Editorial overview: Cell signalling and gene regulation-communication and control as the twin pillars of systems biology.

Curr Opin Plant Biol. 2015 Oct;27:v-viii

Authors: Cao X, Meyers BC

PMID: 26433830 [PubMed - indexed for MEDLINE]

Food metabolomics: from farm to human.

Curr Opin Biotechnol. 2016 Feb;37:16-23

Authors: Kim S, Kim J, Yun EJ, Kim KH

Abstract
Metabolomics, one of the latest components in the suite of systems biology, has been used to understand the metabolism and physiology of living systems, including microorganisms, plants, animals and humans. Food metabolomics can be defined as the application of metabolomics in food systems, including food resources, food processing and diet for humans. The study of food metabolomics has increased gradually in the recent years, because food systems are directly related to nutrition and human health. This review describes the recent trends and applications of metabolomics to food systems, from farm to human, including food resource production, industrial food processing and food intake by humans.

PMID: 26426959 [PubMed - indexed for MEDLINE]

Drug Drug Interaction Extraction from Biomedical Literature Using Syntax Convolutional Neural Network.

Bioinformatics. 2016 Jul 27;

Authors: Zhao Z, Yang Z, Luo L, Lin H, Wang J

Abstract
MOTIVATION: Detecting drug-drug interaction (DDI) has become a vital part of public health safety. Therefore, using text mining techniques to extract DDIs from biomedical literature has received great attentions. However, this research is still at an early stage and its performance has much room to improve.
RESULTS: In this paper, we present a syntax convolutional neural network (SCNN) based DDI extraction method. In this method, a novel word embedding, syntax word embedding, is proposed to employ the syntactic information of a sentence. Then the position and part of speech (POS) features are introduced to extend the embedding of each word. Later, auto-encoder is introduced to encode the traditional bag-of-words feature (sparse 0-1 vector) as the dense real value vector. Finally, a combination of embedding-based convolutional features and traditional features are fed to the softmax classifier to extract DDIs from biomedical literature. Experimental results on the DDIExtraction 2013 corpus show that SCNN obtains a better performance (an F-score of 0.686) than other state-of-the-art methods.
AVAILABILITY: The source code is available for academic use at http://202.118.75.18:8080/DDI/SCNN-DDI.zip CONTACT: yangzh@dlut.edu.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID: 27466626 [PubMed - as supplied by publisher]

42 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 2016/07/28

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.

29 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 2016/07/28

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.

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

"Systems Biology"[Title/Abstract] AND ("2005/01/01"[PDAT] : "3000"[PDAT])

These pubmed results were generated on 2016/07/28

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.

Strategies in the discovery of novel antifungal scaffolds.

Future Med Chem. 2016 Jul 27;

Authors: Liu N, Wang C, Su H, Zhang W, Sheng C

Abstract
The development of next-generation antifungal agents with novel chemical scaffolds and new mechanisms of action is vital due to increased incidence and mortality of invasive fungal infections and severe drug resistance. This review will summarize current strategies to discover novel antifungal scaffolds. In particular, high-throughput screening, drug repurposing, antifungal natural products and new antifungal targets are focused on. New scaffolds with validated antifungal activity, their discovery and optimization process as well as structure-activity relationships are discussed in detail. Perspectives that could inspire future antifungal drug discovery are provided.

PMID: 27463376 [PubMed - as supplied by publisher]

Potential Reuse of Oncology Drugs in the Treatment of Rare Diseases.

Trends Pharmacol Sci. 2016 Jul 22;

Authors: Liu Z, Fang H, Slikker W, Tong W

Abstract
Cancer research has made remarkable progress with the help of advancing genomics techniques, resulting in more precise clinical application and many new anticancer drugs on the market. By contrast, very few treatment options are available for rare diseases that are often progressive, severe, and life-threatening. In this opinion we elaborate on the possible association between cancers and rare diseases across three different levels including clinical observation, crosstalk between germline mutation and somatic mutation, and shared biological pathways. Consequently, by utilizing systematic drug-repositioning approaches, and taking safety issues into consideration, we suggest that oncology drugs have great potential for reuse in the treatment of rare diseases.

PMID: 27461952 [PubMed - as supplied by publisher]

Anti-inflammatory effects of dabrafenib on polyphosphate-mediated vascular disruption.

Chem Biol Interact. 2016 Jul 22;

Authors: Lee S, Ku SK, Bae JS

Abstract
The screening of bioactive compound libraries can be an effective approach for repositioning FDA-approved drugs or discovering new treatments for human diseases. Previous studies have reported polyphosphate (PolyP)-mediated vascular inflammatory responses such as disruption of vascular integrity. Dabrafenib is a B-Raf inhibitor and initially used for the treatment of metastatic melanoma therapy. This study illustrates drug repositioning with dabrafenib (DAB) for the modulation of PolyP-mediated vascular inflammatory responses in human umbilical vein endothelial cells (HUVECs) and mice. The survival rates, septic biomarker levels, behavior of human neutrophils, and vascular permeability were determined in PolyP-activated HUVECs and mice. Dabrafenib suppressed the PolyP-mediated vascular barrier permeability, upregulation of inflammatory biomarkers, adhesion/migration of leukocytes, and activation and/or production of nuclear factor-κB, tumor necrosis factor-α, and interleukin-6. Furthermore, dabrafenib demonstrated protective effects on PolyP-mediated lethal death and the levels of the related septic biomarkers. Therefore, these results indicated the therapeutic potential of dabrafenib on various systemic inflammatory diseases, such as sepsis or septic shock.

PMID: 27458080 [PubMed - as supplied by publisher]

Scoring multiple features to predict drug disease associations using information fusion and aggregation.

SAR QSAR Environ Res. 2016 Jul 25;:1-20

Authors: Moghadam H, Rahgozar M, Gharaghani S

Abstract
Prediction of drug-disease associations is one of the current fields in drug repositioning that has turned into a challenging topic in pharmaceutical science. Several available computational methods use network-based and machine learning approaches to reposition old drugs for new indications. However, they often ignore features of drugs and diseases as well as the priority and importance of each feature, relation, or interactions between features and the degree of uncertainty. When predicting unknown drug-disease interactions there are diverse data sources and multiple features available that can provide more accurate and reliable results. This information can be collectively mined using data fusion methods and aggregation operators. Therefore, we can use the feature fusion method to make high-level features. We have proposed a computational method named scored mean kernel fusion (SMKF), which uses a new method to score the average aggregation operator called scored mean. To predict novel drug indications, this method systematically combines multiple features related to drugs or diseases at two levels: the drug-drug level and the drug-disease level. The purpose of this study was to investigate the effect of drug and disease features as well as data fusion to predict drug-disease interactions. The method was validated against a well-established drug-disease gold-standard dataset. When compared with the available methods, our proposed method outperformed them and competed well in performance with area under cover (AUC) of 0.91, F-measure of 84.9% and Matthews correlation coefficient of 70.31%.

PMID: 27455069 [PubMed - as supplied by publisher]

Computational approaches for innovative antiepileptic drug discovery.

Expert Opin Drug Discov. 2016 Jul 25;

Authors: Talevi A

Abstract
INTRODUCTION: Despite the approval of a large number of antiepileptic agents over the past 25 years, there has been no significant improvement in efficacy of treatments, with one third of patients suffering from intractable epilepsy. This scenario has prompted the search for innovative drug discovery solutions. While network pharmacology and explanations of the drug resistance phenomena have been proposed to drive the search for more efficacious therapeutic solutions, such alternative approaches have not fully taken hold within the antiepileptic drug discovery community so far.
AREAS COVERED: Herein, the author discusses the impact that network pharmacology and the current hypotheses of refractory epilepsy and drug repurposing could have if integrated with anti-epileptic computer-aided discovery.
EXPERT OPINION: With many complex diseases, the advancement in the understanding of disorder pathophysiology in addition to the contribution of systems biology have rapidly translated into the discovery of novel drug candidates. However, antiepileptic drug developers have fallen a little behind in this regard, with fewer examples of computer-aided antiepileptic drug design and network-based approximations appearing in scientific literature. New generation single-target agents have so far shown limited success in terms of enhanced efficacy; although multi-target agents could yet demonstrate improved safety and efficacy.

PMID: 27454246 [PubMed - as supplied by publisher]

Getting the Most out of PubChem for Virtual Screening.

Expert Opin Drug Discov. 2016 Jul 25;

Authors: Kim S

Abstract
INTRODUCTION: With the emergence of the "big data" era, the biomedical research community has great interest in exploiting publicly available chemical information for drug discovery. PubChem is an example of public databases that provide a large amount of chemical information free of charge.
AREAS COVERED: This article provides an overview of how PubChem's data, tools, and services can be used for virtual screening and reviews recent publications that discuss important aspects of exploiting PubChem for drug discovery.
EXPERT OPINION: PubChem offers comprehensive chemical information useful for drug discovery. It also provides multiple programmatic access routes, which are essential to build automated virtual screening pipelines that exploit PubChem data. In addition, PubChemRDF allows users to download PubChem data and load them into a local computing facility, facilitating data integration between PubChem and other resources. PubChem resources have been used in many studies for developing bioactivity and toxicity prediction models, discovering polypharmacologic (multi-target) ligands, and identifying new macromolecule targets of compounds (for drug-repurposing or off-target side effect prediction). These studies demonstrate the usefulness of PubChem as a key resource for computer-aided drug discovery and related area.

PMID: 27454129 [PubMed - as supplied by publisher]

The human milk oligosaccharide 2'-fucosyllactose augments the adaptive response to extensive intestinal.

Am J Physiol Gastrointest Liver Physiol. 2016 Mar 15;310(6):G427-38

Authors: Mezoff EA, Hawkins JA, Ollberding NJ, Karns R, Morrow AL, Helmrath MA

Abstract
Intestinal resection resulting in short bowel syndrome (SBS) carries a heavy burden of long-term morbidity, mortality, and cost of care, which can be attenuated with strategies that improve intestinal adaptation. SBS infants fed human milk, compared with formula, have more rapid intestinal adaptation. We tested the hypothesis that the major noncaloric human milk oligosaccharide 2'-fucosyllactose (2'-FL) contributes to the adaptive response after intestinal resection. Using a previously described murine model of intestinal adaptation, we demonstrated increased weight gain from 21 to 56 days (P < 0.001) and crypt depth at 56 days (P < 0.0095) with 2'-FL supplementation after ileocecal resection. Furthermore, 2'-FL increased small bowel luminal content microbial alpha diversity following resection (P < 0.005) and stimulated a bloom in organisms of the genus Parabacteroides (log2-fold = 4.1, P = 0.035). Finally, transcriptional analysis of the intestine revealed enriched ontologies and pathways related to antimicrobial peptides, metabolism, and energy processing. We conclude that 2'-FL supplementation following ileocecal resection increases weight gain, energy availability through microbial community modulation, and histological changes consistent with improved adaptation.

PMID: 26702137 [PubMed - indexed for MEDLINE]

Coupling News Sentiment with Web Browsing Data Improves Prediction of Intra-Day Price Dynamics.

PLoS One. 2016;11(1):e0146576

Authors: Ranco G, Bordino I, Bormetti G, Caldarelli G, Lillo F, Treccani M

Abstract
The new digital revolution of big data is deeply changing our capability of understanding society and forecasting the outcome of many social and economic systems. Unfortunately, information can be very heterogeneous in the importance, relevance, and surprise it conveys, affecting severely the predictive power of semantic and statistical methods. Here we show that the aggregation of web users' behavior can be elicited to overcome this problem in a hard to predict complex system, namely the financial market. Specifically, our in-sample analysis shows that the combined use of sentiment analysis of news and browsing activity of users of Yahoo! Finance greatly helps forecasting intra-day and daily price changes of a set of 100 highly capitalized US stocks traded in the period 2012-2013. Sentiment analysis or browsing activity when taken alone have very small or no predictive power. Conversely, when considering a news signal where in a given time interval we compute the average sentiment of the clicked news, weighted by the number of clicks, we show that for nearly 50% of the companies such signal Granger-causes hourly price returns. Our result indicates a "wisdom-of-the-crowd" effect that allows to exploit users' activity to identify and weigh properly the relevant and surprising news, enhancing considerably the forecasting power of the news sentiment.

PMID: 26808833 [PubMed - indexed for MEDLINE]

Pharmacogenomics of platinum-based chemotherapy sensitivity in NSCLC: toward precision medicine.

Pharmacogenomics. 2016 Jul 27;

Authors: Yin JY, Li X, Zhou HH, Liu ZQ

Abstract
Lung cancer is one of the leading causes of cancer-related death in the world. Platinum-based chemotherapy is the first-line treatment for non-small-cell lung cancer (NSCLC), however, the therapeutic efficiency varies remarkably among individuals. A large number of pharmacogenomics studies aimed to identify genetic variations which can be used to predict platinum response. Those studies are leading NSCLC treatment to the new era of precision medicine. In the current review, we provided a comprehensive update on the main recent findings of genetic variations which can be used to predict platinum sensitivity in the NSCLC patients.

PMID: 27462924 [PubMed - as supplied by publisher]

Genetic variation in catechol-O-methyltransferase modifies effects of clonidine treatment in chronic fatigue syndrome.

Pharmacogenomics J. 2016 Jul 26;

Authors: Hall KT, Kossowsky J, Oberlander TF, Kaptchuk TJ, Saul JP, Wyller VB, Fagermoen E, Sulheim D, Gjerstad J, Winger A, Mukamal KJ

Abstract
Clonidine, an α2-adrenergic receptor agonist, decreases circulating norepinephrine and epinephrine, attenuating sympathetic activity. Although catechol-O-methyltransferase (COMT) metabolizes catecholamines, main effectors of sympathetic function, COMT genetic variation effects on clonidine treatment are unknown. Chronic fatigue syndrome (CFS) is hypothesized to result in part from dysregulated sympathetic function. A candidate gene analysis of COMT rs4680 effects on clinical outcomes in the Norwegian Study of Chronic Fatigue Syndrome in Adolescents: Pathophysiology and Intervention Trial (NorCAPITAL), a randomized double-blinded clonidine versus placebo trial, was conducted (N=104). Patients homozygous for rs4680 high-activity allele randomized to clonidine took 2500 fewer steps compared with placebo (Pinteraction=0.04). There were no differences between clonidine and placebo among patients with COMT low-activity alleles. Similar gene-drug interactions were observed for sleep (Pinteraction=0.003) and quality of life (Pinteraction=0.018). Detrimental effects of clonidine in the subset of CFS patients homozygous for COMT high-activity allele warrant investigation of potential clonidine-COMT interaction effects in other conditions.The Pharmacogenomics Journal advance online publication, 26 July 2016; doi:10.1038/tpj.2016.53.

PMID: 27457818 [PubMed - as supplied by publisher]

Promoter region variation in NFE2L2 influences susceptibility to ototoxicity in patients exposed to high cumulative doses of cisplatin.

Pharmacogenomics J. 2016 Jul 26;

Authors: Spracklen TF, Vorster AA, Ramma L, Dalvie S, Ramesar RS

Abstract
Ototoxicity is a disabling reaction to cisplatin chemotherapy. Much of the inter-individual variability in the development of hearing impairment among cisplatin-receiving patients has not been fully accounted for. In particular, little is known about the pharmacogenomics of cisplatin-induced ototoxicity. This study sought to investigate the role of variation in five candidate genes in a cohort of South African cancer patients. Five variants within the candidate genes were genotyped in 214 patients, of which SLC22A2 rs316019 and NFE2L2 rs6721961 associated with reduced rates of ototoxicity. In the patients who were exposed to cumulative cisplatin doses ⩾200 mg m(-)(2) (n=113), the variant rs6721961 associated with ototoxicity according to three different grading scales of hearing loss (ASHA, P=0.005; Chang, P=0.028; CTCAE, P=0.004). The NFE2L2 promotor variant rs6721961 may therefore be protective against hearing loss in cisplatin-receiving cancer patients.The Pharmacogenomics Journal advance online publication, 26 July 2016; doi:10.1038/tpj.2016.52.

PMID: 27457817 [PubMed - as supplied by publisher]

Ayurgenomics for stratified medicine: TRISUTRA consortium initiative across ethnically and geographically diverse Indian populations.

J Ethnopharmacol. 2016 Jul 22;

Authors: Prasher B, Varma B, Kumar A, Khuntia BK, Pandey R, Narang A, Tiwari P, Kutum R, Guin D, Kukreti R, Dash D, TRISUTRA Ayurgenomics Consortium, Mukerji M

Abstract
BACKGROUND: Genetic differences in the target proteins, metabolizing enzymes and transporters that contribute to inter-individual differences in drug response are not integrated in contemporary drug development programs. Ayurveda, that has propelled many drug discovery programs albeit for the search of new chemical entities incorporates inter-individual variability "Prakriti" in development and administration of drug in an individualized manner. Prakriti of an individual largely determines responsiveness to external environment including drugs as well as susceptibility to diseases. Prakriti has also been shown to have molecular and genomic correlates. We highlight how integration of Prakriti concepts can augment the efficiency of drug discovery and development programs through a unique initiative of Ayurgenomics TRISUTRA consortium.
METHODS: Five aspects that have been carried out are (1) analysis of variability in FDA approved pharmacogenomics genes/SNPs in exomes of 72 healthy individuals including predominant Prakriti types and matched controls from a North Indian Indo-European cohort (2) establishment of a consortium network and development of five genetically homogeneous cohorts from diverse ethnic and geo-climatic background (3) identification of parameters and development of uniform standard protocols for objective assessment of Prakriti types (4) development of protocols for Prakriti evaluation and its application in more than 7500 individuals in the five cohorts (5) Development of data and sample repository and integrative omics pipelines for identification of genomic correlates.
RESULTS: Highlight of the study are (1) Exome sequencing revealed significant differences between Prakriti types in 28 SNPs of 11 FDA approved genes of pharmacogenomics relevance viz CYP2C19 CYP2B6, ESR1, F2, PGR, HLA-B, HLA-DQA1, HLA-DRB1, LDLR, CFTR, CPS1. These variations are polymorphic in diverse Indian and world populations included in 1000 genomes project. (2) Based on the phenotypic attributes of Prakriti we identified anthropometry for anatomical features, biophysical parameters for skin types, HRV for autonomic function tests, spirometry for vital capacity and gustometry for taste thresholds as objective parameters. (3) Comparison of Prakriti phenotypes across different ethnic, age and gender groups led to identification of invariant features as well as some that require weighted considerations across the cohorts.
CONCLUSION: Considering the molecular and genomics differences underlying Prakriti and relevance in disease pharmacogenomics studies, this novel integrative platform would help in identification of differently susceptible and drug responsive population. Additionally, integrated analysis of phenomic and genomic variations would not only allow identification of clinical and genomic markers of Prakriti for application in personalized medicine but also its integration in drug discovery and development programs.

PMID: 27457695 [PubMed - as supplied by publisher]

Personalized Therapy of Hypertension: the Past and the Future.

Curr Hypertens Rep. 2016 Mar;18(3):24

Authors: Manunta P, Ferrandi M, Cusi D, Ferrari P, Staessen J, Bianchi G

Abstract
During the past 20 years, the studies on genetics or pharmacogenomics of primary hypertension provided interesting results supporting the role of genetics, but no actionable finding ready to be translated into personalized medicine. Two types of approaches have been applied: a "hypothesis-driven" approach on the candidate genes, coding for proteins involved in the biochemical machinery underlying the regulation of BP, and an "unbiased hypothesis-free" approach with GWAS, based on the randomness principles of frequentist statistics. During the past 10-15 years, the application of the latter has overtaken the application of the former leading to an enlargement of the number of previously unknown candidate loci or genes but without any actionable result for the therapy of hypertension. In the present review, we summarize the results of our hypothesis-driven approach based on studies carried out in rats with genetic hypertension and in humans with essential hypertension at the pre-hypertensive and early hypertensive stages. These studies led to the identification of mutant adducin and endogenous ouabain as candidate genetic-molecular mechanisms in both species. Rostafuroxin has been developed for its ability to selectively correct Na(+) pump abnormalities sustained by the two abovementioned mechanisms and to selectively reduce BP in rats and in humans carrying the gene variants underlying the mutant adducin and endogenous ouabain (EO) effects. A clinical trial is ongoing to substantiate these findings. Future studies should apply both the candidate gene and GWAS approaches to fully exploit the potential of genetics in optimizing the personalized therapy.

PMID: 26915067 [PubMed - indexed for MEDLINE]

Endogenous glucose production increases in response to metformin treatment in the glycogen-depleted state in humans: a randomised trial.

Diabetologia. 2015 Nov;58(11):2494-502

Authors: Christensen MM, Højlund K, Hother-Nielsen O, Stage TB, Damkier P, Beck-Nielsen H, Brøsen K

Abstract
AIMS/HYPOTHESIS: Metformin is believed to reduce glucose levels primarily by inhibiting hepatic glucose production. Recent data indicate that metformin antagonises glucagon-dependent glucose output, suggesting that compensatory mechanisms protect against hypoglycaemia. Here, we examined the effect of metformin on glucose metabolism in humans after a glycogen-depleting fast and the role of reduced-function alleles in OCT1 (also known as SLC22A1).
METHODS: In a randomised, crossover trial, healthy individuals with or without reduced-function alleles in OCT1 were fasted for 42 h twice, either with or without prior treatment with 1 g metformin twice daily. Participants were recruited from the Pharmacogenomics Biobank of the University of Southern Denmark. Treatment allocation was generated by the Good Clinical Practice Unit, Odense University Hospital, Denmark. Variables of whole-body glucose metabolism were assessed using [3-(3)H]glucose, indirect calorimetry and measurement of substrates and counter-regulatory hormones. The primary outcome was endogenous glucose production (EGP).
RESULTS: Thirty-seven individuals were randomised. Thirty-four completed the study (12 had none, 13 had one and nine had two reduced-function alleles in OCT1). Three were excluded from the analysis because of early dropout. Metformin significantly stimulated glucose disposal rates and non-oxidative glucose metabolism with no effect on glucose oxidation. This increase in glucose utilisation was explained by a concomitant increase in glycolytic flux and accompanied by increased EGP, most likely mediated by increased plasma lactate, glucagon and cortisol levels. There was no effect of reduced-function OCT1 alleles on any of these measures. All individuals completed the glycogen-depleting fast without hypoglycaemia.
CONCLUSIONS/INTERPRETATION: Metformin stimulates glycolytic glucose utilisation and lactate production in the glycogen-depleted state. This may trigger a rise in glucose counter-regulatory hormones and subsequently an increase in EGP, which protects against hypoglycaemia.
TRIAL REGISTRATION: ClinicalTrials.gov NCT01400191 FUNDING: Danish Research Council for Health and Disease (0602-02695B) and Odense University Hospital Free Research Fund, 2012.

PMID: 26271344 [PubMed - indexed for MEDLINE]