A WNT/p21 circuit directed by the C-clamp, a sequence-specific DNA binding domain in TCFs.

The lymphoid enhancer factor 1/T cell factor (LEF/TCF) family of transcription factors are downstream effectors of the WNT signaling pathway, which drives colon tumorigenesis. LEF/TCFs have a DNA sequence-specific high-mobility group (HMG) box that binds Wnt response elements (WREs). The "E tail" isoforms of TCFs are alternatively spliced to include a second DNA binding domain called the C-clamp. We show that induction of a dominant negative C-clamp version of TCF1 (dnTCF1E) induces p21 expression and a stall in the growth of DLD1 colon cancer cells. Induction of a C-clamp mutant did not efficiently induce p21, nor did it stall cell growth. Microarray analysis revealed that induction of p21 by wild-type dnTCF1E (dnTCF1E(WT)) correlated with a decrease in expression of multiple p21 suppressors that act at multiple levels from transcription (SP5, YAP1, and RUNX1), RNA stability (MSI2), and protein stability (CUL4A). We show that the C-clamp is a sequence-specific DNA binding domain that can make contacts with 5'-RCCG-3' elements upstream or downstream of WREs. The C-clamp-RCCG interaction was critical for TCF1E-mediated transcriptional control of p21-connected target gene promoters. Our results indicate that a rapid-response WNT/p21 circuit is driven by C-clamp target gene selection.

Ontology-based meta-analysis of global collections of high-throughput public data.

BACKGROUND: The investigation of the interconnections between the molecular and genetic events that govern biological systems is essential if we are to understand the development of disease and design effective novel treatments. Microarray and next-generation sequencing technologies have the potential to provide this information. However, taking full advantage of these approaches requires that biological connections be made across large quantities of highly heterogeneous genomic datasets. Leveraging the increasingly huge quantities of genomic data in the public domain is fast becoming one of the key challenges in the research community today. METHODOLOGY/RESULTS: We have developed a novel data mining framework that enables researchers to use this growing collection of public high-throughput data to investigate any set of genes or proteins. The connectivity between molecular states across thousands of heterogeneous datasets from microarrays and other genomic platforms is determined through a combination of rank-based enrichment statistics, meta-analyses, and biomedical ontologies. We address data quality concerns through dataset replication and meta-analysis and ensure that the majority of the findings are derived using multiple lines of evidence. As an example of our strategy and the utility of this framework, we apply our data mining approach to explore the biology of brown fat within the context of the thousands of publicly available gene expression datasets. CONCLUSIONS: Our work presents a practical strategy for organizing, mining, and correlating global collections of large-scale genomic data to explore normal and disease biology. Using a hypothesis-free approach, we demonstrate how a data-driven analysis across very large collections of genomic data can reveal novel discoveries and evidence to support existing hypothesis.

Profiling humoral immune responses to P. falciparum infection with protein microarrays.

A complete description of the serological response following exposure of humans to complex pathogens is lacking and approaches suitable for accomplishing this are limited. Here we report, using malaria as a model, a method which elucidates the profile of antibodies that develop after natural or experimental infection or after vaccination with attenuated organisms, and which identifies immunoreactive antigens of interest for vaccine development or other applications. Expression vectors encoding 250 Plasmodium falciparum (Pf) proteins were generated by PCR/recombination cloning; the proteins were individually expressed with >90% efficiency in Escherichia coli cell-free in vitro transcription and translation reactions, and printed directly without purification onto microarray slides. The protein microarrays were probed with human sera from one of four groups which differed in immune status: sterile immunity or no immunity against experimental challenge following vaccination with radiation-attenuated Pf sporozoites, partial immunity acquired by natural exposure, and no previous exposure to Pf. Overall, 72 highly reactive Pf antigens were identified. Proteomic features associated with immunoreactivity were identified. Importantly, antibody profiles were distinct for each donor group. Information obtained from such analyses will facilitate identifying antigens for vaccine development, dissecting the molecular basis of immunity, monitoring the outcome of whole-organism vaccine trials, and identifying immune correlates of protection.

From protein microarrays to diagnostic antigen discovery: a study of the pathogen Francisella tularensis.

MOTIVATION: An important application of protein microarray data analysis is identifying a serodiagnostic antigen set that can reliably detect patterns and classify antigen expression profiles. This work addresses this problem using antibody responses to protein markers measured by a novel high-throughput microarray technology. The findings from this study have direct relevance to rapid, broad-based diagnostic and vaccine development. RESULTS: Protein microarray chips are probed with sera from individuals infected with the bacteria Francisella tularensis, a category A biodefense pathogen. A two-step approach to the diagnostic process is presented (1) feature (antigen) selection and (2) classification using antigen response measurements obtained from F.tularensis microarrays (244 antigens, 46 infected and 54 healthy human sera measurements). To select antigens, a ranking scheme based on the identification of significant immune responses and differential expression analysis is described. Classification methods including k-nearest neighbors, support vector machines (SVM) and k-Means clustering are applied to training data using selected antigen sets of various sizes. SVM based models yield prediction accuracy rates in the range of approximately 90% on validation data, when antigen set sizes are between 25 and 50. These results strongly indicate that the top-ranked antigens can be considered high-priority candidates for diagnostic development. AVAILABILITY: All software programs are written in R and available at http://www.igb.uci.edu/index.php?page=tools and at http://www.r-project.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identification of humoral immune responses in protein microarrays using DNA microarray data analysis techniques.

MOTIVATION: We present a study of antigen expression signals from a newly developed high-throughput protein microarray technique. These signals are a measure of antibody-antigen binding activity and provide a basis for understanding humoral immune responses to various infectious agents and supporting vaccine and diagnostic development. RESULTS: We investigate the characteristics of these expression profiles and show that noise models, normalization, variance estimation and differential expression analysis techniques developed in the context of DNA microarray analysis can be adapted and applied to these protein arrays. Using a high-dimensional dataset containing measurements of expression profiles of antibody reactivity against each protein (295 antigens and 9 controls) in 42 malaria (Plasmodium falciparum) protein arrays derived from 22 donors with various clinical presentations of malaria, we present a methodology for the analysis and identification of significantly expressed antigens targeted by immune responses for individual sera, groups of sera and across stages of infection. We also conduct a short study highlighting the top immunoreactive antigens where we identify three novel high priority antigens for future evaluation. AVAILABILITY: All software programs (in R) used for the analysis described in this paper are freely available for academic purposes at www.igb.uci.edu/servers/servers.html.

How noisy and replicable are DNA microarry data?

This paper analyses variability in highly replicated measurements of DNA microarray data conducted on nylon filters and Affymetrix GeneChips with different cDNA targets, filters, and imaging technology. Replicability is assessed quantitatively using correlation analysis as a global measure and differential expression analysis and ANOVA at the level of individual genes.