nurr: Vol. 7
Review Article
Nuclear Receptor Research
Vol. 7 (2020), Article ID 101452, 10 pages
doi:10.32527/2020/101452

Lipids and NMR: More Than Mere Acquaintances

Cecilia Castro1 and Julian L. Griffin1,2

1Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, UK

2Biomolecular Medicine, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction. Imperial College London, Exhibition Road, South Kensington Campus, London SW7 2AZ, UK

Received 16 August 2019; Accepted 19 January 2020

Editors: Manuel Vazquez Carrera and Pallavi R. Devchand

Copyright © 2020 Cecilia Castro and Julian L. Griffin. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Recognising the paramount importance of lipids in cell physiology and function, there is an analytical need to measure the composition of lipids within the cell and how different lipid species interact. In this review, we will explore the role NMR spectroscopy can have in this. We will show how the technique can be used to measure lipid concentrations, but we will also provide evidences of its importance to characterise lipid interactions with other molecules, such as proteins, and to measure lipoproteins, the transporters of triglycerides and cholesterol, discussing advantages and limitations. Furthermore, we will highlight its potential for quality control analysis, particularly in food science and industry, if further development of benchtop instruments continues. Complementary to liquid chromatography mass spectrometry, which is able to measure numerous lipids in a complex mixture, NMR is an invaluable tool for fulfilling this need of better characterising lipids.

1. NMR Spectroscopy As an Analytical Tool

NMR is a spectroscopic technique which relies on the nuclear property of spin [16]. When certain isotopes are placed in a magnet field their nuclei either align or oppose the magnetic field. A radiofrequency wave can cause a transition between these states, and in the NMR experiment we observe the relaxation of this transition. As the exact frequency is dependent on the chemical environment, as well as the isotope under observation, this makes for an incredibly powerful tool for structure elucidation.

What nuclei can be detected by NMR spectroscopy? The possibility of detecting a nucleus by NMR spectroscopy depends on the number of protons and neutrons in the nucleus. Nuclei where both the number of protons and neutrons are even have no spin, and cannot be detected by NMR. Examples of nuclei belonging to this group are 12C, 16O, 32S. Nuclei where either the number of protons or neutrons is odd have half-integer spin and can be detected by NMR. Examples include 1H, 13C, 31P. Nuclei where both the number of protons and the number of neutrons are odd have integer spin and can be detected by NMR, although the spectra they produce are both more complicated and highly dependent on the environment they are found in. Examples include 2D, 10B, 14N. This means that every element will have at least one isotope that can be detected by NMR spectroscopy, but how abundant that isotope is can be highly variable. From the point of view of measuring lipids, the relevant elements for detecting are H, C, O, N and P, with 1H, 13C and 31P being the most accessible isotopes, as they are naturally the most abundant ones and spin 1/2.

How is the signal originated? A spin-half nucleus has an interaction with a magnetic field which gives rise to two energy levels [16]. The difference in population between the two states is proportional to the difference in energy. At room temperature that difference is low, explaining in part why NMR spectroscopy is a relatively insensitive technique.

A radio frequency can excite nuclei from the lower to the higher energy state, making their populations the same; removing the frequency will result in the system coming back to the original difference in population, as the extra nuclei promoted go back to the lower energy level emitting a signal in the radio frequency range. The signal is registered in the time domain, and then transformed into the frequency domain through a Fourier transform.

How can the pattern of the resonances be characterised? The pattern of lines that make up the NMR signal is the result of many factors, which give important information about the nuclei originating them. Here we describe them, exemplifying them with the specific case of the fatty acids depicted in Figure 1.

F1
Figure 1: Chemical structures of five common fatty acids: A. palmitic acid (16 Cs, no double bonds); B. palmitoleic acid (16 Cs, 1 double bond); C. stearic acid (18 Cs, no double bonds); D. oleic acid (18 Cs, 1 double bond); E. arachidonic acid (20 Cs, 4 double bonds).
  • •:  The position of the signal depends and gives us information about the electronic cloud around the nucleus (i.e. the type of bond the nucleus is involved in). The more the electronic cloud is close to the nucleus, the more it is “shielded“ and it is close to the zero on the chemical shift scale used by NMR, examples of this type of nuclei are protons attached to aliphatic carbons. On the other hand, if the electron cloud is attracted away from the nucleus, it is “unshielded“ and it is farther away on the opposite extreme of the scale, an example here is the proton found adjacent to an aldehyde group. In the case of fatty acids, saturated carbons have electronic clouds closer to them than unsaturated ones, therefore they are more shielded and closer to zero than unsaturated ones. Furthermore, saturated carbons far away from heteroatoms, such as terminal –CH3 are more shielded than saturated carbons close to heteroatoms, such as the –CH2- directly bonded to the carboxylic group, therefore they are closer to zero.
  • •:  The multiplicity depends on the adjacent atoms and gives us information about the neighbouring groups. The presence of protons that have a distance of three bonds gives rise to a splitting of the signal, which depends on the number of adjacent protons. A singlet means that there is no other group containing proton directly linked to its C; a doublet means that there is a group containing one proton directly linked to its C, and so on. In the case of fatty acids, for example, terminal –CH3s usually have two protons on the group directly bonded, therefore they appear as triplets.
  • •:  The intensity depends on and gives us information about two characteristics, the number of equivalent proton and the concentration of the molecule. The more concentrated the molecule is, the higher is the signal. The classical example to explain this is lactate (shown in Figure 2), originating two signals, one for the CH3 group and the other for the CH. Under fully relaxed conditions, the signal of the CH3 group will have an intensity three times higher than the CH, because they belong to the same molecule, but there are three protons in the first case and only one in the second.
F2
Figure 2: Chemical structures of lactate.

Having surveyed the NMR basis, we are now in a position to see how this can be put to use to understand lipid biology.

2. Lipid-protein Binding

One key aspect in studying of nuclear hormone receptors is to understand their regulators and how such regulation occurs. To fully explain these mechanisms, one needs first to identify the ligand binding site and then to determine the conformation the receptor assumes to interact with such ligand. NMR has been extensively used to study protein structure as it allows recording of small and large-scale protein dynamics. Therefore, information about hydrogen bonds, allosteric processes and protein-ligand interactions can be captured. Here, instead of briefly listing a series of application, we present in more details one specific case, the nuclear receptor-related 1 protein (Nurr1/NR4A2), recently published [17], as a concrete demonstration of the contribution the technique can give to this field.

For long time, Nurr1 was classified as a ligand-independent transcription factor, following the determination by crystal structure of the absence of a ligand-binding pocket, and also of the presence of a bulky hydrophobic residues region where its putative canonical pocket was expected [18,19]. However, NMR spectroscopy has since shown that the putative Nurr1 ligand binding domain is dynamic, accessible from the solvent and in exchange between two or more conformations. The peaks of both the amide and the side-chain methyl groups of the amino acids in the putative ligand binding region were compared with the peaks belonging to amino acids away from that region, revealing features indicative of dynamics in the µs-ms timescale, not captured by the crystal structure. Such dynamics are most likely to be an exchange between two or more conformation of the protein in solution.

This was not the sole contribution that NMR gave to a better understanding of how Nurr1 functions. In vitro metabolomics studies had previously shown that unsaturated fatty acids bind to the ligand binding domain of Nurr1 and of another orphan nuclear receptor related to it [20]. Exploring the behaviour of Nurr1, first in the presence of docosahexaenoic acid [21], then in the presence of arachidonic acid, linoleic acid and oleic acid [17], it was possible to show that, in every case, all the more affected NMR peaks map to the putative ligand binding pocket. These results strongly suggest that the interaction between the fatty acid and the receptor occurs in that area. Overall, this body of work shows how the putative ligand binding pocket can expand from the crystallographic structure and highlights how powerful NMR is in capturing the exchange between two or more conformations on the µs-ms timescale. NMR spectroscopy well complements crystallography and electron microscopy, the other two leading techniques in the field. In fact, many cases exist of proteins that can be analysed only by one method, for example giving good NMR data, but no crystallization, or vice-versa; furthermore, adding the dynamic information obtained by NMR spectroscopy to the crystal structure enriches the understanding of the behaviour of a protein. In conclusion, when taken together, these techniques allow an optimal description of the characteristics of a protein and pipelines have been developed and tested for their combined use in the case of small proteins [22,23].

Other notable examples of the use of NMR spectroscopy to clarify the interaction between lipids and proteins include:

  • •:  examining the interaction between palmitic acid and bovine β-lactoglobuline in the pH range between 8.4 and 2.1. The results show that, at neutral pH, the methyl end of the fatty acid is bound deep within the central cavity of the protein. Furthermore, the ligand binding is completely reversible and change according to pH, with the release of the ligand starting at pH 6 and being completed at acidic pH [24];
  • •:  studying the mechanism through which the fatty acid-binding protein 1 (FABP1) mediates peroxisome proliferator-activated receptor α (PPARα) activation. Substituting key residues adjacent to the ligand-binding portal region of FABP1 showed that the binding of some ligands, such as oleic acid or PPARα activator GW7647, both helps the transport of poorly water-soluble compounds through the cell cytoplasm and stabilizes a conformation of FABP1, able to increase its nuclear localization and the activation of PPARα [25];
  • •:  understanding the structure of the complex between arachidonic acid and the odour binding protein 22 of Aedes Aegypti. Odour binding proteins are key in regulating the feeding behaviour of female mosquitoes, an important step in the transmission of certain viruses. NMR spectroscopy detected a complex between arachidonic acid and an odour binding protein, and also the region where the binding occurs. This region undergoes a significant conformational change in the presence of the fatty acids [26].

3. NMR-based Lipidomics

In addition to give us information about where and how a lipid interacts with another molecule NMR can help us also determining changes in classes and concentrations of lipids as the characteristics making it a good technique for the measure of aqueous metabolites remain true also for lipids.

3.1. 1H-NMR use in lipidomics

Recent examples of the use of 1H-NMR for lipidomics are varied and include:

  • •:  the dose-dependent increase in docosahexaenoic acid and the change in the n-6/n-3 ratio in lipid extracts from adipose tissue, following an increased concentration of polyunsaturated n-3 fatty acids in the diet. With this study, the authors aimed to prove that it is possible to evaluate dietary interventions from biopsies and therefore, have a suitable tool to tease out in the future the nutritional part of the risk associated to breast cancer and the evolution of such a contribution [27];
  • •:  the decrease of hepatic triglycerides in db/db mice and its correlation with the attenuated development of fatty liver, following a dietary intervention with leucine [28];
  • •:  the deregulation of membrane constituent-lipids, such as glycerophospholipids, cholesterol and sphingolipids, and the accumulation of energy storage lipids, such as triglycerides, in fish from wetland contaminated from metals and metalloids [29];
  • •:  the measurement of lipid classes in extracts from rodent furs to monitor sebaceous gland atrophy following the administration of a stearoyl-CoA desaturase 1 inhibitor [30].

Beyond the sensitivity issue already outlined as a drawback, the other characteristic that makes 1H-NMR sub-optimal for lipidomics when compared with mass spectrometry is the high overlapping of the signals. There are two main contributing reasons in the particular case of the lipids, one intrinsic to the technique, the other more relevant for these molecules. Proton signals span a small range of chemical shift (10 ppm) and are highly complex in shape, and thus, many resonances overlap making it difficult to determine what species are being observed. Furthermore, lipids have a high degree of similarity in their structures, therefore making the signals highly similar to each other. In Figure 1, we have depicted five common fatty acids with different chain length (16, 18 and 20 C atoms) and levels of unsaturation (0, 1 or 4 double bonds) as examples, to show this similarity in practice. It is useful here to remember again that NMR detects all non-equivalent protons as separated signals, therefore creating complex and similar spectra even for these common cases. The consequence is that it is really challenging (if not utterly impossible) to identify univocally and quantify a single lipid even in relatively simple mixtures.

3.2. 31P-NMR use in lipidomics

31P is a very interesting isotope from lipidomics point of view as it has 100% abundance, a high gyromagnetic ratio (that make it sensitive even at low field) and a wide chemical shift dispersion (that minimize the signal overlapping). Therefore, it is immediately apparent how the detection of this nucleus can be helpful for molecules containing it.

In the case of lipidomics, it is particularly relevant for the identification of the different classes of phospholipids [31]. For example, 31P spectra of lipid extracts from chocolate were shown to provide information on the type and amount of emulsifier used [32]. One particular attraction of this application is that the analysis can potentially be implemented on a benchtop NMR, making the approach very cost effective and easy to use. Benchtop NMR instruments have a low field (usually 1 Tesla or 2 Tesla) [33] and do not require cryogen fluids, and therefore are transportable, cheaper and easier to access and maintain compared to traditional NMR. All these characteristics make these instruments ideal for process controls in industry. In another recent paper [34], the potential of using low-field 31P for lipidomics applications was explored, with an evaluation of the analytical performances and the proposal of two methods to measure absolute concentrations of phospholipids. The authors concluded that it was possible to detect phospholipids in 2 h with a limit of detection of 0.5 mM at 1 Tesla and 0.2 mM at 2 Tesla, unambiguously assign the headgroups of phosphatidylcholine, phosphatidylethanolamine, phosphatidylinositol, phosphatidylserine, and phosphatidylglycerol, and obtain absolute quantifications for them. In Figure 3, the head groups for these families of lipids are represented, to help understanding the capabilities of the techniques. These new applications demonstrate how the technique is still useful and can be particularly appropriate for quality control, especially in food science and industry.

F3
Figure 3: Chemical structures of phospholipids: A. phosphatidylcholine, B. phosphatidylethanolamine, C. phosphatidylinositol, D. phosphatidylserine and E. phosphatidylglycerol. –R1 and -R2 indicate the two fatty acid moieties.

The limitations of 31P NMR are similar to the ones highlighted above for 1H NMR spectroscopy. It is still challenging (if not almost impossible) to distinguish among the many lipid species: 31P NMR permits to obtain information about the different classes of lipids, but not about the individual species in each class. Furthermore, obviously the lack of sensitivity remains a relevant issue as it can limits the applications it can be implemented for, especially when low fields are considered.

4. Measuring Lipoproteins By NMR Spectroscopy

As cholesterol and triglycerides are insoluble in water, these lipids are transported in the organism in complexes with proteins and other phospholipids. These resulting particles are called lipoproteins and are divided in different classes according to their size and composition [35]. Extensive studies highlighted the crucial role of lipoproteins in the developing of cardiovascular diseases, including type 2 diabetes [36], coronary heart disease [37] and atherosclerosis [38,39]. Moreover, a role for specific lipoproteins, in particular apolipoproteins, in neurological diseases such as Alzheimer [40] and Parkinson [41] has long been recognised. Therefore, it is really important both for clinical practice and research purposes, to measure these particles confidently. NMR has become a widespread tool in epidemiological studies for measuring lipoproteins in plasma [42]. A pioneer in the use of the technique in this context is Ala-Korpela [43], who developed a method combining 1H NMR spectroscopy and a line-fitting analysis, on the basis of measurements for each of the ultracentrifuged lipoprotein fractions obtained from human plasma. The results, validated by comparison with the values from traditional biochemical methods, showed that it was possible to obtain “absolute concentrations of phospholipids, total cholesterol, free cholesterol, esterified cholesterol, total proteins, and total masses were estimated for very low density lipoproteins (VLDL), low density lipoproteins (LDL), and high density lipoproteins (HDL) fractions“ [43]; moreover VLDL and LDL triglycerides were also quantified. The method has been further extended to include low-molecular-weight metabolites together with lipoproteins, allowing the measure of 225 molecules at the same time in each sample [44].

More recently, a second way to measure lipoproteins from NMR, based on the spectral deconvolution of the plasma methyl lipid resonances originally introduced by Otvos [45], has been automated and standardised [46], showing “a high level of reproducibility and accuracy across the individual platforms“.

The number of studies where this technique was used demonstrates its importance [42]. Recent applications include:

  • •:  showing how reduced plasma levels of small HDL particles were associated with poor outcomes in patients with idiopathic and heritable pulmonary arterial hypertension [47];
  • •:  examining the association of lipoproteins and other blood metabolites with risk of incident myocardial infarction, ischemic stroke and intracerebral haemorrhage. The results demonstrate that myocardial infarction and ischemic stroke have broadly similar strengths of association with concentrations of lipoprotein and lipid constituents (with very low-, intermediate-, and low-density lipoproteins positively associated with the diseases and high-density lipoproteins inversely associated with them), but for intracerebral haemorrhage the associations were substantially weaker. In contrast, certain non–lipid-related metabolites, such as glycoprotein acetyls and glucose, showed similar strengths of association for all 3 subtypes of diseases [48].
  • •:  comparing statin treatment with the genetic inhibition of proprotein convertase subtilisin/kexin type 9 (PCSK9), as a naturally occurring trial of PCSK9 inhibitors: both lower blood low-density lipoprotein cholesterol levels and therefore reduce risk of cardiovascular events. The results show similar metabolic and lipid changes, with noteworthy discrepancies, however, observed for very low-density lipoproteins, suggesting that PCSK9 inhibitors could have a smaller effect in reducing cardiovascular events compared to statins [49];
  • •:  comparing diets with different fatty acids composition showed that both monounsaturated fatty acids and the Mediterranean diet decreased exactly the same fractions of LDL, including particle number, lipid, phospholipid and free cholesterol fraction; however the Mediterranean diet also decreased the larger subclasses of VLDL, several related VLDL fractions, VLDL-triglycerides, and serum-triglycerides [50];
  • •:  comparing diets rich in saturated fatty acids and n-6 polyunsaturated fatty acids, in people supplemented with eicosapentaenoic and docosahexaenoic acid, and revealing that both diets reduced the concentration of total very-low-density lipoprotein (VLDL) particles, and their subclasses and increased VLDL and LDL particle size [51];
  • •:  studying psoriasis patients and showing increased lipoprotein(a), oxidized lipoprotein(a), and oxidized HDL compared to controls; furthermore a significant association of oxidized LDL and oxidized HDL with noncalcified burden was found [52];
  • •:  comparing the composition of the HDL fractions in patients with low, normal or high HDL-cholesterol. It is, in fact, not only the quantity of the different lipoproteins in the blood that can increase the risk of specific illnesses, but also their composition. Analysing the NMR spectra of the extract obtained from the HDL fractions only, it was possible to highlight that cholesterol was not the only lipid that was altered. In patients with low cholesterol-HDL, it was found that sphingomyelins and phosphatidylcholines are also lower than for other patients, while triglycerides are higher. Furthermore, a lower degree of unsaturation in the fatty acids esterifying all the different classes of lipids characterise these patients [53];
  • •:  a simultaneous characterization of the metabolic profiles in the maternal, as well as cord blood samples, to increase the knowledge about foetal growth restriction. This condition, affecting up to 10% of pregnancies, can have consequences after the birth, in addition to causing severe problems to the foetus. Significantly lower plasma concentrations of cholesterol-intermediate density lipoprotein (IDL), triglycerides-IDL and HDL were found in mothers of growth-restricted foetuses compared to controls; on the other hand, growth-restricted foetuses had significantly higher plasma concentrations of cholesterol and triglycerides transporting lipoproteins, as well as increased VLDL particle types [54].

In Table 1, we have summarised all the applications presented throughout this review.

T1

Table 1: Summary of the studies, where NMR spectroscopy has been applied to characterise lipids, presented in this review.

5. Conclusions

Notwithstanding some limitations largely associated with sensitivity, NMR spectroscopy remains a powerful technique both to measure lipids and to characterise their physiological role in the cell, with many recent examples demonstrating the versatility of the technique. Furthermore, the continuous development of cryogen-free magnets is expanding the range of its industrial applications for quality control and including the measurement of lipids.

Competing Interests

The authors declare no competing interests.

References

  1. A. D. Watson, “Thematic review series: Systems Biology Approaches to Metabolic and Cardiovascular Disorders. Lipidomics: a global approach to lipid analysis in biological systems ,“ Journal of Lipid Research, vol. 47, no. 10, pp. 2101–2111, 2006. Publisher Full Text | Google Scholar
  2. E. Fahy, S. Subramaniam, H. A. Brown et al., “A comprehensive classification system for lipids,“ Journal of Lipid Research, vol. 46, no. 5, pp. 839–861, 2005. Publisher Full Text | Google Scholar
  3. T. Hu and J.-L. Zhang, “Mass-spectrometry-based lipidomics,“ Journal of Separation Science, vol. 41, no. 1, pp. 351–372, 2018. Publisher Full Text | Google Scholar
  4. H. Lee and T. Yokomizo, “Applications of mass spectrometry-based targeted and non-targeted lipidomics,“ Biochemical and Biophysical Research Communications, vol. 504, no. 3, pp. 576–581, 2018. Publisher Full Text | Google Scholar
  5. C. Hinz, S. Liggi, and J. L. Griffin, “The potential of Ion Mobility Mass Spectrometry for high-throughput and high-resolution lipidomics,“ Current Opinion in Chemical Biology, vol. 42, pp. 42–50, 2018. Publisher Full Text | Google Scholar
  6. F. W. Sanders, A. Acharjee, C. Walker et al., “Hepatic steatosis risk is partly driven by increased de novo lipogenesis following carbohydrate consumption,“ Genome Biology, vol. 19, no. 1, 2018. Publisher Full Text | Google Scholar
  7. Z. Hall, N. J. Bond, T. Ashmore et al., “Lipid zonation and phospholipid remodeling in nonalcoholic fatty liver disease,“ Hepatology, vol. 65, no. 4, pp. 1165–1180, 2017. Publisher Full Text | Google Scholar
  8. K. Jurowski, K. Kochan, J. Walczak, M. Barańska, W. Piekoszewski, and B. Buszewski, “Analytical Techniques in Lipidomics: State of the Art,“ Critical Reviews in Analytical Chemistry, vol. 47, no. 5, pp. 418–437, 2017. Publisher Full Text | Google Scholar
  9. S. Khoury, C. Canlet, M. Lacroix, O. Berdeaux, J. Jouhet, and J. Bertrand-Michel, “Quantification of Lipids: Model, Reality, and Compromise,“ Biomolecules, vol. 8, no. 4, p. 174, 2018. Publisher Full Text | Google Scholar
  10. E. Alexandri, R. Ahmed, H. Siddiqui, M. Choudhary, C. Tsiafoulis, and I. Gerothanassis, “High Resolution NMR Spectroscopy as a Structural and Analytical Tool for Unsaturated Lipids in Solution,“ Molecules, vol. 22, no. 10, p. 1663, 2017. Publisher Full Text | Google Scholar
  11. W. Becker, K. C. Bhattiprolu, N. Gubensäk, and K. Zangger, “Investigating Protein-Ligand Interactions by Solution Nuclear Magnetic Resonance Spectroscopy,“ ChemPhysChem, vol. 19, no. 8, pp. 895–906, 2018. Publisher Full Text | Google Scholar
  12. J. Li, T. Vosegaard, and Z. Guo, “Applications of nuclear magnetic resonance in lipid analyses: An emerging powerful tool for lipidomics studies,“ Progress in Lipid Research, vol. 68, pp. 37–56, 2017. Publisher Full Text | Google Scholar
  13. A. M. Emwas, R. M. Salek, J. L. Griffin, and J. Merzaban, “NMR-based metabolomics in human disease diagnosis: applications, limitations, and recommendations,“ Metabolomics, vol. 9, no. 5, pp. 1048–1072, 2013. Publisher Full Text | Google Scholar
  14. H. Atherton, “Metabolomics of the interaction between, pp. AR-Aalpha and age in the, pp. AR-Aalpha-null mouse,“ Molecular Systems Biology, vol. 5, p. 259, 2009. Publisher Full Text | Google Scholar
  15. E. Holmes, J. K. Nicholson, F. W. Bonner et al., “Mapping the biochemical trajectory of nephrotoxicity by pattern recognition of NMR urinanalysis,“ NMR in Biomedicine, vol. 5, no. 6, pp. 368–372, 1992. Publisher Full Text | Google Scholar
  16. J. Keeler, “Understanding NMR Spectroscopy,“ in Understanding NMR Spectroscopy, 2nd. edition, Ed., United States: John Wiley Sons Inc, New York, 2nd edition, 2010.
  17. I. d. Vera, “Defining a Canonical Ligand-Binding Pocket in the Orphan Nuclear Receptor Nurr1,“ Structure, vol. 27, no. 1, pp. 66–77, 2019. Publisher Full Text | Google Scholar
  18. R. Flaig, H. Greschik, C. Peluso-Iltis, and D. Moras, “Structural Basis for the Cell-specific Activities of the NGFI-B and the Nurr1 Ligand-binding Domain,“ The Journal of Biological Chemistry, vol. 280, no. 19, pp. 19250–19258, 2005. Publisher Full Text | Google Scholar
  19. Z. Wang, G. Benoit, J. Liu et al., “Structure and function of Nurr1 identifies a class of ligand-independent nuclear receptors,“ Nature, vol. 423, no. 6939, pp. 555–560, 2003. Publisher Full Text | Google Scholar
  20. N. Vinayavekhin and A. Saghatelian, “Discovery of a Protein–Metabolite Interaction between Unsaturated Fatty Acids and the Nuclear Receptor Nur77 Using a Metabolomics Approach,“ Journal of the American Chemical Society, vol. 133, no. 43, pp. 17168–17171, 2011. Publisher Full Text | Google Scholar
  21. I. M. de Vera, P. K. Giri, P. Munoz-Tello et al., “Identification of a Binding Site for Unsaturated Fatty Acids in the Orphan Nuclear Receptor Nurr1,“ ACS Chemical Biology, vol. 11, no. 7, pp. 1795–1799, 2016. Publisher Full Text | Google Scholar
  22. D. A. Snyder, Y. Chen, N. G. Denissova et al., “Comparisons of NMR Spectral Quality and Success in Crystallization Demonstrate that NMR and X-ray Crystallography Are Complementary Methods for Small Protein Structure Determination,“ Journal of the American Chemical Society, vol. 127, no. 47, pp. 16505–16511, 2005. Publisher Full Text | Google Scholar
  23. A. A. Yee, A. Savchenko, A. Ignachenko et al., “NMR and X-ray crystallography, complementary tools in structural proteomics of small proteins,“ Journal of the American Chemical Society, vol. 127, no. 47, pp. 16512–16517, 2005. Publisher Full Text | Google Scholar
  24. L. Ragona et al., “Bovine beta-lactoglobulin: interaction studies with palmitic acid,“ Protein Science, vol. 9, no. 7, pp. 1347–1356, 2000.
  25. R. Patil, B. Mohanty, B. Liu et al., “A ligand-induced structural change in fatty acid–binding protein 1 is associated with potentiation of peroxisome proliferator–activated receptor α agonists,“ The Journal of Biological Chemistry, vol. 294, no. 10, pp. 3720–3734, 2019. Publisher Full Text | Google Scholar
  26. D. N. M. Jones, J. Wang, and E. J. Murphy, “Complete NMR chemical shift assignments of odorant binding protein 22 from the yellow fever mosquito, Aedes aegypti, bound to arachidonic acid,“ Biomolecular NMR Assignments, 2019.
  27. L. Ouldamer, L. Nadal-Desbarats, S. Chevalier, G. Body, C. Goupille, and P. Bougnoux, “NMR-Based Lipidomic Approach To Evaluate Controlled Dietary Intake of Lipids in Adipose Tissue of a Rat Mammary Tumor Model,“ Journal of Proteome Research, vol. 15, no. 3, pp. 868–878, 2016. Publisher Full Text | Google Scholar
  28. K. Chen, Y. Chen, H. Tang et al., “Dietary Leucine Supplement Ameliorates Hepatic Steatosis and Diabetic Nephropathy in db/db Mice,“ International Journal of Molecular Sciences, vol. 19, no. 7, p. 1921, 2018. Publisher Full Text | Google Scholar
  29. S. D. Melvin, C. M. Lanctôt, N. J. Doriean, W. W. Bennett, and A. R. Carroll, “NMR-based lipidomics of fish from a metal(loid) contaminated wetland show differences consistent with effects on cellular membranes and energy storage,“ Science of the Total Environment, vol. 654, pp. 284–291, 2019. Publisher Full Text | Google Scholar
  30. P. Khandelwal, S. Stryker, H. Chao et al., “ 1 H NMR-based lipidomics of rodent fur: species-specific lipid profiles and SCD1 inhibitor-related dermal toxicity ,“ Journal of Lipid Research, vol. 55, no. 7, pp. 1366–1374, 2014. Publisher Full Text | Google Scholar
  31. J. Schille and K. Arnold, “Application of high resolution 31P NMR spectroscopy to the characterization of the phospholipid composition of tissues and body fluids - A methodological review,“ Medical Science Monitor, vol. 8, no. 11, pp. MT205–MT222, 2002. PubMed Abstract
  32. K. G. Malmos, B. Gouilleux, P. Sønderskov, T. Andersen, J. V. Frambøl, and T. Vosegaard, “ Quantification of Ammonium Phosphatide Emulsifiers in Chocolate Using 31 P NMR Spectroscopy ,“ Journal of Agricultural and Food Chemistry, vol. 66, no. 39, pp. 10309–10316, 2018. Publisher Full Text | Google Scholar
  33. D. Bouillaud, J. Farjon, O. Gonçalves, and P. Giraudeau, “Benchtop NMR for the monitoring of bioprocesses,“ Magnetic Resonance in Chemistry, 2019.
  34. B. Gouilleux, N. V. Christensen, K. G. Malmos, and T. Vosegaard, “ Analytical Evaluation of Low-Field 31 P NMR Spectroscopy for Lipid Analysis ,“ Analytical Chemistry, vol. 91, no. 4, pp. 3035–3042, 2019. Publisher Full Text | Google Scholar
  35. K. Feingold, C. Grunfeld, and to. Introduction Lipids and Lipoproteins. Endotext, Eds., Introduction to Lipids and Lipoproteins. Endotext, South Dartmouth (MA, 2018.
  36. R. M. Krauss, “Lipids and lipoproteins in patients with type 2 diabetes,“ Diabetes Care, vol. 27, no. 6, pp. 1496–1504, 2004. Publisher Full Text | Google Scholar
  37. P. W. F. Wilson, R. B. D'Agostino, D. Levy, A. M. Belanger, H. Silbershatz, and W. B. Kannel, “Prediction of coronary heart disease using risk factor categories,“ Circulation, vol. 97, no. 18, pp. 1837–1847, 1998. Publisher Full Text | Google Scholar
  38. B. A. Ference, I. Graham, L. Tokgozoglu, and A. L. Catapano, “Impact of Lipids on Cardiovascular Health,“ Journal of the American College of Cardiology, vol. 72, no. 10, pp. 1141–1156, 2018. Publisher Full Text | Google Scholar
  39. J. W. Gofman, F. Lindgren, H. Elliott et al., “The Role of Lipids and Lipoproteins in Atherosclerosis,“ Science, vol. 111, no. 2877, pp. 166–186, 1950. Publisher Full Text | Google Scholar
  40. M. O. W. Grimm, D. M. Michaelson, and T. Hartmann, “Omega-3 fatty acids, lipids, and apoE lipidation in Alzheimer's disease: A rationale for multi-nutrient dementia prevention,“ Journal of Lipid Research, vol. 58, no. 11, pp. 2083–2101, 2017. Publisher Full Text | Google Scholar
  41. F. N. Emamzadeh, “Role of Apolipoproteins and α-Synuclein in Parkinson’s Disease,“ Journal of Molecular Neuroscience, vol. 62, no. 3-4, pp. 344–355, 2017. PubMed Abstract | Publisher Full Text | Google Scholar
  42. P. Würtz, A. J. Kangas, P. Soininen, D. A. Lawlor, G. Davey Smith, and M. Ala-Korpela, “Quantitative Serum Nuclear Magnetic Resonance Metabolomics in Large-Scale Epidemiology: A Primer on -Omic Technologies,“ American Journal of Epidemiology, vol. 186, no. 9, pp. 1084–1096, 2017. Publisher Full Text | Google Scholar
  43. M. Ala-Korpela, A. Korhonen, J. Keisala et al., “1H NMR-based absolute quantitation of human lipoproteins and their lipid contents directly from plasma,“ Journal of Lipid Research, vol. 35, no. 12, pp. 2292–2304, 1994. PubMed Abstract
  44. P. Soininen, A. J. Kangas, P. Würtz et al., “High-throughput serum NMR metabonomics for cost-effective holistic studies on systemic metabolism,“ Analyst, vol. 134, no. 9, p. 1781, 2009. Publisher Full Text | Google Scholar
  45. J. D. Otvos, E. J. Jeyarajah, and D. W. Bennett, “Quantification of plasma lipoproteins by proton nuclear magnetic resonance spectroscopy,“ Clinical Chemistry, vol. 37, no. 3, pp. 377–386, 1991. Publisher Full Text | Google Scholar
  46. B. Jiménez, E. Holmes, C. Heude et al., “ Quantitative Lipoprotein Subclass and Low Molecular Weight Metabolite Analysis in Human Serum and Plasma by 1 H NMR Spectroscopy in a Multilaboratory Trial ,“ Analytical Chemistry, vol. 90, no. 20, pp. 11962–11971, 2018. Publisher Full Text | Google Scholar
  47. L. Harbaum, P. Ghataorhe, J. Wharton et al., “Reduced plasma levels of small HDL particles transporting fibrinolytic proteins in pulmonary arterial hypertension,“ Thorax, vol. 74, no. 4, pp. 380–389, 2019. Publisher Full Text | Google Scholar
  48. M. Holmes, “Lipoproteins, and Metabolites and Risk of Myocardial Infarction and Stroke,“ Journal of the American College of Cardiology, vol. 71, no. 6, pp. 620–632, 2018. Publisher Full Text | Google Scholar
  49. E. Sliz, “Metabolomic consequences of genetic inhibition of PCSK9 compared with statin treatment,“ Circulation, vol. 138, no. 22, pp. 2499–2512, 2018. Publisher Full Text | Google Scholar
  50. C. Michielsen, “Disentangling the Effects of Monounsaturated Fatty Acids from Other Components of a Mediterranean Diet on Serum Metabolite Profiles: A Randomized Fully Controlled Dietary Intervention in Healthy Subjects at Risk of the Metabolic Syndrome,“ Molecular Nutrition Food Research, vol. 63, no. 9, Article ID e095, 1801.
  51. C. B. Dias, N. Amigo, L. G. Wood, X. Correig, and M. L. Garg, “Effect of diets rich in either saturated fat or n-6 polyunsaturated fatty acids and supplemented with long-chain n-3 polyunsaturated fatty acids on plasma lipoprotein profiles,“ European Journal of Clinical Nutrition, vol. 71, no. 11, pp. 1297–1302, 2017. Publisher Full Text | Google Scholar
  52. A. V. Sorokin, K. Kotani, Y. A. Elnabawi et al., “Association Between Oxidation-Modified Lipoproteins and Coronary Plaque in Psoriasis,“ Circulation Research, vol. 123, no. 11, pp. 1244–1254, 2018. Publisher Full Text | Google Scholar
  53. C. E. Kostara, V. Tsimihodimos, M. S. Elisaf, and E. T. Bairaktari, “NMR-Based Lipid Profiling of High Density Lipoprotein Particles in Healthy Subjects with Low, Normal, and Elevated HDL-Cholesterol,“ Journal of Proteome Research, vol. 16, no. 4, pp. 1605–1616, 2017. Publisher Full Text | Google Scholar
  54. J. Miranda, “Metabolic profiling and targeted lipidomics reveals a disturbed lipid profile in mothers and fetuses with intrauterine growth restriction,“ Scientific Reports, vol. 8, no. 1, p. 13614, 2018. Publisher Full Text | Google Scholar
Review Article
Nuclear Receptor Research
Vol. 7 (2020), Article ID 101452, 10 pages
doi:10.32527/2020/101452

Lipids and NMR: More Than Mere Acquaintances

Cecilia Castro1 and Julian L. Griffin1,2

1Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, UK

2Biomolecular Medicine, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction. Imperial College London, Exhibition Road, South Kensington Campus, London SW7 2AZ, UK

Received 16 August 2019; Accepted 19 January 2020

Editors: Manuel Vazquez Carrera and Pallavi R. Devchand

Copyright © 2020 Cecilia Castro and Julian L. Griffin. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Recognising the paramount importance of lipids in cell physiology and function, there is an analytical need to measure the composition of lipids within the cell and how different lipid species interact. In this review, we will explore the role NMR spectroscopy can have in this. We will show how the technique can be used to measure lipid concentrations, but we will also provide evidences of its importance to characterise lipid interactions with other molecules, such as proteins, and to measure lipoproteins, the transporters of triglycerides and cholesterol, discussing advantages and limitations. Furthermore, we will highlight its potential for quality control analysis, particularly in food science and industry, if further development of benchtop instruments continues. Complementary to liquid chromatography mass spectrometry, which is able to measure numerous lipids in a complex mixture, NMR is an invaluable tool for fulfilling this need of better characterising lipids.

Review Article
Nuclear Receptor Research
Vol. 7 (2020), Article ID 101452, 10 pages
doi:10.32527/2020/101452

Lipids and NMR: More Than Mere Acquaintances

Cecilia Castro1 and Julian L. Griffin1,2

1Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, UK

2Biomolecular Medicine, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction. Imperial College London, Exhibition Road, South Kensington Campus, London SW7 2AZ, UK

Received 16 August 2019; Accepted 19 January 2020

Editors: Manuel Vazquez Carrera and Pallavi R. Devchand

Copyright © 2020 Cecilia Castro and Julian L. Griffin. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite this article

Cecilia Castro and Julian L. Griffin, "Lipids and NMR: More Than Mere Acquaintances," Nuclear Receptor Research, Vol. 7, Article ID 101452, 10 pages, 2020. doi:10.32527/2020/101452