Section: New Results

Inferring and analysing the networks of molecular elements

Metamodules in transcriptomic analysis

The human microbiome plays a key role in health and disease. Thanks to comparative metatranscriptomics, the cellular functions that are deregulated by the microbiome in disease can now be computationally explored. Unlike gene-centric approaches, pathway-based methods provide a systemic view of such functions; however, they typically consider each pathway in isolation and in its entirety. They can therefore overlook the key differences that (i) span multiple pathways, (ii) contain bidirectionally deregulated components, (iii) are confined to a pathway region. To capture these properties, computational methods that reach beyond the scope of predefined pathways are needed.

By integrating an existing module discovery algorithm into comparative metatranscriptomic analysis, we developed metaModules , a novel computational framework for automated identification of the key functional differences between health- and disease-associated communities [20]. Using this framework, we recovered significantly deregulated subnetworks that were indeed recognised to be involved in two well-studied, microbiome-mediated oral diseases, such as butanoate production in periodontal disease and metabolism of sugar alcohols in dental caries. More importantly, our results indicated that our method can be used for hypothesis generation based on automated discovery of novel, disease-related functional subnetworks, which would otherwise require extensive and laborious manual assessment.

Metabolic environmental dialog

What an organism needs at least from its environment to produce a set of metabolites, e.g. target(s) of interest and/or biomass, has been called a minimal precursor set. Early approaches to enumerate all minimal precursor sets took into account only the topology of the metabolic network (topological precursor sets). Due to cycles and the stoichiometric values of the reactions, it is often not possible to produce the target(s) from a topological precursor set in the sense that there is no feasible flux. Although considering the stoichiometry makes the problem harder, it enables to obtain biologically reasonable precursor sets that we call stoichiometric. Recently a method to enumerate all minimal stoichiometric precursor sets was proposed in the literature. The relationship between topological and stoichiometric precursor sets had however not yet been studied.

Such relationship was explored in a recently accepted paper [3]. In there, we also presented two algorithms that enumerate all minimal stoichiometric precursor sets. The first one is of theoretical interest only and is based on the above mentioned relationship. The second approach solves a series of mixed integer linear programming (MILP) problems. We compared the computed minimal precursor sets to experimentally obtained growth media of several Escherichia coli strains using genome-scale metabolic networks.

The results showed that the second approach, called Sasita , efficiently enumerates minimal precursor sets taking stoichiometry into account, and allows for broad in silico studies of strains or species interactions that may help to understand e.g. pathotype and niche-specific metabolic capabilities.

This work was also part of the PhD of Martin Wannagat, defended in June 2016 [2].

Metabolic hyperstories

In the context of a PhD in the team (whose defence took place in Dec 8, 2016) [1] and using metabolomics data, we focused on inferring the metabolic behaviour of an organism when it is subjected to a change in conditions. In this case, one can infer the reactions impacted when the changes are controlled and known (e.g. exposition to toxic compounds, changes in culture conditions). However, understanding how the metabolism of an organism changes of equilibrium is also of interest to infer the processes related for example to a transition between a commensal or beneficial bacterium to a pathogenic one. This question led to two different methods. The first, that we called Totoro (for TOpological analysis of Transient metabOlic RespOnse), is based on the topology of metabolic networks to infer the reactions involved in a transient state, when an organism goes from one state of growth to another. We proposed a novel definition using the directed hypergraph representation and discussed its application on a dataset of Yeast exposed to cadmium. We showed that this method suggests more complete solutions of the reactions impacted during the metabolic shift. The second method, called Kotoura (for Kantitative analysis Of Transient metabOlic and regUlatory Response And control), offers a constraint-based perspective in a more quantitative approach. We applied it to a simulated dataset and we are currently trying to infer the possible quantitative responses to mutations with a more complete kinetic model. An image previously used is that condition-specific models provide a snapshot of the metabolism of an organism, whether it is at the evolutionary-time scale or at the scale of a specific environmental condition describing a physiological process. Our idea here is thus to infer the transitions between those snapshots.

Besides the PhD manuscript, two papers are in preparation to present this work. They should be submitted in early 2017. A prototype for the two methods is available at: http://hyperstories.gforge.inria.fr/.