{"id":2658,"date":"2023-11-14T09:10:15","date_gmt":"2023-11-14T08:10:15","guid":{"rendered":"https:\/\/www.uni.lu\/lcsb-en\/?post_type=news&p=2658"},"modified":"2023-11-14T11:31:35","modified_gmt":"2023-11-14T10:31:35","slug":"forecasting-microbiomes","status":"publish","type":"news","link":"https:\/\/www.uni.lu\/lcsb-en\/news\/forecasting-microbiomes\/","title":{"rendered":"Forecasting microbiomes for sustainability and health"},"content":{"rendered":"\n
\n

Microbial communities, or microbiomes, are essential for safeguarding human and environmental health through the most widely used biotechnological process on our planet: biological wastewater treatment. However, the process itself is subject to constant changes, difficult to sustain over long periods of time and emits significant amounts of greenhouse gases. There is thus an urgent need to predict the behavior of its complex microbiome to better control the process and to improve on its engineering. Researchers from the Systems Ecology<\/a> group at the Luxembourg Centre for Systems Biomedicine<\/a> (LCSB) and the Department of Life Sciences and Medicine<\/a> of the University of Luxembourg<\/a> and their international collaborators have now developed a novel modeling approach that can predict the dynamics and functions of such microbial communities several years into the future. The research article is published in the journal Nature Ecology and Evolution<\/em><\/a> alongside a corresponding Research Briefing<\/a>.<\/p>\n\n\n\n

Wastewater treatment plants are essential infrastructure, protecting human and environmental health through water sanitation. However, they are highly complex systems that can be difficult to manage. One immediate challenge is how to accurately predict the dynamics of the microbial communities that drive the treatment process, which is essential to better control it and make it sustainable. In their latest study, a team of researchers has addressed this issue head-on by developing a new framework to make such predictions accurately up to three years into the future.<\/p>\n\n\n\n

For their work, the researchers leveraged a unique set of samples, collected weekly at the wastewater treatment plant in Schifflange (Luxembourg) for a year and a half. They then generated a wide range of high-resolution molecular data (\u201cmeta-omics\u201d) on each sample and combined this with environmental information gathered for the site. Meta-omics allow scientists to study the entire genomes, transcriptomes, and proteomes of a microbial community at once, providing a complete understanding of how the community functions and interacts with its environment. \u201cThe meta-omics data covers, in intricate details, information about the abundance, activity, and metabolism of the broad spectrum of microbes living in the treatment plant, both known and unknown,\u201d explains Dr Francesco Delogu<\/a>, first author of the publication.<\/p>\n\n\n\n

Using mathematical and statistical modelling, the researchers were able to reduce the vast amounts of data to only 17 fundamental signals. These signals represent the key drivers of the microbial community and can therefore be used to forecast its composition and activity years into the future. This approach could be used to predict when and where potential problems, such as foaming or the emergence of pathogens, are likely to occur and allow countermeasures to be taken. \u201cTo consolidate the diverse layers of information, we used a rigorous approach to extract relevant information over time. We used cutting-edge analysis methodologies, including the Prophet open-source forecasting tool from Meta\/Facebook, and validated the accuracy of our predictions with additional samples,\u201d details Dr Delogu further. <\/p>\n\n\n

\n \n\"Wastewater <\/figure>\n\n\n

 \u201cBased on our work, the microbiomes driving biological wastewater treatment are no longer a black box,\u201d explains Dr \u00c9milie Muller<\/a>, co-author of the study, former member of the Systems Ecology group and researcher at the GMGM laboratory<\/a> of the University of Strasbourg<\/a> and the French CNRS<\/a>. \u201cThis strategy can now be applied to other ecosystems, be it the human gut microbiome or microbial communities in pristine environments facing disturbance. This is highly relevant for predicting microbial ecosystems and how they are affected by global environmental change.\u201d<\/p>\n\n\n\n

The new approach has a number of important implications for the future of wastewater treatment and for biotechnological processes that rely on microbiomes in general. The ability to predict and thus avoid problems in biological wastewater treatment plants could lead to an increase in their sustainability and efficiency, reducing costs and environmental impact. This new approach could also lead to the development of new and more effective treatment strategies, improving the overall quality of treated water and contributing to a healthier environment. In addition, these results could have implications for other fields, such as bioenergy production and environmental remediation. By developing a better understanding of microbial responses to environmental conditions, scientists could develop new ways to harness the power of microbes. Finally, the same approach could be used to predict the health and dynamics of the human microbiome in relation to dietary changes, drugs and environmental exposures.<\/p>\n\n\n\n

\u201cOur latest study addresses one of our group\u2019s main objectives: to accurately predict the behaviour of microbiomes. As a next important step, we aim to use our forecasting abilities for the rational control of microbiomes in wastewater treatment plants and beyond,\u201d concludes Prof. Paul Wilmes<\/a>, senior author of the study and head of the Systems Ecology research group at the LCSB.<\/p>\n\n\n\n

Publication reference:<\/strong> Francesco Delogu, Benoit J. Kunath, Pedro M. Queir\u00f3s, Rashi Halder, Laura A. Lebrun, Phillip B. Pope, Patrick May, Stefanie Widder, Emilie E. L. Muller & Paul Wilmes, Forecasting the dynamics of a complex microbial community using integrated meta-omics<\/a>, Nature Ecology & Evolution<\/em>, 14 November 2023.<\/p>\n\n\n\n

DOI: https:\/\/doi.org\/10.1038\/s41559-023-02241-3<\/a><\/p>\n\n\n\n

Briefing reference: <\/strong>Forecasting microbiome dynamics using integrated meta-omics<\/a>.\u00a0Nature Ecology & Evolution<\/em>, 14 November 2023.<\/p>\n\n\n\n

DOI: https:\/\/doi.org\/10.1038\/s41559-023-02248-w<\/a><\/p>\n\n\n\n

Funding:<\/strong>\u00a0This project received funding from\u00a0the Luxembourg National Research Fund (FNR), the European Union ERASysAPP programme, the European Research Council (ERC) under the European Union\u2019s Horizon 2020 research and innovation programme (grant agreement No. 863664), Austrian Science Fund (FWF),\u00a0The Research Council of Norway and The Novo Nordisk Foundation.\u00a0The project also received financial support from the Integrated Biobank of Luxembourg with funds from the Luxembourg Ministry of Higher Education and Research alongside the \u201cPlan Technologies de la Sant\u00e9 du Gouvernement du Grand-Duch\u00e9 de Luxembourg\u201d.

Picture credit: University of Luxembourg\/ScienceRELATIONS<\/em><\/p>\n<\/div><\/section>\n\n\n\n

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