Alterations in lipid, amino acid, and energy metabolism distinguish Crohn’s disease from ulcerative colitis and control subjects by serum metabolomic profiling

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Abstract

<div class="section"> <a class="named-anchor" id="S1">  </a> <h5 class="section-title" id="d1634101e268">Introduction</h5> <p id="P1">Biomarkers are needed in inflammatory bowel disease (IBD) to help define disease activity and identify underlying pathogenic mechanisms. We hypothesized that serum metabolomics, which produces unique metabolite profiles, can aid in this search. </p> </div><div class="section"> <a class="named-anchor" id="S2">  </a> <h5 class="section-title" id="d1634101e273">Objectives</h5> <p id="P2">The aim of this study was to characterize serum metabolomic profiles in patients with IBD, and to assess for differences between patients with ulcerative colitis (UC), Crohn’s disease (CD), and non- IBD subjects. </p> </div><div class="section"> <a class="named-anchor" id="S3">  </a> <h5 class="section-title" id="d1634101e278">Methods</h5> <p id="P3">Serum samples from 20 UC, 20 CD, and 20 non-IBD control subjects were obtained along with patient characteristics, including medication use and clinical disease activity. Non-targeted metabolomic profiling was performed using ultra-high performance liquid chromatography/mass spectrometry (UPLC-MS/MS) optimized for basic or acidic species and hydrophilic interaction liquid chromatography (HILIC/UPLC-MS/MS). </p> </div><div class="section"> <a class="named-anchor" id="S4">  </a> <h5 class="section-title" id="d1634101e283">Results</h5> <p id="P4">In total, 671 metabolites were identified. Comparing IBD and control subjects revealed 173 significantly altered metabolites (27 increased and 146 decreased). The majority of the alterations occurred in lipid-, amino acid-, and energy-related metabolites. Comparing only CD and control subjects revealed 286 significantly altered metabolites (54 increased and 232 decreased), whereas comparing UC and control subjects revealed only 5 significantly altered metabolites (all decreased). Hierarchal clustering using significant metabolites separated CD from UC and control subjects. </p> </div><div class="section"> <a class="named-anchor" id="S5">  </a> <h5 class="section-title" id="d1634101e288">Conclusions</h5> <p id="P5">We demonstrate that a number of lipid-, amino acid-, and tricarboxylic acid (TCA) cycle- related metabolites were significantly altered in IBD patients, more specifically in CD. Therefore, alterations in lipid and amino acid metabolism and energy homeostasis may play a key role in the pathogenesis of CD. </p> </div>

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A SIMPLE INDEX OF CROHN'S-DISEASE ACTIVITY

R.F. Harvey, J.M. Bradshaw (1980)

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Organization of GC/MS and LC/MS metabolomics data into chemical libraries

Corey DeHaven, Anne M Evans, Hongping Dai … (2010)

Background Metabolomics experiments involve generating and comparing small molecule (metabolite) profiles from complex mixture samples to identify those metabolites that are modulated in altered states (e.g., disease, drug treatment, toxin exposure). One non-targeted metabolomics approach attempts to identify and interrogate all small molecules in a sample using GC or LC separation followed by MS or MSn detection. Analysis of the resulting large, multifaceted data sets to rapidly and accurately identify the metabolites is a challenging task that relies on the availability of chemical libraries of metabolite spectral signatures. A method for analyzing spectrometry data to identify and Qu antify I ndividual C omponents in a S ample, (QUICS), enables generation of chemical library entries from known standards and, importantly, from unknown metabolites present in experimental samples but without a corresponding library entry. This method accounts for all ions in a sample spectrum, performs library matches, and allows review of the data to quality check library entries. The QUICS method identifies ions related to any given metabolite by correlating ion data across the complete set of experimental samples, thus revealing subtle spectral trends that may not be evident when viewing individual samples and are likely to be indicative of the presence of one or more otherwise obscured metabolites. Results LC-MS/MS or GC-MS data from 33 liver samples were analyzed simultaneously which exploited the inherent biological diversity of the samples and the largely non-covariant chemical nature of the metabolites when viewed over multiple samples. Ions were partitioned by both retention time (RT) and covariance which grouped ions from a single common underlying metabolite. This approach benefitted from using mass, time and intensity data in aggregate over the entire sample set to reject outliers and noise thereby producing higher quality chemical identities. The aggregated data was matched to reference chemical libraries to aid in identifying the ion set as a known metabolite or as a new unknown biochemical to be added to the library. Conclusion The QUICS methodology enabled rapid, in-depth evaluation of all possible metabolites (known and unknown) within a set of samples to identify the metabolites and, for those that did not have an entry in the reference library, to create a library entry to identify that metabolite in future studies.

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Rapid and noninvasive metabonomic characterization of inflammatory bowel disease.

Elaine Holmes, Yulan Wang, Julian Marchesi … (2007)

Inflammatory bowel diseases (IBD) including Crohn's disease (CD) and ulcerative colitis (UC) have a major impact on the health of individuals and populations. Accurate diagnosis of inflammatory bowel disease (IBD) at an early stage, and correct differentiation between Crohn's disease (CD) and ulcerative colitis (UC), is important for optimum treatment and prognosis. We present here the first characterization of fecal extracts obtained from patients with CD and UC by employing a noninvasive metabonomics approach, which combines high resolution 1H NMR spectroscopy and multivariate pattern recognition techniques. The fecal extracts of both CD and UC patients were characterized by reduced levels of butyrate, acetate, methylamine, and trimethylamine in comparison with a control population, suggesting changes in the gut microbial community. Also, elevated quantities of amino acids were present in the feces from both disease groups, implying malabsorption caused by the inflammatory disease or an element of protein losing enteropathy. Metabolic differences in fecal profiles were more marked in the CD group in comparison with the control group, indicating that the inflammation caused by CD is more extensive in comparison with UC and involves the whole intestine. Furthermore, glycerol resonances were a dominant feature of fecal spectra from patients with CD but were present in much lower intensity in the control and UC groups. This work illustrates the potential of metabonomics to generate novel noninvasive diagnostics for gastrointestinal diseases and may further our understanding of disease mechanisms.