcinogenesis, invasion and metastasis of non small cell lung cancer and its molecular characteristics.

Adenocarcinoma↗

PubMed2003

Diffuse large B-cell lymphoma: insights gained from gene expression profiling.

Analysis of global gene expression with DNA microarrays has great potential to improve the understanding of tumorigenesis advance tumor diagnosis and classification, and affect cancer treatment. Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin's lymphoma. However, we now realize that the disease is extremely heterogeneous. This review summarizes the progress in understanding DLBCL that has been made as a result of the application of gene expression profiling.

Gene Expression Profiling↗

PubMed2006

Expression profiling in brain disorders and beyond.

Large scale expression profiling studies provide a wealth of data that even after stratification by sophisticated biostatistical procedures leave the researcher more or less puzzled and helpless. This report is meant to encourage molecular biologists facing compounding gene lists resulting from expression profiling studies to understand them as intellectual challenges that require the researchers' solid scientific experience plus a good deal of curiosity. Three examples from expression profiling investigations on distinct aspects of brain inflammation are covered here. They show that even without good software programs it is possible to come up with reasonable biological hypotheses to be evaluated in subsequent more detailed experiments.

Encephalitis↗

PubMed2002

Gene expression profiling of osteoclast differentiation by combined suppression subtractive hybridization (SSH) and cDNA microarray analysis.

Bone homeostasis is maintained by the balanced action of bone-forming osteoblasts and bone-resorbing osteoclasts. Multinucleated, mature osteoclasts develop from hematopoietic stem cells via the monocyte-macrophage lineage, which also give rise to macrophages and dendritic cells. Despite their distinct physiologic roles in bone and the immune system, these cell types share many molecular and biochemical features. To provide insights into how osteoclasts differentiate and function to control bone metabolism, we employed a systematic approach to profile patterns of osteoclast-specific gene expression by combining suppression subtractive hybridization (SSH) and cDNA microarray analysis. Here we examined how gene expression profiles of mature osteoclast differ from macrophage or dendritic cells, how gene expression profiles change during osteoclast differentiation, and how Mitf, a transcription factor critical for osteoclast maturation, affects the gene expression profile. This approach revealed a set of genes coordinately regulated for osteoclast function, some of which have previously been implicated in several bone diseases in humans.

Animals↗

PubMed2006

Semi-supervised analysis of gene expression profiles for lineage-specific development in the Caenorhabditis elegans embryo.

MOTIVATION: Gene expression profiling is a powerful approach to identify genes that may be involved in a specific biological process on a global scale. For example, gene expression profiling of mutant animals that lack or contain an excess of certain cell types is a common way to identify genes that are important for the development and maintenance of given cell types. However, it is difficult for traditional computational methods, including unsupervised and supervised learning methods, to detect relevant genes from a large collection of expression profiles with high sensitivity and specificity. Unsupervised methods group similar gene expressions together while ignoring important prior biological knowledge. Supervised methods utilize training data from prior biological knowledge to classify gene expression. However, for many biological problems, little prior knowledge is available, which limits the prediction performance of most supervised methods. RESULTS: We present a Bayesian semi-supervised learning method, called BGEN, that improves upon supervised and unsupervised methods by both capturing relevant expression profiles and using prior biological knowledge from literature and experimental validation. Unlike currently available semi-supervised learning methods, this new method trains a kernel classifier based on labeled and unlabeled gene expression examples. The semi-supervised trained classifier can then be used to efficiently classify the remaining genes in the dataset. Moreover, we model the confidence of microarray probes and probabilistically combine multiple probe predictions into gene predictions. We apply BGEN to identify genes involved in the development of a specific cell lineage in the C. elegans embryo, and to further identify the tissues in which these genes are enriched. Compared to K-means clustering and SVM classification, BGEN achieves higher sensitivity and specificity. We confirm certain predictions by biological experiments. AVAILABILITY: The results are available at http://www.csail.mit.edu/~alanqi/projects/BGEN.html.

Algorithms↗

PubMed2005

Gene expression profiling in lymphoma diagnosis and research.

Gene expression profiling in the past 5 years has generated a large amount of data on a variety of malignancies. Unique gene expression signatures have been identified for the more common types of non-Hodgkin lymphoma (NHL), including clinically and biologically important subsets that have not been defined before. In addition, molecularly defined prognosticators have also been constructed for the major types of NHL and these prognosticators provide added value to the widely used International Prognostic Index. The new information should be included in our evaluation of NHL patients, especially when conducting clinical trials. Studies are ongoing to validate and refine these diagnostic and prognostic signatures and to develop platforms that are suitable for routine clinical applications. Similar studies will be performed on the less common types of NHL to complete the molecular classification of NHLs. It is also anticipated that gene expression profiling studies will lead to the identification of novel targets for the development of new therapeutic agents for NHL.

Gene Expression Profiling↗

PubMed2001

An efficient and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles.

We have developed a statistical regression modeling approach to discover genes that are differentially expressed between two predefined sample groups in DNA microarray experiments. Our model is based on well-defined assumptions, uses rigorous and well-characterized statistical measures, and accounts for the heterogeneity and genomic complexity of the data. In contrast to cluster analysis, which attempts to define groups of genes and/or samples that share common overall expression profiles, our modeling approach uses known sample group membership to focus on expression profiles of individual genes in a sensitive and robust manner. Further, this approach can be used to test statistical hypotheses about gene expression. To demonstrate this methodology, we compared the expression profiles of 11 acute myeloid leukemia (AML) and 27 acute lymphoblastic leukemia (ALL) samples from a previous study (Golub et al. 1999) and found 141 genes differentially expressed between AML and ALL with a 1% significance at the genomic level. Using this modeling approach to compare different sample groups within the AML samples, we identified a group of genes whose expression profiles correlated with that of thrombopoietin and found that genes whose expression associated with AML treatment outcome lie in recurrent chromosomal locations. Our results are compared with those obtained using t-tests or Wilcoxon rank sum statistics.

Acute Disease↗

PubMed2005

Gene-expression profiling and the future of adjuvant therapy.

Gene-expression profiling can help distinguish between patients at high risk and those at low risk for developing distant metastases, and so identify patients for adjuvant therapy. For several years, the Netherlands Cancer Institute has been working on gene-expression profiling of breast cancer using a microarray platform containing 25,000 genes. Using supervised classification, a prognostic classifier consisting of 70 genes could be identified. In addition to providing prognostic information, gene profiling should also enable us to detect patients who are likely to respond to particular adjuvant interventions. Well-known predictors for response to systemic therapy include estrogen receptor status HER-2 status, c-kit mutation, and epidermal growth factor receptor mutation. Because of the long periods required for predicting responsiveness in the adjuvant setting, neoadjuvant trials promise far quicker results. Several neoadjuvant studies are under way or planned to investigate gene-expression profiling as a means of predicting the therapeutic response to docetaxel (Taxotere; Aventis Pharmaceuticals Inc., Bridgewater, NJ, http://www.aventispharma-us.com), paclitaxel (Taxol; Bristol-Myers Squibb, Princeton, NJ, http://www.bms.com), cyclophosphamide, and doxorubicin (Adriamycin; Bedford Laboratories, Bedford, OH, http://www.bedfordlabs.com) in breast cancer patients. It is expected that in the coming years an increasing number of novel prognostic and predictive tests will help in guiding the adjuvant systemic treatment of breast cancer and other malignancies.

Chemotherapy, Adjuvant↗

PubMed2003

Gene expression profiling of Helicobacter pylori reveals a growth-phase-dependent switch in virulence gene expression.

The global pattern of growth-phase-dependent gene expression of Helicobacter pylori during in vitro culture was analyzed by using a high-density DNA microarray. To detect consistent coordinated gene expression in this bacterium, temporal changes in transcription were assessed in two independent time courses. Cluster analysis of the expression profiles highlighted a major switch in gene expression during the late log-to-stationary phase transition that we have termed the Log-Stat switch. Statistical analysis of the genes that were significantly induced or repressed during the Log-Stat switch revealed that many of these genes were related to virulence. Among these, expression of the genes for the neutrophil activating protein (napA) and the major flagellin subunit (flaA) were significantly induced. Additionally, the expression of a number of genes involved in iron homeostasis changed dramatically at this switch; the gene for the iron-storage protein, pfr, was induced, while the genes for two putative iron uptake proteins, fecA and frpB, were significantly repressed. These data suggest that the late log phase may correspond to the most virulent phase of growth in H. pylori and may be intimately related to its pathogenesis. The use of microarrays to analyze the kinetics of the transcriptional response of a bacterial pathogen to a changing environment has enabled the discovery of previously unappreciated relationships between genes by elucidation of coordinated gene expression profiles.

Flagella↗

PubMed2002

Bayesian infinite mixture model based clustering of gene expression profiles.

MOTIVATION: The biologic significance of results obtained through cluster analyses of gene expression data generated in microarray experiments have been demonstrated in many studies. In this article we focus on the development of a clustering procedure based on the concept of Bayesian model-averaging and a precise statistical model of expression data. RESULTS: We developed a clustering procedure based on the Bayesian infinite mixture model and applied it to clustering gene expression profiles. Clusters of genes with similar expression patterns are identified from the posterior distribution of clusterings defined implicitly by the stochastic data-generation model. The posterior distribution of clusterings is estimated by a Gibbs sampler. We summarized the posterior distribution of clusterings by calculating posterior pairwise probabilities of co-expression and used the complete linkage principle to create clusters. This approach has several advantages over usual clustering procedures. The analysis allows for incorporation of a reasonable probabilistic model for generating data. The method does not require specifying the number of clusters and resulting optimal clustering is obtained by averaging over models with all possible numbers of clusters. Expression profiles that are not similar to any other profile are automatically detected, the method incorporates experimental replicates, and it can be extended to accommodate missing data. This approach represents a qualitative shift in the model-based cluster analysis of expression data because it allows for incorporation of uncertainties involved in the model selection in the final assessment of confidence in similarities of expression profiles. We also demonstrated the importance of incorporating the information on experimental variability into the clustering model. AVAILABILITY: The MS Windows(TM) based program implementing the Gibbs sampler and supplemental material is available at http://homepages.uc.edu/~medvedm/BioinformaticsSupplement.htm CONTACT: medvedm@email.uc.edu

Bayes Theorem↗

PubMed2004

GOAL: automated Gene Ontology analysis of expression profiles.

One of the most common problems encountered while deciphering results from expression profiling experiments is in relating differential expression of genes to molecular functions and cellular processes. A second important problem is that of comparing experiments performed by different labs using different microarray platforms, or even unrelated techniques. Gene Ontology (GO) is now used to describe biological features, since GO terms are associated with genes, to overcome the apparent distance between expression profiles and biological comprehension. Here we describe the development, implementation and use of GOAL (Gene Ontology Automated Lexicon), a web-based application for the identification of functions and processes regulated in microarray and SAGE (serial analysis of gene expression) experiments. We applied GOAL to a range of experimental datasets related to different biological problems, including cancer and the cell cycle. By using GOAL, reported and novel relevant processes were identified in a number of experiments by our collaborators and by us. Different datasets could also be compared with each other to define conserved functional modules. GOAL allows a seamless and high-level analysis of expression profiles and is implemented as a free WWW resource (http://microarrays.unife.it).

Data Interpretation, Statistical↗

PubMed1999

Analysis of the gene expression profile of Schistosoma mansoni cercariae using the expressed sequence tag approach.

ESTs constitute rapid and informative tools with which to study gene-expression profiles of the diverse stages of the schistosome life cycle. Following a comprehensive EST study of adult worms, analysis has now targeted the cercaria, the parasite larval form responsible for infection of the vertebrate host. Two Schistosoma mansoni cercarial cDNA libraries were examined and partial sequence obtained from 957 randomly selected clones. On the basis of database searches, 551 (57.6%) ESTs generated had no homologs in the public databases whilst 308 (32.2%) were putatively identified, totaling 859 informative ESTs. The remaining 98 (10.2%) were uninformative ESTs (ribosomal RNA and non-coding mitochondrial sequences). By clustering analysis we have identified 453 different genes. The most common sequences in both libraries represented Sm8 calcium binding protein (8% of ESTs), fructose-1,6-bisphosphate aldolase, glyceraldehyde-3-phosphate dehydrogenase, cytochrome oxidase subunit 1, ATP guanidine kinase and triose phosphate isomerase. One hundred and nineteen identified genes were sorted into 11 functional categories, with genes associated with energy metabolism being the most abundant (13%) and diverse. The diversity and abundance of genes associated with the transcription/translation machinery and with regulatory/signaling functions were also marked. A paramyosin transcript was identified, indicating that this gene is not exclusively expressed in adult worms and sporocysts (as had been suggested previously). The possible physiological relevance to cercariae of the presence of transcripts with homology to calcium binding proteins of the EF-hand superfamily, Gq-coupled rhodopsin photoreceptor, rod phosphodiesterase 8 subunit and peripheral-type benzodiazepine receptor is discussed.

Animals↗

PubMed2003

Analysis of the gene expression profile of Schistosoma japonicum cercariae by a strategy based on expressed sequence tags.

We present an analysis of the expression profile of Schistosoma japonicum cercariae by a strategy based on expressed sequence tags (ESTs). A cDNA library from S. japoniucm cercariae was constructed and was used to generate ESTs. In total, 201 clones randomly selected from the library were sequenced; 136 ESTs were successfully obtained and sent to the BLAST server for homology searching. Among the 136 ESTs, 85 (62.50%) did not match any protein or gene sequence published in the BLAST databases; these comprised 75 (55.15%) ESTs matched (or partly matched) S. japonicum and Schistosoma mansoni ESTs, 4 (2.94%) matched human ESTs and 6 (4.41%) that did not match any sequence. Fifty-one (37.50%) ESTs were identified by the BLAST server; these consisted of 8 (5.88%), 9 (6.62%) and 34 (25.00%) that showed high homology with genes or proteins reported from S. japonicum, S. mansoni and other organisms, respectively. These identified ESTs can be grouped into nine categories: transporters (1.96%), secretory proteins (1.96%), kinases (3.92%), proteases (5.88%), structural and cytoskeletal proteins (13.73%), metabolism-related proteins (9.80%), regulatory and signaling proteins (11.76%), transcription and translation machinery (25.50%), and others (25.50%). Several interesting new genes cloned from this cDNA library are discussed here. These findings will be valuable for the understanding of the biology of this parasite.

Animals↗