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Context

Anaerobic digestion (AD) is a microbiological process of degradation which produces biogas rich in valuable methane. It is commonly used to manage different types of organic waste at industrial scale using anaerobic digesters. However, this bioprocess is not fully controlled and still has an important potential for improvement. One of the major limitations of AD is the important susceptibility of the microbial communities to changes in the operational conditions of the digesters. It can lead to unstable methane formation. Controlling AD microbial community stability, though, is not a trivial task. Knowledge on the determinants of anaerobic microbial process stability over time is still missing. Consequently, the successful operation of digesters mainly relies on the know-how of the industrial operators rather than on objective criteria. It strongly limits the standardisation, transfer and broad use of successful operational strategies. Therefore, microbial-based management of anaerobic reactors is a major hurdle to better control and improve AD.

To build such management strategy and prevent dysfunction, a better knowledge of the succession of events leading from microbial equilibrium disruption to process macroscopic failure is required. Emerging omics high-throughput approaches can now lead to unprecedented data to portray AD microbiome at different levels. Metagenomics, metatranscriptomics, metaproteomics and metabolomics provide the information necessary to represent portraits of a community’s genes, gene expression, and metabolite production. They can allow to unravel the intricate networks of functional processes of AD, provided that appropriate analytical methods are applied to decipher these big datasets. In particular, combining omics information with data on reactor performance during different operational conditions could help elucidate the mechanisms of process instability and propose successful operational strategies. Novel computational and statistical methods have a promising potential to capitalise on this rich data. However they are still at their infancy and have not been broadly used for this type of problems yet.

Objectives of the PhD project

In this context, the aim of this interdisciplinary PhD project is to gain insights into the determinants of AD stability over time, using a model lab-scale longitudinal approach and different omics methodologies in combination with computational statistics.

In details, the PhD student will first conduct sets of replicated longitudinal experiments, in the long run, in lab-scale semi-continuous anaerobic digesters under constant environmental parameters or subject to different model perturbations. Degradation performances will be monitored across time. Then, from samples taken regularly in the digesters, a high-frequency monitoring of different descriptors of microbiota activity will be performed, where non-targeted metabolomics and isotopic analyses will characterise the degradation pathways and metabarcoding of RNA and DNA will target both active and present microorganisms. These sets of data will be thoroughly analysed and integrated using cutting-edge statistical methods. For example, multivariate dimension reduction methods will be used for data mining, omics integration and feature selection; specific analytical framework for longitudinal data will be developed. Finally, on selected samples and conditions, an in-depth monitoring of microbiota functioning with both metagenomics and metatranscriptomics will complement the data already obtained.

The objectives will be 1) to evaluate at different omics levels the dynamics of AD microbiome in long term and replicated time course experiments, 2) to describe the succession of events that, under stress, leads to microbiota equilibrium unbalance and digester disruption or on the contrary microbiota equilibrium preservation and maintenance of stability, 3) to propose an original analytical framework of multi-omics longitudinal studies accounting for temporality, and 4) to deliver generic knowledge to understand the determinants of perturbations and stability.

The experimental set-up that will be used is already available at PROSE and ready to be used. Most of the omics protocols and methodologies that will be used in the project are already mastered at PROSE. The PhD student will be supported the members of PROSE analytical and experimental divisions.

Collaborations

This interdisciplinary PhD project is part of the STABILICS ANR funded project and involves collaborations with the Molecular Chemistry Laboratory at the Ecole Polytechnique (Palaiseau, France), the Toulouse Mathematics Institute (France) and the Melbourne Integrative Genomics at the University of Melbourne (Australia).

Required skills

Applicants will have a master's degree or equivalent in the fields of microbial ecology, molecular biology, analytical chemistry or environmental science. They should have an experience of and taste for laboratory work, as the project includes an important experimental part, combining lab-scale bioreactors approaches and experiments in molecular biology and analytical chemistry, all requiring rigor and thoroughness. Knowledge of statistics, programming or computational analysis of data will be strongly appreciated. Knowledge of English is essential.

Of course, ability to take initiative, curiosity and adaptability are prerequisites for this interdisciplinary project requiring the mobilisation of different skills and competencies.

Contact

Dr Olivier Chapleur olivier.chapleur[at]inrae.fr 

• CV
• Cover letter
• Names and contact details of referees

Location

INRAE - PROcédés biotechnologiques au Service de l’Environnement Research Unit - (Antony, Paris Region, France) https://www6.jouy.inrae.fr/prose 

Supervision

Dr Olivier Chapleur