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•The serum metabolome does not reflect steroids activated in an intracrine manner.•The urinary steroid metabolome reflects steroid biosynthesis and metabolism.•Defined pathways link ...the circulating and urinary steroid metabolomes.•Modern mass spectrometry techniques allow for comprehensive steroid profiling.
Advances in technology have allowed for the sensitive, specific, and simultaneous quantitative profiling of steroid precursors, bioactive steroids and inactive metabolites, facilitating comprehensive characterization of the serum and urine steroid metabolomes. The quantification of steroid panels is therefore gaining favor over quantification of single marker metabolites in the clinical and research laboratories. However, although the biochemical pathways for the biosynthesis and metabolism of steroid hormones are now well defined, a gulf still exists between this knowledge and its application to the measured steroid profiles. In this review, we present an overview of steroid hormone biosynthesis and metabolism by the liver and peripheral tissues, specifically highlighting the pathways linking and differentiating the serum and urine steroid metabolomes. A brief overview of the methodology used in steroid profiling is also provided.
The human gastrointestinal tract is home to an incredibly dense population of microbes. These microbes employ unique strategies to capture energy in this largely anaerobic environment. In the process ...of breaking down dietary- and host-derived substrates, the gut microbiota produce a broad range of metabolic products that accumulate to high levels in the gut. Increasingly, studies are revealing that these chemicals impact host biology, either by acting on cells within the gastrointestinal tract or entering circulation and exerting their effects at distal sites within the body. Given the high level of functional diversity in the gut microbiome and the varied diets that we consume, the repertoire of microbiota-derived molecules within our bodies varies dramatically across individuals. Thus, the microbes in our gut and the metabolic end products they produce represent a phenotypic lever that we can potentially control to develop new therapeutics for personalized medicine. Here, we review current understanding of how microbes in the gastrointestinal tract contribute to the molecules within our gut and those that circulate within our bodies. We also highlight examples of how these molecules affect host physiology and discuss potential strategies for controlling their production to promote human health and to treat disease.
The objective of study was to investigate the underlying mechanisms responsible for the ability of 2-Ethylhexylglycidylether (EHGE) fumigation to inhibit the yellowing and senescence of harvested ...broccoli. We conducted a combination of transcriptomic, metabolomic, physiological, and biochemical analyses of EHGE-fumigated broccoli stored at 20 °C and 90 % relative humidity for five days and compared it to an untreated control. Results indicated that the EHGE treatment inhibited fresh weight loss and the loss of vitamin C, inhibited the reduction in total phenolics, delayed yellowing, as well as the reduction in antioxidant capacity. The data suggest a dual mechanism involving the enhancement of chlorophyll synthesis and the inhibition of chlorophyll degradation is involved. The EHGE treatment also suppressed carotenoid accumulation while promoting the accumulation of flavonoids, resulting in increased antioxidant capacity. However, the specific mechanisms associated with the antioxidant capacity of flavonoids require further study. In summary, the study provides a comprehensive theoretical framework for understanding how EHGE fumigation effectively delays the postharvest senescence of postharvest broccoli.
•This paper first studied the effect of 2-Ethylhexylglycidylether (EHGE) on the postharvest preservation of broccoli.•EHGE treatment extends the green coloring of broccoli.•EHGE treatment delays the rate of chlorophyll degradation in broccoli.•EHGE treatment regulates the biosynthesis and metabolism of chlorophyll, carotenoids, and flavonoids in broccoli.
Metabolomics has become an invaluable tool for both studying metabolism and biomarker discovery. The great technical advances in analytical chemistry and bioinformatics have considerably increased ...the number of measurable metabolites, yet an important part of the human metabolome remains uncovered. Among the various MS hyphenated techniques available, LC-MS stands out as the most used. Here, we aimed to show the capabilities of LC-MS to uncover part of the metabolome and how to best proceed with sample preparation and LC to maximise metabolite detection. The analyses of various open metabolite databases served us to estimate the size of the already detected human metabolome, the expected metabolite composition of most used human biospecimens and which part of the metabolome can be detected when LC-MS is used. Based on an extensive review and on our experience, we have outlined standard procedures for LC-MS analysis of urine, cells, serum/plasma, tissues and faeces, to guide in the selection of the sample preparation method that best matches with one or more LC techniques in order to get the widest metabolome coverage. These standard procedures may be a useful tool to explore, at a glance, the wide spectrum of possibilities available, which can be a good starting point for most of the LC-MS metabolomic studies.
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•The LC-MS detectable human metabolome is estimated.•The metabolite composition of the most widespread human biospecimens in metabolomics is estimated.•The most suitable sample preparation methods for LC-MS are described.•The metabolome coverage of LC techniques in MS-based metabolomics is discussed.•A guide to best select sample preparation and LC configuration based on the chosen biospecimen is proposed.
The human gut microbiome is comprised of densely colonizing microorganisms including bacteriophages, which are in dynamic interaction with each other and the mammalian host. To address how ...bacteriophages impact bacterial communities in the gut, we investigated the dynamic effects of phages on a model microbiome. Gnotobiotic mice were colonized with defined human gut commensal bacteria and subjected to predation by cognate lytic phages. We found that phage predation not only directly impacts susceptible bacteria but also leads to cascading effects on other bacterial species via interbacterial interactions. Metabolomic profiling revealed that shifts in the microbiome caused by phage predation have a direct consequence on the gut metabolome. Our work provides insight into the ecological importance of phages as modulators of bacterial colonization, and it additionally suggests the potential impact of gut phages on the mammalian host with implications for their therapeutic use to precisely modulate the microbiome.
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•Phages can coexist over time with targeted gut bacteria•Phages induce cascading effects on microbiota species that are not directly targeted•Phage-induced bacterial modulation impacts the gut metabolome•Phages can modulate metabolites, which are known to affect the mammalian host
How bacteriophages impact bacterial communities in vivo is unclear. Hsu et al. investigate this in mice carrying a model microbiome. Phage predation directly impacts susceptible bacteria, leading to cascading effects on other bacterial species, with consequences on the gut metabolome. Phages can be harnessed to modulate the microbiome and host.
Recently, we reported that some dairy cows could produce high amounts of milk with high amounts of protein (defined as milk protein yield MPY) when a population was raised under the same nutritional ...and management condition, a potential new trait that can be used to increase high-quality milk production. It is unknown to what extent the rumen microbiome and its metabolites, as well as the host metabolism, contribute to MPY. Here, analysis of rumen metagenomics and metabolomics, together with serum metabolomics was performed to identify potential regulatory mechanisms of MPY at both the rumen microbiome and host levels.
Metagenomics analysis revealed that several Prevotella species were significantly more abundant in the rumen of high-MPY cows, contributing to improved functions related to branched-chain amino acid biosynthesis. In addition, the rumen microbiome of high-MPY cows had lower relative abundances of organisms with methanogen and methanogenesis functions, suggesting that these cows may produce less methane. Metabolomics analysis revealed that the relative concentrations of rumen microbial metabolites (mainly amino acids, carboxylic acids, and fatty acids) and the absolute concentrations of volatile fatty acids were higher in the high-MPY cows. By associating the rumen microbiome with the rumen metabolome, we found that specific microbial taxa (mainly Prevotella species) were positively correlated with ruminal microbial metabolites, including the amino acids and carbohydrates involved in glutathione, phenylalanine, starch, sucrose, and galactose metabolism. To detect the interactions between the rumen microbiome and host metabolism, we associated the rumen microbiome with the host serum metabolome and found that Prevotella species may affect the host's metabolism of amino acids (including glycine, serine, threonine, alanine, aspartate, glutamate, cysteine, and methionine). Further analysis using the linear mixed effect model estimated contributions to the variation in MPY based on different omics and revealed that the rumen microbial composition, functions, and metabolites, and the serum metabolites contributed 17.81, 21.56, 29.76, and 26.78%, respectively, to the host MPY.
These findings provide a fundamental understanding of how the microbiome-dependent and host-dependent mechanisms contribute to varied individualized performance in the milk production quality of dairy cows under the same management condition. This fundamental information is vital for the development of potential manipulation strategies to improve milk quality and production through precision feeding. Video Abstract.
Determining metabolomic differences among samples of different phenotypes is a critical component of metabolomics research. With the rapid advances in analytical tools such as ultrahigh‐resolution ...chromatography and mass spectrometry, an increasing number of metabolites can now be profiled with high quantification accuracy. The increased detectability and accuracy raise the level of stringiness required to reduce or control any experimental artifacts that can interfere with the measurement of phenotype‐related metabolome changes. One of the artifacts is the batch effect that can be caused by multiple sources. In this review, we discuss the origins of batch effects, approaches to detect interbatch variations, and methods to correct unwanted data variability due to batch effects. We recognize that minimizing batch effects is currently an active research area, yet a very challenging task from both experimental and data processing perspectives. Thus, we try to be critical in describing the performance of a reported method with the hope of stimulating further studies for improving existing methods or developing new methods.
Introduction
Separately, both exercise and protein ingestion have been shown to alter the blood and urine metabolome. This study goes a step further and examines changes in the metabolome derived ...from blood, urine and muscle tissue extracts in response to resistance exercise combined with ingestion of three different protein sources.
Methods
In an acute parallel study, 52 young males performed one-legged resistance exercise (leg extension, 4 × 10 repetitions at 10 repetition maximum) followed by ingestion of either cricket (insect), pea or whey protein (0.25 g protein/kg fat free mass). Blood and muscle tissue were collected at baseline and three hours after protein ingestion. Urine was collected at baseline and four hours after protein ingestion. Mixed-effects analyses were applied to examine the effect of the time (baseline vs. post), protein (cricket, pea, whey), and time x protein interaction.
Results
Nuclear magnetic resonance (NMR)-based metabolomics resulted in the annotation and quantification of 25 metabolites in blood, 35 in urine and 21 in muscle tissue. Changes in the muscle metabolome after combined exercise and protein intake indicated effects related to the protein source ingested. Muscle concentrations of leucine, methionine, glutamate and myo-inositol were higher after intake of whey protein compared to both cricket and pea protein. The blood metabolome revealed changes in a more ketogenic direction three hours after exercise reflecting that the trial was conducted after overnight fasting. Urinary concentration of trimethylamine N-oxide was significantly higher after ingestion of cricket than pea and whey protein.
Conclusion
The blood, urine and muscle metabolome showed different and supplementary responses to exercise and ingestion of the different protein sources, and in synergy the summarized results provided a more complete picture of the metabolic state of the body.
To compare the variability in metabolomes between the serum and follicular fluid, as well as across 3 dominant follicles.
Prospective cohort study.
An academic fertility clinic in the northeastern ...United States, 2005–2015.
One hundred thirty-five women undergoing in vitro fertilization treatment who provided a serum sample during ovarian stimulation and up to 3 follicular fluid samples during oocyte retrieval.
None.
Samples were analyzed using liquid chromatography with high-resolution mass spectrometry and 2 chromatography columns (C18 hydrophobic negative and hydrophilic interaction chromatography HILIC positive). We calculated overall, feature-specific, and subject-specific correlation coefficients to describe how strongly the intensity of overlapping metabolic features were associated between the serum and follicular fluid and between the 1st-2nd, 1st-3rd, and 2nd-3rd follicles. Feature-specific correlations were adjusted for age, body mass index, infertility diagnosis, ovarian stimulation protocol, and year.
From the C18-negative column and the high-resolution mass spectrometry, 7,830 serum features and 10,790 follicular fluid features were detected in ≥20% of samples. After screening retention times and checking for 1:1 matching, 1,928 features overlapped between the 2 metabolomes. From the HILIC-positive column and the high-resolution mass spectrometry, after applying the same exclusion criteria, there were 9,074 serum features, 5,542 follicular fluid features, and 1,149 features that overlapped. When comparing the feature intensity of overlapping metabolites in the serum and the follicular fluid, the overall (C18, 0.45; HILIC, 0.63), median feature-specific (C18, 0.35; HILIC, 0.37), and median subject-specific (C18, 0.42; HILIC, 0.59) correlations were low to moderate. In contrast, among the overlapping features across all 3 follicles, the overall (C18, all 0.99; HILIC, all 0.99), median feature-specific (C18, 0.74-0.81; HILIC, 0.79–0.85), and median subject-specific (C18, 0.88–0.89; HILIC, 0.90–0.91) correlations between follicular fluid metabolomics features within a woman were high.
We observed minimal overlap and weak-to-moderate correlation between metabolomic features in the serum and follicular fluid but a large overlap and strong correlation between metabolomic features across follicles within a woman. The follicular fluid appears to represent a novel matrix, distinct from serum, which may be a rich source of biologic predictors of female fertility and reproductive outcomes.
Caracterización del metaboloma del líquido folicular: cuantificación de la correlación entre folículos y diferencias con el metaboloma sérico.
Comparar la variabilidad en metabolomas entre el suero y el líquido folicular, así como entre los tres folículos dominantes.
Estudio prospectivo de cohorte.
Clínica académica de fertilidad en el noreste de Estados Unidos, 2005-2015.
Ciento treinta y cinco mujeres sometidas a tratamientos de fecundación in vitro quienes proporcionaron una muestra de suero durante la estimulación ovárica y hasta 3muestras de líquido folicular durante la recuperación ovocitaria.
Ninguna.
Las muestras fueron analizadas utilizando cromatografía líquida con espectrometría de masa de alta resolución y 2 columnas de cromatografía (C18 hidrofóbica negativa y cromatografía de interacción hidrofílica HILIC positiva). Calculamos la correlación de coeficientes global, sujeto-específica y componente específica para describir el grado de asociación entre los componentes metabolómicos comunes del suero y el líquido folicular, y entre primer y segundo, primer y tercer, y segundo y tercer folículo. Las correlaciones componente-específicas se ajustaron para edad, índice de masa corporal, diagnóstico de infertilidad, protocolo de estimulación ovárica y año.
De la columna C18-negativo y de la espectrometría de masa de alta resolución, se detectaron 7,830 componentes de suero y 10,790 componentes de líquido folicular en más del 20% de las muestras. Después de cribar los tiempos de retención y revisar emparejamiento 1:1, 1,928 componentes coincidieron entre los dos metabolomas. De la columna HILC-positiva y de la espectrometría de masa de alta resolución, después de aplicar los mismos criterios de exclusión, se detectaron 9,074 componentes de suero, 5,542 componentes foliculares y 1,149 componentes que coincidían. Cuando se comparó la intensidad del parámetro de los metabolitos coincidentes en el suero y líquido folicular, las correlaciones globales (C18, 0.45; HILIC, 0,63), mediana componente-específica (C18,0.35; HILIC, 0.37), y mediana sujeto-específica (C18,0.42; HILIC, 0.59) fueron de bajas a moderadas. En contraste, en los componente-coincidentes entre los tres folículos, las correlaciones globales (C18,0.99; HILIC 0.99), mediana componente-específica (C18,0.74-0.81; HILIC, 0.79-0.85), y mediana sujeto- específica (C18,0.88-0.89; HILIC, 0.90-0.91) fueron altas en los componentes metabolómicos foliculares de una mujer.
Observamos mínima coincidencia y débil a moderada entre los componentes metabolómicos en el suero y líquido folicular pero amplia coincidencia y fuerte correlación entre los componentes metabolómicos ente los folículos de una mujer. El líquido folicular aparenta ser una matriz novedosa distinta al suero, que puede ser una rica fuente de predictores biológicos de la fertilidad femenina y resultados reproductivos.
Epidemiologic and murine studies suggest that dietary emulsifiers promote development of diseases associated with microbiota dysbiosis. Although the detrimental impact of these compounds on the ...intestinal microbiota and intestinal health have been demonstrated in animal and in vitro models, impact of these food additives in healthy humans remains poorly characterized.
To examine this notion in humans, we performed a double-blind controlled-feeding study of the ubiquitous synthetic emulsifier carboxymethylcellulose (CMC) in which healthy adults consumed only emulsifier-free diets (n = 9) or an identical diet enriched with 15 g per day of CMC (n = 7) for 11 days.
Relative to control subjects, CMC consumption modestly increased postprandial abdominal discomfort and perturbed gut microbiota composition in a way that reduced its diversity. Moreover, CMC-fed subjects exhibited changes in the fecal metabolome, particularly reductions in short-chain fatty acids and free amino acids. Furthermore, we identified 2 subjects consuming CMC who exhibited increased microbiota encroachment into the normally sterile inner mucus layer, a central feature of gut inflammation, as well as stark alterations in microbiota composition.
These results support the notion that the broad use of CMC in processed foods may be contributing to increased prevalence of an array of chronic inflammatory diseases by altering the gut microbiome and metabolome (ClinicalTrials.gov, number NCT03440229).
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Dietary emulsifier carboxymethylcellulose increased abdominal discomfort and altered microbiota composition and fecal metabolome, supporting the notion that its consumption may be promoting development of chronic inflammatory diseases.
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