Effects of Propidium Monoazide (PMA) Treatment on Mycobiome and Bacteriome Analysis of Cystic Fibrosis Airways during Exacerbation.

PLoS One. 2016;11(12):e0168860

Authors: Nguyen LD, Deschaght P, Merlin S, Loywick A, Audebert C, Van Daele S, Viscogliosi E, Vaneechoutte M, Delhaes L

Abstract
INTRODUCTION AND PURPOSE: Propidium monoazide (PMA)-pretreatment has increasingly been applied to remove the bias from dead or damaged cell artefacts, which could impact the microbiota analysis by high-throughput sequencing. Our study aimed to determine whether a PMA-pretreatment coupled with high-throughput sequencing analysis provides a different picture of the airway mycobiome and bacteriome.
RESULTS AND DISCUSSION: We compared deep-sequencing data of mycobiota and microbiota of 15 sputum samples from 5 cystic fibrosis (CF) patients with and without prior PMA-treatment of the DNA-extracts. PMA-pretreatment had no significant effect on the entire and abundant bacterial community (genera expressed as operational taxonomic units (OTUs) with a relative abundance greater than or equal to 1%), but caused a significant difference in the intermediate community (less than 1%) when analyzing the alpha biodiversity Simpson index (p = 0.03). Regarding PMA impact on the airway mycobiota evaluated for the first time here; no significant differences in alpha diversity indexes between PMA-treated and untreated samples were observed. Regarding beta diversity analysis, the intermediate communities also differed more dramatically than the total and abundant ones when studying both mycobiome and bacteriome. Our results showed that only the intermediate (or low abundance) population diversity is impacted by PMA-treatment, and therefore that abundant taxa are mostly viable during acute exacerbation in CF. Given such a cumbersome protocol (PMA-pretreatment coupled with high-throughput sequencing), we discuss its potential interest within the follow-up of CF patients. Further studies using PMA-pretreatment are warranted to improve our "omic" knowledge of the CF airways.

PMID: 28030619 [PubMed - in process]

[Improved lung function in cystic fibrosis using mechanical insufflation-exsufflation].

An Pediatr (Barc). 2016 Oct 28;:

Authors: Fuentes LA, Caro P, Garcia-Ruiz AJ, Muñoz Gómez G, Martín-Montañez E

PMID: 28029527 [PubMed - as supplied by publisher]

Nutrition and Growth in Chronic Disease.

World Rev Nutr Diet. 2016;114:84-102

Authors: Hartman C, Shamir R

PMID: 26906408 [PubMed - indexed for MEDLINE]

Metabolic systems biology: a brief primer.

J Physiol. 2016 Dec 28;:

Authors: Edwards LM

Abstract
In the early to mid 20(th) Century, reductionism as a concept in biology was challenged by key thinkers, including Ludwig Von Bertalanffy. He proposed that living organisms were specific examples of complex systems and, as such, they should display characteristics including hierarchical organisation and emergent behaviour. Yet the true study of complete biological systems (for example, metabolism) was not possible until technological advances that occurred 60 years later. Technology now exists that permits the measurement of complete levels of the biological hierarchy, for example the genome and transcriptome. The complexity and scale of these data require computational models for their interpretation. The combination of these - systems thinking, high-dimensional data and computation - defines systems biology, typically accompanied by some notion of iterative model refinement. Only sequencing-based technologies, however, offer full coverage. Other 'omics' platforms trade coverage for sensitivity, although the densely-connected nature of biological networks suggest that full coverage may not be necessary. Systems biology models are often characterised as either 'bottom-up' (mechanistic) or 'top-down' (statistical). This distinction can mislead, as all models rely on data and all are, to some degree, 'middle-out'. Systems biology has matured as a discipline, and its methods are commonplace in many laboratories. However, many challenges remain, especially those related to large-scale data integration. This article is protected by copyright. All rights reserved.

PMID: 28028815 [PubMed - as supplied by publisher]

Seqenv: linking sequences to environments through text mining.

PeerJ. 2016;4:e2690

Authors: Sinclair L, Ijaz UZ, Jensen LJ, Coolen MJ, Gubry-Rangin C, Chroňáková A, Oulas A, Pavloudi C, Schnetzer J, Weimann A, Ijaz A, Eiler A, Quince C, Pafilis E

Abstract
Understanding the distribution of taxa and associated traits across different environments is one of the central questions in microbial ecology. High-throughput sequencing (HTS) studies are presently generating huge volumes of data to address this biogeographical topic. However, these studies are often focused on specific environment types or processes leading to the production of individual, unconnected datasets. The large amounts of legacy sequence data with associated metadata that exist can be harnessed to better place the genetic information found in these surveys into a wider environmental context. Here we introduce a software program, seqenv, to carry out precisely such a task. It automatically performs similarity searches of short sequences against the "nt" nucleotide database provided by NCBI and, out of every hit, extracts-if it is available-the textual metadata field. After collecting all the isolation sources from all the search results, we run a text mining algorithm to identify and parse words that are associated with the Environmental Ontology (EnvO) controlled vocabulary. This, in turn, enables us to determine both in which environments individual sequences or taxa have previously been observed and, by weighted summation of those results, to summarize complete samples. We present two demonstrative applications of seqenv to a survey of ammonia oxidizing archaea as well as to a plankton paleome dataset from the Black Sea. These demonstrate the ability of the tool to reveal novel patterns in HTS and its utility in the fields of environmental source tracking, paleontology, and studies of microbial biogeography. To install seqenv, go to: https://github.com/xapple/seqenv.

PMID: 28028456 [PubMed]

Machine learning to assist risk-of-bias assessments in systematic reviews.

Int J Epidemiol. 2016 Feb;45(1):266-77

Authors: Millard LA, Flach PA, Higgins JP

Abstract
BACKGROUND: Risk-of-bias assessments are now a standard component of systematic reviews. At present, reviewers need to manually identify relevant parts of research articles for a set of methodological elements that affect the risk of bias, in order to make a risk-of-bias judgement for each of these elements. We investigate the use of text mining methods to automate risk-of-bias assessments in systematic reviews. We aim to identify relevant sentences within the text of included articles, to rank articles by risk of bias and to reduce the number of risk-of-bias assessments that the reviewers need to perform by hand.
METHODS: We use supervised machine learning to train two types of models, for each of the three risk-of-bias properties of sequence generation, allocation concealment and blinding. The first model predicts whether a sentence in a research article contains relevant information. The second model predicts a risk-of-bias value for each research article. We use logistic regression, where each independent variable is the frequency of a word in a sentence or article, respectively.
RESULTS: We found that sentences can be successfully ranked by relevance with area under the receiver operating characteristic (ROC) curve (AUC) > 0.98. Articles can be ranked by risk of bias with AUC > 0.72. We estimate that more than 33% of articles can be assessed by just one reviewer, where two reviewers are normally required.
CONCLUSIONS: We show that text mining can be used to assist risk-of-bias assessments.

PMID: 26659355 [PubMed - indexed for MEDLINE]

Scaling-up NLP Pipelines to Process Large Corpora of Clinical Notes.

Methods Inf Med. 2015;54(6):548-52

Authors: Divita G, Carter M, Redd A, Zeng Q, Gupta K, Trautner B, Samore M, Gundlapalli A

Abstract
INTRODUCTION: This article is part of the Focus Theme of Methods of Information in Medicine on "Big Data and Analytics in Healthcare".
OBJECTIVES: This paper describes the scale-up efforts at the VA Salt Lake City Health Care System to address processing large corpora of clinical notes through a natural language processing (NLP) pipeline. The use case described is a current project focused on detecting the presence of an indwelling urinary catheter in hospitalized patients and subsequent catheter-associated urinary tract infections.
METHODS: An NLP algorithm using v3NLP was developed to detect the presence of an indwelling urinary catheter in hospitalized patients. The algorithm was tested on a small corpus of notes on patients for whom the presence or absence of a catheter was already known (reference standard). In planning for a scale-up, we estimated that the original algorithm would have taken 2.4 days to run on a larger corpus of notes for this project (550,000 notes), and 27 days for a corpus of 6 million records representative of a national sample of notes. We approached scaling-up NLP pipelines through three techniques: pipeline replication via multi-threading, intra-annotator threading for tasks that can be further decomposed, and remote annotator services which enable annotator scale-out.
RESULTS: The scale-up resulted in reducing the average time to process a record from 206 milliseconds to 17 milliseconds or a 12- fold increase in performance when applied to a corpus of 550,000 notes.
CONCLUSIONS: Purposely simplistic in nature, these scale-up efforts are the straight forward evolution from small scale NLP processing to larger scale extraction without incurring associated complexities that are inherited by the use of the underlying UIMA framework. These efforts represent generalizable and widely applicable techniques that will aid other computationally complex NLP pipelines that are of need to be scaled out for processing and analyzing big data.

PMID: 26534722 [PubMed - indexed for MEDLINE]

Understanding the Relationship between Social Cognition and Word Difficulty. A Language Based Analysis of Individuals with Autism Spectrum Disorder.

Methods Inf Med. 2015;54(6):522-9

Authors: Aramaki E, Shikata S, Miyabe M, Usuda Y, Asada K, Ayaya S, Kumagaya S

Abstract
BACKGROUND: Few quantitative studies have been conducted on the relationship between society and its languages. Individuals with autistic spectrum disorder (ASD) are known to experience social hardships, and a wide range of clinical information about their quality of life has been provided through numerous narrative analyses. However, the narratives of ASD patients have thus far been examined mainly through qualitative approaches.
OBJECTIVES: In this study, we analyzed adults with ASD to quantitatively examine the relationship between language abilities and ASD severity scores.
METHODS: We generated phonetic transcriptions of speeches by 16 ASD adults at an ASD workshop, and divided the participants into 2 groups according to their Social Responsiveness Scale(TM), 2nd Edition (SRS(TM)-2) scores (where higher scores represent more severe ASD): Group A comprised high-scoring ASD adults (SRS(TM)-2 score: ≥ 76) and Group B comprised low- and intermediate-scoring ASD adults (SRS(TM)-2 score: < 76). Using natural language processing (NLP)-based analytical methods, the narratives were converted into numerical data according to four language ability indicators, and the relationships between the language ability scores and ASD severity scores were compared.
RESULTS AND DISCUSSION: Group A showed a marginally negative correlation with the level of Japanese word difficulty (p < .10), while the "social cognition" subscale of the SRS(TM)-2 score showed a significantly negative correlation (p < .05) with word difficulty. When comparing only male participants, Group A demonstrated a significantly lower correlation with word difficulty level than Group B (p < .10).
CONCLUSION: Social communication was found to be strongly associated with the level of word difficulty in speech. The clinical applications of these findings may be available in the near future, and there is a need for further detailed study on language metrics designed for ASD adults.

PMID: 26391807 [PubMed - indexed for MEDLINE]

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

Pharmacogenomics[Title/Abstract]

These pubmed results were generated on 2016/12/28

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

Drug repurposing in anticancer reagent development.

Comb Chem High Throughput Screen. 2016 Dec 26;

Authors: Chen PC, Liu X, Lin Y

Abstract
The development process for cancer drugs is risky, lengthy and costly. Exploration of the anticancer potential of listed non-cancer drugs has become a popular research field in the pharmaceutical industry. The efficacy of various drugs are tested in multiple cancer types and the mechanisms underlying their effect were investigated. In this review, we have summarized the drug relocation instances for different cancer types in recent years and discussed the future direction of drug repurposing.

PMID: 28025934 [PubMed - as supplied by publisher]

HEDD: the human epigenetic drug database.

Database (Oxford). 2016;2016:

Authors: Qi Y, Wang D, Wang D, Jin T, Yang L, Wu H, Li Y, Zhao J, Du F, Song M, Wang R

Abstract
Epigenetic drugs are chemical compounds that target disordered post-translational modification of histone proteins and DNA through enzymes, and the recognition of these changes by adaptor proteins. Epigenetic drug-related experimental data such as gene expression probed by high-throughput sequencing, co-crystal structure probed by X-RAY diffraction and binding constants probed by bio-assay have become widely available. The mining and integration of multiple kinds of data can be beneficial to drug discovery and drug repurposing. HEMD and other epigenetic databases store comprehensively epigenetic data where users can acquire segmental information of epigenetic drugs. However, some data types such as high-throughput datasets are not provide by these databases and they do not support flexible queries for epigenetic drug-related experimental data. Therefore, in reference to HEMD and other epigenetic databases, we developed a relatively comprehensive database for human epigenetic drugs. The human epigenetic drug database (HEDD) focuses on the storage and integration of epigenetic drug datasets obtained from laboratory experiments and manually curated information. The latest release of HEDD incorporates five kinds of datasets: (i) drug, (ii) target, (iii) disease, (vi) high-throughput and (v) complex. In order to facilitate data extraction, flexible search options were built in HEDD, which allowed an unlimited condition query for specific kinds of datasets using drug names, diseases and experiment types.Database URL: http://hedds.org/.

PMID: 28025347 [PubMed - in process]

[Ophthalmogenetics: Rare Diseases - A Challenge for Diagnostic and Treatment].

Klin Monbl Augenheilkd. 2016 Mar;232(3):242

Authors: Rudolph G

PMID: 27011027 [PubMed - indexed for MEDLINE]

GerOSS (German Obstetric Surveillance System). A Project to Improve the Treatment of Obstetric Rare Diseases and Complications Using a Web Based Documentation and Information Platform.

Methods Inf Med. 2015;54(5):406-11

Authors: Berlage S, Grüßner S, Lack N, Franz HB

Abstract
BACKGROUND: Severe and very rare obstetric complications (e.g. eclampsia, postpartum haemorrhage or uterine rupture), typically culminate in a chaotic, uncontrollable sequence of events. Outcome for mother and child depends on whether doctors and midwives are able to quickly take correct decisions and initiate optimal treatment.
OBJECTIVES: GerOSS (German Obstetric Surveillance System) aims at generating deeper insight into relevant risk factors to improve diagnosis and treatment of severe complications during pregnancy and delivery. As such it is primarily conceived as a system for quality improvement and less as a register. Another focus is the provision of an information and communication platform for dissemination of these insights. Finally, incidences of selected rare obstetric events may be derived.
METHODS: These rare events are monitored for two to five years in Lower Saxony, Bavaria and Berlin. Quantitative analyses of aggregate data are complemented with in depth case based anonymised evaluations by experts. The temporal sequence of measures taken as well as the management of care is inspected. Participants receive a feedback of comments on the synopsis of individual cases. Aggregate data results are published and made available through the GerOSS platform. A scientific advisory committee ensures the link with the professional scientific bodies. A comparison within INOSS (International Network of Obstetric Survey Systems) allows additional insights into the treatment of obstetric rare diseases and complications. More reliable estimates of the incidence of such events can be computed and compared within a larger database.
RESULTS: Following the implementation in three federal states in Germany in 2010, participation in GerOSS-Project has increased to 100% of all hospitals with a delivery unit in Lower Saxony, 30% in Bavaria and 80% in Berlin. Feasibility of the project is shown by successful implementation of GerOSS. Quantitative analyses enable construction of risk profiles (e.g. for the prevalence of hysterectomies and uterine ruptures) such that tailored treatment algorithms may be derived. Age, body mass index and previous caesarean section are common risk factors when complications occur. Respective recommendations have not always been adhered to in the diagnosis and therapy of such cases. The presentation of initial GerOSS results has paved the path for first changes in obstetric care.
CONCLUSIONS: The envisaged expansion of GerOSS to an interactive platform will allow dissemination of insights such that optimal obstetric care and transferal among all involved medical facilities may see future enhancements via the internet or even through smartphone applications.

PMID: 26065375 [PubMed - indexed for MEDLINE]

Impact of azithromycin on the clinical and antimicrobial effectiveness of tobramycin in the treatment of cystic fibrosis.

J Cyst Fibros. 2016 Dec 23;:

Authors: Nichols DP, Happoldt CL, Bratcher PE, Caceres SM, Chmiel JF, Malcolm KC, Saavedra MT, Saiman L, Taylor-Cousar JL, Nick JA

Abstract
BACKGROUND: Concomitant use of oral azithromycin and inhaled tobramycin occurs in approximately half of US cystic fibrosis (CF) patients. Recent data suggest that this combination may be antagonistic.
METHODS: Test the hypothesis that azithromycin reduces the clinical benefits of tobramycin by analyses of clinical trial data, in vitro modeling of P. aeruginosa antibiotic killing, and regulation of the MexXY efflux pump.
RESULTS: Ongoing administration of azithromycin associates with reduced ability of inhaled tobramycin, as compared with aztreonam, to improve lung function and quality of life in a completed clinical trial. In users of azithromycin FEV1 (L) increased 0.8% during a 4-week period of inhaled tobramycin and an additional 6.4% during a subsequent 4-week period of inhaled aztreonam (P<0.005). CFQ-R respiratory symptom score decreased 1.8 points during inhaled tobramycin and increased 8.3 points during subsequent inhaled aztreonam (P<0.001). A smaller number of trial participants not using azithromycin had similar improvement in lung function and quality of life scores during inhaled tobramycin and inhaled aztreonam. In vitro, azithromycin selectively reduced the bactericidal effects tobramycin in cultures of clinical strains of P. aeruginosa, while up regulating antibiotic resistance through MexXY efflux.
CONCLUSIONS: Azithromycin appears capable of reducing the antimicrobial benefits of tobramycin by inducing adaptive bacterial stress responses in P. aeruginosa, suggesting that these medications together may not be optimal chronic therapy for at least some patients.

PMID: 28025037 [PubMed - as supplied by publisher]

A Review of Analytical Techniques and Their Application in Disease Diagnosis in Breathomics and Salivaomics Research.

Int J Mol Sci. 2016 Dec 23;18(1):

Authors: Beale DJ, Jones OA, Karpe AV, Dayalan S, Oh DY, Kouremenos KA, Ahmed W, Palombo EA

Abstract
The application of metabolomics to biological samples has been a key focus in systems biology research, which is aimed at the development of rapid diagnostic methods and the creation of personalized medicine. More recently, there has been a strong focus towards this approach applied to non-invasively acquired samples, such as saliva and exhaled breath. The analysis of these biological samples, in conjunction with other sample types and traditional diagnostic tests, has resulted in faster and more reliable characterization of a range of health disorders and diseases. As the sampling process involved in collecting exhaled breath and saliva is non-intrusive as well as comparatively low-cost and uses a series of widely accepted methods, it provides researchers with easy access to the metabolites secreted by the human body. Owing to its accuracy and rapid nature, metabolomic analysis of saliva and breath (known as salivaomics and breathomics, respectively) is a rapidly growing field and has shown potential to be effective in detecting and diagnosing the early stages of numerous diseases and infections in preclinical studies. This review discusses the various collection and analyses methods currently applied in two of the least used non-invasive sample types in metabolomics, specifically their application in salivaomics and breathomics research. Some of the salient research completed in this field to date is also assessed and discussed in order to provide a basis to advocate their use and possible future scientific directions.

PMID: 28025547 [PubMed - in process]

A Network-based Systems Biology Platform for Predicting Disease-Metabolite Links.

Comb Chem High Throughput Screen. 2016 Dec 14;

Authors: Wathieu H, Issa NT, Mohandoss M, Byers SW, Dakshanamurthy S

Abstract
Metabolites constitute phenotypic end products of gene expression, and are key players in biological networks. For this reason, the field of metabolomics has been useful in predicting, explaining, and affecting the mechanisms of disease phenotypes. MSD-MAP (Multi Scale Disease-Metabolite Association Platform) is a powerful computational tool for hypothesizing new links between diseases and metabolites, and characterizing the functional basis of those links in a systems biology context. Upon integrating both predicted and known metabolite-protein associations, MSD-MAP takes a two-pronged approach to associating metabolites to a disease, relying on network-based characterization of disease perturbation at multiple levels of biological activity as well as statistical matching of metabolite- and disease-associated biological profiles. MSD-MAP successfully recapitulated cross-disease links of cancer-associated metabolites, and predicted key metabolites associated with colorectal, esophageal, and prostate cancers after the integration of patient-based gene expression analysis. For example, the catecholamine dopamine was correctly predicted to be strongly associated with colorectal cancer based on statistical coincidence with its disease perturbation network.

PMID: 28024464 [PubMed - as supplied by publisher]

Optimizing personalized treatment of oral mucositis secondary to cancer therapy through systems biology.

J Clin Oncol. 2011 May 20;29(15_suppl):e19690

Authors: Srivastava R, White JR, Lalla RV, Loew LM, Peterson DE

Abstract
e19690 Background: Oral mucositis (OM) can result in clinically significant adverse outcomes that require high resource utilization. Clinical trials for mucositis drug development have typically incorporated a cohort-based methodology. However, patients vary considerably in risk and severity of OM. In addition, the modeling for OM pathobiology is complex and includes up-regulation of NF-kB and TNF-a. We therefore tested the hypothesis that an individual patient-oriented systems biology approach to mucositis management will maximize treatment efficacy, while reducing drug dosage.
METHODS: A mathematical model of the biomolecular reaction network describing the dynamics of NF-kB and TNF-a up-regulation was developed using data derived from published studies. To represent inter-subject variation, 10,000 in silico subjects were generated, with the reaction rate constants of each being randomly distributed by up to ± 30% relative to a pre-specified base-case. OM treatment was optimized using a genetic algorithm designed to down-regulate TNF-a levels to a pre-determined level using the minimum possible drug dosage. Two in silico subjects who deviated from average cohort behavior were randomly selected for comparison.
RESULTS: When optimized for the individual, required drug dosage for the first in silico subject was 20% of the cohort-optimized dosage on average. However, TNF-a levels for the individual were maintained within 5% of the average case. The optimal drug treatment for the second subject consisted of the maximum allowable drug dosage, representing a 10-fold increase versus the cohort-optimized case. Even with maximum drug dosage, TNF-a levels were at least 7.5-fold higher than the average case. In all cases, NF-kB dynamic behavior reflected that seen for TNF-a.
CONCLUSIONS: Individually-optimized OM treatment may improve therapeutic efficacy in selected patients, while limiting drug dosage and associated side-effects. Supported by NIH Career Development Award K23DE016946 and NIH grant P41RR013186.

PMID: 28021950 [PubMed - in process]

Pressing needs of biomedical text mining in biocuration and beyond: opportunities and challenges.

Database (Oxford). 2016;2016:

Authors: Singhal A, Leaman R, Catlett N, Lemberger T, McEntyre J, Polson S, Xenarios I, Arighi C, Lu Z

Abstract
Text mining in the biomedical sciences is rapidly transitioning from small-scale evaluation to large-scale application. In this article, we argue that text-mining technologies have become essential tools in real-world biomedical research. We describe four large scale applications of text mining, as showcased during a recent panel discussion at the BioCreative V Challenge Workshop. We draw on these applications as case studies to characterize common requirements for successfully applying text-mining techniques to practical biocuration needs. We note that system 'accuracy' remains a challenge and identify several additional common difficulties and potential research directions including (i) the 'scalability' issue due to the increasing need of mining information from millions of full-text articles, (ii) the 'interoperability' issue of integrating various text-mining systems into existing curation workflows and (iii) the 'reusability' issue on the difficulty of applying trained systems to text genres that are not seen previously during development. We then describe related efforts within the text-mining community, with a special focus on the BioCreative series of challenge workshops. We believe that focusing on the near-term challenges identified in this work will amplify the opportunities afforded by the continued adoption of text-mining tools. Finally, in order to sustain the curation ecosystem and have text-mining systems adopted for practical benefits, we call for increased collaboration between text-mining researchers and various stakeholders, including researchers, publishers and biocurators.

PMID: 28025348 [PubMed - in process]

Direct transcriptional activation of BT genes by NLP transcription factors is a key component of the nitrate response in Arabidopsis.

Biochem Biophys Res Commun. 2016 Dec 23;:

Authors: Sato T, Maekawa S, Konishi M, Yoshioka N, Sasaki Y, Maeda H, Ishida T, Kato Y, Yamaguchi J, Yanagisawa S

Abstract
Nitrate modulates growth and development, functioning as a nutrient signal in plants. Although many changes in physiological processes in response to nitrate have been well characterized as nitrate responses, the molecular mechanisms underlying the nitrate response are not yet fully understood. Here, we show that NLP transcription factors, which are key regulators of the nitrate response, directly activate the nitrate-inducible expression of BT1 and BT2 encoding putative scaffold proteins with a plant-specific domain structure in Arabidopsis. Interestingly, the 35S promoter-driven expression of BT2 partially rescued growth inhibition caused by reductions in NLP activity in Arabidopsis. Furthermore, simultaneous disruption of BT1 and BT2 affected nitrate-dependent lateral root development. These results suggest that direct activation of BT1 and BT2 by NLP transcriptional activators is a key component of the molecular mechanism underlying the nitrate response in Arabidopsis.

PMID: 28025145 [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:

"cystic fibrosis"

These pubmed results were generated on 2016/12/27

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.