Examination of Mixtures Containing Sphingomyelin and Cholesterol

Apr 11, 2017 - Triplicate replicas of each membrane system were generated in rectangular boxes and tetragonal cells (X = Y ≠ Z) using CHARMM-GUI Mem...
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Examination of Mixtures Containing Sphingomyelin and Cholesterol by Molecular Dynamics Simulations Eric Wang† and Jeffery B. Klauda*,†,‡ †

Department of Chemical and Biomolecular Engineering and ‡Biophysics Graduate Program, University of Maryland, College Park, Maryland 20742, United States S Supporting Information *

ABSTRACT: The all-atom CHARMM36 (C36) force field is used to simulate bilayers of pure palmitoylsphingomyelin (PSM) as well as binary mixtures of PSM and stearoylsphingomyelin (SSM) at various cholesterol concentrations (XC) and temperatures. C36 simulation data is in good agreement with experimental deuterium order parameters and previous computational results, providing evidence of the utility of the force field for potentially studying more complex membranes. The area compressibility modulus is shown to achieve a large value of 2.82 ± 0.08 N/m in cholesterol-rich membranes (XC = 0.50). Surface area per lipid (SA/lip), tilt angle, membrane thicknesses, and acyl chain ordering are shown to have strong dependencies on cholesterol concentration. Relaxation times also indicate cholesterol dependence and show a strong preference for rotational axial motion over wobbling motion. Radial distribution functions and lipid clustering indicate strong relationships between lateral ordering and hydrogen bonding, which is long lived in SM membranes. These interactions lead to strong self-association of cholesterol at high concentrations, causing shielding from further SM−cholesterol interactions. The importance of a ternary component on SM−SM hydrogen bonds is revealed in light of previous results and is consequential in the modeling of lipid rafts.

1. INTRODUCTION Sphingolipids are a class of membrane lipids that are pervasive in eukaryotes and carry out a wide variety of essential cell functions such as lipid homeostasis, metabolism, antigen presentation, and cell trafficking.1 Many lipids contain saturated tails that aid in the membrane’s ability to form tightly packed structures that help membranes endure mechanical stress.2 A particular subset of sphingolipids of great interest in recent years is sphingomyelin (SM). The discovered functions for SM are numerous and continually being revealed. SM is abundant in brain and nervous tissue, and the sphingomyelinases which break down SMs to ceramides are believed to be connected to various neurological disorders.3 The hydrolysis of SM leads to the formation of critical second messengers such as ceramides and its derivatives, affecting properties of membranes such as lipid phases, cholesterol concentration and esterification, and protein recognition.2 Most strikingly, SM and its interactions with cholesterol are known to have important roles in the formation of lipid rafts, which are ordered microdomains within the cell membrane that emerged as a challenge to the prevailing fluid mosaic model and carry profound biological significance.4 Since the initial proposal of the lipid raft hypothesis,5 extensive research has led to many possible explanations for raft function and formation. Rafts have been suggested to play roles in signal transduction, protein sorting, microbial infection,6 and nerve terminal function.7 The details of lipid raft formation are © 2017 American Chemical Society

currently unknown, but it has been found that the properties of rafts are similar to those of liquid-ordered phases and that rafts prefer SM over other lipids such as phosphatidylcholines (PC) because of the deeper intrusion of cholesterol into SM membranes.6 Analysis of in vivo rafts has proven to be quite difficult, so studies often use systems of SM and cholesterol as a model. The rapid advance of computational power has allowed for molecular dynamics (MD) simulations to emerge as a powerful method of studying membranes. An early study in 1993 spent 6 months performing a 100 ps simulation of a simple bilayer,8 whereas present-day simulations can run in the millisecond range using advanced supercomputing architecture.9 MD simulations can be limited by time scales (submicroseconds for most systems on standard computational resources) and potential inaccuracies in the force fields but are cost efficient and provide access to a level of detail that is difficult to impossible with experimental methods. Systems of SM and cholesterol have previously been studied using MD, the most common SM being palmitoylsphingomyelin (PSM) and stearoylsphingomyelin (SSM). The structure of these two lipids are very similar, containing saturated tails that differ only by two carbons. Previous simulation studies of PSM/ Received: February 24, 2017 Revised: April 5, 2017 Published: April 11, 2017 4833

DOI: 10.1021/acs.jpcb.7b01832 J. Phys. Chem. B 2017, 121, 4833−4844

Article

The Journal of Physical Chemistry B

Figure 1. (a) Chemical structure of PSM, SSM, and cholesterol. Terminal tail carbons on SM lipids and the C3 and C17 carbons on cholesterol are labeled. (b) Snapshot of SSM at 50 °C and XC = 0.20 at the end of the simulation. SSM lipids are shown in cyan with colored headgroups, cholesterol molecules are in red, and water is shown as purple dots. (c) Snapshot of SSM at 50 °C and XC = 0.50 at the end of simulation. Color scheme is the same as in (b).

Table 1. System Parameters and General Bilayer Propertiesa lipid

T (°C)

XC

no. of lipids

no. of waters

time (ns)

PSM

45

0.00 0.20 0.33 0.00 0.20 0.33 0.20 0.33 0.50

80 100 100 80 100 100 100 100 100

2400 3500 3500 2400 3500 3500 3500 3500 3500

300 400 400 200 600 400 600 500 400

60

SSM

a

50

SA/lip (Å2) 54.8 43.9 41.7 57.5 46.8 43.1 44.5 41.7 40.3

(0.2) (0.2) (0.2) (0.2) (0.2) (0.1) (0.1) (0.1) (0.1)

KA (N/m) 0.45 1.08 2.01 0.45 0.61 1.14 0.84 2.02 2.82

(0.04) (0.06) (0.10) (0.04) (0.04) (0.06) (0.05) (0.09) (0.08)

Standard errors are represented in parentheses. SA/lip is the average for all lipids (SM and cholesterol).

2. METHODS 2.1. System Setup and Simulation Protocol. Experimental and computational studies have produced results on SM/cholesterol mixtures at cholesterol concentrations of 0.00, 0.20, 0.33, and 0.50,6,13,16,17 so mixtures were built to mimic these systems for simple comparison. PSM systems were built using cholesterol concentrations of 0.00, 0.20, and 0.33 at both 45 and 60 °C, while SSM systems were built using cholesterol concentrations of 0.20, 0.33, and 0.50 only at 50 °C (Figure 1). Pure SM bilayers contained 40 lipids per leaflet with 30 water molecules per lipid, and SM/cholesterol mixtures contained 50 lipids per leaflet with 35 water molecules per lipid. Triplicate replicas of each membrane system were generated in rectangular boxes and tetragonal cells (X = Y ≠ Z) using CHARMM-GUI Membrane Builder and were equilibrated for 385 ps with a 1 fs time step using the Membrane Builder18−21 six-step equilibration process to slowly remove restraints. After this, production runs were ran for 200−600 ns, until every replica had at least 150 ns of stable equilibrated data, which was determined by examination of surface area per lipid (SA/lip) plots. The total production time for each system can be found in Table 1. Most systems assumed a symmetric distribution across leaflets. However, for systems with XC = 0.33, the lipids were asymmetrically distributed between leaflets such that one leaflet would have 16 cholesterol and 34 SM while the other would have 17 cholesterol and 33 SM. Concerns about mismatch changing the lateral pressure of each leaflet are mitigated by

cholesterol and SSM/cholesterol mixtures were run often run 0.09), only becoming significant for the initial introduction of cholesterol (p = 0.004) or for large disparities in concentration such as between XC = 0.20 and XC = 0.50 (p = 0.03). Additionally, greater cholesterol concentration causes a tendency for cluster composition to reflect membrane composition which can be seen as the difference (RC − RN) approaches 0 for both cholesterol and SM. The tendency for cholesterol to participate in lipid clusters in higher proportions and form segregated domains (Figure 1b and 1c, Figure S5) at high concentration possibly explains the previously mentioned increase in SM SA/lip since this causes SM to be excluded from interactions with cholesterol. Decreases in SM−cholesterol H bonding are in agreement with this trend (Table 5). In contrast to cholesterol concentration, temperature has a minimal effect and produces statistically identical values for all cluster properties (p > 0.20). 3.9. Relaxation Times. Details of SM intramolecular motion can be explored by the calculation of three fit exponentials to the correlation functions of carbon−hydrogen and cross-chain carbon−carbon vectors as previously described. The cross-chain vector is expected to limit local torsional motion and only represent axial motion, giving insight into the motion of C2−H, which is an unknown combination of both axial motion and lipid wobble.50 The equilibration times, fit coefficients, and relaxation times are reported in Table S7. However, some systems require considerable time to relax, and the corresponding correlation functions did not plateau within the equilibrated time range, often >300 ns (Figure S13). As a result, the resulting fit coefficients and relaxation times are extracted from incomplete data and sometimes exhibit wide variability between replicas. Additionally, one particular case, PSM at 45 °C and XC = 0.20, was equilibrated long enough to plateau, but one replica plateaued at a significantly different value, producing different fit coefficients. Despite these obstacles, the calculated relaxation times remain useful for providing a general estimate and illustrating trends over temperature and cholesterol concentration. The fast and intermediate relaxation times (τ1 and τ2) do not show a consistent trend with either cholesterol concentration or temperature. However, the slow relaxation time (τ3) which is associated with lipid wobble51 is of most interest. In general, τ3 shows the expected trend of increasing time with cholesterol concentration and decreasing time with temperature (Table 7). The strongest shift occurs when cholesterol is first added in which τ3 increases 2−3-fold, and this occurs for both crosschain and C2−H relaxation times. The C2−H slow relaxation time of pure PSM was previously calculated to be 94 ns,13 so this work’s τ3 values of 150 ns in the 45 °C and XC = 0.20 case is consistent. Discrepancies appear when comparing against this work’s pure PSM τ3 value of 56 to 94 ns from the previous simulation,13 which may be the result of shorter time scales in our current work when compared to the past simulations of 400 ns. It is interesting to note that the τ3 relaxation times for the cross-chain and C2−H vectors are quite similar, implying the preferred mode is likely axial rotation and not lipid wobble. This bias for rotation is consistent with the tendency of cholesterol and SM to remain upright in the membrane which restricts space. Cholesterol’s orientation is clear from the tilt angle (Table 3), and SM can be inferred from the relatively high ordering (Tables S2−4, Figure S14) as well as the high proportion of amide−carbonyl inter H bond relative to amide−

Table 7. Slow Relaxation Time (τ3) Calculated from ThirdOrder Exponential Fits to Reorientational Correlation Functions for Both the Cross-Chain (C4S−C2F) and the C2−H Vector in Each Systema decay

lipid

T (°C)

XC

τ3 (ns)

cross chain

PSM

45

0.00 0.20b 0.33b 0.00 0.20 0.33 0.20b 0.33 0.50b 0.00 0.20b 0.33b 0.00 0.20 0.33 0.20b 0.33 0.50b

56 (7) 117 (9) 306 (66) 24 (1) 63 (6) 62 (3) 132 (21) 101 (17) 194 (18) 56 (4) 150 (25) 255 (10) 30 (3) 58 (6) 65 (2) 131 (25) 88 (5) 183 (15)

60

C2−H

SSM

50

PSM

45

60

SSM

50

a

Standard errors are represented in parentheses. bThe reorientational correlation function did not plateau within the time range, and fits are based on extrapolations of the curve.

phosphate bonds (Table S5). In addition, SM has a tendency to form long-lasting H bonds,11 which itself is known to slow even rotational motion of SM compared to other lipids,52 so the absence of lipid wobble is reasonable.

4. DISCUSSION AND CONCLUSION Many previous simulation studies with SM and cholesterol used relatively short time scales due to computational cost.10−12 Zidar et al.10 performed 30 ns simulations of SSM with 0−40% cholesterol using a force field for SSM that was transferred from analogous functional groups in the CHARMM force field. The unoptimized force field used by Zidar et al.10 resulted in a bilayer that was too dense, and thus, the SCD was higher when compared to that published here with the updated C36 lipid force field for sphingomyelin. Although our work agrees with Zidar et al.10 in that there is a general decrease in SM− cholesterol H bonding with cholesterol concentration, the values in our work are around 30% lower. Ultimately, it is hard to fully compare our work with Zidar et al. because 30 ns is too short to reach lateral equilibration in the bilayer, especially in multicomponent membranes. Other studies using various force fields have also been performed on SM/cholesterol mixtures,11,12 and the work by Zhang et al.12 using the Niemelä et al.53 force field for SM was run long enough to make meaningful comparisons (100 ns). These past results for the EDP of SSM with 34% cholesterol lack the secondary peaks caused by cholesterol at ±10 Å that we observe in our simulation (Figure 3b). Moreover, the DHH is higher with the past work (48.6 Å) compared to this work (47.9 Å). The SCD of the sphingosine chain by Zhang et al.12 lacks the decrease in order for the double-bonded carbons (C4 and C5) in comparison with neighboring carbons, and the order is higher than our work. This is consistent with the SA/lip being lower for the past work (39.4 ± 0.4 Å2) in comparison to our work (41.7 ± 0.1 Å2). Overall, the C36 force field predicts a slightly 4841

DOI: 10.1021/acs.jpcb.7b01832 J. Phys. Chem. B 2017, 121, 4833−4844

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the amide plane through MD simulations and ultimately discover what level of lipid diversity is needed for strong lipid raft modeling. The mixtures presented in this work are either pure bilayers of SM or binary mixtures of SM/cholesterol at varying concentrations and temperatures. Comparison of deuterium order parameters with experimental data showed good agreement, and consistency was demonstrated with past C36 simulations based on order parameters, H bonding, relaxation times, SA/lip, and KA. An inaccuracy in the force field is demonstrated based on order parameter data, which shows an early phase transition to a liquid-ordered state at XC = 0.20 and minimal change from XC = 0.33 to XC = 0.50. However, order parameters for the liquid-ordered state are in agreement with experiment. In general, this study is further evidence that the C36 force field is an overall accurate model of molecular interactions and can be used to study more complex structures. Unexpected breaks from trends at high cholesterol concentrations are explained by the self-association of cholesterol which lead to a shielding effect. H-bonding results (Table 5) provide computational support for the decrease in SM−SM H bonding with cholesterol concentration, which has been experimentally noted and has implications in the study of liquid-ordered domains and consequently lipid rafts.

less dense membrane when compared to past simulations with other force fields and in agreement with simulations of pure SM bilayers.13 Since MD simulations with the C36 force field on pure and SM mixtures with cholesterol agree with experiment better than published work with other force fields, the C36 force field is well tuned for looking at mixtures with SM lipids. Experimental studies of SM/cholesterol binary mixtures by Raman spectroscopy54 and computational studies of ternary mixtures involving SM and cholesterol44 using the C36 force field have shed light on possible factors leading to the formation of liquid-ordered phases. In particular, they highlight the importance of hydrogen-bonding networks which are particularly high in SM/cholesterol mixtures. The formation of these liquid-ordered phases, occurring near a cholesterol concentration of 30%, could be similar to the formation of lipid rafts at the nanoscale.10 Notably, not all SM are relevant as oleoylsphingomyelin has been eliminated as a factor in raft formation because of its inability to form segregated domains with cholesterol, a result of unsaturation in the tail.55 Raman spectroscopy on SSM/cholesterol mixtures has suggested a decrease in SM−SM H bonding with cholesterol concentration which is apparently contradicted by the mechanism of amide plane tilting caused by SM−cholesterol interactions proposed by Sodt et al.44 in a ternary system containing SM, cholesterol, and PC. From these studies it appears that the presence of a ternary PC component has a profound impact on SM−SM H bonding. The results of the work published here are in agreement with a past binary SM/cholesterol simulation in the OPLS system56 and Raman spectroscopy as a clear decrease in SM−SM inter H bonding is demonstrated, which is especially prominent in the amide−carbonyl pair. This drop in H bonding can be explained by the tendency of cholesterol to disrupt groups of associating SM which then spread out while maintaining order, also resulting in an increase in SA/lip. Thus, H bonding decreases because the distance between Hbonding groups decreases. This is more powerful than the modest increases which occur in ternary SM/PC/cholesterol mixtures,14 suggesting that amide plane tilting is not always significant. Nevertheless, while binary mixtures remain suitable models of lipid rafts based on the high proportion of SM and cholesterol, future studies should take lipid diversity into account because the full significance of ternary components on lateral organization has yet to be explored. Despite the difficulties associated with applying experimental methods of studying binary systems to ternary systems and beyond, efforts are being made to take greater lipid diversity into account. The use of unsaturated PCs is relatively common because cholesterol promotes phase separation between the low main transition temperature (TM) PC and the high TM SM.57 Results from experimental studies of ternary mixtures found the presence of SM/cholesterol complexes with partitioning into the PC phase58 which is similar to the existence of such complexes in binary systems.59 Additionally, it was found that the use of different PC lipids such as 1-palmitoyl-2-oleoyl-snglycero-3-phosphocholine versus 1-stearoyl-2-oleoyl-sn-glycero3-phosphocholine yielded similar results, and both could be used to model rafts.60 Even greater diversity has been probed with the inclusion of ganglioside in SM/PC/cholesterol membranes to observe ganglioside-rich microdomains.61 Based on the importance of ternary components on SM−SM H bonding revealed in past work and differences with our observations for binary lipids, it would be interesting to probe how an additional component such as ganglioside would affect

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jpcb.7b01832. Figures of SA/lip vs time, deuterium order parameters, component EDPs, RDFs, XY-plane snapshots, reorientational correlation functions, and lipid simulation snapshots; tables of interpolated SA/lip values, order parameters, hydrogen bonding, clustering fractions, and fitted coefficients of reorientational correlation functions (PDF)

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. ORCID

Jeffery B. Klauda: 0000-0001-8725-1870 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This research was supported by the NSF grant MCB-1149187. The high-performance computational resources used for this research are Deepthought and Deepthought2, which are maintained by the Division of Information Technology at the University of Maryland. Production runs used the Extreme Science and Engineering Discovery Environment (XSEDE) allocation on Stampede (MCB-100139). We especially thank Xiaohong Zhuang for sharing data analysis scripts and Hojin Kang for running simulations of SM/cholesterol mixtures.

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REFERENCES

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DOI: 10.1021/acs.jpcb.7b01832 J. Phys. Chem. B 2017, 121, 4833−4844

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DOI: 10.1021/acs.jpcb.7b01832 J. Phys. Chem. B 2017, 121, 4833−4844