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Identifying and classifying biomedical perturbations in text

Molecular perturbations provide a powerful toolset for biomedical researchers to scrutinize the contributions of individual molecules in biological systems. Perturbations qualify the context of experimental results and, despite their diversity, share properties in different dimensions in ways that can be formalized. We propose a formal framework to describe and classify perturbations that allows accumulation of knowledge in order to inform the process of biomedical scientific experimentation and target analysis. We apply this framework to develop a novel algorithm for automatic detection and characterization of perturbations in text and show its relevance in the study of gene–phenotype associations and protein–protein interactions in diabetes and cancer. Analyzing perturbations introduces a novel view of the multivariate landscape of biological systems

Costly Collaborations: The Impact of Scientific Fraud on Co-authors' Careers

Over the last few years, several major scientific fraud cases have shocked the scientific community. The number of retractions each year has also increased tremendously, especially in the biomedical field, and scientific misconduct accounts for approximately more than half of those retractions. It is assumed that co-authors of retracted papers are affected by their colleagues' misconduct, and the aim of this study is to provide empirical evidence of the effect of retractions in biomedical research on co-authors' research careers. Using data from the Web of Science (WOS), we measured the productivity, impact and collaboration of 1,123 co-authors of 293 retracted articles for a period of five years before and after the retraction. We found clear evidence that collaborators do suffer consequences of their colleagues' misconduct, and that a retraction for fraud has higher consequences than a retraction for error. Our results also suggest that the extent of these consequences is closely linked with the ranking of co-authors on the retracted paper, being felt most strongly by first authors, followed by the last authors, while the impact is less important for middle authors.Comment: Accepted for publication in the Journal of the Association for Information Science and Technolog

Characterizing ABC-Transporter Substrate-Likeness Using a Clean-Slate Genetic Background

Mutations in ATP Binding Cassette (ABC)-transporter genes can have major effects on the bioavailability and toxicity of the drugs that are ABC-transporter substrates. Consequently, methods to predict if a drug is an ABC-transporter substrate are useful for drug development. Such methods traditionally relied on literature curated collections of ABC-transporter dependent membrane transfer assays. Here, we used a single large-scale dataset of 376 drugs with relative efficacy on an engineered yeast strain with all ABC-transporter genes deleted (ABC-16), to explore the relationship between a drug’s chemical structure and ABC-transporter substrate-likeness. We represented a drug’s chemical structure by an array of substructure keys and explored several machine learning methods to predict the drug’s efficacy in an ABC-16 yeast strain. Gradient-Boosted Random Forest models outperformed all other methods with an AUC of 0.723. We prospectively validated the model using new experimental data and found significant agreement with predictions. Our analysis expands the previously reported chemical substructures associated with ABC-transporter substrates and provides an alternative means to investigate ABC-transporter substrate-likeness

Oxidative stress is a mediator for increased lipid accumulation in a newly isolated Dunaliella salina strain

Green algae offer sustainable, clean and eco-friendly energy resource. However, production efficiency needs to be improved. Increasing cellular lipid levels by nitrogen depletion is one of the most studied strategies. Despite this, the underlying physiological and biochemical mechanisms of this response have not been well defined. Algae species adapted to hypersaline conditions can be cultivated in salty waters which are not useful for agriculture or consumption. Due to their inherent extreme cultivation conditions, use of hypersaline algae species is better suited for avoiding culture contamination issues. In this study, we identified a new halophilic Dunaliella salina strain by using 18S ribosomal RNA gene sequencing. We found that growth and biomass productivities of this strain were directly related to nitrogen levels, as the highest biomass concentration under 0.05 mM or 5 mM nitrogen regimes were 495 mg/l and 1409 mg/l, respectively. We also confirmed that nitrogen limitation increased cellular lipid content up to 35% under 0.05 mM nitrogen concentration. In order to gain insight into the mechanisms of this phenomenon, we applied fluorometric, flow cytometric and spectrophotometric methods to measure oxidative stress and enzymatic defence mechanisms. Under nitrogen depleted cultivation conditions, we observed increased lipid peroxidation by measuring an important oxidative stress marker, malondialdehyde and enhanced activation of catalase, ascorbate peroxidase and superoxide dismutase antioxidant enzymes. These observations indicated that oxidative stress is accompanied by increased lipid content in the green alga. In addition, we also showed that at optimum cultivation conditions, inducing oxidative stress by application of exogenous H2O2 leads to increased cellular lipid content up to 44% when compared with non-treated control groups. Our results support that oxidative stress and lipid overproduction are linked. Importantly, these results also suggest that oxidative stress mediates lipid accumulation. Understanding such relationships may provide guidance for efficient production of algal biodiesels

Age-associated alterations in corpus callosum white matter integrity in bipolar disorder assessed using probabilistic tractography

OBJECTIVES: Atypical age-associated changes in white matter integrity may play a role in the neurobiology of bipolar disorder, but no studies have examined the major white matter tracts using nonlinear statistical modeling across a wide age range in this disorder. The goal of this study was to identify possible deviations in the typical pattern of age-associated changes in white matter integrity in patients with bipolar disorder across the age range of 9-62 years. METHODS: Diffusion tensor imaging was performed in 57 (20 male and 37 female) patients with a diagnosis of bipolar disorder and 57 (20 male and 37 female) age- and sex-matched healthy volunteers. Mean diffusivity and fractional anisotropy were computed for the genu and splenium of the corpus callosum, two projection tracts, and five association tracts using probabilistic tractography. RESULTS: Overall, patients had lower fractional anisotropy and higher mean diffusivity compared to healthy volunteers across all tracts (while controlling for the effects of age and age2 ). In addition, there were greater age-associated increases in mean diffusivity in patients compared to healthy volunteers within the genu and splenium of the corpus callosum beginning in the second and third decades of life. CONCLUSIONS: Our findings provide evidence for alterations in the typical pattern of white matter development in patients with bipolar disorder compared to healthy volunteers. Changes in white matter development within the corpus callosum may lead to altered inter-hemispheric communication that is considered integral to the neurobiology of the disorder

POGs/PlantRBP: a resource for comparative genomics in plants

POGs/PlantRBP () is a relational database that integrates data from rice, Arabidopsis, and maize by placing the complete Arabidopsis and rice proteomes and available maize sequences into ‘putative orthologous groups’ (POGs). Annotation efforts will focus on predicted RNA binding proteins (RBPs): i.e. those with known RNA binding domains or otherwise implicated in RNA function. POGs form the heart of the database, and were assigned using a mutual-best-hit-strategy after performing BLAST comparisons of the predicted Arabidopsis and rice proteomes. Each POG entry includes orthologs in Arabidopsis and rice, annotated with domain organization, gene models, phylogenetic trees, and multiple intracellular targeting predictions. A graphical display maps maize sequences on to their most similar rice gene model. The database can be queried using any combination of gene name, accession, domain, and predicted intracellular location, or using BLAST. Useful features of the database include the ability to search for proteins with both a specified domain content and intracellular location, the concurrent display of mutual best hits and phylogenetic trees which facilitates evaluation of POG assignments, the association of maize sequences with POGs, and the display of targeting predictions and domain organization for all POG members, which reveals consistency, or lack thereof, of those predictions

Systematic Exploration of Synergistic Drug Pairs

Drug synergy allows a therapeutic effect to be achieved with lower doses of component drugs. Drug synergy can result when drugs target the products of genes that act in parallel pathways (‘specific synergy’). Such cases of drug synergy should tend to correspond to synergistic genetic interaction between the corresponding target genes. Alternatively, ‘promiscuous synergy’ can arise when one drug non-specifically increases the effects of many other drugs, for example, by increased bioavailability. To assess the relative abundance of these drug synergy types, we examined 200 pairs of antifungal drugs in S. cerevisiae. We found 38 antifungal synergies, 37 of which were novel. While 14 cases of drug synergy corresponded to genetic interaction, 92% of the synergies we discovered involved only six frequently synergistic drugs. Although promiscuity of four drugs can be explained under the bioavailability model, the promiscuity of Tacrolimus and Pentamidine was completely unexpected. While many drug synergies correspond to genetic interactions, the majority of drug synergies appear to result from non-specific promiscuous synergy

Strength of selection pressure is an important parameter contributing to the complexity of antibiotic resistance evolution

Revealing the genetic changes responsible for antibiotic resistance can be critical for developing novel antibiotic therapies. However, systematic studies correlating genotype to phenotype in the context of antibiotic resistance have been missing. In order to fill in this gap, we evolved 88 isogenic Escherichia coli populations against 22 antibiotics for 3 weeks. For every drug, two populations were evolved under strong selection and two populations were evolved under mild selection. By quantifying evolved populations' resistances against all 22 drugs, we constructed two separate cross-resistance networks for strongly and mildly selected populations. Subsequently, we sequenced representative colonies isolated from evolved populations for revealing the genetic basis for novel phenotypes. Bacterial populations that evolved resistance against antibiotics under strong selection acquired high levels of cross-resistance against several antibiotics, whereas other bacterial populations evolved under milder selection acquired relatively weaker cross-resistance. In addition, we found that strongly selected strains against aminoglycosides became more susceptible to five other drug classes compared with their wild-type ancestor as a result of a point mutation on TrkH, an ion transporter protein. Our findings suggest that selection strength is an important parameter contributing to the complexity of antibiotic resistance problem and use of high doses of antibiotics to clear infections has the potential to promote increase of cross-resistance in clinics