Search

Ovarian cancer (OC) is one of the most lethal type of cancer in women due to a lack of effective targeted therapies and high rates of treatment resistance and disease recurrence. Recently Poly (ADP-ribose) polymerase inhibitors (PARPi) have shown promise as chemotherapeutic agents; however, their efficacy is limited to a small fraction of patients with BRCA mutations. Here we show a novel function for the Hedgehog (Hh) transcription factor Glioma associated protein 1 (GLI1) in regulation of key Fanconi anemia (FA) gene, FANCD2 in OC cells. GLI1 inhibition in HR-proficient OC cells induces HR deficiency (BRCAness), replication stress and synergistic lethality when combined with PARP inhibition. Treatment of OC cells with combination of GLI1 and PARP inhibitors shows enhanced DNA damage, synergy in cytotoxicity, and strong in vivo anticancer responses.

Some metals are beneficial to plants and contribute to critical physiological processes. Some metals, however, are not. The presence of aluminum ions (Al3+) can be very toxic, especially in acidic soils. Considerable parts of the world&rsquo, s arable land are acidic in nature, mechanistically elucidating a plant&rsquo, s response to aluminum stress is critical to mitigating this stress and improving the quality of plants. To identify the genes involved in sugarcane response to aluminum stress, we generated 372 million paired-end RNA sequencing reads from the roots of CTC-2 and RB855453, which are two contrasting cultivars. Data normalization resulted in 162,161 contigs (contiguous sequences) and 97,335 genes from a de novo transcriptome assembly (trinity genes). A total of 4858 and 1307 differently expressed genes (DEGs) for treatment versus control were identified for the CTC-2 and RB855453 cultivars, respectively. The DEGs were annotated into 34 functional categories. The majority of the genes were upregulated in the CTC-2 (tolerant cultivar) and downregulated in RB855453 (sensitive cultivar). Here, we present the first root transcriptome of sugarcane under aluminum stress. The results and conclusions of this study are a crucial launch pad for future genetic and genomic studies of sugarcane. The transcriptome analysis shows that sugarcane tolerance to aluminum may be explained by an efficient detoxification mechanism combined with lateral root formation and activation of redox enzymes. We also present a hypothetical model for aluminum tolerance in the CTC-2 cultivar.

This opinion paper recommends a novel methodology to mechanistically elucidate the interactions between olfactory receptors and odorant molecules. Preliminary results presented here show diffusion of odorant-ligand molecules from outside the olfactory receptor protein into the receptor binding region. This notion marks a sharp contrast with computational studies related to olfactory receptor-odorant interactions, which have drawn conclusions from static or dynamic studies that have started from the ligand already docked in a preferred configuration in the receptor’s binding region. Dynamic simulations studies that begin by tracking the approach of the odorant from outside the protein and its transit to the receptor binding region followed by tracking the rest of the receptor-odorant interactions will allow the determination of protein structural changes: 1) that facilitate the odorant path; 2) that facilitate specific interactions between amino acids and the odorant; 3) that identify the activation state of the protein and its transition to the inactive state. The novel methodology will not only improve comparisons of hypotheses generated from these computational studies with experimental studies, it will also provide novel mechanistic insights into these interactions. The primary goal is to establish a predictive paradigm for olfactory receptor-odorant interactions.

Breast cancer is a major cause of morbidity and mortality in women and its metastatic spread is the principal reason behind the fatal outcome. Metastasis-related research of breast cancer is however underdeveloped when compared with the abundant literature on primary tumors. We applied an unexplored approach comparing at high resolution the genomic profiles of primary tumors and synchronous axillary lymph node metastases from 13 patients with breast cancer. Overall, primary tumors displayed 20% higher number of aberrations than metastases. In all but two patients, we detected in total 157 statistically significant differences between primary lesions and matched metastases. We further observed differences that can be linked to metastatic disease and there was also an overlapping pattern of changes between different patients. Many of the differences described here have been previously linked to poor patient survival, suggesting that this is a viable approach toward finding biomarkers for disease progression and definition of new targets useful for development of anticancer drugs. Frequent genetic differences between primary tumors and metastases in breast cancer also question, at least to some extent, the role of primary tumors as a surrogate subject of study for the systemic disease.

This article presents the latest developments in neuroscience information dissemination through the SenseLab suite of databases: NeuronDB, CellPropDB, ORDB, OdorDB, OdorMapDB, ModelDB and BrainPharm. These databases include information related to: (i) neuronal membrane properties and neuronal models, and (ii) genetics, genomics, proteomics and imaging studies of the olfactory system. We describe here: the new features for each database, the evolution of SenseLab’s unifying database architecture and instances of SenseLab database interoperation with other neuroscience online resources.

This paper describes NeuroExtract, a pilot system which facilitates the integrated retrieval of Internet-based information relevant to the neurosciences. The approach involved extracting descriptive metadata from the sources using domain-specific queries; retrieving, processing, and organizing the data into structured text files; searching the data files using text-based queries; and, providing the results in a Web page along with descriptions to entries and URL links to the original sources. NeuroExtract has been implemented for three bioscience resources, SWISSPROT, GEO, and PDB, which provide neuroscience-related information as sub-topics. We discuss several issues that arose in the course of NeuroExtract's implementation. This project is a first step in exploring how this general approach might be used, in conjunction with other query mediation approaches, to facilitate the integration of many Internet-accessible resources relevant to the neurosciences.

The Bürgi-Dunitz trajectory traces points along the pathway of bond formation between a nucleophile and electrophile. Previous X-ray crystallographic studies of some molecules containing a nucleophilic nitrogen atom and electrophilic carbonyl group provided some initial evidence for various degrees of bond formation via initial n→π* interactions. Observation of a complete set of points along the trajectory, however, has not yet been attained. In this paper, we present a DFT computational study investigating substituted 2-(dimethylamino)biphenyl-2'-carboxaldehydes as substrates for further examination of n→π* interactions and as a potential framework for more complete tracing of the Bürgi-Dunitz trajectory. These compounds are particulary suitable for study because of the rotational freedom granted by the C-C bond connecting the two aromatic rings allowing the molecule to choose the degree of interaction between the two complementary groups. The extent of interaction is measured by interatomic distance, NBO second-order perturbative analysis energies, volume of transferred electron density as provided by ETS-NOCV analysis, and differences in energies between models that allow for n→π* interactions and those that do not. A series of substituted biphenyls are ultimately identified as future synthetic targets that have maximum potential for providing improved tracing of the Bürgi-Dunitz trajectory.

The ACTH receptor, known as the melanocortin-2 receptor (MC2R), plays an important role in regulating and maintaining adrenocortical function. MC2R is a subtype of the melanocortin receptor (MCR) family and has unique characteristics among MCRs. Endogenous ACTH is the only endogenous agonist for MC2R, whereas the melanocortin peptides α-, β-, and γ-melanocyte-stimulating hormone and ACTH are full agonists for all other MCRs. In this study, we examined the molecular basis of MC2R responsible for ligand selectivity using ACTH analogs and MC2R mutagenesis. Our results indicate that substitution of Phe(7) with D-Phe or D-naphthylalanine (D-Nal(2')) in ACTH(1-24) caused a significant decrease in ligand binding affinity and potency. Substitution of Phe(7) with D-Nal(2') in ACTH(1-24) did not switch the ligand from agonist to antagonist at MC2R, which was observed in MC3R and MC4R. Substitution of Phe(7) with D-Phe(7) in ACTH(1-17) resulted in the loss of ligand binding and activity. Molecular analysis of MC2R indicated that only mutation of the third transmembrane domain of MC2R resulted in a decrease in D-Phe ACTH binding affinity and potency. Our results suggest that Phe(7) in ACTH plays an important role in ligand selectivity and that the third transmembrane domain of MC2R is crucial for ACTH selectivity and potency.

We have developed a program NeuroText to populate the neuroscience databases in SenseLab (http://senselab.med.yale.edu/senselab) by mining the natural language text of neuroscience articles. NeuroText uses a two-step approach to identify relevant articles. The first step (pre-processing), aimed at 100% sensitivity, identifies abstracts containing database keywords. In the second step, potentially relevant abstracts identified in the first step are processed for specificity dictated by database architecture, and neuroscience, lexical and semantic contexts. NeuroText results were presented to the experts for validation using a dynamically generated interface that also allows expert-validated articles to be automatically deposited into the databases. Of the test set of 912 articles, 735 were rejected at the pre-processing step. For the remaining articles, the accuracy of predicting database-relevant articles was 85%. Twenty-two articles were erroneously identified. NeuroText deferred decisions on 29 articles to the expert. A comparison of NeuroText results versus the experts' analyses revealed that the program failed to correctly identify articles' relevance due to concepts that did not yet exist in the knowledgebase or due to vaguely presented information in the abstracts. NeuroText uses two "evolution" techniques (supervised and unsupervised) that play an important role in the continual improvement of the retrieval results. Software that uses the NeuroText approach can facilitate the creation of curated, special-interest, bibliography databases.

Ab initio, self-consistent field, molecular-orbital, calculations were carried out for two 1,8-disubstituted naphthalene compounds to probe non-bonded, intra-molecular, interactions between electrophilic and nucleophilic substituents located at the 1 and 8 positions of the naphthalene rings. The cyano (–CN) group is the electrophilic substituent; the cyano carbon atom is the electrophilic center. The nucleophilic substituents were each, a nitro (–NO2) group in compound I, and a methoxy (–OCH3) group in compound II. The X-ray structure showed that the nitro-nitrile compound existed as two distinct structures, each with different bond distances and bond-angles of the peri substituents. We probed the nature of intra-molecular interactions in these compounds by calculating the electronic charges and molecular electrostatic potentials adjacent to the substituents. Similar studies for carbonyl group as the electrophilic substituent indicated the presence of electronic interactions for naphthalene compounds with the carbonyl group as the electrophilic substituent.

The Olfactory Receptor Database (ORDB; http://senselab.med.yale.edu/senselab/ordb) is a central repository of olfactory receptor (OR) and olfactory receptor-like gene and protein sequences. To deal with the very large OR gene family, we have constructed an algorithm that automatically downloads sequences from web sources such as GenBank and SWISS-PROT into the database. The algorithm uses hypertext markup language (HTML) parsing techniques that extract information relevant to ORDB. The information is then correlated with the metadata in the ORDB knowledge base to encode the unstructured text extracted into the structured format compliant with the database architecture, entity attribute value with classes and relationship (EAV/CR), which supports the SenseLab project as a whole. Three population methods: batch, automatic and semi-automatic population are discussed. The data is imported into the database using extensible markup language (XML).

Using computational methods, which allow mechanistic insights at a molecular level, we explored the olfactory receptor (OR)–odor interactions for 2 mouse ORs, S79 and S86. Both ORs have been previously experimentally, functionally characterized. The odors used were mostly carboxylic acids, which differed in chain length, substituents on the primary carbon atom-chain and degree of unsaturation. These odors elicited varied activation responses from both ORs. Our studies revealed that both receptors have 2 distinct binding sites. Preferential binding in 1 of the 2 sites is correlated with OR activation. The activating odorants: nonanedioic acid, heptanoic acid, and octanoic acid for OR S79 and nonanoic acid for OR S86 preferentially bind in the region bound by transmembranes (TMs [helical domains]) III, IV, V, and VI. The non excitatory odorants heptanol for S79 and heptanoic acid for S86 showed a greater likelihood of binding in the region bound by TMs I, II, III, and VII. Nanosecond-scale molecular dynamics simulations of the physiologically relevant conditions of docked OR–odorant complexes enabled us to quantitatively assess the roles of individual OR amino acids in odor binding. Amino acid–odorant contact maps and distance determinations over the course of the simulations lend support to our conclusions.

We performed an extensive sequence analysis on the loops of proteins. By dividing a loop databank derived from the Protein Data Bank into groups, we analyzed the chemical characteristics and the sequence preferences of loops of different lengths and loops connecting different secondary structures in proteins. We found that a large population of loops in our loop databank (94.4%) is either partially or completely surface-exposed. A majority of surface loops in proteins are hydrophilic, whereas the chemical characteristics of interior loops are relatively neutral according to Eisenberg's consensus hydrophobicity scale. As a first step in investigating the intrinsic sequence-structure relationship of loop sequences in proteins, we performed a neighbor-dependent sequence analysis that calculated the effect of the neighboring amino acid type on the loop propensity of residues in loops. This method enhances the statistical significance of residue propensity, thus allowing us to explore the positional preference of amino acids in loops. Our analysis yielded a series of amino acid dyads that showed high preference for loop conformation. The data presented in this study should prove useful for developing potential codes in recognizing loop sequences in proteins.

Ab initio Self-Consistent Field molecular orbital calculations were carried out on seven 1,8-disubstituted naphthalene compounds to probe the non-bonded intramolecular interactions between electrophilic and nucleophilic substituents located at the 1 and 8 positions. The electrophilic substituents were characterized by the CyO group with the carbonyl carbon as the electrophilic center. The nucleophilic substituents were amino [(CH 3) 2N‐], methoxy (CH3O‐) and hydroxy (‐OH) groups. The nature of the intramolecular interaction was probed by calculating the molecular electrostatic potential on the molecular surface adjacent to the substituents. Differences in net atomic charges and surface molecular electrostatic potentials with and without an adjacent substituent showed the presence of significant non-bonded interactions between the electrophilic and nucleophilic centers in these compounds. q 1998 Elsevier Science B.V. All rights reserved.

DNA polymerase beta (Pol beta) is one of the key enzymes in the base excision repair pathway. The amino-terminal 8 kDa domain of Pol beta has an activity for excising a 5'-deoxyribose phosphate (dRP) group from preincised apurine/apyrimidine (AP) sites. Recent biochemical studies have identified the catalytic center of the 8 kDa domain and provided new insight into the mechanism of DNA repair by DNA polymerase beta. By incorporating both structural and biochemical data, we present here a reaction mechanism for the 5'-dRP excision activity of the 8 kDa domain. This mechanism focuses on a catalytic groove near the helix-hairpin-helix (HhH) motif of the 8 kDa domain. Our model shows that the dRP group of the AP site can be stabilized in the catalytic groove through extensive interactions with the residues of the groove and be positioned close to the active center, Lys72, which catalyzes a beta-elimination reaction by forming a Schiff base with the C1' of the dRP group.

We present here a paradigm for online bioinformatics education, geared primarily towards bioinformatics applications, and currently tested in a graduate level bioinformatics course. This course is targeted to students and researchers whose primary research and education interests are experimental and who need bioinformatics to supplement their knowledge base and aid their research endeavors. We discuss the types of bioinformatics course content, materials and tools that biological and other science graduate students need and focus on methods for presenting this content in an online mode. Two methods of presentations are discussed: (1) video-captures of the demonstrations of bioinformatics web or stand-alone applications are edited to highlight salient aspects of the resources; (2) presentations that synchronize the videos of the lectures with the accompanying slide-shows. The aim of this type of education is to help users not only within the educational context but also to supplement their research. The psycho-ergonomic factors associated with such content creation as well as usage are also discussed.

Providing a rationale that associates a chemical structure of an odorant to its induced perception has been sought for a long time. To achieve this, a detailed atomic structure of both the odorant and the olfactory receptor must be known. State-of-the-art techniques to model the 3D structure of an olfactory receptor in complex with various odorants are presented here. These range from sequence alignment with known structures to molecular dynamics simulations in a realistic environment.

We present here, the salient aspects of three databases: Olfactory Receptor Database (ORDB) is a repository of genomics and proteomics information of ORs; OdorDB stores information related to odorous compounds, specifically identifying those that have been shown to interact with olfactory rectors; and OdorModelDB disseminates information related to computational models of olfactory receptors (ORs). The data stored among these databases is integrated. Presented in this chapter are descriptions of these resources, which are part of the SenseLab suite of databases, a discussion of the computational infrastructure that enhances the efficacy of information storage, retrieval, dissemination, and automated data population from external sources.

Providing a rationale that associates a chemical structure of an odorant to its induced perception has been sought for a long time. To achieve this, a detailed atomic structure of both the odorant and the olfactory receptor must be known. State-of-the-art techniques to model the 3D structure of an olfactory receptor in complex with various odorants are presented here. These range from sequence alignment with known structures to molecular dynamics simulations in a realistic environment.

Quantitative proteomics applications in mass spectrometry depend on the knowledge of the mass-to-charge ratio (m/z) values of proteotypic peptides for the proteins under study and their product ions. MRMPath and MRMutation, web-based bioinformatics software that are platform independent, facilitate the recovery of this information by biologists. MRMPath utilizes publicly available information related to biological pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. All the proteins involved in pathways of interest are recovered and processed in silico to extract information relevant to quantitative mass spectrometry analysis. Peptides may also be subjected to automated BLAST analysis to determine whether they are proteotypic. MRMutation catalogs and makes available, following processing, known (mutant) variants of proteins from the current UniProtKB database. All these results, available via the web from well-maintained, public databases, are written to an Excel spreadsheet, which the user can download and save. MRMPath and MRMutation can be freely accessed. As a system that seeks to allow two or more resources to interoperate, MRMPath represents an advance in bioinformatics tool development. As a practical matter, the MRMPath automated approach represents significant time savings to researchers.

We present here an exploration of the evolution of three well-established, web-based resources dedicated to the dissemination of information related to olfactory receptors (ORs) and their functional ligands, odorants. These resources are: the Olfactory Receptor Database (ORDB), the Human Olfactory Data Explorer (HORDE) and ODORactor. ORDB is a repository of genomic and proteomic information related to ORs and other chemosensory receptors, such as taste and pheromone receptors. Three companion databases closely integrated with ORDB are OdorDB, ORModelDB and OdorMapDB; these resources are part of the SenseLab suite of databases (http://senselab.med.yale.edu). HORDE (http://genome.weizmann.ac.il/horde/) is a semi-automatically populated database of the OR repertoires of human and several mammals. ODORactor (http://mdl.shsmu.edu.cn/ODORactor/) provides information related to OR-odorant interactions from the perspective of the odorant. All three resources are connected to each other via web-links. Database URL: http://senselab.med.yale.edu; http://genome.weizmann.ac.il/horde/; http://mdl.shsmu.edu.cn/ODORactor/

Olfactory receptors (ORs) are a type of GTP-binding protein-coupled receptor (GPCR). These receptors are responsible for mediating the sense of smell through their interaction with odor ligands. OR-odorant interactions marks the first step in the process that leads to olfaction. Computational studies on model OR structures can validate experimental functional studies as well as generate focused and novel hypotheses for further bench investigation by providing a view of these interactions at the molecular level. Here we have shown the specific advantages of simulating the dynamic environment that is associated with OR-odorant interactions. We present a rigorous methodology that ranges from the creation of a computationally-derived model of an olfactory receptor to simulating the interactions between an OR and an odorant molecule. Given the ubiquitous occurrence of GPCRs in the membranes of cells, we anticipate that our OR-developed methodology will serve as a model for the computational structural biology of all GPCRs.

Background This paper describes the development of a web-based tool, GenDrux, which extracts and presents (over the Internet) information related to the disease-gene-drug nexus. This information is archived from the relevant biomedical literature using automated methods. GenDrux is designed to alleviate the difficulties of manually processing the vast biomedical literature to identify disease-gene-drug relationships. GenDrux will evolve with the literature without additional algorithmic modifications. Results GenDrux, a pilot system, is developed in the domain of breast cancer and can be accessed at http://www.microarray.uab.edu/drug_gene.pl. GenDrux can be queried based on drug, gene and/or disease name. From over 8,000 relevant abstracts from the biomedical literature related to breast cancer, we have archived a corpus of more than 4,000 articles that depict gene expression-drug activity relationships for breast cancer and related cancers. The archiving process has been automated. Conclusions The successful development, implementation, and evaluation of this and similar systems when created may provide clinicians with a tool for literature management, clinical decision making, thus setting the platform for personalized therapy in the future.

Chiquito J CrastoDivision of Research, Department of Genetics, University of Alabama at Birmingham, Alabama, USAAbstract: The lack of a crystallographically derived structure for all but three G (TP [guanosine triphosphate]-binding) protein-coupled receptor (GPCRs) proteins necessitates the use of computationally derived methods to determine their structures. Computational methodologies allow a mechanistic glimpse into GPCR–ligand interactions at a molecular level to better understand the initial steps leading to a protein's biologic functions, ie, protecting the ligands that activate, deactivate, or inhibit the protein, stabilizing protein structure in the membrane's lipid bilayer, and ensuring that the hydrophilic environment within the GPCR-binding pocket is maintained. Described here is a formalism that quantifies the amphiphilic nature of a helix, by determining the effective hydrophobicity (or hydrophilicity) at specific positions around it. This formalism will enable computational protein modelers to position helices so that the functional aspects of GPCRs are adequately represented in the model. Hydro-Eff®, an online tool, allows users to calculate effective helical hydrophobicities.Keywords: GPCR, effective hydrophobicity, amphiphilicity

The melanocortin receptor (MCR) subtype family is a member of the GPCR superfamily, and each of them has a different pharmacological profile with regard to the relative potency of the endogenous and synthetic melanocortin peptides. Alpha-MSH and ACTH are endogenous nonselective agonists for MC1R, MC3R, MC4R, and MC5R. In this study, we examined the role of Phe(7) in ACTH on human (h) MC1R, MC3R, and MC4R binding and signaling. Our results indicate that substitution of Phe(7) with d-Nal(2')(7) in ACTH1-24 yields a pharmacological profile different from that for substitution of Phe(7) with d-Nal(2')(7) in MSH in hMC1R, hMC3R, and hMC4R. N-d-Nal(2')(7)-ACTH1-24 is an agonist at hMC3R and hMC4R which did not change the peptide from an agonist to an antagonist at hMC3R and hMC4R. Further experiments indicate that N-d-Nal(2')(7)-ACTH1-17 is the minimal peptide required for hMC3R and hMC4R activation. Single-amino acid substitution studies of d-Nal(2')(7)-ACTH1-17 indicate that amino acid residues 15-17 in N-d-Nal(2')(7)-ACTH1-17 are crucial for hMC3R and hMC4R activation. Substitutions of these amino acid residues reduced or abolished agonist activity at hMC3R and hMC4R. Conformational studies revealed a new beta-turn (Arg(8)-Trp(9)-Gly(10)-Lys(11)) in N-d-Nal(2')(7)-ACTH1-17, compared to the beta-turn-like structure at NDP-alpha-MSH (His(6)-d-Phe(7)-Arg(8)-Trp(9)). Our results suggest that NDP-alpha-MSH and N-d-Nal(2')(7)-ACTH1-17 do not share the same binding site; the highly basic C-terminal fragment (Lys(15)-Lys(16)-Arg(17)) of N-d-Nal(2')(7)-ACTH1-17 induced a new beta-turn, and this shift contributed the selective agonist activity at hMC3R and hMC4R.

Olfactory receptors, in addition to being involved in first step of the physiological processes that leads to olfaction, occupy an important place in mammalian genomes. ORs constitute super families in these genomes. Elucidating ol-factory receptor function at a molecular level can be aided by a computationally derived structure and an understanding of its interactions with odor molecules. Experimental functional analyses of olfactory receptors in conjunction with computational studies serve to validate findings and generate hypotheses. We present here a review of the research efforts in: creating computational models of olfactory receptors, identifying binding strategies for these receptors with odorant molecules, performing medium to long range simulation studies of odor ligands in the receptor binding region, and identifying amino acid positions within the receptor that are responsible for ligand-binding and olfactory receptor activation. Written as a primer and a teaching tool, this review will help researchers extend the methodologies described herein to other GPCRs.

Human olfactory receptor, hOR17-210, is identified as a pseudogene in the human genome. Experimental data has shown however, that the gene product of frame-shifted, cloned hOR17-210 cDNA was able to bind an odorant-binding protein and is narrowly tuned for excitation by cyclic ketones. Supported by experimental results, we used the bioinformatics methods of sequence analysis (genome-wide and pair-wise), computational protein modeling and docking, to show that functionality in this receptor is retained due to sequence-structure features not previously observed in mammalian ORs. This receptor does not possess the first two transmembrane helical domains (of seven typically seen in GPCRs). It however, possesses an additional TM that has not been observed in other human olfactory receptors. By incorporating these novel structural features, we created two putative models for this receptor. We also docked odor ligands that were experimentally shown to bind hOR17-210. We show how and why structural modifications of OR17-210 do not hinder this receptor’s functionality. Our studies reveal that novel gene rearrangements that result in sequence and structural diversity may have a bearing on OR and GPCR function and evolution.

Two major types of genetic variation are known: single nucleotide polymorphisms (SNPs), and a more recently discovered structural variation, involving changes in copy number (CNVs) of kilobase- to megabase-sized chromosomal segments. It is unknown whether CNVs arise in somatic cells, but it is, however, generally assumed that normal cells are genetically identical. We tested 34 tissue samples from three subjects and, having analyzed for each tissue < or =10(-6) of all cells expected in an adult human, we observed at least six CNVs, affecting a single organ or one or more tissues of the same subject. The CNVs ranged from 82 to 176 kb, often encompassing known genes, potentially affecting gene function. Our results indicate that humans are commonly affected by somatic mosaicism for stochastic CNVs, which occur in a substantial fraction of cells. The majority of described CNVs were previously shown to be polymorphic between unrelated subjects, suggesting that some CNVs previously reported as germline might represent somatic events, since in most studies of this kind, only one tissue is typically examined and analysis of parents for the studied subjects is not routinely performed. A considerable number of human phenotypes are a consequence of a somatic process. Thus, our conclusions will be important for the delineation of genetic factors behind these phenotypes. Consequently, biobanks should consider sampling multiple tissues to better address mosaicism in the studies of somatic disorders.

Processing text from scientific literature has become a necessity due to the burgeoning amounts of information that are fast becoming available, stemming from advances in electronic information technology. We created a program, NeuroText ( http://senselab.med.yale.edu/textmine/neurotext.pl ), designed specifically to extract information relevant to neuroscience-specific databases, NeuronDB and CellPropDB ( http://senselab.med.yale.edu/senselab/ ), housed at the Yale University School of Medicine. NeuroText extracts relevant information from the Neuroscience literature in a two-step process: each step parses text at different levels of granularity. NeuroText uses an expert-mediated knowledge base and combines the techniques of indexing, contextual parsing, semantic and lexical parsing, and supervised and non-supervised learning to extract information. The constrains, metadata elements, and rules for information extraction are stored in the knowledge base. NeuroText was created as a pilot project to process 3 years of publications in Journal of Neuroscience and was subsequently tested for 40,000 PubMed abstracts. We also present here a template to create domain non-specific knowledge base that when linked to a text-processing tool like NeuroText can be used to extract knowledge in other fields of research.

The exploration of copy-number variation (CNV), notably of somatic cells, is an understudied aspect of genome biology. Any differences in the genetic makeup between twins derived from the same zygote represent an irrefutable example of somatic mosaicism. We studied 19 pairs of monozygotic twins with either concordant or discordant phenotype by using two platforms for genome-wide CNV analyses and showed that CNVs exist within pairs in both groups. These findings have an impact on our views of genotypic and phenotypic diversity in monozygotic twins and suggest that CNV analysis in phenotypically discordant monozygotic twins may provide a powerful tool for identifying disease-predisposition loci. Our results also imply that caution should be exercised when interpreting disease causality of de novo CNVs found in patients based on analysis of a single tissue in routine disease-related DNA diagnostics. © 2008 The American Society of Human Genetics.

Human olfactory receptor, hOR17-210, is identified as a pseudogene in the human genome. Experimental data has shown however, that the gene product of cloned hOR17-210 cDNA was able to bind an odorant-binding protein and is narrowly tuned for excitation by cyclic ketones. Supported by experimental results, we used the bioinformatics methods of sequence analysis, computational protein modeling and docking, to show that functionality in this receptor is retained due to sequence-structure features not previously observed in mammalian ORs. This receptor does not possess the first two transmembrane helical domains (of seven typically seen in GPCRs). It however, possesses an additional TM that has not been observed in other human olfactory receptors. By incorporating these novel structural features, we created two putative models for this receptor. We also docked odor ligands that were experimentally shown to bind hOR17-210 model. We show how and why structural modifications of OR17-210 do not hinder this receptor's functionality. Our studies reveal that novel gene rearrangement that result in sequence and structural diversity in has a bearing on OR and GPCR function and evolution.

Background Gene expression patterns of olfactory receptors (ORs) are an important component of the signal encoding mechanism in the olfactory system since they determine the interactions between odorant ligands and sensory neurons. We have developed the Olfactory Receptor Microarray Database (ORMD) to house OR gene expression data. ORMD is integrated with the Olfactory Receptor Database (ORDB), which is a key repository of OR gene information. Both databases aim to aid experimental research related to olfaction. Description ORMD is a Web-accessible database that provides a secure data repository for OR microarray experiments. It contains both publicly available and private data; accessing the latter requires authenticated login. The ORMD is designed to allow users to not only deposit gene expression data but also manage their projects/experiments. For example, contributors can choose whether to make their datasets public. For each experiment, users can download the raw data files and view and export the gene expression data. For each OR gene being probed in a microarray experiment, a hyperlink to that gene in ORDB provides access to genomic and proteomic information related to the corresponding olfactory receptor. Individual ORs archived in ORDB are also linked to ORMD, allowing users access to the related microarray gene expression data. Conclusion ORMD serves as a data repository and project management system. It facilitates the study of microarray experiments of gene expression in the olfactory system. In conjunction with ORDB, ORMD integrates gene expression data with the genomic and functional data of ORs, and is thus a useful resource for both olfactory researchers and the public.

Processing text from scientific literature has become a necessity due to the burgeoning amounts of information that are fast becoming available, stemming from advances in electronic information technology. We created a program, NeuroText ( http://senselab.med.yale.edu/textmine/neurotext.pl ), designed specifically to extract information relevant to neuroscience-specific databases, NeuronDB and CellPropDB ( http://senselab.med.yale.edu/senselab/ ), housed at the Yale University School of Medicine. NeuroText extracts relevant information from the Neuroscience literature in a two-step process: each step parses text at different levels of granularity. NeuroText uses an expert-mediated knowledge base and combines the techniques of indexing, contextual parsing, semantic and lexical parsing, and supervised and non-supervised learning to extract information. The constrains, metadata elements, and rules for information extraction are stored in the knowledge base. NeuroText was created as a pilot project to process 3 years of publications in Journal of Neuroscience and was subsequently tested for 40,000 PubMed abstracts. We also present here a template to create domain non-specific knowledge base that when linked to a text-processing tool like NeuroText can be used to extract knowledge in other fields of research.

We have simulated an odor ligand's dynamic behavior in the binding region of an olfactory receptor (OR). Our short timescale computational studies (up to 200 ps) have helped identify unprecedented postdocking ligand behavior of ligands. From in vacuo molecular dynamics simulations of interactions between models of rat OR I7 and 10 aldehyde ligands, we have identified a dissociative pathway along which the ligand exits and enters the OR-binding pocket--a transit event. The ligand's transit through the receptor's binding region may mark the beginning of a signal transduction cascade leading to odor recognition. We have graphically traced the rotameric changes in key OR amino acid side chains during the transit. Our results have helped substantiate or refute previously held notions of amino acid contribution to ligand stability in the binding pocket. Our observations of ligand activity when compared to those of experimental (electroolfactogram response) OR-activation studies provide a view to predicting the stability of ligands in the binding pocket as a precursor to OR activation by the ligand.

Knowledgebase-mediated text -mining approaches work best when processing the natural language of domain-specific text. To enhance the utility of our successfully tested program-NeuroText, and to extend its methodologies to other domains, we have designed clustering algorithms, which is the principal step in automatically creating a knowledgebase. Our algorithms are designed to improve the quality of clustering by parsing the test corpus to include semantic and syntactic parsing.

The EAV/CR framework, designed for database support of rapidly evolving scientific domains, utilizes metadata to facilitate schema maintenance and automatic generation of Web-enabled browsing interfaces to the data. EAV/CR is used in SenseLab, a neuroscience database that is part of the national Human Brain Project. This report describes various enhancements to the framework. These include (1) the ability to create “portals” that present different subsets of the schema to users with a particular research focus, (2) a generic XML-based protocol to assist data extraction and population of the database by external agents, (3) a limited form of ad hoc data query, and (4) semantic descriptors for interclass relationships and links to controlled vocabularies such as the UMLS.

The olfactory receptors constitute over 1000 genes, making it the largest family in the genome. The completion of the human and mouse genomes presents a tremendous challenge to understanding the odor ligand affinities of these receptors and their relation to odor processing in the olfactory system. We describe briefly the practical use of a set of databases in the SenseLab project (http://senselab.med.yale.edu/) that is devoted to this problem. Olfactory Receptor Database (ORDB) archives almost 2000 sequences, and provides tools for their analysis by type. OdorDB stores data about the structures of odor molecules and their affinities for different olfactory receptors, as established by studies in expression systems. Odor simulation gives rise to odor maps in the olfactory bulb which is believed to be a basis for odor discrimination. OdorMapDB provides a site for integration of data from odor mapping studies by different methods. These databases thus enable students and researchers to integrate molecular analysis of olfactory receptors with the functional organization of the olfactory system to understand the neural basis of the sense of smell.

The construction of functional fusion proteins often requires a linker sequence that adopts an extended conformation to allow for maximal flexibility. Linker sequences are generally selected based on intuition. Without a reliable selection criterion, the design of such linkers is often difficult, particularly in situations where longer linker sequences are required. Here we describe a program called LINKER which can automatically generate a set of linker sequences that are known to adopt extended conformations as determined by X-ray crystallography and NMR. The only required input to the program is the desired linker sequence length. The program is specifically designed to assist in fusion protein construction. A number of optional input parameters have been incorporated so that users are able to enhance sequence selection based on specific applications. The program output simply contains a set of sequences with a specified length. This program should be a useful tool in both the biotechnology industry and biomedical research. It can be accessed through the Web page http://www.fccc. edu/research/labs/feng/linker.html.

Background Olfactory receptors (ORs) are the largest gene family in mammalian genomes. Since nearly all OR genes are orphan receptors, inference of functional similarity or differences between odorant receptors typically relies on sequence comparisons. Based on the alignment of entire coding region sequence, OR genes are classified into families and subfamilies, a classification that is believed to be a proxy for OR gene functional variability. However, the assumption that overall protein sequence diversity is a good proxy for functional properties is untested. Methodology Here, we propose an alternative sequence-based approach to infer the similarities and differences in OR binding capacity. Our approach is based on similarities and differences in the predicted binding pockets of OR genes, rather than on the entire OR coding region. Conclusions Interestingly, our approach yields markedly different results compared to the analysis based on the entire OR coding-regions. While neither approach can be tested at this time, the discrepancy between the two calls into question the assumption that the current classification reliably reflects OR gene functional variability.

Analysis of the human genome draft sequences has revealed a more complete portrait of the olfactory receptor gene repertoire in humans than was available previously. The new information provides a basis for deeper analysis of the functions of the receptors, and promises new insights into the evolutionary history of the family.

Background Neuroscientists often need to access a wide range of data sets distributed over the Internet. These data sets, however, are typically neither integrated nor interoperable, resulting in a barrier to answering complex neuroscience research questions. Domain ontologies can enable the querying heterogeneous data sets, but they are not sufficient for neuroscience since the data of interest commonly span multiple research domains. To this end, e-Neuroscience seeks to provide an integrated platform for neuroscientists to discover new knowledge through seamless integration of the very diverse types of neuroscience data. Here we present a Semantic Web approach to building this e-Neuroscience framework by using the Resource Description Framework (RDF) and its vocabulary description language, RDF Schema (RDFS), as a standard data model to facilitate both representation and integration of the data. Results We have constructed a pilot ontology for BrainPharm (a subset of SenseLab) using RDFS and then converted a subset of the BrainPharm data into RDF according to the ontological structure. We have also integrated the converted BrainPharm data with existing RDF hypothesis and publication data from a pilot version of SWAN (Semantic Web Applications in Neuromedicine). Our implementation uses the RDF Data Model in Oracle Database 10g release 2 for data integration, query, and inference, while our Web interface allows users to query the data and retrieve the results in a convenient fashion. Conclusion Accessing and integrating biomedical data which cuts across multiple disciplines will be increasingly indispensable and beneficial to neuroscience researchers. The Semantic Web approach we undertook has demonstrated a promising way to semantically integrate data sets created independently. It also shows how advanced queries and inferences can be performed over the integrated data, which are hard to achieve using traditional data integration approaches. Our pilot results suggest that our Semantic Web approach is suitable for realizing e-Neuroscience and generic enough to be applied in other biomedical fields.