Inferring Biochemical Reactions and Metabolite Structures to Understand Metabolic Pathway Drift

Belcour, Arnaud; Girard, Jean; Aïte, Méziane; Delage, Ludovic; Trottier, Camille; Marteau, Charlotte; Leroux, Cédric; Dittami, Simon M.; Sauleau, Pierre; Corre, Matthieu Le; Nicolas, Jacques; Boyen, Catherine; Leblanc, Catherine; Collén, Jonas; Siegel, Anne; Markov, Gabriel V.

doi:10.1016/j.isci.2020.100849

Cited by 16 publications

(31 citation statements)

References 74 publications

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“…Among the remaining sterols, cycloartenol was found in all algae except C. crispus , as previously noticed ( Belcour et al, 2020 ), and L. digitata , where we could also interpret this absence as it being below the detection limit. Indeed, its precursor, squalene, is also found in the lowest concentration in L. digitata compared to the other examined brown algae.…”

Section: Resultssupporting

confidence: 86%

“…In order to build a general biosynthesis model for the brown algal sterols confirmed by GC-MS profiling, we used the previously developed Pathmodel program ( Belcour et al, 2020 ) to infer pathways based on the data about the presence and absence of sterols in brown alga and the related biochemical reactions registered in the MetaCyc database ( Caspi et al, 2020 ). This model shows a mixture of features that are either conserved in other lineages or specific to brown algae ( Figure 2 ).…”

Section: Resultsmentioning

confidence: 99%

“…Ab-initio inference of biosynthesis pathway was performed using the Pathmodel program ( Belcour et al, 2020 ). The encoding source files for the mozukulins pathway and the brown sterol biosynthetic pathways were added in a new version of Pathmodel (0.2.0).…”

Section: Methodsmentioning

confidence: 99%

“…The associated biosynthetic pathways are good models to study metabolic pathway evolution because the enzymes involved are well-known across eukaryotes and relatively conserved despite some variation ( Desmond and Gribaldo, 2009 ). Recently, several studies have enabled to better understand the history of this pathway in land plants ( Sonawane et al, 2016 ), diatoms ( Gallo et al, 2020 ), or red algae ( Belcour et al, 2020 ). However, a general model for sterol biosynthesis in brown algae – a vast diversified and independent eukaryotic lineage ( Bringloe et al, 2020 ) – is still lacking.…”

Section: Introductionmentioning

confidence: 99%

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Semi-Quantitative Targeted Gas Chromatography-Mass Spectrometry Profiling Supports a Late Side-Chain Reductase Cycloartenol-to-Cholesterol Biosynthesis Pathway in Brown Algae

et al. 2021

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Sterols are biologically important molecules that serve as membrane fluidity regulators and precursors of signaling molecules, either endogenous or involved in biotic interactions. There is currently no model of their biosynthesis pathways in brown algae. Here, we benefit from the availability of genome data and gas chromatography-mass spectrometry (GC-MS) sterol profiling using a database of internal standards to build such a model. We expand the set of identified sterols in 11 species of red, brown, and green macroalgae and integrate these new data with genomic data. Our analyses suggest that some metabolic reactions may be conserved despite the loss of canonical eukaryotic enzymes, like the sterol side-chain reductase (SSR). Our findings are consistent with the principle of metabolic pathway drift through enzymatic replacement and show that cholesterol synthesis from cycloartenol may be a widespread but variable pathway among chlorophyllian eukaryotes. Among the factors contributing to this variability, one could be the recruitment of cholesterol biosynthetic intermediates to make signaling molecules, such as the mozukulins. These compounds were found in some brown algae belonging to Ectocarpales, and we here provide a first mozukulin biosynthetic model. Our results demonstrate that integrative approaches can already be used to infer experimentally testable models, which will be useful to further investigate the biological roles of those newly identified algal pathways.

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Section: Resultssupporting

confidence: 86%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Semi-Quantitative Targeted Gas Chromatography-Mass Spectrometry Profiling Supports a Late Side-Chain Reductase Cycloartenol-to-Cholesterol Biosynthesis Pathway in Brown Algae

et al. 2021

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“…The complete approach allowed studying biosynthetic pathways for carotenoids production, highlighting both reactions preserved through evolution and the specificities related to brown algae. Similar difficulties were encountered in the study of the red alga Chondrus crispus [89]. For this organism, an exhaustive confrontation of the experimentally detected metabolites and knowledge databases of metabolic reactions evidenced that for many compounds, biosynthetic pathways could not be inferred with gap-filling algorithms, because of incomplete biochemical knowledge and incomplete conservation of biochemical pathways during evolution.…”

Section: Applications To Unconventional Organisms: the Example Of Macroalgaementioning

confidence: 79%

Using automated reasoning to explore the metabolism of unconventional organisms: a first step to explore host–microbial interactions

Frioux

Dittami

Siegel

2020

Biochemical Society Transactions

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Systems modelled in the context of molecular and cellular biology are difficult to represent with a single calibrated numerical model. Flux optimisation hypotheses have shown tremendous promise to accurately predict bacterial metabolism but they require a precise understanding of metabolic reactions occurring in the considered species. Unfortunately, this information may not be available for more complex organisms or non-cultured microorganisms such as those evidenced in microbiomes with metagenomic techniques. In both cases, flux optimisation techniques may not be applicable to elucidate systems functioning. In this context, we describe how automatic reasoning allows relevant features of an unconventional biological system to be identified despite a lack of data. A particular focus is put on the use of Answer Set Programming, a logic programming paradigm with combinatorial optimisation functionalities. We describe its usage to over-approximate metabolic responses of biological systems and solve gap-filling problems. In this review, we compare steady-states and Boolean abstractions of metabolic models and illustrate their complementarity via applications to the metabolic analysis of macro-algae. Ongoing applications of this formalism explore the emerging field of systems ecology, notably elucidating interactions between a consortium of microbes and a host organism. As the first step in this field, we will illustrate how the reduction in microbiotas according to expected metabolic phenotypes can be addressed with gap-filling problems.

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Deciphering Complex Natural Mixtures through Metabolome Mining of Mass Spectrometry Data

Hooft

Ernst

Papenberg

et al. 2023

Recent Advances in Polyphenol Research

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Inferring Biochemical Reactions and Metabolite Structures to Understand Metabolic Pathway Drift

Cited by 16 publications

References 74 publications

Semi-Quantitative Targeted Gas Chromatography-Mass Spectrometry Profiling Supports a Late Side-Chain Reductase Cycloartenol-to-Cholesterol Biosynthesis Pathway in Brown Algae

Semi-Quantitative Targeted Gas Chromatography-Mass Spectrometry Profiling Supports a Late Side-Chain Reductase Cycloartenol-to-Cholesterol Biosynthesis Pathway in Brown Algae

Using automated reasoning to explore the metabolism of unconventional organisms: a first step to explore host–microbial interactions

Deciphering Complex Natural Mixtures through Metabolome Mining of Mass Spectrometry Data

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