Leptin substitution in patients with lipodystrophy: neural correlates for long-term success in the normalization of eating behavior.

Diabetes. 2016 May 10;

Authors: Schlögl H, Müller K, Horstmann A, Miehle K, Püschel J, Villringer A, Pleger B, Stumvoll M, Fasshauer M

Abstract
Lipodystrophy (LD) is a rare disease with a paucity of subcutaneous adipocytes and leptin-deficiency. Patients often develop severe diabetes mellitus and show disturbed eating behavior with reduced satiety that can be restored by substitution with the leptin analogue metreleptin. However, long-term effects of metreleptin on resting-state brain connectivity in treatment-naïve LD patients have not been assessed. In this study, resting-state functional magnetic resonance imaging (fMRI) scans and extensive behavioral testing assessing changes in hunger/satiety regulation were performed during the first 52 weeks of metreleptin treatment in nine LD patients. Resting-state connectivity significantly increased over the course of metreleptin treatment in three brain areas, i.e. hypothalamus, insula/superior temporal gyrus, and medial prefrontal cortex. Behavioral tests demonstrated that perceived hunger, importance of eating, eating frequencies, and liking ratings of food pictures significantly decreased during metreleptin therapy. Taken together, leptin substitution was accompanied by long-term changes of hedonic and homeostatic central nervous networks regulating eating behavior, as well as decreased hunger feelings and diminished incentive value of food. It needs to be assessed in future studies whether metreleptin treatment in LD restores physiological processes important for the development of satiety.

PMID: 27207511 [PubMed - as supplied by publisher]

Pharmaceutical expenditure on drugs for rare diseases in Canada: a historical (2007-13) and prospective (2014-18) MIDAS sales data analysis.

Orphanet J Rare Dis. 2016;11(1):68

Authors: Divino V, DeKoven M, Kleinrock M, Wade RL, Kim T, Kaura S

Abstract
BACKGROUND: Health Canada has defined rare diseases as life-threatening, seriously debilitating, or serious chronic conditions affecting a very small number of patients (~1 in 2,000 persons). An estimated 9 % of Canadians suffer from a rare disease. Drugs treating rare diseases (DRDs) are also known as orphan drugs. While Canada is currently developing an orphan drug framework, in the United States (US), the Orphan Drug Act (ODA) of 1983 established incentives for the development of orphan drugs. This study measured total annual expenditure of orphan drugs in Canada (2007-13) and estimated future (2014-18) orphan drug expenditure.
METHODS: Orphan drugs approved by the US Food and Drug Administration (FDA) in the US were used as a proxy for the orphan drug landscape in Canada. Branded, orphan drugs approved by the FDA between 1983 through 2013 were identified (N = 356 unique products). Only US orphan drugs with the same orphan indication(s) approved in Canada were included in the analysis. Adjustment via an indication factoring was applied to products with both orphan and non-orphan indications using available data sources to isolate orphan-indication sales. The IMS Health MIDAS database of audited biopharmaceutical sales was utilized to measure total orphan drug expenditure, calculated annually from 2007-2013 and evaluated as a proportion of total annual pharmaceutical drug expenditure (adjusted to 2014 CAD).
RESULTS: Between 2007 and 2013, expenditure was measured for a final N = 147 orphan drugs. Orphan drug expenditure totaled $610.2 million (M) in 2007 and $1,100.0 M in 2013, representing 3.3- 5.6 % of total Canadian pharmaceutical drug expenditure in 2007-2013, respectively. Future trend analysis suggests orphan drug expenditure will remain under 6 % of total expenditure in 2014-18.
CONCLUSIONS: While the number of available orphan drugs and associated expenditure increased over time, access remains an issue, and from the perspectives of society and equity, overall spending on orphan drugs is lower relative to the number of patients affected with an orphan disease in Canada. The overall budget impact of orphan drugs is small and fairly stable relative to total pharmaceutical expenditure. Concerns that growth in orphan drug expenditure may lead to unsustainable drug expenditure do not appear to be justified.

PMID: 27207271 [PubMed - as supplied by publisher]

A Decade of Change: Recent Developments in Pharmacotherapy of Hereditary Angioedema (HAE).

Clin Rev Allergy Immunol. 2016 May 20;

Authors: Bork K

Abstract
Hereditary angioedema (HAE) due to C1 esterase inhibitor (C1-INH) deficiency (HAE-C1-INH) is a rare but medically significant disease that can be associated with considerable morbidity and mortality. Research into the pathogenesis of HAE-C1-INH has expanded greatly in the last six decades and has led to new clinical trials with novel therapeutic agents and treatment strategies. Mechanisms of pharmacotherapy include (a) supplementing C1-INH, the missing serine-protease inhibitor in HAE; (b) inhibiting the activation of the contact system and the uncontrolled release of proteases in the kallikrein-kinin system, by blocking the production/function of its components; (c) inhibiting the fibrinolytic system by blocking the production/function of its components; and (d) inhibiting the function of bradykinin at the endothelial level. Strategies for managing HAE-C1-INH are aimed at treating acute attacks, or preventing attacks, through the use of prophylactic treatment. Available agents for treating acute attacks include plasma-derived C1-INH concentrates, a recombinant C1-INH, a bradykinin B2 receptor antagonist, and a plasma kallikrein inhibitor. Long-term prophylactic treatments include attenuated androgens, plasma-derived C1-INH concentrates, and anti-fibrinolytics. Plasma-derived C1-INH and a bradykinin B2 receptor antagonist are already approved for self-administration at home. The number of management options for HAE-C1-INH has increased considerably within the past decade, thus helping to alleviate the burden of this rare disease.

PMID: 27207174 [PubMed - as supplied by publisher]

Text mining, a race against time? An attempt to quantify possible variations in text corpora of medical publications throughout the years.

Comput Biol Med. 2016 Apr 20;73:173-185

Authors: Wagner M, Vicinus B, Muthra ST, Richards TA, Linder R, Frick VO, Groh A, Rubie C, Weichert F

Abstract
BACKGROUND: The continuous growth of medical sciences literature indicates the need for automated text analysis. Scientific writing which is neither unitary, transcending social situation nor defined by a timeless idea is subject to constant change as it develops in response to evolving knowledge, aims at different goals, and embodies different assumptions about nature and communication. The objective of this study was to evaluate whether publication dates should be considered when performing text mining.
METHODS: A search of PUBMED for combined references to chemokine identifiers and particular cancer related terms was conducted to detect changes over the past 36 years. Text analyses were performed using freeware available from the World Wide Web. TOEFL Scores of territories hosting institutional affiliations as well as various readability indices were investigated. Further assessment was conducted using Principal Component Analysis. Laboratory examination was performed to evaluate the quality of attempts to extract content from the examined linguistic features.
RESULTS: The PUBMED search yielded a total of 14,420 abstracts (3,190,219 words). The range of findings in laboratory experimentation were coherent with the variability of the results described in the analyzed body of literature. Increased concurrence of chemokine identifiers together with cancer related terms was found at the abstract and sentence level, whereas complexity of sentences remained fairly stable.
CONCLUSIONS: The findings of the present study indicate that concurrent references to chemokines and cancer increased over time whereas text complexity remained stable.

PMID: 27208610 [PubMed - as supplied by publisher]

Members of BTB gene family regulate negatively nitrate uptake and nitrogen use efficiency in Arabidopsis thaliana and Oryza sativa.

Plant Physiol. 2016 Apr 27;

Authors: Araus V, Vidal EA, Puelma T, Alamos S, Mieulet D, Guiderdoni E, Gutiérrez RA

Abstract
Development of crops with improved nitrogen use efficiency (NUE) is essential for sustainable agriculture. However, achieving this goal has proven difficult due to NUE is a complex trait encompassing physiological and developmental processes. We thought to tackle this problem by taking a systems biology approach to identify candidate target genes. First, we used a supervised machine-learning algorithm to predict a NUE gene network in Arabidopsis thaliana. Second, we identified BT2, a member of the Bric-a-Brac/Tramtrack/Broad (BTB) gene family, as the most central and connected gene in the NUE network. Third, we experimentally tested BT2 for a role in NUE. We found NUE decreased in plants overexpressing BT2 gene as compared to wild-type plants under limiting nitrate conditions. Additionally, NUE increased as compared to wild-type plants under low nitrate conditions in double mutant plants in bt2 and its closely related homolog bt1, indicating a functional redundancy of BT1 and BT2 for NUE. Expression of the nitrate transporter genes NRT2.1 and NRT2.4 increased in the bt1/bt2 double mutant as compared to wild-type plants, with a concomitant 65% increase in nitrate uptake under low nitrate conditions. Similar to Arabidopsis, we found that mutation of the BT1/BT2 ortholog gene in rice OsBT increased NUE by 20% as compared to wild-type rice plants under low nitrogen conditions. These results indicate BT gene family members act as conserved negative regulators of nitrate uptake genes and NUE in plants and highlight them as prime targets for future strategies to improve NUE in crops.

PMID: 27208309 [PubMed - as supplied by publisher]

It may Seem Inflammatory, but Some T Cells are Innately Healing to the Bone.

J Bone Miner Res. 2016 May 21;

Authors: Kalyan S

Abstract
Among the most significant developments to have taken place in osteology over the last few decades is an evolution from treating and viewing bone disorders primarily through an endocrine lens to instead seeing them as metabolic disorders that interface at the molecular and cellular level with the immune system. Osteoimmunology was officially born in response to accumulating evidence that the immune system is integrally involved in bone remodelling, but much of the early work focused on the role of conventional αβ T cells in driving bone loss. There is, however, emerging data indicating that innate lymphocytes, in particular γδ T cells, may in fact be important for bone regeneration. We first observed that bisphosphonate-associated osteonecrosis of the jaw (ONJ), a rare but serious adverse drug effect characterized by non-healing necrotic bone tissue of the mandible or maxilla, was linked to a deficiency in a subset of γδ T cells found in human peripheral blood. Patients who developed ONJ while on bisphosphonate therapy not only lacked the main subset of circulating γδ T cells, but they also all had underlying conditions that compromised their immune integrity. A number of recent studies have unraveled the role of γδ T cells (and lymphocytes sharing their characteristics) in bone regeneration - particularly for fracture healing. These findings seem to contradict the prevailing view of such "inflammatory" T cells as being bone degenerative rather than restorative. This viewpoint melds together the emerging evidence of these so-called inflammatory T cells in bone remodeling and healing - showing that they are not in fact "all bad to the bone". This article is protected by copyright. All rights reserved.

PMID: 27207251 [PubMed - as supplied by publisher]

Text mining, a race against time? An attempt to quantify possible variations in text corpora of medical publications throughout the years.

Comput Biol Med. 2016 Apr 20;73:173-185

Authors: Wagner M, Vicinus B, Muthra ST, Richards TA, Linder R, Frick VO, Groh A, Rubie C, Weichert F

Abstract
BACKGROUND: The continuous growth of medical sciences literature indicates the need for automated text analysis. Scientific writing which is neither unitary, transcending social situation nor defined by a timeless idea is subject to constant change as it develops in response to evolving knowledge, aims at different goals, and embodies different assumptions about nature and communication. The objective of this study was to evaluate whether publication dates should be considered when performing text mining.
METHODS: A search of PUBMED for combined references to chemokine identifiers and particular cancer related terms was conducted to detect changes over the past 36 years. Text analyses were performed using freeware available from the World Wide Web. TOEFL Scores of territories hosting institutional affiliations as well as various readability indices were investigated. Further assessment was conducted using Principal Component Analysis. Laboratory examination was performed to evaluate the quality of attempts to extract content from the examined linguistic features.
RESULTS: The PUBMED search yielded a total of 14,420 abstracts (3,190,219 words). The range of findings in laboratory experimentation were coherent with the variability of the results described in the analyzed body of literature. Increased concurrence of chemokine identifiers together with cancer related terms was found at the abstract and sentence level, whereas complexity of sentences remained fairly stable.
CONCLUSIONS: The findings of the present study indicate that concurrent references to chemokines and cancer increased over time whereas text complexity remained stable.

PMID: 27208610 [PubMed - as supplied by publisher]

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Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data.

Mol Pharm. 2016 May 20;

Authors: Aliper A, Plis S, Artemov A, Ulloa A, Mamoshina P, Zhavoronkov A

Abstract
Deep learning is rapidly advancing many areas of science and technology with multiple success stories in image, text, voice and video recognition, robotics and autonomous driving. In this paper we demonstrate how deep neural networks (DNN) trained on large transcriptional response data sets can classify various drugs to therapeutic categories solely based on their transcriptional profiles. We used the perturbation samples of 678 drugs across A549, MCF-7 and PC-3 cell lines from the LINCS project and linked those to 12 therapeutic use categories derived from MeSH. To train the DNN, we utilized both gene level transcriptomic data and transcriptomic data processed using a pathway activation scoring algorithm, for a pooled dataset of samples perturbed with different concentrations of the drug for 6 and 24 hours. When applied to normalized gene expression data for "landmark genes," DNN showed cross-validation mean F1 scores of 0.397, 0.285 and 0.234 on 3-, 5- and 12-category classification problems, respectively. At the pathway level DNN performed best with cross-validation mean F1 scores of 0.701, 0.596 and 0.546 on the same tasks. In both gene and pathway level classification, DNN convincingly outperformed support vector machine (SVM) model on every multiclass classification problem. For the first time we demonstrate a deep learning neural net trained on transcriptomic data to recognize pharmacological properties of multiple drugs across different biological systems and conditions. We also propose using deep neural net confusion matrices for drug repositioning. This work is a proof of principle for applying deep learning to drug discovery and development.

PMID: 27200455 [PubMed - as supplied by publisher]

Web Ontologies to Categorialy Structure Reality: Representations of Human Emotional, Cognitive, and Motivational Processes.

Front Psychol. 2016;7:551

Authors: López-Gil JM, Gil R, García R

Abstract
This work presents a Web ontology for modeling and representation of the emotional, cognitive and motivational state of online learners, interacting with university systems for distance or blended education. The ontology is understood as a way to provide the required mechanisms to model reality and associate it to emotional responses, but without committing to a particular way of organizing these emotional responses. Knowledge representation for the contributed ontology is performed by using Web Ontology Language (OWL), a semantic web language designed to represent rich and complex knowledge about things, groups of things, and relations between things. OWL is a computational logic-based language such that computer programs can exploit knowledge expressed in OWL and also facilitates sharing and reusing knowledge using the global infrastructure of the Web. The proposed ontology has been tested in the field of Massive Open Online Courses (MOOCs) to check if it is capable of representing emotions and motivation of the students in this context of use.

PMID: 27199796 [PubMed]

Strategies for new and improved vaccines against ticks and tick-borne diseases.

Parasite Immunol. 2016 May 20;

Authors: de la Fuente J, Kopáček P, Lew-Tabor A, Maritz-Olivier C

Abstract
Ticks infest a variety of animal species and transmit pathogens causing disease in both humans and animals worldwide. Tick-host-pathogen interactions have evolved through dynamic processes that accommodated the genetic traits of the hosts, pathogens transmitted and the vector tick species that mediate their development and survival. New approaches for tick control are dependent on defining molecular interactions between hosts, ticks and pathogens to allow for discovery of key molecules that could be tested in vaccines or new generation therapeutics for intervention of tick-pathogen cycles. Currently, tick vaccines constitute an effective and environmentally sound approach for the control of ticks and the transmission of the associated tick-borne diseases. New candidate protective antigens will most likely be identified by focusing on proteins with relevant biological function in the feeding, reproduction, development, immune response, subversion of host immunity of the tick vector and/or molecules vital for pathogen infection and transmission. This review addresses different approaches and strategies used for the discovery of protective antigens, including focusing on relevant tick biological functions and proteins, reverse genetics, vaccinomics and tick protein evolution and interactomics. New and improved tick vaccines will most likely contain multiple antigens to control tick infestations and pathogen infection and transmission. This article is protected by copyright. All rights reserved.

PMID: 27203187 [PubMed - as supplied by publisher]

The Obama Administration's Cancer Moonshot: A Call for Proteomics.

Clin Cancer Res. 2016 May 19;

Authors: Conrads TP, Petricoin EF

Abstract
The Cancer Moonshot Program has been launched and represents a potentially paradigm-shifting initiative with the goal to implement a focused national effort to double the rate of progress against cancer. The placement of precision medicine, immunotherapy, genomics, and combination therapies was placed at the central nexus of this initiative. While we are extremely enthusiastic about the goals of the program, it is time we meet this revolutionary project with equally bold and cutting-edge ideas: its time we move firmly into the post-genome era and provide the necessary resources to propel and seize on innovative recent gains in the field of proteomics required for it to stand on equal footing in this narrative as a combined, synergistic engine for molecular profiling. After all, while the genome is the information archive, it is the proteins that actually do the work of the cell and represent the structural cellular machinery. It is the proteins that comprise most of the biomarkers that are measured to detect cancers, constitute the antigens that drive immune response and inter and itntracellular communications, and it is the proteins that are the drug targets for nearly every targeted therapy that is being evaluated in cancer trials today. We believe that a combined systems biology view of the tumor microenvironment that orients cancer studies back to the functional proteome, phosphoproteome and biochemistry of the cell will be essential to deliver on the promise of the Cancer Moonshot program.

PMID: 27199492 [PubMed - as supplied by publisher]

Reconstruction of gene regulatory networks reveals chromatin remodelers and key transcription factors in tumorigenesis.

Genome Med. 2016;8(1):57

Authors: Malysheva V, Mendoza-Parra MA, Saleem MA, Gronemeyer H

Abstract
BACKGROUND: Alterations in genetic and epigenetic landscapes are known to contribute to the development of different types of cancer. However, the mechanistic links between transcription factors and the epigenome which coordinate the deregulation of gene networks during cell transformation are largely unknown.
METHODS: We used an isogenic model of stepwise tumorigenic transformation of human primary cells to monitor the progressive deregulation of gene networks upon immortalization and oncogene-induced transformation. We applied a systems biology approach by combining transcriptome and epigenome data for each step during transformation and integrated transcription factor-target gene associations in order to reconstruct the gene regulatory networks that are at the basis of the transformation process.
RESULTS: We identified 142 transcription factors and 24 chromatin remodelers/modifiers (CRMs) which are preferentially associated with specific co-expression pathways that originate from deregulated gene programming during tumorigenesis. These transcription factors are involved in the regulation of divers processes, including cell differentiation, the immune response, and the establishment/modification of the epigenome. Unexpectedly, the analysis of chromatin state dynamics revealed patterns that distinguish groups of genes which are not only co-regulated but also functionally related. Decortication of transcription factor targets enabled us to define potential key regulators of cell transformation which are engaged in RNA metabolism and chromatin remodeling.
CONCLUSIONS: We reconstructed gene regulatory networks that reveal the alterations occurring during human cellular tumorigenesis. Using these networks we predicted and validated several transcription factors as key players for the establishment of tumorigenic traits of transformed cells. Our study suggests a direct implication of CRMs in oncogene-induced tumorigenesis and identifies new CRMs involved in this process. This is the first comprehensive view of the gene regulatory network that is altered during the process of stepwise human cellular tumorigenesis in a virtually isogenic system.

PMID: 27198694 [PubMed - in process]

Simple biophysics underpins collective conformations of the intrinsically disordered proteins of the Nuclear Pore Complex.

Elife. 2016;5

Authors: Vovk A, Gu C, Opferman MG, Kapinos LE, Lim RY, Coalson RD, Jasnow D, Zilman A

Abstract
Nuclear Pore Complexes (NPCs) are key cellular transporter that control nucleocytoplasmic transport in eukaryotic cells, but its transport mechanism is still not understood. The centerpiece of NPC transport is the assembly of intrinsically disordered polypeptides, known as FG nucleoporins, lining its passageway. Their conformations and collective dynamics during transport are difficult to assess in vivo. In vitro investigations provide partially conflicting results, lending support to different models of transport, which invoke various conformational transitions of the FG nucleoporins induced by the cargo-carrying transport proteins. We show that the spatial organization of FG nucleoporin assemblies with the transport proteins can be understood within a first principles biophysical model with a minimal number of key physical variables, such as the average protein interaction strengths and spatial densities. These results address some of the outstanding controversies and suggest how molecularly divergent NPCs in different species can perform essentially the same function.

PMID: 27198189 [PubMed - in process]

Metabolomics in cardiovascular diseases.

J Pharm Biomed Anal. 2015 Sep 10;113:121-36

Authors: Kordalewska M, Markuszewski MJ

Abstract
Cardiovascular diseases (CVDs) are the main cause of death globally. There is a need for the development of specific diagnostic methods, more effective therapeutic procedures as well as drugs, which can decrease the risk of deaths in the course of CVDs. For this reason, better understanding and explanation of molecular pathomechanisms of CVDs are essential. Metabolomics is focused on analysis of metabolites, small molecules which reflect the state of an organism in a certain point of time. Application of metabolomics approach in the investigation of molecular processes responsible for CVDs development may provide valuable information. In this article we overviewed recent reports employing application of untargeted and targeted metabolomic analyses in particular CVDs. Moreover, we focused on applications of various analytical platforms and metabolomics approaches which may contribute to the explanation of the pathomechanisms of different cardiovascular diseases.

PMID: 25958299 [PubMed - indexed for MEDLINE]

Metabolomics for laboratory diagnostics.

J Pharm Biomed Anal. 2015 Sep 10;113:108-20

Authors: Bujak R, Struck-Lewicka W, Markuszewski MJ, Kaliszan R

Abstract
Metabolomics is an emerging approach in a systems biology field. Due to continuous development in advanced analytical techniques and in bioinformatics, metabolomics has been extensively applied as a novel, holistic diagnostic tool in clinical and biomedical studies. Metabolome's measurement, as a chemical reflection of a current phenotype of a particular biological system, is nowadays frequently implemented to understand pathophysiological processes involved in disease progression as well as to search for new diagnostic or prognostic biomarkers of various organism's disorders. In this review, we discussed the research strategies and analytical platforms commonly applied in the metabolomics studies. The applications of the metabolomics in laboratory diagnostics in the last 5 years were also reviewed according to the type of biological sample used in the metabolome's analysis. We also discussed some limitations and further improvements which should be considered taking in mind potential applications of metabolomic research and practice.

PMID: 25577715 [PubMed - indexed for MEDLINE]

Text Mining of Journal Articles for Sleep Disorder Terminologies.

PLoS One. 2016;11(5):e0156031

Authors: Lam C, Lai FC, Wang CH, Lai MH, Hsu N, Chung MH

Abstract
OBJECTIVE: Research on publication trends in journal articles on sleep disorders (SDs) and the associated methodologies by using text mining has been limited. The present study involved text mining for terms to determine the publication trends in sleep-related journal articles published during 2000-2013 and to identify associations between SD and methodology terms as well as conducting statistical analyses of the text mining findings.
METHODS: SD and methodology terms were extracted from 3,720 sleep-related journal articles in the PubMed database by using MetaMap. The extracted data set was analyzed using hierarchical cluster analyses and adjusted logistic regression models to investigate publication trends and associations between SD and methodology terms.
RESULTS: MetaMap had a text mining precision, recall, and false positive rate of 0.70, 0.77, and 11.51%, respectively. The most common SD term was breathing-related sleep disorder, whereas narcolepsy was the least common. Cluster analyses showed similar methodology clusters for each SD term, except narcolepsy. The logistic regression models showed an increasing prevalence of insomnia, parasomnia, and other sleep disorders but a decreasing prevalence of breathing-related sleep disorder during 2000-2013. Different SD terms were positively associated with different methodology terms regarding research design terms, measure terms, and analysis terms.
CONCLUSION: Insomnia-, parasomnia-, and other sleep disorder-related articles showed an increasing publication trend, whereas those related to breathing-related sleep disorder showed a decreasing trend. Furthermore, experimental studies more commonly focused on hypersomnia and other SDs and less commonly on insomnia, breathing-related sleep disorder, narcolepsy, and parasomnia. Thus, text mining may facilitate the exploration of the publication trends in SDs and the associated methodologies.

PMID: 27203858 [PubMed - as supplied by publisher]

An Integrated Data Driven Approach to Drug Repositioning Using Gene-Disease Associations.

PLoS One. 2016;11(5):e0155811

Authors: Mullen J, Cockell SJ, Woollard P, Wipat A

Abstract
Drug development is both increasing in cost whilst decreasing in productivity. There is a general acceptance that the current paradigm of R&D needs to change. One alternative approach is drug repositioning. With target-based approaches utilised heavily in the field of drug discovery, it becomes increasingly necessary to have a systematic method to rank gene-disease associations. Although methods already exist to collect, integrate and score these associations, they are often not a reliable reflection of expert knowledge. Furthermore, the amount of data available in all areas covered by bioinformatics is increasing dramatically year on year. It thus makes sense to move away from more generalised hypothesis driven approaches to research to one that allows data to generate their own hypothesis. We introduce an integrated, data driven approach to drug repositioning. We first apply a Bayesian statistics approach to rank 309,885 gene-disease associations using existing knowledge. Ranked associations are then integrated with other biological data to produce a semantically-rich drug discovery network. Using this network, we show how our approach identifies diseases of the central nervous system (CNS) to be an area of interest. CNS disorders are identified due to the low numbers of such disorders that currently have marketed treatments, in comparison to other therapeutic areas. We then systematically mine our network for semantic subgraphs that allow us to infer drug-disease relations that are not captured in the network. We identify and rank 275,934 drug-disease has_indication associations after filtering those that are more likely to be side effects, whilst commenting on the top ranked associations in more detail. The dataset has been created in Neo4j and is available for download at https://bitbucket.org/ncl-intbio/genediseaserepositioning along with a Java implementation of the searching algorithm.

PMID: 27196054 [PubMed - as supplied by publisher]

Whole Exome Sequencing in a Rare Disease: A Patient with Anomalous Left Coronary Artery from the Pulmonary Artery (Bland-White-Garland Syndrome).

OMICS. 2016 May;20(5):325-327

Authors: Hekim N, Batyraliev T, Trujillano D, Wang W, Dandara C, Karben Z, Saygılı Eİ, Çetin Z, Mıhcıoğlu D, Türkmen S, İkidağ MA, Cüce MA, Rolfs A

PMID: 27195969 [PubMed - as supplied by publisher]

A Novel ECM1 Splice Site Mutation in Lipoid Proteinosis: Case Report plus Review of the Literature.

Mol Syndromol. 2016 Apr;7(1):26-31

Authors: Rey LK, Kohlhase J, Möllenhoff K, Dekomien G, Epplen JT, Hoffjan S

Abstract
Lipoid proteinosis (LP) is an autosomal recessive genodermatosis known to be caused by mutations in ECM1. Nonsense and missense mutations are the most common variations in LP. Up to date, only 6 splice site mutations have been observed. We report on a 26-year-old female LP patient from a Turkish consanguineous family carrying a novel homozygous splice site mutation in intron 8 of the ECM1 gene and summarize the current knowledge on ECM1 mutations and possible genotype-phenotype correlations.

PMID: 27194970 [PubMed]