Whole proteome analyses on Ruminiclostridium cellulolyticum show a modulation of the cellulolysis machinery in response to cellulosic materials with subtle differences in chemical and structural properties

Lignocellulosic materials from municipal solid waste emerge as attractive resources for anaerobic digestion biorefinery. To increase the knowledge required for establishing efficient bioprocesses, dynamics of batch fermentation by the cellulolytic bacterium Ruminiclostridium cellulolyticum were compared using three cellulosic materials, paper handkerchief, cotton discs and Whatman filter paper. Fermentation of paper handkerchief occurred the fastest and resulted in a specific metabolic profile: it resulted in the lowest acetate-to-lactate and acetate-to-ethanol ratios. By shotgun proteomic analyses of paper handkerchief and Whatman paper incubations, 151 proteins with significantly different levels were detected, including 20 of the 65 cellulosomal components, 8 non-cellulosomal CAZymes and 44 distinct extracytoplasmic proteins. Consistent with the specific metabolic profile observed, many enzymes from the central carbon catabolic pathways had higher levels in paper handkerchief incubations. Among the quantified CAZymes and cellulosomal components, 10 endoglucanases mainly from the GH9 families and 7 other cellulosomal subunits had lower levels in paper handkerchief incubations. An in-depth characterization of the materials used showed that the lower levels of endoglucanases in paper handkerchief incubations could hypothetically result from its lower crystallinity index (50%) and degree of polymerization (970). By contrast, the higher hemicellulose rate in paper handkerchief (13.87%) did not result in the enhanced expression of enzyme with xylanase as primary activity, including enzymes from the xyl-doc cluster. It suggests the absence, in this material, of molecular structures that specifically lead to xylanase induction. The integrated approach developed in this work shows that subtle differences among cellulosic materials regarding chemical and structural characteristics have significant effects on expressed bacterial functions, in particular the cellulolysis machinery, resulting in different metabolic patterns and degradation dynamics.This work was supported by a grant [R2DS 2010-08] from Conseil Regional d'Ile-de-France through DIM R2DS programs (http://www.r2ds-ile-de-france.com/). Irstea (www.irstea.fr/) contributed to the funding of a PhD grant for the first author. The funders provided support in the form of salaries for author [NB], funding for consumables and laboratory equipment, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Omics Services provided support in the form of salaries for authors [VS, MD], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors [NB, VS, MD] are articulated in the 'author contributions' section.info:eu-repo/semantics/publishedVersio

Structuration de la diversité métabolique chez escherichia coli (Intégration du réseau métabolique, du protéome, des paramètres enzymatiques et des phénotypes de croissance)

Les microorganismes sont remarquablement adaptés à des environnements divers, changeants et imprévisibles. Escherischia coli est une bactérie d'importance en santé publique et compte parmi les plus versatiles. Sa diversité génétique intra-spécifique a été très étudiée et indique l'existence d'une plasticité du génome et d'une structure en phylogroupes. Alors que le métabolisme détermine la capacité d'une bactérie à exploiter les ressources, la diversité métabolique de l'espèce est mal connue. Pour comprendre le rôle des facteurs écologiques dans l'évolution de cette espèce, nous avons étudié l'importance et la structure de la diversité pour trois caractères métaboliques : la présence/absence de réactions au sein du réseau métabolique, la capacité à utiliser différentes sources de carbone et la variation des concentrations des protéines dans différents milieux. Nous avons montré que les réseaux métaboliques partagent un large noyau de réactions communes, et que leur part variable est structurée en fonction de la phylogénie. Toutefois, les phénotypes métaboliques ne sont pas liés aux phylogroupes et E. coli constitue un unique groupe phénotypique, ce qui suggère l'absence de spécialisation pour l'utilisation de source de carbone. Ce travail révèle l'importance de la diversité métabolique intra-spécifique et suggère de nouvelles hypothèses à propos des relations génotype/phénotype. La diversité métabolique intra-spécifique étant très structurée par les ressources en interaction avec les souches mais peu par la phylogénie de l'espèce ou le mode de vie, elle pourrait brouiller le signal phylogénétique. En perspective, l'intégration des données expérimentales dans les modèles métaboliques permettrait de mettre en relation les concentrations des enzymes avec les taux de croissance.Microorganisms are remarkably adapted to diverse, changing and unpredictable environments. Although metabolism is directly linked to the bacterial ability to grow in an ecological niche, the intra-species metabolic diversity is poorly known. Escherichia coli is one of the most versatile medically important species. Its intra-species genetic diversity has been thoroughly studied showing its genome plasticity and phylogroup structure. In order to better understand the role of ecological factors in the species evolution, we studied the extent and structure of metabolic diversity throughout the species for three metabolic traits: the presence/absence of reactions in the metabolic networks, the ability to grow on different carbon sources and the variation of protein concentrations in different environments. We found that metabolic networks share a large core of common reactions, and that its variable part is structured according to the species phylogeny. Nevertheless, metabolic phenotypes are not linked to phylogroups and E. coli constitutes a single phenotypic group, which suggests that no specialization occurred for carbon source usage within the species. This work reveals the extent of the intra-species metabolic diversity and suggests new hypotheses about genotype-phenotype relationships that could blur the phylogenetic signal. The main emerging picture is that the intra-species metabolic diversity is highly structured by the resources in interaction with the strains but weakly by the strain phylogeny or lifestyle. Further prospects consist in integrating these experimental data into metabolic models to relate variation of enzyme concentrations to growth rates.ORSAY-PARIS 11-BU Sciences (914712101) / SudocSudocFranceF

Core and Panmetabolism in Escherichia coli▿ †

Escherichia coli exhibits a wide range of lifestyles encompassing commensalism and various pathogenic behaviors which its highly dynamic genome contributes to develop. How environmental and host factors shape the genetic structure of E. coli strains remains, however, largely unknown. Following a previous study of E. coli genomic diversity, we investigated its diversity at the metabolic level by building and analyzing the genome-scale metabolic networks of 29 E. coli strains (8 commensal and 21 pathogenic strains, including 6 Shigella strains). Using a tailor-made reconstruction strategy, we significantly improved the completeness and accuracy of the metabolic networks over default automatic reconstruction processes. Among the 1,545 reactions forming E. coli panmetabolism, 885 reactions were common to all strains. This high proportion of core reactions (57%) was found to be in sharp contrast to the low proportion (13%) of core genes in the E. coli pangenome, suggesting less diversity of metabolic functions compared to that of all gene functions. Core reactions were significantly overrepresented among biosynthetic reactions compared to the more variable degradation processes. Differences between metabolic networks were found to follow E. coli phylogeny rather than pathogenic phenotypes, except for Shigella networks, which were significantly more distant from the others. This suggests that most metabolic changes in non-Shigella strains were not driven by their pathogenic phenotypes. Using a supervised method, we were yet able to identify small sets of reactions related to pathogenicity or commensalism. The quality of our reconstructed networks also makes them reliable bases for building metabolic models

Interactions between genotype and environment drive the metabolic phenotype within Escherichia coli isolates

To gain insights into the adaptation of the Escherichia coli species to different environments, we monitored protein abundances using quantitative proteomics and measurements of enzymatic activities of central metabolism in a set of five representative strains grown in four contrasted culture media including human urine. Two hundred and thirty seven proteins representative of the genome-scale metabolic network were identified and classified into pathway categories. We found that nutrient resources shape the general orientation of metabolism through coordinated changes in the average abundances of proteins and in enzymatic activities that all belong to the same pathway category. For example, each culture medium induces a specific oxidative response whatever the strain. On the contrary, differences between strains concern isolated proteins and enzymes within pathway categories in single environments. Our study confirms the predominance of genotype by environment interactions at the proteomic and enzyme activity levels. The buffering of genetic variation when considering life-history traits suggests a multiplicity of evolutionary strategies. For instance, the uropathogenic isolate CFT073 shows a deregulation of iron demand and increased oxidative stress response

A Multimodal Omics Exploration of the Motor and Non-Motor Symptoms of Parkinson’s Disease

Parkinson’s disease (PD) is the second most common neurodegenerative disease clinically characterized by classical motor symptoms and a range of associated non-motor symptoms. Due to the heterogeneity of symptoms and variability in patient prognosis, the discovery of blood biomarkers is of utmost importance to identify the biological mechanisms underlying the different clinical manifestations of PD, monitor its progression and develop personalized treatment strategies. Whereas studies often rely on motor symptoms alone or composite scores, our study focused on finding relevant molecular markers associated with three clinical models describing either motor, cognitive or emotional symptoms. An integrative multiblock approach was performed using regularized generalized canonical correlation analysis to determine specific associations between lipidomics, transcriptomics and clinical data in 48 PD patients. We identified omics signatures confirming that clinical manifestations of PD in our cohort could be classified according to motor, cognition or emotion models. We found that immune-related genes and triglycerides were well-correlated with motor variables, while cognitive variables were linked to triglycerides as well as genes involved in neuronal growth, synaptic plasticity and mitochondrial fatty acid oxidation. Furthermore, emotion variables were associated with phosphatidylcholines, cholesteryl esters and genes related to endoplasmic reticulum stress and cell regulation

A Multimodal Omics Exploration of the Motor and Non-Motor Symptoms of Parkinson’s Disease

Parkinson’s disease (PD) is the second most common neurodegenerative disease clinically characterized by classical motor symptoms and a range of associated non-motor symptoms. Due to the heterogeneity of symptoms and variability in patient prognosis, the discovery of blood biomarkers is of utmost importance to identify the biological mechanisms underlying the different clinical manifestations of PD, monitor its progression and develop personalized treatment strategies. Whereas studies often rely on motor symptoms alone or composite scores, our study focused on finding relevant molecular markers associated with three clinical models describing either motor, cognitive or emotional symptoms. An integrative multiblock approach was performed using regularized generalized canonical correlation analysis to determine specific associations between lipidomics, transcriptomics and clinical data in 48 PD patients. We identified omics signatures confirming that clinical manifestations of PD in our cohort could be classified according to motor, cognition or emotion models. We found that immune-related genes and triglycerides were well-correlated with motor variables, while cognitive variables were linked to triglycerides as well as genes involved in neuronal growth, synaptic plasticity and mitochondrial fatty acid oxidation. Furthermore, emotion variables were associated with phosphatidylcholines, cholesteryl esters and genes related to endoplasmic reticulum stress and cell regulation.</jats:p

A Multimodal Omics Exploration of the Motor and Non-Motor Symptoms of Parkinson&rsquo;s Disease

Parkinson&rsquo;s disease (PD) is the second most common neurodegenerative disease clinically characterized by classical motor symptoms and a range of associated non-motor symptoms. Due to the heterogeneity of symptoms and variability in patient prognosis, the discovery of blood biomarkers is of utmost importance to identify the biological mechanisms underlying the different clinical manifestations of PD, monitor its progression and develop personalized treatment strategies. Whereas studies often rely on motor symptoms alone or composite scores, our study focused on finding relevant molecular markers associated with three clinical models describing either motor, cognitive or emotional symptoms. An integrative multiblock approach was performed using regularized generalized canonical correlation analysis to determine specific associations between lipidomics, transcriptomics and clinical data in 48 PD patients. We identified omics signatures confirming that clinical manifestations of PD in our cohort could be classified according to motor, cognition or emotion models. We found that immune-related genes and triglycerides were well-correlated with motor variables, while cognitive variables were linked to triglycerides as well as genes involved in neuronal growth, synaptic plasticity and mitochondrial fatty acid oxidation. Furthermore, emotion variables were associated with phosphatidylcholines, cholesteryl esters and genes related to endoplasmic reticulum stress and cell regulation

Cellulosomal endoglucanases with significantly different levels when comparing Tissue and Whatman Paper incubations.

<p>Cellulosomal endoglucanases with significantly different levels when comparing Tissue and Whatman Paper incubations.</p

Detailed characteristics of Tissue, Whatman Paper and Cotton.

<p>Detailed characteristics of Tissue, Whatman Paper and Cotton.</p

Growth and fermentation dynamics of <i>R</i>. <i>cellulolyticum</i> on Tissue (black symbols), Whatman Paper (grey symbols) and Cotton (light grey symbols).

<p>Acetate (A), ethanol (B) and lactate (C) are the three most abundant fermentation products and their concentration ratios are shown in (D-E). Genome copy numbers estimated from the amount of total extracted DNA are shown in (F). Error bars indicate standard deviations calculated from triplicate samples, except in F (duplicate samples). Light grey areas indicate the time points selected for subsequent proteomic analyses.</p