• History & Evolution
• Biosynthesis vs. dietary uptake
• Methionine and the microbiome
• Methionine and DNA methylation
• Methionine and cardiovascular disease
• Methionine and neurogenerative disorders
• Methionine and cancer

History & Evolution

1921: discovery (Mueller, J., 1921) | 1928: structure identified (Barger, G., 1928) | 1945-7: first synthesis (Livak, J. et al., 1945).

Methionine’s story begins just over a century ago, when it was discovered in casein by JH Mueller in 1921 (Mueller, J., 1921). In 1928, Barger and Coyne determined the chemical structure of what was until then known as γ-methylthiol-α-aminobutyric acid and suggested a more straightforward name: methionine (Barger, G., 1928). By the 1930s, methionine was recognized as an essential and limiting amino acid involved in protein synthesis, with subsequent research focusing on its vital role in human and animal diets (Rose, W., 1976). In the aftermath of World War II, protein shortages and widespread malnutrition prompted the hunt for a synthetic form of methionine (Livak, J. et al., 1945).

Now, over one million tons of methionine are produced each year from petroleum, with many applications including poultry feed, flavor enhancers, cosmetics, drugs and pesticides (Neubauer, C., 2021).

Methionine is found in humans, animals, plants and bacteria. In humans, methionine has been described as a “double-edged sword” (Navik, U. et al., 2021). It plays a crucial role in cellular, metabolic, epigenetic and genomic processes, influencing a broad range of physiological functions. It has been shown to support immune function, digestion, insulin resistance, to extend lifespan, and to reduce DNA damage and carcinogenesis (Martínez, Y. et al., 2017). However, excessive amounts can be toxic, with adverse effects including increased cholesterol and hyperhomocysteinemia (Navik, U. et al., 2021); (Garlick, P., 2006).

There is a theory that not only is this ancient metabolite essential for life, but – as a self-activating and sulfur containing amino acid – it may have triggered the development of life on Earth itself (Heinen, W., 1996).

Biosynthesis vs. dietary uptake

As an essential amino acid, methionine must be obtained via the diet and gut microbiota (Parkhitko, A. et al., 2019). Primary dietary sources of methionine include eggs, fish, meat, dairy and plant protein sources (Navik, U. et al., 2021) (Rajavel, E., 2020). In bacteria, methionine is synthesized from cysteine and aspartic acid. Plasma methionine concentration in healthy subjects ranges from 13 to 45 µM (Navik, U. et al., 2021).

The Quantitative Metabolomics Database (QMDB), cataloguing the concentrations of over 600 metabolites in healthy individuals reports average levels of 28 µM for women and 30 µM for men.

Methionine is one of four sulfur-containing amino acids, and, along with cysteine, one of two incorporated into proteins (Brosnan, T., 2006). Daily dietary requirements for methionine are usually taken together with cysteine, and the total sulfur amino acid requirement (TSAA) is 13-15 mg/kg/d for adults (Rajavel, E., 2020).

Methionine metabolism involves three key stages (Parkhitko, A. et al., 2019); (Lauinger, L., 2021):
– the methionine cycle,
– the transsulfuration/transmethylation pathway
– the methionine salvage cycle

In the methionine cycle, methionine and adenosine triphosphate (ATP) are converted into the methyl donor S-adenosylmethionine (SAM) by the enzyme methionine adenosyltransferase. After transferring the methyl group, SAM is converted to S-adenosylhomocysteine (SAH) and hydrolyzed into adenosine and homocysteine. This homocysteine can be remethylated to methionine by the methionine synthase enzyme (with vitamin B12 as a coenzyme), which completes the cycle, or diverted to the transssulfuration pathway to form cysteine (Rajavel, E., 2020); (Parkhitko, A. et al., 2019). In the salvage cycle, methionine is regenerated from decarboxylated SAM, following a series of enzymatic steps involving polyamine synthesis.

Methionine is a precursor to other molecules including antioxidants such as glutathione and cystathionine, and amino acids including cysteine, carnitine, taurine and creatine (Navik, U. et al., 2021).

Methionine and the microbiome

Gut microbiota play a major role in methionine metabolism, with around 20% of dietary methionine metabolized in the gut (Wu, X. et al., 2022); (Bauchart C. et al., 2009). In turn, methionine metabolism influences gut health, as evidenced by the interactions between methionine levels, microbial composition and disease states.

A multi-omics study comparing vegan and omnivore diets found that methionine positively correlates with Bacteroides, Blautia, Dorea, Lachnoclostridium and Fusicatenibacter, with all except Bacteroides being more abundant in omnivores (Prochazkova, M. et al., 2022). Another study found that restricting sulfur amino acids like methionine increases the abundance of Firmicutes, Clostridaceae and Turicibacteraceae, while decreasing Verrucomicrobia, compared to a low-calorie diet (Nichenametla, S. et al., 2021). These findings show how methionine intake influences the diversity and abundance of bacterial species in the gut.

Animal studies have shown that restricting dietary methionine, e.g. through a plant-based diet, may improve gut health. Methionine restriction in mice was found to increase short chain fatty acid (SCFA)-producing bacteria such as Bifidobacterium, Lactobacillus, Bacteroides, Roseburia, Coprococcus, and Ruminococcus, and increase inflammation-inhibiting bacteria like Oscillospira and Corynebacterium (Yang, Y. et al., 2019). In contrast, the high fat diet reduced SCFA production and induced gut dysbiosis.

Methionine appears to play a role in several inflammatory diseases associated with disrupted gut microbiota (Wu, X. et al., 2022). For example, a study in patients with inflammatory bowel disease (IBD) showed reduced levels of methionine, serine and sarcosine, along with fewer SCFA-producing gut bacteria (Borren, N. et al., 2021). Another study found changes in methionine and homoserine levels in patients with Parkinson’s disease (PD) (Hertel, J. et al., 2019). PD patients showed an increase in Akkermansia muciniphila, which is involved in sulfur metabolism. This contributed more than 70% of potential methionine production, suggesting a clear link between gut microbiota and the methionine cycle (Wu, X. et al., 2022).

Methionine and DNA methylation

Methionine is essential for DNA methylation, a key epigenetic mechanism affecting gene expression related to growth and development (Lauinger, L., 2021). In this process, DNA methyltransferases transfer a methyl group from SAM to the C-5 position of the pyrimidine ring of cytosine (Jin, B. et al., 2011). Changes in dietary methionine change the ratio of SAM to SAH (the “methylation index”), which influences methylation capacity. This can be measured using immunoassays to detect levels of SAM and SAH in blood and tissue (Hao, X. et al., 2016).

Dysregulation of DNA methylation is associated with some cancers (Bauchart C. et al., 2009). Studies suggest that methionine supplementation can affect DNA methylation, with both positive and negative effects depending on the gene region affected (Waterland, R., 2006).

More research is needed to fully understand methionine’s role in DNA methylation. Metabolomics offers a useful tool with which to understand epigenomics and DNA methylation in more detail, especially when combined with other omics. For example, a 2020 study combined metabolomics, proteomics and genomics to show that activated T cells use methionine to synthesize SAM, identifying methionine as a key nutritional factor in shaping T helper cell activity through histone methylation (Roy, D. et al., 2020). The findings suggest restricting dietary methionine could be a viable intervention in the treatment of autoimmune conditions such as multiple sclerosis.

Layering insights from different omics analyses in this way gives a fuller picture of the interactions between metabolites, proteins, genes and environmental factors that drive disease processes.

Methionine and cardiovascular disease

Too much methionine may increase the risk of cardiovascular disease (CVD). Methionine is involved in DNA synthesis, lipid regulation, oxidative defense and cellular regulation – processes all thought to play a role in CVD and other diseases (Aissa, A. et al., 2017). Impaired DNA methylation may also be a factor, as this increases circulating LDL cholesterol and lowers HDL cholesterol, both of which are risk factors for CVD (Calderón, A. et al., 2020); (Blachier, F. et al., 2020).

Another key mechanism is linked to methionine’s downstream metabolite homocysteine, which is known to be vasotoxic (Troen, A. et al., 2003). In excess, homocysteine can cause hyperhomocysteinemia, which is a well-established risk factor for CVD. A longitudinal population-based study found that a lower concentration of methionine, higher concentration of homocysteine and lower ratio of methionine to homocysteine were associated with an increased risk of CVD (Calderón, A. et al., 2020).

Excess methionine may also damage endothelial cells and increase plasma lipid levels, which both contribute to atherosclerosis. High levels of dietary methionine have been found to increase the risk of acute coronary events in middle-aged men (Virtanen, J. et al., 2006).

By contrast, methionine restriction appears to delay development of CVD and associated risk factors such as obesity (Zhang, Y. et al., 2022).

Methionine and neurogenerative disorders

Excess methionine is linked to neurodevelopmental disorders, like autism and schizophrenia, and neurodegenerative disorders such as Alzheimer’s disease (AD) and PD (Alachkar, A. et al., 2022); (Hertel, J. et al., 2019). A 2023 study involving animal and human AD phenotypes found that methionine intake is associated with mild cognitive impairment, and methionine restriction improves cognitive function (Xi, Y. et al., 2022).

More research is needed to understand the mechanisms, but again, methionine’s role in DNA methylation and homocysteine production may be relevant. A population-based study found links between methionine, homocysteine and dementia development, suggesting that a higher methionine to homocysteine ratio may reduce brain atrophy and lower the risk of dementia (Hooshmand, B. et al., 2019).

As noted above, a longitudinal metabolomics study investigated links between PD, sulfur metabolism and the gut microbiome (Hertel, J. et al., 2019). This showed differences between methionine and cysteine production via cystathionine in PD patients and healthy subjects. Using multiomics, the researchers were able to identify patterns in microbial-host sulfur metabolism that may contribute to PD severity.

Methionine and cancer

Methionine restriction is also an “exciting potential tool in the treatment of cancer” (Wanders, D. et al., 2020). Methionine inhibits cancer cell proliferation and growth in several types of cancer, without damaging healthy cells (in the presence of homocysteine). It also appears to improve efficacy of chemotherapy and radiation therapy in animal models. A 2020 review summarized evidence showing that methionine restriction could inhibit cancer development and/or progression in prostate cancer, breast cancer and colorectal cancer (Wanders, D. et al., 2020). Possible mechanisms vary by cancer type. It may be that methionine restriction inhibits polyamine biosynthesis, induces apoptosis, alters DNA methylation and glutathione formation, and reduces activity of thymidylate synthase, the enzyme that converts dUMP to dTMP.

 
More about the Metabolite of the Month
Reference ranges of methionine in healthy humans
Quantify the impact of methionine

References

Aissa, A. et al.: Methionine-supplemented diet affects the expression of cardiovascular disease-related genes and increases inflammatory cytokines in mice heart. (2017). J Toxicol Environ Health A | DOI: 10.1080/15287394.2017.1357366

Alachkar, A. et al.: L-methionine enhances neuroinflammation and impairs neurogenesis: implication for alzheimer’s disease. (2022). Journal of Neuroimmunology | DOI: 10.1016/j.jneuroim.2022.577843

Barger, G. et al.: The amino-acid methionine; constitution and synthesis. (1928). Biochem J. | DOI: 10.1042/bj0221417

Bauchart, C. et al.: Intestinal metabolism of sulfur amino acids. (2009). Research Reviews | DOI: 10.1017/S0954422409990138

Blachier, F. et al.: Sulfur-containing amino Acids and lipid metabolism. (2020). The Journal of Nutrition | DOI: 10.1093/jn/nxaa243

Borren, N. et al.: Alterations in fecal microbiomes and serum metabolomes of fatigued patients with quiescent inflammatory bowel diseases. (2021). Clin Gastroenterol Hepatol | DOI: 10.1016/j.cgh.2020.03.013

Brosnan, T. et al.: The sulfur-containing amino acids: an overview. (2006). J Nutr | DOI: 10.1093/jn/136.6.1636S

Calderón, A. et al.: Association of homocysteine, methionine, and mthfr 677c>t polymorphism with rate of cardiovascular multimorbidity development in older adults in sweden. (2020). JAMA Netw Open | DOI: 10.1001/jamanetworkopen.2020.5316

Garlick, P. et al.: Toxicity of methionine in humans. (2006). The Journal of Nutrition | DOI: 10.1093/jn/136.6.1722S

Hao, X. et al.: Immunoassay of S-adenosylmethionine and S-adenosylhomocysteine: the methylation index as a biomarker for disease and health status. (2016). BMC Res Notes | DOI: 10.1186/s13104-016-2296-8

Heinen, W. et al.: Organic sulfur compounds resulting from the interaction of iron sulfide, hydrogen sulfide and carbon dioxide in an anaerobic aqueous environment. (1996). Orig Life Evol Biosph | DOI: 10.1007/BF01809852

Hertel, J. et al.: Integrated analyses of microbiome and longitudinal metabolome data reveal microbial-host interactions on sulfur metabolism in parkinson’s disease. (2019). Cell Rep | DOI: 10.1016/j.celrep.2019.10.035

Hooshmand, B. et al.: Association of methionine to homocysteine status with brain magnetic resonance imaging measures and risk of dementia. (2019). JAMA Psychiatry | DOI: 10.1001/jamapsychiatry.2019.1694

Jin, B. et al.: DNA methylation. (2011). Genes Cancer | DOI: 10.1177/1947601910393957

Kaiser, P. et al.: Methionine dependence of cancer. (2020). Biomolecules | DOI: 10.3390/biom10040568

Lauinger, L. et al.: Sensing and signaling of methionine metabolism. (2021). Metabolites | DOI: 10.3390/metabo11020083

Livak, J. et al.: Synthesis of dl-methionine. (1945). J Am Chem Soc | DOI: 10.1021/ja01228a050

Martínez, Y. et al.: The role of methionine on metabolism, oxidative stress, and diseases. (2017). Amino Acids | DOI: 10.1007/s00726-017-2494-2

Mueller, J. et al.: Growth-determining substances in bacteriological culture media. (1921). Proceedings of the Society for Experimental Biology and Medicine | DOI: 10.3181/00379727-18-113

Navik, U. et al.: Methionine as a double-edged sword in health and disease: Current perspective and future challenges. (2021). Ageing Research Reviews | DOI: 10.1016/j.arr.2021.101500

Neubauer, C. et al.: A planetary health perspective on synthetic methionine. (2021). The Lancet Planetary Health | DOI: 10.1016/S2542-5196(21)00138-8

Nichenametla, S. et al.: Differential Effects of Sulfur Amino Acid-Restricted and Low-Calorie Diets on Gut Microbiome Profile and Bile Acid Composition in Male C57BL6/J Mice. (2021). The Journals of Gerontology: Series A | DOI: 10.1093/gerona/glaa270

Parkhitko, A. et al.: Methionine metabolism and methyltransferases in the regulation of aging and lifespan extension across species. (2019). Aging Cell | DOI: 10.1111/acel.13034

Prochazkova, M. et al.: Vegan diet Is associated with favorable effects on the metabolic performance of intestinal microbiota: A cross-sectional multi-omics study. Front. (2022). Nutr | DOI: 10.3389/fnut.2021.783302

Rajavel, E. et al.: Methionine Nutrition and Metabolism: Insights from Animal Studies to Inform Human Nutrition. (2020). The Journal of Nutrition | DOI: 10.1093/jn/nxaa155

Rose, W. et al.: Amino Acid Requirements of Man. (1976). Nutrition Reviews | DOI: 10.1111/j.1753-4887.1976.tb05679.x

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• History & Evolution
• Biosynthesis vs. dietary uptake
• Indoxyl sulfate and the microbiome
• Indoxyl sulfate and nephrology
• Indoxyl sulfate and cardiovascular disease
• Indoxyl sulfate and bone disease
• Indoxyl sulfate and neurological disease

History & Evolution

1911: discovery of indoxyl sulfate (Obermayer, F. and Popper, H., 1911) | 1936: confirmation of liver’s role in indoxyl sulfate production (Houssay, B., 1936) | 1950s: discovery of role in kidney disease (Leong, S. and Sirich, T., 2016).

In 1911, Obermayer and Popper discovered high concentrations of a metabolite, then called “indican,” in patients with kidney disease (Obermayer, F. and Popper, H., 1911). Indoxyl sulfate, as it later came to be known, was initially studied as a “putrefaction” product of intestinal microbial metabolism. In the 1950s, researchers explored whether urinary excretion of indoxyl sulfate might indicate various diseases, eventually focusing on its role in kidney disease. This was a major leap forwards in understanding uremia, which had puzzled scientists for decades (Leong, S. and Sirich, T., 2016).

As a key uremic toxin, accumulation of indoxyl sulfate affects the kidneys, cardiovascular system and bones (Colombo, G. et al., 2022). Indoxyl sulfate has been linked to cognitive impairment, oxidative stress and blood-brain barrier permeability, and is also thought to play a role in remote sensing and signaling in renal and extra-renal tissues (Lowenstein, J. and Nigam, S., 2021). Recent research has focused on IS as a potential therapeutic target in chronic kidney disease and cardiovascular disease, especially when these conditions are comorbid.

Indoxyl sulfate is a precursor of indigo, which has been used as a dye for thousands of years. This connection is illustrated vividly in “purple urine bag syndrome,” where constipation or urinary tract infections cause an accumulation of bacteria in urine collection bags, leading to the conversion of indoxyl sulfate into indigo and indirubin, and resulting in purple urine (Tan, C. et al., 2008).

Biosynthesis vs. dietary uptake

Indoxyl sulfate is produced from precursors synthesized via the tryptophan-indole pathway in the gut (Banoglu, E. et al., 2001). In the first step, L-tryptophan, an amino acid obtained from dietary protein, is metabolized by tryptophanase-expressing bacteria in the intestine to produce indole. Indole is absorbed into the bloodstream and is converted to indoxyl in the liver via hydroxylation by cytochrome enzymes including CYP2E1. Sulfotransferase enzymes in the liver complete the process by converting indoxyl into indoxyl sulfate.

Research suggests that increasing dietary tryptophan (typically found in protein-rich foods) may increase indoxyl sulfate production (Lauriola, M. et al., 2023; Leong, S. and Sirich, T., 2016). I ndividuals following high-protein diets show higher levels of indoxyl sulfate in both plasma and urine compared to those on low-protein or vegetarian diets (Patel, K. et al., 2012). Conversely, very low protein diets have been shown to reduce indoxyl sulfate levels (Marzocco, S. et al., 2013). A study of 56 hemodialysis patients found that increasing dietary fiber also reduced indoxyl sulfate levels (Sirich, T. et al., 2014).

In healthy individuals, the indoxyl sulfate concentrations range is10-130 mg/day, clearing rapidly through the kidneys (Patel, K. et al., 2012). Kidney dysfunction inhibits clearing, causing indoxyl sulfate to accumulate, which contributes to renal and cardiovascular toxicity as discussed below (Hung, S. et al., 2017). The full extent of IS’ role in healthy phenotypes remains to be discovered.

Indoxyl sulfate and the microbiome

More than 85 species of bacteria produce indole through the action of tryptophanase on tryptophan (Melander, R. et al., 2014). Tryptophanase has been found in bacteria including Lactobacillus, Bifidobacterium longum, Bacteroides fragilis, Parabacteroides distasonis, Clostridium bartlettii and E. hallii (Zhang, L. and Davies, S., 2016). Even more species respond to the presence of indole, despite lacking tryptophanase and thus not producing indole themselves.



The role of the liver in indoxyl sulfate production was known as far back as 1936, when animal studies revealed that indoxyl sulfate could be produced even in the absence of a digestive tract, as long as the liver was intact (Houssay, B., 1936). More recently, metabolomics has confirmed the involvement of the gut microbiome in indoxyl sulfate production: studies comparing indoxyl sulfate levels in conventional and germ-free rats, and in hemodialysis patients with and without colons, highlight the significant role of colon bacteria (Leong, S. and Sirich, T., 2016).

Indoxyl sulfate and nephrology

As a uremic toxin, indoxyl sulfate is strongly implicated in the progression of chronic kidney disease (CKD) (Caggiano, G. et al., 2023). As renal function declines, indoxyl sulfate concentrations rise, making it a useful biomarker of CKD. Both indoxyl sulfate and p-cresol sulfate, another protein-bound uremic toxin, are linked to symptoms of renal disease such as inflammation and fibrosis (Caggiano, G. et al., 2023). Indoxyl sulfate levels increase progressively with each stage of CKD (Clark, W. et al., 2019). Patients with hospital-acquired acute kidney injury have also been found to have higher serum levels of indoxyl sulfate (Menez, S. et al., 2019). Hemodialysis is limited in its ability to remove plasma protein bond, and dialysis patients with end-stage renal disease have been found to have indoxyl sulfate levels more than 20 times higher than those of healthy subjects (Wang, Y. et al., 2020).

Emerging evidence suggests that interventions that reduce indoxyl sulfate levels may slow CKD progression, though results remain inconclusive (Wang, Y. et al., 2020). Generally, there appears to be a good indication that administering prebiotics, probiotics and synbiotics can reduce indoxyl sulfate in patients with CKD and renal failure, although further research is needed (Lim, Y. et al., 2021).

Administration of AST-120 seems to be particularly promising for CKD (Lim, Y. et al., 2021). This treatment works by adsorbing indole and p-cresol to minimize downstream indoxyl sulfate production. AST-120 has been shown to slow the decline in glomerular filtration rate of CKD patients in some clinical studies, although randomized controlled trials have been inconclusive. More research is needed to determine whether gut microbiome modulation is a viable strategy in kidney disease.

Indoxyl sulfate and cardiovascular disease

Cardiovascular disease is higher among patients with CKD than the general population. While this relationship is not entirely understood, uremic toxins are thought to play a role, acting as vascular toxins as they accumulate in the blood (Barreto, F. et al., 2009; Zhao, Y. and Wang, Z., 2020). IS disrupts endothelial integrity and is linked to pro-oxidant, pro-inflammatory and prothrombotic processes, contributing to atherosclerosis, aortic calcification and vascular disease (Lano, G. et al., 2020). Indoxyl sulfateis also an agonist for the aryl hydrocarbon receptor (AhR) on vascular smooth muscle cells, further promoting the formation of atherosclerotic lesions and thrombosis (Leong, S. and Sirich, T., 2016). These mechanisms highlight indoxyl sulfate as a key player in the progression of cardiovascular complications in CKD.



Metabolomics studies have provided further insight into the role of indoxyl sulfate in cardiovascular health. For example, research shows that elevated indoxyl sulfate in plasma predicts cardiovascular events in patients with congestive heart failure and hypertrophic cardiomyopathy (Imazu, M. et al., 2020). Another study showed an association between plasma indoxyl sulfate levels and arterial stiffness in patients with type 2 diabetes (Katakami, N. et al., 2020). Diabetes can disrupt gut microbial composition and metabolism, which may contribute to increased indoxyl sulfate levels.

Indoxyl sulfate and bone disease

Patients with CKD often develop mineral bone disorders (CKD-MBD). Research suggests a link between this disorder and IS, though the precise mechanism is unclear (Liu, W. et al., 2018). One possibility is that the accumulation of uremic toxins weakens bone quality and quantity, potentially leading to uremic osteoporosis. Bone responses to parathyroid hormone (PTH) are also progressively lower in CKD patients, with studies suggesting a connection between PTH resistance, low bone turnover and increased indoxyl sulfate levels. Furthermore, indoxyl sulfate acts on AhR and activates signaling pathways that can induce ferroptosis and disrupt osteoblast differentiation (Chen, H. et al., 2024). Uremic toxin adsorbents may be a potential therapeutic strategy to improve bone health in patients with renal disease.

Indoxyl sulfate and neurological disease

A relatively new area of interest is the link between indoxyl sulfate and neurology, particularly its involvement in Alzheimer’s disease, Parkinson’s disease, anxiety and other neurological disorders. In CKD patients, accumulation of uremic toxins can lead to cerebrovascular lesions, increasing the risk of cognitive disorders and dementia (Sankowski, B. et al., 2020).

Using metabolomics, Sankowski et al. demonstrated that patients with Parkinson’s disease had higher levels of indoxyl sulfate and p-cresol sulfate in cerebrospinal fluid compared to controls, and higher levels of another uremic toxin, trimethylamine N-oxide (TMAO), in plasma (Sankowski, B. et al., 2020).

Another metabolomics study quantified molecular markers in whole blood of dementia patients, and found elevated levels of indoxyl sulfate compared to healthy controls, again suggesting that indoxyl sulfate may be neurotoxic (Teruya, T. et al., 2021).

Indoxyl sulfate levels also positively correlate with severity of anxiety, according to an exploratory metabolomics study (Brydges, C. et al., 2021). While modulating indole levels did not seem to affect treatment outcomes, the fact that a gut microbiome-derived metabolite influenced neural processing provides further evidence of a link between the gut microbiome and neuropsychiatric disorders.

 
Quantify the impact of indoxyl sulfate
Reference ranges of indoxyl sulfate in healthy humans
Learn more about the Metabolite of the Month

References

Using mass spectrometry in metabolic profiling

Metabolomics relies on mass spectrometry (MS), a prevalent methodology known for its high sensitivity, reproducibility, and versatility. MS is a quantitative and qualitative analytical technique that uses the mass-to-charge (m/z) ratio of previously ionized molecules to provide insights into biochemical pathways, metabolic fluxes, and the physiological status of organisms. Due to the diverse physical and chemical properties of compounds such as lipids, amino acids, and organic acids, as well as their widespread and fluctuating distribution across biological matrices, different MS-based technologies are used (Ren et al. 2018; Zhang et al. 2015).

Mass spectrometry, which consists of three main components: an ion source, a mass analyzer, and a detector. Ionization techniques such as electrospray ionization (ESI) and matrix-assisted laser desorption/ionization (MALDI) ionize molecules from solutions/solid-state, mixtures, while electron ionization (EI) is used to ionize molecules from the gas phase (after gas-chromatography (GC) separation).

Mass analyzers like time-of-flight (TOF), quadrupole, and ion trap separate ions based on their m/z ratios. Detectors such as electron multipliers and photomultiplier tubes then amplify ion signals for analysis. Liquid chromatography-mass spectrometry (LC-MS) combines liquid chromatography separation with MS detection, suitable for complex mixtures, whereas GC-MS offers high resolution for volatile compounds but requires derivatization for non-volatile ones. Each platform has advantages and limitations, guiding their selection for metabolomics studies based on the nature of the analytes and analytical requirements (Ahmad et al. 2020, Hoffmann et al. 2007).

Sample preparation

Effective metabolomic analysis calls for meticulous sample preparation to extract metabolites from the biological matrices, enhance detectability, and mitigate potential interference in subsequent mass spectrometry (MS) analysis.

If you’re unsure about the optimal matrix for your study, consider referring to ‘Which sample matrix should I use for my metabolomics study?’.

While preparation techniques play a crucial role in improving the detection and stability of metabolites, they also introduce complexities and variability into the sample preparation workflow. Sample clean-up procedures are necessary to eliminate unwanted matrix components, ameliorate matrix effects and enhance the sensitivity and selectivity of MS detection (Gong et al. 2017). Despite efforts to optimize sample preparation protocols, striking a balance between maximizing metabolite detection and minimizing sample variability and matrix effects remains challenging (Fan et al. 2012).

Extraction and analysis

Metabolomics analysis relies on various extraction techniques, each tailored to specific metabolite proprieties and sample matrices. Liquid-liquid extraction (LLE), solid-phase extraction (SPE), and protein precipitation are a few examples, each offering distinct advantages and applicability across different sample types (Nováková et al. 2024).

LLE involves partitioning analytes between two immiscible liquid phases, making it ideal for extracting non-polar and moderately polar metabolites. SPE uses solid-phase sorbents to selectively retain analytes, making it applicable to a wide range of sample types with high selectivity (Schomakers et al. 2022, Chetwynd et al. 2015). By contrast, protein precipitation removes proteins using organic solvents or acids, providing a rapid and effective approach for high-throughput analyses.

Derivatization methods enhance metabolite detection and stability, particularly those with low volatility or poor ionization efficiency, but as with preparation techniques, they can complicate the sample preparation workflow (Skov et al 2015).

Data analysis and interpretation

After data preprocessing, which involves peak picking, retention time alignment, and normalization, researchers strive to mitigate systematic variations that may be lurking in the raw data. To facilitate the identification of differences between experimental conditions, the data interpretation phase uses statistical methods, which may include the following (Lamichhane et al. 2018, Cambiaghi et al. 2017): 

• t-tests
• analysis of variance (ANOVA)
• linear mixed models
• multivariate analyses such as principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA)
• metabolite set analysis such as the hypergeometric test and Fisher Exact test
• data fusion techniques such as joint and individual explained analysis (JIVE) and deeply integrated single-cell omics (DISCO).

A few common challenges to look out for at this stage are handling missing values, managing high data dimensionality, and controlling false discovery rates. Researchers address these through various strategies such as imputation techniques, dimensionality reduction methods, and stringent statistical corrections. Quality control measures and validation strategies are also imperative for ensuring the robustness and reproducibility of results (Perez de Souza et al. 2024, Anwardeen et al. 2023). Multivariate data analysis techniques help find patterns and correlations within metabolomics datasets. Limonciel, 2023, introduces the “STORY Principle”, a systematic framework for interpreting metabolomics data and guiding researchers through each step.

Applications of MS-based metabolomics in recent research publications

Metabolomics can be applied in diagnostics, prognostics, biomarker discovery, treatment monitoring, and understanding disease mechanisms across various fields such as cardiometabolic diseases, diabetology, oncology, and toxicology.

The study of cardiometabolic diseases, including diabetes, is an especially important application of MS in metabolomics research. Huang et al. 2024, investigated the protective effect of Liu-Wei-Di-Huang-Wan (a traditional Chinese formulation composed of six commonly used Chinese herbs) in mice with type 2 diabetes mellitus. They identified 30 endogenous metabolites including 3-hydroxybutyric acid, citric acid, hexadecanoic acid, and octadecanoic acid. They found that treatment with Liu-Wei-Di-Huang-Wan improved metabolic patterns in diabetic kidney disease mice, comparable to gliquidone, by regulating metabolites associated with key metabolic pathways. In another study, Matter-Rieck et al. 2024, showcase the potential role of omentin in regulating systemic metabolism, particularly lipid metabolism, particularly lipid metabolism, and influencing insulin sensitivity, further exploring its involvement in type 2 diabetes. Omentin levels were positively associated with lipids such as phosphatidylcholines (PCs) and acylcarnitines, and negatively associated with certain amino acids.

Epidemiology and genetics offer insights into disease etiology and progression. Rosario et al. 2023, identified significant metabolic and transcriptional dysregulation associated with gastrointestinal adenocarcinomas. The findings reveal alterations in pathways such as steroid metabolism and tryptophan/kynurenine metabolism, with sex-specific differences. Investigating gut microbiota stability, Frost et al. 2021, found that instability, characterized by an increase in potential pathogens like Enterobacteriaceae and a decrease in beneficial bacteria such as Bifidobacteria, was significantly associated with metabolic disorders including diabetes mellitus and fatty liver disease.

Metabolomics is also an important investigative tool in oncology, presenting the metabolic signatures linked to cancer development and treatment response. Zhang N et al. 2024, used LC-MS and GC-MS analysis of plasma metabolites to study biomarkers for neoadjuvant therapy efficacy in HER2 + breast cancer. Significant differences were found between responders and non-responders, with 100 metabolites identified and enriched in 40 pathways. Area under the curve (AUC) values for discriminating groups exceeded 0.910, and 18 metabolites showed potential for monitoring efficacy. In another study, Decker et al. 2024, described connections between cholesterol precursors, oxysterols, and diverse factors in women diagnosed with breast cancer. Their research shows the linkage of cholesterol precursors to metabolic factors like body mass index (BMI), cardiovascular disease (CVD) and oxysterols, particularly those generated via reactive oxygen species (ROS), and key characteristics of breast cancer tumors, including obesity.

Metabolic profiling is increasingly recognized in cardiology studies. A study by Kretzschmar et al. 2024, analyzed plasma metabolites in patients with acute decompensated heart failure (ADHF) and chronic heart failure (CHF). They found distinct metabolic signatures associated with each phenotype, including potential novel biomarkers for heart failure such as 1-methyl histidine and 3-indolepropionic acid. Another interesting study by Andrews et al. 2024, highlights significant changes in lipid metabolism in mice, specifically focusing on alterations in acylcarnitines, amino acids, bile acids, ceramides, sphingomyelins, and triacylglycerols. These findings pinpoint the mechanisms underlying hepatic steatosis and atherosclerosis development in mice fed the hyperhomocysteinemic diet.

Finally, in neuroscience, Ge et al. 2024, conducted a metabolomic analysis from 48 patients with disorders of consciousness (DoC), showing distinct metabolic profiles linked to etiology, consciousness levels, and prognosis. Their findings highlight the critical role of phospholipid metabolism and identify potential biomarkers for improved diagnosis and treatment of DoC.

As mass spectrometry evolves, we’ll see more opportunities to apply the methodology within metabolomics studies. If you are considering using this technique in your own study, feel free to contact the biocrates team with any questions.

References

Andrews, S.G. et al.: Diet-Induced Severe Hyperhomocysteinemia Promotes Atherosclerosis Progression and Dysregulates the Plasma Metabolome in Apolipoprotein-E-Deficient Mice (2024) Nutrients | DOI: 10.3390/nu16030330

Anwardeen, N.R.et al.: Statistical methods and resources for biomarker discovery using metabolomics (2023) BMC Bioinformatics 24 | DOI:10.1186/s12859-023-05383-0

Cambiaghi A et al.: Analysis of metabolomic data: tools, current strategies and future challenges for omics data integration (2017) Briefings in Bioinformatics | DOI: 10.1093/bib/bbw031

Causon J.: Review of sample preparation strategies for MS-based metabolomic studies in industrial biotechnology (2016) Analytica Chimica Acta | DOI: 10.1016/j.aca.2016.07.033

Chetwynd J. et al.: Solid-Phase Extraction and Nanoflow Liquid Chromatography-Nanoelectrospray Ionization Mass Spectrometry for Improved Global Urine Metabolomics (2014) Analytical Chemistry | DOI: doi.org/10.1021/ac503769q

Decker, N.S et al.: Associations between lifestyle, health, and clinical characteristics and circulating oxysterols and cholesterol precursors in women diagnosed with breast cancer: a cross-sectional study. (2024) Sci Rep 14 | DOI: 10.1038/s41598-024-55316-x

Fan, T.WM.: Considerations of Sample Preparation for Metabolomics Investigation. (2012) The Handbook of Metabolomics. Methods in Pharmacology and Toxicology | DOI: 10.1007/978-1-61779-618-0_2

Frost F. et al.: Long-term instability of the intestinal microbiome is associated with metabolic liver disease, low microbiota diversity, diabetes mellitus and impaired exocrine pancreatic function (2021) Gut | DOI: 10.1136/gutjnl-2020-322753

Ge Q, et al.: Serum metabolism alteration behind different etiology, diagnosis, and prognosis of disorders of consciousness (2024) Chin Neurosurg J. | DOI: 10.1186/s41016-024-00365-4

Gong Z-G. et al.: The Recent Developments in Sample Preparation for Mass Spectrometry-Based Metabolomics (2017) Critical Reviews in Analytical Chemistry, 47(4), 325–331 | DOI: 10.1080/10408347.2017.1289836

Hoffmann E. et al.: Mass Spectrometry: Principles and Applications (2007) Hoboken, NJ: John Wiley & Sons. | ISBN: 978-0-470-03310-4

Huang JH. et al.: A GC-MS-Based Metabolomics Investigation of the Protective Effect of Liu-Wei-Di-Huang-Wan in Type 2 Diabetes Mellitus Mice (2020) Int J Anal Chem. Aug | DOI: 10.1155/2020/1306439

Javed A et al.: Basic Principles and Fundamental Aspects of Mass Spectrometry (2022) Mass Spectrometry in Food Analysis | DOI: 10.1201/9781003091226-2

Kretzschmar T. et al.: Metabolic Profiling Identifies 1-MetHis and 3-IPA as Potential Diagnostic Biomarkers for Patients With Acute and Chronic Heart Failure With Reduced Ejection Fraction. (2024) Circ Heart Fail | DOI: 10.1161/CIRCHEARTFAILURE.123.010813

Lamichhane S.:Chapter Fourteen – An Overview of Metabolomics Data Analysis: Current Tools and Future Perspectives (2018) Comprehensive Analytical Chemistry | DOI: 10.1016/bs.coac.2018.07.001

Limonciel A. The STORY principle A guide to the biological interpretation of metabolomics (2023) biocrates life sciences gmbh | DOI: 10.1016/bs.coac.2018.07.001

Nováková S. et al.: Comparison of Various Extraction Approaches for Optimized Preparation of Intracellular Metabolites from Human Mesenchymal Stem Cells and Fibroblasts for NMR-Based Study (2024) Metabolites | DOI: 10.3390/metabo14050268

Perez de Souza L. et al.: Computational methods for processing and interpreting mass spectrometry-based metabolomics. (2024) Essays Biochem | DOI: 10.1042/EBC20230019

Ratter-Rieck J.: Omentin associates with serum metabolite profiles indicating lower diabetes risk: KORA F4 Study (2024) BMJ Open Diabetes Research and Care | DOI: 10.1136/bmjdrc-2023-003865

Ren J-L. et al.: Advances in mass spectrometry-based metabolomics for investigation of metabolites (2018) Royal Society of Chemistry | DOI: 10.1039/C8RA01574K

Rosario D. et al.: Systematic analysis of gut microbiome reveals the role of bacterial folate and homocysteine metabolism in Parkinson’s disease (2021) Cell Reports 34 | DOI: 10.1016/j.celrep.2021.108807

Schomakers V. Et al.: Polar metabolomics in human muscle biopsies using a liquid-liquid extraction and full-scan LC-MS (2022) TAR Protocols | DOI: 10.1016/j.xpro.2022.101302

Skov K. et al.: LC–MS analysis of the plasma metabolome—A novel sample preparation strategy (2015) Journal of Chromatography B | DOI: 10.1016/j.jchromb.2014.11.033

Zhang A. et al.: Metabolomics for Biomarker Discovery: Moving to the Clinic (2015) Biomed Res Int. | DOI: 10.1155/2015/354671

Zhang N et al.: Metabolomics assisted by transcriptomics analysis to reveal metabolic characteristics and potential biomarkers associated with treatment response of neoadjuvant therapy with TCbHP regimen in HER2 + breast cancer. (2024) Breast Cancer Res. | DOI: 10.1186/s13058-024-01813

Long-term intake of proton pump inhibitors (PPIs) has been associated with elevated risk of cardiovascular events. Nevertheless, they are still widely used to treat increased gastric-acid production, even without medical supervision. Demonstrating and understanding a causal link between PPIs and cardiovascular risk could help to inform regulatory decisions around the safe use of PPIs in future.

It has been suggested that PPIs block the enzyme activity of dimethylarginine dimethylaminohydrolase (DDAH). This leads to increased levels of endothelial asymmetrical dimethylarginine (ADMA), which in turn inhibits endothelial nitric oxide synthase (eNOS) and lowers nitric oxide (NO) levels. Since NO is responsible for endothelial functions, like vasorelaxation, disruption of its regulatory circuit might cause cardiovascular events.

So far, this mechanism of action has been proved only in human cell cultures and mouse models by altered ADMA levels. Based on this, Dr. Baumeister and colleagues in Munich hypothesized an association between decreased levels of citrulline, which is the product of both DDAH and eNOS activity, and daily intake of PPIs.

The study included participants of the population-based Study of Health in Pomerania (SHIP-0 and SHIP-2). The research team analyzed the association of regular long-term intake of PPIs and serum metabolites of the NO pathway. Additionally, the effect of PPIs on flow-mediated vasodilatation (FMD) was assessed.

PPI users showed 0.99% lower FMD than non-users. Similarly, PPI intake resulted in 3.03 µmol/L reduced citrulline levels. These results are quite substantial considering the population mean FMD of 5.59% and citrulline of 31.46 µmol/L. The analysis also showed that to refute these observed associations, unmeasured confounders would have to be related to PPI intake by a risk ratio of 1.9 above those measured. No association was found between PPI intake and the other metabolites of the NO pathway, ADMA, symmetric dimethylarginine (SDMA), and arginine, which is consistent with previous evidence.

Directly assessing the relation between PPI intake and NO levels is challenging because NO is reactive and complex to measure. ADMA does not appear to be a practical indicator of DDAH activity since its plasma levels do not reliably reflect its endothelial levels. Since citrulline is the product of ADMA degradation by DDAH and eNOS activity, it might show the biggest response to PPI intake. Finally, this study confirmed that citrulline could be a suitable marker for disruptions of the NO pathway leading to decreasing NO levels and increasing risk for cardiovascular events.

Explore our recent articles to find out more about how metabolomics in population-based cohort studies can help reveal mechanisms of actions.

Nolde M, Bahls M, Friedrich N, Dörr M, Dreischulte T, Felix SB et al.: Association of proton pump inhibitor use with endothelial function and metabolites of the nitric oxide pathway: A cross-sectional study. (2021) Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy | https://doi.org/10.1002/phar.2504

Coronary heart disease (CHD) is a complex and heterogenic disease that is increasingly becoming a public health burden worldwide. Current risk assessment is based on classic risk factors (BMI, systolic blood pressure, diabetes, total cholesterol) and two established clinical biomarkers, high-sensitivity C-reactive protein (hsCRP) and high-sensitivity troponin I (hsTnI). An improved risk stratification process would advance preventive actions.

The multinational Biomarker for Cardiovascular Risk Assessment in Europe (BiomarCaRE) consortium aimed to evaluate the association between circulating metabolites and CHD in a large, prospective population-based cohort and to assess the capability of metabolomics for CHD risk stratification. For the present study, baseline serum samples from more than 12,000 individuals with over 2,000 incident CHD events over a median follow-up time of 9.2 years were analyzed.

Of the 141 metabolites quantified, five phosphatidylcholines (PC ae C40:6, PC aa C40:6, PC ae C38:6, PC aa C38:6, PC aa C38:5) showed significant inverse association with the risk of incident CHD after correction for multiple testing: increasing levels of phosphatidylcholines were protective against incident CHD, which is in accordance with previous studies. These circulating metabolites showed a comparable discrimination to classic risk factors and established clinical biomarkers.

These findings not only contribute to a better understanding of the pathophysiology of CHD, but also demonstrate the potential of phosphatidylcholines in the risk assessment of coronary heart disease, underlining the value of metabolomics for biomarker discovery.

If you are interested in carrying out a large, multi-center study, our standardized metabolomics kits are the ideal tool to accomplish such a task. Please visit the products and services webpages for additional information, or contact us for support.

Cavus E, Karakas M, Ojeda FM, Kontto J, Veronesi G, Ferrario MM et al. Association of Circulating Metabolites With Risk of Coronary Heart Disease in a European Population: Results From the Biomarkers for Cardiovascular Risk Assessment in Europe (BiomarCaRE) Consortium. (2019) JAMA Cardiol | https://doi.org/10.1001/jamacardio.2019.4130

The composition of our individual gut microbiota contributes to the digestion and absorption of nutrients from food.  Similarly, orally administered medications experience the same digestive processes that may lead to an enhancement or a reduction in drug efficacy. In turn, the medications that we ingest can dramatically alter our gut microbiota composition, as is well-known with antibiotics.

In this publication by Zimmermann et al., metabolomics was used in conjunction with host gene expression and microbiota species profiling with 16S rRNA qPCR. These methods were used to investigate the cross-talk between gut microbiota and the cholesterol-lowering drug atorvastatin in a mouse model. The most striking result was a reduction in atorsvastatin efficacy when the gut microbiota was impaired. In addition, metabolomics showed that plasma levels of sphingolipids were affected by the drug, but only in mice with normal gut microbiota.

Together, these results demonstrate the importance of the gut microbiota to enhance or hinder the efficacy of drugs, in particular when metabolites such as cholesterol and lipids are the target of treatment. Such findings could help anticipate inter-individual differences in patients that arise, not only from their own genetic background, but from their differences in gut microbiota composition.

To find out if such lipid panels could be applied to your research, check out our products or contact us. For more information on the application of metabolomics to microbiota research, please check our microbiome application page.

Zimmermann F, Roessler J, Schmidt D, Jasina A, Schumann P, Gast M, Poller W, Leistner D, Giral H, Kränkel N, Kratzer A, Schuchardt S, Heimesaat MM, Landmesser U, Haghikia A: Impact of the gut microbiota on atorvastatin mediated effects on blood lipids. (2020) J Clin Med. | https://doi.org/10.3390/jcm9051596

The Fontan procedure is a surgical technique established in the 1970’s indicated for children who were born with a single functional ventricle, which can lead to a series of cardiac conditions. The procedure consists in diverting parts of the venous blood arriving at the heart towards the pulmonary arteries in order to reduce ventricular workload. Following this surgery, many patients are able to enjoy a normal quality of life which includes normal development and the ability to tolerate exercise.

In this study by Michel et al., young adults who underwent the Fontan procedure as children (univentricular group) were compared to healthy controls (biventricular group) to investigate the long-term effects of the procedure on their metabolome.

While cardiac function was comparable between the two groups, lipidomics performed on serum samples demonstrated that the univentricular patients had higher acylcarnitines levels than the biventricular controls. In addition, the levels of phosphatidylcholines and sphingomyelins were reduced in univentricular patients compared to controls, while lyso-phosphatidylcholines levels seemed to be unaffected.

The authors concluded on the potential of targeted metabolomics and lipidomics to identify biomarker patterns to provide new diagnostic strategies with minimally intensive follow-up procedures for Cardiac patients. This could extend to conditions such as heart failure in biventricular patients, certain types of inflammation, and alterations of the lymphatic or endothelial systems.

To find out more about the measurement of large panels of lipids with targeted metabolomics and lipidomics, see Biocrates’ metabolic profiling kits: MxP® Quant 500, Quant HR Xpress™, AbsoluteIDQ® p180.

Miriam Michel, Karl-Otto Dubowy, Manuela Zlamy, Daniela Karall, Mark Gordian Adam, Andreas Entenmann, Markus Andreas Keller, Jakob Koch, Irena Odri Komazec, Ralf Geiger, Christina Salvador, Christian Niederwanger, Udo Müller, Sabine Scholl-Bürgi and Kai Thorsten Laser: Targeted metabolomic analysis of serum phospholipid and acylcarnitine in the adult Fontan patient with a dominant left ventricle. Therapeutic Advances in Chronic Diseases. 2020. https://doi.org/10.1177/2040622320916031

Lipoxygenases are enzymes involved in inflammatory signaling and they do play a role in the mediation of acute and chronic heart failure (HF). What happens if a key lipoxygenase (12/15LOX) is knocked out? On the top level, survival after myocardial infarction was enhanced (89% 56 d survival in knock-out versus 58% in wild-type mice). 

Changes are also reported at the molecular level:, which was studied using metabolomics: Compared to control conditions (pre-myocardial infarct), lack of 12/15LOX leads to changes in amino acid pathways with accumulation of citrulline, arginine, lysine, and spermidine in plasma. The lysine degradation product 2-aminoadipic acid is heavily decreased in concentration which already hints at a protective effect according to the authors. Acute heart failure is characterized by a heavily reorganized metabolic phenotype especially in amino acids and lipids in both 12/15LOX knock-out mice and controls. What makes this study worth reading is the detailed dissection of the metabolic pathways and the time course analysis of acute to chronic HF.

If you are inerested in other applications of metabolomics in cardiology, take a look in the technology section or contact us!

Ganesh V Halade, Vasundhara Kain, Bochra Tourki, Jeevan K Jadapalli, Lipoxygenase drives lipidomic and metabolic reprogramming in ischemic heart failure, Metabolism Clinical and Experimental, 2019 .

https//:doi: 10.1016/j.metabol.2019.04.011