Machine learning and comparative genomics approaches for the discovery of xylose transporters in yeast
Abstract Background The need to mitigate and substitute the use of fossil fuels as the main energy matrix has led to the study and development of biofuels as an alternative. Second-generation (2G) ethanol arises as one biofuel with great potential, due to not only maintaining food security, but also...
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| Format: | Article |
| Language: | English |
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BMC
2022-05-01
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| Series: | Biotechnology for Biofuels and Bioproducts |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13068-022-02153-7 |
| _version_ | 1811341276446982144 |
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| author | Mateus Bernabe Fiamenghi João Gabriel Ribeiro Bueno Antônio Pedro Camargo Guilherme Borelli Marcelo Falsarella Carazzolle Gonçalo Amarante Guimarães Pereira Leandro Vieira dos Santos Juliana José |
| author_facet | Mateus Bernabe Fiamenghi João Gabriel Ribeiro Bueno Antônio Pedro Camargo Guilherme Borelli Marcelo Falsarella Carazzolle Gonçalo Amarante Guimarães Pereira Leandro Vieira dos Santos Juliana José |
| author_sort | Mateus Bernabe Fiamenghi |
| collection | DOAJ |
| description | Abstract Background The need to mitigate and substitute the use of fossil fuels as the main energy matrix has led to the study and development of biofuels as an alternative. Second-generation (2G) ethanol arises as one biofuel with great potential, due to not only maintaining food security, but also as a product from economically interesting crops such as energy-cane. One of the main challenges of 2G ethanol is the inefficient uptake of pentose sugars by industrial yeast Saccharomyces cerevisiae, the main organism used for ethanol production. Understanding the main drivers for xylose assimilation and identify novel and efficient transporters is a key step to make the 2G process economically viable. Results By implementing a strategy of searching for present motifs that may be responsible for xylose transport and past adaptations of sugar transporters in xylose fermenting species, we obtained a classifying model which was successfully used to select four different candidate transporters for evaluation in the S. cerevisiae hxt-null strain, EBY.VW4000, harbouring the xylose consumption pathway. Yeast cells expressing the transporters SpX, SpH and SpG showed a superior uptake performance in xylose compared to traditional literature control Gxf1. Conclusions Modelling xylose transport with the small data available for yeast and bacteria proved a challenge that was overcome through different statistical strategies. Through this strategy, we present four novel xylose transporters which expands the repertoire of candidates targeting yeast genetic engineering for industrial fermentation. The repeated use of the model for characterizing new transporters will be useful both into finding the best candidates for industrial utilization and to increase the model’s predictive capabilities. Graphical Abstract |
| first_indexed | 2024-04-13T18:53:38Z |
| format | Article |
| id | doaj.art-719b1e97be8a4d3eb81ca1dbe103c4fb |
| institution | Directory Open Access Journal |
| issn | 2731-3654 |
| language | English |
| last_indexed | 2024-04-13T18:53:38Z |
| publishDate | 2022-05-01 |
| publisher | BMC |
| record_format | Article |
| series | Biotechnology for Biofuels and Bioproducts |
| spelling | doaj.art-719b1e97be8a4d3eb81ca1dbe103c4fb2022-12-22T02:34:19ZengBMCBiotechnology for Biofuels and Bioproducts2731-36542022-05-0115111510.1186/s13068-022-02153-7Machine learning and comparative genomics approaches for the discovery of xylose transporters in yeastMateus Bernabe Fiamenghi0João Gabriel Ribeiro Bueno1Antônio Pedro Camargo2Guilherme Borelli3Marcelo Falsarella Carazzolle4Gonçalo Amarante Guimarães Pereira5Leandro Vieira dos Santos6Juliana José7Genomics and Bioenergy Laboratory (LGE), Institute of Biology, University of Campinas (UNICAMP)Genetics and Molecular Biology Graduate Program, Institute of Biology, University of Campinas (UNICAMP)Genomics and Bioenergy Laboratory (LGE), Institute of Biology, University of Campinas (UNICAMP)Genomics and Bioenergy Laboratory (LGE), Institute of Biology, University of Campinas (UNICAMP)Genomics and Bioenergy Laboratory (LGE), Institute of Biology, University of Campinas (UNICAMP)Genomics and Bioenergy Laboratory (LGE), Institute of Biology, University of Campinas (UNICAMP)Genetics and Molecular Biology Graduate Program, Institute of Biology, University of Campinas (UNICAMP)Genomics and Bioenergy Laboratory (LGE), Institute of Biology, University of Campinas (UNICAMP)Abstract Background The need to mitigate and substitute the use of fossil fuels as the main energy matrix has led to the study and development of biofuels as an alternative. Second-generation (2G) ethanol arises as one biofuel with great potential, due to not only maintaining food security, but also as a product from economically interesting crops such as energy-cane. One of the main challenges of 2G ethanol is the inefficient uptake of pentose sugars by industrial yeast Saccharomyces cerevisiae, the main organism used for ethanol production. Understanding the main drivers for xylose assimilation and identify novel and efficient transporters is a key step to make the 2G process economically viable. Results By implementing a strategy of searching for present motifs that may be responsible for xylose transport and past adaptations of sugar transporters in xylose fermenting species, we obtained a classifying model which was successfully used to select four different candidate transporters for evaluation in the S. cerevisiae hxt-null strain, EBY.VW4000, harbouring the xylose consumption pathway. Yeast cells expressing the transporters SpX, SpH and SpG showed a superior uptake performance in xylose compared to traditional literature control Gxf1. Conclusions Modelling xylose transport with the small data available for yeast and bacteria proved a challenge that was overcome through different statistical strategies. Through this strategy, we present four novel xylose transporters which expands the repertoire of candidates targeting yeast genetic engineering for industrial fermentation. The repeated use of the model for characterizing new transporters will be useful both into finding the best candidates for industrial utilization and to increase the model’s predictive capabilities. Graphical Abstracthttps://doi.org/10.1186/s13068-022-02153-7XyloseXylose transporterMachine learningFeature selectionPentose metabolismIndustrial biotechnology |
| spellingShingle | Mateus Bernabe Fiamenghi João Gabriel Ribeiro Bueno Antônio Pedro Camargo Guilherme Borelli Marcelo Falsarella Carazzolle Gonçalo Amarante Guimarães Pereira Leandro Vieira dos Santos Juliana José Machine learning and comparative genomics approaches for the discovery of xylose transporters in yeast Biotechnology for Biofuels and Bioproducts Xylose Xylose transporter Machine learning Feature selection Pentose metabolism Industrial biotechnology |
| title | Machine learning and comparative genomics approaches for the discovery of xylose transporters in yeast |
| title_full | Machine learning and comparative genomics approaches for the discovery of xylose transporters in yeast |
| title_fullStr | Machine learning and comparative genomics approaches for the discovery of xylose transporters in yeast |
| title_full_unstemmed | Machine learning and comparative genomics approaches for the discovery of xylose transporters in yeast |
| title_short | Machine learning and comparative genomics approaches for the discovery of xylose transporters in yeast |
| title_sort | machine learning and comparative genomics approaches for the discovery of xylose transporters in yeast |
| topic | Xylose Xylose transporter Machine learning Feature selection Pentose metabolism Industrial biotechnology |
| url | https://doi.org/10.1186/s13068-022-02153-7 |
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