Commentary: A Novel Disease-Drug Database Demonstrating Applicability for Pharmacogenomic-Based Prescribing.

Clin Pharmacol Ther. 2016 Jul 1;

Authors: Whirl-Carrillo M, Sangkuhl K, Gong L, Klein TE

PMID: 27367543 [PubMed - as supplied by publisher]

A tale of two sites: how inflammation can reshape the microbiomes of the gut and lungs.

J Leukoc Biol. 2016 Jun 30;

Authors: Scales BS, Dickson RP, Huffnagle GB

Abstract
Inflammation can directly and indirectly modulate the bacterial composition of the microbiome. Although studies of inflammation primarily focus on its function to negatively select against potential pathogens, some bacterial species have the ability to exploit inflammatory byproducts for their benefit. Inflammatory cells release reactive nitrogen species as antimicrobial effectors against infection, but some facultative anaerobes can also utilize the increase in extracellular nitrate in their environment for anaerobic respiration and growth. This phenomenon has been studied in the gastrointestinal tract, where blooms of facultative anaerobic Gammaproteobacteria, primarily Escherichia coli, often occur during colonic inflammation. In cystic fibrosis, Pseudomonas aeruginosa, another Gammaproteobacteria facultative anaerobe, can reduce nitrogen for anaerobic respiration and it blooms in the airways of the chronically inflamed cystic fibrosis lung. This review focuses on the evidence that inflammation can provide terminal electron acceptors for anaerobic respiration and can support blooms of facultative anaerobes, such as E. coli and P. aeruginosa in distinct, but similar, environments of the inflamed gastrointestinal and respiratory tracts.

PMID: 27365534 [PubMed - as supplied by publisher]

Fungal epidemiology and diversity in cystic fibrosis patients over a 5-year period in a national reference center.

Med Mycol. 2016 Jun 30;

Authors: Ziesing S, Suerbaum S, Sedlacek L

Abstract
The knowledge on prevalence rates of yeasts and moulds in patients with cystic fibrosis (CF) in Germany is scarce. The aim of this report is to give an overview of the diversity and epidemiology of fungal species in CF patients. Over a 5-year period, all fungal isolates cultured from microbiological specimen from CF patients were recorded. Beside standard bacteriological culture media two fungal media were used for cultivation. Species were identified by microscopy, biochemical profiling, MALDI-TOF analysis or DNA sequencing methods. In sum, 25,975 clinical samples from CF patients were analyzed. About 75% of CF patients were colonized by yeasts, mainly Candida albicans (38%) and Candida dubliniensis (12%). In 35% of the patients Aspergillus spp. (Aspergillus fumigatus: 29%) were detected, followed by Exophiala dermatitidis and Scedosporium/Lomentospora complex isolates (4% each). Data for other fungal species are shown. Over a 5-year period, the epidemiology of fungal species detected in CF patients was relatively constant. Clinical microbiology laboratories should carefully monitor samples from CF patients for newly occurring fungal pathogens.

PMID: 27364649 [PubMed - as supplied by publisher]

Phenotypic variability of R117H-CFTR expression within monozygotic twins.

Paediatr Respir Rev. 2016 Jun 15;

Authors: Waller MD, Simmonds NJ

Abstract
Whilst cystic fibrosis is a monogenic condition, variation in phenotype exists for the same CFTR genotype, which is influenced by multiple genetic and non-genetic (environmental) factors. The R117H-CFTR mutation has variability directly relating to in cis poly-thymidine alleles, producing a differing spectrum of disease. This paper provides evidence of extreme phenotype variability-including fertility status - in the context of male monogenetic twins, discussing mechanisms and highlighting the diagnostic and treatment challenges.

PMID: 27364092 [PubMed - as supplied by publisher]

Promising gene therapies pose million-dollar conundrum.

Nature. 2016 Jun 16;534(7607):305-6

Authors: Check Hayden E

PMID: 27306167 [PubMed - indexed for MEDLINE]

Proteomic screening and lasso regression reveal differential signaling in insulin and insulin-like growth factor I pathways.

Mol Cell Proteomics. 2016 Jun 30;

Authors: Erdem C, Nagle AM, Casa AJ, Litzenburger BC, Wang YF, Taylor DL, Lee AV, Lezon TR

Abstract
Insulin and insulin-like growth factor I (IGF1) influence cancer risk and progression through poorly understood mechanisms. To better understand the roles of insulin and IGF1 signaling in breast cancer, we combined proteomic screening with computational network inference to uncover differences in IGF1 and insulin induced signaling. Using reverse phase protein array, we measured the levels of 134 proteins in 21 breast cancer cell lines stimulated with IGF1 or insulin for up to 48 hours. We then constructed directed protein expression networks using three separate methods: (i) lasso regression, (ii) conventional matrix inversion, and (iii) entropy maximization. These networks, named here as the time translation models, were analyzed and the inferred interactions were ranked by differential magnitude to identify pathway differences. The two top candidates, chosen for experimental validation, were shown to regulate IGF1/insulin induced phosphorylation events. First, acetyl-CoA carboxylase (ACC) knock-down was shown to increase the level of MAPK phosphorylation. Second, stable knock-down of E-Cadherin increased the phospho-Akt protein levels. Both of the knock-down perturbations incurred phosphorylation responses stronger in IGF1 stimulated cells compared to insulin. Overall, the time-translation modeling coupled to wet-lab experiments has proven to be powerful in inferring differential interactions downstream of IGF1 and insulin signaling, in vitro.

PMID: 27364358 [PubMed - as supplied by publisher]

Digital signaling decouples activation probability and population heterogeneity.

Elife. 2015;4:e08931

Authors: Kellogg RA, Tian C, Lipniacki T, Quake SR, Tay S

Abstract
Digital signaling enhances robustness of cellular decisions in noisy environments, but it is unclear how digital systems transmit temporal information about a stimulus. To understand how temporal input information is encoded and decoded by the NF-κB system, we studied transcription factor dynamics and gene regulation under dose- and duration-modulated inflammatory inputs. Mathematical modeling predicted and microfluidic single-cell experiments confirmed that integral of the stimulus (or area, concentration × duration) controls the fraction of cells that activate NF-κB in the population. However, stimulus temporal profile determined NF-κB dynamics, cell-to-cell variability, and gene expression phenotype. A sustained, weak stimulation lead to heterogeneous activation and delayed timing that is transmitted to gene expression. In contrast, a transient, strong stimulus with the same area caused rapid and uniform dynamics. These results show that digital NF-κB signaling enables multidimensional control of cellular phenotype via input profile, allowing parallel and independent control of single-cell activation probability and population heterogeneity.

PMID: 26488364 [PubMed - indexed for MEDLINE]

Calibration and analysis of genome-based models for microbial ecology.

Elife. 2015;4:e08208

Authors: Louca S, Doebeli M

Abstract
Microbial ecosystem modeling is complicated by the large number of unknown parameters and the lack of appropriate calibration tools. Here we present a novel computational framework for modeling microbial ecosystems, which combines genome-based model construction with statistical analysis and calibration to experimental data. Using this framework, we examined the dynamics of a community of Escherichia coli strains that emerged in laboratory evolution experiments, during which an ancestral strain diversified into two coexisting ecotypes. We constructed a microbial community model comprising the ancestral and the evolved strains, which we calibrated using separate monoculture experiments. Simulations reproduced the successional dynamics in the evolution experiments, and pathway activation patterns observed in microarray transcript profiles. Our approach yielded detailed insights into the metabolic processes that drove bacterial diversification, involving acetate cross-feeding and competition for organic carbon and oxygen. Our framework provides a missing link towards a data-driven mechanistic microbial ecology.

PMID: 26473972 [PubMed - indexed for MEDLINE]

Codon-level information improves predictions of inter-residue contacts in proteins by correlated mutation analysis.

Elife. 2015;4:e08932

Authors: Jacob E, Unger R, Horovitz A

Abstract
Methods for analysing correlated mutations in proteins are becoming an increasingly powerful tool for predicting contacts within and between proteins. Nevertheless, limitations remain due to the requirement for large multiple sequence alignments (MSA) and the fact that, in general, only the relatively small number of top-ranking predictions are reliable. To date, methods for analysing correlated mutations have relied exclusively on amino acid MSAs as inputs. Here, we describe a new approach for analysing correlated mutations that is based on combined analysis of amino acid and codon MSAs. We show that a direct contact is more likely to be present when the correlation between the positions is strong at the amino acid level but weak at the codon level. The performance of different methods for analysing correlated mutations in predicting contacts is shown to be enhanced significantly when amino acid and codon data are combined.

PMID: 26371555 [PubMed - indexed for MEDLINE]

Advanced continuous cultivation methods for systems microbiology.

Microbiology. 2015 Sep;161(9):1707-19

Authors: Adamberg K, Valgepea K, Vilu R

Abstract
Increasing the throughput of systems biology-based experimental characterization of in silico-designed strains has great potential for accelerating the development of cell factories. For this, analysis of metabolism in the steady state is essential as only this enables the unequivocal definition of the physiological state of cells, which is needed for the complete description and in silico reconstruction of their phenotypes. In this review, we show that for a systems microbiology approach, high-resolution characterization of metabolism in the steady state--growth space analysis (GSA)--can be achieved by using advanced continuous cultivation methods termed changestats. In changestats, an environmental parameter is continuously changed at a constant rate within one experiment whilst maintaining cells in the physiological steady state similar to chemostats. This increases the resolution and throughput of GSA compared with chemostats, and, moreover, enables following of the dynamics of metabolism and detection of metabolic switch-points and optimal growth conditions. We also describe the concept, challenge and necessary criteria of the systematic analysis of steady-state metabolism. Finally, we propose that such systematic characterization of the steady-state growth space of cells using changestats has value not only for fundamental studies of metabolism, but also for systems biology-based metabolic engineering of cell factories.

PMID: 26220303 [PubMed - indexed for MEDLINE]

Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts.

J Biomed Inform. 2016 Jun 27;

Authors: Korkontzelos I, Nikfarjam A, Shardlow M, Sarker A, Ananiadou S, Gonzalez GH

Abstract
OBJECTIVE: The abundance of text available in social media and health related forums along with the rich expression of public opinion have recently attracted the interest of the public health community to use these sources for pharmacovigilance. Based on the intuition that patients post about Adverse Drug Reactions (ADRs) expressing negative sentiments, we investigate the effect of sentiment analysis features in locating ADR mentions.
METHODS: We enrich the feature space of a state-of-the-art ADR identification method with sentiment analysis features. Using a corpus of posts from the DailyStrength forum and tweets annotated for ADR and indication mentions, we evaluate the extent to which sentiment analysis features help in locating ADR mentions and distinguishing them from indication mentions.
RESULTS: Evaluation results show that sentiment analysis features marginally improve ADR identification in tweets and health related forum posts. Adding sentiment analysis features achieved a statistically significant F-measure increase from 72.14% to 73.22% in the Twitter part of an existing corpus using its original train/test split. Using stratified 10 × 10-fold cross-validation, statistically significant F-measure increases were shown in the DailyStrength part of the corpus, from 79.57% to 80.14%, and in the Twitter part of the corpus, from 66.91% to 69.16%. Moreover, sentiment analysis features are shown to reduce the number of ADRs being recognised as indications.
CONCLUSION: This study shows that adding sentiment analysis features can marginally improve the performance of even a state-of-the-art ADR identification method. This improvement can be of use to pharmacovigilance practice, due to the rapidly increasing popularity of social media and health forums.

PMID: 27363901 [PubMed - as supplied by publisher]

12 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 2016/07/01

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.

12 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 2016/07/01

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.

Sex-differential heterologous (non-specific) effects of vaccines: an emerging public health issue that needs to be understood and exploited.

Expert Rev Vaccines. 2016 Jun 30;:1-9

Authors: Flanagan KL, Plebanski M

Abstract
INTRODUCTION: Vaccines have heterologous effects on the immune system, leading to altered susceptibility to a range of pathogens, and possibly allergy and autoimmunity. Effects are often sex-differential. This review discusses the evidence, mechanisms and public health implications of the non-specific effects of vaccines (NSEs).
AREAS COVERED: This article firstly discusses the World Health Organization systematic review of the evidence for sex-differential heterologous effects of vaccines, and further PubMed indexed studies on NSEs on susceptibility to infectious diseases, allergy, autoimmunity and malignancy in animals and humans. Potential immunological mechanisms are evaluated, including sex-differential effects. Finally it describes how advances in systems biology might be applied to study such effects. Expert commentary: This section points out the need to understand immune mechanisms in order to exploit beneficial vaccine effects, and diminish deleterious ones. It suggests analysis of vaccine effects by sex is important, and discusses the future for personalised vaccines that take these effects into account.

PMID: 27362915 [PubMed - as supplied by publisher]

Recon 2.2: from reconstruction to model of human metabolism.

Metabolomics. 2016;12:109

Authors: Swainston N, Smallbone K, Hefzi H, Dobson PD, Brewer J, Hanscho M, Zielinski DC, Ang KS, Gardiner NJ, Gutierrez JM, Kyriakopoulos S, Lakshmanan M, Li S, Liu JK, Martínez VS, Orellana CA, Quek LE, Thomas A, Zanghellini J, Borth N, Lee DY, Nielsen LK, Kell DB, Lewis NE, Mendes P

Abstract
INTRODUCTION: The human genome-scale metabolic reconstruction details all known metabolic reactions occurring in humans, and thereby holds substantial promise for studying complex diseases and phenotypes. Capturing the whole human metabolic reconstruction is an on-going task and since the last community effort generated a consensus reconstruction, several updates have been developed.
OBJECTIVES: We report a new consensus version, Recon 2.2, which integrates various alternative versions with significant additional updates. In addition to re-establishing a consensus reconstruction, further key objectives included providing more comprehensive annotation of metabolites and genes, ensuring full mass and charge balance in all reactions, and developing a model that correctly predicts ATP production on a range of carbon sources.
METHODS: Recon 2.2 has been developed through a combination of manual curation and automated error checking. Specific and significant manual updates include a respecification of fatty acid metabolism, oxidative phosphorylation and a coupling of the electron transport chain to ATP synthase activity. All metabolites have definitive chemical formulae and charges specified, and these are used to ensure full mass and charge reaction balancing through an automated linear programming approach. Additionally, improved integration with transcriptomics and proteomics data has been facilitated with the updated curation of relationships between genes, proteins and reactions.
RESULTS: Recon 2.2 now represents the most predictive model of human metabolism to date as demonstrated here. Extensive manual curation has increased the reconstruction size to 5324 metabolites, 7785 reactions and 1675 associated genes, which now are mapped to a single standard. The focus upon mass and charge balancing of all reactions, along with better representation of energy generation, has produced a flux model that correctly predicts ATP yield on different carbon sources.
CONCLUSION: Through these updates we have achieved the most complete and best annotated consensus human metabolic reconstruction available, thereby increasing the ability of this resource to provide novel insights into normal and disease states in human. The model is freely available from the Biomodels database (http://identifiers.org/biomodels.db/MODEL1603150001).

PMID: 27358602 [PubMed - as supplied by publisher]

Mapping transcription factor interactome networks using HaloTag protein arrays.

Proc Natl Acad Sci U S A. 2016 Jun 29;

Authors: Yazaki J, Galli M, Kim AY, Nito K, Aleman F, Chang KN, Carvunis AR, Quan R, Nguyen H, Song L, Alvarez JM, Huang SC, Chen H, Ramachandran N, Altmann S, Gutiérrez RA, Hill DE, Schroeder JI, Chory J, LaBaer J, Vidal M, Braun P, Ecker JR

Abstract
Protein microarrays enable investigation of diverse biochemical properties for thousands of proteins in a single experiment, an unparalleled capacity. Using a high-density system called HaloTag nucleic acid programmable protein array (HaloTag-NAPPA), we created high-density protein arrays comprising 12,000 Arabidopsis ORFs. We used these arrays to query protein-protein interactions for a set of 38 transcription factors and transcriptional regulators (TFs) that function in diverse plant hormone regulatory pathways. The resulting transcription factor interactome network, TF-NAPPA, contains thousands of novel interactions. Validation in a benchmarked in vitro pull-down assay revealed that a random subset of TF-NAPPA validated at the same rate of 64% as a positive reference set of literature-curated interactions. Moreover, using a bimolecular fluorescence complementation (BiFC) assay, we confirmed in planta several interactions of biological interest and determined the interaction localizations for seven pairs. The application of HaloTag-NAPPA technology to plant hormone signaling pathways allowed the identification of many novel transcription factor-protein interactions and led to the development of a proteome-wide plant hormone TF interactome network.

PMID: 27357687 [PubMed - as supplied by publisher]

Quantitative deep-mapping of the cultured podocyte proteome uncovers shifts in proteostatic mechanisms during differentiation.

Am J Physiol Cell Physiol. 2016 Jun 29;:ajpcell.00121.2016

Authors: Rinschen MM, Schroeter CB, Koehler S, Ising C, Schermer B, Kann M, Benzing T, Brinkkoetter PT

Abstract
The renal filtration barrier is maintained by the renal podocyte, an epithelial postmitotic cell. Immortalized mouse podocyte cell lines - both in the differentiated and undifferentiated state - are widely utilized tools to estimate podocyte injury and cytoskeletal rearrangement processes in vitro. Here, we mapped the cultured podocyte proteome at a depth of more than 8800 proteins and quantified 7240 proteins. Copy numbers of proteins mutated in forms of hereditary nephrotic syndrome or focal segmental glomerulosclerosis (FSGS) were assessed. We found that cultured podocytes express abundant copy numbers of endogenous receptors such as tyrosine kinase membrane receptors, the G-protein coupled receptor (GPCR), NPR3 (ANP receptor) and several poorly characterized GPCRs. The dataset was correlated with deep mapping mRNA sequencing ("mRNAseq") data from the native mouse podocyte, the native mouse podocyte proteome and staining intensities from the human protein atlas. The generated dataset was similar to these previously published resources, but several native and high-abundant podocyte-specific proteins were not identified in the dataset. Notably, this dataset detected general perturbations in proteostatic mechanisms as a dominant alteration during podocyte differentiation, with high proteasome activity in the undifferentiated state and markedly increased expression of lysosomal proteins in the differentiated state. Phosphoproteomics analysis of mouse podocytes at a resolution of more than 3000 sites suggested a preference of phosphorylation of actin-filament associated proteins in the differentiated state. The dataset obtained here provides a resource and provides the means for deep mapping of the native podocyte proteome and phosphoproteome in a similar manner.

PMID: 27357545 [PubMed - as supplied by publisher]

Unified pre- and postsynaptic long-term plasticity enables reliable and flexible learning.

Elife. 2015;4

Authors: Costa RP, Froemke RC, Sjöström PJ, van Rossum MC

Abstract
Although it is well known that long-term synaptic plasticity can be expressed both pre- and postsynaptically, the functional consequences of this arrangement have remained elusive. We show that spike-timing-dependent plasticity with both pre- and postsynaptic expression develops receptive fields with reduced variability and improved discriminability compared to postsynaptic plasticity alone. These long-term modifications in receptive field statistics match recent sensory perception experiments. Moreover, learning with this form of plasticity leaves a hidden postsynaptic memory trace that enables fast relearning of previously stored information, providing a cellular substrate for memory savings. Our results reveal essential roles for presynaptic plasticity that are missed when only postsynaptic expression of long-term plasticity is considered, and suggest an experience-dependent distribution of pre- and postsynaptic strength changes.

PMID: 26308579 [PubMed - indexed for MEDLINE]

Systems Biology Approaches to a Rational Drug Discovery Paradigm.

Curr Top Med Chem. 2016;16(9):1009-25

Authors: Prathipati P, Mizuguchi K

Abstract
Ligand- and structure-based drug design approaches complement phenotypic and target screens, respectively, and are the two major frameworks for guiding early-stage drug discovery efforts. Since the beginning of this century, the advent of the genomic era has presented researchers with a myriad of high throughput biological data (parts lists and their interaction networks) to address efficacy and toxicity, augmenting the traditional ligand- and structure-based approaches. This data rich era has also presented us with challenges related to integrating and analyzing these multi-platform and multi-dimensional datasets and translating them into viable hypotheses. Hence in the present paper, we review these existing approaches to drug discovery research and argue the case for a new systems biology based approach. We present the basic principles and the foundational arguments/underlying assumptions of the systems biology based approaches to drug design. Also discussed are systems biology data types (key entities, their attributes and their relationships with each other, and data models/representations), software and tools used for both retrospective and prospective analysis, and the hypotheses that can be inferred. In addition, we summarize some of the existing resources for a systems biology based drug discovery paradigm (open TG-GATEs, DrugMatrix, CMap and LINCs) in terms of their strengths and limitations.

PMID: 26306988 [PubMed - indexed for MEDLINE]

The Next Generation of Dietitians: Implementing Dietetics Education and Practice in Integrative Medicine.

J Am Coll Nutr. 2015;34(5):430-5

Authors: Wagner LE, Evans RG, Noland D, Barkley R, Sullivan DK, Drisko J

Abstract
Integrative medicine is a quickly expanding field of health care that emphasizes nutrition as a key component. Dietitians and nutritionists have an opportunity to meet workforce demands by practicing dietetics and integrative medicine (DIM). The purpose of this article is to describe a DIM education program and practicum. We report the results of an interprofessional nutrition education and practicum program between the University of Kansas Medical Center (KUMC) Department of Dietetics and Nutrition and KU Integrative Medicine. This partnered program provides training that builds on the strong foundation of the Nutrition Care Process and adds graduate-level educational and practicum experiences in foundational integrative medicine knowledge, including nutritional approaches from a systems biology perspective, nutrigenomics, and biochemistry as the core knowledge to understand the root cause of a chronic disorder and to choose appropriate nutritional tools for interventions. This interprofessional KUMC program provides a dietetic internship, master's degree, and graduate certificate in DIM and fulfills a need for dietitians and nutritionists who seek careers practicing in an integrative medicine setting. The program fulfills expanding workforce needs to provide quality health care for patients with chronic illnesses.

PMID: 25961884 [PubMed - indexed for MEDLINE]