[Using (1)H-nuclear magnetic resonance metabolomics and gene ontology to establish pathological staging model for esophageal cancer patients].

Zhonghua Wai Ke Za Zhi. 2016 Jul 1;54(7):540-545

Authors: Chen X, Wang K, Chen W, Jiang H, Deng PC, Li ZJ, Peng J, Zhou ZY, Yang H, Huang GX, Zeng J

Abstract
Objectives: By combining the metabolomics and computational biology, to explore the relationship between metabolic phenotype and pathological stage in esophageal cancer patients, to find the mechanism of metabolic network disturbance and develop a new method for fast preoperative clinical staging. Methods: A prospective cohort study (from April 2013 to January 2016) was conducted. The preoperative patients from Sichuan Provincial People's Hospital, who were diagnosed with esophageal cancer from May 2013 to April 2014 were included, and their serum samples were collected to detect (1)H-nuclear magnetic resonance (NMR) metabolomics for the purpose of drawing the metabolic fingerprinting in different stages of patients with esophageal cancer. The data were processed with these methods-principal components analysis: partial least squares regression and support vector machine, for the exploration of the enzyme-gene network regulatory mechanism in abnormal esophageal cancer metabolic network regulation and to build the quantitative prediction model of esophageal cancer staging in the end. All data were processed on high-performance computing platforms Matalab. The comparison of data had used Wilcoxon test, variance analysis, χ(2) test and Fisher exact test. Results: Twenty patients with different stages of esophageal cancer were included; and their serum metabolic fingerprinting could differentiate different tumor stages. There were no difference among the five teams in the age (F=1.086, P>0.05), the body mass index (F=1.035, P>0.05), the distance from the incisors to tumor (F=1.078, P>0.05). Among the patients with different TNM stages, there was a significant difference in plasma metabolome. Compared to ⅡB, ⅢA, Ⅳstage patients, increased levels of butanone, ethanol amine, homocysteine, hydroxy acids and estriol, together with decreased levels of glycoprotein, creatine, choline, isobutyricacid, alanine, leucine, valine, were observed inⅠB, ⅡA stage patients. Four metabolic markers (ethanol amine, hydroxy-propionic acid, homocysteine and estriol) were eventually selected. gene ontology analysis showed that 54 enzymes and genes regulated the 4 key metabolic markers. The quantitative prediction model of esophageal cancer staging based on esophageal cancer NMR spectrum were established. Cross-validation results showed that the predicted effect was good (root mean square error=5.3, R(2)=0.47, P=0.036). Conclusions: The systems biology approaches based on metabolomics and enzyme-gene regulatory network analysis can be used to quantify the metabolic network disturbance of patients with advanced esophageal cancer, and to predict preoperative clinical staging of esophageal cancer patients by plasma NMR metabolomics.

PMID: 27373482 [PubMed - as supplied by publisher]

Mining Health-Related Issues in Consumer Product Reviews by Using Scalable Text Analytics.

Biomed Inform Insights. 2016;8(Suppl 1):1-11

Authors: Torii M, Tilak SS, Doan S, Zisook DS, Fan JW

Abstract
In an era when most of our life activities are digitized and recorded, opportunities abound to gain insights about population health. Online product reviews present a unique data source that is currently underexplored. Health-related information, although scarce, can be systematically mined in online product reviews. Leveraging natural language processing and machine learning tools, we were able to mine 1.3 million grocery product reviews for health-related information. The objectives of the study were as follows: (1) conduct quantitative and qualitative analysis on the types of health issues found in consumer product reviews; (2) develop a machine learning classifier to detect reviews that contain health-related issues; and (3) gain insights about the task characteristics and challenges for text analytics to guide future research.

PMID: 27375358 [PubMed]

Coreference resolution improves extraction of Biological Expression Language statements from texts.

Database (Oxford). 2016;2016

Authors: Choi M, Liu H, Baumgartner W, Zobel J, Verspoor K

Abstract
We describe a system that automatically extracts biological events from biomedical journal articles, and translates those events into Biological Expression Language (BEL) statements. The system incorporates existing text mining components for coreference resolution, biological event extraction and a previously formally untested strategy for BEL statement generation. Although addressing the BEL track (Track 4) at BioCreative V (2015), we also investigate how incorporating coreference resolution might impact event extraction in the biomedical domain. In this paper, we report that our system achieved the best performance of 20.2 and 35.2 in F-score for the full BEL statement level on both stage 1, and stage 2 using provided gold standard entities, respectively. We also report that our results evaluated on the training dataset show benefit from integrating coreference resolution with event extraction.

PMID: 27374122 [PubMed - as supplied by publisher]

Mineral and metabolic profiles in tea leaves and flowers during flower development.

Plant Physiol Biochem. 2016 Jun 14;106:316-326

Authors: Jia S, Wang Y, Hu J, Ding Z, Liang Q, Zhang Y, Wang H

Abstract
Tea [Camellia sinensis (L.) O. Kuntze] is one of the most popular non-alcoholic beverage crops in the world, and the physiological processes and gene regulations involved in development in tea plants have been well characterized. However, relatively little is known about the metabolic changes combined with mineral distributions that occur during flower development. Here we detected the contents of 11 elements in tea leaves and flowers and found that, some of them, especially phosphorus, sulfur and copper, showed significant changes during tea flowering. We also detected 122 metabolites in tea leaves and flowers and found that, 72 of them showed significant differences between flowers and leaves, of which sugars, organic acids, and flavonoids dominated. The sugars, such as trehalose and galactose, all accumulated in tea flowers, and the organic acids, such as malic acid, citric acid and fumaric acid involved in TCA cycle. The flavonoids, like epicatechin, catechin gallate and epigallocatechin, were more abundant in leaves. Furthermore, we found that the contents of 33 metabolites changed during the development of flowers. Especially, citric acid, phenylalanine and most flavonoids decreased while fructose and galactose increased during flowering stages in flowers. We also analyzed the correlations between the ions and metabolites and found that, some mineral nutrients including phosphorus, sulfur, manganese and zinc had close relations to organic acids, flavonoids, sugars and several amino acids during flowering. We mapped the metabolic pathway according to the KEGG database. This work will serve as the foundation for a systems biology approach to the understanding of mineral metabolism.

PMID: 27372442 [PubMed - as supplied by publisher]

6 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 2016/07/03

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.

Research and drug development activities in rare diseases: differences between Japan and Europe regarding influence of prevalence.

Drug Discov Today. 2016 Jun 28;

Authors: Mizoguchi H, Yamanaka T, Kano S

Abstract
Orphan drug legislation has contributed enormously to promote drug development for rare diseases but further effective and sustainable approaches are required. This study focused on the difference of rare disease prevalence between Japan and Europe, classified the rare diseases comprehensively using cluster analysis and analyzed the influence of prevalence on research activity and drug development. Although overall strong correlative progress of research was found and absolute numbers of values were greater in Europe than in Japan, the regional higher prevalent diseases demonstrated more progress of research and development relatively in the region by examining clusters. Our findings suggest potential optimal drug development in consideration of regional differences. Moreover, an in-depth analysis of diseases that showed exceptional research achievements compared with prevalence speculated important determinants of progress.

PMID: 27371505 [PubMed - as supplied by publisher]

9 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 2016/07/02

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.

Methylene blue inhibits lumefantrine-resistant Plasmodium berghei.

J Infect Dev Ctries. 2016;10(6):635-642

Authors: Mwangi VI, Mumo RM, Kiboi DM, Omar SA, Ng'ang'a ZW, Ozwara HS

Abstract
INTRODUCTION: Chemotherapy still is the most effective way to control malaria, a major public health problem in sub-Saharan Africa. The large-scale use of the combination therapy artemether-lumefantrine for malaria treatment in Africa predisposes lumefantrine to emergence of resistance. There is need to identify drugs that can be used as substitutes to lumefantrine for use in combination therapy. Methylene blue, a synthetic anti-methemoglobinemia drug, has been shown to contain antimalarial properties, making it a candidate for drug repurposing. The present study sought to determine antiplasmodial effects of methylene blue against lumefantrine- and pyrimethamine-resistant strains of P. berghei.
METHODOLOGY: Activity of methylene blue was assessed using the classical four-day test on mice infected with lumefantrine-resistant and pyrimethamine-resistant P. berghei. A dose of 45 mg/kg/day was effective for testing ED90. Parasitemia and mice survival was determined.
RESULTS: At 45 mg/kg/day, methylene blue sustained significant parasite inhibition, over 99%, for at least 6 days post-treatment against lumefantrine-resistant and pyrimethamine-resistant P. berghei (p = 0.0086 and p = 0.0191, respectively). No serious adverse effects were observed.
CONCLUSIONS: Our results indicate that methylene blue at a concentration of 45 mg/kg/day confers over 99% inhibition against lumefantrine- and pyrimethamine-resistant P. berghei for six days. This shows the potential use methylene blue in the development of antimalarials against lumefantrine- and pyrimethamine-resistant parasites.

PMID: 27367013 [PubMed - as supplied by publisher]

Drug repositioning approaches to parasitic diseases: a medicinal chemistry perspective.

Drug Discov Today. 2016 Jun 27;

Authors: Ferreira LG, Andricopulo AD

Abstract
Identifying new indications for clinically useful drugs is a worthwhile approach for neglected tropical diseases. The number of successful repurposing cases in the field is growing as not-for-profit organizations, in association with academia and pharmaceutical companies, enable screening campaigns for the identification of new repositioning candidates. Current programs have delivered encouraging results as the use of state-of-the-art technologies, such as genomic and structural biology tools, and high-throughput screening platforms have become increasingly common in infectious disease research. Drug repositioning has played a key part in improving the lives of those suffering from these conditions, as evidenced by successful precedents and recent studies on preeminent parasitic disorders.

PMID: 27365271 [PubMed - as supplied by publisher]

Can you teach old drugs new tricks?

Nature. 2016 Jun 16;534(7607):314-6

Authors: Nosengo N

PMID: 27306171 [PubMed - indexed for MEDLINE]

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]