Integration Of Functional Data Research Articles

Identification and Validation of Candidate Genes from Genome-Wide Association Studies.

Exploiting the statistical associations coming out from a GWAS experiment to identify and validate candidate genes may be potentially difficult and time consuming. To fill the gap between the identification of candidate genes toward their functional validation onto the trait performance, the prioritization of variants underlying the GWAS-associated regions is necessary. In parallel, recent developments in genomics and statistical methods have been achieved notably in human genetic and they are accordingly being adopted in plant breeding toward the study of the genetic architecture of traits to sustain genetic gains. In this chapter, we aim at providing both theoretical and practical aspects underlying three main options including (1) the MetaGWAS analysis, (2) the statistical fine mapping and (3) the integration of functional data toward the identification and validation of candidate genes from a GWAS experiment.

Abstract 3211: Evolution of the CIViC knowledgebase for community driven curation of clinical variants in cancer

Abstract With increasing adoption of next generation sequencing into clinical practice, the problem of clinical interpretation of tumor variants arises as a bottleneck for patient care, as effective annotation of these variants draws from a constantly increasing body of largely unstructured clinical and preclinical results. One model to sustain clinical variant curation is to house variant annotation behind a paywall, using access fees to fund further curation effort. An alternate approach is to leverage public curation and expert moderation to create a free public resource to house and distribute this knowledge. The Clinical Interpretation of Variants in Cancer (CIViC, www.civicdb.org) knowledgebase employs the latter approach, and is a free and open-access public resource with an intuitive user interface and flexible public API for programmatic access to all content, which is available to the public with no restrictions on usage. All data is available for retrieval without login, while registration with a free account is required to contribute curation. The provenance of all curation and revision in CIViC is viewable through the web interface, and curators may also leave public comments on all content. Selected expert editors review and revise submitted content, which is clearly labeled as accepted once it has fully undergone moderation. All content in CIViC adheres to a structured data model which follows a published standard operating procedure for curation. This data model incorporates ontologies, standards and guidelines from across the field to promote interoperability and compatibility with other efforts. The CIViC interface also allows curators and organizations to track and display summary statistics of all their activity. CIViC currently has a community of over 190 curators and 16,000 clinical and research users around the world. CIViC continually develops and improves both new and existing features in response to user feedback as well as collaborative and internal development goals. Recently, the drugs and treatment terms used in predictive/therapeutic annotation have been normalized to the NCI Thesaurus. A conflict of interest (COI) statement is now required for all CIViC Editors, and functionality for writing and displaying the COI has been built into the interface. CIViC employs Predictive (Therapeutic), Prognostic, Diagnostic, Predisposing evidence types, and we highlight the recently introduced Functional evidence type, which has seen continued development. We will present the rationale for these changes including demonstrating how adding a Dominant Negative term better supports curation of functional genomics data sets. A focus of functional curation has been TP53, with over 50 evidence items to date. With multiple use cases for this type of data including targeted therapeutics, identification of relevant hotspots, or characterization of cancer driver mechanisms, functional evidence can be used to support conventional concepts of clinical utility and expand the CIViC data model. These developments provide a mechanism for discussion and integration of functional data into somatic variant interpretation guidelines, an area being explored but lacking expert consensus. Citation Format: Arpad Danos, Kilannin Krysiak, Erica K. Barnell, Adam C. Coffman, Joshua F. McMichael, Susanna Kiwala, Nicholas C. Spies, Lana M. Sheta, Shahil P. Pema, Lynzey Kujan, Kaitlin A. Clark, Sydney Anderson, Amber Wollam, Brian Li, Justin Guerra, Shruti Rao, Deborah I. Ritter, Cameron J. Grisdale, Gordana Raca, Alex H. Wagner, Subha Madhavan, Malachi Griffith, Obi L. Griffith. Evolution of the CIViC knowledgebase for community driven curation of clinical variants in cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3211.

Molecular diagnostics of Mendelian disorders via combined DNA and RNA sequencing

Abstract The diagnostic yield in rare disorders is currently less than 50% although sequencing technologies in use are able to detect the majority of possible variants in our genome. The diagnostic gap is in part due to limitations in prioritizing and interpreting identified variants. The integration of functional data, such as transcriptomics, is emerging as a powerful complementary tool in diagnostics. It is able to quantify aberrant splicing, validate nonsense-mediated mRNA decay for potential loss-of-function variants, identify mono-allelically expressed variants, and help prioritize variants not predicted to change the encoded protein. Moreover, RNA-sequencing has been validated as a tool for the discovery of pathogenic variants in novel Mendelian disease genes. As RNA sequencing provides complementary information to DNA sequencing and can easily be established in addition to DNA sequencing, it has great potential for implementation as a routine tool for improving molecular diagnosis.

T61 - Integration Of Gene Expression With Gwas To Identify Risk Genes For Nicotine Dependence

Background Cigarette smoking is addictive and persistent smoking leads to nicotine dependence. Both family and twin studies have indicated that it is influenced by genetic factors. In recent years, several risk genes for nicotine dependence have been identified. However, these genes account for only a small proportion of observed heritability. It is a challenge to discover those remaining genetic factors. FTND score is a common measure for nicotine dependence, and the time to smoke the first cigarette in the morning (TFC) can be considered as a measure of nicotine withdrawal since the half-life of nicotine in human blood is about 2 hours. Methods To identify genes involved in these phenotypes, we performed a gene-based genome-wide meta-analysis of FTND score and TFC using several African American samples from dbGaP. We also conducted transcriptome sequencing for samples isolated from nucleus accumbens of chronic nicotine treated and withdrawal mice. Results By integrating differentially expressed genes in nicotine treated and withdrawal mice with GWAS meta-analyses, we discover some novel genes associated with nicotine dependence and withdrawal. These include TEAD3, ORC3, TCTN3, and SERPINE2. These genes are reported associated with schizophrenia, neuronal maturation, orofaciodigital syndromes and cancers. Discussion Our results indicate that integration of functional data with GWAS analyses could significantly improve power to discover new risk genes.

A Strategy for Functional Interpretation of Metabolomic Time Series Data in Context of Metabolic Network Information.

The functional connection of experimental metabolic time series data with biochemical network information is an important, yet complex, issue in systems biology. Frequently, experimental analysis of diurnal, circadian, or developmental dynamics of metabolism results in a comprehensive and multidimensional data matrix comprising information about metabolite concentrations, protein levels, and/or enzyme activities. While, irrespective of the type of organism, the experimental high-throughput analysis of the transcriptome, proteome, and metabolome has become a common part of many systems biological studies, functional data integration in a biochemical and physiological context is still challenging. Here, an approach is presented which addresses the functional connection of experimental time series data with biochemical network information which can be inferred, for example, from a metabolic network reconstruction. Based on a time-continuous and variance-weighted regression analysis of experimental data, metabolic functions, i.e., first-order derivatives of metabolite concentrations, were related to time-dependent changes in other biochemically relevant metabolic functions, i.e., second-order derivatives of metabolite concentrations. This finally revealed time points of perturbed dependencies in metabolic functions indicating a modified biochemical interaction. The approach was validated using previously published experimental data on a diurnal time course of metabolite levels, enzyme activities, and metabolic flux simulations. To support and ease the presented approach of functional time series analysis, a graphical user interface including a test data set and a manual is provided which can be run within the numerical software environment Matlab®.

Accelerated Discovery of Novel Benzodiazepine Ligands by Experiment-Guided Virtual Screening

High throughput discovery of ligand scaffolds for target proteins can accelerate development of leads and drug candidates enormously. Here we describe an innovative workflow for the discovery of high affinity ligands for the benzodiazepine-binding site on the so far not crystallized mammalian GABAA receptors. The procedure includes chemical biology techniques that may be generally applied to other proteins. Prerequisites are a ligand that can be chemically modified with cysteine-reactive groups, knowledge of amino acid residues contributing to the drug-binding pocket, and crystal structures either of proteins homologous to the target protein or, better, of the target itself. Part of the protocol is virtual screening that without additional rounds of optimization in many cases results only in low affinity ligands, even when a target protein has been crystallized. Here we show how the integration of functional data into structure-based screening dramatically improves the performance of the virtual screening. Thus, lead compounds with 14 different scaffolds were identified on the basis of an updated structural model of the diazepam-bound state of the GABAA receptor. Some of these compounds show considerable preference for the α3β2γ2 GABAA receptor subtype.

Integration of functional neuroimaging in CyberKnife radiosurgery: feasibility and dosimetric results

The integration of state-of-the-art neuroimaging into treatment planning may increase the therapeutic potential of stereotactic radiosurgery. Functional neuroimaging, including functional MRI, navigated brain stimulation, and diffusion tensor imaging-based tractography, may guide the orientation of radiation beams to decrease the dose to critical cortical and subcortical areas. The authors describe their method of integrating functional neuroimaging technology into radiosurgical treatment planning using the CyberKnife radiosurgery system. The records of all patients who had undergone radiosurgery for brain lesions at the CyberKnife Center of the University of Messina, Italy, between July 2010 and July 2012 were analyzed. Among patients with brain lesions in critical areas, treatment planning with the integration of functional neuroimaging was performed in 25 patients. Morphological and functional imaging data sets were coregistered using the Multiplan dedicated treatment planning system. Treatment planning was initially based on morphological data; radiation dose distribution was then corrected in relation to the functionally relevant cortical and subcortical areas. The change in radiation dose distribution was then calculated. The data sets could be easily and reliably integrated into the Cyberknife treatment planning. Using an inverse planning algorithm, the authors achieved an average 17% reduction in the radiation dose to functional areas. Further gain in terms of dose sparing compromised other important treatment parameters, including target coverage, conformality index, and number of monitor units. No neurological deficit due to radiation was recorded at the short-term follow-up. Radiosurgery treatments rely on the quality of neuroimaging. The integration of functional data allows a reduction in radiation doses to functional organs at risk, including critical cortical areas, subcortical tracts, and vascular structures. The relative simplicity of integrating functional neuroimaging into radiosurgery warrants further research to implement, standardize, and identify the limits of this procedure.

Protein biomarkers for in vitro testing of toxicology

The vision of the toxicology in the 21st century movement is to overcome the currently used animal tests and identify molecular pathways of toxicity, using human in vitro systems with the aim to provide the most relevant mechanistic information for human risk assessment. It is expected to translate key surrogate biomarkers to novel types of toxicity-related high throughput screening of the many thousands of compounds which need to be tested during development phases of the pharmaceutical industry and with regard to the REACH legislation in Europe.Systems biology, an emerging and increasingly popular field of research, appears to be the discipline of choice to integrate results from transcriptomics, proteomics, epigenomics and metabonomics technologies used to analyze samples from toxicological models. The challenges, however, with respect to data generation, statistical treatment, bioinformatic integration and interpretation or in silico modeling remain formidable.One of the main difficulties is the fact that the sheer number of molecular species is inflated enormously in the course of translation from genes to proteins due to post-translational modifications. Moreover, at the level of proteins, time scales of cellular reactions to toxic insults can be very fast, ranging from milliseconds to seconds. Linear dynamic ranges of concentration differences between conditions can also differ by several orders of magnitude.So, the search for protein biomarkers of toxicity requires sophisticated strategies for time-resolved quantitative differential approaches. The statistical principles, normalization of primary data and principal component and cluster analysis have been well developed for genomics/transcriptomics and partly for proteomics, but have not been widely adapted to technologies like metabonomics. Also, the integration of functional data, in particular data from mass spectrometry, with the aim of modeling pathways of toxicity for human risk assessment, is still at an infant stage.

Brain Shift Compensation and Neurosurgical Image Fusion Using Intraoperative MRI: Current Status and Future Challenges

Navigation systems are commonly used in neurosurgical operating theaters. Generally, they either rely on the use of preoperative or intraoperative image data. Using preoperative image data, the phenomenon of brain shift contributes most to errors, in addition to various other sources of decreased reliability, such as image-related errors or registration inaccuracy. Updating navigation after intraoperative magnetic resonance imaging (iMRI) serves as immediate feedback on the surgical result and furthermore compensates for the effects of brain shift. Together with an integration of functional data in the navigation such as diffusion tensor imaging (DTI)-based fiber tracking or functional MRI, there is evidence that iMRI contributes to maximize extent of resection in glioma surgery with a preservation of neurological function. The following article summarizes the work flow and clinical impact of iMRI and functional navigation, as well as current problems and possible solutions.

Approaches for the study of cancer: towards the integration of genomics, proteomics and metabolomics

Recent technological advances, combined with the development of bioinformatic tools, allow us to better address biological questions combining -omic approaches (i.e., genomics, metabolomics and proteomics). This novel comprehensive perspective addresses the identification, characterisation and quantitation of the whole repertoire of genes, proteins and metabolites occurring in living organisms. Here we provide an overview of recent significant advances and technologies used in genomics, metabolomics and proteomics. We also underline the importance and limits of mass accuracy in mass spectrometry-based -omics and briefly describe emerging types of fragmentation used in mass spectrometry. The range of instruments and techniques used to address the study of each -omic approach, which provide vast amounts of information (usually termed "high-throughput" technologies in the literature) is briefly discussed, including names, links and descriptions of the main databases, data repositories and resources used. Integration of multiple -omic results and procedures seems necessary. Therefore, an emerging challenge is the integration of the huge amount of data generated and the standardisation of the procedures and methods used. Functional data integration will lead to answers to unsolved questions, hopefully, applicable to clinical practice and management of patients.