| 1. |
- Nandy, Subir Kumar, 1978, et al.
(författare)
-
Reconstruction of the yeast protein-protein interaction network involved in nutrient sensing and global metabolic regulation
- 2010
-
Ingår i: BMC Systems Biology. - : Springer Science and Business Media LLC. - 1752-0509. ; 4, s. 68-
-
Tidskriftsartikel (refereegranskat)abstract
- Background: Several protein-protein interaction studies have been performed for the yeast Saccharomyces cerevisiae using different high-throughput experimental techniques. All these results are collected in the BioGRID database and the SGD database provide detailed annotation of the different proteins. Despite the value of BioGRID for studying protein-protein interactions, there is a need for manual curation of these interactions in order to remove false positives.Results: Here we describe an annotated reconstruction of the protein-protein interactions around four key nutrient-sensing and metabolic regulatory signal transduction pathways (STP) operating in Saccharomyces cerevisiae. The reconstructed STP network includes a full protein-protein interaction network including the key nodes Snf1, Tor1, Hog1 and Pka1. The network includes a total of 623 structural open reading frames (ORFs) and 779 protein-protein interactions. A number of proteins were identified having interactions with more than one of the protein kinases. The fully reconstructed interaction network includes all the information available in separate databases for all the proteins included in the network (nodes) and for all the interactions between them (edges). The annotated information is readily available utilizing the functionalities of network modelling tools such as Cytoscape and CellDesigner.Conclusions: The reported fully annotated interaction model serves as a platform for integrated systems biology studies of nutrient sensing and regulation in S. cerevisiae. Furthermore, we propose this annotated reconstruction as a first step towards generation of an extensive annotated protein-protein interaction network of signal transduction and metabolic regulation in this yeast.
|
|
| 2. |
- Gibson, B., et al.
(författare)
-
Adaptive Laboratory Evolution of Ale and Lager Yeasts for Improved Brewing Efficiency and Beer Quality
- 2020
-
Ingår i: Annual review of food science and technology. - : Annual Reviews. - 1941-1421 .- 1941-1413. ; 11, s. 23-44
-
Forskningsöversikt (refereegranskat)abstract
- Yeasts directly impact the efficiency of brewery fermentations as well as the character of the beers produced. In recent years, there has been renewed interest in yeast selection and development inspired by the demand to utilize resources more efficiently and the need to differentiate beers in a competitive market. Reviewed here are the different, non-genetically modified (GM) approaches that have been considered, including bioprospecting, hybridization, and adaptive laboratory evolution (ALE). Particular emphasis is placed on the latter, which represents an extension of the processes that have led to the domestication of strains already used in commercial breweries. ALE can be used to accentuate the positive traits of brewing yeast as well as temper some of the traits that are less desirable from a modern brewer's perspective. This method has the added advantage of being non-GM and therefore suitable for food and beverage production.
|
|
| 3. |
- Tramontano, Melanie, et al.
(författare)
-
Nutritional preferences of human gut bacteria reveal their metabolic idiosyncrasies
- 2018
-
Ingår i: Nature Microbiology. - : Springer Science and Business Media LLC. - 2058-5276. ; 3:4, s. 514-522
-
Tidskriftsartikel (refereegranskat)abstract
- Bacterial metabolism plays a fundamental role in gut microbiota ecology and host-microbiome interactions. Yet the metabolic capabilities of most gut bacteria have remained unknown. Here we report growth characteristics of 96 phylogenetically diverse gut bacterial strains across 4 rich and 15 defined media. The vast majority of strains (76) grow in at least one defined medium, enabling accurate assessment of their biosynthetic capabilities. These do not necessarily match phylogenetic similarity, thus indicating a complex evolution of nutritional preferences. We identify mucin utilizers and species inhibited by amino acids and short-chain fatty acids. Our analysis also uncovers media for in vitro studies wherein growth capacity correlates well with in vivo abundance. Further value of the underlying resource is demonstrated by correcting pathway gaps in available genome-scale metabolic models of gut microorganisms. Together, the media resource and the extracted knowledge on growth abilities widen experimental and computational access to the gut microbiota.
|
|
