Reconstruction and analysis of the genetic and metabolic regulatory networks of the central metabolism of Bacillus subtilis Goelzer , Anne (INRA , Jouy-En-Josas (France). UR 1077 Mathématique, Informatique et Génome); Bekkal Brikci , Fadia (INRA , Jouy-En-Josas (France). UR 1077 Mathématique, Informatique et Génome); Martin-Verstraete , Isabelle (Institut PasteurUniversité Paris 7, ParisParis(France). URA CNRS 2171 Unité de Génétique des Génomes BactériensUnité de Formation et de Recherche de Biochimie) ...
2008
Publication
Background. Few genome-scale models of organisms focus on the regulatory networks and none of them integrates all known levels of regulation. In particular, the regulations involving metabolite pools ...are often neglected. However, metabolite pools link the metabolic to the genetic network through genetic regulations, including those involving effectors of transcription factors or riboswitches. Consequently, they play pivotal roles in the global organization of the genetic and metabolic regulatory networks. Results. We report the manually curated reconstruction of the genetic and metabolic regulatory networks of the central metabolism of Bacillus subtilis (transcriptional, translational and post-translational regulations and modulation of enzymatic activities). We provide a systematic graphic representation of regulations of each metabolic pathway based on the central role of metabolites in regulation. We show that the complex regulatory network of B. subtilis can be decomposed as sets of locally regulated modules, which are coordinated by global regulators. Conclusion. This work reveals the strong involvement of metabolite pools in the general regulation of the metabolic network. Breaking the metabolic network down into modules based on the control of metabolite pools reveals the functional organization of the genetic and metabolic regulatory networks of B. subtilis
Two glyceraldehyde-3-phosphate dehydrogenases with opposite physiological roles in a nonphotosynthetic bacterium Fillinger , Sabine, Helma (INRA , Thiverval-Grignon (France). UMR 1238 Unité mixte de recherche microbiologie et génétique moléculaire); Boshi-Muller , Sandrine (Centre National de la Recherche ScientifiqueUniversité de Lorraine, Vandoeuvre-les-NançyVandoeuvre-les-Nançy(France). UMR 7567, Maturation des ARN et Enzymologie MoléculaireFaculté des Sciences); Azza , Saïd (Centre National de la Recherche ScientifiqueUniversité de Lorraine, Vandoeuvre-lès-NancyVandoeuvre-lès-Nancy(France). UMR 7567, Maturation des ARN et Enzymologie MoléculaireFaculté des Sciences) ...
2000
Publication
The mechanisms explaining the co-existence of asthma, eczema and rhinitis (allergic multimorbidity) are largely unknown. We investigated the mechanisms underlying multimorbidity between three main ...allergic diseases at a molecular level by identifying the proteins and cellular processes that are common to them.
An in silico study based on computational analysis of the topology of the protein interaction network was performed in order to characterize the molecular mechanisms of multimorbidity of asthma, eczema and rhinitis. As a first step, proteins associated to either disease were identified using data mining approaches, and their overlap was calculated. Secondly, a functional interaction network was built, allowing to identify cellular pathways involved in allergic multimorbidity. Finally, a network-based algorithm generated a ranked list of newly predicted multimorbidity-associated proteins.
Asthma, eczema and rhinitis shared a larger number of associated proteins than expected by chance, and their associated proteins exhibited a significant degree of interconnectedness in the interaction network. There were 15 pathways involved in the multimorbidity of asthma, eczema and rhinitis, including IL4 signaling and GATA3-related pathways. A number of proteins potentially associated to these multimorbidity processes were also obtained.
These results strongly support the existence of an allergic multimorbidity cluster between asthma, eczema and rhinitis, and suggest that type 2 signaling pathways represent a relevant multimorbidity mechanism of allergic diseases. Furthermore, we identified new candidates contributing to multimorbidity that may assist in identifying new targets for multimorbid allergic diseases.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Several studies have suggested an interaction between air pollution and pollen exposure with an impact on allergy symptoms. However, large studies with real-life data are not available.
To ...investigate associations between major air pollutants (ozone and particulate matter with a diameter of <2.5 μm) and allergic rhinitis (AR) control during grass and birch pollen seasons as well as outside the pollen season.
The daily impact of allergic symptoms was recorded by the Allergy Diary (Mobile Airways Sentinel NetworK MASK-air) app (a validated mHealth tool for rhinitis management) using visual analog scales (VASs) in Northern and Central Europe users in 2017 and 2018. Uncontrolled AR was defined using symptoms and medications. Pollutant levels were assessed using the System for Integrated modeLing of Atmospheric coMposition database. Pollen seasons were assessed by regions using Google Trends. Generalized estimating equation models were used to account for repeated measures per user, adjusting for sex, age, treatment, and country. Analyses were stratified by pollen seasons to investigate interactions between air pollutants and pollen exposure.
A total of 3323 geolocated individuals (36,440 VAS-days) were studied. Associations between uncontrolled rhinitis and pollutants were stronger during the grass pollen season. Days with uncontrolled AR increased by 25% for an interquartile range increase in ozone levels during the grass pollen season (odds ratio of 1.25 95% CI, 1.11-1.41 in 2017 and of 1.14 95% CI, 1.04-1.25 in 2018). A similar trend was found for particulate matter with a diameter of less than 2.5 μm, especially in 2017.
These results suggest that the relationship between uncontrolled AR and air pollution is modified by the presence of grass pollens. This study confirms the impact of pollutants in the grass pollen season but not in the birch pollen season.
Allergic rhinitis (AR) is impacted by allergens and air pollution but interactions between air pollution, sleep and allergic diseases are insufficiently understood. POLLAR (Impact of air POLLution on ...sleep, Asthma and Rhinitis) is a project of the European Institute of Innovation and Technology (EIT Health). It will use a freely‐existing application for AR monitoring that has been tested in 23 countries (the Allergy Diary, iOS and Android, 17,000 users, TLR8). The Allergy Diary will be combined with a new tool allowing queries on allergen, pollen (TLR2), sleep quality and disorders (TRL2) as well as existing longitudinal and geolocalized pollution data. Machine learning will be used to assess the relationship between air pollution, sleep and AR comparing polluted and non‐polluted areas in 6 EU countries. Data generated in 2018 will be confirmed in 2019 and extended by the individual prospective assessment of pollution (portable sensor, TLR7) in AR. Sleep apnea patients will be used as a demonstrator of sleep disorder that can be modulated in terms of symptoms and severity by air pollution and AR. The geographic information system GIS will map the results. Consequences on quality of life (EQ‐5D), asthma, school, work and sleep will be monitored and disseminated towards the population. The impacts of POLLAR will be (1) to propose novel care pathways integrating pollution, sleep and patients’ literacy, (2) to study sleep consequences of pollution and its impact on frequent chronic diseases, (3) to improve work productivity, (4) to propose the basis for a sentinel network at the EU level for pollution and allergy, (5) to assess the societal implications of the interaction. MASK paper N°32.
Asthma, rhinitis, and eczema are complex diseases with multiple genetic and environmental factors interlinked through IgE-associated and non–IgE-associated mechanisms. Mechanisms of the Development ...of ALLergy (MeDALL; EU FP7-CP-IP; project no: 261357; 2010-2015) studied the complex links of allergic diseases at the clinical and mechanistic levels by linking epidemiologic, clinical, and mechanistic research, including in vivo and in vitro models. MeDALL integrated 14 European birth cohorts, including 44,010 participants and 160 cohort follow-ups between pregnancy and age 20 years. Thirteen thousand children were prospectively followed after puberty by using a newly standardized MeDALL Core Questionnaire. A microarray developed for allergen molecules with increased IgE sensitivity was obtained for 3,292 children. Estimates of air pollution exposure from previous studies were available for 10,000 children. Omics data included those from historical genome-wide association studies (23,000 children) and DNA methylation (2,173), targeted multiplex biomarker (1,427), and transcriptomic (723) studies. Using classical epidemiology and machine-learning methods in 16,147 children aged 4 years and 11,080 children aged 8 years, MeDALL showed the multimorbidity of eczema, rhinitis, and asthma and estimated that only 38% of multimorbidity was attributable to IgE sensitization. MeDALL has proposed a new vision of multimorbidity independent of IgE sensitization, and has shown that monosensitization and polysensitization represent 2 distinct phenotypes. The translational component of MeDALL is shown by the identification of a novel allergic phenotype characterized by polysensitization and multimorbidity, which is associated with the frequency, persistence, and severity of allergic symptoms. The results of MeDALL will help integrate personalized, predictive, preventative, and participatory approaches in allergic diseases.
Background
Allergic diseases often occur in combination (multimorbidity). Human blood transcriptome studies have not addressed multimorbidity. Large‐scale gene expression data were combined to ...retrieve biomarkers and signaling pathways to disentangle allergic multimorbidity phenotypes.
Methods
Integrated transcriptomic analysis was conducted in 1233 participants with a discovery phase using gene expression data (Human Transcriptome Array 2.0) from whole blood of 786 children from three European birth cohorts (MeDALL), and a replication phase using RNA Sequencing data from an independent cohort (EVA‐PR, n = 447). Allergic diseases (asthma, atopic dermatitis, rhinitis) were considered as single disease or multimorbidity (at least two diseases), and compared with no disease.
Results
Fifty genes were differentially expressed in allergic diseases. Thirty‐two were not previously described in allergy. Eight genes were consistently overexpressed in all types of multimorbidity for asthma, dermatitis, and rhinitis (CLC, EMR4P, IL5RA, FRRS1, HRH4, SLC29A1, SIGLEC8, IL1RL1). All genes were replicated the in EVA‐PR cohort. RT‐qPCR validated the overexpression of selected genes. In MeDALL, 27 genes were differentially expressed in rhinitis alone, but none was significant for asthma or dermatitis alone. The multimorbidity signature was enriched in eosinophil‐associated immune response and signal transduction. Protein‐protein interaction network analysis identified IL5/JAK/STAT and IL33/ST2/IRAK/TRAF as key signaling pathways in multimorbid diseases. Synergistic effect of multimorbidity on gene expression levels was found.
Conclusion
A signature of eight genes identifies multimorbidity for asthma, rhinitis, and dermatitis. Our results have clinical and mechanistic implications, and suggest that multimorbidity should be considered differently than allergic diseases occurring alone.
This study compares gene expression from whole blood of European children (4‐16 years) with asthma and/or dermatitis and/or rhinitis to controls without allergy. Eight genes are overlapping among DEGs found in multimorbidity for asthma, dermatitis and rhinitis, which had synergistic effects along the number of co‐occurrent diseases. Results were replicated in North American cohort with similar features. Abbreviations: AstM, asthma multimorbidity; CLC, charcot‐leyden crystal galectin; DEGs, differentially expressed genes; DerM, dermatitis multimorbidity; EMR4P, adhesion G protein‐coupled receptor E4; FRRS1, ferric chelate reductase 1; HRH4, histamine receptor H4; IL1RL1, interleukin 1 receptor like 1; IL5RA, interleukin 5 receptor subunit alpha; RhiM, rhinitis multimorbidity; SIGLEC8, sialic acid binding Ig like lectin 8; SLC29A1, solute carrier family 29 member 1
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