Networking the way towards antimicrobial combination therapies

Publicado em "8th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2014)"The exploration of new antimicrobial combinations is a pressing concern for Clinical Microbiology due to the growing number of resistant strains emerging in healthcare settings and in the general community. Researchers are screening agents with alternative modes of action and interest is rising for the potential of antimicrobial peptides (AMPs). This work presents the first ever network reconstruction of AMP combinations reported in the literature fighting Pseudomonas aeruginosa infections. The network, containing 193 combinations of AMPs with 39 AMPs and 154 traditional antibiotics, is expected to help in the design of new studies, notably by unveiling different mechanisms of action and helping in the prediction of new combinations and synergisms. The challenges faced in the attempted text-mining approaches and other considerations regarding the manual curation of the data are pointed out, reflecting about the future automation of this type of reconstruction as means to widen the scope of analysis

Extraction of pharmacokinetic evidence of drug-drug interactions from the literature

Drug-drug interaction (DDI) is a major cause of morbidity and mortality and a subject of intense scientific interest. Biomedical literature mining can aid DDI research by extracting evidence for large numbers of potential interactions from published literature and clinical databases. Though DDI is investigated in domains ranging in scale from intracellular biochemistry to human populations, literature mining has not been used to extract specific types of experimental evidence, which are reported differently for distinct experimental goals. We focus on pharmacokinetic evidence for DDI, essential for identifying causal mechanisms of putative interactions and as input for further pharmacological and pharmacoepidemiology investigations. We used manually curated corpora of PubMed abstracts and annotated sentences to evaluate the efficacy of literature mining on two tasks: first, identifying PubMed abstracts containing pharmacokinetic evidence of DDIs; second, extracting sentences containing such evidence from abstracts. We implemented a text mining pipeline and evaluated it using several linear classifiers and a variety of feature transforms. The most important textual features in the abstract and sentence classification tasks were analyzed. We also investigated the performance benefits of using features derived from PubMed metadata fields, various publicly available named entity recognizers, and pharmacokinetic dictionaries. Several classifiers performed very well in distinguishing relevant and irrelevant abstracts (reaching F10.93, MCC0.74, iAUC0.99) and sentences (F10.76, MCC0.65, iAUC0.83). We found that word bigram features were important for achieving optimal classifier performance and that features derived from Medical Subject Headings (MeSH) terms significantly improved abstract classification. We also found that some drug-related named entity recognition tools and dictionaries led to slight but significant improvements, especially in classification of evidence sentences. Based on our thorough analysis of classifiers and feature transforms and the high classification performance achieved, we demonstrate that literature mining can aid DDI discovery by supporting automatic extraction of specific types of experimental evidence.National Institutes of Health, National Library of Medicine Program, grant 01LM011945-01 "BLR: Evidence-based Drug-Interaction Discovery: In-Vivo, In-Vitro and Clinical," a grant from the Indiana University Collaborative Research Program 2013, "Drug-Drug Interaction Prediction from Large-scale Mining of Literature and Patient Records," as well as a grant from the joint program between the Fundação Luso-Americana para o Desenvolvimento (Portugal) and National Science Foundation (USA), 2012-2014, "Network Mining For Gene Regulation And Biochemical Signaling.

Analyzing and Modeling Real-World Phenomena with Complex Networks: A Survey of Applications

The success of new scientific areas can be assessed by their potential for contributing to new theoretical approaches and in applications to real-world problems. Complex networks have fared extremely well in both of these aspects, with their sound theoretical basis developed over the years and with a variety of applications. In this survey, we analyze the applications of complex networks to real-world problems and data, with emphasis in representation, analysis and modeling, after an introduction to the main concepts and models. A diversity of phenomena are surveyed, which may be classified into no less than 22 areas, providing a clear indication of the impact of the field of complex networks.Comment: 103 pages, 3 figures and 7 tables. A working manuscript, suggestions are welcome

Characterization of Functional and Structural Integrity in Experimental Focal Epilepsy: Reduced Network Efficiency Coincides with White Matter Changes

BACKGROUND: Although focal epilepsies are increasingly recognized to affect multiple and remote neural systems, the underlying spatiotemporal pattern and the relationships between recurrent spontaneous seizures, global functional connectivity, and structural integrity remain largely unknown. METHODOLOGY/PRINCIPAL FINDINGS: Here we utilized serial resting-state functional MRI, graph-theoretical analysis of complex brain networks and diffusion tensor imaging to characterize the evolution of global network topology, functional connectivity and structural changes in the interictal brain in relation to focal epilepsy in a rat model. Epileptic networks exhibited a more regular functional topology than controls, indicated by a significant increase in shortest path length and clustering coefficient. Interhemispheric functional connectivity in epileptic brains decreased, while intrahemispheric functional connectivity increased. Widespread reductions of fractional anisotropy were found in white matter regions not restricted to the vicinity of the epileptic focus, including the corpus callosum. CONCLUSIONS/SIGNIFICANCE: Our longitudinal study on the pathogenesis of network dynamics in epileptic brains reveals that, despite the locality of the epileptogenic area, epileptic brains differ in their global network topology, connectivity and structural integrity from healthy brains

Top 20 predictions of new drug-gene relationships for PharmGKB, and whether a PGx relationship has been documented in the literature.

<p>*** indicates that an association has been demonstrated experimentally between changes in the expression/activity of the gene/protein and the efficacy of the drug</p><p>** indicates that such an association is likely, but has not yet been studied</p><p>* indicates that the association has been studied experimentally, and the experiment refuted the association. Here we include only associations between pharmaceutical compounds and single genes; predicted associations involving endogenous compounds and/or groups of genes are included in the supplement, however.</p><p>Top 20 predictions of new drug-gene relationships for PharmGKB, and whether a PGx relationship has been documented in the literature.</p

Explanation of the clusters shown in Fig 4.

<p>Clusters with 20 or fewer members are not described in the table in the interest of space.</p

Classifier performance at the task of recognizing (a) PGx associations (dense matrix), (b) drug-target associations (dense matrix), (c) PGx associations (sparse matrix) and (d) drug-target associations (sparse matrix).

<p>Classifier performance at the task of recognizing (a) PGx associations (dense matrix), (b) drug-target associations (dense matrix), (c) PGx associations (sparse matrix) and (d) drug-target associations (sparse matrix).</p

Top 20 predictions of new drug-target relationships for DrugBank.

<p>*** indicates that the drug has been shown experimentally to have modified the activity of the gene/protein</p><p>** means that the interaction is known to DrugBank but is listed under an alternate drug or gene name</p><p>* means the interaction has been studied and is unlikely; P refers to a particular type of parser error in which the ligand of a receptor is mistaken for that receptor; L refers to a lexicon error (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004216#sec008" target="_blank">Discussion</a>).</p><p>Top 20 predictions of new drug-target relationships for DrugBank.</p

Selected dependency paths and representative sentences.

<p>The drug and gene names flanking each path are bolded. Some key abbreviations are listed here: <i>appos</i>: appositional modifier, <i>amod</i>: adjectival modifier, <i>prep</i>: prepositional modifier (if <i>prep_of</i>, the specific preposition used is “of”, if <i>prep_to</i>, it’s “to”, if <i>prep_for</i>, it’s “for”), <i>nsubjpass</i>: passive nominal subject, <i>agent</i>: complement of passive verb, <i>dobj</i>: direct object of active verb, <i>nsubj</i>: noun subject of active verb.</p><p>Selected dependency paths and representative sentences.</p