Anil G. Jegga

ToppGene Suite for gene list enrichment analysis and candidate gene prioritization

ToppGene Suite (http://toppgene.cchmc.org; this web site is free and open to all users and does not require a login to access) is a one-stop portal for (i) gene list functional enrichment, (ii) candidate gene prioritization using either functional annotations or network analysis and (iii) identification and prioritization of novel disease candidate genes in the interactome. Functional annotation-based disease candidate gene prioritization uses a fuzzy-based similarity measure to compute the similarity between any two genes based on semantic annotations. The similarity scores from individual features are combined into an overall score using statistical meta-analysis. A P-value of each annotation of a test gene is derived by random sampling of the whole genome. The protein–protein interaction network (PPIN)-based disease candidate gene prioritization uses social and Web networks analysis algorithms (extended versions of the PageRank and HITS algorithms, and the K-Step Markov method). We demonstrate the utility of ToppGene Suite using 20 recently reported GWAS-based gene–disease associations (including novel disease genes) representing five diseases. ToppGene ranked 19 of 20 (95%) candidate genes within the top 20%, while ToppNet ranked 12 of 16 (75%) candidate genes among the top 20%.

An integrated database of genes responsive to the Myc oncogenic transcription factor: identification of direct genomic targets

We report a database of genes responsive to the Myc oncogenic transcription factor. The database Myc Target Gene prioritizes candidate target genes according to experimental evidence and clusters responsive genes into functional groups. We coupled the prioritization of target genes with phylogenetic sequence comparisons to predict c-Myc target binding sites, which are in turn validated by chromatin immunoprecipitation assays. This database is essential for the understanding of the genetic regulatory networks underlying the genesis of cancers.

Activation of IKKα target genes depends on recognition of specific κB binding sites by RelB: p52 dimers

IκB Kinase (IKK)α is required for activation of an alternative NF‐κB signaling pathway based on processing of the NF‐κB2/p100 precursor protein, which associates with RelB in the cytoplasm. This pathway, which activates RelB:p52 dimers, is required for induction of several chemokine genes needed for organization of secondary lymphoid organs. We investigated the basis for the IKKα dependence of the induction of these genes in response to engagement of the lymphotoxin β receptor (LTβR). Using chromatin immunoprecipitation, we found that the promoters of organogenic chemokine genes are recognized by RelB:p52 dimers and not by RelA:p50 dimers, the ubiquitous target for the classical NF‐κB signaling pathway. We identified in the IKKα‐dependent promoters a novel type of NF‐κB‐binding site that is preferentially recognized by RelB:p52 dimers. This site links induction of organogenic chemokines and other important regulatory molecules to activation of the alternative pathway.

Transcriptional recapitulation and subversion of embryonic colon development by mouse colon tumor models and human colon cancer

BackgroundThe expression of carcino-embryonic antigen by colorectal cancer is an example of oncogenic activation of embryonic gene expression. Hypothesizing that oncogenesis-recapitulating-ontogenesis may represent a broad programmatic commitment, we compared gene expression patterns of human colorectal cancers (CRCs) and mouse colon tumor models to those of mouse colon development embryonic days 13.5-18.5.ResultsWe report here that 39 colon tumors from four independent mouse models and 100 human CRCs encompassing all clinical stages shared a striking recapitulation of embryonic colon gene expression. Compared to normal adult colon, all mouse and human tumors over-expressed a large cluster of genes highly enriched for functional association to the control of cell cycle progression, proliferation, and migration, including those encoding MYC, AKT2, PLK1 and SPARC. Mouse tumors positive for nuclear β-catenin shifted the shared embryonic pattern to that of early development. Human and mouse tumors differed from normal embryonic colon by their loss of expression modules enriched for tumor suppressors (EDNRB, HSPE, KIT and LSP1). Human CRC adenocarcinomas lost an additional suppressor module (IGFBP4, MAP4K1, PDGFRA, STAB1 and WNT4). Many human tumor samples also gained expression of a coordinately regulated module associated with advanced malignancy (ABCC1, FOXO3A, LIF, PIK3R1, PRNP, TNC, TIMP3 and VEGF).ConclusionCross-species, developmental, and multi-model gene expression patterning comparisons provide an integrated and versatile framework for definition of transcriptional programs associated with oncogenesis. This approach also provides a general method for identifying pattern-specific biomarkers and therapeutic targets. This delineation and categorization of developmental and non-developmental activator and suppressor gene modules can thus facilitate the formulation of sophisticated hypotheses to evaluate potential synergistic effects of targeting within- and between-modules for next-generation combinatorial therapeutics and improved mouse models.

Evaluation of Myc E-Box Phylogenetic Footprints in Glycolytic Genes by Chromatin Immunoprecipitation Assays

ABSTRACT Prediction of gene regulatory sequences using phylogenetic footprinting has advanced considerably but lacks experimental validation. Here, we report whether transcription factor binding sites predicted by dot plotting or web-based Trafac analysis could be validated by chromatin immunoprecipitation assays. MYC overexpression enhances glycolysis without hypoxia and hence may contribute to altered tumor metabolism. Because the full spectrum of glycolytic genes directly regulated by Myc is not known, we chose Myc as a model transcription factor to determine whether it binds target glycolytic genes that have conserved canonical Myc binding sites or E boxes (5′-CACGTG-3′). Conserved canonical E boxes in ENO1, HK2, and LDHA occur in 31- to 111-bp islands with high interspecies sequence identity (>65%). Trafac analysis revealed another region in ENO1 that corresponds to a murine region with a noncanonical E box. Myc bound all these conserved regions well in the human P493-6 B lymphocytes. We also determined whether Myc could bind nonconserved canonical E boxes found in the remaining human glycolytic genes. Myc bound PFKM, but it did not significantly bind GPI, PGK1, and PKM2. Binding to BPGM, PGAM2, and PKLR was not detected. Both GAPD and TPI1 do not have conserved E boxes but are induced and bound by Myc through regions with noncanonical E boxes. Our results indicate that Myc binds well to conserved canonical E boxes, but not nonconserved E boxes. However, the binding of Myc to unpredicted genomic regions with noncanonical E boxes reveals a limitation of phylogenetic footprinting. In aggregate, these observations indicate that Myc is an important regulator of glycolytic genes, suggesting that MYC plays a key role in a switch to glycolytic metabolism during cell proliferation or tumorigenesis.

Atlas of Gene Expression in the Developing Kidney at Microanatomic Resolution

Kidney development is based on differential cell-type-specific expression of a vast number of genes. While multiple critical genes and pathways have been elucidated, a genome-wide analysis of gene expression within individual cellular and anatomic structures is lacking. Accomplishing this could provide significant new insights into fundamental developmental mechanisms such as mesenchymal-epithelial transition, inductive signaling, branching morphogenesis, and segmentation. We describe here a comprehensive gene expression atlas of the developing mouse kidney based on the isolation of each major compartment by either laser capture microdissection or fluorescence-activated cell sorting, followed by microarray profiling. The resulting data agree with known expression patterns and additional in situ hybridizations. This kidney atlas allows a comprehensive analysis of the progression of gene expression states during nephrogenesis, as well as discovery of potential growth factor-receptor interactions. In addition, the results provide deeper insight into the genetic regulatory mechanisms of kidney development.

Improved human disease candidate gene prioritization using mouse phenotype

BackgroundThe majority of common diseases are multi-factorial and modified by genetically and mechanistically complex polygenic interactions and environmental factors. High-throughput genome-wide studies like linkage analysis and gene expression profiling, tend to be most useful for classification and characterization but do not provide sufficient information to identify or prioritize specific disease causal genes.ResultsExtending on an earlier hypothesis that the majority of genes that impact or cause disease share membership in any of several functional relationships we, for the first time, show the utility of mouse phenotype data in human disease gene prioritization. We study the effect of different data integration methods, and based on the validation studies, we show that our approach, ToppGene http://toppgene.cchmc.org, outperforms two of the existing candidate gene prioritization methods, SUSPECTS and ENDEAVOUR.ConclusionThe incorporation of phenotype information for mouse orthologs of human genes greatly improves the human disease candidate gene analysis and prioritization.

Large-Scale Molecular Comparison of Human Schwann Cells to Malignant Peripheral Nerve Sheath Tumor Cell Lines and Tissues

Malignant peripheral nerve sheath tumors (MPNST) are highly invasive soft tissue sarcomas that arise within the peripheral nerve and frequently metastasize. To identify molecular events contributing to malignant transformation in peripheral nerve, we compared eight cell lines derived from MPNSTs and seven normal human Schwann cell samples. We found that MPNST lines are heterogeneous in their in vitro growth rates and exhibit diverse alterations in expression of pRb, p53, p14(Arf), and p16(INK4a) proteins. All MPNST cell lines express the epidermal growth factor receptor and lack S100beta protein. Global gene expression profiling using Affymetrix oligonucleotide microarrays identified a 159-gene molecular signature distinguishing MPNST cell lines from normal Schwann cells, which was validated in Affymetrix microarray data generated from 45 primary MPNSTs. Expression of Schwann cell differentiation markers (SOX10, CNP, PMP22, and NGFR) was down-regulated in MPNSTs whereas neural crest stem cell markers, SOX9 and TWIST1, were overexpressed in MPNSTs. Previous studies have implicated TWIST1 in apoptosis inhibition, resistance to chemotherapy, and metastasis. Reducing TWIST1 expression in MPNST cells using small interfering RNA did not affect apoptosis or chemoresistance but inhibited cell chemotaxis. Our results highlight the use of gene expression profiling in identifying genes and molecular pathways that are potential biomarkers and/or therapeutic targets for treatment of MPNST and support the use of the MPNST cell lines as a primary analytic tool.

ToppCluster: a multiple gene list feature analyzer for comparative enrichment clustering and network-based dissection of biological systems

ToppCluster is a web server application that leverages a powerful enrichment analysis and underlying data environment for comparative analyses of multiple gene lists. It generates heatmaps or connectivity networks that reveal functional features shared or specific to multiple gene lists. ToppCluster uses hypergeometric tests to obtain list-specific feature enrichment P-values for currently 17 categories of annotations of human-ortholog genes, and provides user-selectable cutoffs and multiple testing correction methods to control false discovery. Each nameable gene list represents a column input to a resulting matrix whose rows are overrepresented features, and individual cells per-list P-values and corresponding genes per feature. ToppCluster provides users with choices of tabular outputs, hierarchical clustering and heatmap generation, or the ability to interactively select features from the functional enrichment matrix to be transformed into XGMML or GEXF network format documents for use in Cytoscape or Gephi applications, respectively. Here, as example, we demonstrate the ability of ToppCluster to enable identification of list-specific phenotypic and regulatory element features (both cis-elements and 3′UTR microRNA binding sites) among tissue-specific gene lists. ToppCluster’s functionalities enable the identification of specialized biological functions and regulatory networks and systems biology-based dissection of biological states. ToppCluster can be accessed freely at http://toppcluster.cchmc.org.

Canonical genetic signatures of the adult human brain

The structure and function of the human brain are highly stereotyped, implying a conserved molecular program responsible for its development, cellular structure and function. We applied a correlation-based metric called differential stability to assess reproducibility of gene expression patterning across 132 structures in six individual brains, revealing mesoscale genetic organization. The genes with the highest differential stability are highly biologically relevant, with enrichment for brain-related annotations, disease associations, drug targets and literature citations. Using genes with high differential stability, we identified 32 anatomically diverse and reproducible gene expression signatures, which represent distinct cell types, intracellular components and/or associations with neurodevelopmental and neurodegenerative disorders. Genes in neuron-associated compared to non-neuronal networks showed higher preservation between human and mouse; however, many diversely patterned genes displayed marked shifts in regulation between species. Finally, highly consistent transcriptional architecture in neocortex is correlated with resting state functional connectivity, suggesting a link between conserved gene expression and functionally relevant circuitry.