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  1. Article ; Online: Genome-scale metabolic reconstruction of 7,302 human microorganisms for personalized medicine.

    Heinken, Almut / Hertel, Johannes / Acharya, Geeta / Ravcheev, Dmitry A / Nyga, Malgorzata / Okpala, Onyedika Emmanuel / Hogan, Marcus / Magnúsdóttir, Stefanía / Martinelli, Filippo / Nap, Bram / Preciat, German / Edirisinghe, Janaka N / Henry, Christopher S / Fleming, Ronan M T / Thiele, Ines

    Nature biotechnology

    2023  Volume 41, Issue 9, Page(s) 1320–1331

    Abstract: The human microbiome influences the efficacy and safety of a wide variety of commonly prescribed drugs. Designing precision medicine approaches that incorporate microbial metabolism would require strain- and molecule-resolved, scalable computational ... ...

    Abstract The human microbiome influences the efficacy and safety of a wide variety of commonly prescribed drugs. Designing precision medicine approaches that incorporate microbial metabolism would require strain- and molecule-resolved, scalable computational modeling. Here, we extend our previous resource of genome-scale metabolic reconstructions of human gut microorganisms with a greatly expanded version. AGORA2 (assembly of gut organisms through reconstruction and analysis, version 2) accounts for 7,302 strains, includes strain-resolved drug degradation and biotransformation capabilities for 98 drugs, and was extensively curated based on comparative genomics and literature searches. The microbial reconstructions performed very well against three independently assembled experimental datasets with an accuracy of 0.72 to 0.84, surpassing other reconstruction resources and predicted known microbial drug transformations with an accuracy of 0.81. We demonstrate that AGORA2 enables personalized, strain-resolved modeling by predicting the drug conversion potential of the gut microbiomes from 616 patients with colorectal cancer and controls, which greatly varied between individuals and correlated with age, sex, body mass index and disease stages. AGORA2 serves as a knowledge base for the human microbiome and paves the way to personalized, predictive analysis of host-microbiome metabolic interactions.
    MeSH term(s) Humans ; Precision Medicine ; Genome ; Genomics ; Gastrointestinal Microbiome/genetics ; Microbiota
    Language English
    Publishing date 2023-01-19
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 1311932-1
    ISSN 1546-1696 ; 1087-0156
    ISSN (online) 1546-1696
    ISSN 1087-0156
    DOI 10.1038/s41587-022-01628-0
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

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  2. Article: APOLLO: A genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites.

    Heinken, Almut / Hulshof, Timothy Otto / Nap, Bram / Martinelli, Filippo / Basile, Arianna / O'Brolchain, Amy / O'Sullivan, Neil Francis / Gallagher, Celine / Magee, Eimer / McDonagh, Francesca / Lalor, Ian / Bergin, Maeve / Evans, Phoebe / Daly, Rachel / Farrell, Ronan / Delaney, Rose Marie / Hill, Saoirse / McAuliffe, Saoirse Roisin / Kilgannon, Trevor /
    Fleming, Ronan M T / Thinnes, Cyrille C / Thiele, Ines

    bioRxiv : the preprint server for biology

    2023  

    Abstract: Computational modelling of microbiome metabolism has proved instrumental to catalyse our understanding of diet-host-microbiome-disease interactions through the interrogation of mechanistic, strain- and molecule-resolved metabolic models. We present ... ...

    Abstract Computational modelling of microbiome metabolism has proved instrumental to catalyse our understanding of diet-host-microbiome-disease interactions through the interrogation of mechanistic, strain- and molecule-resolved metabolic models. We present APOLLO, a resource of 247,092 human microbial genome-scale metabolic reconstructions spanning 19 phyla and accounting for microbial genomes from 34 countries, all age groups, and five body sites. We explored the metabolic potential of the reconstructed strains and developed a machine learning classifier able to predict with high accuracy the taxonomic strain assignments. We also built 14,451 sample-specific microbial community models, which could be stratified by body site, age, and disease states. Finally, we predicted faecal metabolites enriched or depleted in gut microbiomes of people with Crohn's disease, Parkinson disease, and undernourished children. APOLLO is compatible with the human whole-body models, and thus, provide unprecedented opportunities for systems-level modelling of personalised host-microbiome co-metabolism. APOLLO will be freely available under https://www.vmh.life/.
    Language English
    Publishing date 2023-10-03
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.10.02.560573
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: From Eat to trEat: engineering the mitochondrial Eat1 enzyme for enhanced ethyl acetate production in

    Kruis, Aleksander J / Bohnenkamp, Anna C / Nap, Bram / Nielsen, Jochem / Mars, Astrid E / Wijffels, Rene H / van der Oost, John / Kengen, Servé W M / Weusthuis, Ruud A

    Biotechnology for biofuels

    2020  Volume 13, Page(s) 76

    Abstract: Background: Genetic engineering of microorganisms has become a common practice to establish microbial cell factories for a wide range of compounds. Ethyl acetate is an industrial solvent that is used in several applications, mainly as a biodegradable ... ...

    Abstract Background: Genetic engineering of microorganisms has become a common practice to establish microbial cell factories for a wide range of compounds. Ethyl acetate is an industrial solvent that is used in several applications, mainly as a biodegradable organic solvent with low toxicity. While ethyl acetate is produced by several natural yeast species, the main mechanism of production has remained elusive until the discovery of Eat1 in
    Results: Unprocessed and synthetically truncated eat1 variants of
    Conclusion: By analysing and predicting the N-terminal pre-sequences of different Eat1 proteins and systematically trimming them, the stability of the enzymes in vitro could be improved, leading to an overall improvement of in vivo ethyl acetate production in
    Language English
    Publishing date 2020-04-19
    Publishing country England
    Document type Journal Article
    ZDB-ID 2421351-2
    ISSN 1754-6834
    ISSN 1754-6834
    DOI 10.1186/s13068-020-01711-1
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article: From Eat to trEat: engineering the mitochondrial Eat1 enzyme for enhanced ethyl acetate production in Escherichia coli

    Kruis, Aleksander J / Bohnenkamp, Anna C / Nap, Bram / Nielsen, Jochem / Mars, Astrid E / Wijffels, Rene H / van der Oost, John / Kengen, Servé W. M / Weusthuis, Ruud A

    Biotechnology for biofuels. 2020 Dec., v. 13, no. 1

    2020  

    Abstract: BACKGROUND: Genetic engineering of microorganisms has become a common practice to establish microbial cell factories for a wide range of compounds. Ethyl acetate is an industrial solvent that is used in several applications, mainly as a biodegradable ... ...

    Abstract BACKGROUND: Genetic engineering of microorganisms has become a common practice to establish microbial cell factories for a wide range of compounds. Ethyl acetate is an industrial solvent that is used in several applications, mainly as a biodegradable organic solvent with low toxicity. While ethyl acetate is produced by several natural yeast species, the main mechanism of production has remained elusive until the discovery of Eat1 in Wickerhamomyces anomalus. Unlike other yeast alcohol acetyl transferases (AATs), Eat1 is located in the yeast mitochondria, suggesting that the coding sequence contains a mitochondrial pre-sequence. For expression in prokaryotic hosts such as E. coli, expression of heterologous proteins with eukaryotic signal sequences may not be optimal. RESULTS: Unprocessed and synthetically truncated eat1 variants of Kluyveromyces marxianus and Wickerhamomyces anomalus have been compared in vitro regarding enzyme activity and stability. While the specific activity remained unaffected, half-life improved for several truncated variants. The same variants showed better performance regarding ethyl acetate production when expressed in E. coli. CONCLUSION: By analysing and predicting the N-terminal pre-sequences of different Eat1 proteins and systematically trimming them, the stability of the enzymes in vitro could be improved, leading to an overall improvement of in vivo ethyl acetate production in E. coli. Truncated variants of eat1 could therefore benefit future engineering approaches towards efficient ethyl acetate production.
    Keywords Escherichia coli ; Kluyveromyces marxianus ; Wickerhamomyces anomalus ; acetyltransferases ; alcohols ; biodegradability ; biofuels ; biotechnology ; engineering ; enzyme activity ; ethyl acetate ; factories ; genetic engineering ; half life ; hosts ; mitochondria ; prediction ; proteins ; signal peptide ; solvents ; toxicity ; yeasts
    Language English
    Dates of publication 2020-12
    Size p. 76.
    Publishing place BioMed Central
    Document type Article
    Note NAL-light
    ZDB-ID 2421351-2
    ISSN 1754-6834
    ISSN 1754-6834
    DOI 10.1186/s13068-020-01711-1
    Database NAL-Catalogue (AGRICOLA)

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  5. Article ; Online: From Eat to trEat

    Kruis, Aleksander J. / Bohnenkamp, Anna C. / Nap, Bram / Nielsen, Jochem / Mars, Astrid E. / Wijffels, Rene H. / Van Der Oost, John / Kengen, Servé W.M. / Weusthuis, Ruud A.

    Biotechnology for Biofuels

    Engineering the mitochondrial Eat1 enzyme for enhanced ethyl acetate production in Escherichia coli

    2020  Volume 13, Issue 1

    Abstract: Background: Genetic engineering of microorganisms has become a common practice to establish microbial cell factories for a wide range of compounds. Ethyl acetate is an industrial solvent that is used in several applications, mainly as a biodegradable ... ...

    Abstract Background: Genetic engineering of microorganisms has become a common practice to establish microbial cell factories for a wide range of compounds. Ethyl acetate is an industrial solvent that is used in several applications, mainly as a biodegradable organic solvent with low toxicity. While ethyl acetate is produced by several natural yeast species, the main mechanism of production has remained elusive until the discovery of Eat1 in Wickerhamomyces anomalus. Unlike other yeast alcohol acetyl transferases (AATs), Eat1 is located in the yeast mitochondria, suggesting that the coding sequence contains a mitochondrial pre-sequence. For expression in prokaryotic hosts such as E. coli, expression of heterologous proteins with eukaryotic signal sequences may not be optimal. Results: Unprocessed and synthetically truncated eat1 variants of Kluyveromyces marxianus and Wickerhamomyces anomalus have been compared in vitro regarding enzyme activity and stability. While the specific activity remained unaffected, half-life improved for several truncated variants. The same variants showed better performance regarding ethyl acetate production when expressed in E. coli. Conclusion: By analysing and predicting the N-terminal pre-sequences of different Eat1 proteins and systematically trimming them, the stability of the enzymes in vitro could be improved, leading to an overall improvement of in vivo ethyl acetate production in E. coli. Truncated variants of eat1 could therefore benefit future engineering approaches towards efficient ethyl acetate production.
    Keywords Alcohol acetyl transferase (AAT) ; Eat1 ; Escherichia coli ; Ethyl acetate ; Mitochondria
    Subject code 570
    Language English
    Publishing country nl
    Document type Article ; Online
    ZDB-ID 2421351-2
    ISSN 1754-6834
    ISSN 1754-6834
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article: A toolbox of machine learning software to support microbiome analysis.

    Marcos-Zambrano, Laura Judith / López-Molina, Víctor Manuel / Bakir-Gungor, Burcu / Frohme, Marcus / Karaduzovic-Hadziabdic, Kanita / Klammsteiner, Thomas / Ibrahimi, Eliana / Lahti, Leo / Loncar-Turukalo, Tatjana / Dhamo, Xhilda / Simeon, Andrea / Nechyporenko, Alina / Pio, Gianvito / Przymus, Piotr / Sampri, Alexia / Trajkovik, Vladimir / Lacruz-Pleguezuelos, Blanca / Aasmets, Oliver / Araujo, Ricardo /
    Anagnostopoulos, Ioannis / Aydemir, Önder / Berland, Magali / Calle, M Luz / Ceci, Michelangelo / Duman, Hatice / Gündoğdu, Aycan / Havulinna, Aki S / Kaka Bra, Kardokh Hama Najib / Kalluci, Eglantina / Karav, Sercan / Lode, Daniel / Lopes, Marta B / May, Patrick / Nap, Bram / Nedyalkova, Miroslava / Paciência, Inês / Pasic, Lejla / Pujolassos, Meritxell / Shigdel, Rajesh / Susín, Antonio / Thiele, Ines / Truică, Ciprian-Octavian / Wilmes, Paul / Yilmaz, Ercument / Yousef, Malik / Claesson, Marcus Joakim / Truu, Jaak / Carrillo de Santa Pau, Enrique

    Frontiers in microbiology

    2023  Volume 14, Page(s) 1250806

    Abstract: The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning ( ... ...

    Abstract The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.
    Language English
    Publishing date 2023-11-22
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 2587354-4
    ISSN 1664-302X
    ISSN 1664-302X
    DOI 10.3389/fmicb.2023.1250806
    Database MEDical Literature Analysis and Retrieval System OnLINE

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