- Styliani A. Chasapi (1)
- Georgios A. Spyroulias (1)
- Jonathan Adam (1)
- Jerzy Adamski (1)
- M. Assfalg (1)
- J. Bouwman (1)
- A. Calabrò (1)
- Veronica Ghini (1)
- V. Ghini (1)
- E. Gralka (1)
- J. Greef de (1)
- Claudio Luchinat (3)
- C. Luchinat (4)
- Giulia Menichetti (1)
- Annette Peters (1)
- Cornelia Prehn (1)
- Daniel Remondini (1)
- E. Saccenti (4)
- Edoardo Saccenti (3)
- Claudio Santucci (1)
- C. Santucci (1)
- A.K. Smilde (1)
- Alexandros Spyridonidis (1)
- M. Suarez Diez (1)
- Maria Suarez-Diez (1)
- Leonardo Tenori (3)
- L. Tenori (4)
- M.E. Timmerman (1)
- P. Verbruggen (1)
- Alessia Vignoli (1)
- Rui Wang-Sattler (1)
Age and Sex Effects on Plasma Metabolite Association Networks in Healthy Subjects
Vignoli, Alessia ; Tenori, Leonardo ; Luchinat, Claudio ; Saccenti, Edoardo - \ 2018
Journal of Proteome Research 17 (2018)1. - ISSN 1535-3893 - p. 97 - 107.
differential network analysis - metabolism - metabolomics - network inference - NMR
In the era of precision medicine, the analysis of simple information like sex and age can increase the potential to better diagnose and treat conditions that occur more frequently in one of the two sexes, present sex-specific symptoms and outcomes, or are characteristic of a specific age group. We present here a study of the association networks constructed from an array of 22 plasma metabolites measured on a cohort of 844 healthy blood donors. Through differential network analysis we show that specific association networks can be associated with sex and age: Different connectivity patterns were observed, suggesting sex-related variability in several metabolic pathways (branched-chain amino acids, ketone bodies, and propanoate metabolism). Reduction in metabolite hub connectivity was also found to be associated with age in both sex groups. Network analysis was complemented with standard univariate and multivariate statistical analysis that revealed age- and sex-specific metabolic signatures. Our results demonstrate that the characterization of metabolite-metabolite association networks is a promising and powerful tool to investigate the human phenotype at a molecular level.
Plasma and Serum Metabolite Association Networks : Comparability within and between Studies Using NMR and MS Profiling
Suarez-Diez, Maria ; Adam, Jonathan ; Adamski, Jerzy ; Chasapi, Styliani A. ; Luchinat, Claudio ; Peters, Annette ; Prehn, Cornelia ; Santucci, Claudio ; Spyridonidis, Alexandros ; Spyroulias, Georgios A. ; Tenori, Leonardo ; Wang-Sattler, Rui ; Saccenti, Edoardo - \ 2017
Journal of Proteome Research 16 (2017)7. - ISSN 1535-3893 - p. 2547 - 2559.
blood - correlations - differential network analysis - low molecular weight metabolites - mutual information - network inference - network topology - plasma - serum
Blood is one of the most used biofluids in metabolomics studies, and the serum and plasma fractions are routinely used as a proxy for blood itself. Here we investigated the association networks of an array of 29 metabolites identified and quantified via NMR in the plasma and serum samples of two cohorts of ∼1000 healthy blood donors each. A second study of 377 individuals was used to extract plasma and serum samples from the same individual on which a set of 122 metabolites were detected and quantified using FIA-MS/MS. Four different inference algorithms (ARANCE, CLR, CORR, and PCLRC) were used to obtain consensus networks. The plasma and serum networks obtained from different studies showed different topological properties with the serum network being more connected than the plasma network. On a global level, metabolite association networks from plasma and serum fractions obtained from the same blood sample of healthy people show similar topologies, and at a local level, some differences arise like in the case of amino acids.
Entropy-Based Network Representation of the Individual Metabolic Phenotype
Saccenti, Edoardo ; Menichetti, Giulia ; Ghini, Veronica ; Remondini, Daniel ; Tenori, Leonardo ; Luchinat, Claudio - \ 2016
Journal of Proteome Research 15 (2016)9. - ISSN 1535-3893 - p. 3298 - 3307.
metabolite modules - metabolite-metabolite association networks - metabolomics - network multiplex
We approach here the problem of defining and estimating the nature of the metabolite-metabolite association network underlying the human individual metabolic phenotype in healthy subjects. We retrieved significant associations using an entropy-based approach and a multiplex network formalism. We defined a significantly over-represented network formed by biologically interpretable metabolite modules. The entropy of the individual metabolic phenotype is also introduced and discussed.
Allostasis and Resilience of the Human Individual Metabolic Phenotype
Ghini, V. ; Saccenti, E. ; Tenori, L. ; Assfalg, M. ; Luchinat, C. - \ 2015
Journal of Proteome Research 14 (2015)7. - ISSN 1535-3893 - p. 2951 - 2962.
nmr metabolomics - gut microbiota - health - stress - biomarkers - nutrition - disease - urine - load - discovery
The urine metabotype of 12 individuals was followed over a period of 8-10 years, which provided the longest longitudinal study of metabolic phenotypes to date. More than 2000 NMR metabolic profiles were analyzed. The majority of subjects have a stable metabotype. Subjects who were exposed to important pathophysiological stressful conditions had a significant metabotype drift. When the stress conditions ceased, the original metabotypes were regained, while an irreversible stressful condition resulted in a permanent metabotype change. These results suggest that each individual occupies a well-defined region in the broad metabolic space, within which a limited degree of allostasis is permitted. The insurgence of significant stressful conditions causes a shift of the metabotype to another distinct region. The spontaneous return to the original metabolic region when the stressful conditions are removed suggests that the original metabotype has some degree of resilience. In this picture, precision medicine should aim at reinforcing the patient's metabolic resilience, that is, his or her ability to revert to his or her specific metabotype rather than to a generic healthy one
Probabilistic networks of blood metabolites in healthy subjects as indicators of latent cardiovascular risk
Saccenti, E. ; Suarez Diez, M. ; Luchinat, C. ; Santucci, C. ; Tenori, L. - \ 2015
Journal of Proteome Research 14 (2015)2. - ISSN 1535-3893 - p. 1101 - 1111.
l-arginine supplementation - gene-coexpression network - insulin-resistance - metabolomic networks - disease - obesity - expression - cholesterol - association - validation
The complex nature of the mechanisms behind cardiovascular diseases prevents the detection of latent early risk conditions. Network representations are ideally suited to investigate the complex interconnections between the individual components of a biological system underlying complex diseases. Here we investigate the patterns of correlations of an array of 29 metabolites identified and quantified in the plasma of 864 healthy blood donors and use a systems biology approach to define metabolite probabilistic networks specific for low and high latent cardiovascular risk. We adapted methods based on the likelihood of correlation and methods from information theory and combined them with resampling techniques. Our results show that plasma metabolite networks can be defined that associate with latent cardiovascular disease risk. The analysis of the networks supports our previous finding of a possible association between cardiovascular risk and impaired mitochondrial activity and highlights post-translational modifications (glycosilation and oxidation) of lipoproteins as a possible target-mechanism for early detection of latent cardiovascular risk.
Of Monkeys and Men: A Metabolomic Analysis of Static and Dynamic Urinary Metabolic Phenotypes in Two Species
Saccenti, E. ; Tenori, L. ; Verbruggen, P. ; Timmerman, M.E. ; Bouwman, J. ; Greef, J. de; Luchinat, C. ; Smilde, A.K. - \ 2014
PLoS One 9 (2014)9. - ISSN 1932-6203
multilevel component analysis - time - creatinine - evolution - pathways - humans - diet
Background Metabolomics has attracted the interest of the medical community for its potential in predicting early derangements from a healthy to a diseased metabolic phenotype. One key issue is the diversity observed in metabolic profiles of different healthy individuals, commonly attributed to the variation of intrinsic (such as (epi)genetic variation, gut microbiota, etc.) and extrinsic factors (such as dietary habits, life-style and environmental conditions). Understanding the relative contributions of these factors is essential to establish the robustness of the healthy individual metabolic phenotype. Methods To assess the relative contribution of intrinsic and extrinsic factors we compared multilevel analysis results obtained from subjects of Homo sapiens and Macaca mulatta, the latter kept in a controlled environment with a standardized diet by making use of previously published data and results. Results We observed similarities for the two species and found the diversity of urinary metabolic phenotypes as identified by nuclear magnetic resonance (NMR) spectroscopy could be ascribed to the complex interplay of intrinsic factors and, to a lesser extent, of extrinsic factors in particular minimizing the role played by diet in shaping the metabolic phenotype. Moreover, we show that despite the standardization of diet as the most relevant extrinsic factor, a clear individual and discriminative metabolic fingerprint also exists for monkeys. We investigate the metabolic phenotype both at the static (i.e., at the level of the average metabolite concentration) and at the dynamic level (i.e., concerning their variation over time), and we show that these two components sum up to the overall phenotype with different relative contributions of about 1/4 and 3/4, respectively, for both species. Finally, we show that the great degree diversity observed in the urinary metabolic phenotype of both species can be attributed to differences in both the static and dynamic part of their phenotype
A Metabolomic Perspective on Coeliac Disease
Calabrò, A. ; Gralka, E. ; Luchinat, C. ; Saccenti, E. ; Tenori, L. - \ 2014
Autoimmune Diseases 2014 (2014). - ISSN 2090-0422 - 13 p.
Metabolomics is an “omic” science that is now emerging with the purpose of elaborating a comprehensive analysis of the metabolome, which is the complete set of metabolites (i.e., small molecules intermediates) in an organism, tissue, cell, or biofluid. In the past decade, metabolomics has already proved to be useful for the characterization of several pathological conditions and offers promises as a clinical tool. A metabolomics investigation of coeliac disease (CD) revealed that a metabolic fingerprint for CD can be defined, which accounts for three different but complementary components: malabsorption, energy metabolism, and alterations in gut microflora and/or intestinal permeability. In this review, we will discuss the major advancements in metabolomics of CD, in particular with respect to the role of gut microbiome and energy metabolism