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BackgroundCystic fibrosis (CF) is a disorder affecting the respiratory, digestive, reproductive systems and sweat glands. This lethal hereditary disease has known or suspected links to the dysbiosis gut microbiota. High-throughput meta-omics-based approaches may assist in unveiling this complex network of symbiosis modifications.ObjectivesThe aim of this study was to provide a predictive and functional model of the gut microbiota enterophenotype of pediatric patients affected by CF under clinical stability.MethodsThirty-one fecal samples were collected from CF patients and healthy children (HC) (age range, 1-6 years) and analysed using targeted-metagenomics and metabolomics to characterize the ecology and metabolism of CF-linked gut microbiota. The multidimensional data were low fused and processed by chemometric classification analysis.ResultsThe fused metagenomics and metabolomics based gut microbiota profile was characterized by a high abundance of Propionibacterium, Staphylococcus and Clostridiaceae, including Clostridium difficile, and a low abundance of Eggerthella, Eubacterium, Ruminococcus, Dorea, Faecalibacterium prausnitzii, and Lachnospiraceae, associated with overexpression of 4-aminobutyrate (GABA), choline, ethanol, propylbutyrate, and pyridine and low levels of sarcosine, 4-methylphenol, uracil, glucose, acetate, phenol, benzaldehyde, and methylacetate. The CF gut microbiota pattern revealed an enterophenotype intrinsically linked to disease, regardless of age, and with dysbiosis uninduced by reduced pancreatic function and only partially related to oral antibiotic administration or lung colonization/infection.ConclusionsAll together, the results obtained suggest that the gut microbiota enterophenotypes of CF, together with endogenous and bacterial CF biomarkers, are direct expression of functional alterations at the intestinal level. Hence, it's possible to infer that CFTR impairment causes the gut ecosystem imbalance.This new understanding of CF host-gut microbiota interactions may be helpful to rationalize novel clinical interventions to improve the affected children's nutritional status and intestinal function.

Metabolomics has the capability of providing predisposition, diagnostic, prognostic, and therapeutic biomarker profiles of individual patients, since a large number of metabolites can be measured in an unbiased manner from biological samples. In this setting

Metabolomics has the capability of providing predisposition, diagnostic, prognostic, and therapeutic biomarker profiles of individual patients, since a large number of metabolites can be measured in an unbiased manner from biological samples. In this setting, 1H-Nuclear Magnetic Resonance (NMR) spectroscopy of biofluids such as plasma, urine, and fecal water offers the opportunity to identify patterns of biomarker changes that reflects the physiological or pathological status of an individual patient.In this chapter, we show as a metabolomics study can be used to diagnose a disease, classifying patients as healthy or as pathological taking into account individual variability.

Metabolomics belongs to the family of "-omics" sciences, also comprised of genomics, transcriptomics, and proteomics, all of which share the advantage of a non-targeted approach for identifying biomarkers and profiling the patient. This means that they do not require a preliminary knowledge of the substances to be studied. Moreover, even small quantities of biological fluids or tissues may be utilized for analysis. Metabolomic procedure has become feasible only recently with the advent and accessibility of new high-throughput technologies, including mass spectrometry and nuclear magnetic resonance. The methodology generally involves three defining steps: 1) the acquisition of experimental data, 2) the multivariate statistical analysis, and 3) the projection of the acquired information (profiles) to construct the patient map. Metabolomic analysis has been applied to several disorders: as far as rheumatic diseases are concerned, a few studies have focused on rheumatoid arthritis, spondyloarthritis, systemic lupus erythematosus, and osteoarthritis. Both murine models and clinical data have shown the potential of this novel tool to contribute to deciding a diagnosis, discriminate between patients based on disease activity, and even predict the response to a particular treatment. The present review fully reports these findings and offers a critical view of the challenges still to be met.

To shed light on the specific contribution of HDA101 in modulating metabolic pathways in the maize seed, changes in the metabolic profiles of kernels obtained from hda101 mutant plants have been investigated by a metabonomic approach. Dynamic properties of chromatin folding can be mediated by enzymes that modify DNA and histones. The enzymes responsible for the steady-state of histone acetylation are histone acetyltransferase and histone deacetylase (HDA). Therefore, it is interesting to evaluate the effects of up- and down-regulation of a Rpd-3 type HDA on the development of maize seeds in terms of metabolic changes. This has been reached by analysing nuclear magnetic resonance spectra by different chemometrician approaches, such as Orthogonal Projection to Latent Structure-Discriminant Analysis, Parallel Factors Analysis, and Multi-way Partial Least Squares-Discriminant Analysis (N-PLS-DA). In particular, the latter approaches were chosen because they explicitly take time into account, organizing data into a set of slices that refer to different steps of the developing process. The results show the good discriminating capabilities of the N-PLS-DA approach, even if the number of samples ought be increased to obtain better predictive capabilities. However, using this approach, it was possible to show differences in the accumulation of metabolites during development and to highlight the changes occuring in the modified seeds. In particular, the results confirm the role of this gene in cell cycle control.

Objective A considerable proportion of patients with rheumatoid arthritis (RA) do not have a satisfactory response to biological therapies. We investigated the use of metabolomics approach to identify biomarkers able to anticipate the response to biologics in RA patients. Methods Due to gender differences in metabolomic profiling, the analysis was restricted to female patients starting etanercept as the first biological treatment and having a minimum of six months’ follow-up. Each patient was evaluated by the same rheumatologist before and after six months of treatment. At this time, the clinical response (good, moderate, none) was determined according to the EUropean League Against Rheumatism (EULAR) criteria, based on both erythrocyte sedimentation rate (EULAR-ESR) and C-reactive protein (EULAR-CRP). Sera collected prior and after six months of etanercept were analyzed by 1H-nuclear magnetic resonance (NMR) spectroscopy in combination with multivariate data analysis. Results Twenty-seven patients were enrolled: 18 had a good/moderate response and 9 were non responders according to both EULAR-ESR and EULAR-CRP after six months of etanercept. Metabolomic analysis at baseline was able to discriminate good, moderate, and non-responders with a very good predictivity (Q2 = 0.68) and an excellent sensitivity, specificity, and accuracy (100%). In good responders, we found an increase in isoleucine, leucine, valine, alanine, glutamine, tyrosine, and glucose levels and a decrease in 3-hydroxybutyrate levels after six months of treatment with etanercept with respect to baseline. Conclusion Our study confirms the potential of metabolomic analysis to predict the response to biological agents. Changes in metabolic profiles during treatment may help elucidate their mechanism of action.

Objective: Microgravity was often shown to cause cell damage and impair cell cycle in a variety of biological systems. Since the effects on the neural system were poorly investigated, we aimed to gain insight into how biological processes such as cell cycle, cell damage, stemness features and metabolic status are involved in neural stem cells (NSC) when they experience simulated microgravity. We also wished to investigate whether these modulations were transient or permanent once cells were returned to normal gravity. Methods: NSC were isolated from mouse cerebella and cultured in the Rotary Cell Culture System (RCCS) to model microgravity. We analyzed cell cycle, stress and apoptotic response. We also performed a 1H NMR-based metabolomic analysis and evaluation of stemness features of NSC in simulated microgravity and once in the returned to normogravity cell culture. Results: Biological processes and metabolic status were modulated by simulated microgravity. Cells were arrested in S-phase together with enhanced apoptosis. Metabolic changes occurred in NSC after simulated microgravity. Interestingly, these modulations were transient. Indeed, stemness features and metabolic footprint returned to basal levels after few days of culture in normal conditions. Moreover NSC clonogenic ability was not impaired. Conclusions: Our data suggest that simulated microgravity impacts on NSC biological processes, including cell cycle and apoptosis. However, NSC does not suffer from permanent damage.

The aim of the research was to investigate metabolic variations associated with genetic modifications in the grains of Zea mays using metabonomic techniques. With this in mind, the non-targeted characteristic of the technique is useful to identify metabolites peculiar to the genetic modification and initially undefined. The results obtained showed that the genetic modification, introducing Cry1Ab gene expression, induces metabolic variations involving the primary nitrogen pathway. Concerning the methodological aspects, the experimental protocol used has been applied in this field for the first time. It consists of a combination of partial least square-discriminant analysis and principal component analysis. The most important metabolites for discrimination were selected and the metabolic correlations linking them are identified. Principal component analysis on selected signals confirms metabolic variations, highlighting important details about the changes induced on the metabolic network by the presence of a Bt transgene in the maize genome.

Metabolomics is the analysis of the concentration profiles of low molecular weight compounds present in biological fluids. Metabolites are nonpeptide molecules representing the end products of cellular activity. Therefore, changes in metabolite concentrations reveal the range of biochemical effects induced by a disease or its therapeutic intervention. Metabolomics has recently become feasible with the accessibility of new technologies, including mass spectrometry and high-resolution proton nuclear magnetic resonance, and has already been applied to several disorders. Indeed, it has the advantage of being a nontargeted approach for identifying potential biomarkers, which means that it does not require a preliminary knowledge of the substances to be studied. In this review, we summarize the main studies in which metabolomic approach was used in some allergic (asthma, atopic dermatitis) and rheumatic diseases (rheumatoid arthritis, systemic lupus erythematosus) to explore the feasibility of this technique as a novel diagnostic tool in these complex disorders.

The astonishing development of high-throughput techniques in the last decades has fostered a renewed, dynamic comprehension of cell and tissue metabolism, giving unexpected insights into the ‘systemic aspects’ of cancer, namely pointing out that metabolism should be considered a truly “systems property”. Both internal and microenvironmental cues tightly cooperate in shaping the tissue metabolomic fingerprint. Tumour metabolome hardly could be mechanistically linked to the linear dynamics of few gene regulatory networks thus, it is more likely to be the complex end point of several interacting non-linear pathways, involving both cells and their microenvironment. As such, tumour metabolism might be considered an emerging, “systems property”, arising at the integrated scale of the whole system and behaving like an “attractor” in a specific space phase defined by thermodynamic constraints.

Fractal analysis in cancer cell investigation provided meaningful insights into the relationship between morphology and phenotype. Some reports demonstrated that changes in cell shape precede and trigger dramatic modifications in both gene expression and enzymatic function. Nonetheless, metabolomic pattern in cells undergoing shape changes have been not still reported. Our study was aimed to investigate if modifications in cancer cell morphology are associated to relevant transition in tumour metabolome, analyzed by nuclear magnetic resonance spectroscopy and principal component analysis. MCF-7 and MDA-MB-231 breast cancer cells, exposed to an experimental morphogenetic field, undergo a dramatic change in their membrane profiles. Both cell lines recover a more rounded shape, loosing spindle and invasive protrusions, acquiring a quite “normal” morphology. This result, quantified by fractal analysis, shows that normalized bending energy (a global shape characterization expressing the amount of energy needed to transform a specific shape into its lowest energy state) decreases after 48 h. Later on, a significant shift from a high to a low glycolytic phenotype was observed on both cell lines: glucose flux begins to drop off at 48 h, leading to reduced lactate accumulation, and fatty acids and citrate synthesis slow-down after 72 h. Moreover, de novo lipidogenesis is inhibited and nucleotide synthesis is reduced, as indicated by the positive correlation between glucose and formate. In conclusion, these data indicate that the reorganization of cell membrane architecture, induced by environmental cues, is followed by a relevant transition of the tumour metabolome, suggesting cells undergo a dramatic phenotypic reversion.

Metabolic profiling is defined as the simultaneous assessment of substrate fluxes within and among the different pathways of metabolite synthesis and energy production under various physiological conditions. The use of stable-isotope tracers and the analysis of the distribution of labeled carbons in various intermediates, by both mass spectrometry and NMR spectroscopy, allow the role of several metabolic processes in cell growth and death to be defined. In the present paper we describe the metabolic profiling of Jurkat cells by isotopomer analysis using 13 C-NMR spectroscopy and [1,2- 13 C 2 ]glucose as the stable-isotope tracer. The isotopomer analysis of the lactate, alanine, glutamate, proline, serine, glycine, malate and ribose-5-phosphate moiety of nucleotides has allowed original integrated information regarding the pentose phosphate pathway, TCA cycle, and amino acid metabolism in proliferating human leukemia T cells to be obtained. In particular, the contribution of the glucose-6-phosphate dehydrogenase and transketolase activities to phosphoribosyl-pyrophosphate synthesis was evaluated directly by the determination of isotopomers of the [1′- 13 C], [4′,5′- 13 C 2 ]ribosyl moiety of nucleotides. Furthermore, the relative contribution of the glycolysis and pentose cycle to lactate production was estimated via analysis of lactate isotopomers. Interestingly, pyruvate carboxylase and pyruvate dehydrogenase flux ratios measured by glutamate isotopomers and the production of isotopomers of several metabolites showed that the metabolic processes described could not take place simultaneously in the same macrocompartments (cells). Results revealed a heterogeneous metabolism in an asynchronous cell population that may be interpreted on the basis of different metabolic phenotypes of subpopulations in relation to different cell cycle phases.

The network metaphor is currently one ofthe most common general paradigms in biological sciences: this paradigm spans di3erent scales ofde4nition going f rom gene regulation to protein–protein interaction studies and metabolic regulation networks. Generally, the networks are de4ned by the nature ofthe connected elements (nodes) and their relative relations (edges). In this paper we demonstrate how the same biochemical regulation network can assume di3erent shapes in terms ofboth constituting elements and intervening relations while remaining recognizable as a speci4c entity. This behaviour can be explained by the general scaling properties ofbiological networks and points to regulation pathways as emergent features of biochemical systems posited at a di3erent hierarchical level with respect to the intervening metabolites. c

Background Metabolomics belongs to the family of “-omics” sciences, all of which share the advantage of a non-targeted approach for identifying biomarkers and profiling the patient. Metabolomic procedure has become feasible recently with the advent and accessibility of new high-throughput technologies, including mass spectrometry and 1H Nuclear Magnetic Resonance (1H NMR) and a few studies have been published in rheumatic disorders. Objectives To evaluate whether a 1H NMR-based metabolomic analysis in serum from patients with rheumatoid arthritis (RA) could predict the response to etanercept evaluated at 6 months. Methods Adult patients fulfilling the 1987 ACR revised criteria for the classification of RA and designated to start anti-TNF therapy were prospectively enrolled. The analysis was restricted to female patients with active disease starting etanercept as the first biological treatment and having a minimum of 6 months9 follow-up. Each patient was evaluated by the same rheumatologist at baseline before starting etanercept and after 6 months following the onset of biological treatment. DAS28 was calculated and the clinical response (good, moderate, none) was evaluated according to the EULAR criteria, based on both erythrocyte sedimentation rate (EULAR-ESR) and C-reactive protein (EULAR-CRP). We merged good and moderate categories as response in comparison with no response. Sera collected prior to the onset of etanercept were analyzed via 1H NMR-based metabolomics. Discriminating metabolites were identified, and the relationship between metabolic profiles and clinical outcomes was assessed. Results Twenty-seven patients were included (mean age 57.8 years, ± SD 12.5; mean disease duration 102.5 months, ± SD 78.05). Eighteen patients had a good/moderate response and 9 were non responders according to both EULAR-ESR and EULAR-CRP after 6 months of etanercept. Baseline serum metabolic profiles discriminated between RA patients who did or did not have a response to etanercept. Unsupervised Principal Component Analysis (PCA) results according to EULAR-ESR or EULAR-CRP response criteria highlighted significant differences between the metabolic profiles of responders and non responders (p Conclusions Metabolomics approach is a potentially useful technique for predicting the response of RA patients to etanercept, as demonstrated by differences in serum metabolic profiles at baseline. This observation deserves further investigation in larger cohorts of patients to confirm the capability of predicting clinical response without the need for empirical treatment. Disclosure of Interest : None declared DOI 10.1136/annrheumdis-2014-eular.5080