| 1. |
- Lu, Hongzhong, 1987, et al.
(författare)
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A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism
- 2019
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723 .- 2041-1723. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Genome-scale metabolic models (GEMs) represent extensive knowledgebases that provide a platform for model simulations and integrative analysis of omics data. This study introduces Yeast8 and an associated ecosystem of models that represent a comprehensive computational resource for performing simulations of the metabolism of Saccharomyces cerevisiae––an important model organism and widely used cell-factory. Yeast8 tracks community development with version control, setting a standard for how GEMs can be continuously updated in a simple and reproducible way. We use Yeast8 to develop the derived models panYeast8 and coreYeast8, which in turn enable the reconstruction of GEMs for 1,011 different yeast strains. Through integration with enzyme constraints (ecYeast8) and protein 3D structures (proYeast8DB), Yeast8 further facilitates the exploration of yeast metabolism at a multi-scale level, enabling prediction of how single nucleotide variations translate to phenotypic traits.
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| 2. |
- Wang, Hao, 1975, et al.
(författare)
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RAVEN 2.0: A versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor
- 2018
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Ingår i: PLoS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 14:10, s. e1006541-
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Tidskriftsartikel (refereegranskat)abstract
- RAVEN is a commonly used MATLAB toolbox for genome-scale metabolic model (GEM) reconstruction, curation and constraint-based modelling and simulation. Here we present RAVEN Toolbox 2.0 with major enhancements, including: (i) de novo reconstruction of GEMs based on the MetaCyc pathway database; (ii) a redesigned KEGG-based reconstruction pipeline; (iii) convergence of reconstructions from various sources; (iv) improved performance, usability, and compatibility with the COBRA Toolbox. Capabilities of RAVEN 2.0 are here illustrated through de novo reconstruction of GEMs for the antibiotic-producing bacterium Streptomyces coelicolor. Comparison of the automated de novo reconstructions with the iMK1208 model, a previously published high-quality S. coelicolor GEM, exemplifies that RAVEN 2.0 can capture most of the manually curated model. The generated de novo reconstruction is subsequently used to curate iMK1208 resulting in Sco4, the most comprehensive GEM of S. coelicolor, with increased coverage of both primary and secondary metabolism. This increased coverage allows the use of Sco4 to predict novel genome editing targets for optimized secondary metabolites production. As such, we demonstrate that RAVEN 2.0 can be used not only for de novo GEM reconstruction, but also for curating existing models based on up-to-date databases. Both RAVEN 2.0 and Sco4 are distributed through GitHub to facilitate usage and further development by the community (https://github.com/SysBioChalmers/RAVEN and https://github.com/SysBioChalmers/Streptomyces_coelicolor-GEM).
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| 3. |
- Das, Promi, 1990, et al.
(författare)
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Metagenomic analysis of bile salt biotransformation in the human gut microbiome
- 2019
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Ingår i: BMC Genomics. - : Springer Science and Business Media LLC. - 1471-2164. ; 20:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: In the biochemical milieu of human colon, bile acids act as signaling mediators between the host and its gut microbiota. Biotransformation of primary to secondary bile acids have been known to be involved in the immune regulation of human physiology. Several 16S amplicon-based studies with inflammatory bowel disease (IBD) subjects were found to have an association with the level of fecal bile acids. However, a detailed investigation of all the bile salt biotransformation genes in the gut microbiome of healthy and IBD subjects has not been performed. Results: Here, we report a comprehensive analysis of the bile salt biotransformation genes and their distribution at the phyla level. Based on the analysis of shotgun metagenomes, we found that the IBD subjects harbored a significantly lower abundance of these genes compared to the healthy controls. Majority of these genes originated from Firmicutes in comparison to other phyla. From metabolomics data, we found that the IBD subjects were measured with a significantly low level of secondary bile acids and high levels of primary bile acids compared to that of the healthy controls. Conclusions: Our bioinformatics-driven approach of identifying bile salt biotransformation genes predicts the bile salt biotransformation potential in the gut microbiota of IBD subjects. The functional level of dysbiosis likely contributes to the variation in the bile acid pool. This study sets the stage to envisage potential solutions to modulate the gut microbiome with the objective to restore the bile acid pool in the gut.
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| 4. |
- Marcisauskas, Simonas, 1988
(författare)
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An insight towards food-related microbial sets through metabolic modelling and functional analysis
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The dietary food digestion depends on the human gastrointestinal tract, where host cells and gut microbes mutually interact. This interplay may also mediate host metabolism, as shown by microbial-derived secondary bile acids, needed for receptor signalling. Microbes are also crucial in the production of fermented foods, such as wine and dairy. Kefir is fermented milk processed by the symbiotic community of bacteria and yeasts. One such species is a yeast Kluyveromyces marxianus . Its thermotolerance is a desired trait in biotechnology since it may reduce the cooling demands during cultivation. The systems biology tools allow analysing various size microbial communities under the different functional scope. For example, the homology prediction tools can give detailed functional insights when working with metagenomics data. The whole-cell metabolic processes can be summarised in genome-scale metabolic models (GEMs), which enable to predict the metabolic capabilities and allow for the integration of omics data. The work shown in this thesis includes i) in silico analysis of food-related microbes; ii) the development of GEMs and RAVEN. With a focus on bile acid metabolism, hundreds of human gut microbes were annotated based on metagenomics data, thereby suggesting the differences in the potential for bile acid processing between healthy and diseased subjects. These findings may be exploitable once aiming to restore the bile acid metabolism for the patients having inflammatory bowel disease. Also, the metabolism of yeast K. marxianus was characterised in genome-scale. Two K. marxianus strains from kefir grains were isolated, sequenced, assembled, and functionally annotated. They were compared with the other ten strains, providing the core and dispensable physiological features for K. marxianus . Furthermore, the first GEM for K. marxianus , namely iSM996, was reconstructed. It was integrated with transcriptomics data to predict its metabolic capabilities in rich medium and high-temperature conditions. The results might be useful to optimise strain-specific medium for high-temperature applications. The final paper comprises the efforts to improve the usability for RAVEN, a toolbox for GEM reconstruction and analysis. Altogether the outcomes of this thesis suggest the potential applications for medicine and industrial biotechnology, which may be facilitated by the newly upgraded RAVEN toolbox.
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| 5. |
- Marcisauskas, Simonas, 1988, et al.
(författare)
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Draft genome sequences of five fungal strains isolated from Kefir
- 2021
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Ingår i: Microbiology Resource Announcements. - 2576-098X. ; 10:21
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Tidskriftsartikel (refereegranskat)abstract
- We present the annotated draft genome sequences of five fungal strains isolated from kefir grains. These isolates included three ascomycetous (Candida californica, Kazachstania exigua, and Kazachstania unispora) and one basidiomycetous (Rhodotorula mucilaginosa) species. The results revealed a detailed overview of the metabolic features of kefir fungi that will be potentially useful in biotechnological applications.
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| 6. |
- Marcisauskas, Simonas, 1988, et al.
(författare)
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Reconstruction and analysis of a Kluyveromyces marxianus genome-scale metabolic model
- 2019
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Ingår i: BMC Bioinformatics. - : Springer Science and Business Media LLC. - 1471-2105. ; 20:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Kluyveromyces marxianus is a thermotolerant yeast with multiple biotechnological potentials for industrial applications, which can metabolize a broad range of carbon sources, including less conventional sugars like lactose, xylose, arabinose and inulin. These phenotypic traits are sustained even up to 45 °C, what makes it a relevant candidate for industrial biotechnology applications, such as ethanol production. It is therefore of much interest to get more insight into the metabolism of this yeast. Recent studies suggested, that thermotolerance is achieved by reducing the number of growth-determining proteins or suppressing oxidative phosphorylation. Here we aimed to find related factors contributing to the thermotolerance of K. marxianus. Results: Here, we reported the first genome-scale metabolic model of Kluyveromyces marxianus, iSM996, using a publicly available Kluyveromyces lactis model as template. The model was manually curated and refined to include the missing species-specific metabolic capabilities. The iSM996 model includes 1913 reactions, associated with 996 genes and 1531 metabolites. It performed well to predict the carbon source utilization and growth rates under different growth conditions. Moreover, the model was coupled with transcriptomics data and used to perform simulations at various growth temperatures. Conclusions: K. marxianus iSM996 represents a well-annotated metabolic model of thermotolerant yeast, which provides a new insight into theoretical metabolic profiles at different temperatures of K. marxianus. This could accelerate the integrative analysis of multi-omics data, leading to model-driven strain design and improvement.
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| 7. |
- Marcisauskas, Simonas, 1988, et al.
(författare)
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Univariate and classification analysis reveals potential diagnostic biomarkers for early stage ovarian cancer Type 1 and Type 2
- 2019
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Ingår i: Journal of Proteomics. - : Elsevier BV. - 1874-3919 .- 1876-7737. ; 196, s. 57-68
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Tidskriftsartikel (refereegranskat)abstract
- Biomarkers for early detection of ovarian tumors are urgently needed. Tumors of the ovary grow within cysts and most are benign. Surgical sampling is the only way to ensure accurate diagnosis, but often leads to morbidity and loss of female hormones. The present study explored the deep proteome in well-defined sets of ovarian tumors, FIGO stage I, Type 1 (low-grade serous, mucinous, endometrioid; n = 9), Type 2 (high-grade serous; n = 9), and benign serous (n = 9) using TMT–LC–MS/MS. Data are available via ProteomeXchange with identifier PXD010939. We evaluated new bioinformatics tools in the discovery phase. This innovative selection process involved different normalizations, a combination of univariate statistics, and logistic model tree and naive Bayes tree classifiers. We identified 142 proteins by this combined approach. One biomarker panel and nine individual proteins were verified in cyst fluid and serum: transaldolase-1, fructose-bisphosphate aldolase A (ALDOA), transketolase, ceruloplasmin, mesothelin, clusterin, tenascin-XB, laminin subunit gamma-1, and mucin-16. Six of the proteins were found significant (p <.05) in cyst fluid while ALDOA was the only protein significant in serum. The biomarker panel achieved ROC AUC 0.96 and 0.57 respectively. We conclude that classification algorithms complement traditional statistical methods by selecting combinations that may be missed by standard univariate tests. Significance: In the discovery phase, we performed deep proteome analyses of well-defined histology subgroups of ovarian tumor cyst fluids, highly specified for stage and type (histology and grade). We present an original approach to selecting candidate biomarkers combining several normalization strategies, univariate statistics, and machine learning algorithms. The results from validation of selected proteins strengthen our prior proteomic and genomic data suggesting that cyst fluids are better than sera in early stage ovarian cancer diagnostics.
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