Ontology highlight
ABSTRACT:
PROVIDER: PRJNA996098 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR25320286_1.fastq.gz | Fastqsanger.gz | |||
| SRR25320286_2.fastq.gz | Fastqsanger.gz | |||
| SRR25320287_1.fastq.gz | Fastqsanger.gz | |||
| SRR25320287_2.fastq.gz | Fastqsanger.gz | |||
| SRR25320288_1.fastq.gz | Fastqsanger.gz |
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Project description:Flahaut2013 - Genome-scale metabolic model of L.lactis (MG1363)
Genome-scale metabolic model for Lactococcus lactis
MG1363 and its application to the analysis of flavor formation.
This model is described in the article:
Genome-scale metabolic model for Lactococcus lactis MG1363 and its application to the analysis of flavor formation.
Flahaut NA, Wiersma A, van de Bunt B, Martens DE, Schaap PJ, Sijtsma L, Dos Santos VA, de Vos WM
Applied Microbiology and Biotechnology. 2013; 97(19):8729-8739
Abstract:
Lactococcus lactis subsp. cremoris MG1363 is a paradigm strain for lactococci used in industrial dairy fermentations. However, despite of its importance for process development, no genome-scale metabolic model has been reported thus far. Moreover, current models for other lactococci only focus on growth and sugar degradation. A metabolic model that includes nitrogen metabolism and flavor-forming pathways is instrumental for the understanding and designing new industrial applications of these lactic acid bacteria. A genome-scale, constraint-based model of the metabolism and transport in L. lactis MG1363, accounting for 518 genes, 754 reactions, and 650 metabolites, was developed and experimentally validated. Fifty-nine reactions are directly or indirectly involved in flavor formation. Flux Balance Analysis and Flux Variability Analysis were used to investigate flux distributions within the whole metabolic network. Anaerobic carbon-limited continuous cultures were used for estimating the energetic parameters. A thorough model-driven analysis showing a highly flexible nitrogen metabolism, e.g., branched-chain amino acid catabolism which coupled with the redox balance, is pivotal for the prediction of the formation of different flavor compounds. Furthermore, the model predicted the formation of volatile sulfur compounds as a result of the fermentation. These products were subsequently identified in the experimental fermentations carried out. Thus, the genome-scale metabolic model couples the carbon and nitrogen metabolism in L. lactis MG1363 with complete known catabolic pathways leading to flavor formation. The model provided valuable insights into the metabolic networks underlying flavor formation and has the potential to contribute to new developments in dairy industries and cheese-flavor research.
This model is hosted on BioModels Database
and identified by: MODEL1310300000
.
To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models
.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication
for more information.
2005-01-01 | MODEL1310300000 | BioModels
Project description:Rice is one of the main raw materials for Baijiu fermentation and plays an important role in the process of Baijiu brewing. Different varieties of rice may have different compositions of aroma substances in Baijiu due to different contents of physical and chemical components (amylopectin, tannin, fat, etc.), which may affect liquor quality. However, there is currently no systematic understanding of the differences in the types and contents of physicochemical components among different varieties of rice. This study mainly uses modern technological methods to conduct in-depth analysis and precise determination of the physicochemical components of different rice varieties, exploring the differences in physicochemical components of different rice varieties. This study will lay the foundation for the directional breeding of rice for liquor making, and strongly promote the Baijiu industry to accurately screen rice raw materials with higher quality and adaptability.
2024-12-22 | PXD059140 |
Project description:An evolved strain (ECA5) presented two successive yields of bioconversion of higher alcohols to acetate esters, while its parental strain (EC1118) had a constant yield through the fermentation. Transcriptomic analysis was performed during wine fermentation in SM330 containing 8mg/L phytosterols, at 35 g/l and at 70 g/l of CO2 released. For ECA5, this corresponds to a sample before and one after the change of bioconversion yield. This analysis helped us to understand the different management of lipid source by the evolved strain, which is probably linked to a greater availability in acetyl-CoA. Two strains (EC1118 and ECA5) were compared during wine fermentation at 2 released CO2 time point (35g/L and 70g/L). Each condition is in triplicat.
2016-06-29 | E-GEOD-68354 | biostudies-arrayexpress
Project description:Saccharomyces cerevisiae (SC) is the main driver of alcoholic fermentation however for aroma and flavour formation in wine non-Saccharomyces species can have a powerful effect. This study aimed to compare untargeted volatile compound profiles from SPME-GCxGC-TOF-MS and sensory analysis data of Sauvignon blanc wine inoculated with six different non-Saccharomyces yeasts followed by SC. Torulaspora delbrueckii (TD), Lachancea thermotolerans (LT), Pichia kluyveri (PK) and Metschnikowia pulcherrima (MP) where commercial starter strains, while Candida zemplinina (CZ) and Kazachstania aerobia (KA), were isolated from wine grape environments. Each fermentation produced a distinct profile both sensorially and chemically. SC and CZ were the most distinct in both of these cases. SC had guava, grapefruit, banana, and pineapple aromas while CZ was driven by fermented apple, dried peach/apricot, and stewed fruit as well as sour flavor. Chemically over 300 unique features were identified as significantly different across the fermentations. SC had the highest number of esters in the highest relative concentration but all the yeasts had distinct ester profiles. CZ displayed the highest number of terpenes in high concentration but also produced a large amount of acetic acid. KA was high in ethyl acetate. TD had fewer esters but three distinctly higher thiol compounds. LT showed a relatively high number of increased acetate esters and certain terpenes. PK had some off odor compounds while the MP had high levels of different methyl butyl-, methyl propyl-, and phenethyl esters. Overall, this study gives a more detailed profile of these yeasts than anything previously reported.
2015-08-19 | MTBLS212 | MetaboLights