Exploring the functional impact of alternative splicing on human protein isoforms using available annotation sources.

In recent years, the emphasis of scientific inquiry has shifted from whole-genome analyses to an understanding of cellular responses specific to tissue, developmental stage or environmental conditions. One of the central mechanisms underlying the diversity and adaptability of the contextual responses is alternative splicing (AS). It enables a single gene to encode multiple isoforms with distinct biological functions. However, to date, the functions of the vast majority of differentially spliced protein isoforms are not known. Integration of genomic, proteomic, functional, phenotypic and contextual information is essential for supporting isoform-based modeling and analysis. Such integrative proteogenomics approaches promise to provide insights into the functions of the alternatively spliced protein isoforms and provide high-confidence hypotheses to be validated experimentally. This manuscript provides a survey of the public databases supporting isoform-based biology. It also presents an overview of the potential global impact of AS on the human canonical gene functions, molecular interactions and cellular pathways.

Lynx: a knowledge base and an analytical workbench for integrative medicine.

Lynx (http://lynx.ci.uchicago.edu) is a web-based database and a knowledge extraction engine. It supports annotation and analysis of high-throughput experimental data and generation of weighted hypotheses regarding genes and molecular mechanisms contributing to human phenotypes or conditions of interest. Since the last release, the Lynx knowledge base (LynxKB) has been periodically updated with the latest versions of the existing databases and supplemented with additional information from public databases. These additions have enriched the data annotations provided by Lynx and improved the performance of Lynx analytical tools. Moreover, the Lynx analytical workbench has been supplemented with new tools for reconstruction of co-expression networks and feature-and-network-based prioritization of genetic factors and molecular mechanisms. These developments facilitate the extraction of meaningful knowledge from experimental data and LynxKB. The Service Oriented Architecture provides public access to LynxKB and its analytical tools via user-friendly web services and interfaces.

Lynx: a database and knowledge extraction engine for integrative medicine.

We have developed Lynx (http://lynx.ci.uchicago.edu)--a web-based database and a knowledge extraction engine, supporting annotation and analysis of experimental data and generation of weighted hypotheses on molecular mechanisms contributing to human phenotypes and disorders of interest. Its underlying knowledge base (LynxKB) integrates various classes of information from >35 public databases and private collections, as well as manually curated data from our group and collaborators. Lynx provides advanced search capabilities and a variety of algorithms for enrichment analysis and network-based gene prioritization to assist the user in extracting meaningful knowledge from LynxKB and experimental data, whereas its service-oriented architecture provides public access to LynxKB and its analytical tools via user-friendly web services and interfaces.

Lynx web services for annotations and systems analysis of multi-gene disorders.

Lynx is a web-based integrated systems biology platform that supports annotation and analysis of experimental data and generation of weighted hypotheses on molecular mechanisms contributing to human phenotypes and disorders of interest. Lynx has integrated multiple classes of biomedical data (genomic, proteomic, pathways, phenotypic, toxicogenomic, contextual and others) from various public databases as well as manually curated data from our group and collaborators (LynxKB). Lynx provides tools for gene list enrichment analysis using multiple functional annotations and network-based gene prioritization. Lynx provides access to the integrated database and the analytical tools via REST based Web Services (http://lynx.ci.uchicago.edu/webservices.html). This comprises data retrieval services for specific functional annotations, services to search across the complete LynxKB (powered by Lucene), and services to access the analytical tools built within the Lynx platform.

Mapping autism risk loci using genetic linkage and chromosomal rearrangements.

Autism spectrum disorders (ASDs) are common, heritable neurodevelopmental conditions. The genetic architecture of ASDs is complex, requiring large samples to overcome heterogeneity. Here we broaden coverage and sample size relative to other studies of ASDs by using Affymetrix 10K SNP arrays and 1,181 [corrected] families with at least two affected individuals, performing the largest linkage scan to date while also analyzing copy number variation in these families. Linkage and copy number variation analyses implicate chromosome 11p12-p13 and neurexins, respectively, among other candidate loci. Neurexins team with previously implicated neuroligins for glutamatergic synaptogenesis, highlighting glutamate-related genes as promising candidates for contributing to ASDs.

High-throughput translational medicine: challenges and solutions.

Recent technological advances in genomics now allow producing biological data at unprecedented tera- and petabyte scales. Yet, the extraction of useful knowledge from this voluminous data presents a significant challenge to a scientific community. Efficient mining of vast and complex data sets for the needs of biomedical research critically depends on seamless integration of clinical, genomic, and experimental information with prior knowledge about genotype-phenotype relationships accumulated in a plethora of publicly available databases. Furthermore, such experimental data should be accessible to a variety of algorithms and analytical pipelines that drive computational analysis and data mining. Translational projects require sophisticated approaches that coordinate and perform various analytical steps involved in the extraction of useful knowledge from accumulated clinical and experimental data in an orderly semiautomated manner. It presents a number of challenges such as (1) high-throughput data management involving data transfer, data storage, and access control; (2) scalable computational infrastructure; and (3) analysis of large-scale multidimensional data for the extraction of actionable knowledge.We present a scalable computational platform based on crosscutting requirements from multiple scientific groups for data integration, management, and analysis. The goal of this integrated platform is to address the challenges and to support the end-to-end analytical needs of various translational projects.

Mutations in LGI1 cause autosomal-dominant partial epilepsy with auditory features.

The epilepsies are a common, clinically heterogeneous group of disorders defined by recurrent unprovoked seizures. Here we describe identification of the causative gene in autosomal-dominant partial epilepsy with auditory features (ADPEAF, MIM 600512), a rare form of idiopathic lateral temporal lobe epilepsy characterized by partial seizures with auditory disturbances. We constructed a complete, 4.2-Mb physical map across the genetically implicated disease-gene region, identified 28 putative genes (Fig. 1) and resequenced all or part of 21 genes before identifying presumptive mutations in one copy of the leucine-rich, glioma-inactivated 1 gene (LGI1) in each of five families with ADPEAF. Previous studies have indicated that loss of both copies of LGI1 promotes glial tumor progression. We show that the expression pattern of mouse Lgi1 is predominantly neuronal and is consistent with the anatomic regions involved in temporal lobe epilepsy. Discovery of LGI1 as a cause of ADPEAF suggests new avenues for research on pathogenic mechanisms of idiopathic epilepsies.

Unique somatic variants in DNA from urine exosomes of individuals with bladder cancer.

Bladder cancer (BC), a heterogeneous disease characterized by high recurrence rates, is diagnosed and monitored by cystoscopy. Accurate clinical staging based on biopsy remains a challenge, and additional, objective diagnostic tools are needed urgently. We used exosomal DNA (exoDNA) as an analyte to examine cancer-associated mutations and compared the diagnostic utility of exoDNA from urine and serum of individuals with BC. In contrast to urine exosomes from healthy individuals, urine exosomes from individuals with BC contained significant amounts of DNA. Whole-exome sequencing of DNA from matched urine and serum exosomes, bladder tumors, and normal tissue (peripheral blood mononuclear cells) identified exonic and 3' UTR variants in frequently mutated genes in BC, detectable in urine exoDNA and matched tumor samples. Further analyses identified somatic variants in driver genes, unique to urine exoDNA, possibly because of the inherent intra-tumoral heterogeneity of BC, which is not fully represented in random small biopsies. Multiple variants were also found in untranslated portions of the genome, such as microRNA (miRNA)-binding regions of the KRAS gene. Gene network analyses revealed that exoDNA is associated with cancer, inflammation, and immunity in BC exosomes. Our findings show utility of exoDNA as an objective, non-invasive strategy to identify novel biomarkers and targets for BC.

Recurrent 16p11.2 microdeletions in autism.

Autism is a childhood neurodevelopmental disorder with a strong genetic component, yet the identification of autism susceptibility loci remains elusive. We investigated 180 autism probands and 372 control subjects by array comparative genomic hybridization (aCGH) using a 19K whole-genome tiling path bacterial artificial chromosome microarray to identify submicroscopic chromosomal rearrangements specific to autism. We discovered a recurrent 16p11.2 microdeletion in two probands with autism and none in controls. The deletion spans approximately 500-kb and is flanked by approximately 147-kb segmental duplications (SDs) that are >99% identical, a common characteristic of genomic disorders. We assessed the frequency of this new autism genomic disorder by screening an additional 532 probands and 465 controls by quantitative PCR and identified two more patients but no controls with the microdeletion, indicating a combined frequency of 0.6% (4/712 autism versus 0/837 controls; Fisher exact test P = 0.044). We confirmed all 16p11.2 deletions using fluorescence in situ hybridization, microsatellite analyses and aCGH, and mapped the approximate deletion breakpoints to the edges of the flanking SDs using a custom-designed high-density oligonucleotide microarray. Bioinformatic analysis localized 12 of the 25 genes within the microdeletion to nodes in one interaction network. We performed phenotype analyses and found no striking features that distinguish patients with the 16p11.2 microdeletion as a distinct autism subtype. Our work reports the first frequency, breakpoint, bioinformatic and phenotypic analyses of a de novo 16p11.2 microdeletion that represents one of the most common recurrent genomic disorders associated with autism to date.

Inducible enhancement of memory storage and synaptic plasticity in transgenic mice expressing an inhibitor of ATF4 (CREB-2) and C/EBP proteins.

To examine the role of C/EBP-related transcription factors in long-term synaptic plasticity and memory storage, we have used the tetracycline-regulated system and expressed in the forebrain of mice a broad dominant-negative inhibitor of C/EBP (EGFP-AZIP), which preferentially interacts with several inhibiting isoforms of C/EBP. EGFP-AZIP also reduces the expression of ATF4, a distant member of the C/EBP family of transcription factors that is homologous to the Aplysia memory suppressor gene ApCREB-2. Consistent with the removal of inhibitory constraints on transcription, we find an increase in the pattern of gene transcripts in the hippocampus of EGFP-AZIP transgenic mice and both a reversibly enhanced hippocampal-based spatial memory and LTP. These results suggest that several proteins within the C/EBP family including ATF4 (CREB-2) act to constrain long-term synaptic changes and memory formation. Relief of this inhibition lowers the threshold for hippocampal-dependent long-term synaptic potentiation and memory storage in mice.

Chemokine expression in the early response to injury in human airway epithelial cells.

Basal airway epithelial cells (AEC) constitute stem/progenitor cells within the central airways and respond to mucosal injury in an ordered sequence of spreading, migration, proliferation, and differentiation to needed cell types. However, dynamic gene transcription in the early events after mucosal injury has not been studied in AEC. We examined gene expression using microarrays following mechanical injury (MI) in primary human AEC grown in submersion culture to generate basal cells and in the air-liquid interface to generate differentiated AEC (dAEC) that include goblet and ciliated cells. A select group of ~150 genes was in differential expression (DE) within 2-24 hr after MI, and enrichment analysis of these genes showed over-representation of functional categories related to inflammatory cytokines and chemokines. Network-based gene prioritization and network reconstruction using the PINTA heat kernel diffusion algorithm demonstrated highly connected networks that were richer in differentiated AEC compared to basal cells. Similar experiments done in basal AEC collected from asthmatic donor lungs demonstrated substantial changes in DE genes and functional categories related to inflammation compared to basal AEC from normal donors. In dAEC, similar but more modest differences were observed. We demonstrate that the AEC transcription signature after MI identifies genes and pathways that are important to the initiation and perpetuation of airway mucosal inflammation. Gene expression occurs quickly after injury and is more profound in differentiated AEC, and is altered in AEC from asthmatic airways. Our data suggest that the early response to injury is substantially different in asthmatic airways, particularly in basal airway epithelial cells.

Selection for contextual fear conditioning affects anxiety-like behaviors and gene expression.

Conditioned fear and anxiety-like behaviors have many similarities at the neuroanatomical and pharmacological levels, but their genetic relationship is less well defined. We used short-term selection for contextual fear conditioning (FC) to produce outbred mouse lines with robust genetic differences in FC. The high and low selected lines showed differences in fear learning that were stable across various training parameters and were not secondary to differences in sensitivity to the unconditioned stimulus (foot shock). They also showed a divergence in fear potentiated startle, indicating that differences induced by selection generalized to another measure of fear learning. However, there were no differences in performance in a Pavlovian approach conditioning task or the Morris water maze, indicating no change in general learning ability. The high fear learning line showed greater anxiety-like behavior in the open field and zero maze, confirming a genetic relationship between FC and anxiety-like behavior. Gene expression analysis of the amygdala and hippocampus identified genes that were differentially expressed between the two lines. Quantitative trait locus (QTL) analysis identified several chromosomal regions that may underlie the behavioral response to selection; cis-acting expression QTL were identified in some of these regions, possibly identifying genes that underlie these behavioral QTL. These studies support the validity of a broad genetic construct that includes both learned fear and anxiety and provides a basis for further studies aimed at gene identification.

DRD2 C957T polymorphism interacts with the COMT Val158Met polymorphism in human working memory ability.

The C957T polymorphism in the dopamine D2 receptor (DRD2) gene and the Val158Met polymorphism in the Catechol-O-Methyl-Transferase (COMT) gene affect dopamine transmission and have been found to be associated with schizophrenia. Since DRD2 in mice and the COMT gene in humans modulate working memory, we examined the relationship and possible interaction of both polymorphisms to working memory performance in 188 healthy adults. Subjects having the DRD2 C/C allele showed the poorest performance in a word serial position test. Moreover, the effect of the C957T genotype was strengthened when interaction with the COMT Val158Met polymorphism was included in the analysis. We propose that an interaction of the DRD2 C957T and COMT Val158Met may be involved in the generation of some working memory deficits in schizophrenia.

A p53-regulated apoptotic gene signature predicts treatment response and outcome in pediatric acute lymphoblastic leukemia.

Gene signatures have been associated with outcome in pediatric acute lymphoblastic leukemia (ALL) and other malignancies. However, determining the molecular drivers of these expression changes remains challenging. In ALL blasts, the p53 tumor suppressor is the primary regulator of the apoptotic response to genotoxic chemotherapy, which is predictive of outcome. Consequently, we hypothesized that the normal p53-regulated apoptotic response to DNA damage would be altered in ALL and that this alteration would influence drug response and treatment outcome. To test this, we first used global expression profiling in related human B-lineage lymphoblastoid cell lines with either wild type or mutant TP53 to characterize the normal p53-mediated transcriptional response to ionizing radiation (IR) and identified 747 p53-regulated apoptotic target genes. We then sorted these genes into six temporal expression clusters (TECs) based upon differences over time in their IR-induced p53-regulated gene expression patterns, and found that one cluster (TEC1) was associated with multidrug resistance in leukemic blasts in one cohort of children with ALL and was an independent predictor of survival in two others. Therefore, by investigating p53-mediated apoptosis in vitro, we identified a gene signature significantly associated with drug resistance and treatment outcome in ALL. These results suggest that intersecting pathway-derived and clinically derived expression data may be a powerful method to discover driver gene signatures with functional and clinical implications in pediatric ALL and perhaps other cancers as well.

Catechol-O-methyltransferase (COMT) genotypes and working memory: associations with differing cognitive operations.

BACKGROUND: Catechol-O-methyltransferase (COMT) is a strong candidate gene for schizophrenia and cognitive functions disrupted in this disorder. This report examines the relation of COMT genotypes to performance on a battery of working memory tests differing in the cognitive operations to be performed on the material. METHODS: A large sample of 402 healthy adults were tested on four working memory tests: Spatial Delayed Response (SDR), Word Serial Position Test (WSPT), N-back, and Letter-Number Sequencing. A subsample (n = 246) was tested on the Wisconsin Card Sorting Test (WCST). A saliva swab was used to obtain DNA from all participants. RESULTS: Letter-Number Sequencing, which requires both storage and manipulation of information, was the only working memory test that showed expected differences among COMT genotypes, with the met/met group showing the best performance and the val/val group the poorest performance. As in previous studies, the met/met group also performed better than the val/val group on the WCST. CONCLUSIONS: COMT genotypes were not associated with performance on tests measuring simple storage, maintenance of temporal order or updating of information in working memory. Genotype differences in Letter-Number Sequencing and WCST suggest that higher-order components of processing (e.g., mental manipulation) are more closely related to this gene.

Disease gene prioritization using network and feature.

Identifying high-confidence candidate genes that are causative for disease phenotypes, from the large lists of variations produced by high-throughput genomics, can be both time-consuming and costly. The development of novel computational approaches, utilizing existing biological knowledge for the prioritization of such candidate genes, can improve the efficiency and accuracy of the biomedical data analysis. It can also reduce the cost of such studies by avoiding experimental validations of irrelevant candidates. In this study, we address this challenge by proposing a novel gene prioritization approach that ranks promising candidate genes that are likely to be involved in a disease or phenotype under study. This algorithm is based on the modified conditional random field (CRF) model that simultaneously makes use of both gene annotations and gene interactions, while preserving their original representation. We validated our approach on two independent disease benchmark studies by ranking candidate genes using network and feature information. Our results showed both high area under the curve (AUC) value (0.86), and more importantly high partial AUC (pAUC) value (0.1296), and revealed higher accuracy and precision at the top predictions as compared with other well-performed gene prioritization tools, such as Endeavour (AUC-0.82, pAUC-0.083) and PINTA (AUC-0.76, pAUC-0.066). We were able to detect more target genes (9/18/19/27) on top positions (1/5/10/20) compared to Endeavour (3/11/14/23) and PINTA (6/10/13/18). To demonstrate its usability, we applied our method to a case study for the prediction of molecular mechanisms contributing to intellectual disability and autism. Our approach was able to correctly recover genes related to both disorders and provide suggestions for possible additional candidates based on their rankings and functional annotations.

Optimal step length EM algorithm (OSLEM) for the estimation of haplotype frequency and its application in lipoprotein lipase genotyping.

BACKGROUND: Haplotype based linkage disequilibrium (LD) mapping has become a powerful and cost-effective method for performing genetic association studies, particularly in the search for genetic markers in linkage disequilibrium with complex disease loci. Various methods (e.g. Monte-Carlo (Gibbs sampling); EM (expectation maximization); and Clark's method) have been used to estimate haplotype frequencies from routine genotyping data. RESULTS: These algorithms can be very slow for large number of SNPs. In order to speed them up, we have developed a new algorithm using numerical analysis technology, a so-called optimal step length EM (OSLEM) that accelerates the calculation. By optimizing approximately the step length of the EM algorithm, OSLEM can run at about twice the speed of EM. This algorithm has been used for lipoprotein lipase (LPL) genotyping analysis. CONCLUSIONS: This new optimal step length EM (OSLEM) algorithm can accelerate the calculation for haplotype frequency estimation for genotyping data without pedigree information. An OSLEM on-line server is available, as well as a free downloadable version.

Population-based study of SR-BI genetic variation and lipid profile.

The variability of the Class B Type I Scavenger Receptor (SR-BI) gene in human populations and the relation of its variants to blood lipids was investigated in a random sample of 1756 untreated adult residents of Geneva, Switzerland, during 1999-2000. A three-step study approach yielded the following results: (1) resequencing the gene's exons and flanking regions in 95 subjects identified four common single nucleotide polymorphisms (SNPs with rare allele frequency >3%); (2) association study of the four common SNPs in subjects with extreme HDL-cholesterol (HDL-C) and LDL-C phenotypes (186 "atherogenic cases" and 185 "non-atherogenic controls") showed that the synonymous exon 8 C-T (allelic frequency 48%) polymorphism, A350A, was associated with atheroprotection in men (odds ratios (OR) = 0.36, 95% confidence intervals (CI) = 0.15-0.90, P < 0.03), but not in women (2.09, 0.79-5.49, P = 0.14); and (3) population clinical effects of A350A genotypes assessed in all 1756 subjects, showed that the case-control study findings reflected a protective HDL-C effect in men (CC: 1.17 mmol/L, CT: 1.22 mmol/L, and TT: 1.24 mmol/L, trend P = 0.0062) and a deleterious LDL-C effect in women (CC: 3.58 mmol/L, CT: 3.72 mmol/L, and TT: 3.79 mmol/L, trend P = 0.014). The allelic frequencies of the common SR-BI variants appear to be very similar in European and North American populations. The HDL-C effect increased with age. SR-BI A350A appears to have gender-specific and age-related effects on cholesterol transport lipoproteins.

An integrative computational approach for prioritization of genomic variants.

An essential step in the discovery of molecular mechanisms contributing to disease phenotypes and efficient experimental planning is the development of weighted hypotheses that estimate the functional effects of sequence variants discovered by high-throughput genomics. With the increasing specialization of the bioinformatics resources, creating analytical workflows that seamlessly integrate data and bioinformatics tools developed by multiple groups becomes inevitable. Here we present a case study of a use of the distributed analytical environment integrating four complementary specialized resources, namely the Lynx platform, VISTA RViewer, the Developmental Brain Disorders Database (DBDB), and the RaptorX server, for the identification of high-confidence candidate genes contributing to pathogenesis of spina bifida. The analysis resulted in prediction and validation of deleterious mutations in the SLC19A placental transporter in mothers of the affected children that causes narrowing of the outlet channel and therefore leads to the reduced folate permeation rate. The described approach also enabled correct identification of several genes, previously shown to contribute to pathogenesis of spina bifida, and suggestion of additional genes for experimental validations. The study demonstrates that the seamless integration of bioinformatics resources enables fast and efficient prioritization and characterization of genomic factors and molecular networks contributing to the phenotypes of interest.