BRENDA in 2013: integrated reactions, kinetic data, enzyme function data, improved disease classification: new options and contents in BRENDA

Schomburg, Ida; Chang, Antje; Placzek, Sandra; Söhngen, Carola; Rother, Michael; Lang, Maren; Munaretto, Cornelia; Ulas, Susanne; Stelzer, Michael; Grote, Andreas; Scheer, Maurice; Schomburg, Dietmar

doi:10.1093/nar/gks1049

Cited by 383 publications

(309 citation statements)

References 31 publications

(31 reference statements)

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“…Enzymes are macromolecular biological catalysts that can increase the reaction rate by lowering the activation energy. They are able to catalyze more than 5000 biochemical reactions (Schomburg et al 2013), and some can make reactions of substrates to products occur millions of times faster. Orotidine 5′-phosphate decarboxylase is an extreme example, which catalyzes a reaction that would otherwise take millions of years to occur in milliseconds (Radzicka and Wolfenden 1995;Callahan and Miller 2007).…”

Section: Discussionmentioning

confidence: 99%

The identification and characteristics of salinity-related microRNAs in gills of Portunus trituberculatus

Liu

Gao

et al. 2016

Cell Stress and Chaperones

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MicroRNAs (miRNAs) are small noncoding RNAs that post-transcriptionally regulate gene expression in organisms. To understand the underlying mechanisms behind the molecular response of the crab to low salt-stress, highthroughput Illumina/Solexa deep sequencing technology was used to investigate the expression profiles of miRNAs under low salinity challenged. Two mixed RNA pool libraries of gill tissues from low salinity challenged (LC) and the control groups (NC) were sequenced on the Illumina platform. A total of 6,166,057 and 7,032,973 high-quality reads were obtained from the NC and LC libraries, respectively. Sixty-seven miRNAs consisting of 16 known and 51 novel ones were identified, among which, 12 miRNAs were differentially expressed in LC compared to NC. Thirty-four of the target genes predicted were differentially expressed in the opposite direction to the miRNAs, which were involved in crucial processes related to osmoregulation by gene ontology (GO) functional enrichment analysis, such as anion transport processes (GO:0006820) and chitin metabolic process (GO:0006030). These results provide a basis for further investigation of the miRNA-modulating networks in osmoregulation of Portunus trituberculatus.

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Section: Discussionmentioning

confidence: 99%

The identification and characteristics of salinity-related microRNAs in gills of Portunus trituberculatus

Liu

Gao

et al. 2016

Cell Stress and Chaperones

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“…We then integrated available values of kinetic parameters of enzymes from the literature and databases (Schomburg et al, 2013;Teusink et al, 2000;Wittig et al, 2012). For the enzymes with no prior or incomplete knowledge of values, we sampled the space of values through sampling of the degree of saturation of the enzyme active site (Wang et al, 2004), which is an alternative to sampling of the enzyme states and therefore to sampling of the space (Miskovic and Hatzimanikatis, 2011).…”

Section: Integration Of Kinetic Information and Parameterization Of Mmentioning

confidence: 99%

“…For the enzymes with no prior or incomplete knowledge of values, we sampled the space of values through sampling of the degree of saturation of the enzyme active site (Wang et al, 2004), which is an alternative to sampling of the enzyme states and therefore to sampling of the space (Miskovic and Hatzimanikatis, 2011). We found experimental information about 69 Michaelis constants (Schomburg et al, 2013;Wittig et al, 2012), for 37 out of 153 enzymatic reactions in the model (Supplementary material S5). Within these reactions, complete kinetic information (i.e., values for every substrate and product) was available for 8 reactions (22%), whereas for 16 (43%) of the reactions values for only one metabolite were found in the databases.…”

Section: Integration Of Kinetic Information and Parameterization Of Mmentioning

confidence: 99%

Identification of metabolic engineering targets for the enhancement of 1,4-butanediol production in recombinant E. coli using large-scale kinetic models

Andreozzi

Chakrabarti

Soh

et al. 2016

Metabolic Engineering

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Identification of metabolic engineering targets for the enhancement of 1,4-butanediol production in recombinant E. coli using largescale kinetic models, Metabolic Engineering, http://dx.doi.org/10. 1016/j.ymben.2016.01.009 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Entities) to analyze the physiology of recombinant E. coli producing 1,4-butanediol (BDO) and to identify potential strategies for improved production of BDO. The framework allowed us to integrate data across multiple levels and to construct a population of large-scale kinetic models despite the lack of available information about kinetic properties of every enzyme in the metabolic pathways. We analyzed these models and we found that the enzymes that primarily control the fluxes leading to BDO production are part of central glycolysis, the lower branch of tricarboxylic acid (TCA) cycle and the novel BDO production route. Interestingly, among the enzymes between the glucose uptake and the BDO pathway, the enzymes belonging to the lower branch of TCA cycle have been identified as the most important for improving BDO production and yield. We also quantified the effects of changes of the target enzymes on other intracellular states like energy charge, cofactor levels, redox state, cellular growth, and byproduct formation.Independent earlier experiments on this strain confirmed that the computationally 3 obtained conclusions are consistent with the experimentally tested designs, and the findings of the present studies can provide guidance for future work on strain improvement. Overall, these studies demonstrate the potential and effectiveness of ORACLE for the accelerated design of microbial cell factories.

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“…In the current retrospective cohort study, we further evaluated the prognostic predictive power of angiotensinogen and its combination with renin. Renin was evaluated because we hypothesized that it would predict AKI severity because it cleaves angiotensinogen in the rate-limiting step of the renin-angiotensin system (RAS) (21). Because renin and angiotensinogen concentrations reflect different components within the renal RAS, the combination of the two candidate markers may improve prediction.…”

Section: Introductionmentioning

confidence: 99%

Association of Elevated Urinary Concentration of Renin-Angiotensin System Components and Severe AKI

Alge

Karakala

Neely

et al. 2013

Clinical Journal of the American Society of Nephrology

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SummaryBackground Prognostic biomarkers that predict the severity of AKI at an early time point are needed. Urinary angiotensinogen was recently identified as a prognostic AKI biomarker. The study hypothesis is that urinary renin could also predict AKI severity and that in combination angiotensinogen and renin would be a strong predictor of prognosis at the time of AKI diagnosis.Design, setting, participants, & measurements In this multicenter, retrospective cohort study, urine was obtained from 204 patients who developed AKI after cardiac surgery from August 2008 to June 1, 2012. All patients were classified as having Acute Kidney Injury Network (AKIN) stage 1 disease by serum creatinine criteria at the time of sample collection. Urine output was not used for staging. Urinary angiotensinogen and renin were measured, and the area under the receiver-operating characteristic curve (AUC) was used to test for prediction of progression to AKIN stage 3 or in-hospital 30-day mortality. These biomarkers were added stepwise to a clinical model, and improvement in prognostic predictive performance was evaluated by category free net reclassification improvement (cfNRI) and chi-squared automatic interaction detection (CHAID).Results Both the urinary angiotensinogen-to-creatinine ratio (uAnCR; AUC, 0.75; 95% confidence interval [CI], 0.65 to 0.85) and the urinary renin-to-creatinine ratio (uRenCR; AUC, 0.70; 95% CI, 0.57 to 0.83) predicted AKIN stage 3 or death. Addition of uAnCR to a clinical model substantially improved prediction of the outcome (AUC, 0.85; cfNRI, 0.673), augmenting sensitivity and specificity. Further addition of uRenCR increased the sensitivity of the model (cfNRI events , 0.44). CHAID produced a highly accurate model (AUC, 0.91) and identified the combination of uAnCR .337.89 ng/mg and uRenCR .893.41 pg/mg as the strongest predictors (positive predictive value, 80.4%; negative predictive value, 90.7%; accuracy, 90.2%). ConclusionThe combination of urinary angiotensinogen and renin predicts progression to very severe disease in patients with early AKI after cardiac surgery.

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BRENDA in 2013: integrated reactions, kinetic data, enzyme function data, improved disease classification: new options and contents in BRENDA

Cited by 383 publications

References 31 publications

The identification and characteristics of salinity-related microRNAs in gills of Portunus trituberculatus

The identification and characteristics of salinity-related microRNAs in gills of Portunus trituberculatus

Identification of metabolic engineering targets for the enhancement of 1,4-butanediol production in recombinant E. coli using large-scale kinetic models

Association of Elevated Urinary Concentration of Renin-Angiotensin System Components and Severe AKI

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