Periodic Reporting for period 3 - DECIPHER (A computational framework to interpret the chemical language of the microbiome)
Reporting period: 2023-12-01 to 2025-05-31
Humans, animals and plants are covered in microbes. Such microbiomes have a major impact on the health of their hosts and have been linked to traits like growth promotion, stress resilience, and diseases. However, the mechanisms underlying microbiome-host interactions remain poorly understood. Recent studies have shown that microbiome-associated phenotypes are often mediated by specific molecules, a ‘chemical language’ that enables microbes to interact with each other and with the host. The biosynthesis of these molecules is encoded in metabolic gene clusters (MGCs) that are subject to frequent horizontal transfer and are therefore highly strain-specific.
Current computational methods for analysing microbiomes largely focus on comparative taxonomic analyses and generic metabolism, and overlook this complex "chemical dialog". Indeed, no adequate methods are available to systematically study the roles of MGCs in microbiomes. At the same time, metabolomics data from microbiomes are full of ‘dark matter’: unknown molecules that cannot be traced to their producers. Here, I propose to develop the first comprehensive computational framework to study the chemical language of the microbiome.
DECIPHER aims to develop new algorithms to link MGCs to their metabolic products and to predict their molecular and ecological functions in microbiomes. Subsequently, the new framework will be applied in systematic studies of microbiome- associated phenotypes in plants and humans. Together, the innovations proposed here will fill a key gap in the analysis of microbiome function and pave the way toward precision-engineering of microbiomes with specific metabolic capabilities for designer soils and microbiome-based therapies.
Current computational methods for analysing microbiomes largely focus on comparative taxonomic analyses and generic metabolism, and overlook this complex "chemical dialog". Indeed, no adequate methods are available to systematically study the roles of MGCs in microbiomes. At the same time, metabolomics data from microbiomes are full of ‘dark matter’: unknown molecules that cannot be traced to their producers. Here, I propose to develop the first comprehensive computational framework to study the chemical language of the microbiome.
DECIPHER aims to develop new algorithms to link MGCs to their metabolic products and to predict their molecular and ecological functions in microbiomes. Subsequently, the new framework will be applied in systematic studies of microbiome- associated phenotypes in plants and humans. Together, the innovations proposed here will fill a key gap in the analysis of microbiome function and pave the way toward precision-engineering of microbiomes with specific metabolic capabilities for designer soils and microbiome-based therapies.
In the first 2.5 years, the project has made significant strides in understanding how microorganisms communicate and produce valuable compounds that hold immense potential for applications in medicine, agriculture, and beyond.
One of the key achievements of the project was the development of (prototypes of) innovative software packages, which leverage cutting-edge artificial intelligence techniques to predict the functions of biosynthetic gene clusters (BGCs). These packages have already unveiled fascinating insights into the roles of microorganisms in producing diverse chemical compounds, and have led to several collaborations with experimental scientists to confirm the predicted functions of these BGCs.
Moreover, the creation of an in silico retrobiosynthesis software may enables us and other researchers to identify the building blocks encoded within BGCs, and identify structure-function relationships that connect specific chemical substructures to functions. New collaborations with industry partners have been instrumental in collecting a vast library of almost 4000 purified natural products. This treasure trove of compounds serves as a valuable resource for cell painting assays, allowing us to predict how these compounds may interact with cells. A draft model for this has now been created.
The isolation of hundreds of strains from disease-suppressive soils and their (ongoing) genome sequencing will further help us to validate our approaches. We performed paired metagenomics/metatranscriptomics/metabolomics analyses of the synthetic communities associated with the disease-suppressive phenotype, and their results are being analysed.
One of the key achievements of the project was the development of (prototypes of) innovative software packages, which leverage cutting-edge artificial intelligence techniques to predict the functions of biosynthetic gene clusters (BGCs). These packages have already unveiled fascinating insights into the roles of microorganisms in producing diverse chemical compounds, and have led to several collaborations with experimental scientists to confirm the predicted functions of these BGCs.
Moreover, the creation of an in silico retrobiosynthesis software may enables us and other researchers to identify the building blocks encoded within BGCs, and identify structure-function relationships that connect specific chemical substructures to functions. New collaborations with industry partners have been instrumental in collecting a vast library of almost 4000 purified natural products. This treasure trove of compounds serves as a valuable resource for cell painting assays, allowing us to predict how these compounds may interact with cells. A draft model for this has now been created.
The isolation of hundreds of strains from disease-suppressive soils and their (ongoing) genome sequencing will further help us to validate our approaches. We performed paired metagenomics/metatranscriptomics/metabolomics analyses of the synthetic communities associated with the disease-suppressive phenotype, and their results are being analysed.
Building on the achievements of the DECIPHER project thus far, the next 2.5 years hold exciting potential for unlocking new horizons in microbiome science and natural product research. With a solid foundation in place, the project is poised to make significant advancements and generate valuable insights into the chemical language of the microbiome.
Over the next two years, we anticipate further refinements to our software packages. Through iterative learning and advancements in algorithms, we expect enhanced accuracy and efficiency in predicting BGC functions and identifying substructures within BGCs and metabolomic data. These improvements will deepen our understanding of the complex interactions between microorganisms and their chemical products, opening up new possibilities for drug discovery and biotechnological applications.
The collaboration with industry partners is set to yield exciting outcomes as we apply cell painting assays to predict the mechanisms of action specialized metabolites. The extensive new dataset that will be soon generated, combined with machine learning approaches, is anticipated to unravel structure-function relationships and shed light on the therapeutic potential of these compounds. The outcomes of these efforts have the potential to revolutionize drug development and shape the future of pharmaceutical research.
A key focus in the coming years will be the continued exploration of disease-suppressive rhizosphere microbiomes. While challenges in isolating certain bacterial strains have been encountered, the cultivated community, representing a substantial portion of the natural microbiome, remains invaluable for further investigations. Through in-depth analysis of the isolated strains and their phenotypic characteristics, we aim to uncover the underlying mechanisms of disease suppression using synthetic microbial communities and metatranscriptomics and metabolomics analysis of their interactions, potentially leading to novel biocontrol strategies for sustainable agriculture.
As the project progresses, we anticipate exciting breakthroughs in our understanding of the microbiome's chemical language and the hidden potential of microorganisms. The integration of multidisciplinary approaches, in collaboration with renowned academic and industry partners, is expected to drive innovation and lead to impactful outcomes.
Over the next two years, we anticipate further refinements to our software packages. Through iterative learning and advancements in algorithms, we expect enhanced accuracy and efficiency in predicting BGC functions and identifying substructures within BGCs and metabolomic data. These improvements will deepen our understanding of the complex interactions between microorganisms and their chemical products, opening up new possibilities for drug discovery and biotechnological applications.
The collaboration with industry partners is set to yield exciting outcomes as we apply cell painting assays to predict the mechanisms of action specialized metabolites. The extensive new dataset that will be soon generated, combined with machine learning approaches, is anticipated to unravel structure-function relationships and shed light on the therapeutic potential of these compounds. The outcomes of these efforts have the potential to revolutionize drug development and shape the future of pharmaceutical research.
A key focus in the coming years will be the continued exploration of disease-suppressive rhizosphere microbiomes. While challenges in isolating certain bacterial strains have been encountered, the cultivated community, representing a substantial portion of the natural microbiome, remains invaluable for further investigations. Through in-depth analysis of the isolated strains and their phenotypic characteristics, we aim to uncover the underlying mechanisms of disease suppression using synthetic microbial communities and metatranscriptomics and metabolomics analysis of their interactions, potentially leading to novel biocontrol strategies for sustainable agriculture.
As the project progresses, we anticipate exciting breakthroughs in our understanding of the microbiome's chemical language and the hidden potential of microorganisms. The integration of multidisciplinary approaches, in collaboration with renowned academic and industry partners, is expected to drive innovation and lead to impactful outcomes.