The Orthology Ontology: development and applications.

J Biomed Semantics. 2016;7(1):34

Authors: Fernández-Breis JT, Chiba H, Legaz-García MD, Uchiyama I

Abstract
BACKGROUND: Computational comparative analysis of multiple genomes provides valuable opportunities to biomedical research. In particular, orthology analysis can play a central role in comparative genomics; it guides establishing evolutionary relations among genes of organisms and allows functional inference of gene products. However, the wide variations in current orthology databases necessitate the research toward the shareability of the content that is generated by different tools and stored in different structures. Exchanging the content with other research communities requires making the meaning of the content explicit.
DESCRIPTION: The need for a common ontology has led to the creation of the Orthology Ontology (ORTH) following the best practices in ontology construction. Here, we describe our model and major entities of the ontology that is implemented in the Web Ontology Language (OWL), followed by the assessment of the quality of the ontology and the application of the ORTH to existing orthology datasets. This shareable ontology enables the possibility to develop Linked Orthology Datasets and a meta-predictor of orthology through standardization for the representation of orthology databases. The ORTH is freely available in OWL format to all users at http://purl.org/net/orth .
CONCLUSIONS: The Orthology Ontology can serve as a framework for the semantic standardization of orthology content and it will contribute to a better exploitation of orthology resources in biomedical research. The results demonstrate the feasibility of developing shareable datasets using this ontology. Further applications will maximize the usefulness of this ontology.

PMID: 27259657 [PubMed - as supplied by publisher]

Towards Consensus Gene Ages.

Genome Biol Evol. 2016 Jun 3;

Authors: Liebeskind BJ, McWhite CD, Marcotte EM

Abstract
Correctly estimating the age of a gene or gene family is important for a variety of fields, including molecular evolution, comparative genomics, and phylogenetics, and increasingly for systems biology and disease genetics. However, most studies use only a point estimate of a gene's age, neglecting the substantial uncertainty involved in this estimation. Here, we characterize this uncertainty by investigating the effect of algorithm choice on gene-age inference and calculate consensus gene ages with attendant error distributions for a variety of model eukaryotes. We use thirteen orthology inference algorithms to create gene-age datasets and then characterize the error around each age-call on a per-gene and per-algorithm basis. Systematic error was found to be a large factor in estimating gene age, suggesting that simple consensus algorithms are not enough to give a reliable point estimate. We also found that different sources of error can affect downstream analyses, such as gene ontology enrichment. Our consensus gene-age datasets, with associated error terms, are made fully available at so that researchers can propagate this uncertainty through their analyses ( GENEAGESORG: ).

PMID: 27259914 [PubMed - as supplied by publisher]

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RANKS: a flexible tool for node label ranking and classification in biological networks.

Bioinformatics. 2016 Jun 2;

Authors: Valentini G, Armano G, Frasca M, Lin J, Mesiti M, Re M

Abstract
RANKS is a flexible software package that can be easily applied to any bioinformatics task formalisable as ranking of nodes with respect to a property given as a label, such as automated protein function prediction, gene disease prioritization and drug repositioning. To this end RANKS provides an efficient and easy-to-use implementation of kernelized score functions, a semi-supervised algorithmic scheme embedding both local and global learning strategies for the analysis of biomolecular networks. To facilitate comparative assessment, baseline network-based methods, e.g. label propagation and random walk algorithms, have also been implementedAvailability and implementation: The package is available from CRAN: https://cran.r-project.org/ The package is written in R, except for the most computationally intensive functionalities which are implemented in C.
CONTACT: valentini@di.unimi.it SUPPLEMENTARY INFORMATION: Supplementary Information are available at Bioinformatics online.

PMID: 27256314 [PubMed - as supplied by publisher]

Generation of open biomedical datasets through ontology-driven transformation and integration processes.

J Biomed Semantics. 2016;7:32

Authors: Carmen Legaz-García MD, Miñarro-Giménez JA, Menárguez-Tortosa M, Fernández-Breis JT

Abstract
BACKGROUND: Biomedical research usually requires combining large volumes of data from multiple heterogeneous sources, which makes difficult the integrated exploitation of such data. The Semantic Web paradigm offers a natural technological space for data integration and exploitation by generating content readable by machines. Linked Open Data is a Semantic Web initiative that promotes the publication and sharing of data in machine readable semantic formats.
METHODS: We present an approach for the transformation and integration of heterogeneous biomedical data with the objective of generating open biomedical datasets in Semantic Web formats. The transformation of the data is based on the mappings between the entities of the data schema and the ontological infrastructure that provides the meaning to the content. Our approach permits different types of mappings and includes the possibility of defining complex transformation patterns. Once the mappings are defined, they can be automatically applied to datasets to generate logically consistent content and the mappings can be reused in further transformation processes.
RESULTS: The results of our research are (1) a common transformation and integration process for heterogeneous biomedical data; (2) the application of Linked Open Data principles to generate interoperable, open, biomedical datasets; (3) a software tool, called SWIT, that implements the approach. In this paper we also describe how we have applied SWIT in different biomedical scenarios and some lessons learned.
CONCLUSIONS: We have presented an approach that is able to generate open biomedical repositories in Semantic Web formats. SWIT is able to apply the Linked Open Data principles in the generation of the datasets, so allowing for linking their content to external repositories and creating linked open datasets. SWIT datasets may contain data from multiple sources and schemas, thus becoming integrated datasets.

PMID: 27255189 [PubMed - in process]

Genomewide Association Studies in Pharmacogenomics: Meeting Report of the NIH Pharmacogenomics Research Network-RIKEN (PGRN-RIKEN) Collaboration.

Clin Pharmacol Ther. 2016 Jun 3;

Authors: Yee SW, Momozawa Y, Kamatani Y, Tyndale RF, Weinshilboum RM, Ratain MJ, Giacomini KM, Kubo M

Abstract
Introduction Genomewide association studies (GWAS) have resulted in the identification of many heritable genetic factors that underlie risk for human disease or variation in physiologic traits. In contrast, there are fewer GWAS of drug response phenotypes, despite extensive unexplained interindividual variability. To address this urgent need, the NIH Pharmacogenomics Research Network (PGRN) and the Center for Integrative Medical Sciences (IMS) at RIKEN support a collaboration, PGRN-RIKEN, with the goal of accelerating GWAS of drug response phenotypes. This article is protected by copyright. All rights reserved.

PMID: 27256705 [PubMed - as supplied by publisher]

CAN ADJUVANT AGENTS REDUCE GASTRIC ACIDITY IN PATIENTS WITH CYSTIC FIBROSIS: EVIDENCE FROM A COCHRANE REVIEW.

Gastroenterol Nurs. 2016 May-Jun;39(3):246-248

Authors: Zhao S

PMID: 27258469 [PubMed - as supplied by publisher]

Sources of Variation in Sweat Chloride Measurements in Cystic Fibrosis.

Am J Respir Crit Care Med. 2016 Jun 3;

Authors: Collaco JM, Blackman SM, Raraigh KS, Corvol H, Rommens JM, Pace RG, Boelle PY, McGready J, Sosnay PR, Strug LJ, Knowles MR, Cutting GR

Abstract
RATIONALE: Expanding the use of CFTR potentiators and correctors for the treatment of cystic fibrosis (CF) requires precise and accurate biomarkers. Sweat chloride concentration provides an in vivo assessment of CFTR function, but it is unknown the degree to which CFTR mutations account for sweat chloride variation.
METHODS: 2639 sweat chloride measurements were obtained in 1761 twins/siblings from the CF Twin-Sibling Study, French CF Modifier Gene Study, and Canadian Consortium for Genetic Studies. Variance component estimation was performed by nested mixed modelling.
RESULTS: Across the tested CF population as a whole, CFTR gene mutations were found to be the primary determinant of sweat chloride variability (56.1% of variation) with contributions from variation over time (e.g., factors related to testing on different days; 13.8%), environmental factors (e.g., climate, family diet; 13.5%), other residual factors (e.g., test variability; 9.9%) and unique individual factors (e.g., modifier genes, unique exposures; 6.8%) (LR test p<0.001). Twin analysis suggested that modifier genes did not play a significant role as the heritability estimate was negligible (H2=0; 95%CI: 0.0, 0.35). For an individual with CF, variation in sweat chloride was primarily due to variation over time (58.1%) with the remainder attributable to residual/random factors (41.9%).
CONCLUSIONS: Variation in the CFTR gene is the predominant cause of sweat chloride variation; most of the non-CFTR variation is due to testing variability and unique environmental factors. If test precision and accuracy can be improved, sweat chloride measurement could be a valuable biomarker for assessing response to therapies directed at mutant CFTR.

PMID: 27258095 [PubMed - as supplied by publisher]

Adults with cystic fibrosis in Portugal: A first step towards improvement.

Rev Port Pneumol (2006). 2016 May-Jun;22(3):139-40

Authors: Raja TH, Flume PA

PMID: 27256624 [PubMed - in process]

Evaluation of a New Newborn Screening Model for Cystic Fibrosis.

J Pediatr. 2016 May 30;

Authors: Kharrazi M

PMID: 27255858 [PubMed - as supplied by publisher]

A Handling Study to Assess Use of the Respimat(®) Soft Mist™ Inhaler in Children Under 5 Years Old.

J Aerosol Med Pulm Drug Deliv. 2015 Oct;28(5):372-81

Authors: Kamin W, Frank M, Kattenbeck S, Moroni-Zentgraf P, Wachtel H, Zielen S

Abstract
BACKGROUND: Respimat(®) Soft Mist(™) Inhaler (SMI) is a hand-held device that generates an aerosol with a high, fine-particle fraction, enabling efficient lung deposition. The study objective was to assess inhalation success among children using Respimat SMI, and the requirement for assistance by the parent/caregiver and/or a valved holding chamber (VHC).
METHODS: This open-label study enrolled patients aged <5 years with respiratory disease and history of coughing and/or recurrent wheezing. Patients inhaled from the Respimat SMI (air only; no aerosol) using a stepwise configuration: "1" (dose released by child); "2" (dose released by parent/caregiver), and "3" (Respimat SMI with VHC, facemask, and parent/caregiver help). Co-primary endpoints included the ability to perform successful inhalation as assessed by the investigators using a standardized handling questionnaire and evaluation of the reasons for success. Inhalation profile in the successful handling configuration was verified with a pneumotachograph. Patient satisfaction and preferences were investigated in a questionnaire.
RESULTS: Of the children aged 4 to <5 years (n=27) and 3 to <4 years (n=30), 55.6% and 30.0%, respectively, achieved success without a VHC or help; with assistance, another 29.6% and 10.0%, respectively, achieved success, and the remaining children were successful with VHC. All children aged 2 to <3 years (n=20) achieved success with the Respimat SMI and VHC. Of those aged <2 years (n=22), 95.5% had successful handling of the Respimat SMI with VHC and parent/caregiver help. Inhalation flow profiles generally confirmed the outcome of the handling assessment by the investigators. Most parent/caregiver and/or child respondents were satisfied with operation, instructions for use, handling, and ease of holding the Respimat SMI with or without a VHC.
CONCLUSIONS: The Respimat SMI is suitable for children aged <5 years; however, children aged <5 years are advised to add a VHC to complement its use.

PMID: 25844687 [PubMed - indexed for MEDLINE]

Mathematical models of breast and ovarian cancers.

Wiley Interdiscip Rev Syst Biol Med. 2016 Jun 3;

Authors: Botesteanu DA, Lipkowitz S, Lee JM, Levy D

Abstract
Women constitute the majority of the aging United States (US) population, and this has substantial implications on cancer population patterns and management practices. Breast cancer is the most common women's malignancy, while ovarian cancer is the most fatal gynecological malignancy in the US. In this review, we focus on these subsets of women's cancers, seen more commonly in postmenopausal and elderly women. In order to systematically investigate the complexity of cancer progression and response to treatment in breast and ovarian malignancies, we assert that integrated mathematical modeling frameworks viewed from a systems biology perspective are needed. Such integrated frameworks could offer innovative contributions to the clinical women's cancers community, as answers to clinical questions cannot always be reached with contemporary clinical and experimental tools. Here, we recapitulate clinically known data regarding the progression and treatment of the breast and ovarian cancers. We compare and contrast the two malignancies whenever possible in order to emphasize areas where substantial contributions could be made by clinically inspired and validated mathematical modeling. We show how current paradigms in the mathematical oncology community focusing on the two malignancies do not make comprehensive use of, nor substantially reflect existing clinical data, and we highlight the modeling areas in most critical need of clinical data integration. We emphasize that the primary goal of any mathematical study of women's cancers should be to address clinically relevant questions. For further resources related to this article, please visit the WIREs website.

PMID: 27259061 [PubMed - as supplied by publisher]

Chronic Lung Allograft Dysfunction: A Systems Review of Mechanisms.

Transplantation. 2016 Jun 2;

Authors: Royer PJ, Olivera-Botello G, Koutsokera A, Aubert JD, Bernasconi E, Tissot A, Pison C, Nicod L, Boissel JP, Magnan A, SysCLAD consortium

Abstract
Chronic lung allograft dysfunction (CLAD) is the major limitation of long-term survival after lung transplantation. Chronic lung allograft dysfunction manifests as bronchiolitis obliterans syndrome or the recently described restrictive allograft syndrome. Although numerous risk factors have been identified so far, the physiopathological mechanisms of CLAD remain poorly understood. We investigate here the immune mechanisms involved in the development of CLAD after lung transplantation. We explore the innate or adaptive immune reactions induced by the allograft itself or by the environment and how they lead to allograft dysfunction. Because current literature suggests bronchiolitis obliterans syndrome and restrictive allograft syndrome as 2 distinct entities, we focus on the specific factors behind one or the other syndromes. Chronic lung allograft dysfunction is a multifactorial disease that remains irreversible and unpredictable so far. We thus finally discuss the potential of systems-biology approach to predict its occurrence and to better understand its underlying mechanisms.

PMID: 27257997 [PubMed - as supplied by publisher]

Gene Co-Expression Network Analysis for Identifying Modules and Functionally Enriched Pathways in Type 1 Diabetes.

PLoS One. 2016;11(6):e0156006

Authors: Riquelme Medina I, Lubovac-Pilav Z

Abstract
Type 1 diabetes (T1D) is a complex disease, caused by the autoimmune destruction of the insulin producing pancreatic beta cells, resulting in the body's inability to produce insulin. While great efforts have been put into understanding the genetic and environmental factors that contribute to the etiology of the disease, the exact molecular mechanisms are still largely unknown. T1D is a heterogeneous disease, and previous research in this field is mainly focused on the analysis of single genes, or using traditional gene expression profiling, which generally does not reveal the functional context of a gene associated with a complex disorder. However, network-based analysis does take into account the interactions between the diabetes specific genes or proteins and contributes to new knowledge about disease modules, which in turn can be used for identification of potential new biomarkers for T1D. In this study, we analyzed public microarray data of T1D patients and healthy controls by applying a systems biology approach that combines network-based Weighted Gene Co-Expression Network Analysis (WGCNA) with functional enrichment analysis. Novel co-expression gene network modules associated with T1D were elucidated, which in turn provided a basis for the identification of potential pathways and biomarker genes that may be involved in development of T1D.

PMID: 27257970 [PubMed - as supplied by publisher]

A Graph Based Framework to Model Virus Integration Sites.

Comput Struct Biotechnol J. 2016;14:69-77

Authors: Fronza R, Vasciaveo A, Benso A, Schmidt M

Abstract
With next generation sequencing thousands of virus and viral vector integration genome targets are now under investigation to uncover specific integration preferences and to define clusters of integration, termed common integration sites (CIS), that may allow to assess gene therapy safety or to detect disease related genomic features such as oncogenes. Here, we addressed the challenge to: 1) define the notion of CIS on graph models, 2) demonstrate that the structure of CIS enters in the category of scale-free networks and 3) show that our network approach analyzes CIS dynamically in an integrated systems biology framework using the Retroviral Transposon Tagged Cancer Gene Database (RTCGD) as a testing dataset.

PMID: 27257470 [PubMed]

Integrated omics approaches provide strategies for rapid erythromycin yield increase in Saccharopolyspora erythraea.

Microb Cell Fact. 2016;15(1):93

Authors: Karničar K, Drobnak I, Petek M, Magdevska V, Horvat J, Vidmar R, Baebler Š, Rotter A, Jamnik P, Fujs Š, Turk B, Fonovič M, Gruden K, Kosec G, Petković H

Abstract
BACKGROUND: Omics approaches have significantly increased our understanding of biological systems. However, they have had limited success in explaining the dramatically increased productivity of commercially important natural products by industrial high-producing strains, such as the erythromycin-producing actinomycete Saccharopolyspora erythraea. Further yield increase is of great importance but requires a better understanding of the underlying physiological processes.
RESULTS: To reveal the mechanisms related to erythromycin yield increase, we have undertaken an integrated study of the genomic, transcriptomic, and proteomic differences between the wild type strain NRRL2338 (WT) and the industrial high-producing strain ABE1441 (HP) of S. erythraea at multiple time points of a simulated industrial bioprocess. 165 observed mutations lead to differences in gene expression profiles and protein abundance between the two strains, which were most prominent in the initial stages of erythromycin production. Enzymes involved in erythromycin biosynthesis, metabolism of branched chain amino acids and proteolysis were most strongly upregulated in the HP strain. Interestingly, genes related to TCA cycle and DNA-repair were downregulated. Additionally, comprehensive data analysis uncovered significant correlations in expression profiles of the erythromycin-biosynthetic genes, other biosynthetic gene clusters and previously unidentified putative regulatory genes. Based on this information, we demonstrated that overexpression of several genes involved in amino acid metabolism can contribute to increased yield of erythromycin, confirming the validity of our systems biology approach.
CONCLUSIONS: Our comprehensive omics approach, carried out in industrially relevant conditions, enabled the identification of key pathways affecting erythromycin yield and suggests strategies for rapid increase in the production of secondary metabolites in industrial environment.

PMID: 27255285 [PubMed - in process]

Systems Biology and Noninvasive Imaging of Atherosclerosis.

Arterioscler Thromb Vasc Biol. 2016 Feb;36(2):e1-8

Authors: Calcagno C, Mulder WJ, Nahrendorf M, Fayad ZA

PMID: 26819466 [PubMed - indexed for MEDLINE]

A tuberculosis ontology for host systems biology.

Tuberculosis (Edinb). 2015 Sep;95(5):570-4

Authors: Levine DM, Dutta NK, Eckels J, Scanga C, Stein C, Mehra S, Kaushal D, Karakousis PC, Salamon H

Abstract
A major hurdle facing tuberculosis (TB) investigators who want to utilize a rapidly growing body of data from both systems biology approaches and omics technologies is the lack of a standard vocabulary for data annotation and reporting. Lacking a means to readily compare samples from different research groups, a significant quantity of potentially informative data is largely ignored by researchers. To facilitate standardizing data across studies, a simple ontology of TB terms was developed to provide a common vocabulary for annotating data sets. New terminology was developed to address animal models and experimental systems, and existing clinically focused terminology was modified and adapted. This ontology can be used to annotate host TB data in public databases and collaborations, thereby standardizing database searches and allowing researchers to more easily compare results. To demonstrate the utility of a standard TB ontology for host systems biology, a web application was developed to annotate and compare human and animal model gene expression data sets.

PMID: 26190839 [PubMed - indexed for MEDLINE]

Systems metabolic engineering of microorganisms to achieve large-scale production of flavonoid scaffolds.

J Biotechnol. 2014 Oct 20;188:72-80

Authors: Wu J, Du G, Zhou J, Chen J

Abstract
Flavonoids possess pharmaceutical potential due to their health-promoting activities. The complex structures of these products make extraction from plants difficult, and chemical synthesis is limited because of the use of many toxic solvents. Microbial production offers an alternate way to produce these compounds on an industrial scale in a more economical and environment-friendly manner. However, at present microbial production has been achieved only on a laboratory scale and improvements and scale-up of these processes remain challenging. Naringenin and pinocembrin, which are flavonoid scaffolds and precursors for most of the flavonoids, are the model molecules that are key to solving the current issues restricting industrial production of these chemicals. The emergence of systems metabolic engineering, which combines systems biology with synthetic biology and evolutionary engineering at the systems level, offers new perspectives on strain and process optimization. In this review, current challenges in large-scale fermentation processes involving flavonoid scaffolds and the strategies and tools of systems metabolic engineering used to overcome these challenges are summarized. This will offer insights into overcoming the limitations and challenges of large-scale microbial production of these important pharmaceutical compounds.

PMID: 25160917 [PubMed - indexed for MEDLINE]

Xenbase: Core features, data acquisition, and data processing.

Genesis. 2015 Aug;53(8):486-97

Authors: James-Zorn C, Ponferrada VG, Burns KA, Fortriede JD, Lotay VS, Liu Y, Brad Karpinka J, Karimi K, Zorn AM, Vize PD

Abstract
Xenbase, the Xenopus model organism database (www.xenbase.org), is a cloud-based, web-accessible resource that integrates the diverse genomic and biological data from Xenopus research. Xenopus frogs are one of the major vertebrate animal models used for biomedical research, and Xenbase is the central repository for the enormous amount of data generated using this model tetrapod. The goal of Xenbase is to accelerate discovery by enabling investigators to make novel connections between molecular pathways in Xenopus and human disease. Our relational database and user-friendly interface make these data easy to query and allows investigators to quickly interrogate and link different data types in ways that would otherwise be difficult, time consuming, or impossible. Xenbase also enhances the value of these data through high-quality gene expression curation and data integration, by providing bioinformatics tools optimized for Xenopus experiments, and by linking Xenopus data to other model organisms and to human data. Xenbase draws in data via pipelines that download data, parse the content, and save them into appropriate files and database tables. Furthermore, Xenbase makes these data accessible to the broader biomedical community by continually providing annotated data updates to organizations such as NCBI, UniProtKB, and Ensembl. Here, we describe our bioinformatics, genome-browsing tools, data acquisition and sharing, our community submitted and literature curation pipelines, text-mining support, gene page features, and the curation of gene nomenclature and gene models.

PMID: 26150211 [PubMed - indexed for MEDLINE]