Reference : Forecasting of a complex microbial community using meta-omics

	Document type :	E-prints/Working papers : Already available on another site
	Discipline(s) :	Life sciences : Environmental sciences & ecology Life sciences : Genetics & genetic processes Life sciences : Microbiology
	Focus Areas :	Systems Biomedicine
	To cite this reference:	http://hdl.handle.net/10993/52862

Title :	Forecasting of a complex microbial community using meta-omics
Language :	English
Author, co-author :	Delogu, Francesco [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Systems Ecology]
	Kunath, Benoît [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Systems Ecology]
	Queirós, P. M. [> >]
	Halder, Rashi [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Scientific Central Services]
	Lebrun, Laura [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Systems Ecology]
	Pope, P. B. [> >]
	May, Patrick [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Bioinformatics Core]
	Widder, S. [> >]
	Muller, E. E. L. [> >]
	Wilmes, Paul [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Systems Ecology]
Publication date :	20-Oct-2022
Publisher :	Cold Spring Harbor Laboratory
Peer reviewed :	No
Keywords :	[en] forecasting ; microbial communities ; time-series analysis ; wastewater treatment plant ; multi-omics ; prediction
Abstract :	[en] Microbial communities are complex assemblages whose dynamics are shaped by abiotic and biotic factors. A major challenge concerns correctly forecasting the community behaviour in the future. In this context, communities in biological wastewater treatment plants (BWWTPs) represent excellent model systems, because forecasting them is required to ultimately control and operate the plants in a sustainable manner. Here, we forecast the microbial community from the water-air interface of the anaerobic tank of a BWWTP via longitudinal meta-omics (metagenomics, metatranscriptomics and metaproteomics) data covering 14 months at weekly intervals. We extracted all the available time-dependent information, summarised it in 17 temporal signals (explaining 91.1 of the temporal variance) and linked them over time to rebuild the sequence of ecological phenomena behind the community dynamics. We forecasted the signals over the following five years and tested the predictions with 21 extra samples. We were able to correctly forecast five signals accounting for 22.5 of the time-dependent information in the system and generate mechanistic predictions on the ecological events in the community (e.g. a predation cycle involving bacteria, viruses and amoebas). Through the forecasting of the 17 signals and the environmental variables readings we reconstructed the gene abundance and expression for the following 5 years, showing a nearly perfect trend prediction (coefficient of determination >= 0.97) for the first 2 years. The study demonstrates the maturity of microbial ecology to forecast composition and gene expression of open microbial ecosystems using year-spanning interactions between community cycles and environmental parameters.
Research centres :	Luxembourg Centre for Systems Biomedicine (LCSB): Eco-Systems Biology (Wilmes Group) ; Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) ; University of Luxembourg: High Performance Computing - ULHPC
Funders :	The project received financial support from the Integrated Biobank of Luxembourg with funds from the Luxembourg Ministry of Higher Education and Research. This work was also supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 863664). The work of P.M. was funded by the ‘Plan Technologies de la Santé du Gouvernement du Grand-Duché de Luxembourg’ through the Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg. S.W. was supported by the Austrian Science Fund (FWF) Elise Richter V585- B31. P.B.P is grateful for support from The Research Council of Norway (FRIPRO program: 250479) and The Novo Nordisk Foundation (Project no. 0054575).
Target :	Researchers ; Professionals
Permalink :	http://hdl.handle.net/10993/52862
DOI :	10.1101/2022.10.19.512887
Other URL :	https://www.biorxiv.org/content/early/2022/10/20/2022.10.19.512887
source URL :	https://www.biorxiv.org/content/10.1101/2022.10.19.512887v1
FnR project :	FnR ; FNR11823097 > Paul Wilmes > MICROH-DTU > Microbiomes In One Health > 01/09/2018 > 28/02/2025 > 2017

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