Beyond Academia - Interrogating Research Impact in the Research Excellence Framework.

PLoS One. 2016;11(12):e0168533

Authors: Terämä E, Smallman M, Lock SJ, Johnson C, Austwick MZ

Abstract
Big changes to the way in which research funding is allocated to UK universities were brought about in the Research Excellence Framework (REF), overseen by the Higher Education Funding Council, England. Replacing the earlier Research Assessment Exercise, the purpose of the REF was to assess the quality and reach of research in UK universities-and allocate funding accordingly. For the first time, this included an assessment of research 'impact', accounting for 20% of the funding allocation. In this article we use a text mining technique to investigate the interpretations of impact put forward via impact case studies in the REF process. We find that institutions have developed a diverse interpretation of impact, ranging from commercial applications to public and cultural engagement activities. These interpretations of impact vary from discipline to discipline and between institutions, with more broad-based institutions depicting a greater variety of impacts. Comparing the interpretations with the score given by REF, we found no evidence of one particular interpretation being more highly rewarded than another. Importantly, we also found a positive correlation between impact score and [overall research] quality score, suggesting that impact is not being achieved at the expense of research excellence.

PMID: 27997599 [PubMed - in process]

Integrating text mining, data mining, and network analysis for identifying genetic breast cancer trends.

BMC Res Notes. 2016 Apr 26;9:236

Authors: Jurca G, Addam O, Aksac A, Gao S, Özyer T, Demetrick D, Alhajj R

Abstract
BACKGROUND: Breast cancer is a serious disease which affects many women and may lead to death. It has received considerable attention from the research community. Thus, biomedical researchers aim to find genetic biomarkers indicative of the disease. Novel biomarkers can be elucidated from the existing literature. However, the vast amount of scientific publications on breast cancer make this a daunting task. This paper presents a framework which investigates existing literature data for informative discoveries. It integrates text mining and social network analysis in order to identify new potential biomarkers for breast cancer.
RESULTS: We utilized PubMed for the testing. We investigated gene-gene interactions, as well as novel interactions such as gene-year, gene-country, and abstract-country to find out how the discoveries varied over time and how overlapping/diverse are the discoveries and the interest of various research groups in different countries.
CONCLUSIONS: Interesting trends have been identified and discussed, e.g., different genes are highlighted in relationship to different countries though the various genes were found to share functionality. Some text analysis based results have been validated against results from other tools that predict gene-gene relations and gene functions.

PMID: 27112211 [PubMed - indexed for MEDLINE]

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Cystic Fibrosis

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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.

Orphan receptor ligand discovery by pickpocketing pharmacological neighbors.

Nat Chem Biol. 2016 Dec 19;:

Authors: Ngo T, Ilatovskiy AV, Stewart AG, Coleman JL, McRobb FM, Riek RP, Graham RM, Abagyan R, Kufareva I, Smith NJ

Abstract
Understanding the pharmacological similarity of G protein-coupled receptors (GPCRs) is paramount for predicting ligand off-target effects, drug repurposing, and ligand discovery for orphan receptors. Phylogenetic relationships do not always correctly capture pharmacological similarity. Previous family-wide attempts to define pharmacological relationships were based on three-dimensional structures and/or known receptor-ligand pairings, both unavailable for orphan GPCRs. Here, we present GPCR-CoINPocket, a novel contact-informed neighboring pocket metric of GPCR binding-site similarity that is informed by patterns of ligand-residue interactions observed in crystallographically characterized GPCRs. GPCR-CoINPocket is applicable to receptors with unknown structure or ligands and accurately captures known pharmacological relationships between GPCRs, even those undetected by phylogeny. When applied to orphan receptor GPR37L1, GPCR-CoINPocket identified its pharmacological neighbors, and transfer of their pharmacology aided in discovery of the first surrogate ligands for this orphan with a 30% success rate. Although primarily designed for GPCRs, the method is easily transferable to other protein families.

PMID: 27992882 [PubMed - as supplied by publisher]

Ribavirin suppresses bacterial virulence by targeting LysR-type transcriptional regulators.

Sci Rep. 2016 Dec 19;6:39454

Authors: Mandal RS, Ta A, Sinha R, Theeya N, Ghosh A, Tasneem M, Bhunia A, Koley H, Das S

Abstract
Targeting bacterial virulence mechanisms without compromising bacterial growth is a promising strategy to prevent drug resistance. LysR-type transcriptional regulators (LTTRs) possess structural conservation across bacterial species and regulate virulence in numerous pathogens, making them attractive targets for antimicrobial agents. We targeted AphB, a Vibrio cholerae LTTR, which regulates the expression of genes encoding cholera toxin and toxin-co-regulated pilus for inhibitor designing. Since AphB ligand is unknown, we followed a molecular fragment-based approach for ligand designing using FDA-approved drugs and subsequent screen to identify molecules that exhibited high-affinity binding to AphB ligand-binding pocket. Among the identified compounds, ribavirin, an anti-viral drug, antagonized AphB functions. Ribavirin perturbed Vibrio cholerae pathogenesis in animal models. The inhibitory effects of the drug was limited to the bacteria expressing wild type AphB, but not its constitutively active mutant (AphBN100E), which represents the ligand-bound state, suggesting that ribavirin binds to the active site of AphB to exert its inhibitory role and there exists no AphB-independent mechanism of its action. Similarly, ribavirin suppressed the functions of Salmonella Typhi LTTR Hrg, indicating its broad spectrum efficacy. Moreover, ribavirin did not affect the bacterial viability in culture. This study cites an example of drug repurposing for anti-infective therapy.

PMID: 27991578 [PubMed - in process]

The KIT Motion-Language Dataset.

Big Data. 2016 Dec;4(4):236-252

Authors: Plappert M, Mandery C, Asfour T

Abstract
Linking human motion and natural language is of great interest for the generation of semantic representations of human activities as well as for the generation of robot activities based on natural language input. However, although there have been years of research in this area, no standardized and openly available data set exists to support the development and evaluation of such systems. We, therefore, propose the Karlsruhe Institute of Technology (KIT) Motion-Language Dataset, which is large, open, and extensible. We aggregate data from multiple motion capture databases and include them in our data set using a unified representation that is independent of the capture system or marker set, making it easy to work with the data regardless of its origin. To obtain motion annotations in natural language, we apply a crowd-sourcing approach and a web-based tool that was specifically build for this purpose, the Motion Annotation Tool. We thoroughly document the annotation process itself and discuss gamification methods that we used to keep annotators motivated. We further propose a novel method, perplexity-based selection, which systematically selects motions for further annotation that are either under-represented in our data set or that have erroneous annotations. We show that our method mitigates the two aforementioned problems and ensures a systematic annotation process. We provide an in-depth analysis of the structure and contents of our resulting data set, which, as of October 10, 2016, contains 3911 motions with a total duration of 11.23 hours and 6278 annotations in natural language that contain 52,903 words. We believe this makes our data set an excellent choice that enables more transparent and comparable research in this important area.

PMID: 27992262 [PubMed]

The Impact of Genetic and Non-Genetic Factors on Warfarin Dose Prediction in MENA Region: A Systematic Review.

PLoS One. 2016;11(12):e0168732

Authors: Bader LA, Elewa H

Abstract
BACKGROUND: Warfarin is the most commonly used oral anticoagulant for the treatment and prevention of thromboembolic disorders. Pharmacogenomics studies have shown that variants in CYP2C9 and VKORC1 genes are strongly and consistently associated with warfarin dose variability. Although different populations from the Middle East and North Africa (MENA) region may share the same ancestry, it is still unclear how they compare in the genetic and non-genetic factors affecting their warfarin dosing.
OBJECTIVE: To explore the prevalence of CYP2C9 and VKORC1 variants in MENA, and the effect of these variants along with other non-genetic factors in predicting warfarin dose.
METHODS: In this systematic review, we included observational cross sectional and cohort studies that enrolled patients on stable warfarin dose and had the genetics and non-genetics factors associated with mean warfarin dose as the primary outcome. We searched PubMed, Medline, Scopus, PharmGKB, PHGKB, Google scholar and reference lists of relevant reviews.
RESULTS: We identified 17 studies in eight different populations: Iranian, Israeli, Egyptian, Lebanese, Omani, Kuwaiti, Sudanese and Turkish. Most common genetic variant in all populations was the VKORC1 (-1639G>A), with a minor allele frequency ranging from 30% in Egyptians and up to 52% and 56% in Lebanese and Iranian, respectively. Variants in the CYP2C9 were less common, with the highest MAF for CYP2C9*2 among Iranians (27%). Variants in the VKORC1 and CYP2C9 were the most significant predictors of warfarin dose in all populations. Along with other genetic and non-genetic factors, they explained up to 63% of the dose variability in Omani and Israeli patients.
CONCLUSION: Variants of VKORC1 and CYP2C9 are the strongest predictors of warfarin dose variability among the different populations from MENA. Although many of those populations share the same ancestry and are similar in their warfarin dose predictors, a population specific dosing algorithm is needed for the prospective estimation of warfarin dose.

PMID: 27992547 [PubMed - in process]

What is the future of pharmacogenomics in pain management?

Pharmacogenomics. 2016 Dec 19;

Authors: Peiró AM, Margarit C, LLerena A

PMID: 27992298 [PubMed - as supplied by publisher]

Current and future directions in the treatment and prevention of drug-induced liver injury: a systematic review.

Expert Rev Gastroenterol Hepatol. 2016;10(4):517-36

Authors: Stine JG, Lewis JH

Abstract
While the pace of discovery of new agents, mechanisms and risk factors involved in drug-induced liver injury (DILI) remains brisk, advances in the treatment of acute DILI seems slow by comparison. In general, the key to treating suspected DILI is to stop using the drug prior to developing irreversible liver failure. However, predicting when to stop is an inexact science, and commonly used ALT monitoring is an ineffective strategy outside of clinical trials. The only specific antidote for acute DILI remains N-acetylcysteine (NAC) for acetaminophen poisoning, although NAC is proving to be beneficial in some cases of non-acetaminophen DILI in adults. Corticosteroids can be effective for DILI associated with autoimmune or systemic hypersensitivity features. Ursodeoxycholic acid, silymarin and glycyrrhizin have been used to treat DILI for decades, but success remains anecdotal. Bile acid washout regimens using cholestyramine appear to be more evidenced based, in particular for leflunomide toxicity. For drug-induced acute liver failure, the use of liver support systems is still investigational in the United States and emergency liver transplant remains limited by its availability. Primary prevention appears to be the key to avoiding DILI and the need for acute treatment. Pharmacogenomics, including human leukocyte antigen genotyping and the discovery of specific DILI biomarkers offers significant promise for the future. This article describes and summarizes the numerous and diverse treatment and prevention modalities that are currently available to manage DILI.

PMID: 26633044 [PubMed - indexed for MEDLINE]

Long-Read Single Molecule Real-Time Full Gene Sequencing of Cytochrome P450-2D6.

Hum Mutat. 2016 Mar;37(3):315-23

Authors: Qiao W, Yang Y, Sebra R, Mendiratta G, Gaedigk A, Desnick RJ, Scott SA

Abstract
The cytochrome P450-2D6 (CYP2D6) enzyme metabolizes ∼25% of common medications, yet homologous pseudogenes and copy number variants (CNVs) make interrogating the polymorphic CYP2D6 gene with short-read sequencing challenging. Therefore, we developed a novel long-read, full gene CYP2D6 single molecule real-time (SMRT) sequencing method using the Pacific Biosciences platform. Long-range PCR and CYP2D6 SMRT sequencing of 10 previously genotyped controls identified expected star (*) alleles, but also enabled suballele resolution, diplotype refinement, and discovery of novel alleles. Coupled with an optimized variant-calling pipeline, CYP2D6 SMRT sequencing was highly reproducible as triplicate intra- and inter-run nonreference genotype results were completely concordant. Importantly, targeted SMRT sequencing of upstream and downstream CYP2D6 gene copies characterized the duplicated allele in 15 control samples with CYP2D6 CNVs. The utility of CYP2D6 SMRT sequencing was further underscored by identifying the diplotypes of 14 samples with discordant or unclear CYP2D6 configurations from previous targeted genotyping, which again included suballele resolution, duplicated allele characterization, and discovery of a novel allele and tandem arrangement. Taken together, long-read CYP2D6 SMRT sequencing is an innovative, reproducible, and validated method for full-gene characterization, duplication allele-specific analysis, and novel allele discovery, which will likely improve CYP2D6 metabolizer phenotype prediction for both research and clinical testing applications.

PMID: 26602992 [PubMed - indexed for MEDLINE]

Cardiovascular pharmacogenomics: current status and future directions.

J Hum Genet. 2016 Jan;61(1):79-85

Authors: Roden DM

Abstract
Drugs are widely used and highly effective in the treatment of heart disease. Nevertheless, in some instances, even drugs effective in a population display lack of efficacy or adverse drug reactions in individual patients, often in an apparently unpredictable fashion. This review summarizes the genomic factors now known to influence variability in responses to widely used cardiovascular drugs such as clopidogrel, warfarin, heparin and statins. Genomic approaches being used to discover new pathways in common cardiovascular diseases and thus potential new targets for drug development are described. Finally, the way in which this new information is likely to be used in an electronic medical record environment is discussed.

PMID: 26178435 [PubMed - indexed for MEDLINE]

Using Sub-Network Combinations to Scale Up an Enumeration Method for Determining the Network Structures of Biological Functions.

PLoS One. 2016;11(12):e0168214

Authors: Xi JY, Ouyang Q

Abstract
Deduction of biological regulatory networks from their functions is one of the focus areas of systems biology. Among the different techniques used in this reverse-engineering task, one powerful method is to enumerate all candidate network structures to find suitable ones. However, this method is severely limited by calculation capability: due to the brute-force approach, it is infeasible for networks with large number of nodes to be studied using traditional enumeration method because of the combinatorial explosion. In this study, we propose a new reverse-engineering technique based on the enumerating method: sub-network combinations. First, a complex biological function is divided into several sub-functions. Next, the three-node-network enumerating method is applied to search for sub-networks that are able to realize each of the sub-functions. Finally, complex whole networks are constructed by enumerating all possible combinations of sub-networks. The optimal ones are selected and analyzed. To demonstrate the effectiveness of this new method, we used it to deduct the network structures of a Pavlovian-like function. The whole Pavlovian-like network was successfully constructed by combining robust sub-networks, and the results were analyzed. With sub-network combination, the complexity has been largely reduced. Our method also provides a functional modular view of biological systems.

PMID: 27992476 [PubMed - in process]

Biological causal links on physiological and evolutionary time scales.

Elife. 2016 Apr 26;5:e14424

Authors: Karmon A, Pilpel Y

Abstract
Correlation does not imply causation. If two variables, say A and B, are correlated, it could be because A causes B, or that B causes A, or because a third factor affects them both. We suggest that in many cases in biology, the causal link might be bi-directional: A causes B through a fast-acting physiological process, while B causes A through a slowly accumulating evolutionary process. Furthermore, many trained biologists tend to consistently focus at first on the fast-acting direction, and overlook the slower process in the opposite direction. We analyse several examples from modern biology that demonstrate this bias (codon usage optimality and gene expression, gene duplication and genetic dispensability, stem cell division and cancer risk, and the microbiome and host metabolism) and also discuss an example from linguistics. These examples demonstrate mutual effects between the fast physiological processes and the slow evolutionary ones. We believe that building awareness of inference biases among biologists who tend to prefer one causal direction over another could improve scientific reasoning.

PMID: 27113916 [PubMed - indexed for MEDLINE]

Dissecting mechanisms of mouse embryonic stem cells heterogeneity through a model-based analysis of transcription factor dynamics.

J R Soc Interface. 2016 Apr;13(117):

Authors: Herberg M, Glauche I, Zerjatke T, Winzi M, Buchholz F, Roeder I

Abstract
Pluripotent mouse embryonic stem cells (mESCs) show heterogeneous expression levels of transcription factors (TFs) involved in pluripotency regulation, among them Nanog and Rex1. The expression of both TFs can change dynamically between states of high and low activity, correlating with the cells' capacity for self-renewal. Stochastic fluctuations as well as sustained oscillations in gene expression are possible mechanisms to explain this behaviour, but the lack of suitable data hampered their clear distinction. Here, we present a systems biology approach in which novel experimental data on TF heterogeneity is complemented by an agent-based model of mESC self-renewal. Because the model accounts for intracellular interactions, cell divisions and heredity structures, it allows for evaluating the consistency of the proposed mechanisms with data on population growth and on TF dynamics after cell sorting. Our model-based analysis revealed that a bistable, noise-driven network model fulfils the minimal requirements to consistently explain Nanog and Rex1 expression dynamics in heterogeneous and sorted mESC populations. Moreover, we studied the impact of TF-related proliferation capacities on the frequency of state transitions and demonstrate that cellular genealogies can provide insights into the heredity structures of mESCs.

PMID: 27097654 [PubMed - indexed for MEDLINE]

Systems Vaccinology: Applications, Trends, and Perspectives.

Methods Mol Biol. 2016;1403:107-30

Authors: Sollner J

Abstract
The strategies employed in vaccinology have improved since the seminal work of Edward Jenner in the eighteenth century. Stimulated by failure to develop vaccines for cancers and chronic infectious diseases as well as an emergence of a multitude of new technologies not available earlier, vaccinology has moved from a largely experimental art to a new phase of innovation. Currently, immune reactions can be predicted and modeled before they occur and formulations can be optimized in advance for genetic background, age, sex, lifestyle, environmental factors, and microbiome. A multitude of scientific insights and technological advancements have led us to this current status, yet possibly none of the recent developments is individually more promising to achieve these goals than the interdisciplinary science of systems vaccinology. This review summarizes current trends and applications of systems vaccinology, including technically tangible areas of vaccine and immunology research which allow the transformative process into a truly broad understanding of vaccines, thereby effectively modeling interaction of vaccines with health and disease. It is becoming clear that a multitude of factors have to be considered to understand inter-patient variability of vaccine responses including those characterized from the interfaces between the immune system, microbiome, metabolome, and the nervous system.

PMID: 27076127 [PubMed - indexed for MEDLINE]

Synthetic biology and regulatory networks: where metabolic systems biology meets control engineering.

J R Soc Interface. 2016 Apr;13(117):

Authors: He F, Murabito E, Westerhoff HV

Abstract
Metabolic pathways can be engineered to maximize the synthesis of various products of interest. With the advent of computational systems biology, this endeavour is usually carried out through in silico theoretical studies with the aim to guide and complement further in vitro and in vivo experimental efforts. Clearly, what counts is the result in vivo, not only in terms of maximal productivity but also robustness against environmental perturbations. Engineering an organism towards an increased production flux, however, often compromises that robustness. In this contribution, we review and investigate how various analytical approaches used in metabolic engineering and synthetic biology are related to concepts developed by systems and control engineering. While trade-offs between production optimality and cellular robustness have already been studied diagnostically and statically, the dynamics also matter. Integration of the dynamic design aspects of control engineering with the more diagnostic aspects of metabolic, hierarchical control and regulation analysis is leading to the new, conceptual and operational framework required for the design of robust and productive dynamic pathways.

PMID: 27075000 [PubMed - indexed for MEDLINE]

Noise modulation in retinoic acid signaling sharpens segmental boundaries of gene expression in the embryonic zebrafish hindbrain.

Elife. 2016 Apr 12;5:e14034

Authors: Sosnik J, Zheng L, Rackauckas CV, Digman M, Gratton E, Nie Q, Schilling TF

Abstract
Morphogen gradients induce sharply defined domains of gene expression in a concentration-dependent manner, yet how cells interpret these signals in the face of spatial and temporal noise remains unclear. Using fluorescence lifetime imaging microscopy (FLIM) and phasor analysis to measure endogenous retinoic acid (RA) directly in vivo, we have investigated the amplitude of noise in RA signaling, and how modulation of this noise affects patterning of hindbrain segments (rhombomeres) in the zebrafish embryo. We demonstrate that RA forms a noisy gradient during critical stages of hindbrain patterning and that cells use distinct intracellular binding proteins to attenuate noise in RA levels. Increasing noise disrupts sharpening of rhombomere boundaries and proper patterning of the hindbrain. These findings reveal novel cellular mechanisms of noise regulation, which are likely to play important roles in other aspects of physiology and disease.

PMID: 27067377 [PubMed - indexed for MEDLINE]

An Immunological Perspective on Neonatal Sepsis.

Trends Mol Med. 2016 Apr;22(4):290-302

Authors: Kan B, Razzaghian HR, Lavoie PM

Abstract
Despite concerted international efforts, mortality from neonatal infections remains unacceptably high in some areas of the world, particularly for premature infants. Recent developments in flow cytometry and next-generation sequencing technologies have led to major discoveries over the past few years, providing a more integrated understanding of the developing human immune system in the context of its microbial environment. We review these recent findings, focusing on how in human newborns incomplete maturation of the immune system before a full term of gestation impacts on their vulnerability to infection. We also discuss some of the clinical implications of this research in guiding the design of more-accurate age-adapted diagnostic and preventive strategies for neonatal sepsis.

PMID: 26993220 [PubMed - indexed for MEDLINE]

Mixed-mode oscillations and population bursting in the pre-Bötzinger complex.

Elife. 2016 Mar 14;5:e13403

Authors: Bacak BJ, Kim T, Smith JC, Rubin JE, Rybak IA

Abstract
This study focuses on computational and theoretical investigations of neuronal activity arising in the pre-Bötzinger complex (pre-BötC), a medullary region generating the inspiratory phase of breathing in mammals. A progressive increase of neuronal excitability in medullary slices containing the pre-BötC produces mixed-mode oscillations (MMOs) characterized by large amplitude population bursts alternating with a series of small amplitude bursts. Using two different computational models, we demonstrate that MMOs emerge within a heterogeneous excitatory neural network because of progressive neuronal recruitment and synchronization. The MMO pattern depends on the distributed neuronal excitability, the density and weights of network interconnections, and the cellular properties underlying endogenous bursting. Critically, the latter should provide a reduction of spiking frequency within neuronal bursts with increasing burst frequency and a dependence of the after-burst recovery period on burst amplitude. Our study highlights a novel mechanism by which heterogeneity naturally leads to complex dynamics in rhythmic neuronal populations.

PMID: 26974345 [PubMed - indexed for MEDLINE]