History & Evolution

2000 BC: cinnamon is used to embalm mummies (Abdel-Maksoud et al. 2011) | 1834: isolation of cinnamaldehyde from cinnamon oil (Dumas et al. 1834) | 1854: first synthesis from unrelated compounds (Richmond 1947)

As the name suggests, cinnamaldehyde is a compound found in cinnamon, contributing to cinnamon’s flavor, aroma and potential health benefits. Cinnamaldehyde has antimicrobial, antioxidant and anti-inflammatory properties, and is also studied for its potential effects on cardiovascular and metabolic diseases.

Cinnamon is prepared from the inner bark of Asian evergreen trees, with Sri Lanka its primary producer. Tree bark is typically removed from the branches of mature trees and left to dry in the sun without additional treatment (Shang et al. 2021). Dried bark curls into cinnamon sticks and may be ground into powdered form. Different species of cinnamon tree contain different amounts of cinnamaldehyde and other metabolites. Cinnamomum verum (native to Sri Lanka and later introduced in other countries of the Indian subcontinent) is considered the original cinnamon tree for international trade.

Metabolomics has been used to find signatures of the different cinnamon tree species in cinnamon samples (Avula et al. 2015; Wang et al. 2020; Zhang et al. 2022). Cinnamon from C. verum is typically high in cinnamaldehyde and low in coumarin (Wang et al. 2020). Metabolic profiling can differentiate ‘true’ cinnamon from C. verum from other plants used to produce cinnamon such as C. cassia, simply by measuring the proportion of cinnamaldehyde, coumarin and other metabolites in the samples (Zhang et al. 2022). C. cassia and other species growing in China are prevalent in traditional Chinese medicine.

Cinnamaldehyde is also synthesized by a broad range of microorganisms that exploit its antibacterial and antifungal properties (Gan et al. 2020). In addition, bacteria (e.g., E. coli) can be engineered to synthesize cinnamaldehyde from phenylalanine (Bang et al. 2016).

Biosynthesis vs. dietary uptake

In plants, cinnamaldehyde is synthesized via the Shikimate pathway, a pathway that also yields aromatic amino acids and folates (Morrow 2013). Starting with phosphoenolpyruvate (PEP), this pathway generates aromatic amino acids that are precursors to cinnamaldehyde. Interestingly, bacteria and other microorganisms can also synthesize cinnamaldehyde through this pathway, for example, from phenylalanine.

In C. verum, phenylalanine ammonia-lyase catalyzes the conversion of phenylalanine into trans-cinnamic acid, a compound with antioxidant and anti-inflammatory properties also responsible for some of cinnamon’s biological activities.

It has also been suggested that cinnamic acid plays a role in improving insulin sensitivity (Stevens et al. 2022) and in protection of the cardiovascular system, making it potentially beneficial for people with diabetes and early-stage metabolic disease. Kinetic analysis in rat blood showed that cinnamaldehyde was quickly converted to cinnamic acid via a protein-driven mechanism (Yuan J. et al. 1992).

Cinnamaldehyde and infectious diseases

Cinnamaldehyde and its derivatives have attracted attention for their antimicrobial potential, for example in the development of tuberculosis treatment (Nordqvist et al. 2011). Metabolomics has shown that exposing cultures of Mycobacterium tuberculosis (the strain responsible for the disease) to cinnamon essential oil alters small molecules, including biotin levels and tetrahydrofolate biosynthesis, which is essential for optimal one-carbon metabolism (Sieniawska et al. 2020).

The same study revealed a significant effect on many lipid classes, with most changes seen in phospholipids (primarily phosphatidylethanolamines and phosphatidylglycerols) and glycerophospholipids (primarily triglycerides and monoglycerides).

Cinnamaldehyde is not the only antibacterial compound in cinnamon; other metabolites such as eugenol may contribute to the antimicrobial effects of cinnamon essential oil and extracts (Vasconcelos et al. 2018).

Cinnamaldehyde and metabolic disease

Cinnamon has been long considered a beneficial food for patients with type 2 diabetes. There is mounting evidence that cinnamon and its metabolites may improve glycemic and lipidemic indicators (Silva et al. 2022). For instance, a 2007 study in male rats with streptozotocin-induced diabetes showed that a 45-day treatment with 20 mg/kg bw of cinnamaldehyde reduced plasma glucose and glycosylated hemoglobin levels, serum total cholesterol and triglyceride levels while increasing insulin, high-density lipoprotein (HDL) cholesterol and liver glycogen levels (Subash Babu et al. 2007).

Randomized controlled clinical trials have investigated the effects of cinnamon and shown that 1 to 3 g of cinnamon per day could reduce glycosylated hemoglobin levels (Silva et al. 2022). Clinical trials also confirmed its anti-inflammatory effect in humans (Davari et al. 2020).

Of note, while C. verum is the plant of choice for culinary cinnamon, many studies focus on C. cassia, C. zeylanicum and others. Whether this is due to easier access, a higher prevalence of those species in traditional Chinese medicine, or a higher therapeutic potential in those species is unclear.

Nevertheless, there appear to be large differences in the effects and required doses depending on the tree of origin for the cinnamon used in these trials. This may explain why a recent meta-analysis of epidemiological studies found no associations between cinnamon intake and levels of low-density lipoprotein (LDL) cholesterol, HDL cholesterol or glycosylated hemoglobin (Krittanawong et al. 2022). Thus, more work is needed to fully understand this spice.

Cinnamaldehyde and chronic diseases

Finally, research into the health benefits of cinnamon points to potential to address multiple complex chronic diseases, even beyond its anti-inflammatory effect. For example, cinnamaldehyde may have applications in cancer, owing to its capacity to induce apoptosis in cancer cells (Sadeghi et al. 2019). Cinnamon’s anti-inflammatory properties and unique flavor have been hypothesized to help breast cancer survivors better adhere to a Mediterranean diet (Zuniga et al. 2019).

Cinnamon’s effects on the immune system have also made it a spice of interest in the field of autoimmune diseases (Pahan et al. 2020). There are also ongoing trials focusing on the effects of cinnamon in various chronic diseases, from Alzheimer’s disease to autoimmune diseases. These encouraging findings suggest that cinnamon and its key metabolites could play an important role in redressing the metabolic imbalance at the origin of many complex chronic diseases (Hariri et al. 2016).

References

Abdel-Maksoud et al. 2011: A review on the materials used during the mummification processes in ancient Egypt | A review on the materials used during mummification processes in Ancient Egypt

Avula et al. 2015: Authentication of true cinnamon (Cinnamon verum) utilising direct analysis in real time (DART)-QToF-MS. Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment | https://doi.org/10.1080/19440049.2014.981763

Bang et al. 2016: Metabolic engineering of Escherichia coli for the production of cinnamaldehyde. Microbial Cell Factories | https://doi.org/10.1186/s12934-016-0415-9

Davari et al. 2020: Effects of cinnamon supplementation on expression of systemic inflammation factors, NF-kB and Sirtuin-1 (SIRT1) in type 2 diabetes: a randomized, double blind, and controlled clinical trial. Nutrition Journal | https://doi.org/10.1186/s12937-019-0518-3

Dumas & Péligot 1834: Recherches de chimie organique – Sur l’huile de cannelle, l’acide hippurique et l’acide sébacique. Annales de chimie et de physique | https://gallica.bnf.fr/ark:/12148/bpt6k6568974z/f311.image.r

Gan et al. 2020: Synthesis and Antifungal Activities of Cinnamaldehyde Derivatives against Penicillium digitatum Causing Citrus Green Mold. Journal of Food Quality | https://doi.org/10.1155/2020/8898692

Hariri & Ghiasvand 2016: Cinnamon and Chronic Diseases. Advances in experimental medicine and biology | https://doi.org/10.1007/978-3-319-41342-6_1

Krittanawong et al. 2022: Association Between Cinnamon Consumption and Risk of Cardiovascular Health: A Systematic Review and Meta-Analysis. The American journal of medicine | https://doi.org/10.1016/j.amjmed.2021.07.019

Nordqvist et al. 2011: Synthesis of functionalized cinnamaldehyde derivatives by an oxidative Heck reaction and their use as starting materials for preparation of Mycobacterium tuberculosis 1-deoxy-D-xylulose-5-phosphate reductoisomerase inhibitors. The Journal of Organic Chemistry | https://doi.org/10.1021/jo201715x

Pahan & Prahan 2020 : Can cinnamon spice down autoimmune diseases? Journal of clinical & experimental immunology | https://doi.org/10.33140/jcei.05.06.01

Richmond 1947: Preparation of cinnamaldehyde (1947) Patent US2529186A | https://patents.google.com/patent/US2529186A/en

Sadeghi et al. 2019: Anti-cancer effects of cinnamon: Insights into its apoptosis effects. European journal of medicinal chemistry | https://doi.org/10.1016/j.ejmech.2019.05.067

Shang et al. 2021: Beneficial effects of cinnamon and its extracts in the management of cardiovascular diseases and diabetes. Food & Function | https://doi.org/10.1039/D1FO01935J

Sieniawska et al. 2020: Untargetted Metabolomic Exploration of the Mycobacterium tuberculosis Stress Response to Cinnamon Essential Oil. Biomolecules | https://doi.org/10.3390/biom10030357

Silva et al. 2022: Cinnamon as a Complementary Therapeutic Approach for Dysglycemia and Dyslipidemia Control in Type 2 Diabetes Mellitus and Its Molecular Mechanism of Action: A Review. Nutrients | https://doi.org/ 10.3390/nu14132773

Stevens et al. 2022: A Review and Exploration of Mechanisms Using In Silico Molecular Docking Simulations. Molecules | https://doi.org/10.3390/molecules27030853

Subash Babu et al. 2007: Cinnamaldehyde–a potential antidiabetic agent. Phytomedicine | https://doi.org/10.1016/j.phymed.2006.11.005

Morrow 2013: The Shikimate Pathway: Biosynthesis of phenolic products from shikimic acid. | https://doi.org/10.1093/oso/9780199860531.003.0009

Tohge et al. 2013: Shikimate and phenylalanine biosynthesis in the green lineage. Frontiers in Plant Science | https://doi.org/10.3389/fpls.2013.00062

Vasconcelos et al. 2018: Antibacterial mechanisms of cinnamon and its constituents: A review. Microbial pathogenesis | https://doi.org/10.1016/j.micpath.2018.04.036

Wang et al. 2020: Metabolomic profiling and comparison of major cinnamon species using UHPLC-HRMS. Analytical and bioanalytical chemistry | https://doi.org/10.1007/s00216-020-02904-1

Yuan et al. 1992: Quantitation of cinnamaldehyde and cinnamic acid in blood by HPLC. Journal of analytical toxicology | https://doi.org/10.1093/jat/16.6.359

Zhang et al. 2022: Development of a Metabolite Ratio Rule-Based Method for Automated Metabolite Profiling and Species Differentiation of Four Major Cinnamon Species. Journal of agricultural and food chemistry | https://doi.org/10.1021/acs.jafc.2c01245

Zuniga et al. 2019: Dietary intervention among breast cancer survivors increased adherence to a Mediterranean-style, anti-inflammatory dietary pattern: the Rx for Better Breast Health Randomized Controlled Trial (2019) Breast cancer research and treatment | https://doi.org/10.1007/s10549-018-4982-9

• History & Evolution
• Biosynthesis vs. dietary uptake
• Structure and nomenclature
• Phosphatidylcholines and membrane properties
• Phosphatidylcholines and inflammation
• Phosphatidylcholines, surfactant and the lungs
• Sex differences in phosphatidylcholine levels
• Phosphatidylcholines and central nervous system infections

History and evolution

1846: discovery of lecithin (Vance 2014) | 1954: identification of pathways for biosynthesis (Zeisel 2012) | 1990s: phosphatidylcholine shown to be essential for human health

Phosphatidylcholines (PCs) are one of the most abundant glycerophospholipids found in animal and plant eukaryotic cell membranes (van der Veen et al. 2017). PCs were first identified as a component of egg yolk in 1846 by Theodore Gobley, who named them “lecithin”, after the Greek word for egg yolk (lekithos) (Vance 2014). In 1862, Adolph Strecker found that heating lecithin from bile produced a substance he called “choline”.

Lecithin was later identified as phosphatidylcholine and the two terms were often used interchangeably, though PCs are part of the broader lecithin family (Zeisel 2012).

Pathways for the biosynthesis of PCs were discovered in the 1950s. The existence of various PC molecules with fatty acyl chains of varying chain lengths and saturation statuses gave rise to a more detailed nomenclature for this family of lipids detailed below.

PCs are present in multiple tissues, including brain and nerve, and can also act as an emulsifier in the lungs. They are often referred to as membrane lipids, but animal and human studies have revealed roles for PCs in energy metabolism, lipoprotein transport and cell signaling (van der Veen et al. 2017).

Biosynthesis vs. dietary uptake

PCs are found in foods high in lecithin, such as egg yolks, soybeans, sunflower seeds, meat and fish. Dietary PC is metabolized in the small intestine by pancreatic and mucosal enzymes to generate 1-lyso-phosphatidylcholine, which then enters various pathways for lipid and glucose metabolism and fat storage (Nilsson et al. 2019).

Dietary PC is the main source of choline, an essential nutrient that supports lipid and amino acid metabolism and contributes to cell membrane structure (Tang et al. 2013). Choline also acts as precursor for the neurotransmitter acetylcholine, which supports brain and muscle function.

PCs are synthesized primarily in the endoplasmic reticulum (ER) through two main pathways: the cytidine 5-diphosphocoline (CDP-choline) or Kennedy pathway, and the phosphatidylethanolamine methyl transferase (PEMT) pathway.

In the CDP-choline pathway, cytidine triphosphate activates phosphocholine and converts it to diacylglyceride (DG) to form PC (Moessinger et al. 2014). Cytoplasmic cytidylyltransferase and cholinephosphotransferase-1 mediate the reaction (Henneberry et al. 2002). This pathway accounts for around 70% of PC synthesis in the liver (Vance 2014).

The remaining 30% of PC synthesis occurs through the PEMT pathway (Moessinger et al. 2014). Here, S-adenosyl methionine (SAM) methylates phosphatidylethanolamine (PE) to form PC. This pathway is also the main route to PC synthesis in bacteria.

Intestinal PCs, whether derived from dietary uptake, bile or de novo synthesis, are hydrolyzed by phospholipase A2 (PLA2) to lysoPCs and fatty acids, then absorbed by enterocytes (Kennelly et al. 2018). This is known as the Lands cycle. The reverse reaction is also a route to PC synthesis.

Structure and nomenclature

Phospholipids include two main categories: glycerophospholipids and sphingolipids. PCs are a member of the former, along with PEs, phosphatidylserines (PSs), phosphatidylinositols (PIs), and cardiolipins (Li et al. 2015).

PCs have a phosphocholine head group linked to two fatty acyl side chains by a glycerol backbone. They can be sub-classified into diacyls, alkylacyls or alkenylacyls, depending on the type of bond at the sn-1 position (first carbon of the glycerol backbone).

Scientific nomenclature for PCs varies. At biocrates, we use a short nomenclature where the name of the PC denotes the type of bond linking the fatty acyl groups to the glycerol backbone, and the number of carbon atoms and double bonds. For example:

• PC aa C32:1 describes a PC with two fatty acyl chains adding up to 32 carbon atoms and one double bond. The ‘aa’ indicates that both moieties at the sn-1 and sn-2 positions are fatty acyl residues bound to the glycerol backbone by ester bonds.
• PC ae C44:6 describes a PC with two fatty acyl chains adding up to 44 carbon atoms and six double bonds. The ‘ae’ denotes that one of the moieties, either in the sn-1 or sn-2 position, is a fatty alcohol residue bound by an ether bond.
• LysoPC a C18:0 describes a lysoPC with a fatty acyl chain with 18 carbon atoms and no double bonds. The ‘a’ indicates that the moiety usually at the sn-1 position is a fatty acid residue bound to the glycerol backbone by an ester bond.

Lysophosphatidylcholines are produced when one of the fatty acyl groups in PCs is removed by phospholipase.

Phosphatidylcholines and membrane properties

PCs are the most abundant phospholipid in cell membranes, accounting for 40–50% of total cellular phospholipids (van der Veen et al. 2017). Mammalian cells contain large and diverse populations of PCs, derived from the remodeling action of phospholipases and lysophospholipid acyltransferases.

PCs are commonly found in the outer layer of the cell membrane, while other glycerophospholipids (PE, PS, PI) are predominant in the inner membrane leaflet. Intracellular transport is not yet fully understood. PC-specific transfer protein and non-specific lipid transfer proteins may play a role, but animal studies suggest that neither accounts for the entirety of PC movement (Vance 2014).

PCs contribute to cell function and structure. They are central to membrane-mediated cell signaling, protein synthesis, cholesterol homeostasis and triglycerides storage and secretion (Lagace et al. 2013).

Phosphatidylcholines and inflammation

PCs are involved in the early stages of the inflammatory cascade. When PCs with a C20:4 fatty acyl group are cleaved to form lysoPCs, this releases arachidonic acid (FA (20:4)). This specific fatty acid is the precursor for a family of active lipids called eicosanoids. These include prostaglandins and leukotrienes that serve as signaling molecules during inflammation .

LysoPCs also have a role in modulating the immune response through the activation and transportation of immune cells. These functions have been associated with inflammatory diseases such as diabetes, obesity, atherosclerosis, cancer and rheumatoid arthritis (Dei Cas et al. 2020).

A high ratio of lysoPCs to PCs may indicate increased enzyme activity associated with progression of inflammatory conditions, such as osteoarthritis (Zhai et al. 2019). The amount of PC affects the size and dynamics of lipid droplets in immune cells and this variation in lipid activity can trigger stress responses. The location of PC synthesis in the ER may affect the etiology of diseases that arise from ER dysfunction (Lagace et al. 2013).

Changes in phospholipid ratios can also affect energy production and have been associated with metabolic disorders such as obesity, diabetes and atherosclerosis (van der Veen J, et al., 2017). For example, PCs are central to the established association between a PC metabolite through microbial metabolism, trimethylamine-N-oxide (TMAO), and increased risk of cardiovascular disease (Tang et al. 2013).

Phosphatidylcholines, surfactant and the lungs

PCs comprise around 80% of surfactant lipids in the lungs (Agassandian et al. 2013). The majority are in disaturated form, as dipalmitoylphosphatidylcholine (DPPC). Saturated PCs are essential components of pulmonary surfactants due to their ability to lower the surface tension on alveolar structures in the lungs, which can inhibit lung expansion and cause pulmonary edema.

This makes PCs an interesting subject for the study of respiratory disease. Surfactant proteins have been well studied as a treatment for respiratory distress syndrome in neonates (Speer et al. 2013). Efficacy of surfactant treatment in adult respiratory conditions is less well established, but studies point to a possible role in modulating the immune response in pulmonary disease (Wang et al. 2021).

Lipidomic analyses have shown links between circulating lipids, including PCs, and the severity of COVID-19 (Pimentel et al. 2021). Individuals with metabolic comorbidities have been reported to be at greater risk of more severe COVID-19. Therefore, PCs may be relevant both through their role in the immune cascade and for their surfactant properties.

Sex differences in phosphatidylcholine levels

Sex-based differences in the human blood metabolome are reasonably well established. Several metabolomic investigations have shown that women have higher levels of PCs than men (Barupal et al. 2019). A 2011 study of more than 3000 participants in the Cooperative Health Research in the Augsburg Region (KORA) cohort found sex differences for up to 78% of metabolites, including PCs (Mittelstrass et al. 2011). Concentrations of PCs were found to be significantly higher in females than in males, while the reverse was true of lysoPCs.

A 2017 study found similar results: women tended to have higher levels of PCs, while men were found to have higher concentrations of lysoPCs (Trabado et al. 2017). In the same study, older subjects were found to have higher plasma levels of PCs than younger subjects.

These findings suggest that PCs concentrations may contribute to sex differences in susceptibility for many chronic diseases.

Phosphatidylcholines and central nervous system infections

PC levels may be a useful mechanism for distinguishing bacterial and viral central nervous system (CNS) infections. A 2021 study used targeted metabolomics to develop lipid profiles for bacterial meningitis, viral meningitis or encephalitis, and noninflamed controls (Al-Mekhlafi et al. 2021). PCs were found to be significantly elevated in the cerebrospinal fluid (CSF) of patients with bacterial meningitis, compared to both viral infection and controls. Ten robust biomarkers were identified, with four of the top five PCs showing better results than standard CSF parameters.

In bacterial meningitis, changes in PCs were more strongly correlated with local CNS disease than systemic inflammation, which suggests dysfunction of the blood-CSF barrier leading to cell death.

Learn more about the roles of PCs and other phospholipids in complex chronic diseases such as cancer, Alzheimer’s disease, depression, inflammatory bowel disease, multiple sclerosis and diabetes in our whitepaper “Complex chronic diseases have a common origin”.

References

Agassandian M. et al.: Surfactant phospholipid metabolism. (2013) Biochimica et Biophysica Acta | https://doi.org//10.1016/j.bbalip.2012.09.010

Al-Mekhlafi A. et al.: Elevated Free Phosphatidylcholine Levels in Cerebrospinal Fluid Distinguish Bacterial from Viral CNS Infections.(2021) Cells | https://doi.org/10.3390/cells10051115

Barupal D. et al.: The circulating lipidome is largely defined by sex descriptors in the GOLDN, GeneBank and the ADNI studies. (2019) bioRxiv | https://doi.org/10.1101/731448

Dei Cas M. et al.: Functional Lipids in Autoimmune Inflammatory Diseases.(2020) International Journal of Molecular Sciences | https://doi.org/10.3390/ijms21093074

Henneberry A. et al.: The Major Sites of Cellular Phospholipid Synthesis and Molecular Determinants of Fatty Acid and Lipid Head Group Specificity. (2002) Molecular Biology of the Cell | https://doi.org/10.1091/mbc.01-11-0540

Kennelly J. et al.: Intestinal de novo phosphatidylcholine synthesis is required for dietary lipid absorption and metabolic homeostasis. (2018) Journal of Lipid Research | https://doi.org/10.1194/jlr.M087056

Lagace T. et al.: The role of phospholipids in the biological activity and structure of the endoplasmic reticulum. (2013) Biochimica et Biophysica Acta | https://doi.org/10.1016/j.bbamcr.2013.05.018

Li J. et al.: A review on phospholipids and their main applications in drug delivery systems. (2015) Asian Journal of Pharmaceutical Sciences | https://doi.org//10.1016/j.ajps.2014.09.004

Mittelstrass K.et al.: Discovery of Sexual Dimorphisms in Metabolic and Genetic Biomarkers. (2011) PLoS Genetics | https://doi.org/10.1371/journal.pgen.1002215

Moessinger C. et al.: Two different pathways of phosphatidylcholine synthesis, the Kennedy Pathway and the Lands Cycle, differentially regulate cellular triacylglycerol storage. (2014) BMC Cell Biology | https://doi.org/10.1186/s12860-014-0043-3

Nilsson, Å. et al.: Pancreatic and mucosal enzymes in choline phospholipid digestion. (2019) American Journal of Physiology | https://doi.org/doi.org/10.1152/ajpgi.00320.2018

Pimentel L. et al.: Cholesterol, inflammation, and phospholipids: COVID-19 share traits with cardiovascular disease. (2021) Biochimica et Biophysica Acta | https://doi.org/10.1016/j.bbalip.2020.158839

Speer C. et al.: Surfactant therapy: past, present and future. (2013) Early Human Development | https://doi.org/10.1016/S0378-3782(13)70008-2

Tang W. et al.: Intestinal Microbial Metabolism of Phosphatidylcholine and Cardiovascular Risk. (2013) The New England Journal of Medicine | https://doi.org/10.1056/NEJMoa1109400

Trabado S. et al.: The human plasma-metabolome: Reference values in 800 French healthy volunteers; impact of cholesterol, gender and age. (2017) PLoS ONE | https://doi.org/10.1371/journal.pone.0173615

van der Veen J. et al.: The critical role of phosphatidylcholine and phosphatidylethanolamine metabolism in health and disease. (2017) Biochimica et Biophysica Acta | https://doi.org/10.1016/j.bbamem.2017.04.006

Vance, D.: Phospholipid methylation in mammals: from biochemistry to physiological function. (2014) Biochimica et Biophysica Acta | https://doi.org/10.1016/j.bbamem.2013.10.018

Wang S. et al.: The Role of Pulmonary Surfactants in the Treatment of Acute Respiratory Distress Syndrome in COVID-19. (2021) Frontiers in Pharmacology | https://doi.org/10.3389/fphar.2021.698905

Zeisel, S.: A brief history of choline. (2012) Annals of Nutrition and Metabolism | https://doi.org/10.1159/000343120

Zhai G. et al.: Serum lysophosphatidylcholines to phosphatidylcholines ratio is associated with symptomatic responders to symptomatic drugs in knee osteoarthritis patients. (2019) Arthritis Research & Therapy | https://doi.org/10.1186/s13075-019-2006-8

Central nervous system (CNS) infections, especially bacterial meningitis, remain a severe health issue with high morbidity and mortality. Since an early and differential diagnosis of the infection is crucial for fast and effective treatment, biomarkers for rapid assessment and identification of different CNS infections would be a huge advantage.

This was the ambitious goal of a recent study led by PD Dr. Frank Pessler from the Helmholtz Centre for Infection Research in Hannover, Germany. The group chose cerebrospinal fluid (CSF) as sample matrix, and included 132 patients with bacterial meningitis, viral meningitis or encephalitis, and noninflamed controls. Since lipids have previously been shown to represent promising biomarkers for CNS disorders, lipid profiles in CSF were obtained through targeted metabolomics.

The analyses revealed that in patients with bacterial meningitis, CSF levels of 54 phosphatidylcholines (PCs) were significantly elevated compared to both viral infection and controls. Following internal cross-validation, 10 PCs were determined as the most robust markers. Indeed, 4 of the top 5 PCs showed a better overall discriminative performance compared to standard CSF parameters. Although promising, the results have not yet been validated in an independent data set.

Since PCs are the main building blocks of lipid bilayers in biological systems, the elevated levels of free PCs in CSF can be explained by disruptions of neuronal membranes resulting from CNS infection. Although an increase in released PCs is not unique to bacterial infections, the levels measured here are considerably higher than in viral CNS infections. This most likely reflects more pronounced cell damage. In addition, there was very little overlap between the top phosphatidylcholine biomarkers observed between bacterial and viral meningitis.

Interestingly, the changes in PCs due to bacterial meningitis correlated more strongly with markers for local CNS disease than systemic inflammation. These markers reflected dysfunction of the blood-CSF barrier and cell death. In serum, PC concentrations did not change significantly. Together, this indicates that increased PC levels do not reflect systemic inflammation, but result from local disease activity. PC levels in CSF could also distinguish bacterial meningitis caused by classical pathogens from that caused by atypical pathogens. In patients with a classical infection, concentrations were significantly higher, which is, again, likely due to a more severe cell damage.

Overall, the authors of this study have identified free PCs in CSF as highly promising biomarker candidates for an improved and more differential diagnosis of bacterial meningitis. It would be interesting to see if and how the top markers could be combined in a marker signature, instead of considered as single markers.

Please visit our focus pages on neurology and infectious diseases, if you are interested in more applications from these fields.

Al-Mekhlafi A, Sühs K-W, Schuchardt S, Kuhn M, Müller-Vahl K, Trebst C et al.: Elevated Free Phosphatidylcholine Levels in Cerebrospinal Fluid Distinguish Bacterial from Viral CNS Infections. (2021) | https://doi.org/10.3390/cells10051115

Up to three quarters of all malaria cases are chronic, which presents a major hurdle for combatting the disease. These chronic cases are often asymptomatic, but can have long term negative health effects for the infected, while serving as source for mosquito-borne transmission. While malaria worldwide still presents a major health concern, the biological mechanisms behind chronic malaria are not well understood.

In a new study by Cordy et al, metabolomics and transcriptomics techniques were used to observe alterations in metabolism between chronic and acute cases of malaria. The goal was to identify metabolites and parasite transcriptional features that could help distinguish the two cases to better understand the biology behind disease progression. To determine the relevance of animal models, the authors monitored macaques infected with Plasmodium coatneyi longitudinally, and compared with human samples infected with Plasmodium falciparum. Throughout the study, the authors observed clinical parameters, plasma metabolomes, and parasite transcriptional profiles.

Untargeted high-resolution mass spectrometry analysis was used to measure and annotate metabolic profiles. Targeted metabolomics was then used to further characterize and quantify clinically relevant metabolites for each type of infection. Metabolomics results showed global changes between both infection types in amino acid, biogenic amine, carnitine, and lipid metabolism, while transcriptomics in macaques revealed alterations in amine, fatty acid, lipid, and energy metabolism.

Targeted, tandem mass spectrometry data also revealed significant perturbations in clinically related metabolic ratios in human samples. The Fischer ratio (liver dysfunction), kynurenine to tryptophan (immunosuppression/tolerance), mono-unsaturated to saturated phosphatidylcholines (fatty acid desaturases activity), and total lyso-phosphatidylcholines to total phosphatidylcholines (phospholipases activity) were all found to be significantly altered, peaking in the acute case where the disease was most active. The metabolic ratio indicators correlated well with the pathway analysis from the untargeted data and demonstrated that both fatty acid metabolism and lipid degradation were elevated during the acute cases.

When looked at together, the authors discovered a large set of metabolites and indicators are differentially and significantly expressed in chronic malaria compared to acute cases. These observed alterations in metabolites may be key to better understanding the biology of host-parasite interactions in malarial disease progression. Further, it was demonstrated that metabolite alterations in the macaques were comparable to those from humans, providing a robust animal model for future studies. The study demonstrates a first attempt at integrating various data types and animal models to provide a wholistic picture for better understanding the dynamics between acute and chronic malaria.

Visit our applications page to learn more about metabolomics in disease and wellbeing.
Learn more about how metabolic indicators can help answer biological questions and provide new data insights with our blog post on MetaboINDICATOR™.

Cordy RJ, Patrapuvich R, Lili LN, Cabrera-Mora M, Chien J, Tharp GK, Khadka M, Meyer EVS, Lapp SA, Joyner CJ, Garcia A, Banton S, Tran V, Luvira V, Rungin S, Saeseu T, Rachaphaew N, Pakala SB, DeBarry JD, MaHPIC Consortium, Kissinger JC, Ortlund EA, Bosinger SE, Barnwell JW, Jones DP, Uppal K, Li S, Sattabongkot J, Moreno A, Galinski MR: Distinct amino acid and lipid perturbations characterize acute versus chronic malaria (2019) JCI Insight | https://doi.org/10.1172/jci.insight.125156

In spite of the intensive research activities that have spun in the wake of the Coronavirus pandemic, several aspects of the biology of the virus are still poorly understood. One big unanswered question is: “Why is one patient asymptomatic, while others suffer a severe course of disease, with a range of symptoms?” Age and aging-related diseases have been identified as major risk factors for severe disease, but they fail to fully explain the variability in outcomes.

As metabolic risk factors appear to have a major impact on the risk profile associated with infection, metabolomics has emerged as an appealing technology for learning more about how the pathogenesis and outcome of this novel disease are defined. Recent articles in the “Infectiology” section of our blog explore various metabolic pathways of interest in this context.

A recent study from the University of Naples sheds new light on the pathways associated with the severity of disease, focusing on a specific group of metabolites: lipids. The study finds a large number of lipid metabolites from various classes to be differentially expressed between patients with mild, moderate and severe disease. Ceramides, di- and triglycerides appear to be the most important classes in this context. Interestingly, only a few of the lipids correlated with pro-inflammatory cytokines.

Marianna Caterino and colleagues discuss a variety of biological explanations for how an infection might alter the blood lipidome and how lipid metabolites might influence the severity of the disease. The paper discusses the role of lipids in affecting the virus’ entry into host cells, the replication process, and the potential role of lipid inflammatory mediators in contributing to the dysregulation of the immune response. It also considers their role in causing respiratory distress syndrome. The discovery of a correlation between adiponectin, lipids and disease severity suggests that adipose tissue is actively involved in early COVID-19 pathophysiology – an important potential link that may explain why obesity and metabolic diseases predispose patients for more severe courses of disease.

This study highlights the importance of both the interaction between the immune system and metabolism and the interaction between different organs in fighting infection. The finding that adipose tissue may be involved in the early stages of COVID-19 is striking. But it is just as significant to confirm that a group of lipids that is involved in the core cellular metabolic process of autophagy is also strongly associated with disease severity – even though this process has previously been identified in other infectious diseases.

The findings of this paper confirm that a whole-body response is involved in battling severe infections. Metabolomics could play a pivotal role in providing biomarker signatures that could be clinically relevant for patient stratification.

Caterino, M., Gelzo, M., Sol, S. et al. Dysregulation of lipid metabolism and pathological inflammation in patients with COVID-19. (2021) Sci Rep | https://doi.org/10.1038/s41598-021-82426-7

Giardia lamblia is an enteropathogenic protozoan that causes acute diarrhea in adults. It is one of the most common causes of diarrheal disease and accounts for about 280 million cases per year worldwide. In adults, the disease is usually self-limiting. In young children however, G. lamblia can cause chronic disease, reduced weight gain and reduced height-for-age (linear growth).

A new study by Dr. Ambre Riba and colleagues established a mouse model to study the effects of G. lamblia on bile acid physiology, in hope to understand more about the underlying metabolism behind this infection.

Neonatal mice exposed to G. lamblia developed a persistent infection, without the diarrhea commonly associated with acute G. lamblia infection. When 3-day-old mice were infected, this infection lasted into adulthood and was associated with reduced linear growth and reduced body weight gain (as is common in human children). By contrast, mice exposed to the parasite in adulthood were resistant to the infection. This suggested that the parasite manipulated the host during the post-natal period.

The team found that the G. lamblia population was highest in the proximal small intestine, which was similar to the situation in humans. This mouse model also demonstrated that chronic G. lamblia infection was associated with altered microbial composition.

Metabolomics analysis revealed that bile acid secretion was induced by G. lamblia infection and that bile acids are important growth factors for the parasite. The infection also caused an increase in bile acid deconjugation, probably as a result of altered microbiota composition.

The neonatal mouse model established in this study successfully mimics the infection model of G. lamblia in human infants. The study is a good example of how the gut microbiome can interact with the host metabolome to affect host metabolism.

Reference: Riba A, Hassani K, Walker A, van Best N, von Zeschwitz D, Anslinger T, Sillner N, Rosenhain S, Eibach D, Maiga-Ascofaré O, Rolle-Kampczyk U, Basic M, Binz A, Mocek S, Sodeik B, Bauerfeind R, Mohs A, Trautwein C, Kiessling F, May J, Klingenspor M, Gremse F, Schmitt-Kopplin P, Bleich A, Torow N, von Bergen M, Hornef MW: Disturbed gut microbiota and bile homeostasis in Giardia-infected mice contributes to metabolic dysregulation and growth impairment (2020) Science Translational Medicine | https://doi.org/10.1126/scitranslmed.aay7019

• History & Evolution
• Biosynthesis vs. dietary uptake
• Arachidonic acid and cellular membranes
• Arachidonic acid, eicosanoids and inflammation
• Arachidonic acid and the brain
• Arachidonic acid supplements and athletes
• Arachidonic acid and COVID-19
• References

History & Evolution

1909: isolation from mammal tissues by Percival Hartley | 1940: definition of four double bonds locations (Martin et al. 2016)

Unlike its name suggests (derived from the Latin, arachis, meaning peanut), arachidonic acid is not present in high amounts in peanuts. This polyunsaturated fatty acid was named in 1913 after its saturated cousin, arachidic acid, which is commonly found in peanuts and other nuts (Martin et al. 2016). In contrast, arachidonic acid is rarely found in plants, but can be produced by many animal and microbial species (Abedi and Sahari 2014).

Biosynthesis vs. dietary uptake

Arachidonic acid (C20:4, standing for 20 carbons with 4 double bonds) is available in high amounts in foods of animal origin: meats, fish, dairy products and eggs (Mann et al. 1995; Taber et al. 1998; Abedi and Sahari 2014). In humans, arachidonic acid can be synthesized by elongation and saturation of linoleic acid (C18:2), an essential fatty acid. This means that a lack of linoleic acid can hinder processes requiring arachidonic acid. However, the degradation of phospholipids by phospholipases such as PLA2 can also be a source of arachidonic acid from endogenous metabolism. Both exogenous (dietary) and endogenous arachidonic acid may be used as substrates (Brash 2001).

Arachidonic acid and cellular membranes

The role of lipids in cellular organization and signalling is gaining momentum, putting fatty acids and complex lipids at the forefront of modern biomedical research.  As a fatty acid present in phospholipids, arachidonic acid plays an important role in cellular structure. Thanks to its four cis double bonds, it contributes to the flexibility of cellular membranes which is essential for cell function, particularly in the nervous system, skeletal muscle and immune system (Rich 1993; Tallima and El Ridi 2018).

Arachidonic acid, eicosanoids and inflammation

The four cis double bonds of arachidonic acid make it particularly prone to oxidation, from which eicosanoids are derived. This family of molecules includes prostaglandins, leukotrienes, and other lipids, which act as mediators and regulators of inflammation and wound healing. They also contribute to vascular tone, lipid metabolism, epithelial barrier function, pain, and more (Dennis and Norris 2015). For this reason, lipidomics is a popular method when investigating processes such as inflammation and anti-inflammatory therapies (Mazaleuskaya et al. 2016). Recently, one of these eicosanoids (12-HETE) was identified as an enterosyne, a bioactive molecule able to control the enteric nervous system (ENS) and contribute to the gut-brain axis (Abot et al 2020).

Arachidonic acid and the brain

Arachidonic acid and other polyunsaturated fatty acids (PUFA) are essential to brain development, repair, and maintenance, and to neuron protection (Liu et al. 2015). Although results are inconclusive, studies have explored the role of arachidonic acid and eicosanoids in depression (Lin et al. 2010; Gopaldas et al. 2019), amyotrophic lateral sclerosis (ALS) (Carter et al. 2020), Alzheimer’s disease (Snowden et al. 2017; Goozee et al. 2017), Parkinson’s disease (Willkommen et al. 2018) and bipolar disorder (Rapoport 2014). Research shows arachidonic acid supplements could also have a beneficial effect on cognitive dysfunction (Kotani et al. 2006).

Arachidonic acid supplements and athletes

The effects of arachidonic acid supplements have been investigated in fields as varied as neurology, cardiology, hepatology or nutrition (Kawashima 2019). But it is athletes that probably represent the largest market for arachidonic acid supplements. However, given the role of arachidonic acid in the regulation of inflammation, there are concerns about the safety of such supplements. Supplementation of the diet with arachidonic acid may help increase lean body mass, upper-body strength and peak power in trained males (De Souza E. O. et al. 2016). Studies on the effects of arachidonic acid supplements in resistance training also showed effects on skeletal muscle and blood lipid profiles and peak power without a significant induction of inflammation signalling (Markworth et al. 2018; Roberts et al. 2007).

Arachidonic acid and COVID-19

Recent metabolomic and lipidomic investigations on the effects of the SARS-CoV-2 virus revealed a link to imbalances in arachidonic acid and eicosanoid levels (Di Wu et al. 2020; Barberis et al. 2020). Arachidonic acid was identified as a marker of the severity of the disease (Barberis et al. 2020), leading the authors to conclude that PLA2 may be a potential target for the treatment of COVID-19. The suggested anti-viral properties of arachidonic acid and related metabolites also led to their recommendation as potential therapeutics (Shoieb et al. 2020; Das 2020).

Learn more about the roles of arachidonic acid and other fatty acids in complex chronic diseases such as cancer, Alzheimer’s disease, depression, inflammatory bowel disease, multiple sclerosis and diabetes in our whitepaper “Complex chronic diseases have a common origin”.

References

Abedi E, Sahari MA: Long-chain polyunsaturated fatty acid sources and evaluation of their nutritional and functional properties (2014) Food science & nutrition | https://doi.org/10.1002/fsn3.121 .

Abot A, Wemelle E, Laurens C, Paquot A, Pomie N, Carper D, Bessac A, Orea XM, Fremez C, Fontanie M, Lucas A, Lesage J, Everard A, Meunier E, Dietrich G, Muccioli GG, Moro C, Cani PD, Knauf C: Identification of new enterosynes using prebiotics: roles of bioactive lipids and mu-opioid receptor signalling in humans and mice (2020) Gut. | http://dx.doi.org/10.1136/gutjnl-2019-320230

Barberis E, Timo S et al.: Large-Scale Plasma Analysis Revealed New Mechanisms and Molecules Associated with the Host Response to SARS-CoV-2 (2020) International journal of molecular sciences | https://doi.org/10.3390/ijms21228623 .

Brash AR: Arachidonic acid as a bioactive molecule (2001) The Journal of clinical investigation | https://doi.org/10.1172/JCI13210 .

Carter GT, McLaughlin RJ et al.: Endocannabinoids and related lipids in serum from patients with amyotrophic lateral sclerosis (2020) Muscle & nerve | https://doi.org/10.1002/mus.27096 .

Das UN: Can Bioactive Lipids Inactivate Coronavirus (COVID-19)? (2020) Archives of medical research | https://doi.org/10.1016/j.arcmed.2020.03.004 .

De Souza EO, Lowery RP et al.: Effects of Arachidonic Acid Supplementation on Acute Anabolic Signaling and Chronic Functional Performance and Body Composition Adaptations (2016) PloS one | https://doi.org/10.1371/journal.pone.0155153 .

Dennis EA, Norris PC: Eicosanoid Storm in Infection and Inflammation (2015) Nature reviews Immunology | https://doi.org/10.1038/nri3859 .

Wu D, Shu T et al.: Plasma metabolomic and lipidomic alterations associated with COVID-19 (2020) National Science Review | https://doi.org/10.1093/nsr/nwaa086 .

Goozee K, Chatterjee P et al.: Alterations in erythrocyte fatty acid composition in preclinical Alzheimer’s disease (2017) Scientific Reports | https://doi.org/10.1038/s41598-017-00751-2 .

Gopaldas M, Zanderigo F et al.: Brain serotonin transporter binding, plasma arachidonic acid and depression severity: A positron emission tomography study of major depression (2019) Journal of Affective Disorders | https://doi.org/10.1016/j.jad.2019.07.035 .

Kawashima H: Intake of arachidonic acid-containing lipids in adult humans: dietary surveys and clinical trials (2019) Lipids in health and disease | https://doi.org/ 10.1186/s12944-019-1039-y .

Kotani S, Sakaguchi E et al.: Dietary supplementation of arachidonic and docosahexaenoic acids improves cognitive dysfunction (2006) Neuroscience research | https://doi.org/10.1016/j.neures.2006.06.010 .

Lin PY, Huang SY & Su KP: A meta-analytic review of polyunsaturated fatty acid compositions in patients with depression (2010) Biological psychiatry | https://doi.org/10.1016/j.biopsych.2010.03.018 .

Liu JJ, Green P et al.: Pathways of polyunsaturated fatty acid utilization: Implications for brain function in neuropsychiatric health and disease (2015) Brain Research | https://doi.org/10.1016/j.brainres.2014.11.059

Mann NJ, Johnson LG et al.: The arachidonic acid content of the Australian diet is lower than previously estimated (1995) The Journal of nutrition | https://doi.org/10.1093/jn/125.10.2528 .

Markworth JF, Mitchell CJ et al.: Arachidonic acid supplementation modulates blood and skeletal muscle lipid profile with no effect on basal inflammation in resistance exercise trained men (2018) Prostaglandins, leukotrienes, and essential fatty acids | https://doi.org/10.1016/j.plefa.2017.12.003 .

Martin SA, Brash AR et al.: The discovery and early structural studies of arachidonic acid (2016) Journal of lipid research | https://doi.org/10.1194/jlr.R068072.

Mazaleuskaya, Liudmila L.; Lawson, John A.; Li, Xuanwen; Grant, Gregory; Mesaros, Clementina; Grosser, Tilo; Blair, Ian A.; Ricciotti, Emanuela; FitzGerald, Garret A.: A broad-spectrum lipidomics screen of antiinflammatory drug combinations in human blood (2016) JCI insight | https://doi.org/10.1172/jci.insight.87031 .

Rapoport SI: Lithium and the other mood stabilizers effective in bipolar disorder target the rat brain arachidonic acid cascade (2014) ACS chemical neuroscience | https://doi.org/10.1021/cn500058v .

Rich MR: Conformational analysis of arachidonic and related fatty acids using molecular dynamics simulations (1993) Biochimica et Biophysica Acta (BBA) – Molecular Cell Research | https://doi.org/10.1016/0167-4889(93)90113-4 .

Roberts MD, Iosia M et al.: Effects of arachidonic acid supplementation on training adaptations in resistance-trained males (2007) Journal of the International Society of Sports Nutrition | https://doi.org/10.1186/1550-2783-4-21 .

Shoieb SM, El-Ghiaty MA et al.: Targeting arachidonic acid-related metabolites in COVID-19 patients: potential use of drug-loaded nanoparticles (2020) Emergent materials | https://doi.org/10.1007/s42247-020-00136-8 .

Snowden SG, Ebshiana AA et al.: Association between fatty acid metabolism in the brain and Alzheimer disease neuropathology and cognitive performance: A nontargeted metabolomic study (2017) PLoS medicine | https://doi.org/10.1371/journal.pmed.1002266 .

Taber L, Chiu CH & Whelan J: Assessment of the arachidonic acid content in foods commonly consumed in the American diet (1998) Lipids | https://doi.org/10.1007/s11745-998-0317-4 .

Willkommen D, Lucio M et al.: Metabolomic investigations in cerebrospinal fluid of Parkinson’s disease (2018) PloS one | https://doi.org/10.1371/journal.pone.0208752 .

While one of the greatest health crises in a century is raging, the need for methods that could provide value in infectious disease research is apparent.

The recognition that metabolic processes and immune regulation are intimately related has resulted in increasing interest in immunometabolism. In fact, fewer than 100 publications in 2015 featured the term “immunometabolism”, compared to >500 in 2020. Biocrates is proud of its contribution to this research area, through projects such as the INITIATE project, which seeks to investigate immunometabolism in order to define novel antiviral targets.

Besides its demonstrated value in pharmaceutical and basic research, there is significant and likely underappreciated potential for metabolomics to advance clinical research on infectious diseases. While several aspects have been discussed in our series of articles on Covid-19, here, we take a closer look at a recent publication in the field of infectious diseases.
Banoei and colleagues sought to uncover prognostic signatures of mortality from patient plasma collected upon hospitalization for bacterial Community-Acquired Pneumonia (CAP). They further examined the ability of biomarkers to predict in-hospital mortality, ICU admission, and disease severity. The group employed biocrates’ kit technology, supplemented with complementary GC-MS and 1H-NMR technologies to further enrich the data set. The results detailed below are based on biocrates’ technology as it has been found to provide more informative data compared to the other technologies.

In this study, plasma from 75 patients who survived >90 days was compared to plasma from 75 patients who died in the same time span. Among the non-survivors, 26 included in-hospital fatalities. A group of 31 patients that were ICU-ventilated for different reasons than pneumonia was used as an independent control group. A signature of Lysophosphatidylcholines and Acylcarnitines was associated with CAP mortality. More specifically, decreased Lysophosphatidylcholines and Phosphatidylcholines, together with an increase in Acylcarnitines, was predictive of both ICU admission and subsequent in-hospital mortality. In addition, low tryptophan levels were associated with mortality, confirming earlier metabolomics studies on outcome in CAP (Meier et al., Clin Chem Lab Med 2016).

The significance of this study is highlighted by the poor predictive value of current clinical scoring systems. In addition, given that morbidity and mortality in viral pneumonia is often caused by bacterial superinfection, it is likely that these results could be translatable to viral pneumonia. In fact, similar metabolite alterations have been reported in COVID-19 patients. Finally, this publication sheds light on the importance and potential of nutritional support, especially regarding essential amino acids and fatty acid composition, in achieving optimum outcomes in CAP.

Further information about the INITIATE project can be found at https://cordis.europa.eu/project/id/813343.

More on metabolomics and COVID-19

Banoei MM, Vogel H J, Weljie AM, Yende S, Angus DC, Winston BW: Plasma lipid profiling for the prognosis of 90-day mortality, in-hospital mortality, ICU admission, and severity in bacterial community-acquired pneumonia (CAP) (2020). Critical care (London, England) | https://doi.org/10.1186/s13054-020-03147-3

Introduction

Those who recover from COVID-19, especially after severe cases, have experienced a great deal of pain and discomfort. However, because of potential multi-organ injuries that more closely resemble the situation after sepsis rather than recovery from an Influenza virus infection, COVID-19 survivors often experience long-term consequences of the infection.

For many patients, even after viral clearance, the road to recovery is far from over. For some, these complications are as mild as the inability to smell or taste, while those who are less fortunate can experience lasting respiratory problems, cardiovascular complications or new onset of metabolic disorders.

In this article, we will discuss selected long-term health challenges associated with COVID-19, as well as the metabolic processes that may be associated. We will furthermore highlight the potential to address these challenges through metabolomic research.

Cardiometabolic outcomes

Recent work has revealed associations between COVID-19 and new onset metabolic syndrome, diabetes and changes in the vasculature. This may ultimately contribute to an increased risk for stroke and cardiovascular disease. Moreover, it has been suggested that the elevated risk for diabetes in COVID-19 survivors may be due to a deterioration of metabolic control and direct damage to pancreatic beta cells caused by the virus (Apicella et al., 2020).

It may be unsurprising that COVID-19 has a significant and sustained impact on metabolic homeostasis, given that a long-term follow up study on survivors of the 2002/2003 SARS outbreak found that 50 % of patients lacking a history of Type 2 diabetes (T2D) developed it during the infection. Furthermore, 5 % of cases persisted for an extended period of time. The onset of infection-induced T2D in these patients was accompanied by dysregulated glucose metabolism, hyperlipidemia, and cardiovascular abnormalities, as well as changes in lysophosphatidylcholines and lysophosphatidylinositol. (Wu et al., 2017).

In addition to the direct impact of COVID-19, intensive care is associated with additional health challenges. It is well established that intensive care patients with acute respiratory distress experience dramatic weight loss. While these patients demonstrate rapid recovery back to a normal weight, they tend to gain adipose tissue rather than regaining lean body mass, thereby adding to the risk of cardiometabolic disease. (Chan et al., 2018)

Metabolomics is an attractive technology for assessing and monitoring cardiometabolic risk factors associated with the long-term health impact of SARS-CoV-2 infections. For example, established biomarkers of prediabetes (Wang-Sattler et al., 2012) and the transition from gestational diabetes to T2D (Wheeler et al., 2020) may also aid in determining the risk of diabetes resulting from infection-mediated metabolic dysregulation. In either of the cited cases, the predictive biomarker signatures are multi-factorial. They cover various aspects of the pathophysiology, such as altered glucose metabolism, abnormalities in lipid homeostasis and the metabolism of branched-chain amino acids, as well as perturbations in mitochondrial dynamics. Besides predicting the risk of diabetes, metabolomic signatures have also been helpful in analyzing frailty (Westbrook et al., 2016; Corona et al., 2014) and muscle quality (Moaddel et al., 2016). Moreover, nitric oxide metabolism and arachidonic acid metabolism are two metabolic processes closely related with angiogenesis and immune regulation, thus providing a link between the hyper-inflammatory status during infection and the risk of vascular disease. Thus, such markers may serve as a window into the future, revealing the health status that a patient will achieve upon recovery from COVID-19, and providing a rationale for supportive care.

Neuropsychiatric outcomes

It has been suggested that COVID-19 patients are at greater risk for a range of neuropsychiatric outcomes, including stroke, impaired cognitive function, depression and post-traumatic stress disorder (Varatharaj et al., 2020).

The increased risk of stroke may stem from dysregulation in the coagulation cascade, which could result in bleeding as well as thrombotic events. As tryptophan metabolism is involved in regulating coagulation (Pawlak et al., 2009), this pathway may be valuable for evaluating the risk of stroke. As previously discussed, metabolic dysregulations observed in metabolic syndrome constitute a risk factor for vascular events. especiallycerebrovascular ones, as metabolism is crucial for maintaining healthy blood vessels and facilitating angiogenesis (Draoui et al., 2017).

Extensive studies on Alzheimer’s Disease and Parkinson’s Disease have shown that metabolic parameters exhibit a clear relationship with different components of brain pathology and a good correlation with cognitive scores. In addition, metabolomics has been used in the context of traumatic brain injury (Fiandaca et al., 2018), as well as Huntington’s disease (Graham et al., 2018), which is characterized by a severe inflammatory state, similar to that observed in COVID-19. These studies prove the utility of metabolomics as a minimally-invasive tool for monitoring cognitive and CNS function by means of a small blood sample analysis.

Finally, mental trauma, as from intensive care stress, can have long-term metabolic consequences (Boeck et al., 2016). Depression is increasingly being understood as a disorder that involves inflammatory and bioenergetic components. Metabolomics has shown promise in monitoring pharmaceutical (Cysz et al., 2019) as well as cognitive behavioral therapy (Bhattacharyya et al., 2019) for depression.

Other health challenges

Some COVID-19 survivors, particularly following a severe disease course, suffer from a persistently impaired lung function. In this context, it is noteworthy that cholesterol metabolites are involved in local immune regulation in the lung (Gowdy & Fessler, 2012), which may give them prognostic value regarding the recovery from lung injury. ADMA (Asymmetric Dimethylarginine), a biomarker associated with proteolysis, has been shown to be a strong predictor of long-term outcome in COPD (Vögeli et al., 2017) and thus, may similarly predict long term lung functional outcomes following COVID-19.

Impaired kidney function may be a complication of COVID-19 infections, and kidney damage is being investigated as a potential side effect of Remdesivir treatment (Xu et al., 2020). A number of metabolites have been suggested or are currently in clinical use as biomarkers for impaired kidney function, including creatinine and SDMA (Symmetric Dimethylarginine). The latter, as ADMA, is associated with proteolysis, but relies on renal clearance, thus accumulating when the kidney function is impaired.

Conclusion

COVID-19 is a multifactorial disease with a wide range of pathophysiological processes implicated in defining long-term outcomes. Metabolomics, by profiling of hundreds of metabolites, can inform on many of the involved processes, thus providing a means to better understand the long-term impact of this novel disease.

References

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Bhattacharyya S, Dunlop BW, Mahmoudiandehkordi S, Ahmed AT, Louie G, Frye MA, Weinshilboum RM, Krishnan RR, Rush AJ, Mayberg HS, Craighead WE, Kaddurah-Daouk R. Pilot: Study of Metabolomic Clusters as State Markers of Major Depression and Outcomes to CBT Treatment. (2019) Front Neurosci | https://doi.org/10.3389/fnins.2019.00926

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Corona, G., Polesel, J., Fratino, L., Miolo, G., Rizzolio, F., Crivellari, D., Addobbati, R., Cervo, S. and Toffoli, G.: Metabolomics Biomarkers of Frailty in Elderly Breast Cancer Patients. (2014), J. Cell. Physiol | https://doi.org/10.1002/jcp.24520

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The global impact of the COVID-19 pandemic has prompted collaborative research efforts across the world. Thus far, the bulk of these efforts have been geared towards vaccine development and discovery of therapeutic strategies that mitigate disease severity.

It is clear that SARS-CoV-2 will not merely go away, rather, we will likely be battling this virus for much longer than anticipated. On top of this, many COVID-19 survivors experience long-term complications of the infection, as discussed in a recent post. This not only impacts the quality of life of survivors, it also signifies a threat to the burden on healthcare systems. Until the physiological basis for such complications is clarified, we may continue to see negative impacts on healthcare and the overall economy. Thus, it is crucial to understand the biology of the SARS-CoV-2 pathogen, as well as the disease pathophysiology.

Fortunately, several vaccine and therapeutic candidates are now in the pipeline, some of which are close to market authorization. With this in mind, we anticipate a shift in the focus of COVID-19 research. There is still much to learn about the pathophysiology that determines long-term outcomes, in order to provide treatment options that mitigate these long-term effects. Besides clinical research on this topic, it is imperative that basic research studies on COVID-19 utilize appropriate animal models that best mimic human disease. For more on this, see also the review by Ehaideb et al., 2020.

Here, we discuss the potential of various model systems for COVID-19 research. We especially focus on the convergence with human metabolic control, which would be expected to improve the transferability of findings. This may be particularly useful for studies on the long-term complications in survivors where clinical research is complicated, increasing the necessity to resort to studies in model systems.

Compared to proteins, metabolites have been considerably more conserved in evolution. Metabolites are generally identical across species and most metabolic pathways are identical. This makes metabolomics well-suited for translational research, especially for emerging infectious diseases, as fast and efficient transfer of results is of utmost importance. Nonetheless, not all animal models reflect human metabolism to the same extent. Thus, we review here specific areas of metabolism that may affect model suitability.

SARS-CoV-2 preclinical models

• In vitro cell lines.
  Though not considered an animal model, cell lines are key for understanding SARS-CoV-2 biology and for early evaluation of therapeutic candidates. As cell lines are derived from humans, they mimic the physiology of human cells. Yet, it is worth noting that differences in glucose utilization have been observed between in vitro activated T-cells compared to T-cells activated physiologically via infection. T-cells activated by infection show greater rates of oxidative metabolism and carbon flux to anabolic pathways, such as synthesis of serine and nucleotides (Ma et al., 2019). Further, certain metabolites are not universally produced by all cell types, so cell-based assays do not fully reflect metabolic interactions between cell types, tissues and organs.

Though SARS-CoV-2 can replicate in the animal models discussed below, studies have also revealed that infection elicits only mild disease in these animals, apart from infrequent exceptions (Ehaideb et al., 2020).

• Small Animals (Hamsters and Mice).
  Mice and rats are generally regarded as the go-to animal models for a variety of preclinical studies, due to their size, short life cycles, low costs for upkeep and the availability of tools for genetic manipulation.

It is well established that SARS-CoV-2 enters cells by way of the ACE2 enzyme. Interestingly, it was discovered that the virus is unable to enter cells through mouse ACE2. Thus, researchers now utilize transgenic mice that express human ACE2. However, even if the virus can enter the cells of hACE2 transgenic mice, it is likely that there still may not be full transferability to human studies, due to differences in corticosteroid hormone homeostasis between humans and rodents (Joels et al., 2018). Along with ACE2, these hormones are involved in the Renin-Angiotensin-Aldosterone System (RAAS).

Hamsters have emerged as an excellent replacement for mice in COVID-19 studies. The ACE2 enzyme in hamsters has homology to human ACE2 and molecular docking has suggested sufficient binding between hamster ACE2 and the SARS-CoV-2 S protein. Hamsters were found to exhibit the most consistency in terms of lung disease. Yet, fewer tools are available for hamster studies as they are less commonly used compared to other small animals (Khoury et al., 2020).

• Large Animals (Ferrets and Cats).
  These animal models are best suited for studies in which the pathology of COVID-19 in humans does not need to be exactly replicated, but its physiological impact must be assessed. SARS-CoV-2 is capable of replicating in ferrets and cats, though ferrets were found to exhibit greater disease severity. It is worth noting that transmission of a SARS-CoV-2 variant from minks to humans was recently reported in Denmark. The variant contained mutations in the S protein that rendered it less susceptible to neutralizing antibodies. While vaccine or therapeutic candidates are not expected to be any less effective against this variant, further investigation is needed to verify this (ECDC). As both minks and ferrets belong to the Mustelidae family, studies in ferrets may be valuable for these efforts.

• Non-Human Primates.
  The pathology of COVID-19 in humans is most closely mimicked by that seen in primates. Though cynomolgus and rhesus macaques typically experience mild disease, they do exhibit some pathologies seen in humans and cases of lethal lung disease have been reported (Khoury et al., 2020). Unsurprisingly, primates have the best physiological similarity to humans (~99% genetic similarity) compared to other animal models, due to their proximity within the evolutionary process. Of course, these models have limitations, including high maintenance costs, long gestation periods and lifetimes.

Metabolic differences between humans and animal models

It is well-acknowledged that studies in animal models are not always fully transferable to humans, and differences in metabolic control may contribute to this effect despite high degrees of evolutionary conservation. In the following section, we discuss possible influences on the transferability of animal model based COVID-19 research, apart from the immediate capability of SARS-CoV-2 to infect those animals and the ACE2 homology described above.

The advantages associated with using rodents in animal studies are reflected in the sheer number of publications using mice and rats compared to other animal models. Their widespread use in a variety of studies has resulted in the identification of metabolic differences that occur in these mammals compared to humans. On the other hand, less is known about the metabolic differences between humans and the other animal models discussed here. However, they certainly may exist. Thus, one should be cognizant of such differences when selecting an animal model for a research project.

Rodents exhibit differences in ascorbate synthesis, purine degradation, and glycan metabolism, as well as hepatocyte growth, compared to humans (Blais et al 2017). Further, prior work has demonstrated notable differences in the adaptive and innate immune systems of mice and humans, as well as in their responses to inflammatory conditions (Mrochen et al, 2020; Akhtar, 2015), which may be reflected in immune modulatory metabolic pathways. Rodents also exhibit differences in lipid metabolism. For example, mice adipocytes exhibit marked differences in the receptor types that control lipid catabolism, and this corresponds to differences in the elicited metabolic responses (Bergen and Mersmann, 2005).

Humanized mouse models of atherosclerosis require significant alterations. While circulating cholesterol is transported via LDL in humans and other mammals, in mice it is transported via HDL. As a result, mice have much lower cholesterol levels, which confers protection against atherosclerosis. As HDL and LDL differ in phospholipid composition, discrepancies may exist between humans and mice in certain phospholipid levels. This can be mimicked in humanized mouse models. Still, as several mouse models of atherosclerosis exist, phospholipid signatures can vary depending on the manipulations used to generate the model (Saulnier-Blache et al, 2018). Further, mice exhibit low absorption of dietary cholesterol and lack a homolog of a human protein that transfers cholesterol esters and triglycerides between lipoproteins (von Scheidt et al., 2017). Further discrepancies in cholesterol/oxysterol metabolism have also been reported (Kilk 2019). Such differences may impact the apparent association between metabolic disease and COVID-19 disease course that have been discussed in previous articles of this series

Rodents can modify bile acids in different ways than humans. For example, a number of hydroxylated bile acids, the so-called muricholic acids, typically make up a large proportion of serum bile acids in rodents but are hardly found in many other species. In addition, in the rodent liver, secondary bile acids can be converted back into primary bile acids – a reaction that does not occur in the human liver. For that reason, studies on bile acid metabolism in these models may not be truly reflective of the processes occurring in humans. (Wishart 2019).

Differences in bile acid metabolism compared to humans are not only known for rodents. Cats also differ in their metabolism of bile acids, as they cannot conjugate bile acids with glycine, only taurine (Hickman, 1992). Cats also differ in the preferred site and carbon source for de novo lipogenesis (DNL). Whereas in humans and rodents, DNL primarily occurs in the liver, with glucose as the preferred carbon source, cats prefer acetate, with DNL mainly occurring in adipose tissue. In cats, the use of acetate obviates the need for carbon flow to pass through the mitochondria prior to cytosolic fatty acid synthesis, thus eliminating the role of certain enzymes involved in regulating lipid metabolism (Bergen and Mersmann, 2005).

Other considerations

Of course, animal models also differ greatly in their gut microbiomes. Given the important functions of the microbiome in shaping both metabolism and immunity, this is an important consideration.

Animal diets should also be well-considered. For example, metabolic diseases that pre-dispose patients to a severe course of COVID-19 often occur against a background of adverse nutritional habits (“Western diet”). High-fat diet is frequent in research on metabolic diseases and related disorders, such as obesity. However, the terms “Western diet” or “High-Fat diet” are poorly defined. Moreover, dietary intervention studies have shown that obesity and adiposity phenotypes are less dramatic in mice fed a high fat diet at too early of an age (Kleinert et al., 2018). Given that lipid metabolism is heavily discussed in relation to the pro- and anti-inflammatory actions of different lipids, the composition and timing of diets in experimental systems should be considered, at least as potential confounders.

Finally, it is recommended that animal model-based research not rely on a single experimental setting. In the authors’ opinion, diversification of experimental procedures serves to boost the validity of results. For example, Pandey et al. (2014) showed that various mouse strains differ in their metabolic reaction to 3,5-diethoxycarbonyl-1,4-dihydrocollidine (DDC) induced steatohepatitis. In using three different mouse strains, robustly perturbed pathways could be differentiated from strain-specific reactions. In addition, Pann et al. (2020) have shown that the metabolic dynamics in early life differs between different mouse strains.

Summary

Preclinical studies that use animal models are undeniably valuable and obligatory to ensure safety and efficacy of therapeutics prior to clinical evaluation. At the same time, there are limitations associated with the use of animal models in mimicking human disease and treatment response. It is unlikely that such challenges, stemming from metabolic differences between species, will ever be fully resolved. Still, conscientious efforts to humanize animal models, based on known metabolic divergences from humans, can aid in the translatability of results and ultimately elucidate unknown aspects of pathophysiology.

Metabolomics represents a means to circumvent some of these challenges. Comparative metabolomics can shed light on metabolic processes that differ between species. This can reveal aspects of a study that researchers should be cautious of when advancing a drug to the clinic. Importantly, the non-invasive sampling approach and minimal sample volume requirements allow for more frequent sample collection. This can offer a comprehensive picture of the mechanisms governing pathophysiological changes in animals and humans. Moreover, such deep insights can rapidly advance our understanding of the long-term complications seen in patients. In particular, targeted metabolomics is highly valuable in COVID-19 studies, as it allows separate groups to pool their data and draw interpretations from separate studies.

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