Assessing clusters and motifs from gene expression data

The identification of functional motifs in temporal gene expression analysis

The identification of transcription factor binding sites is essential to the understanding of the regulation of gene expression and the reconstruction of genetic regulatory networks. The in silico identification of cis-regulatory motifs is challenging due to sequence variability and lack of sufficient data to generate consensus motifs that are of quantitative or even qualitative predictive value. To determine functional motifs in gene expression, we propose a strategy to adopt false discovery rate (FDR) and estimate motif effects to evaluate combinatorial analysis of motif candidates and temporal gene expression data. The method decreases the number of predicted motifs, which can then be confirmed by genetic analysis. To assess the method we used simulated motif/expression data to evaluate parameters. We applied this approach to experimental data for a group of iron responsive genes in Salmonella typhimurium 14028S. The method identified known and potentially new ferric-uptake regulator (Fur) binding sites. In addition, we identified uncharacterized functional motif candidates that correlated with specific patterns of expression. A SAS code for the simulation and analysis gene expression data is available from the first author upon request.

Protein arginine methyltransferase 5 regulates multiple signaling pathways to promote lung cancer cell proliferation

BACKGROUND

Protein arginine methyltransferase 5 (PRMT5) catalyzes the formation of symmetrical dimethylation of arginine residues in proteins. WD repeat domain 77 (WDR77), also known as p44, MEP50, or WD45, forms a stoichiometric complex with PRMT5. The PRMT5/p44 complex is required for cellular proliferation of lung and prostate epithelial cells during earlier stages of development and is re-activated during prostate and lung tumorigenesis. The molecular mechanisms by which PRMT5 and p44 promote cellular proliferation are unknown.

METHODS

Expression of PRMT5 and p44 in lung and prostate cancer cells was silenced and their target genes were identified. The regulation of target genes was validated in various cancer cells during lung development and tumorigenesis. Altered expression of target genes was achieved by ectopic cDNA expression and shRNA-mediated silencing.

RESULTS

PRMT5 and p44 regulate expression of a specific set of genes encoding growth and anti-growth factors, including receptor tyrosine kinases and antiproliferative proteins. Genes whose expression was suppressed by PRMT5 and p44 encoded anti-growth factors and inhibited cell growth when ectopically expressed. In contrast, genes whose expression was enhanced by PRMT5 and p44 encoded growth factors and increased cell growth when expressed. Altered expression of target genes is associated with re-activation of PRMT5 and p44 during lung tumorigenesis.

CONCLUSIONS

Our data provide the molecular basis by which PRMT5 and p44 regulate cell growth and lay a foundation for further investigation of their role in lung tumor initiation.

XIAP downregulation promotes caspase-dependent inhibition of proteasome activity in AML cells

To further understand the role of XIAP in acute myeloid leukemia (AML), we suppressed XIAP expression by antisense oligonucleotides and determined the effect on gene expression profiles and biological pathways. XIAP inhibition upregulated expression of proteasome genes in a manner similar to the proteasome inhibitor bortezomib or MG132; decreased 20S proteasome activity, an effect which was diminished in the presence of a pan-caspase inhibitor; and increased IκBα, Mcl-1, and HSP70 in AML cells. In addition to multiple functions already described, XIAP contributes to increased proteasome activity in AML cells, and the antitumor effect of XIAP inhibition may be mediated in part through caspase-dependent proteasome inhibition.

Persistent antigen at vaccination sites induces tumor-specific CD8⁺ T cell sequestration, dysfunction and deletion

To understand why cancer vaccine-induced T cells often fail to eradicate tumors, we studied immune responses in mice vaccinated with gp100 melanoma peptide in incomplete Freund’s adjuvant (IFA), commonly used in clinical cancer vaccine trials. Peptide/IFA vaccination primed tumor-specific CD8⁺ T cells, which accumulated not in tumors but at the persisting, antigen-rich vaccination site. Once there, primed T cells became dysfunctional and underwent antigen-driven, Interferon-γ (IFN-γ) and Fas ligand (FasL)-mediated apoptosis, resulting in hyporesponsiveness to subsequent vaccination. Provision of anti-CD40 antibody, Toll-like receptor 7 (TLR7) agonist and interleukin-2 (IL-2) reduced T cell apoptosis but did not prevent vaccination site sequestration. A non-persisting vaccine formulation shifted T cell localization towards tumors, inducing superior anti-tumor activity while reducing systemic T cell dysfunction and promoting memory formation. Persisting peptide/IFA vaccine depots can induce specific T cell sequestration, dysfunction and deletion at vaccination sites; short-lived formulations may overcome these limitations and result in greater therapeutic efficacy of peptide-based cancer vaccines.

The p44/wdr77-dependent cellular proliferation process during lung development is reactivated in lung cancer

During lung development, cells proliferate for a defined length of time before they begin to differentiate. Factors that control this proliferative process and how this growth process is related to lung cancer are currently unknown. Here, we found that the WD40-containing protein (p44/wdr77) was expressed in growing epithelial cells at the early stages of lung development. In contrast, p44/wdr77 expression was diminished in fully differentiated epithelial cells in the adult lung. Loss of p44/wdr77 gene expression led to cell growth arrest and differentiation. Re-expression of p44/wdr77 caused terminally differentiated cells to re-enter the cell cycle. Our findings suggest that p44/wdr77 is essential and sufficient for proliferation of lung epithelial cells. P44/Wdr77 was re-expressed in lung cancer, and silencing p44/wdr77 expression strongly inhibited growth of lung adenocarcinoma cells in tissue culture and abolished growth of lung adenocarcinoma tumor xenografts in mice. The growth arrest induced by loss of p44/wdr77 expression was partially through the p21-Rb signaling. Our results suggest that p44/wdr77 controls cellular proliferation during lung development, and this growth process is reactivated during lung tumorigenesis.

The identification and interpretation of differences in the transcriptomes of organically and conventionally grown potato tubers

In the European integrated research project SAFEFOODS, one of the aims was to further establish the potential of transcriptomics for the assessment of differences between plant varieties grown under different environmental conditions. Making use of the knowledge of cellular processes and interactions is one of the ways to obtain a better understanding of the differences found with transcriptomics. For the present study the potato genotype Santé was grown under both organic and conventional fertilizer, and each combined with either organic or conventional crop protection, giving four different treatments. Samples were derived from the European project QualityLowInputFood (QLIF). Microarray data were analyzed using different statistical tools (multivariate, principal components analysis (PCA); univariate, analysis of variance (ANOVA)) and with pathway analysis (hypergeometric distribution (HGD) and gene set enrichment analysis (GSEA)). Several biological processes were implicated as a result of the different treatments of the plants. Most obvious were the lipoxygenase pathway, with higher expression in organic fertilizer and lower expression in organic crop protection; the starch synthase pathway, with higher expression in both organic crop protection and fertilizer; and the biotic stress pathway, with higher expression in organic fertilizer. This study confirmed that gene expression profiling in combination with pathway analysis can identify and characterize differences between plants grown under different environmental conditions.

Gene set-based module discovery decodes cis-regulatory codes governing diverse gene expression across human multiple tissues

Decoding transcriptional programs governing transcriptomic diversity across human multiple tissues is a major challenge in bioinformatics. To address this problem, a number of computational methods have focused on cis-regulatory codes driving overexpression or underexpression in a single tissue as compared to others. On the other hand, we recently proposed a different approach to mine cis-regulatory codes: starting from gene sets sharing common cis-regulatory motifs, the method screens for expression modules based on expression coherence. However, both approaches seem to be insufficient to capture transcriptional programs that control gene expression in a subset of all samples. Especially, this limitation would be serious when analyzing multiple tissue data. To overcome this limitation, we developed a new module discovery method termed BEEM (Biclusering-based Extraction of Expression Modules) in order to discover expression modules that are functional in a subset of tissues. We showed that, when applied to expression profiles of human multiple tissues, BEEM finds expression modules missed by two existing approaches that are based on the coherent expression and the single tissue-specific differential expression. From the BEEM results, we obtained new insights into transcriptional programs controlling transcriptomic diversity across various types of tissues. This study introduces BEEM as a powerful tool for decoding regulatory programs from a compendium of gene expression profiles.

DISCLOSE : DISsection of CLusters Obtained by SEries of transcriptome data using functional annotations and putative transcription factor binding sites

BACKGROUND

A typical step in the analysis of gene expression data is the determination of clusters of genes that exhibit similar expression patterns. Researchers are confronted with the seemingly arbitrary choice between numerous algorithms to perform cluster analysis.

RESULTS

We developed an exploratory application that benchmarks the results of clustering methods using functional annotations. In addition, a de novo DNA motif discovery algorithm is integrated in our program which identifies overrepresented DNA binding sites in the upstream DNA sequences of genes from the clusters that are indicative of sites of transcriptional control. The performance of our program was evaluated by comparing the original results of a time course experiment with the findings of our application.

CONCLUSION

DISCLOSE assists researchers in the prokaryotic research community in systematically evaluating results of the application of a range of clustering algorithms to transcriptome data. Different performance measures allow to quickly and comprehensively determine the best suited clustering approach for a given dataset.

The identification of functional motifs in temporal gene expression analysis

The identification of transcription factor binding sites is essential to the understanding of the regulation of gene expression and the reconstruction of genetic regulatory networks. The in silico identification of cis-regulatory motifs is challenging due to sequence variability and lack of sufficient data to generate consensus motifs that are of quantitative or even qualitative predictive value. To determine functional motifs in gene expression, we propose a strategy to adopt false discovery rate (FDR) and estimate motif effects to evaluate combinatorial analysis of motif candidates and temporal gene expression data. The method decreases the number of predicted motifs, which can then be confirmed by genetic analysis. To assess the method we used simulated motif/expression data to evaluate parameters. We applied this approach to experimental data for a group of iron responsive genes in Salmonella typhimurium 14028S. The method identified known and potentially new ferric-uptake regulator (Fur) binding sites. In addition, we identified uncharacterized functional motif candidates that correlated with specific patterns of expression. A SAS code for the simulation and analysis gene expression data is available from the first author upon request.

From promoter analysis to transcriptional regulatory network prediction using PAINT

Highly parallel gene-expression analysis has led to analysis of gene regulation, in particular coregulation, at a system level. Promoter analysis and interaction network toolset (PAINT) was developed to provide the biologist a computational tool to integrate functional genomics data, for example, from microarray-based gene-expression analysis with genomic sequence data to carry out transcriptional regulatory network analysis (TRNA). TRNA combines bioinformatics, used to identify and analyze gene-regulatory regions, and statistical significance testing, used to rank the likelihood of the involvement of individual transcription factors (TF), with visualization tools to identify TF likely to play a role in the cellular process under investigation. In summary, given a list of gene identifiers PAINT can: (1) fetch potential promoter sequences for the genes in the list, (2) find TF-binding sites on the sequences, (3) analyze the TF-binding site occurrences for over/under-representation compared with a reference, with or without coexpression clustering information, and (4) generate multiple visualizations for these analyses. At present, PAINT supports TRNA of the human, mouse, and rat genomes. PAINT is currently available as an online, web-based service located at: http://www.dbi.tju.edu/dbi/tools/paint.

Systems analysis of circadian time-dependent neuronal epidermal growth factor receptor signaling

BACKGROUND

Identifying the gene regulatory networks governing physiological signal integration remains an important challenge in circadian biology. Epidermal growth factor receptor (EGFR) has been implicated in circadian function and is expressed in the suprachiasmatic nuclei (SCN), the core circadian pacemaker. The transcription networks downstream of EGFR in the SCN are unknown but, by analogy to other SCN inputs, we expect the response to EGFR activation to depend on circadian timing.

RESULTS

We have undertaken a systems-level analysis of EGFR circadian time-dependent signaling in the SCN. We collected gene-expression profiles to study how the SCN response to EGFR activation depends on circadian timing. Mixed-model analysis of variance (ANOVA) was employed to identify genes with circadian time-dependent EGFR regulation. The expression data were integrated with transcription-factor binding predictions through gene group enrichment analyses to generate robust hypotheses about transcription-factors responsible for the circadian phase-dependent EGFR responses.

CONCLUSION

The analysis results suggest that the transcriptional response to EGFR signaling in the SCN may be partly mediated by established transcription-factors regulated via EGFR transcription-factors (AP1, Ets1, C/EBP), transcription-factors involved in circadian clock entrainment (CREB), and by core clock transcription-factors (Ror alpha). Quantitative real-time PCR measurements of several transcription-factor expression levels support a model in which circadian time-dependent EGFR responses are partly achieved by circadian regulation of upstream signaling components. Our study suggests an important role for EGFR signaling in SCN function and provides an example for gaining physiological insights through systems-level analysis.

Validation and functional annotation of expression-based clusters based on gene ontology

Background

The biological interpretation of large-scale gene expression data is one of the paramount challenges in current bioinformatics. In particular, placing the results in the context of other available functional genomics data, such as existing bio-ontologies, has already provided substantial improvement for detecting and categorizing genes of interest. One common approach is to look for functional annotations that are significantly enriched within a group or cluster of genes, as compared to a reference group.

Results

In this work, we suggest the information-theoretic concept of mutual information to investigate the relationship between groups of genes, as given by data-driven clustering, and their respective functional categories. Drawing upon related approaches (Gibbons and Roth, Genome Research 12:1574-1581, 2002), we seek to quantify to what extent individual attributes are sufficient to characterize a given group or cluster of genes.

Conclusion

We show that the mutual information provides a systematic framework to assess the relationship between groups or clusters of genes and their functional annotations in a quantitative way. Within this framework, the mutual information allows us to address and incorporate several important issues, such as the interdependence of functional annotations and combinatorial combinations of attributes. It thus supplements and extends the conventional search for overrepresented attributes within a group or cluster of genes. In particular taking combinations of attributes into account, the mutual information opens the way to uncover specific functional descriptions of a group of genes or clustering result. All datasets and functional annotations used in this study are publicly available. All scripts used in the analysis are provided as additional files.

Overflow metabolism in Escherichia coli during steady-state growth: transcriptional regulation and effect of the redox ratio

Overflow metabolism in the form of aerobic acetate excretion by Escherichia coli is an important physiological characteristic of this common industrial microorganism. Although acetate formation occurs under conditions of high glucose consumption, the genetic mechanisms that trigger this phenomenon are not clearly understood. We report on the role of the NADH/NAD ratio (redox ratio) in overflow metabolism. We modulated the redox ratio in E. coli through the expression of Streptococcus pneumoniae (water-forming) NADH oxidase. Using steady-state chemostat cultures, we demonstrated a strong correlation between acetate formation and this redox ratio. We furthermore completed genome-wide transcription analyses of a control E. coli strain and an E. coli strain overexpressing NADH oxidase. The transcription results showed that in the control strain, several genes involved in the tricarboxylic acid (TCA) cycle and respiration were repressed as the glucose consumption rate increased. Moreover, the relative repression of these genes was alleviated by expression of NADH oxidase and the resulting reduced redox ratio. Analysis of a promoter binding site upstream of the genes which correlated with redox ratio revealed a degenerate sequence with strong homology with the binding site for ArcA. Deletion of arcA resulted in acetate reduction and increased the biomass yield due to the increased capacities of the TCA cycle and respiration. Acetate formation was completely eliminated by reducing the redox ratio through expression of NADH oxidase in the arcA mutant, even at a very high glucose consumption rate. The results provide a basis for studying new regulatory mechanisms prevalent at reduced NADH/NAD ratios, as well as for designing more efficient bioprocesses.

Finding Motifs with Insufficient Number of Strong Binding Sites

A molecule called transcription factor usually binds to a set of promoter sequences of coexpressed genes. As a result, these promoter sequences contain some short substrings, or binding sites, with similar patterns. The motif discovering problem is to find these similar patterns and motifs in a set of sequences. Most existing algorithms find the motifs based on strong-signal sequences only (i.e., those containing binding sites very similar to the motif). In this paper, we use a probability matrix to represent a motif to calculate the minimum total number of binding sites required to be in the input dataset in order to confirm that the discovered motifs are not artifacts. Next, we introduce a more general and realistic energy-based model, which considers all sequences with varying degrees of binding strength to the transcription factors (as measured experimentally). By treating sequences with varying degrees of binding strength, we develop a heuristic algorithm called EBMF (Energy-Based Motif Finding Algorithm) to find the motif, which can handle sequences ranging from those that contain more than one binding site to those that contain none. EBMF can find motifs for datasets that do not even have the required minimum number of binding sites as previously derived. EBMF compares favorably with common motif-finding programs AlignACE and MEME. In particular, for some simulated and real datasets, EBMF finds the motif when both AlignACE and MEME fail to do so.

Conserved transcription factor binding sites of cancer markers derived from primary lung adenocarcinoma microarrays

Gene transcription in a set of 49 human primary lung adenocarcinomas and 9 normal lung tissue samples was examined using Affymetrix GeneChip technology. A total of 3442 genes, called the set M_AD, were found to be either up- or down-regulated by at least 2-fold between the two phenotypes. Genes assigned to a particular gene ontology term were found, in many cases, to be significantly unevenly distributed between the genes in and outside M_AD. Terms that were overrepresented in M_AD included functions directly implicated in the cancer cell metabolism. Based on their functional roles and expression profiles, genes in M_AD were grouped into likely co-regulated gene sets. Highly conserved sequences in the 5 kb region upstream of the genes in these sets were identified with the motif discovery tool, MoDEL. Potential oncogenic transcription factors and their corresponding binding sites were identified in these conserved regions using the TRANSFAC 8.3 database. Several of the transcription factors identified in this study have been shown elsewhere to be involved in oncogenic processes. This study searched beyond phenotypic gene expression profiles in cancer cells, in order to identify the more important regulatory transcription factors that caused these aberrations in gene expression.

Rationale and study design of the CardioGene Study: genomics of in-stent restenosis

BACKGROUND AND AIMS

in-stent restenosis is a major limitation of stent therapy for atherosclerosis coronary artery disease. The CardioGene Study is an ongoing study of restenosis in bare mental stents (BMS) for the treatment of coronary artery disease. The overall goal is to understand the genetic determinants of the responses to vascular injury that result in the development of restenosis in some patients but not in others. Gene expression profiling at transcriptional and translational levels provides global assessment of gene activity after vascular injury and mechanistic insight. Furthermore, the delineation of genetic biomarkers would be of value in the clinical setting of risk-stratify patients prior to stent therapy. Prospective risk stratification would allow for the rational selection of specialized treatments against the development of in-stent restenosis (ISR), such as drug-eluting stents.

SETTING

Patients are enrolled at two sites in the US with high-volume cardiac catheterization facilities: the William Beaumont Hospital in Royal Oak, MI, USA, and the Mayo Clinic in Rochester, MN, USA.

STUDY DESIGN

Two complementary study designs are used to understand the molecular mechanisms of restenosis and the genetic biomarkers predictive of restenosis. First, 350 patients are enrolled prospectively at the time of stent implantation. Blood is sampled prior to stent placement and afterwards at 2 weeks and 6 months. The clinical outcome of restenosis is determined 6 and 12 months after stent placement. The primary outcome is clinical restenosis at 6 months. The major secondary outcome is clinical restenosis at 12 months. Second, a corollary case-control analysis will be carried out with the enrollment of an additional 250 cases with a history of recurrent restenosis after treatment with BMS. Controls for this analysis are derived from the prospective cohort.

PATIENTS AND METHODS

Consecutive patients presenting to the cardiac catheterization laboratory are screened, informed about the study and enrolled after signing the consent form. Enrollment has been completed for the prospective cohort, and enrollment of the additional group is ongoing. A standardized questionnaire is used to collect clinical data primarily through direct patient interview to assess medical history, medication use, functional status, family history, environmental factors, and social history. Further data are abstracted from the medical charts and catheterization reports. A total of 276 clinical variables are collected per individual at baseline, and 49 variables are collected at each of the 6- and 12-month follow-up visits. A Clinical Events Committee adjudicates clinical outcomes. Blood samples are processed at each clinical enrollment site using standardized operating procedures. From each blood sample, several aliquots are prepared and stored of peripheral blood mononuclear cells, granulocytes, platelets, serum, and plasma. Additionally, a portion of each patient's leukocytes is cryopreserved for future cell-line creation. Samples are frozen and shipped to the National Heart, Lung and Blood Institute (NHLBI). Additional materials generated in the analysis of the samples at the NHLBI are frozen and stored, including isolated genomic DNA, total RNA, reverse transcribed cDNA libraries and labeled RNA hybridization mixtures used in microarray analysis. Per individual in the prospective cohort, high-quality transcript profiles of peripheral blood mononuclear cells at each time of blood sampling are obtained using Affymetrix U133A microarrays (Affymetrix, Santa Clara, CA, USA). Per chip, this yields 495,930 features per individual per time of sampling. This represents expression levels for 22,283 genes per patients oer time of blood sampling, including 14,500 well-characterized human genes. Proteomics of plasma is performed with multidimensional liquid chromatography and tandem mass spectrometry. Protein expression is examined similarly to mRNA expression as a measure of gene expression. Genotyping is performed in two manners. First, those genes showing differential expression at the levels of mRNA and protein are investigated using a candidate gene approach. Specific variants in known gene regulatory regions, such as promoters, are sought initially, as those variants may explain differences in expression level. Second, a genome-wide scan is used to identify genetic loci that are associated with ISR. Those regions identified are further examined for genes that show differential expression in the mRNA microarray profiling or proteomics investigations. These genes are finely investigated for candidate SNPs and other gene variants. Complementary genomic and proteomic approaches are expected to be robust. Integration of data sets is accomplished using a variety of informatics tools, organization of gene expression into functional pathways, and investigation of physical maps of up- and downregulated sets of genes.

CONCLUSIONS

The CardioGene Study is designed to understand ISR. Global gene and protein expression profiling define molecular phenotypes of patients. Well-defined clinical phenotypes will be paired with genomic data to define analyses aimed to achieve several goals. These include determining blood gene and protein expression in patients with ISR, investigating the genetic basis of ISR, developing predictive gene and protein biomarkers, and the identification of new targets for treatment.

Clustering of diverse genomic data using information fusion

MOTIVATION

Genome sequencing projects and high-through-put technologies like DNA and Protein arrays have resulted in a very large amount of information-rich data. Microarray experimental data are a valuable, but limited source for inferring gene regulation mechanisms on a genomic scale. Additional information such as promoter sequences of genes/DNA binding motifs, gene ontologies, and location data, when combined with gene expression analysis can increase the statistical significance of the finding. This paper introduces a machine learning approach to information fusion for combining heterogeneous genomic data. The algorithm uses an unsupervised joint learning mechanism that identifies clusters of genes using the combined data.

RESULTS

The correlation between gene expression time-series patterns obtained from different experimental conditions and the presence of several distinct and repeated motifs in their upstream sequences is examined here using publicly available yeast cell-cycle data. The results show that the combined learning approach taken here identifies correlated genes effectively. The algorithm provides an automated clustering method, but allows the user to specify apriori the influence of each data type on the final clustering using probabilities.

AVAILABILITY

Software code is available by request from the first author.

CONTACT

jkasturi@cse.psu.edu.

Using the gene ontology for microarray data mining: a comparison of methods and application to age effects in human prefrontal cortex

One of the challenges in the analysis of gene expression data is placing the results in the context of other data available about genes and their relationships to each other. Here, we approach this problem in the study of gene expression changes associated with age in two areas of the human prefrontal cortex, comparing two computational methods. The first method, "overrepresentation analysis" (ORA), is based on statistically evaluating the fraction of genes in a particular gene ontology class found among the set of genes showing age-related changes in expression. The second method, "functional class scoring" (FCS), examines the statistical distribution of individual gene scores among all genes in the gene ontology class and does not involve an initial gene selection step. We find that FCS yields more consistent results than ORA, and the results of ORA depended strongly on the gene selection threshold. Our findings highlight the utility of functional class scoring for the analysis of complex expression data sets and emphasize the advantage of considering all available genomic information rather than sets of genes that pass a predetermined "threshold of significance."

PAINT: a promoter analysis and interaction network generation tool for gene regulatory network identification

We have developed a bioinformatics tool named PAINT that automates the promoter analysis of a given set of genes for the presence of transcription factor binding sites. Based on coincidence of regulatory sites, this tool produces an interaction matrix that represents a candidate transcriptional regulatory network. This tool currently consists of (1) a database of promoter sequences of known or predicted genes in the Ensembl annotated mouse genome database, (2) various modules that can retrieve and process the promoter sequences for binding sites of known transcription factors, and (3) modules for visualization and analysis of the resulting set of candidate network connections. This information provides a substantially pruned list of genes and transcription factors that can be examined in detail in further experimental studies on gene regulation. Also, the candidate network can be incorporated into network identification methods in the form of constraints on feasible structures in order to render the algorithms tractable for large-scale systems. The tool can also produce output in various formats suitable for use in external visualization and analysis software. In this manuscript, PAINT is demonstrated in two case studies involving analysis of differentially regulated genes chosen from two microarray data sets. The first set is from a neuroblastoma N1E-115 cell differentiation experiment, and the second set is from neuroblastoma N1E-115 cells at different time intervals following exposure to neuropeptide angiotensin II. PAINT is available for use as an agent in BioSPICE simulation and analysis framework (www.biospice.org), and can also be accessed via a WWW interface at www.dbi.tju.edu/dbi/tools/paint/.

Molecular heterogeneity in acute renal allograft rejection identified by DNA microarray profiling

BACKGROUND

The causes and clinical course of acute rejection vary, and it is not possible to predict graft outcome reliably on the basis of available clinical, pathological, and genetic markers. We hypothesized that previously unrecognized molecular heterogeneity might underlie some of the variability in the clinical course of acute renal allograft rejection and in its response to treatment.

METHODS

We used DNA microarrays in a systematic study of gene-expression patterns in biopsy samples from normal and dysfunctional renal allografts. A combination of exploratory and supervised bioinformatic methods was used to analyze these profiles.

RESULTS

We found consistent differences among the gene-expression patterns associated with acute rejection, nephrotoxic effects of drugs, chronic allograft nephropathy, and normal kidneys. The gene-expression patterns associated with acute rejection suggested at least three possible distinct subtypes of acute rejection that, although indistinguishable by light microscopy, were marked by differences in immune activation and cellular proliferation. Since the gene-expression patterns pointed to substantial variation in the composition of immune infiltrates, we used immunohistochemical staining to define these subtypes further. This analysis revealed a striking association between dense CD20+ B-cell infiltrates and both clinical glucocorticoid resistance (P=0.01) and graft loss (P<0.001).

CONCLUSIONS

Systematic analysis of gene-expression patterns provides a window on the biology and pathogenesis of renal allograft rejection. Biopsy samples from patients with acute rejection that are indistinguishable on conventional histologic analysis reveal extensive differences in gene expression, which are associated with differences in immunologic and cellular features and clinical course. The presence of dense clusters of B cells in a biopsy sample was strongly associated with severe graft rejection, suggesting a pivotal role of infiltrating B cells in acute rejection.

An approach to identify over-represented cis-elements in related sequences

Computational identification of transcription factor binding sites is an important research area of computational biology. Positional weight matrix (PWM) is a model to describe the sequence pattern of binding sites. Usually, transcription factor binding sites prediction methods based on PWMs require user-defined thresholds. The arbitrary threshold and also the relatively low specificity of the algorithm prevent the result of such an analysis from being properly interpreted. In this study, a method was developed to identify over-represented cis-elements with PWM-based similarity scores. Three sets of closely related promoters were analyzed, and only over- represented motifs with high PWM similarity scores were reported. The thresholds to evaluate the similarity scores to the PWMs of putative transcription factors binding sites can also be automatically determined during the analysis, which can also be used in further research with the same PWMs. The online program is available on the website: http://www.bioinfo.tsinghua.edu.cn/- zhengjsh/OTFBS/.

EZ-Retrieve: a web-server for batch retrieval of coordinate-specified human DNA sequences and underscoring putative transcription factor-binding sites

The availability of a draft human genome sequence and ability to monitor the transcription of thousands of genes with DNA microarrays has necessitated the need for new computational tools that can analyze cis-regulatory elements controlling genes that display similar expression patterns. We have developed a tool designated EZ-Retrieve that can: (i) retrieve any particular region of human genome sequence from the NCBI database and (ii) analyze retrieved sequences for putative transcription factor-binding sites (TFBSs) as they appear on the TRANSFAC database. The tool is web-based, user-friendly and offers both batch sequence retrieval and batch TFBS prediction. A major application of EZ-Retrieve is the analysis of co-expressed genes that are highlighted as expression clusters in DNA microarray experiments.

Judging the quality of gene expression-based clustering methods using gene annotation

We compare several commonly used expression-based gene clustering algorithms using a figure of merit based on the mutual information between cluster membership and known gene attributes. By studying various publicly available expression data sets we conclude that enrichment of clusters for biological function is, in general, highest at rather low cluster numbers. As a measure of dissimilarity between the expression patterns of two genes, no method outperforms Euclidean distance for ratio-based measurements, or Pearson distance for non-ratio-based measurements at the optimal choice of cluster number. We show the self-organized-map approach to be best for both measurement types at higher numbers of clusters. Clusters of genes derived from single- and average-linkage hierarchical clustering tend to produce worse-than-random results.

Microarray analysis of chitin elicitation in Arabidopsis thaliana

Summary Chitin oligomers, released from fungal cell walls by endochitinase, induce defence and related cellular responses in many plants. However, little is known about chitin responses in the model plant Arabidopsis. We describe here a large-scale characterization of gene expression patterns in Arabidopsis in response to chitin treatment using an Arabidopsis microarray consisting of 2375 EST clones representing putative defence-related and regulatory genes. Transcript levels for 71 ESTs, representing 61 genes, were altered three-fold or more in chitin-treated seedlings relative to control seedlings. A number of transcripts exhibited altered accumulation as early as 10 min after exposure to chitin, representing some of the earliest changes in gene expression observed in chitin-treated plants. Included among the 61 genes were those that have been reported to be elicited by various pathogen-related stimuli in other plants. Additional genes, including genes of unknown function, were also identified, broadening our understanding of chitin-elicited responses. Among transcripts with enhanced accumulation, one cluster was enriched in genes with both the W-box promoter element and a novel regulatory element. In addition, a number of transcripts had decreased abundance, encoding several proteins involved in cell wall strengthening and wall deposition. The chalcone synthase promoter element was identified in the upstream regions of these genes, suggesting that pathogen signals may suppress the expression of some genes. These data indicate that Arabidopsis should be an excellent model to elucidate the mechanisms of chitin elicitation in plant defence.

Transcriptional regulation: a genomic overview

The availability of the Arabidopsis thaliana genome sequence allows a comprehensive analysis of transcriptional regulation in plants using novel genomic approaches and methodologies. Such a genomic view of transcription first necessitates the compilation of lists of elements. Transcription factors are the most numerous of the different types of proteins involved in transcription in eukaryotes, and the Arabidopsis genome codes for more than 1,500 of them, or approximately 6% of its total number of genes. A genome-wide comparison of transcription factors across the three eukaryotic kingdoms reveals the evolutionary generation of diversity in the components of the regulatory machinery of transcription. However, as illustrated by Arabidopsis, transcription in plants follows similar basic principles and logic to those in animals and fungi. A global view and understanding of transcription at a cellular and organismal level requires the characterization of the Arabidopsis transcriptome and promoterome, as well as of the interactome, the localizome, and the phenome of the proteins involved in transcription.

Regulatory sequence analysis: application to the interpretation of gene expression

Microarray technologies for measuring mRNA abundances in cells allow monitoring of gene expression levels for tens of thousands of genes in parallel. By measuring expression responses across hundreds of different conditions or timepoints a relatively detailed gene expression map starts to emerge. Using cluster analysis techniques, it is possible to identify genes that are consistently coexpressed under several different conditions or treatments. These sets of coexpressed genes can then be compared to existing knowledge about biochemical or signalling pathways, the function of unknown genes can be hypothesised by comparing them to other genes with characterised function, or from trends in expression profiles in general - why cell needs to transcribe or silence the genes during particular treatment. The regulation of genes on the DNA level is largely guided by particular sequence features, the transcription factor binding sites, and other signals encaptured in DNA. By analyzing the regulatory regions of the DNA of the genes consistently coexpressed, we can discover the potential signals hidden in DNA by computational analysis methods. The prerequisite for this kind of analysis is the existence of genomic DNA sequence, knowledge about gene locations, and experimental gene expression measurements for a variety of conditions. This article surveys some of the analysis methods and studies for such a computational discovery approach for yeast Saccharomyces cerevisiae.