Propionate as a health-promoting microbial metabolite in the human gut.Propionate is mehabolism major microbial metaboilsm metabolite in the human gut with putative health effects that extend beyond the gut epithelium. Propionate is thought to lower lipogenesis, serum cholesterol levels, and carcinogenesis in other tissues. Steering microbial propionate production propionate metabolism in humans diet could therefore be a potent strategy to increase health effects from microbial carbohydrate fermentation. The present review first discusses the two main propionate-production pathways and provides an extended gene-based propionate metabolism in humans of microorganisms with the potential to produce propionate. Second, it evaluates the promising potential of arabinoxylan, polydextrose, and Propionaye to act as substrates to increase microbial propionate. Third, given the complexity of the gut microbiota, propionate production is approached from a microbial-ecological perspective that includes interaction processes such as cross-feeding mechanisms. Finally, it introduces the development of functional gene-based analytical tools to detect and characterize propionate-producing microorganisms in types of inhaled corticosteroids for asthma complex community.
Propionate as a health-promoting microbial metabolite in the human gut. - PubMed - NCBI
We applied untargeted mass spectrometry-based metabolomics to the diseases methylmalonic acidemia MMA and propionic acidemia PA.
We used a screening platform that used untargeted, mass-based metabolomics of methanol-extracted plasma to find significantly different molecular features in human plasma samples from MMA and PA patients and from healthy individuals. Of the approximately features measured, propionyl carnitine was easily identified as the best biomarker of disease P value 1.
Many metabolites that do not appear in any public database, and that remain unidentified, varied significantly between normal, MMA, and PA, underscoring the complex downstream metabolic effects resulting from the defect in a single enzyme. This proof-of-concept study demonstrates that metabolomics can expand the range of metabolites associated with human disease and shows that this method may be useful for disease diagnosis and patient clinical evaluation.
Inborn errors of metabolism can have a severe impact on human health, so comprehensive diagnostic neonatal screening is used for early diagnosis to avoid potentially catastrophic physical and neurological effects 1. These defects can cause a buildup of toxic metabolites, resulting in serious, often fatal, disease early in life 2 3. Neonatal blood screening with mass spectrometry MS 1 is now commonly used to test newborns for a wide array of inborn errors of metabolism using specific metabolites for diagnosis 4.
We studied 2 diseases using metabolomics with nontargeted LC-MS to simultaneously profile thousands of metabolites and obtain a more comprehensive metabolic profile of plasma. The platform we developed Fig. A , outline of the methods used for untargeted metabolomics comparing disease and normal plasmas.
Data are converted and aligned in the time domain using the program XCMS, with — features found. Statistically significant differences are ranked using a t -test. These metabolites are identified by use of the methods outlined in B. B , accurate mass information from the metabolite is used either directly for a database search or for calculating the possible elemental compositions, which are then used for database searching.
Methylmalonic acidemia MMA and propionic acidemia PA are inborn errors of amino acid metabolism affecting 1 in 50 to 1 in individuals 6 7. PA results from a defect in the enzyme propionyl-CoA carboxylase, which catalyzes the biotin-dependent conversion of propionyl-CoA to methylmalonyl-CoA 8. MMA results from deficiency of the immediately downstream enzyme methylmalonyl-CoA mutase, which catalyzes the vitamin B 12 -dependent conversion of methylmalonyl-CoA to succinyl-CoA Fig.
Patients have considerable variability in symptoms and clinical prognosis, which are correlated with genetic locus 6 7. Abnormalities in the processing of vitamin B 12 to the active coenzyme adenosylcobalamin cbl A, B, C, and D are amenable to treatment with B 12 supplementation, and present later with the best prognosis 9. Within each of these groups there is substantial variability in disease severity and prognosis, for which there is no biomarker.
For example, in the complete apoenzyme deficiency form, both MMA and PA often include varying degrees of neurological involvement, the underlying biochemical cause of which is not known 10 11 12 13 Perhaps differences in metabolite concentrations correlating with neurological damage could eventually be identified using untargeted metabolomics.
Both produce a buildup of toxic upstream acyl-CoA precursors, and their corresponding acylcarnitines, propionyl-carnitine, and methylmalonyl-carnitine. B , simplified pathway illustrating conversion of toxic acyl-CoA intermediates to the corresponding acylcarnitines and cleared into the plasma. Long-chain acylcarnitines require carnitine acetyltransferase CAT to pass through the membrane, whereas shorter- chain acylcarnitines can pass directly through the cell membrane.
The level of propionyl carnitine is highly increased in both diseases, arising from the transesterification of propionyl-CoA Fig. Propionyl carnitine is now used as the primary biomarker, screened by tandem electrospray MS after derivatizing bloodspot methanol extracts by butylation The power of untargeted metabolomics lies in its potential to broaden our understanding of disease biochemistry, identify new biomarkers, and provide finer disease categorization and treatment.
We hypothesized that a metabolomics study may reveal additional differences between disease and normal plasma, and thus a more complete biochemical profile. This study demonstrated the application of a new toolbox of methods to clinical chemistry, an approach with the potential to provide new insights into the biochemical mechanisms of disease. The pellet was removed by centrifuging at 16 g for 10 min.
The supernatant was removed to a clean tube and the centrifugation step was repeated, and the samples were dried in a SpeedVac to dryness. Existing samples referred to a clinical laboratory for prior testing were anonymized, and the investigation was in accordance with the University of California—San Diego Human Research Protection Program.
In an initial experiment, plasma samples from individuals with MMA and healthy individuals were collected, comparing 9 MMA samples to 10 normal plasma samples. After finding significantly increased concentrations of several metabolites in MMA, including various acylcarnitines, the sample size was expanded.
Samples from patients with PA were included, along with healthy adults before and after supplementation with l-carnitine TwinLabs , mg twice a day for 1 week.
The patient group was restricted to patients with complete apoenzyme deficiency termed mut 0 in MMA. We used a metabolomics workflow Fig. Briefly, the plasma samples were extracted with methanol to remove proteins and extract the maximum number of metabolites Data were collected and converted to a common data format.
We used the program XCMS 5 to integrate the chromatographic peaks and assign the peaks into groups, followed by nonlinear alignment of the grouped data in the time domain. Reverse phase chromatography was performed using either a by 0.
The LC system was an Agilent with a capillary pump. Buffer A was water with 0. The instrument was calibrated immediately before use. The data files were converted from the instrument format. To confirm the identification of significant ions, we used a linear ion trap Thermo LTQ , with a custom nanospray interface operated at a voltage of 2 kV.
Masses for CID were targeted from a mass list and fragmented during the chromatography run. The common fragment of 85 and the neutral loss of 59 are both characteristic CID fragmentation patterns of acylcarnitines. This process, in combination with accurate mass data from the ESI-TOF, was used to identify the compound as propionyl carnitine.
In addition, the retention time of a propionyl carnitine calibrator was checked against the retention time of the endogenous peak in patient plasma. The endcapped Aquasil column provided better retention of propionyl carnitine, with a retention time of 16 min, than a standard C18 column, on which it is not retained and elutes at 5.
Box plots showing integrated ion intensities linear, arbitrary scale for the identified ions that differ most significantly between normal individuals with and without carnitine supplementation and MMA and PPA. Individual values are indicated by dots in red normal , green MMA , and blue PA , with SD indicated by the upper and lower extent of the box. In the initial experiment comparing MMA and normal plasma, a number of compounds were found to be significantly increased in MMA plasma, several of which were identified as acylcarnitines.
To test whether treatment with carnitine produced this effect, samples from 3 healthy children treated with carnitine and from 3 healthy adults before and after carnitine supplementation were included in the experimental controls.
The acylcarnitine levels of the 6 supplemented individuals were not significantly increased relative to the unsupplemented controls, and levels were within the general range of the other normal samples. Although neither compound is known to be associated with MMA or PA as a biomarker, 2-methyl-branched acylcarnitines have been reported in MMA and PA patient urines during acute illness 21 , including 2-methylpentenoic carnitine, which could correspond to the C6: In another recent study, novel branched-chain acylcarnitines were recently identified in the urine of 1 patient with another metabolic disorder, medium-chain CoA dehydrogenase disorder Isovaleryl carnitine was increased in MMA relative to normal samples by a factor of 5.
The P value was 5. Methylmalonyl carnitine was not observed in the untargeted metabolomic analysis, although it is usually increased in MMA. Number and percentage of the features of total differentiating normal, MMA, and PA at 2 significance cutoffs. We observed several statistically significant differences between disease and normal plasma. Using an endcapped C18 column Aquasil; Thermo with a run time of 75 min, features were detected.
The simultaneous detection and profiling of many hundreds to thousands metabolites increases the probability of identifying new compounds associated with disease using untargeted metabolomics.
Indeed, the most significant feature in the untargeted metabolomic analysis that distinguished disease patient vs normal plasma was propionyl carnitine P value 1. This result validates the untargeted metabolomics approach for identification of biomarkers, and is the most important finding, demonstrating proof-of-concept application of metabolomics to clinical chemistry.
Among the identified compounds were C5: These compounds do not seem to be the result of nonspecific transesterification of CoA esters, because they are not increased in samples from metabolically normal children on carnitine supplementation or the adults treated with carnitine.
There are many more significant differences, however, and C6: More significantly, isovaleryl carnitine was increased by a factor of 2. There are other significant ions differentiating MMA and PA, which are clearly not acylcarnitines based on fragmentation pattern but have not yet been identified. The task of de novo identification of metabolites can be very time-consuming, however, and is an open-ended process; identification of additional compounds awaits further study and validation with additional samples.
By combining multiple ions, it is possible to clearly differentiate between the 2 diseases, but larger numbers of patient samples are needed for validation. Methylmalonyl carnitine was not identified in the untargeted analysis, although it was fold increased in MMA vs PA and normal individuals in an independent targeted analysis data not shown. Methylmalonyl carnitine may not have been observed because of its high polarity and lack of retention on the reverse phase column. Box plots showing integrated ion intensities for selected ions that differentiate between PA and MMA.
Statistics and mean differences are shown in Supplemental Data Table 1. Propionyl carnitine was identified as a biomarker using a completely untargeted approach, illustrating the usefulness of metabolomics for identifying biomarkers of disease. New metabolites continue to be discovered even in well-studied diseases 21 22 31 , and the metabolomics approach permits analysis of an unprecedented range of compounds.
The finding of many significant differences underscores the evolving understanding of metabolic disease as having complex, diverse phenotypes that transcend simple concepts of a single-locus genotype 17 The ratio of the average intensity of all disease samples to normal samples is shown in column 8.
NA, not applicable; ND, not determined. Skip to main content. Research Article Automation and Analytical Techniques. Wikoff , Jon A. Gangoiti , Bruce A. Barshop , Gary Siuzdak.
View inline View popup. Identification and significance of compounds. We thank Andy Gieschen for helpful advice on data collection. Footnotes 1 A longer retention time for the acyl carnitines on the reverse-phase column correlates with a longer acyl chain length. Neonatal biochemical screening for disease. Clin Chim Acta ; Methylmalonic and propionic aciduria. Metabolic disorders and mental retardation. The application of tandem mass spectrometry to neonatal screening for inherited disorders of intermediary metabolism.