Challenges in integrating Escherichia coli molecular biology data

One key challenge in Systems Biology is to provide mechanisms to collect and integrate the necessary data to be able to meet multiple analysis requirements. Typically, biological contents are scattered over multiple data sources and there is no easy way of comparing heterogeneous data contents. This work discusses ongoing standardisation and interoperability efforts and exposes integration challenges for the model organism Escherichia coli K-12. The goal is to analyse the major obstacles faced by integration processes, suggest ways to systematically identify them, and whenever possible, propose solutions or means to assist manual curation. Integration of gene, protein and compound data was evaluated by performing comparisons over EcoCyc, KEGG, BRENDA, ChEBI, Entrez Gene and UniProt contents. Cross-links, a number of standard nomenclatures and name information supported the comparisons. Except for the gene integration scenario, in no other scenario an element of integration performed well enough to support the process by itself. Indeed, both the integration of enzyme and compound records imply considerable curation. Results evidenced that, even for a well-studied model organism, source contents are still far from being as standardized as it would be desired and metadata varies considerably from source to source. Before designing any data integration pipeline, researchers should decide on the sources that best fit the purpose of analysis and be aware of existing conflicts/inconsistencies to be able to intervene in their resolution. Moreover, they should be aware of the limits of automatic integration such that they can define the extent of necessary manual curation for each application.     

In silico gene prioritization by integrating multiple data sources

Identifying disease genes is crucial to the understanding of disease pathogenesis, and to the improvement of disease diagnosis and treatment. In recent years, many researchers have proposed approaches to prioritize candidate genes by considering the relationship of candidate genes and existing known disease genes, reflected in other data sources. In this paper, we propose an expandable framework for gene prioritization that can integrate multiple heterogeneous data sources by taking advantage of a unified graphic representation. Gene-gene relationships and gene-disease relationships are then defined based on the overall topology of each network using a diffusion kernel measure. These relationship measures are in turn normalized to derive an overall measure across all networks, which is utilized to rank all candidate genes. Based on the informativeness of available data sources with respect to each specific disease, we also propose an adaptive threshold score to select a small subset of candidate genes for further validation studies. We performed large scale cross-validation analysis on 110 disease families using three data sources. Results have shown that our approach consistently outperforms other two state of the art programs. A case study using Parkinson disease (PD) has identified four candidate genes (UBB, SEPT5, GPR37 and TH) that ranked higher than our adaptive threshold, all of which are involved in the PD pathway. In particular, a very recent study has observed a deletion of TH in a patient with PD, which supports the importance of the TH gene in PD pathogenesis. A web tool has been implemented to assist scientists in their genetic studies.     

Prioritizing complex disease risk genes by integrating multiple data

Complex diseases, such as obesity, type II diabetes and chronic obstructive pulmonary disease (COPD) as metabolic disorder-related diseases are major concern for worldwide public health in the 21st century. The identification of these disease risk genes has attracted increasing interest in computational systems biology. In this paper, a novel method was proposed to prioritize disease risk genes (PDRG) by integrating functional annotations, protein interactions and gene expression information to assess similarity between genes in a disease-related metabolic network. The gene prioritization method was successfully carried out for obesity and COPD, the effectiveness of which was superior to those of ToppGene and ToppNet in both literature validation and recall rate by LOOCV. Our method could be applied broadly to other metabolism-related diseases, helping to prioritize novel disease risk genes, and could shed light on diagnosis and effective therapies.     

Discovering disease associations by integrating electronic clinical data and medical literature

Electronic health record (EHR) systems offer an exceptional opportunity for studying many diseases and their associated medical conditions within a population. The increasing number of clinical record entries that have become available electronically provides access to rich, large sets of patients' longitudinal medical information. By integrating and comparing relations found in the EHRs with those already reported in the literature, we are able to verify existing and to identify rare or novel associations. Of particular interest is the identification of rare disease co-morbidities, where the small numbers of diagnosed patients make robust statistical analysis difficult. Here, we introduce ADAMS, an Application for Discovering Disease Associations using Multiple Sources, which contains various statistical and language processing operations. We apply ADAMS to the New York-Presbyterian Hospital's EHR to combine the information from the relational diagnosis tables and textual discharge summaries with those from PubMed and Wikipedia in order to investigate the co-morbidities of the rare diseases Kaposi sarcoma, toxoplasmosis, and Kawasaki disease. In addition to finding well-known characteristics of diseases, ADAMS can identify rare or previously unreported associations. In particular, we report a statistically significant association between Kawasaki disease and diagnosis of autistic disorder.     

Improving and integrating data on invasive species collected by citizen scientists

Limited resources make it difficult to effectively document, monitor, and control invasive species across large areas, resulting in large gaps in our knowledge of current and future invasion patterns. We surveyed 128 citizen science program coordinators and interviewed 15 of them to evaluate their potential role in filling these gaps. Many programs collect data on invasive species and are willing to contribute these data to public databases. Although resources for education and monitoring are readily available, groups generally lack tools to manage and analyze data. Potential users of these data also retain concerns over data quality. We discuss how to address these concerns about citizen scientist data and programs while preserving the advantages they afford. A unified yet flexible national citizen science program aimed at tracking invasive species location, abundance, and control efforts could be designed using centralized data sharing and management tools. Such a system could meet the needs of multiple stakeholders while allowing efficiencies of scale, greater standardization of methods, and improved data quality testing and sharing. Finally, we present a prototype for such a system (see ).     

Craniometric variation in the Aleutians: integrating morphological, molecular, spatial, and temporal data

Several hypotheses have been put forward about the origins and evolution of the inhabitants of the Aleutian Islands. Both Hrdli?ka [The Aleutian and Commander Islands and Their Inhabitants (Philadelphia: Wistar Institute of Anatomy and Biology, 1945)] and Laughlin ["The Alaska gateway viewed from the Aleutian Islands," in Papers on the Physical Anthropology of the American Indian, W. S. Laughlin, ed. (New York: Viking Fund, 1951), 98-126] analyzed cranial morphology and came to somewhat different conclusions using a typological approach and limited analytical methods. Subsequent investigations using morphological data have not significantly improved our understanding of Aleut prehistory. More recently, radiocarbon dating and mitochondrial DNA analyses have shed light on Aleut genetic variation and changes over time, but better morphological methods using multivariate statistical analysis have not yet been used. We analyzed craniometric data using multivariate procedures and found that Aleuts demonstrate significant changes in cranial morphology over time, and these changes correspond to Hrdli?ka's observations but may not necessarily reflect in-migration. The morphological changes were concentrated in the very aspects of morphology that are easily observable and that Hrdli?ka most often measured, namely, cranial length, breadth, and height, but they were obscured when craniometric variation as a whole was analyzed. Also, we found that the morphological changes over time were not related to the changes in haplogroup frequencies over time, suggesting that migration into the Aleutians did not play a significant role in producing the morphological changes. However, craniometric variability apparently increases over time, suggesting in-migration, localized selection, and/or greater environmental heterogeneity. Our results contradict Laughlin's observations but may be more in line with his hypothesis of in situ evolutionary changes absent gene flow. In addition to selection, gene flow, and gene drift, however, sociocultural changes must also be considered as a factor in why morphology changed over time.     

Optimal drug combinations and minimal hitting sets

Background  

Identifying effective drug combinations that significantly improve over single agents is a challenging problem. Pairwise combinations already represent a huge screening effort. Beyond two drug combinations the task seems unfeasible.     

Anti-SARS drug screening by molecular docking

Summary. Starting from a collection of 1386 druggable compounds obtained from the 3D pharmacophore search, we performed a similarity search to narrow down the scope of docking studies. The template molecule is KZ7088 (Chou et al., 2003, Biochem Biophys Res Commun 308: 148–151). The MDL MACCS keys were used to fingerprint the molecules. The Tanimoto coefficient is taken as the metric to compare fingerprints. If the similarity threshold was 0.8, a set of 50 unique hits and 103 conformers were retrieved as a result of similarity search. The AutoDock 3.011 was used to carry out molecular docking of 50 ligands to their macromolecular protein receptors. Three compounds, i.e., C₂₈H₃₄O₄N₇Cl, C₂₁H₃₆O₅N₆, and C₂₁H₃₆O₅N₆, were found that may be promising candidates for further investigation. The main feature shared by these three potential inhibitors as well as the information of the involved side chains of SARS Cov Mpro may provide useful insights for the development of potent inhibitors against SARS enzyme.     

Optimizing control programmes by integrating data from fine-scale space use by introduced predators

The control of introduced mammalian predators (IMP) through trapping campaigns relies on operator experience to deploy traps in sites with an expected high probability of IMP presence, where the maximum number of captures is anticipated. We tested the limitations of available information on fine-scale spatial use by feral cats modelled from remote data collection methods (small-resolution satellite imagery and GPS-telemetry) in an intensive control campaign conducted over 8 years in an ecologically sensitive area of New Zealand. We calculated dichotomous optimal/sub-optimal areas for cats and found that operators placed traps in or close to optimal areas. Over a continuous range of probabilities of cat use, trap sites were not principally placed in hot spots of cat use. Logistic regression revealed that the probability of cat use was significantly associated with the probability of capture. However, regressing catch-effort against the probability of cat use showed no association between sites of high probability of cat use and higher capture rates. The incorporation in the models of bait, trap type, and habitat suitability for rabbits, as variables of operator’s choice showed that rabbit suitability, and the combination of baits/traps were significant. Results suggest that trapping feral cats is a complex process that likely relies on variables of space, time, and individual cognition. However, control programmes could improve trap deployment by identifying sites of high probability of cat use to maximize capture probability, while traps in sub-optimal areas could be removed (cost reduction), reallocated to optimal areas, or used to “fence” core conservation areas.     

Drug uptake and pharmacological modulation of drug sensitivity in leukemia by AQP9

Leukemia is the most common childhood cancer. Trisenox, the active ingredient of which is trivalent arsenic, is the first line of treatment for acute promyelocytic leukemia. Since drug action usually requires uptake of the drug, it is of importance to determine the transport system responsible for Trisenox uptake. Recently, human aquaglyceroporin 9 (AQP9) has been shown to transport As(III) in Xenopus oocytes. In this study we report to show that AQP9 expression modulates the drug sensitivity of leukemic cells. AQP9 was transfected into the chronic myelogenous leukemia cell line K562. The transfectants became hypersensitive to Trisenox and Sb(III). The promyelocytic leukemia cell line HL60 treated with vitamin D showed higher expression of AQP9 and hypersensitivity to Trisenox and Sb(III). This sensitivity was due to higher rates of uptake of the trivalent metalloids by the cell lines overexpressing AQP9. Trisenox hypersensitivity results from increased expression of AQP9 drug uptake system. The possibility of using pharmacological agents to increase expression of AQP9 gene delivers the promise of new therapies for the treatment of leukemia. Thus, drug hypersensitivity can be correlated with increased expression of the drug uptake system. This is the first demonstration that AQP9 can modulate drug sensitivity in cancer.     

Classification of dopamine-antagonists from pharmacological data

A graphical method is described that allows to classify the dopamine- antagonists on the basis of three pharmacological observations. The so-called spectral map represents the relative affinities of these drugs for three receptor sites.