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.

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.

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.

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.

Strategic Integration of Multiple Bioinformatics Resources for System Level Analysis of Biological Networks.

Recent technological advances in genomics allow the production of biological data at unprecedented tera- and petabyte scales. Efficient mining of these vast and complex datasets for the needs of biomedical research critically depends on a seamless integration of the clinical, genomic, and experimental information with prior knowledge about genotype-phenotype relationships. Such experimental data accumulated in publicly available databases should be accessible to a variety of algorithms and analytical pipelines that drive computational analysis and data mining.We present an integrated computational platform Lynx (Sulakhe et al., Nucleic Acids Res 44:D882-D887, 2016) ( http://lynx.cri.uchicago.edu ), a web-based database and knowledge extraction engine. It provides advanced search capabilities and a variety of algorithms for enrichment analysis and network-based gene prioritization. It gives public access to the Lynx integrated knowledge base (LynxKB) and its analytical tools via user-friendly web services and interfaces. The Lynx service-oriented architecture supports annotation and analysis of high-throughput experimental data. Lynx tools assist the user in extracting meaningful knowledge from LynxKB and experimental data, and in the generation of weighted hypotheses regarding the genes and molecular mechanisms contributing to human phenotypes or conditions of interest. The goal of this integrated platform is to support the end-to-end analytical needs of various translational projects.

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.

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.

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.

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.

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.

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.

Panic disorder is associated with the serotonin transporter gene (SLC6A4) but not the promoter region (5-HTTLPR).

Panic disorder (PD) and social anxiety disorder (SAD) are moderately heritable anxiety disorders. We analyzed five genes, derived from pharmacological or translational mouse models, in a new case-control study of PD and SAD in European Americans: (1) the serotonin transporter (SLC6A4), (2) the serotonin receptor 1A, (3) catechol-O-methyltransferase, (4) a regulator of g-protein signaling and (5) the gastrin-releasing peptide receptor. Cases were interviewed using the schedule for affective disorders and schizophrenia and were required to have a probable or definite lifetime diagnosis of PD (N=179), SAD (161) or both (140), with first onset by age 31 and a family history of anxiety. Final diagnoses were determined using the best estimate procedure, blind to genotyping data. Controls were obtained from the National Institute of Mental Health Human Genetics Initiative; only subjects above 25 years of age who screened negative for all psychiatric symptoms were included (N=470). A total of 45 single nucleotide polymorphisms were successfully genotyped over the five selected genes using Applied Biosystems SNPlex protocol. SLC6A4 provided strong and consistent evidence of association with the PD and PD+SAD groups, with the most significant association in both groups being at rs140701 (chi(2)=10.72, P=0.001 with PD and chi(2)=8.59, P=0.003 in the PD+SAD group). This association remained significant after multiple test correction. Those carrying at least one copy of the haplotype A-A-G constructed from rs3794808, rs140701 and rs4583306 have 1.7 times the odds of PD than those without the haplotype (95% confidence interval: 1.2-2.3). The SAD only group did not provide evidence of association, suggesting a PD-driven association. The findings remained after adjustment for age and sex, and there was no evidence that the association was due to population stratification. The promoter region of the gene, 5-HTTLPR, did not provide any evidence of association, regardless of whether analyzed as a triallelic or biallelic locus, nor did any of the other four candidate genes tested. Our findings suggest that the serotonin transporter gene may play a role in PD; however, the findings require replication. Future studies should attend to the entire genetic region rather than the promoter.

Novel submicroscopic chromosomal abnormalities detected in autism spectrum disorder.

BACKGROUND: One genetic mechanism known to be associated with autism spectrum disorders (ASD) is chromosomal abnormalities. The identification of copy number variants (CNV), i.e., microdeletions and microduplications that are undetectable at the level of traditional cytogenetic analysis, allows the potential association of submicroscopic chromosomal imbalances and human disease. METHODS: We performed array comparative genomic hybridization (aCGH) utilizing a 19K whole genome tiling path bacterial artificial chromosome (BAC) microarray on 397 unrelated subjects with autism spectrum disorder. Common CNV were excluded using a control group comprised of 372 individuals from the National Institute of Mental Health (NIMH) Genetics Initiative Control samples. Confirmation studies were performed on all remaining CNV using fluorescence in situ hybridization (FISH), microsatellite analysis, and/or quantitative polymerase chain reaction (PCR) analysis. RESULTS: A total of 51 CNV were confirmed in 46 ASD subjects. Three maternal interstitial duplications of 15q11-q13 known to be associated with ASD were identified. The other 48 CNV ranged in size from 189 kilobase (kb) to 5.5 megabase (Mb) and contained from 0 to approximately 40 National Center for Biotechnology Information (NCBI) Reference Sequence (RefSeq) genes. Seven CNV were de novo and 44 were inherited. CONCLUSIONS: Fifty-one autism-specific CNV were identified in 46 of 397 ASD patients using a 19K BAC microarray for an overall rate of 11.6%. These microdeletions and microduplications cause gene dosage imbalance in 272 genes, many of which could be considered as candidate genes for autism.

Genetic-linkage mapping of complex hereditary disorders to a whole-genome molecular-interaction network.

Common hereditary neurodevelopmental disorders such as autism, bipolar disorder, and schizophrenia are most likely both genetically multifactorial and heterogeneous. Because of these characteristics traditional methods for genetic analysis fail when applied to such diseases. To address the problem we propose a novel probabilistic framework that combines the standard genetic linkage formalism with whole-genome molecular-interaction data to predict pathways or networks of interacting genes that contribute to common heritable disorders. We apply the model to three large genotype-phenotype data sets, identify a small number of significant candidate genes for autism (24), bipolar disorder (21), and schizophrenia (25), and predict a number of gene targets likely to be shared among the disorders.

Rapid selection response for contextual fear conditioning in a cross between C57BL/6J and A/J: behavioral, QTL and gene expression analysis.

We used short-term selection to produce outbred mouse lines with differences in contextual fear conditioning. Within two generations of selection all low selected mice were homozygous for the recessive tyrc allele and showed the corresponding albino coat color. Freezing differed in the high and low selected lines across a range of parameters. We identified several QTLs for the selection response, including a highly significant QTL at the tyr locus (p < 9.6(-10)). To determine whether the tyrc allele was directly responsible for the response to selection, we examined B6 mice that have a mutant tyr allele (tyr(c-2j-)) and an AJ congenic strain that has the wild-type B6 allele for tyr. These studies showed that the tyr allele had a small influence on fear learning. We used Affymetrix microarrays to identify many differentially expressed genes in the amygdala and hippocampus of the selected lines. We conclude that tyr is one of many alleles that influence fear conditioning.

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.

Genetic architecture of fear conditioning in chromosome substitution strains: relationship to measures of innate (unlearned) anxiety-like behavior.

We measured fear conditioning (FC) in a panel of chromosome substitution strains (CSS) created using the C57BL/6J (B6) and A/J (AJ) inbred strains. Mice were trained to associate a specific context and tone with a foot shock. FC was measured by observing freezing behavior during re-exposure to the context and tone. Freezing to context was more than twofold greater in the AJ strain relative to the B6 strain. Among the CSS we identified four strains with higher (CSS-6, -10, -11, and -18) and two strains with lower (CSS-7 and -14) freezing to context. CSS-10 and -18 also showed higher freezing to tone, while CSS-12 showed less freezing to tone. CSS-1 has been implicated in open-field (OF) and light-dark box (LDB); we observed significant activity differences prior to training but no differences in FC. Chromosomes 6 and 10 have been associated with differences in anxiety-like behaviors, suggesting the existence of pleiotropic alleles that influence both learned and innate fear. By utilizing a genetic reference population, we have identified chromosomes that pleiotropically influence multiple phenotypes hypothesized to reflect a common ethologic construct that has been termed emotionality. The CSS provide a straightforward means of isolating the underlying genetic factors.

Strong association of de novo copy number mutations with autism.

We tested the hypothesis that de novo copy number variation (CNV) is associated with autism spectrum disorders (ASDs). We performed comparative genomic hybridization (CGH) on the genomic DNA of patients and unaffected subjects to detect copy number variants not present in their respective parents. Candidate genomic regions were validated by higher-resolution CGH, paternity testing, cytogenetics, fluorescence in situ hybridization, and microsatellite genotyping. Confirmed de novo CNVs were significantly associated with autism (P = 0.0005). Such CNVs were identified in 12 out of 118 (10%) of patients with sporadic autism, in 2 out of 77 (3%) of patients with an affected first-degree relative, and in 2 out of 196 (1%) of controls. Most de novo CNVs were smaller than microscopic resolution. Affected genomic regions were highly heterogeneous and included mutations of single genes. These findings establish de novo germline mutation as a more significant risk factor for ASD than previously recognized.

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.