Summary
Bacteria are extraordinarily adaptive organisms spread all over the globe and making up about half of the cells in our bodies. Their main source of novel functions is horizontal gene transfer, usually prompted by mobile genetic elements such as plasmids, that provide them with a wide range of traits. Plasmid conjugation is the main mechanism of dissemination of antimicrobial resistance (AMR), an increasingly important health problem. However, more than half of the plasmids found in bacterial genomes lack genes encoding conjugation functions and it is not known how they are transferred between cells. Since extensive data shows that many of these plasmids are transferred, it is important and urgent to study how this takes place to understand and predict their epidemiological patterns.
EvoPlas has two main objectives. First, to unravel the mechanisms by which the putatively non-transmissible plasmids are transferred between cells, thereby showing the actual ability of plasmids to spread within populations. Second, to identify the evolutionary origins of non-transmissible plasmids and understand their stability in bacterial lineages.
EvoPlas will use bioinformatics and experimental biology to leverage massive genomic data in order to unravel key aspects of bacterial evolution by horizontal gene transfer. It will additionally provide cutting-edge knowledge on the evolution of bacterial conjugation, novel insights into integrative mobile genetic elements and provide major advances on the understanding of how AMR spreads. At the later stages of EvoPlas, we will develop machine learning models of plasmid mobility to anticipate the dissemination of antimicrobial resistance genes. Hence, by combining my expertise in plasmid biology with the host lab’s expertise in big data and computational biology, EvoPlas has the potential to uncover new insights into bacterial and plasmid evolution and provide novel approaches to fight against the spread of AMR.
EvoPlas has two main objectives. First, to unravel the mechanisms by which the putatively non-transmissible plasmids are transferred between cells, thereby showing the actual ability of plasmids to spread within populations. Second, to identify the evolutionary origins of non-transmissible plasmids and understand their stability in bacterial lineages.
EvoPlas will use bioinformatics and experimental biology to leverage massive genomic data in order to unravel key aspects of bacterial evolution by horizontal gene transfer. It will additionally provide cutting-edge knowledge on the evolution of bacterial conjugation, novel insights into integrative mobile genetic elements and provide major advances on the understanding of how AMR spreads. At the later stages of EvoPlas, we will develop machine learning models of plasmid mobility to anticipate the dissemination of antimicrobial resistance genes. Hence, by combining my expertise in plasmid biology with the host lab’s expertise in big data and computational biology, EvoPlas has the potential to uncover new insights into bacterial and plasmid evolution and provide novel approaches to fight against the spread of AMR.
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More information & hyperlinks
Web resources: | https://cordis.europa.eu/project/id/101062386 |
Start date: | 01-07-2022 |
End date: | 30-06-2024 |
Total budget - Public funding: | - 195 914,00 Euro |
Cordis data
Original description
Bacteria are extraordinarily adaptive organisms spread all over the globe and making up about half of the cells in our bodies. Their main source of novel functions is horizontal gene transfer, usually prompted by mobile genetic elements such as plasmids, that provide them with a wide range of traits. Plasmid conjugation is the main mechanism of dissemination of antimicrobial resistance (AMR), an increasingly important health problem. However, more than half of the plasmids found in bacterial genomes lack genes encoding conjugation functions and it is not known how they are transferred between cells. Since extensive data shows that many of these plasmids are transferred, it is important and urgent to study how this takes place to understand and predict their epidemiological patterns.EvoPlas has two main objectives. First, to unravel the mechanisms by which the putatively non-transmissible plasmids are transferred between cells, thereby showing the actual ability of plasmids to spread within populations. Second, to identify the evolutionary origins of non-transmissible plasmids and understand their stability in bacterial lineages.
EvoPlas will use bioinformatics and experimental biology to leverage massive genomic data in order to unravel key aspects of bacterial evolution by horizontal gene transfer. It will additionally provide cutting-edge knowledge on the evolution of bacterial conjugation, novel insights into integrative mobile genetic elements and provide major advances on the understanding of how AMR spreads. At the later stages of EvoPlas, we will develop machine learning models of plasmid mobility to anticipate the dissemination of antimicrobial resistance genes. Hence, by combining my expertise in plasmid biology with the host lab’s expertise in big data and computational biology, EvoPlas has the potential to uncover new insights into bacterial and plasmid evolution and provide novel approaches to fight against the spread of AMR.
Status
SIGNEDCall topic
HORIZON-MSCA-2021-PF-01-01Update Date
09-02-2023
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