@MInter: Automated Text-mining of Microbial Interactions.

Bioinformatics. 2016 Jun 16;

Authors: Lim KM, Li C, Chng KR, Nagarajan N

Abstract
MOTIVATION: Microbial consortia are frequently defined by numerous interactions within the community that are key to understanding their function. While microbial interactions have been extensively studied experimentally, information regarding them is dispersed in the scientific literature. As manual collation is an infeasible option, automated data processing tools are needed to make this information easily accessible.
RESULTS: We present @MInter, an automated information extraction system based on Support Vector Machines to analyze paper abstracts and infer microbial interactions. @MInter was trained and tested on a manually curated gold standard dataset of 735 species interactions and 3,917 annotated abstracts, constructed as part of this study. Cross-validation analysis showed that @MInter is able to detect abstracts pertaining to one or more microbial interactions with high specificity (specificity = 95%, AUC = 0.97). Despite challenges in identifying specific microbial interactions in an abstract (interaction level recall = 95%, precision = 25%), @MInter was shown to reduce annotator workload 13-fold compared to alternate approaches. Applying @MInter to 175 bacterial species abundant on human skin, we identified a network of 357 literature-reported microbial interactions, demonstrating its utility for the study of microbial communities.
AVAILABILITY: @MInter is freely available at https://github.com/CSB5/atminter CONTACT: nagarajann@gis.a-star.edu.sg SUPPLEMENTARY INFORMATION: Supplementary data is available at Bioinformatics online.

PMID: 27312413 [PubMed - as supplied by publisher]

10 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

("orphan disease" OR "rare disease" OR "orphan diseases" OR "rare diseases")

These pubmed results were generated on 2016/06/17

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

6 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"Cystic Fibrosis"

These pubmed results were generated on 2016/06/17

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

Progress and prospects in pharmacogenetics of antidepressant drugs.

Expert Opin Drug Metab Toxicol. 2016 Jun 16;

Authors: Fabbri C, Crisafulli C, Calabrò M, Spina E, Serretti A

Abstract
INTRODUCTION: Depression is responsible for the most part of the personal and socio-economic burden due to psychiatric disorders. Since antidepressant response clusters in families, pharmacogenetics represents a meaningful tool to provide tailored treatments and improve the prognosis of depression.
AREAS COVERED: This review aims to summarize and discuss the pharmacogenetics of antidepressant drugs in major depressive disorder, with a focus on the most replicated genes, genome-wide association studies (GWAS), but also on the findings provided by new and promising analysis methods. In particular, multimarker tests such as pathway analysis and polygenic risk scores increase the power of detecting associations compared to the analysis of individual polymorphisms. Since genetic variants are not necessarily associated with a change in protein level, gene expression studies may provide complementary information to genetic studies. Finally, the pharmacogenetic tests that have been investigated for clinical application are discussed.
EXPERT OPINION: Despite the lack of widespread clinical applications, preliminary results suggest that pharmacogenetics may be useful to guide antidepressant treatment. The US Food and Drug Administration included pharmacogenetic indications in the labeling of several antidepressants. This represented an important official recognition of the clinical relevance of genetic polymorphisms in antidepressant treatment.

PMID: 27310483 [PubMed - as supplied by publisher]

Identification of genetic polymorphisms of CYP2W1 in the three main Chinese ethnicities: Han, Tibetan, and Uighur.

Drug Metab Dispos. 2016 Jun 15;

Authors: Li YW, Kang X, Yang G, Dai PG, Chen C, Wang HJ

Abstract
CYP2W1 is an orphan member of the cytochrome P450 (CYP) superfamily. Recently, CYP2W1 has gained great research interest because of its unknown enzymatic function and tumor-specific expression property. This study aims to investigate the genetic polymorphisms of the CYP2W1 gene in Chinese populations and explore the functions of the detected variants. All the nine exons and exon-intron junction regions of the CYP2W1 gene were sequenced in 150 Chinese subjects, including 50 Han Chinese, 50 Tibetans, and 50 Uighurs. A total of 26 genetic variants were identified in this study, and 19 polymorphisms were detected in each population. Frequency comparison between populations showed that nine variants exhibited significantly different allelic distributions. A total of 12 different haplotypes were inferred from 150 samples by using the genotype data of nine exonic variants found in this study. CYP2W1*1A, *1B, *2, *4, and *6 were detected as the main alleles/haplotypes. Moreover, one, three, and two ethnically specific haplotypes were observed in the Han, Tibetan and Uighur samples, respectively. Then, the effects of four detected missense mutations (Ala181Thr, Gly376Ser, Val432Ile, and Pro488Leu) on the CYP2W1 protein function were predicted using three in-silico tools, namely, PolyPhen-2, SIFT, and MutationTaster. The results showed that Gly376Ser and Pro488Leu may have deleterious effects. In summary, this study showed that the genetic pattern of CYP2W1 is inter-ethnically different among the three Chinese populations, and this finding can extend our understanding of population genetics of CYP2W1 in the Chinese population.

PMID: 27307299 [PubMed - as supplied by publisher]

Drug-Gene Interactions of Antihypertensive Medications and Risk of Incident Cardiovascular Disease: A Pharmacogenomics Study from the CHARGE Consortium.

PLoS One. 2015;10(10):e0140496

Authors: Bis JC, Sitlani C, Irvin R, Avery CL, Smith AV, Sun F, Evans DS, Musani SK, Li X, Trompet S, Krijthe BP, Harris TB, Quibrera PM, Brody JA, Demissie S, Davis BR, Wiggins KL, Tranah GJ, Lange LA, Sotoodehnia N, Stott DJ, Franco OH, Launer LJ, Stürmer T, Taylor KD, Cupples LA, Eckfeldt JH, Smith NL, Liu Y, Wilson JG, Heckbert SR, Buckley BM, Ikram MA, Boerwinkle E, Chen YD, de Craen AJ, Uitterlinden AG, Rotter JI, Ford I, Hofman A, Sattar N, Slagboom PE, Westendorp RG, Gudnason V, Vasan RS, Lumley T, Cummings SR, Taylor HA, Post W, Jukema JW, Stricker BH, Whitsel EA, Psaty BM, Arnett D

Abstract
BACKGROUND: Hypertension is a major risk factor for a spectrum of cardiovascular diseases (CVD), including myocardial infarction, sudden death, and stroke. In the US, over 65 million people have high blood pressure and a large proportion of these individuals are prescribed antihypertensive medications. Although large long-term clinical trials conducted in the last several decades have identified a number of effective antihypertensive treatments that reduce the risk of future clinical complications, responses to therapy and protection from cardiovascular events vary among individuals.
METHODS: Using a genome-wide association study among 21,267 participants with pharmaceutically treated hypertension, we explored the hypothesis that genetic variants might influence or modify the effectiveness of common antihypertensive therapies on the risk of major cardiovascular outcomes. The classes of drug treatments included angiotensin-converting enzyme inhibitors, beta-blockers, calcium channel blockers, and diuretics. In the setting of the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, each study performed array-based genome-wide genotyping, imputed to HapMap Phase II reference panels, and used additive genetic models in proportional hazards or logistic regression models to evaluate drug-gene interactions for each of four therapeutic drug classes. We used meta-analysis to combine study-specific interaction estimates for approximately 2 million single nucleotide polymorphisms (SNPs) in a discovery analysis among 15,375 European Ancestry participants (3,527 CVD cases) with targeted follow-up in a case-only study of 1,751 European Ancestry GenHAT participants as well as among 4,141 African-Americans (1,267 CVD cases).
RESULTS: Although drug-SNP interactions were biologically plausible, exposures and outcomes were well measured, and power was sufficient to detect modest interactions, we did not identify any statistically significant interactions from the four antihypertensive therapy meta-analyses (Pinteraction > 5.0×10-8). Similarly, findings were null for meta-analyses restricted to 66 SNPs with significant main effects on coronary artery disease or blood pressure from large published genome-wide association studies (Pinteraction ≥ 0.01). Our results suggest that there are no major pharmacogenetic influences of common SNPs on the relationship between blood pressure medications and the risk of incident CVD.

PMID: 26516778 [PubMed - indexed for MEDLINE]

Pharmacogenomic Testing and Warfarin Management.

Oncol Nurs Forum. 2015 Sep;42(5):563-5

Authors: Maluso A

Abstract
Warfarin has been used for the prevention of thrombosis for more than 50 years and is the most frequently prescribed vitamin K antagonist in North America (Gage & Eby, 2003). Its mode of action is to prevent vitamin K from converting to vitamin KH2, thereby inhibiting clotting factors (Johnson & Cavallari, 2015). Warfarin metabolism is affected by variations in the cytochrome P450 2C9 (CYP2C9) and the vitamin K epoxide reductase complex 1 (VKORC1) genotypes. CYP2C9 affects the drug's pharmacokinetics, or metabolism, whereas VKORC1, the target protein of warfarin, affects the drug's pharmacodynamics, or its impact on cell proteins
.

PMID: 26302287 [PubMed - indexed for MEDLINE]

Dihydropyrimidinase and β-ureidopropionase gene variation and severe fluoropyrimidine-related toxicity.

Pharmacogenomics. 2015;16(12):1367-77

Authors: Kummer D, Froehlich TK, Joerger M, Aebi S, Sistonen J, Amstutz U, Largiadèr CR

Abstract
AIMS: To assess the association of DPYS and UPB1 genetic variation, encoding the catabolic enzymes downstream of dihydropyrimidine dehydrogenase, with early-onset toxicity from fluoropyrimidine-based chemotherapy.
PATIENTS & METHODS: The coding and exon-flanking regions of both genes were sequenced in a discovery subset (164 patients). Candidate variants were genotyped in the full cohort of 514 patients.
RESULTS & CONCLUSIONS: Novel rare deleterious variants in DPYS (c.253C > T and c.1217G > A) were detected once each in toxicity cases and may explain the occurrence of severe toxicity in individual patients, and associations of common variants in DPYS (c.1-1T > C: p(adjusted) = 0.003; OR = 2.53; 95% CI: 1.39-4.62, and c.265-58T > C: p(adjusted) = 0.039; OR = 0.61; 95% CI: 0.38-0.97) with 5-fluorouracil toxicity were replicated.

PMID: 26244261 [PubMed - indexed for MEDLINE]

Community-level cohesion without cooperation.

Elife. 2016 Jun 16;5

Authors: Tikhonov M

Abstract
Recent work draws attention to community-community encounters ('coalescence') as likely an important factor shaping natural ecosystems. This work builds on MacArthur's classic model of competitive coexistence to investigate such community-level competition in a minimal theoretical setting. It is shown that the ability of a species to survive a coalescence event is best predicted by a community-level 'fitness' of its native community rather than the intrinsic performance of the species itself. The model presented here allows formalizing a macroscopic perspective whereby a community harboring organisms at varying abundances becomes equivalent to a single organism expressing genes at different levels. While most natural communities do not satisfy the strict criteria of multicellularity developed by multi-level selection theory, the effective cohesion described here is a generic consequence of resource partitioning, requires no cooperative interactions, and can be expected to be widespread in microbial ecosystems.

PMID: 27310530 [PubMed - as supplied by publisher]

Mechanistic Mathematical Modeling Tests Hypotheses of the Neurovascular Coupling in fMRI.

PLoS Comput Biol. 2016 Jun;12(6):e1004971

Authors: Lundengård K, Cedersund G, Sten S, Leong F, Smedberg A, Elinder F, Engström M

Abstract
Functional magnetic resonance imaging (fMRI) measures brain activity by detecting the blood-oxygen-level dependent (BOLD) response to neural activity. The BOLD response depends on the neurovascular coupling, which connects cerebral blood flow, cerebral blood volume, and deoxyhemoglobin level to neuronal activity. The exact mechanisms behind this neurovascular coupling are not yet fully investigated. There are at least three different ways in which these mechanisms are being discussed. Firstly, mathematical models involving the so-called Balloon model describes the relation between oxygen metabolism, cerebral blood volume, and cerebral blood flow. However, the Balloon model does not describe cellular and biochemical mechanisms. Secondly, the metabolic feedback hypothesis, which is based on experimental findings on metabolism associated with brain activation, and thirdly, the neurotransmitter feed-forward hypothesis which describes intracellular pathways leading to vasoactive substance release. Both the metabolic feedback and the neurotransmitter feed-forward hypotheses have been extensively studied, but only experimentally. These two hypotheses have never been implemented as mathematical models. Here we investigate these two hypotheses by mechanistic mathematical modeling using a systems biology approach; these methods have been used in biological research for many years but never been applied to the BOLD response in fMRI. In the current work, model structures describing the metabolic feedback and the neurotransmitter feed-forward hypotheses were applied to measured BOLD responses in the visual cortex of 12 healthy volunteers. Evaluating each hypothesis separately shows that neither hypothesis alone can describe the data in a biologically plausible way. However, by adding metabolism to the neurotransmitter feed-forward model structure, we obtained a new model structure which is able to fit the estimation data and successfully predict new, independent validation data. These results open the door to a new type of fMRI analysis that more accurately reflects the true neuronal activity.

PMID: 27310017 [PubMed - as supplied by publisher]

Mitochondrial dysfunction remodels one-carbon metabolism in human cells.

Elife. 2016;5

Authors: Bao XR, Ong SE, Goldberger O, Peng J, Sharma R, Thompson DA, Vafai SB, Cox AG, Marutani E, Ichinose F, Goessling W, Regev A, Carr SA, Clish CB, Mootha VK

Abstract
Mitochondrial dysfunction is associated with a spectrum of human disorders, ranging from rare, inborn errors of metabolism to common, age-associated diseases such as neurodegeneration. How these lesions give rise to diverse pathology is not well understood, partly because their proximal consequences have not been well-studied in mammalian cells. Here we provide two lines of evidence that mitochondrial respiratory chain dysfunction leads to alterations in one-carbon metabolism pathways. First, using hypothesis-generating metabolic, proteomic, and transcriptional profiling, followed by confirmatory experiments, we report that mitochondrial DNA depletion leads to an ATF4-mediated increase in serine biosynthesis and transsulfuration. Second, we show that lesioning the respiratory chain impairs mitochondrial production of formate from serine, and that in some cells, respiratory chain inhibition leads to growth defects upon serine withdrawal that are rescuable with purine or formate supplementation. Our work underscores the connection between the respiratory chain and one-carbon metabolism with implications for understanding mitochondrial pathogenesis.

PMID: 27307216 [PubMed - in process]

Advanced Multidimensional Separations in Mass Spectrometry: Navigating the Big Data Deluge.

Annu Rev Anal Chem (Palo Alto Calif). 2016 Jun 12;9(1):387-409

Authors: May JC, McLean JA

Abstract
Hybrid analytical instrumentation constructed around mass spectrometry (MS) is becoming the preferred technique for addressing many grand challenges in science and medicine. From the omics sciences to drug discovery and synthetic biology, multidimensional separations based on MS provide the high peak capacity and high measurement throughput necessary to obtain large-scale measurements used to infer systems-level information. In this article, we describe multidimensional MS configurations as technologies that are big data drivers and review some new and emerging strategies for mining information from large-scale datasets. We discuss the information content that can be obtained from individual dimensions, as well as the unique information that can be derived by comparing different levels of data. Finally, we summarize some emerging data visualization strategies that seek to make highly dimensional datasets both accessible and comprehensible.

PMID: 27306312 [PubMed - in process]

Qualitative dynamics semantics for SBGN process description.

BMC Syst Biol. 2016;10(1):42

Authors: Rougny A, Froidevaux C, Calzone L, Paulevé L

Abstract
BACKGROUND: Qualitative dynamics semantics provide a coarse-grain modeling of networks dynamics by abstracting away kinetic parameters. They allow to capture general features of systems dynamics, such as attractors or reachability properties, for which scalable analyses exist. The Systems Biology Graphical Notation Process Description language (SBGN-PD) has become a standard to represent reaction networks. However, no qualitative dynamics semantics taking into account all the main features available in SBGN-PD had been proposed so far.
RESULTS: We propose two qualitative dynamics semantics for SBGN-PD reaction networks, namely the general semantics and the stories semantics, that we formalize using asynchronous automata networks. While the general semantics extends standard Boolean semantics of reaction networks by taking into account all the main features of SBGN-PD, the stories semantics allows to model several molecules of a network by a unique variable. The obtained qualitative models can be checked against dynamical properties and therefore validated with respect to biological knowledge. We apply our framework to reason on the qualitative dynamics of a large network (more than 200 nodes) modeling the regulation of the cell cycle by RB/E2F.
CONCLUSION: The proposed semantics provide a direct formalization of SBGN-PD networks in dynamical qualitative models that can be further analyzed using standard tools for discrete models. The dynamics in stories semantics have a lower dimension than the general one and prune multiple behaviors (which can be considered as spurious) by enforcing the mutual exclusiveness between the activity of different nodes of a same story. Overall, the qualitative semantics for SBGN-PD allow to capture efficiently important dynamical features of reaction network models and can be exploited to further refine them.

PMID: 27306057 [PubMed - in process]

DeepMeSH: deep semantic representation for improving large-scale MeSH indexing.

Bioinformatics. 2016 Jun 15;32(12):i70-i79

Authors: Peng S, You R, Wang H, Zhai C, Mamitsuka H, Zhu S

Abstract
MOTIVATION: Medical Subject Headings (MeSH) indexing, which is to assign a set of MeSH main headings to citations, is crucial for many important tasks in biomedical text mining and information retrieval. Large-scale MeSH indexing has two challenging aspects: the citation side and MeSH side. For the citation side, all existing methods, including Medical Text Indexer (MTI) by National Library of Medicine and the state-of-the-art method, MeSHLabeler, deal with text by bag-of-words, which cannot capture semantic and context-dependent information well.
METHODS: We propose DeepMeSH that incorporates deep semantic information for large-scale MeSH indexing. It addresses the two challenges in both citation and MeSH sides. The citation side challenge is solved by a new deep semantic representation, D2V-TFIDF, which concatenates both sparse and dense semantic representations. The MeSH side challenge is solved by using the 'learning to rank' framework of MeSHLabeler, which integrates various types of evidence generated from the new semantic representation.
RESULTS: DeepMeSH achieved a Micro F-measure of 0.6323, 2% higher than 0.6218 of MeSHLabeler and 12% higher than 0.5637 of MTI, for BioASQ3 challenge data with 6000 citations.
AVAILABILITY AND IMPLEMENTATION: The software is available upon request.
CONTACT: zhusf@fudan.edu.cn
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID: 27307646 [PubMed - in process]

Combining machine learning, crowdsourcing and expert knowledge to detect chemical-induced diseases in text.

Database (Oxford). 2016;2016

Authors: Bravo À, Li TS, Su AI, Good BM, Furlong LI

Abstract
Drug toxicity is a major concern for both regulatory agencies and the pharmaceutical industry. In this context, text-mining methods for the identification of drug side effects from free text are key for the development of up-to-date knowledge sources on drug adverse reactions. We present a new system for identification of drug side effects from the literature that combines three approaches: machine learning, rule- and knowledge-based approaches. This system has been developed to address the Task 3.B of Biocreative V challenge (BC5) dealing with Chemical-induced Disease (CID) relations. The first two approaches focus on identifying relations at the sentence-level, while the knowledge-based approach is applied both at sentence and abstract levels. The machine learning method is based on the BeFree system using two corpora as training data: the annotated data provided by the CID task organizers and a new CID corpus developed by crowdsourcing. Different combinations of results from the three strategies were selected for each run of the challenge. In the final evaluation setting, the system achieved the highest Recall of the challenge (63%). By performing an error analysis, we identified the main causes of misclassifications and areas for improving of our system, and highlighted the need of consistent gold standard data sets for advancing the state of the art in text mining of drug side effects.Database URL: https://zenodo.org/record/29887?ln¼en#.VsL3yDLWR_V.

PMID: 27307137 [PubMed - in process]

Design of Automatic Extraction Algorithm of Knowledge Points for MOOCs.

Comput Intell Neurosci. 2015;2015:123028

Authors: Chen H, Han D, Dai Y, Zhao L

Abstract
In recent years, Massive Open Online Courses (MOOCs) are very popular among college students and have a powerful impact on academic institutions. In the MOOCs environment, knowledge discovery and knowledge sharing are very important, which currently are often achieved by ontology techniques. In building ontology, automatic extraction technology is crucial. Because the general methods of text mining algorithm do not have obvious effect on online course, we designed automatic extracting course knowledge points (AECKP) algorithm for online course. It includes document classification, Chinese word segmentation, and POS tagging for each document. Vector Space Model (VSM) is used to calculate similarity and design the weight to optimize the TF-IDF algorithm output values, and the higher scores will be selected as knowledge points. Course documents of "C programming language" are selected for the experiment in this study. The results show that the proposed approach can achieve satisfactory accuracy rate and recall rate.

PMID: 26448738 [PubMed - indexed for MEDLINE]

14 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

("orphan disease" OR "rare disease" OR "orphan diseases" OR "rare diseases")

These pubmed results were generated on 2016/06/16

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

Discovery of New Potential Anti-Infective Compounds Based on Carbonic Anhydrase Inhibitors by Rational Target-Focused Repurposing Approaches.

ChemMedChem. 2016 Jun 15;

Authors: Annunziato G, Angeli A, D'Alba F, Bruno A, Pieroni M, Vullo D, De Luca V, Capasso C, Supuran CT, Costantino G

Abstract
In academia, compound recycling represents an alternative drug discovery strategy to identify new pharmaceutical targets from a library of chemical compounds available in house. Herein we report the application of a rational target-based drug-repurposing approach to find diverse applications for our in-house collection of compounds. The carbonic anhydrase (CA, EC 4.2.1.1) metalloenzyme superfamily was identified as a potential target of our compounds. The combination of a thoroughly validated docking screening protocol, together with in vitro assays against various CA families and isoforms, allowed us to identify two unprecedented chemotypes as CA inhibitors. The identified compounds have the capacity to preferentially bind pathogenic (bacterial/protozoan) CAs over human isoforms and represent excellent hits for further optimization in hit-to-lead campaigns.

PMID: 27304878 [PubMed - as supplied by publisher]

Drug Repurposing Screening Identifies Novel Compounds That Effectively Inhibit Toxoplasma gondii Growth.

mSphere. 2016 Mar-Apr;1(2)

Authors: Dittmar AJ, Drozda AA, Blader IJ

Abstract
The urgent need to develop new antimicrobial therapies has spawned the development of repurposing screens in which well-studied drugs and other types of compounds are tested for potential off-label uses. As a proof-of-principle screen to identify compounds effective against Toxoplasma gondii, we screened a collection of 1,120 compounds for the ability to significantly reduce Toxoplasma replication. A total of 94 compounds blocked parasite replication with 50% inhibitory concentrations of <5 µM. A significant number of these compounds are established inhibitors of dopamine or estrogen signaling. Follow-up experiments with the dopamine receptor inhibitor pimozide revealed that the drug impacted both parasite invasion and replication but did so independently of inhibition of dopamine or other neurotransmitter receptor signaling. Tamoxifen, which is an established inhibitor of the estrogen receptor, also reduced parasite invasion and replication. Even though Toxoplasma can activate the estrogen receptor, tamoxifen inhibits parasite growth independently of this transcription factor. Tamoxifen is also a potent inducer of autophagy, and we find that the drug stimulates recruitment of the autophagy marker light chain 3-green fluorescent protein onto the membrane of the vacuolar compartment in which the parasite resides and replicates. In contrast to other antiparasitic drugs, including pimozide, tamoxifen treatment of infected cells leads to a time-dependent elimination of intracellular parasites. Taken together, these data suggest that tamoxifen restricts Toxoplasma growth by inducing xenophagy or autophagic destruction of this obligate intracellular parasite. IMPORTANCE There is an urgent need to develop new therapies to treat microbial infections, and the repurposing of well-characterized compounds is emerging as one approach to achieving this goal. Using the protozoan parasite Toxoplasma gondii, we screened a library of 1,120 compounds and identified several compounds with significant antiparasitic activities. Among these were pimozide and tamoxifen, which are well-characterized drugs prescribed to treat patients with psychiatric disorders and breast cancer, respectively. The mechanisms by which these compounds target these disorders are known, but we show here that these drugs kill Toxoplasma through novel pathways, highlighting the potential utility of off-target effects in the treatment of infectious diseases.

PMID: 27303726 [PubMed]

Gene-set analysis based on the pharmacological profiles of drugs to identify repurposing opportunities in schizophrenia.

J Psychopharmacol. 2016 Jun 14;

Authors: de Jong S, Vidler LR, Mokrab Y, Collier DA, Breen G

Abstract
Genome-wide association studies (GWAS) have identified thousands of novel genetic associations for complex genetic disorders, leading to the identification of potential pharmacological targets for novel drug development. In schizophrenia, 108 conservatively defined loci that meet genome-wide significance have been identified and hundreds of additional sub-threshold associations harbour information on the genetic aetiology of the disorder. In the present study, we used gene-set analysis based on the known binding targets of chemical compounds to identify the 'drug pathways' most strongly associated with schizophrenia-associated genes, with the aim of identifying potential drug repositioning opportunities and clues for novel treatment paradigms, especially in multi-target drug development. We compiled 9389 gene sets (2496 with unique gene content) and interrogated gene-based p-values from the PGC2-SCZ analysis. Although no single drug exceeded experiment wide significance (corrected p<0.05), highly ranked gene-sets reaching suggestive significance including the dopamine receptor antagonists metoclopramide and trifluoperazine and the tyrosine kinase inhibitor neratinib. This is a proof of principle analysis showing the potential utility of GWAS data of schizophrenia for the direct identification of candidate drugs and molecules that show polypharmacy.

PMID: 27302942 [PubMed - as supplied by publisher]