Ornella Cominetti

Systems Biology Approaches for Inflammatory Bowel Disease: Emphasis on Gut Microbial Metabolism

Abstract:Although the prevalence of main idiopathic forms of inflammatory bowel disease (IBD) has risen considerably over the last decades, their clinical features do not allow accurate prediction of prognosis, likelihood of disease progression, or response to specific therapy. Through a better understanding of the molecular pathways involved in IBD and the promise of more targeted therapies, the personalized approach to the management of IBD shows potential. To achieve this, there remains a significant need to better understand the disease process at cellular and molecular levels for any given individual with IBD. The complexity of biological functional networks behind the etiology of IBD highlights the need for their comprehensive analysis. In this, omics technologies can generate a systemic view of IBD pathogenesis on which to base novel, multiple pathway-integrated therapies. Omics sciences have just started to contribute here by generating gene, protein expression, metabolite data at global level and large scale, and more recently by offering new opportunities to explore gut functional ecology. In particular, there is much expectation regarding the putative role of the gut microbiome in IBD. No doubt it will provide additional insights and lead to the development of alternative, hopefully better, diagnostic, prognostic, and monitoring tools in the management of IBD. This review discusses perspectives of relevance to clinical translation with emphasis on gut microbial metabolic activities.

Longitudinal omics modeling and integration in clinical metabonomics research: challenges in childhood metabolic health research.

Systems biology is an important approach for deciphering the complex processes in health maintenance and the etiology of metabolic diseases. Such integrative methodologies will help better understand the molecular mechanisms involved in growth and development throughout childhood, and consequently will result in new insights about metabolic and nutritional requirements of infants, children and adults. To achieve this, a better understanding of the physiological processes at anthropometric, cellular and molecular level for any given individual is needed. In this respect, novel omics technologies in combination with sophisticated data modeling techniques are key. Due to the highly complex network of influential factors determining individual trajectories, it becomes imperative to develop proper tools and solutions that will comprehensively model biological information related to growth and maturation of our body functions. The aim of this review and perspective is to evaluate, succinctly, promising data analysis approaches to enable data integration for clinical research, with an emphasis on the longitudinal component. Approaches based on empirical and mechanistic modeling of omics data are essential to leverage findings from high dimensional omics datasets and enable biological interpretation and clinical translation. On the one hand, empirical methods, which provide quantitative descriptions of patterns in the data, are mostly used for exploring and mining datasets. On the other hand, mechanistic models are based on an understanding of the behavior of a systems components and condense information about the known functions, allowing robust and reliable analyses to be performed by bioinformatics pipelines and similar tools. Herein, we will illustrate current examples, challenges and perspectives in the applications of empirical and mechanistic modeling in the context of childhood metabolic health research.

The differential plasma proteome of obese and overweight individuals undergoing a nutritional weight loss and maintenance intervention

The nutritional intervention program “DiOGenes” focuses on how obesity can be prevented and treated from a dietary perspective. We generated differential plasma proteome profiles in the DiOGenes cohort to identify proteins associated with weight loss and maintenance and explore their relation to body mass index, fat mass, insulin resistance, and sensitivity.

High-throughput method for the quantitation of metabolites and co-factors from homocysteine-methionine cycle for nutritional status assessment

AIM There is increasing interest in the profiling and quantitation of methionine pathway metabolites for health management research. Currently, several analytical approaches are required to cover metabolites and co-factors. RESULTS We report the development and the validation of a method for the simultaneous detection and quantitation of 13 metabolites in red blood cells. The method, validated in a cohort of healthy human volunteers, shows a high level of accuracy and reproducibility. CONCLUSION This high-throughput protocol provides a robust coverage of central metabolites and co-factors in one single analysis and in a high-throughput fashion. In large-scale clinical settings, the use of such an approach will significantly advance the field of nutritional research in health and disease.

A Highly Automated Shotgun Proteomic Workflow: Clinical Scale and Robustness for Biomarker Discovery in Blood

With recent technological developments, protein biomarker discoveries directly from blood have regained interest due to elevated feasibility. Mass spectrometry (MS)-based proteomics can now characterize human plasma proteomes to a greater extent than has ever been possible before. Such deep proteome coverage comes, however, with important limitations in terms of analysis time which is a critical factor in the case of clinical studies. As a consequence, compromises still need to be made to balance the proteome coverage with realistic analysis time frame in clinical research. The analysis of a sufficient number of samples is compulsory to empower statistically robust candidate biomarker findings. We have, therefore, recently developed a scalable automated proteomic pipeline (ASAP2) to enable the proteomic analysis of large numbers of plasma and cerebrospinal fluid (CSF) samples, from dozens to a thousand of samples, with the latter number being currently processed in 15 weeks. A distinct characteristic of ASAP2 relies on the possibility to prepare samples in a highly automated way, mostly using 96-well plates. We describe herein a sample preparation procedure for human plasma that includes internal standard spiking, abundant protein removal, buffer exchange, reduction, alkylation, tryptic digestion, isobaric labeling, pooling, and sample purification. Other key elements of the pipeline (i.e., study design, sample tracking, liquid chromatography (LC) tandem MS (MS/MS), data processing, and data analysis) are also highlighted.

Coordinated activation of mitochondrial respiration and exocytosis mediated by PKC signaling in pancreatic β cells

Mitochondria play a central role in pancreatic β‐cell nutrient sensing by coupling their metabolism to plasma membrane excitability and insulin granule exocytosis. Whether non‐nutrient secretagogues stimulate mitochondria as part of the molecular mechanism to promote insulin secretion is not known. Here, we show that PKC signaling, which is employed by many non‐nutrient secretagogues, augments mitochondrial respiration in INS‐1E (rat insulinoma cell line clone 1E) and human pancreatic β cells. The phorbol ester, phorbol 12‐myristate 13‐acetate, accelerates mitochondrial respiration at both resting and stimulatory glucose concentrations. A range of inhibitors of novel PKC isoforms prevent phorbol ester–induced respiration. Respiratory response was blocked by oligomycin that demonstrated PKC‐dependent acceleration of mitochondrial ATP synthesis. Enhanced respiration was observed even when glycolysis was bypassed or fatty acid transport was blocked, which suggested that PKC regulates mitochondrial processes rather than upstream catabolic fluxes. A phosphoproteome study of phorbol ester–stimulated INS‐1E cells maintained under resting (2.5 mM) glucose revealed a large number of phosphorylation sites that were altered during short‐term activation of PKC signaling. The data set was enriched for proteins that are involved in gene expression, cytoskeleton remodeling, secretory vesicle transport, and exocytosis. Interactome analysis identified PKC, C‐Raf, and ERK1/2 as the central phosphointeraction cluster. Prevention of ERK1/2 signaling by using a MEK1 inhibitor caused a marked decreased in phorbol 12‐myristate 13‐acetate–induced mitochondrial respiration. ERK1/2 signaling module therefore links PKC activation to downstream mitochondrial activation. We conclude that non‐nutrient secretagogues act, in part, via PKC and downstream ERK1/2 signaling to stimulate mitochondrial energy production to compensate for energy expenditure that is linked to β‐cell activation.—Santo‐Domingo, J., Chareyron, I., Dayon, L., Galindo, A. N., Cominetti, O., Giménez, M. P. G., De Marchi, U., Canto, C., Kussmann, M., Wiederkehr, A. Coordinated activation of mitochondrial respiration and exocytosis mediated by PKC signaling in pancreatic β cells. FASEB J. 31, 1028–1045 (2017). www.fasebj.org

Urinary Metabolic Phenotyping Reveals Differences in the Metabolic Status of Healthy and Inflammatory Bowel Disease (IBD) Children in Relation to Growth and Disease Activity

Background: Growth failure and delayed puberty are well known features of children and adolescents with inflammatory bowel disease (IBD), in addition to the chronic course of the disease. Urinary metabonomics was applied in order to better understand metabolic changes between healthy and IBD children. Methods: 21 Pediatric patients with IBD (mean age 14.8 years, 8 males) were enrolled from the Pediatric Gastroenterology Outpatient Clinic over two years. Clinical and biological data were collected at baseline, 6, and 12 months. 27 healthy children (mean age 12.9 years, 16 males) were assessed at baseline. Urine samples were collected at each visit and subjected to 1H Nuclear Magnetic Resonance (NMR) spectroscopy. Results: Using 1H NMR metabonomics, we determined that urine metabolic profiles of IBD children differ significantly from healthy controls. Metabolic differences include central energy metabolism, amino acid, and gut microbial metabolic pathways. The analysis described that combined urinary urea and phenylacetylglutamine—two readouts of nitrogen metabolism—may be relevant to monitor metabolic status in the course of disease. Conclusion: Non-invasive sampling of urine followed by metabonomic profiling can elucidate and monitor the metabolic status of children in relation to disease status. Further developments of omic-approaches in pediatric research might deliver novel nutritional and metabolic hypotheses.

Modeling Longitudinal Metabonomics and Microbiota Interactions in C57BL/6 Mice Fed a High Fat Diet.

Longitudinal studies aim typically at following populations of subjects over time and are important to understand the global evolution of biological processes. When it comes to longitudinal omics data, it will often depend on the overall objective of the study, and constraints imposed by the data, to define the appropriate modeling tools. Here, we report the use of multilevel simultaneous component analysis (MSCA), orthogonal projection on latent structures (OPLS), and regularized canonical correlation analysis (rCCA) to study associations between specific longitudinal urine metabonomics data and microbiome data in a diet-induced obesity model using C57BL/6 mice. (1)H NMR urine metabolic profiling was performed on samples collected weekly over a period of 13 weeks, and stool microbial composition was assessed using 16S rRNA gene sequencing at three specific time periods (baseline, first week response, end of study). MSCA and OPLS allowed us to explore longitudinal urine metabonomics data in relation to the dietary groups, as well as dietary effects on body weight. In addition, we report a data integration strategy based on regularized CCA and correlation analyses of urine metabonomics data and 16S rRNA gene sequencing data to investigate the functional relationships between metabolites and gut microbial composition. Thanks to this workflow enabling the breakdown of this data set complexity, the most relevant patterns could be extracted to further explore physiological processes at an anthropometric, cellular, and molecular level.

Systematic Evaluation of the Use of Human Plasma and Serum for Mass-Spectrometry-Based Shotgun Proteomics

Over the last two decades, EDTA-plasma has been used as the preferred sample matrix for human blood proteomic profiling. Serum has also been employed widely. Only a few studies have assessed the difference and relevance of the proteome profiles obtained from plasma samples, such as EDTA-plasma or lithium-heparin-plasma, and serum. A more complete evaluation of the use of EDTA-plasma, heparin-plasma, and serum would greatly expand the comprehensiveness of shotgun proteomics of blood samples. In this study, we evaluated the use of heparin-plasma with respect to EDTA-plasma and serum to profile blood proteomes using a scalable automated proteomic pipeline (ASAP2). The use of plasma and serum for mass-spectrometry-based shotgun proteomics was first tested with commercial pooled samples. The proteome coverage consistency and the quantitative performance were compared. Furthermore, protein measurements in EDTA-plasma and heparin-plasma samples were comparatively studied using matched sample pairs from 20 individuals from the Australian Imaging, Biomarkers and Lifestyle (AIBL) Study. We identified 442 proteins in common between EDTA-plasma and heparin-plasma samples. Overall agreement of the relative protein quantification between the sample pairs demonstrated that shotgun proteomics using workflows such as the ASAP2 is suitable in analyzing heparin-plasma and that such sample type may be considered in large-scale clinical research studies. Moreover, the partial proteome coverage overlaps (e.g., ∼70%) showed that measures from heparin-plasma could be complementary to those obtained from EDTA-plasma.

An adaptive pipeline to maximize isobaric tagging data in large-scale MS-based proteomics

Isobaric tagging is the method of choice in mass-spectrometry-based proteomics for comparing several conditions at a time. Despite its multiplexing capabilities, some drawbacks appear when multiple experiments are merged for comparison in large sample-size studies due to the presence of missing values, which result from the stochastic nature of the data-dependent acquisition mode. Another indirect cause of data incompleteness might derive from the proteomic-typical data-processing workflow that first identifies proteins in individual experiments and then only quantifies those identified proteins, leaving a large number of unmatched spectra with quantitative information unexploited. Inspired by untargeted metabolomic and label-free proteomic workflows, we developed a quantification-driven bioinformatic pipeline (Quantify then Identify (QtI)) that optimizes the processing of isobaric tandem mass tag (TMT) data from large-scale studies. This pipeline includes innovative features, such as peak filtering with a self-adaptive preprocessing pipeline optimization method, Peptide Match Rescue, and Optimized Post-Translational Modification. QtI outperforms a classical benchmark workflow in terms of quantification and identification rates, significantly reducing missing data while preserving unmatched features for quantitative comparison. The number of unexploited tandem mass spectra was reduced by 77 and 62% for two human cerebrospinal fluid and plasma data sets, respectively.