The metabonomic signature of celiac disease

Rapid diagnosis of celiac disease based on plasma Raman spectroscopy combined with deep learning

Celiac Disease (CD) is a primary malabsorption syndrome resulting from the interplay of genetic, immune, and dietary factors. CD negatively impacts daily activities and may lead to conditions such as osteoporosis, malignancies in the small intestine, ulcerative jejunitis, and enteritis, ultimately causing severe malnutrition. Therefore, an effective and rapid differentiation between healthy individuals and those with celiac disease is crucial for early diagnosis and treatment. This study utilizes Raman spectroscopy combined with deep learning models to achieve a non-invasive, rapid, and accurate diagnostic method for celiac disease and healthy controls. A total of 59 plasma samples, comprising 29 celiac disease cases and 30 healthy controls, were collected for experimental purposes. Convolutional Neural Network (CNN), Multi-Scale Convolutional Neural Network (MCNN), Residual Network (ResNet), and Deep Residual Shrinkage Network (DRSN) classification models were employed. The accuracy rates for these models were found to be 86.67%, 90.76%, 86.67% and 95.00%, respectively. Comparative validation results revealed that the DRSN model exhibited the best performance, with an AUC value and accuracy of 97.60% and 95%, respectively. This confirms the superiority of Raman spectroscopy combined with deep learning in the diagnosis of celiac disease.

Metabolomics and lipidomics signature in celiac disease: a narrative review

Celiac disease (CD) is a chronic immune-mediated inflammatory disease of the small intestine caused by aberrant immune responses to consumed gluten proteins. CD is diagnosed by a combination of the patients reported symptoms, serologic and endoscopic biopsy evaluation of the small intestine; and adherence to a strict gluten-free diet (GFD) is considered the only available therapeutic approach for this disorder. Novel approaches need to be considered for finding new biomarkers to help this disorder diagnosis and finding a new alternative therapeutic method for this group of patients. Metabolomics and lipidomics are powerful tools to provide highly accurate and sensitive biomarkers. Previous studies indicated a metabolic fingerprint for CD deriving from alterations in gut microflora or intestinal permeability, malabsorption, and energy metabolism. Moreover, since CD is characterized by increased intestinal permeability and due to the importance of membrane lipid components in controlling barrier integrity, conducting lipidomics studies in this disorder is of great importance. In the current study, we tried to provide a critical overview of metabolomic and lipidomic changes in CD.

Exploring the Effects of Probiotic Treatment on Urinary and Serum Metabolic Profiles in Healthy Individuals

Probiotics are live microorganisms that confer health benefits when administered in adequate amounts. They are used to promote gut health and alleviate various disorders. Recently, there has been an increasing interest in the potential effects of probiotics on human physiology. In the presented study, the effects of probiotic treatment on the metabolic profiles of human urine and serum using a nuclear magnetic resonance (NMR)-based metabonomic approach were investigated. Twenty-one healthy volunteers were enrolled in the study, and they received two different dosages of probiotics for 8 weeks. During the study, urine and serum samples were collected from volunteers before and during probiotic supplementation. The results showed that probiotics had a significant impact on the urinary and serum metabolic profiles without altering their phenotypes. This study demonstrated the effects of probiotics in terms of variations of metabolite levels resulting also from the different probiotic posology. Overall, the results suggest that probiotic administration may affect both urine and serum metabolomes, although more research is needed to understand the mechanisms and clinical implications of these effects. NMR-based metabonomic analysis of biofluids is a powerful tool for monitoring host-gut microflora dynamic interaction as well as for assessing the individual response to probiotic treatment.

The effects of a hydrolyzed protein diet on the plasma, fecal and urine metabolome in cats with chronic enteropathy

Hydrolyzed protein diets are extensively used to treat chronic enteropathy (CE) in cats. However, the biochemical effects of such a diet on feline CE have not been characterized. In this study an untargeted 1H nuclear magnetic resonance spectroscopy-based metabolomic approach was used to compare the urinary, plasma, and fecal metabolic phenotypes of cats with CE to control cats with no gastrointestinal signs recruited at the Royal Veterinary College (RVC). In addition, the biomolecular consequences of a hydrolyzed protein diet in cats with CE was also separately determined in cats recruited from the RVC (n = 16) and the University of Bristol (n = 24) and whether these responses differed between dietary responders and non-responders. Here, plasma metabolites related to energy and amino acid metabolism significantly varied between CE and control cats in the RVC cohort. The hydrolyzed protein diet modulated the urinary metabolome of cats with CE (p = 0.005) in both the RVC and Bristol cohort. In the RVC cohort, the urinary excretion of phenylacetylglutamine, p-cresyl-sulfate, creatinine and taurine at diagnosis was predictive of dietary response (p = 0.025) although this was not observed in the Bristol cohort. Conversely, in the Bristol cohort plasma betaine, glycerol, glutamine and alanine at diagnosis was predictive of outcome (p = 0.001), but these same results were not observed in the RVC cohort. The biochemical signature of feline CE in the RVC cohort was consistent with that identified in human and animal models of inflammatory bowel disease. The hydrolyzed protein diet had the same effect on the urinary metabolome of cats with CE at both sites. However, biomarkers that were predictive of dietary response at diagnosis differed between the 2 sites. This may be due to differences in disease severity, disease heterogeneity, factors unrelated to the disease or small sample size at both sites. As such, further studies utilizing larger number of cats are needed to corroborate these findings.

Potential of in vitro nuclear magnetic resonance of biofluids and tissues in clinical research

Body fluids, cells, and tissues contain a wide variety of metabolites that consist of a mixture of various low-molecular-weight compounds, including amino acids, peptides, lipids, nucleic acids, and organic acids, which makes comprehensive analysis more difficult. Quantitative nuclear magnetic resonance (NMR) spectroscopy is a well-established analytical technique for analyzing the metabolic profiles of body fluids, cells, and tissues. It enables fast and comprehensive detection, characterization, a high level of experimental reproducibility, minimal sample preparation, and quantification of various endogenous metabolites. In recent times, NMR-based metabolomics has been appreciably utilized in diverse branches of medicine, including microbiology, toxicology, pathophysiology, pharmacology, nutritional intervention, and disease diagnosis/prognosis. In this review, the utility of NMR-based metabolomics in clinical studies is discussed. The significance of in vitro NMR-based metabolomics as an effective tool for detecting metabolites and their variations in different diseases are discussed, together with the possibility of identifying specific biomarkers that can contribute to early detection and diagnosis of disease.

Gluten-Free Bread Enriched with Artichoke Leaf Extract In Vitro Exerted Antioxidant and Anti-Inflammatory Properties

Due to its high nutritional value and broad beneficial effects, the artichoke plant (Cynara cardunculus L.) is an excellent healthy food candidate. Additionally, the artichoke by-products are usually discarded even though they still contain a huge concentration of dietary fibers, phenolic acids, and other micronutrients. The present work aimed to characterize a laboratory-made gluten-free bread (B) using rice flour supplemented with a powdered extract from artichoke leaves (AEs). The AE, accounting for the 5% of titratable chlorogenic acid, was added to the experimental gluten-free bread. Accounting for different combinations, four different bread batches were prepared. To evaluate the differences, a gluten-free type-II sourdough (tII-SD) was added in two doughs (SB and SB-AE), while the related controls (YB and YB-AE) did not contain the tII-SD. Profiling the digested bread samples, SB showed the lowest glycemic index, while SB-AE showed the highest antioxidant properties. The digested samples were also fermented in fecal batches containing viable cells from fecal microbiota samples obtained from healthy donors. Based on plate counts, no clear tendencies emerged concerning the analyzed microbial patterns; by contrast, when profiling volatile organic compounds, significant differences were observed in SB-AE, exhibiting the highest scores of hydrocinnamic and cyclohexanecarboxylic acids. The fecal fermented supernatants were recovered and assayed for healthy properties on human keratinocyte cell lines against oxidative stress and for effectiveness in modulating the expression of proinflammatory cytokines in Caco-2 cells. While the first assay emphasized the contribution of AE to protect against stressor agents, the latter enlightened how the combination of SB with AE decreased the cellular TNF-α and IL1-β expression. In conclusion, this preliminary study suggests that the combination of AE with sourdough biotechnology could be a promising tool to increase the nutritional and healthy features of gluten-free bread.

The Field-Dependent Nature of PageRank Values in Citation Networks

The value of scientific research can be easier to assess at the collective level than at the level of individual contributions. Several journal-level and article-level metrics aim to measure the importance of journals or individual manuscripts. However, many are citation-based and citation practices vary between fields. To account for these differences, scientists have devised normalization schemes to make metrics more comparable across fields. We use PageRank as an example metric and examine the extent to which field-specific citation norms drive estimated importance differences. In doing so, we recapitulate differences in journal and article PageRanks between fields. We also find that manuscripts shared between fields have different PageRanks depending on which field's citation network the metric is calculated in. We implement a degree-preserving graph shuffling algorithm to generate a null distribution of similar networks and find differences more likely attributed to field-specific preferences than citation norms. Our results suggest that while differences exist between fields' metric distributions, applying metrics in a field-aware manner rather than using normalized global metrics avoids losing important information about article preferences. They also imply that assigning a single importance value to a manuscript may not be a useful construct, as the importance of each manuscript varies by the reader's field.

NMR-Based Metabolomics to Evaluate Individual Response to Treatments

The aim of this chapter is to highlight the various aspects of metabolomics in relation to health and diseases, starting from the definition of metabolic space and of how individuals tend to maintain their own position in this space. Physio-pathological stimuli may cause individuals to lose their position and then regain it, or move irreversibly to other positions. By way of examples, mostly selected from our own work using ¹H NMR on biological fluids, we describe the effects on the individual metabolomic fingerprint of mild external interventions, such as diet or probiotic administration. Then we move to pathologies (such as celiac disease, various types of cancer, viral infections, and other diseases), each characterized by a well-defined metabolomic fingerprint. We describe the effects of drugs on the disease fingerprint and on its reversal to a healthy metabolomic status. Drug toxicity can be also monitored by metabolomics. We also show how the individual metabolomic fingerprint at the onset of a disease may discriminate responders from non-responders to a given drug, or how it may be prognostic of e.g., cancer recurrence after many years. In parallel with fingerprinting, profiling (i.e., the identification and quantification of many metabolites and, in the case of selected biofluids, of the lipoprotein components that contribute to the ¹H NMR spectral features) can provide hints on the metabolic pathways that are altered by a disease and assess their restoration after treatment.

Applications of machine learning in metabolomics: Disease modeling and classification

Metabolomics research has recently gained popularity because it enables the study of biological traits at the biochemical level and, as a result, can directly reveal what occurs in a cell or a tissue based on health or disease status, complementing other omics such as genomics and transcriptomics. Like other high-throughput biological experiments, metabolomics produces vast volumes of complex data. The application of machine learning (ML) to analyze data, recognize patterns, and build models is expanding across multiple fields. In the same way, ML methods are utilized for the classification, regression, or clustering of highly complex metabolomic data. This review discusses how disease modeling and diagnosis can be enhanced via deep and comprehensive metabolomic profiling using ML. We discuss the general layout of a metabolic workflow and the fundamental ML techniques used to analyze metabolomic data, including support vector machines (SVM), decision trees, random forests (RF), neural networks (NN), and deep learning (DL). Finally, we present the advantages and disadvantages of various ML methods and provide suggestions for different metabolic data analysis scenarios.

Chemometrics assisted untargeted metabolomic analysis to explore metabolic alterations in chronic urticaria via LC/Q-TOF/MS/MS

Chronic urticaria (CU) is a common disease characterized by the development of recurrent itchy blisters and/or angioedema lasting longer than 6 weeks. The evidence-based diagnosis of CU is described in the most recent urticaria guideline. Metabolomics has the potential to offer diagnostic biomarkers for the detection and prognosis of diseases and predict the efficacy and safety of pharmaceutical interventions. Determining the variation in metabolites found in the plasma of CU patients (n = 20) and 20 controls has therefore been the goal of this investigation. Samples were analyzed using liquid chromatography quadrupole time-of-flight mass spectrometry after applying acetonitrile precipitation. For the purpose of identifying and characterizing metabolites, the METLIN database was utilized. According to results, 21 metabolites were found to be significantly (VIP score > 0.7, p < .05 and fold analysis >1.5) altered. Differentiations between each group were successful via both OPLS-DA and ROC analysis. While plasma allantoate, homogentisate, indole acetate, proline, phenylalanine levels decreased in CU patients compared to healthy subjects, tryptophan, spermidine, phenyl pyruvate, oleic acid, lysine, valine, ornithine, histidine, glutamate, leucine, kynurenine, hypoxanthine, tyrosine, glucose, creatine and cortisol levels were significantly increased. Diagnosis of CU could be achieved by evaluating the metabolic profile of patients.

Characterization of the “gut microbiota-immunity axis” and microbial lipid metabolites in atrophic and potential celiac disease

Introduction

Potential celiac disease (pCD) is characterized by genetic predisposition, positive anti-endomysial and anti-tissue transglutaminase antibodies, but a normal or almost normal jejunal mucosa (e.g., minor histological abnormalities without villous atrophy). To gain further insights into basic mechanisms involved in the development of intestinal villous atrophy, we evaluated and compared the microbial, lipid, and immunological signatures of pCD and atrophic CD (aCD).

Materials and methods

This study included 17 aCD patients, 10 pCD patients, and 12 healthy controls (HC). Serum samples from all participants were collected to analyze free fatty acids (FFAs). Duodenal mucosa samples of aCD and pCD patients were taken to evaluate histology, tissue microbiota composition, and mucosal immune response.

Results

We found no significant differences in the mucosa-associated microbiota composition of pCD and aCD patients. On the other hand, in pCD patients, the overall abundance of serum FFAs showed relevant and significant differences in comparison with aCD patients and HC. In detail, compared to HC, pCD patients displayed higher levels of propionic, butyric, valeric, 2-ethylhexanoic, tetradecanoic, hexadecanoic, and octadecanoic acids. Instead, aCD patients showed increased levels of propionic, isohexanoic, and 2-ethylhexanoic acids, and a lower abundance of isovaleric and 2-methylbutyricacids when compared to HC. In addition, compared to aCD patients, pCD patients showed a higher abundance of isobutyric and octadecanoic acid. Finally, the immunological analysis of duodenal biopsy revealed a lower percentage of CD4⁺ T lymphocytes in pCD infiltrate compared to that observed in aCD patients. The functional characterization of T cells documented a pro-inflammatory immune response in both aCD and pCD patients, but the pCD patients showed a higher percentage of Th0/Th17 and a lower percentage of Th1/Th17.

Conclusion

The results of the present study show, for the first time, that the duodenal microbiota of patients with pCD does not differ substantially from that of aCD; however, serum FFAs and local T cells displayed a distinctive profile between pCD, aCD, and HC. In conclusion, our result may help to shed new light on the “gut microbiota-immunity axis,” lipid metabolites, and duodenal immune response in overt CD and pCD patients, opening new paradigms in understanding the pathogenesis behind CD progression.

The metabolic adaptation of the adult offspring after maternal high-dosed folic acid supplementation based on the proteomics and metabolomics in rats

BACKGROUND

The onset of complex diseases at a later stage of life has been evidently linked with maternal folic acid (FA) ingestion. However, little is known regarding the underlying molecule fingerprints of the offspring.

METHODS

We integrated proteomics-metabolomics profiles and analyzed the influence of maternal FA supplementation on the metabolism of the adult offspring rats. 20 pregnant female rats were randomly assigned to a FA supplementation (FolS group, 10 mg/kg FA) or control group (2 mg/kg FA respectively).

RESULTS

Such omics approach revealed that dopaminergic synapse pathway, tricarboxylic acid cycle and neural development related metabolites such as glutamic acid and γ-aminobutyric acid were significantly up-regulated in the FolS group, whereas pyruvic acid, oxalic acid and adipic acid was reduced.

CONCLUSIONS

Maternal FA supplementation can cause the alterations of metabolites and protein in the offspring rats.

Untargeted LC-HRMS Based-Plasma Metabolomics Reveals 3-O-Methyldopa as a New Biomarker of Poor Prognosis in High-Risk Neuroblastoma

Neuroblastoma (NB) is the most common extracranial malignant tumor in children. Although the survival rate of NB has improved over the years, the outcome of NB still remains poor for over 30% of cases. A more accurate risk stratification remains a key point in the study of NB and the availability of novel prognostic biomarkers of “high-risk” at diagnosis could help improving patient stratification and predicting outcome.

In this paper we show a biomarker discovery approach applied to the plasma of 172 NB patients. Plasma samples from a first cohort of NB patients and age-matched healthy controls were used for untargeted metabolomics analysis based on high-resolution mass spectrometry (HRMS). Differential expression analysis highlighted a number of metabolites annotated with a high degree of identification. Among them, 3-O-methyldopa (3-O-MD) was validated in a second cohort of NB patients using a targeted metabolite profiling approach and its prognostic potential was also analyzed by survival analysis on patients with 3 years follow-up. High expression of 3-O-MD was associated with worse prognosis in the subset of patients with stage M tumor (log-rank p < 0.05) and, among them, it was confirmed as a prognostic factor able to stratify high-risk patients older than 18 months. 3-O-MD might be thus considered as a novel prognostic biomarker of NB eligible to be included at diagnosis among catecholamine metabolite panels in prospective clinical studies. Further studies are warranted to exploit other potential biomarkers highlighted using our approach.

How Metabolomics Provides Novel Insights on Celiac Disease and Gluten-Free Diet: A Narrative Review

Celiac disease (CD) is an inflammatory autoimmune disorder triggered by the ingestion of gluten from wheat and other cereals. Nowadays, its positive diagnosis is based on invasive approaches such as the histological examination of intestinal biopsies and positive serology screening of antibodies. After proven diagnosis, the only admissible treatment for CD individuals is strict life-long adherence to gluten-free diet (GFD), although it is not a conclusive therapy. Acting by different mechanisms and with different etiologies, both CD and GFD have a great impact on gut microbiota that result in a different taxa composition. Altered production of specific metabolites reflects these microbiota changes. In this light, the currently available literature reports some suggestions about the possible use of specific metabolites, detected by meta-omics analyses, as potential biomarkers for a CD non-invasive diagnosis. To highlight insights about metabolomics application in CD study, we conducted a narrative dissertation of selected original articles published in the last decade. By applying a systematic search, it clearly emerged how the metabolomic signature appears to be contradictory, as well as poorly investigated.

NMR based metabolic profiling of patients with potential celiac disease elucidating early biochemical changes of gluten-sensitivity: A pilot Study

BACKGROUND

The patients with positive celiac disease (CeD) specific serology, but no evidence of intestinal inflammation are defined as potential celiac disease (PCeD) patients. About one-third of PCeD patients develop intestinal inflammation over time. The present study investigated the metabolome of small intestinal biopsies, blood plasma, and urine of patients with PCeD to understand the biochemical changes underlying the CeD.

METHODS

The metabolic profiles of small intestinal biopsies, blood plasma, and urine of patients with PCeD (n=7) were compared with CeD (n=64) and controls (n=15) [disease controls (DC) and healthy controls (HC)] using ¹H NMR spectroscopy.

RESULTS

The intestinal mucosa of PCeD showed lower levels of histidine, glycine, tyrosine, and tryptophan compared to DC. Altered levels of 6 metabolites (glucose, acetate, acetoacetate, β-hydroxybutyrate, pyruvate, arginine) in blood plasma and two metabolites (succinate and aminohippurate) in urine were observed in PCeD compared to HC. The PLS-DA model built on the concentration of blood plasma showed separate clustering for PCeD and CeD patients.

CONCLUSION

Altered metabolic profile of PCeD suggested that gluten intolerance was evident at the metabolic level before the intestinal damage. Altered energy metabolism and lower cytoprotective activity (histidine, glycine, arginine) indicated vulnerability to develop intestinal inflammation in PCeD over time. Our study may provide an insight into early biochemical processes of the progression of PCeD to CeD.

Interactions between the intestinal microbiota and epigenome in individuals with autism spectrum disorder

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by variable impairment of cognitive function and interpersonal relationships. Furthermore, some individuals with ASD have gastrointestinal disorders that have been correlated with impairments in intestinal microbiota. Gut microbiota are important not only for intestinal health, but also for many other functions including food digestion, energy production, immune system regulation, and, according to current data, behavior. Disruption of the indigenous microbiota, microbial dysbiosis (imbalance between microorganisms present in the gut), overgrowth of potentially pathogenic microorganisms, a less diverse microbiome, or lower levels of beneficial bacteria in children with ASD can affect behavior. Metabolome analysis in children with ASD has identified perturbations in multiple metabolic pathways that might be associated with cognitive functions. Recent studies have shown that the intestinal microbiome provides environmental signals that can modify host response to stimuli by modifying the host epigenome, which affects DNA methylation, histone modification, and non-coding RNAs. The most studied microbiota-produced epigenetic modifiers are short-chain fatty acids, although other products of intestinal microbiota might also cause epigenetic modifications in the host's DNA. Here we review evidence suggesting that epigenetic alterations caused by modification of gene expression play an important role in understanding ASD.

Preserved central cholinergic functioning to transcranial magnetic stimulation in de novo patients with celiac disease

BACKGROUND

Celiac disease (CD) is now viewed as a systemic disease with multifaceted clinical manifestations. Among the extra-intestinal features, neurological and neuropsychiatric symptoms are still a diagnostic challenge, since they can precede or follow the diagnosis of CD. In particular, it is well known that some adults with CD may complain of cognitive symptoms, that improve when the gluten-free diet (GFD) is started, although they may re-appear after incidental gluten intake. Among the neurophysiological techniques, motor evoked potentials (MEPs) to transcranial magnetic stimulation (TMS) can non-invasively probe in vivo the excitation state of cortical areas and cortico-spinal conductivity, being also able to unveil preclinical impairment in several neurological and psychiatric disorders, as well as in some systemic diseases affecting the central nervous system (CNS), such as CD. We previously demonstrated an intracortical disinhibition and hyperfacilitation of MEP responses to TMS in newly diagnosed patients. However, no data are available on the central cholinergic functioning indexed by specific TMS measures, such as the short-latency afferent inhibition (SAI), which might represent the neurophysiological correlate of cognitive changes in CD patients, also at the preclinical level.

METHODS

Cognitive and depressive symptoms were screened by means of the Montreal Cognitive Assessment (MoCA) and the 17-item Hamilton Depression Rating Scale (HDRS), respectively, in 15 consecutive de novo CD patients and 15 healthy controls. All patients were on normal diet at the time of the enrolment. Brain computed tomography (CT) was performed in all patients. SAI, recorded at two interstimulus intervals (2 and 8 ms), was assessed as the percentage amplitude ratio between the conditioned and the unconditioned MEP response. Resting motor threshold, MEP amplitude and latency, and central motor conduction time were also measured.

RESULTS

The two groups were comparable for age, sex, anthropometric features, and educational level. Brain CT ruled out intracranial calcifications and clear radiological abnormalities in all patients. Scores at MoCA and HDRS were significantly worse in patients than in controls. The comparison of TMS data between the two groups revealed no statistically significant difference for all measures, including SAI at both interstimulus intervals.

CONCLUSIONS

Central cholinergic functioning explored by the SAI of the motor cortex resulted to be not affected in these de novo CD patients compared to age-matched healthy controls. Although the statistically significant difference in MoCA, an overt cognitive impairment was not clinically evident in CD patients. Coherently, to date, no study based on TMS or other diagnostic techniques has shown any involvement of the central acetylcholine or the cholinergic fibers within the CNS in CD. This finding might add support to the vascular inflammation hypothesis underlying the so-called "gluten encephalopathy", which seems to be due to an aetiology different from that of the cholinergic dysfunction. Longitudinal studies correlating clinical, TMS, and neuroimaging data, both before and after GFD, are needed.

Gh BF, Taherkhani A, Rostami-Nejad M, Asri N, Haidari MH. Screening of Altered Metabolites and Metabolic Pathways in Celiac Disease Using NMR Spectroscopy

BACKGROUND

Celiac disease (CeD) is an autoimmune intestinal disorder caused by gluten protein consumption in genetically predisposed individuals. As biopsy sampling is an invasive procedure, finding novel noninvasive serological markers for screening of at-risk CeD population is a priority. Metabolomics is helpful in monitoring metabolite changes in body fluids and tissues. In the present study, we evaluated serum metabolite levels of CeD patients relative to healthy controls with the aim of introducing new biomarkers for population screening.

METHOD

We compared the serum metabolic profile of CeD patients (n = 42) and healthy controls (n = 22) using NMR spectroscopy and multivariate analysis.

RESULT

25 metabolites were identified by serum metabolic profiling. Levels of 3-hydroxyisobutyric acid and isobutyrate showed significant differences in CeD patients' samples compared with healthy controls (p < 0.05). According to pathway analysis, our data demonstrated that changes in nine metabolic pathways were significantly disrupted/affected in patients with CeD. These enriched pathways are involved in aminoacyl-tRNA biosynthesis; primary bile acid biosynthesis; nitrogen metabolism; glutamine and glutamate metabolism; valine, leucine, and isoleucine biosynthesis and degradation; taurine and hypotaurine metabolism; glyoxylate and dicarboxylate metabolism; glycine, serine, and threonine metabolism; and arginine biosynthesis.

CONCLUSION

In summary, our results demonstrated that changes in the serum level of 25 metabolites may be useful in distinguishing CeD patients from healthy controls, which have the potential to be considered candidate biomarkers of CeD.

Up-Regulation of Specific Bioactive Lipids in Celiac Disease

Celiac disease (CD) is an autoimmune enteropathy linked to alterations of metabolism. Currently, limited untargeted metabolomic studies evaluating differences in the plasma metabolome of CD subjects have been documented. We engage in a metabolomic study that analyzes plasma metabolome in 17 children with CD treated with a gluten-free diet and 17 healthy control siblings in order to recognize potential changes in metabolic networks. Our data demonstrates the persistence of metabolic defects in CD subjects in spite of the dietary treatment, affecting a minor but significant fraction (around 4%, 209 out of 4893 molecular features) of the analyzed plasma metabolome. The affected molecular species are mainly, but not exclusively, lipid species with a particular affectation of steroids and derivatives (indicating an adrenal gland affectation), glycerophospholipids (to highlight phosphatidic acid), glycerolipids (with a special affectation of diacylglycerols), and fatty acyls (eicosanoids). Our findings are suggestive of an activation of the diacylglycerol-phosphatidic acid signaling pathway in CD that may potentially have detrimental effects via activation of several targets including protein kinases such as mTOR, which could be the basis of the morbidity and mortality connected with untreated CD. However, more studies are necessary to validate this idea regarding CD.

Diagnosis and management of secondary causes of steatohepatitis

The term non-alcoholic fatty liver disease (NAFLD) was originally coined to describe hepatic fat deposition as part of the metabolic syndrome. However, a variety of rare hereditary liver and metabolic diseases, intestinal diseases, endocrine disorders and drugs may underlie, mimic, or aggravate NAFLD. In contrast to primary NAFLD, therapeutic interventions are available for many secondary causes of NAFLD. Accordingly, secondary causes of fatty liver disease should be considered during the diagnostic workup of patients with fatty liver disease, and treatment of the underlying disease should be started to halt disease progression. Common genetic variants in several genes involved in lipid handling and metabolism modulate the risk of progression from steatosis to fibrosis, cirrhosis and hepatocellular carcinoma development in NAFLD, alcohol-related liver disease and viral hepatitis. Hence, we speculate that genotyping of common risk variants for liver disease progression may be equally useful to gauge the likelihood of developing advanced liver disease in patients with secondary fatty liver disease.

Preserved central cholinergic functioning to transcranial magnetic stimulation in de novo patients with celiac disease

BACKGROUND

Celiac disease (CD) is now viewed as a systemic disease with multifaceted clinical manifestations. Among the extra-intestinal features, neurological and neuropsychiatric symptoms are still a diagnostic challenge, since they can precede or follow the diagnosis of CD. In particular, it is well known that some adults with CD may complain of cognitive symptoms, that improve when the gluten-free diet (GFD) is started, although they may re-appear after incidental gluten intake. Among the neurophysiological techniques, motor evoked potentials (MEPs) to transcranial magnetic stimulation (TMS) can non-invasively probe in vivo the excitation state of cortical areas and cortico-spinal conductivity, being also able to unveil preclinical impairment in several neurological and psychiatric disorders, as well as in some systemic diseases affecting the central nervous system (CNS), such as CD. We previously demonstrated an intracortical disinhibition and hyperfacilitation of MEP responses to TMS in newly diagnosed patients. However, no data are available on the central cholinergic functioning indexed by specific TMS measures, such as the short-latency afferent inhibition (SAI), which might represent the neurophysiological correlate of cognitive changes in CD patients, also at the preclinical level.

METHODS

Cognitive and depressive symptoms were screened by means of the Montreal Cognitive Assessment (MoCA) and the 17-item Hamilton Depression Rating Scale (HDRS), respectively, in 15 consecutive de novo CD patients and 15 healthy controls. All patients were on normal diet at the time of the enrolment. Brain computed tomography (CT) was performed in all patients. SAI, recorded at two interstimulus intervals (2 and 8 ms), was assessed as the percentage amplitude ratio between the conditioned and the unconditioned MEP response. Resting motor threshold, MEP amplitude and latency, and central motor conduction time were also measured.

RESULTS

The two groups were comparable for age, sex, anthropometric features, and educational level. Brain CT ruled out intracranial calcifications and clear radiological abnormalities in all patients. Scores at MoCA and HDRS were significantly worse in patients than in controls. The comparison of TMS data between the two groups revealed no statistically significant difference for all measures, including SAI at both interstimulus intervals.

CONCLUSIONS

Central cholinergic functioning explored by the SAI of the motor cortex resulted to be not affected in these de novo CD patients compared to age-matched healthy controls. Although the statistically significant difference in MoCA, an overt cognitive impairment was not clinically evident in CD patients. Coherently, to date, no study based on TMS or other diagnostic techniques has shown any involvement of the central acetylcholine or the cholinergic fibers within the CNS in CD. This finding might add support to the vascular inflammation hypothesis underlying the so-called "gluten encephalopathy", which seems to be due to an aetiology different from that of the cholinergic dysfunction. Longitudinal studies correlating clinical, TMS, and neuroimaging data, both before and after GFD, are needed.

Non-Celiac Gluten Sensitivity in the Context of Functional Gastrointestinal Disorders

Gluten-free diets are increasingly chosen in the Western world, even in the absence of a diagnosis of celiac disease. Around 10% of people worldwide self-report gluten-related complaints, including intestinal and extra-intestinal symptoms. In most cases, these subjects would be labeled as patients suffering from irritable bowel syndrome (IBS) who place themselves on a gluten-free diet even in the absence of celiac disease. In some instances, patients report a clear benefit by avoiding gluten from their diet and/or symptom worsening upon gluten reintroduction. This clinical entity has been termed non-celiac gluten sensitivity (NCGS). The symptoms referred by these patients are both intestinal and extra-intestinal, suggesting that similarly to functional gastrointestinal disorders, NCGS is a disorder of gut-brain interaction. It remains unclear if gluten is the only wheat component involved in NCGS. The mechanisms underlying symptom generation in NCGS remain to be fully clarified, although in the past few years, the research has significantly moved forward with new data linking NCGS to changes in gut motility, permeability and innate immunity. The diagnosis is largely based on the self-reported reaction to gluten by the patient, as there are no available biomarkers, and confirmatory double-blind challenge protocols are unfeasible in daily clinical practice. Some studies suggest that a small proportion of patients with IBS have an intolerance to gluten. However, the benefits of gluten-free or low-gluten diets in non-celiac disease-related conditions are limited, and the long-term consequences of this practice may include nutritional and gut microbiota unbalance. Here, we summarize the role of gluten in the clinical features, pathophysiology, and management of NCGS and disorders of gut-brain interaction.

Alterations in One-Carbon Metabolism in Celiac Disease

Celiac disease (CD) is an autoimmune enteropathy associated with alterations of metabolism. Metabolomics studies, although limited, showed changes in choline, choline-derived lipids, and methionine concentrations, which could be ascribed to alterations in one-carbon metabolism. To date, no targeted metabolomics analysis investigating differences in the plasma choline/methionine metabolome of CD subjects are reported. This work is a targeted metabolomic study that analyzes 37 metabolites of the one-carbon metabolism in 17 children with CD, treated with a gluten-free diet and 17 healthy control siblings, in order to establish the potential defects in this metabolic network. Our results demonstrate the persistence of defects in the transsulfuration pathway of CD subjects, despite dietary treatment, while choline metabolism, methionine cycle, and folate cycle seem to be reversed and preserved to healthy levels. These findings describe for the first time, a metabolic defect in one-carbon metabolism which could have profound implications in the physiopathology and treatment of CD.

Hyperpolarized NMR Metabolomics at Natural 13C Abundance

Metabolomics plays a pivotal role in systems biology, and NMR is a central tool with high precision and exceptional resolution of chemical information. Most NMR metabolomic studies are based on 1H 1D spectroscopy, severely limited by peak overlap. 13C NMR benefits from a larger signal dispersion but is barely used in metabolomics due to ca. 6000-fold lower sensitivity. We introduce a new approach, based on hyperpolarized 13C NMR at natural abundance, that circumvents this limitation. A new untargeted NMR-based metabolomic workflow based on dissolution dynamic nuclear polarization (d-DNP) for the first time enabled hyperpolarized natural abundance 13C metabolomics. Statistical analysis of resulting hyperpolarized 13C data distinguishes two groups of plant (tomato) extracts and highlights biomarkers, in full agreement with previous results on the same biological model. We also optimize parameters of the semiautomated d-DNP system suitable for high-throughput studies.

Persistent Alterations in Plasma Lipid Profiles Before Introduction of Gluten in the Diet Associated With Progression to Celiac Disease

Celiac disease (CD) is a chronic enteropathy characterized by an autoimmune reaction in the small intestine of genetically susceptible individuals. The underlying causes of autoimmune reaction and its effect on host metabolism remain largely unknown. Herein, we apply lipidomics to elucidate the early events preceding clinical CD in a cohort of Finnish children, followed up in the Type 1 Diabetes Prediction and Prevention study.

Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology

The multitude of multi-omics data generated cost-effectively using advanced high-throughput technologies has imposed challenging domain for research in Artificial Intelligence (AI). Data curation poses a significant challenge as different parameters, instruments, and sample preparations approaches are employed for generating these big data sets. AI could reduce the fuzziness and randomness in data handling and build a platform for the data ecosystem, and thus serve as the primary choice for data mining and big data analysis to make informed decisions. However, AI implication remains intricate for researchers/clinicians lacking specific training in computational tools and informatics. Cancer is a major cause of death worldwide, accounting for an estimated 9.6 million deaths in 2018. Certain cancers, such as pancreatic and gastric cancers, are detected only after they have reached their advanced stages with frequent relapses. Cancer is one of the most complex diseases affecting a range of organs with diverse disease progression mechanisms and the effectors ranging from gene-epigenetics to a wide array of metabolites. Hence a comprehensive study, including genomics, epi-genomics, transcriptomics, proteomics, and metabolomics, along with the medical/mass-spectrometry imaging, patient clinical history, treatments provided, genetics, and disease endemicity, is essential. Cancer Moonshot℠ Research Initiatives by NIH National Cancer Institute aims to collect as much information as possible from different regions of the world and make a cancer data repository. AI could play an immense role in (a) analysis of complex and heterogeneous data sets (multi-omics and/or inter-omics), (b) data integration to provide a holistic disease molecular mechanism, (c) identification of diagnostic and prognostic markers, and (d) monitor patient's response to drugs/treatments and recovery. AI enables precision disease management well beyond the prevalent disease stratification patterns, such as differential expression and supervised classification. This review highlights critical advances and challenges in omics data analysis, dealing with data variability from lab-to-lab, and data integration. We also describe methods used in data mining and AI methods to obtain robust results for precision medicine from "big" data. In the future, AI could be expanded to achieve ground-breaking progress in disease management.

Worldwide public policies for celiac disease: are patients well assisted?

OBJECTIVES

To evaluate public policies (PP) to celiac disease (CD) patients and classify countries regarding the level of assistance provided by the Public Policies for Celiac Disease Score.

METHODS

Countries were scored from 0 to 6 according to the existence of PP regarding industrial food and meal regulations, health service support, food allowance/financial incentive, gluten-free (GF) food certification, and CD associations. Subsequently, countries were allocated to continents. In total, 192 countries are registered as members of the World Health Organization.

RESULTS

The European continent (score 3.63) is the most advanced in CD patient care, followed by South America (2.86), North America (1.05), Asia (0.53), Oceania (0.5), and Africa (0.27). Industrial food regulations were the most frequent PP (40.6%). 15.6% of the countries display regulations for meals; 13.5% have health service support; 13.5% have policies of food allowance/financial incentive; 19.3% have GF certification; and 34.4% have celiac associations.

CONCLUSIONS

Policies regarding GF meals and food safety certification, health service support, and financial incentives need improvement to ensure correct treatment and reduce the diseases' financial burden for celiac patients and governments.

Abnormalities in metabolic pathways in celiac disease investigated by the metabolic profiling of small intestinal mucosa, blood plasma and urine by NMR spectroscopy

Celiac disease (CeD) is an autoimmune enteropathy caused by gluten intake in genetically predisposed individuals. We investigated the metabolism of CeD by metabolic profiling of intestinal mucosa, blood plasma and urine using NMR spectroscopy and multivariate analysis. The metabolic profile of the small intestinal mucosa was compared between patients with CeD (n = 64) and disease controls (DCs, n = 30). The blood plasma and urinary metabolomes of CeD patients were compared with healthy controls (HCs, n = 39). Twelve metabolites (proline (Pro), arginine (Arg), glycine (Gly), histidine (His), glutamate (Glu), aspartate, tryptophan (Trp), fumarate, formate, succinate (Succ), glycerophosphocholine (GPC) and allantoin (Alln)) of intestinal mucosa differentiated CeD from controls. The metabolome of blood plasma with 18 metabolites (Pro, Arg, Gly, alanine, Glu, glutamine, glucose (Glc), lactate (Lac), acetate (Ace), acetoacetate (AcAc), β-hydroxybutyrate (β-OHB), pyruvate (Pyr), Succ, citrate (Cit), choline (Cho), creatine (Cr), phosphocreatine (PCr) and creatinine) and 9 metabolites of urine (Pro, Trp, β-OHB, Pyr, Succ, N-methylnicotinamide (NMN), aminohippurate (AHA), indoxyl sulfate (IS) and Alln) distinguished CeD from HCs. Our data demonstrated changes in nine metabolic pathways. The altered metabolites were associated with increased oxidative stress (Alln), impaired healing and repair mechanisms (Pro, Arg), compromised anti-inflammatory and cytoprotective processes (Gly, His, NMN), altered energy metabolism (Glc, Lac, β-OHB, Ace, AcAc, Pyr, Succ, Cit, Cho, Cr and PCr), impaired membrane metabolism (GPC and Cho) and intestinal dysbiosis (AHA and IS). An orthogonal partial least square discriminant analysis model provided clear differentiation between patients with CeD and controls in all three specimens. A classification model built by combining the distinguishing metabolites of blood plasma and urine samples gave an AUC of 0.99 with 97.7% sensitivity, 93.3% specificity and a predictive accuracy of 95.1%, which was higher than for the models built separately using small intestinal mucosa, blood plasma and urine. In conclusion, a panel of metabolic biomarkers in intestinal biopsies, plasma and urine samples has potential to differentiate CeD from controls and may complement traditional tests to improve the diagnosis of CeD.

Endometriosis phenotypes are associated with specific serum metabolic profiles determined by proton-nuclear magnetic resonance

RESEARCH QUESTION

What is the correlation between serum metabolic profile and endometriosis phenotype?

DESIGN

A pilot study nestled in a prospective cohort study at a university hospital, including 46 patients with painful endometriosis who underwent surgery and 21 controls who did not have macroscopic endometriotic lesions. Endometriosis was strictly classified into two groups of 23 patients each: endometrioma (OMA) and deep infiltrating endometriosis (DIE). Serum samples were collected before surgery for metabolomic profiling based on proton-nuclear magnetic resonance spectroscopy in combination with statistical approaches. Comparative identification of the metabolites in the serum from endometriosis patients and from controls was carried out, including an analysis according to endometriosis phenotype.

RESULTS

The serum metabolic profiles of the endometriosis patients revealed significantly lower concentrations of several amino acids compared with the controls, whereas the concentrations of free fatty acids and ketone bodies were significantly higher. The OMA and the DIE phenotypes each had a specific metabolic profile, with higher concentrations of two ketone bodies in the OMA group, and higher concentrations of free fatty acids and lipids in the DIE group.

CONCLUSION

Proton-nuclear magnetic resonance-based metabolomics of serum samples were found to have ample potential for identifying metabolic changes associated with endometriosis phenotypes. This information may improve our understanding of the pathogenesis of endometriosis.

Integration of metabolomics and proteomics to highlight altered neural development related pathways in the adult offspring after maternal folic acid supplement

BACKGROUND & AIMS

Maternal folic acid (FA) supplement (FolS) programs the early development of an offspring. The onset of complex diseases at a later stage of life has been evidently linked with maternal FA ingestion. However, little is known regarding the underlying molecule fingerprints of the offspring. Here, we analyze the influence of maternal FolS on the metabolism of the adult offspring rats using the integrated metabolomics-proteomics.

METHODS

Twenty pregnant female rats were randomly assigned to a FA supplement (FolS group) or control group which were fed AIN93G diet with 2 or 5 mg/kg FA, respectively. The blood samples from the offspring at 0, 3 and 7 weeks after birth were collected. The brain samples were obtained from the offspring at 7 weeks after birth. Serum and brain metabolite profiles were performed by UPLC-MS/MS and the brain proteomics analysis was obtained using iTRAQ-based quantitative proteomics.

RESULTS

The metabolic change of the offspring for the maternal FA supplement is characterized by the phospholipids, fatty acid and amino acids, which are involved in linoleic acid, docosahexaenoic acid, glycerophosphocholine, lysophosphatidylcholine, tryptophan, glycine, arachidonic acid, γ-aminobutyric acid, and so on. Using iTRAQ-based quantitative proteomics analysis, 51 differential proteins in the brain are identified, which provides valuable insight into the underlying mechanisms of the offspring after the maternal FolS. These results demonstrate neural development related metabolites and proteins, such as docosahexaenoic acid, glycine, tryptophan, γ-aminobutyric acid, dopaminergic synapse related proteins including G protein, PPP1R1B and CAMK2G, are significantly altered, which suggests that the active neural conduction occurs in the offspring after maternal FA supplement. The behavioral testing demonstrates that the high level of memory is observed in rats with FA supplement.

CONCLUSIONS

We conceive that the alterations of metabolites and protein in the offspring are associated with the maternal FA supplement and these alterations are involved in the neural development, although such animal data are limited in their ability to mimic metabolic outcomes in humans.

DHA-Induced Perturbation of Human Serum Metabolome. Role of the Food Matrix and Co-Administration of Oat β-glucan and Anthocyanins

Docosahexaenoic acid (DHA) has been reported to have a positive impact on many diet-related disease risks, including metabolic syndrome. Although many DHA-enriched foods have been marketed, the impact of different food matrices on the effect of DHA is unknown. As well, the possibility to enhance DHA effectiveness through the co-administration of other bioactives has seldom been considered. We evaluated DHA effects on the serum metabolome administered to volunteers at risk of metabolic syndrome as an ingredient of three different foods. Foods were enriched with DHA alone or in combination with oat beta-glucan or anthocyanins and were administered to volunteers for 4 weeks. Serum samples collected at the beginning and end of the trial were analysed by NMR-based metabolomics. Multivariate and univariate statistical analyses were used to characterize modifications in the serum metabolome and to evaluate bioactive-bioactive and bioactive-food matrix interactions. DHA administration induces metabolome perturbation that is influenced by the food matrix and the co-presence of other bioactives. In particular, when co-administered with oat beta-glucan, DHA induces a strong rearrangement in the lipoprotein profile of the subjects. The observed modifications are consistent with clinical results and indicate that metabolomics represents a possible strategy to choose the most appropriate food matrices for bioactive enrichment.

Probiotic treatment induced change of inflammation related metabolites in IBS-D patients/double-blind, randomized, placebo-controlled trial

There have been many studies suggesting that probiotics are effective in patients with diarrhea-predominant irritable bowel syndrome (IBS-D). However, its mechanism of action as well as prediction of response is still to be elucidated. In the present study, to find out metabolomic characteristics of probiotic effect in IBS-D, we compared IBS symptom changes and metabolomic characteristics in the subjects' urine samples between multi-strain probiotics (one strain of Lactobacillus sp. and four strains of Bifidobacterium sp.) group (n = 32) and placebo group (n = 31). After 8 weeks' administration (3 times/day), dissatisfaction in bowel habits and stool frequencies were significantly improved. Also, probiotics group had significantly changed seven metabolites including palmitic acid methyl ester (PAME) and 4,6-dihydroxyquinoline, 4-(2-aminophenyl)-2,4-dioxobutanoic acid (DOBA). According to IBS-SSS and IBS-QoL questionnaires, IBS-SSS responders showed higher PAME levels and IBS-QoL responders showed lower DOBA levels. This suggests potential role of these metabolites as a biomarker to predict probiotics effect in IBS-D patients.

Non-Invasive Biomarkers for Celiac Disease

Once thought to be uncommon, celiac disease has now become a common disease globally. While avoidance of the gluten-containing diet is the only effective treatment so far, many new targets are being explored for the development of new drugs for its treatment. The endpoints of therapy include not only reversal of symptoms, normalization of immunological abnormalities and healing of mucosa, but also maintenance of remission of the disease by strict adherence of the gluten-free diet (GFD). There is no single gold standard test for the diagnosis of celiac disease and the diagnosis is based on the presence of a combination of characteristics including the presence of a celiac-specific antibody (anti-tissue transglutaminase antibody, anti-endomysial antibody or anti-deamidated gliadin peptide antibody) and demonstration of villous abnormalities. While the demonstration of enteropathy is an important criterion for a definite diagnosis of celiac disease, it requires endoscopic examination which is perceived as an invasive procedure. The capability of prediction of enteropathy by the presence of the high titer of anti-tissue transglutaminase antibody led to an option of making a diagnosis even without obtaining mucosal biopsies. While present day diagnostic tests are great, they, however, have certain limitations. Therefore, there is a need for biomarkers for screening of patients, prediction of enteropathy, and monitoring of patients for adherence of the gluten-free diet. Efforts are now being made to explore various biomarkers which reflect different changes that occur in the intestinal mucosa using modern day tools including transcriptomics, proteomics, and metabolomics. In the present review, we have discussed comprehensively the pros and cons of available biomarkers and also summarized the current status of emerging biomarkers for the screening, diagnosis, and monitoring of celiac disease.

NMR for sample quality assessment in metabolomics

The EU Framework 7 project SPIDIA was the occasion for development of NMR approaches to evaluate the impact of different pre-analytical treatments on the quality of biological samples dedicated to metabolomics. Systematic simulation of different pre-analytical procedures was performed on urine and blood serum and plasma. Here we review the key aspects of these studies that have led to the development of CEN technical specifications, to be translated into ISO/IS in the course of the EU Horizon 2020 project SPIDIA4P. Inspired by the SPIDIA results, follow-up research was performed, extending the analysis to different sample types and to the different effects of long-term storage. The latter activity was in conjunction with the local European da Vinci Biobank. These results (which partially contributed to the ANNEX of CEN/TS 16945"MOLECULAR IN VITRO DIAGNOSTIC EXAMINATIONS - SPECIFICATIONS FOR PRE-EXAMINATION PROCESSES FOR METABOLOMICS IN URINE, VENOUS BLOOD SERUM AND PLASMA") are presented in detail.

High-Throughput Metabolomics by 1D NMR

Metabolomics deals with the whole ensemble of metabolites (the metabolome). As one of the -omic sciences, it relates to biology, physiology, pathology and medicine; but metabolites are chemical entities, small organic molecules or inorganic ions. Therefore, their proper identification and quantitation in complex biological matrices requires a solid chemical ground. With respect to for example, DNA, metabolites are much more prone to oxidation or enzymatic degradation: we can reconstruct large parts of a mammoth's genome from a small specimen, but we are unable to do the same with its metabolome, which was probably largely degraded a few hours after the animal's death. Thus, we need standard operating procedures, good chemical skills in sample preparation for storage and subsequent analysis, accurate analytical procedures, a broad knowledge of chemometrics and advanced statistical tools, and a good knowledge of at least one of the two metabolomic techniques, MS or NMR. All these skills are traditionally cultivated by chemists. Here we focus on metabolomics from the chemical standpoint and restrict ourselves to NMR. From the analytical point of view, NMR has pros and cons but does provide a peculiar holistic perspective that may speak for its future adoption as a population-wide health screening technique.

Metabolic Signature of Primary Biliary Cholangitis and Its Comparison with Celiac Disease

Primary biliary cholangitis (PBC) is a chronic autoimmune liver disease characterized by ongoing inflammatory destruction of the interlobular bile ducts, eventually leading to chronic cholestasis and biliary cirrhosis. This study primarily aims to define the metabolomic signature of PBC after comparison with healthy controls (HC). Second, it aims to evaluate the possible metabolic association between PBC and celiac disease (CD), an immune-mediated disorder frequently associated with PBC. Serum and urine samples from 20 PBC, 21 CD, and 19 sex-matched HC subjects were collected. ¹H nuclear magnetic resonance (NMR) spectra for all samples were acquired, and multivariate statistics were used to evaluate the differences among the three groups and to provide information about the involved metabolites. The classification accuracies to discriminate PBC and HC groups were 78.9-84.6% for serum and 76.9% for urine. In comparison to HC, PBC patient sera were characterized by altered levels ( p value <0.05) of pyruvate, citrate, glutamate, glutamine, serine, tyrosine, phenylalanine, and lactate. PBC patient urine showed lower levels ( p value <0.05) of trigonelline and hippurate with respect to HC. Furthermore, the NMR metabolomic fingerprint was able to cluster PBC with respect to CD patients, and the classification accuracies in the discriminations between these groups were 81.9-91.7% for serum and 77.7% for urine. Our results show that PBC displays a unique metabolomic fingerprint, which led to speculation about an impaired energy metabolism, probably associated with an altered gut microbiota. PBC and CD showed two distinct metabolic fingerprints. These data could provide clues for the comprehension of the PBC pathogenetic mechanisms and the detection of novel therapeutic targets.

Machine Learning in Untargeted Metabolomics Experiments

Machine learning is a form of artificial intelligence (AI) that provides computers with the ability to learn generally without being explicitly programmed. Machine learning refers to the ability of computer programs to adapt when exposed to new data. Here we examine the use of machine learning for use with untargeted metabolomics data, when it is appropriate to use, and questions it can answer. We provide an example workflow for training and testing a simple binary classifier, a multiclass classifier and a support vector machine using the Waikato Environment for Knowledge Analysis (Weka), a toolkit for machine learning. This workflow should provide a framework for greater integration of machine learning with metabolomics study.

The Effect of Gluten-Free Diet on Health and the Gut Microbiota Cannot Be Extrapolated from One Population to Others

Gluten-related disorders (GRD) affect millions of people worldwide and have been related to the composition and metabolism of the gut microbiota. These disorders present differently in each patient and the only treatment available is a strict life-long gluten-free diet (GFD). Several studies have investigated the effect of a GFD on the gut microbiota of patients afflicted with GRD as well as healthy people. The purpose of this review is to persuade the biomedical community to think that, while useful, the results from the effect of GFD on health and the gut microbiota cannot be extrapolated from one population to others. This argument is primarily based on the highly individualized pattern of gut microbial composition and metabolic activity in each person, the variability of the gut microbiota over time and the plethora of factors associated with this variation. In addition, there is wide variation in the composition, economic viability, and possible deleterious effects to health among different GFD, both within and among countries. Overall, this paper encourages the conception of more collaborative efforts to study local populations in an effort to reach biologically and medically useful conclusions that truly contribute to improve health in patients afflicted with GRD.

Plasma profile and urine excretion of amino acids in children with celiac disease on gluten-free diet after oligofructose-enriched inulin intervention: results of a randomised placebo-controlled pilot study

The circulating amino acid (AAs) concentrations are indicators of dietary protein intake and metabolic status. In celiac disease (CD), the AA imbalance is frequently observed. Prebiotics are found to alleviate nutrient deficiencies. Therefore, the aim of this study was to analyse the impact of oligrofructose-enriched inulin (Synergy 1), administered for 3 months as a gluten-free diet (GFD) supplement to children with CD, on the plasma and urine concentrations of AAs. CD children (N = 34) were randomised into two groups, receiving Synergy 1 (10 g/day) or placebo (maltodextrin) for 3 months. The AA profile and concentration was determined in plasma and urine before and after the dietary intervention by gas chromatography. 22 and 28 AAs were determined in plasma and urine samples, respectively. After the intervention, the plasma concentrations of several AAs (Ala, Pro, Asn, Glu, Tyr, Lys, His, Orn) increased significantly in both experimental groups, while Gln increased only in the Synergy 1 group. The urinary excretion of Asn, Lys and Aaa increased significantly in the Synergy 1 group, and the excretion of Asp and Met decreased (p < 0.05) in the placebo group. The Gln:Glu ratio in urine increased in both groups after the intervention. An increased urinary excretion of AAs observed in Synergy 1 group with a simultaneous increase in the content of circulating AAs could be attributed to higher absorption or intensified metabolism of AAs, and on the other hand further healing of the intestinal mucosa being the result of continuous treatment with GFD. Moreover, the observed changes in Glu concentration suggest that oligofructose-enriched inulin could improve the intestinal condition and permeability. To conclude, a prebiotic-supplemented GFD influences beneficially the overall AAs metabolism in CD children; however, further prospective cohort studies are needed to confirm the results obtained.

Altered peripheral amino acid profile indicate a systemic impact of active celiac disease and a possible role of amino acids in disease pathogenesis

BACKGROUND

We have previously performed a Genome Wide Association and linkage study that indicated a new disease triggering mechanism involving amino acid metabolism and nutrient sensing signaling pathways.

OBJECTIVE

The aim of this study was to investigate if plasma amino acid levels differed among children with celiac disease compared with disease controls.

MATERIALS AND METHODS

Fasting plasma samples from 141 children with celiac disease and 129 non-celiac disease controls, were analyzed for amino acid levels by liquid chromatography-tandem mass spectrometry (LC/MS). A general linear model using age and experimental effects as covariates was used to compare amino acid levels between children with a diagnosis of celiac disease and controls.

RESULTS

Seven out of twenty-three analyzed amino acids were elevated in children with celiac disease compared with controls (tryptophan, taurine, glutamic acid, proline, ornithine, alanine and methionine). The significance of the individual amino acids do not survive multiple correction, however, multivariate analyses of the amino acid profile showed significantly altered amino acid levels in children with celiac disease overall and after correction for age, sex and experimental effects (p = 8.4 × 10-8).

CONCLUSION

These findings support the idea that amino acids could influence systemic inflammation and play a possible role in disease pathogenesis.

Gas chromatography/mass spectrometry based metabolomic study in a murine model of irritable bowel syndrome

AIM

To study the role of microbial metabolites in the modulation of biochemical and physiological processes in irritable bowel syndrome (IBS).

METHODS

In the current study, using a metabolomic approach, we analyzed the key metabolites differentially excreted in the feces of control mice and mice with IBS, with or without Clostridium butyricum (C. butyricum) treatment. C57BL/6 mice were divided into control, IBS, and IBS + C. butyricum groups. In the IBS and IBS + C. butyricum groups, the mice were subjected to water avoidance stress (WAS) for 1 h/d for ten days. Gas chromatography/mass spectrometry (GC-MS) together with multivariate analysis was employed to compare the fecal samples between groups.

RESULTS

WAS exposure established an appropriate model of IBS in mice, with symptoms of visceral hyperalgesia and diarrhea. The differences in the metabolite profiles between the control group and IBS group significantly changed with the progression of IBS (days 0, 5, 10, and 17). A total of 14 differentially excreted metabolites were identified between the control and IBS groups, and phenylethylamine was a major metabolite induced by stress. In addition, phenylalanine metabolism was found to be the most relevant metabolic pathway. Between the IBS group and IBS + C. butyricum group, 10 differentially excreted metabolites were identified. Among these, pantothenate and coenzyme A (CoA) biosynthesis metabolites, as well as steroid hormone biosynthesis metabolites were identified as significantly relevant metabolic pathways.

CONCLUSION

The metabolic profile of IBS mice is significantly altered compared to control mice. Supplementation with C. butyricum to IBS mice may provide a considerable benefit by modulating host metabolism.

Metabolomic analysis with 1H-NMR for non-invasive diagnosis of hepatic fibrosis degree in patients with chronic hepatitis C

BACKGROUND

The assessment of fibrosis degree in liver diseases is based on several non-invasive techniques, but none has been accurate.

AIM

This study employed proton nuclear magnetic resonance spectroscopy to identify metabolic profiles in serum and urine, specific for different fibrosis degree in chronic hepatitis C patients.

METHOD

71 plasma, 73 serum, and 578 urine samples were collected. All samples were analyzed using ¹H-NMR spectroscopy technique and three different NMR spectra were acquired for each serum/plasma sample. The data analyses were performed by partial least square regression, principal component analysis, and Monte Carlo cross-validation in a supervised methodology.

RESULTS

The cross-validation test correctly assigned each sample to its specific donor with 98.44% accuracy for urine samples and 65% for serum/plasma samples. Advanced fibrosis and cirrhosis were recognized with 71% sensitivity for CPMG plasma spectra and 69% specificity for NOESY serum spectra. Accuracy for NOESY serum spectra was 68%. Noesy spectra recognized advanced fibrosis and cirrhosis with 71% sensitivity, 30% specificity, and 50% accuracy in urine samples.

CONCLUSION

Metabolomic analysis of urine spectra using ¹H-NMR spectroscopy can recognize a specific individual profile in all patients with chronic hepatitis C. However, this method cannot yet differentiate with sufficient accuracy, patients with advanced fibrosis from patients with milder disease.

Intestinal epithelium, intraepithelial lymphocytes and the gut microbiota - Key players in the pathogenesis of celiac disease

Celiac disease (CD) is a chronic immune-mediated disorder triggered by the ingestion of gluten in genetically predisposed individuals. Before activating the immune system, gluten peptides are transferred by the epithelial barrier to the mucosal lamina propria, where they are deamidated by intestinal tissue transglutaminase 2. As a result, they strongly bind to human leucocyte antigens (HLAs), especially HLA-DQ2 and HLA-DQ8, expressed on antigen-presenting cells. This induces an inflammatory response, which results in small bowel enteropathy. Although gluten is the main external trigger activating both innate and adaptive (specific) immunity, its presence in the intestinal lumen does not fully explain CD pathogenesis. It has been hypothesized that an early disruption of the gut barrier in genetically susceptible individuals, which would result in an increased intestinal permeability, could precede the onset of gluten-induced immune events. The intestinal barrier is a complex functional structure, whose functioning is dependent on intestinal microbiota homeostasis, epithelial layer integrity, and the gut-associated lymphoid tissue with its intraepithelial lymphocytes (IELs). The aim of this paper was to review the current literature and summarize the role of the gut microbiota, epithelial cells and their intercellular junctions, and IELs in CD development.

New Insights into the Pathogenesis of Celiac Disease

Celiac disease (CD) is an autoimmune and multisystem gluten-related disorder that causes symptoms involving the gastrointestinal tract and other organs. Pathogenesis of CD is only partially known. It had been established that ingestion of gluten proteins present in wheat and other cereals are necessary for the disease and develops in individuals genetically predisposed carrying the DQ2 or DQ8 human leukocyte antigen haplotypes. In this review, we had pay specific attention on the last discoveries regarding the three cellular components mainly involved in the development and maintenance of CD: T-cells, B-cells, and microbioma. All of them had been showed critical for the interaction between inflammatory immune response and gluten peptides. Although the mechanisms of interaction among overall these components are not yet fully understood, recent proteomics and molecular studies had shed some lights in the pathogenic role of tissue transglutaminase 2 in CD and in the alteration of the intestinal barrier function induced by host microbiota.

Rats' urinary metabolomes reveal the potential roles of functional foods and exercise in obesity management

The complexity of the metabolic changes in obese individuals still presents a challenge for the understanding of obesity-related metabolic disruptions and for obesity management. In this study, a gas chromatography mass spectrometry (GC-MS) based metabolomics approach targeting urine metabolism has been applied to assess the potential roles of functional foods and exercise for obesity management in rats. Male albino rats diagnosed as obese via histopathology and biochemical assays were administered functional foods in common use for obesity management including pomegranate, grapefruit, and red cabbage juice extracts in parallel with swimming exercise. Urine samples were collected from these rats, and likewise from healthy control animals, for metabolite analysis using (GC-MS) coupled to multivariate data analysis. The results revealed a significant elevation in oxalate and phosphate levels in obese rat urine concurrent with lower lactate levels as compared to the control group. Furthermore, and to pinpoint the bioactive agents in the administered functional foods, ultra performance liquid chromatography (UPLC) coupled to high resolution time-of-flight mass spectrometry (TOF-MS) was employed for secondary metabolite profiling. The different phenolic classes found in the examined functional foods, viz. ellagitannins in pomegranate, flavanones in grapefruit and flavonols in red cabbage, are likely to mediate their anti-obesity effects. The results indicate that these functional foods and exercise were quite effective in reverting obesity-related metabolic disruptions back to normal status, as revealed by orthogonal partial least squares-discriminant analysis (OPLS-DA).

Plasma and Serum Metabolite Association Networks: Comparability within and between Studies Using NMR and MS Profiling

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.