Purinergic inhibition of Na+,K+,Cl− cotransport in C11-MDCK cells: Role of stress-activated protein kinases

Previously, we observed that sustained activation of P2Y1 leads to inhibition of Na+,K+,Cl− cotransport (NKCC) in C11 cells resembling intercalated cells from collecting ducts of the Madin-Darby canine kidney. This study examined the role of stress-activated protein kinases (SAPK) in NKCC inhibition triggered by purinergic receptors. Treatment of C11 cells with ATP led to sustained phosphorylation of SAPK such as JNK and p38. Activation of these kinases also occurred in anisomycin-treated cells. Surprisingly, we observed that compounds SP600125 and SB202190, known as potent inhibitors of JNK and p38 in cell-free systems, activated rather than inhibited phosphorylation of the kinases in C11 cells. Importantly, similarly to ATP, all the above-listed activators of JNK and p38 phosphorylation inhibited NKCC. Thus, our results suggest that activation of JNK and/or p38 contributes to NKCC suppression detected in intercalated-like cells from distal tubules after their exposure to P2Y1 agonists.
Na+,K+,Cl− cotransporter (NKCC), providing electroneutral symport of monovalent ions in the stoichiometry of 1Na+:1K+:2Cl− and selectively inhibited by bumetanide and other high-ceiling diuretics, belongs to the superfamily of Cl−-coupled monovalent cation cotransporters. Two isoforms of this carrier have been cloned from vertebrate cDNA libraries. A ubiquitous NKCC1 isoform is expressed in all types of cells studied so far [1], including the basolateral membrane of epithelial cells derived from collecting ducts of the Madin-Darby canine kidney (MDCK) [2]. This isoform contributes to cell volume regulation, adjustment of [Cl−]i above the values predicted by Nernst equilibrium potential and transcellular movement of salt and  in the secretory epithelium [1, 3]. Unlike NKCC1, three alternatively spliced variants of the NKCC2 isoform were found exclusively on the apical membrane of renal epithelial cells from the macula densa and thick ascending limb of Henle’s loop. In the thick ascending limb, NKCC2 plays a key role in bulk salt reabsorption [3], whereas in the macula densa this carrier is involved in sensing extracellular Cl− concentration and regulating renal function via tubuloglomerular feedback [4].
Because of compensatory reactions occurring in proximal segments via tubuloglomerular feedback, collecting ducts are considered a major target for regulation of water/salt and acid-base homeostasis by hormones and neurotransmitters, including vasopressin, bradykinin, atrial natriuretic peptide, prostanoids, mineralocorticoids, and catecholamines [5]. Under certain circumstances, extracellular nucleotides, such as ATP, UTP, and ADP, can also regulate renal epithelium function by activating two receptor subtypes. P2Y receptors are coupled to heterotrimeric G proteins and expressed in all types of renal cells studied so far, whereas P2X receptors correspond to ligand-gated cation channels, and their expression in the kidney is mainly limited to mesangial and vascular smooth muscle cells [6–10].
The renal ion transport systems affected by P2Y receptors have been explored mainly in MDCK cells where extracellular ATP leads to transient Cl− secretion [11]. Two populations of cells have been isolated from commercially available stocks of MDCK cells: C7- and C11-MDCK cells [12]. C7 cells have high transepithelial electrical resistance (Rte), are peanut lectin negative, maintain pHi at 7.39, and have large K+ conductance, thus resembling principal cells from the collecting ducts. C11 cells, on the other hand, resemble intercalated cells; they have low Rte, are peanut lectin positive, maintain pHi at 7.16, and have large Cl− and H+ conductances [12]. We were the first to report that transient activation of basolateral NKCC in ATP-treated C7 cells is evoked by elevation of [Ca2+]i [13] and contributes to transepithelial Cl− secretion [14]. We also noted that in contrast to C7 and other cells studied so far, sustained application of ATP to C11 cells resulted in sharp inhibition of this carrier [15] that was absent under the addition of other modulators of transepithelial ion fluxes [16].
Several hypotheses might be proposed to explain the cell type-specific impact of extracellular nucleotides on NKCC activity. First, NKCC inhibition is mediated by the P2 receptor isoform whose expression is limited to intercalated cells. However, in a recent study, we established that NKCC inhibition in ATP-treated C11 cells is caused by activation of P2Y1 receptors [13] whose expression has been detected in most of the cell types studied so far, including C7 cells [17]. Second, in intercalated cells, sustained activation of P2Y1 receptors leads to inhibition of the cell type-specific NKCC isoform. This hypothesis contradicts data showing that prolonged exposure of C11 cell monolayers to ATP abolished the increment of bumetanide-sensitive 86Rb uptake across the basolateral membrane by transfection with human NKCC1 [18]. Third, delayed NKCC1 inhibition seen in ATP-treated C11 cells is mediated by the de novo expression of intercalated cell-specific inhibitor(s) of this carrier. However, the inhibitory action of ATP on NKCC activity in C11 monolayers was preserved in the presence of inhibitors of RNA and protein synthesis [18]. Fourth, NKCC1 inhibition is caused by the intercalated cell-specific signaling cascade that couples P2Y1 activation with NKCC1 inhibition.
In renal epithelial cells, P2Y receptors trigger diverse signaling, including transient elevation of [Ca2+]i, cAMP production, activation of phospholipase C (PLC), phospholipase A2 (PLA2), prostaglandin synthase, protein kinase C (PKC), and protein kinase A (PKA) [19, 20]. We did not observe any effect of extracellular Ca2+ and intracellular Ca2+ chelator on NKCC inhibition in C11 cells [15, 21]. The inhibitory action of ATP is also preserved in the presence of inhibitors of PLC, PLA2, and prostaglandin synthase (U73122, AACOCF3, and indomethacin, respectively) as well as under activation of adenylate cyclase with forskolin and isoproterenol and inhibition of PKA with H-89 [15, 21]. In C11 cells, NKCC inhibition was also revealed in the presence of the PKC activator 4β-phorbol-12-myristate-13-acetate (PMA). However, the same action of PMA was also documented in C7 cells [15]. Moreover, suppression of NKCC in ATP-treated cells was not affected in the presence of PKC inhibitors (staurosporine and calphostin C) and after downregulation of this enzyme by chronic exposure to PMA [15, 21]. Viewed collectively, these data strongly suggest that none of the above-listed intermediates of the intracellular signaling cascade is involved in the purinergic inhibition of NKCC [15, 21].
In contrast to the above-listed signals documented in both principal- and intercalated-like cells, activation of P2Y receptors resulted in phosphorylation of members of the superfamily of mitogen-activated protein kinases (MAPK), such as the extracellular signal-activated protein kinases Erk1 and Erk2 and the Jun N-terminal kinase JNK1. Compound PD98059, a potent Erk inhibitor, strongly suppressed Erk1 and Erk2 phosphorylation in ATP-treated C11 cells, but did not affect the inhibitory action of ATP on NKCC activity [15, 21]. Considering these results, we designed the present investigation to examine the involvement of JNK and p38, a member of the stress-activated protein kinases (SAPK), in P2Y-induced NKCC inhibition.
Methods
Cell culture C11-MDCK cells were obtained from Dr. M. Gekle (University of Würzburg, Germany) and cultured in DMEM supplemented with 2.5 g/l sodium bicarbonate, 2 g/l hydroxyethylpiperazine ethanesulfonic acid (HEPES), 100 U/ml penicillin, 100 μg/ml streptomycin, and 10% fetal bovine serum (Gibco Laboratories, Burlington, ON, Canada). The cells were passaged upon reaching subconfluent density by treatment in Ca2+- and Mg2+-free Dulbecco’s phosphate-buffered saline (PBS) with 0.1% trypsin from Sigma (St. Louis, MO, USA), then scraped from the flasks with a rubber policeman. Dispersed cells were counted and inoculated at 1.25 × 103 cells/cm2. Before experimentation, the cells were subjected to 24-h serum deprivation in the presence of 0.1% bovine serum albumin (BSA).
NKCC measurement Serum-deprived cells seeded in 24-well plates were washed twice with 2 ml of PBS and incubated for 30 min at 37°C in 1 ml of Cl−-depleted medium A containing 140 mM Na gluconate, 5 mM K gluconate, 1 mM MgSO4, 1 mM CaCl2, 5 mM D-glucose, and 20 mM HEPES-Tris (pH 7.4). After 30 min of Cl− depletion, ATP and other test compounds were added at concentrations indicated in the figure legends. In a major part of the experiments, preincubation with ATP was limited to 30 min. Then, Cl−-depleted medium was replaced with 0.25 ml of medium B containing 140 mM NaCl, 5 mM KCl, 1 mM MgCl2, 1 mM CaCl2, 5 mM D-glucose, 20 mM HEPES-Tris (pH 7.4), ~1 μCi/ml 86Rb, and 50 μM ouabain ± 10 μM bumetanide. In 5 min, 86Rb uptake was terminated by the addition of 2 ml of ice-cold medium C containing 100 mM MgCl2 and 10 mM HEPES-Tris buffer (pH 7.4). The cells were then transferred on ice, washed 4 times with 2 ml of ice-cold medium C, and lysed with 1 ml of a 1% SDS/4 mM ethylenediaminetetraacetate (EDTA) mixture. The radioactivity of the cell lysate was measured with a liquid scintillation analyzer. The rate of 86Rb (K+) influx was calculated as V = A/amt where A was the radioactivity in the sample (cpm), a was the specific radioactivity of 86Rb (K+) (cpm/nmol) in the incubation medium, m was the protein content in the sample measured by modified Lowry’s method (mg), and t was the incubation time (min). NKCC activity was estimated as the rate of ouabain-resistant, bumetanide-sensitive 86Rb influx.
Western blotting C11 cells grown in 6-well plates were incubated for 24 h in DMEM containing 0.1% BSA and stimulated with ATP in Cl−-depleted medium A. Then, the cells were washed twice with ice-cold PBS and lysed in 200 μl of lysis buffer containing 20 mM Tris-HCl (pH 7.5), 150 mM NaCl, 10% glycerol, 1% Triton X-100, 0.1% SDS, 2 mM EDTA, 2 mM ethyleneglycoltetraacetic acid (EGTA), 0.25% deoxycholate, 1 mM phenylmethylsulfonyl fluoride, 1 μg/ml leupeptin, 1 μg/ml aprotinin, 200 μM Na-orthovanadate, and 1 mM NaF. The lysed cells were scraped and centrifuged at 13,000 rpm for 5 min, and an equal volume of clear lysates containing 20 μg of protein was treated for 5 min at 95°C and applied on 10% polyacrylamide gel, followed by electrophoresis and transfer to Immobilon-P membranes (Millipore Corp., Bedford, MA, USA). The membranes were washed with PBS containing 0.05% Tween 20 (PBS-Tween) and 0.5% skim milk, and incubated overnight at 4°C with antibodies. After incubation, the membranes were washed 3 times with PBS-Tween, incubated for 1 h with horseradish peroxidase-conjugated antibody (Santa Cruz Biotechnology, Santa Cruz, CA, USA), washed with PBS-Tween, and the protein bands were visualized with an enhanced chemiluminescence detection kit (Santa Cruz Biotechnology, Santa Cruz, CA, USA) before exposure to X-ray film. Relative protein content was quantified by the NIH image program.
Chemicals ATP, ouabain, bumetanide, forskolin, and PMA were obtained from Sigma (St. Louis, MO, USA); anisomycin, cell-permeable inhibitors of JNK and p38 kinases (compounds SP600125 and SB202190, respectively) and their negative controls [N1-methyl-1,9-pyrazoloanthrone (MPA) and compound SB202474, respectively] were purchased from Calbiochem (La Jolla, CA, USA), and 86RbCl from Dupont (Boston, MA, USA). Anti-phospho-SAPK/JNK (Thr183/Tyr185) and anti-phospho-p38 (Thr180/tyr182) antibodies were provided by Cell Signaling Technology Inc. (Hornby, ON, Canada). Salts, D-glucose, and buffers were obtained from Sigma (St. Louis, MO, USA) and Anachemia (Montreal, QC, Canada).
Statistics The data were analyzed by Student’s t-test or the t-test for dependent samples, as appropriate. Significance was defined as p < 0.05.
Results
In a previous study, we observed that 1-h preincubation of C11 cells in Cl−-depleted medium abolished the transient activation of NKCC by ATP but sharply increased baseline NKCC activity, allowing more precise estimation of the inhibitory action of P2Y-induced signaling [13]. Considering these results, Cl−-depleted cells were used in the present experiments. Figure 1 shows that NKCC inhibition in Cl−-depleted cells occurred after a 10-min lag phase with maximal attenuation of the carrier’s activity in 30–40 min of ATP addition.
Fig. 1Time dependence of NKCC inhibition in C11 cells triggered by the addition of 100 μM ATP
Incubation for 2 min with ATP was sufficient to induce full-scale phosphorylation of JNK1 (p46) and p38 kinases (Fig. 2). Phosphorylation of JNK2 in ATP-treated cells was observed after a 5-min lag phase, and its time course was negatively correlated with NKCC inhibition. In contrast to sustained JNK phosphorylation, p38 phosphorylation was partially normalized in 40 min of ATP addition. Activation of adenylate cyclase with forskolin did not affect SAPK phosphorylation. Very modest p38 and JNK phosphorylation was detected in 20 min of PMA addition. These results show that neither PKA nor PKC is involved in SAPK phosphorylation by ATP, which is consistent with the lack of involvement of these serine-threonine kinases in inhibition of NKCC in ATP-treated C11 cells [15, 16, 21].
Fig. 2Representative blots showing the time-dependent action of 100 μM ATP, 10 μM forskolin, and 0.1 μM PMA on SAPK phosphorylation in C11 cells
Consistent with numerous previous observations, anisomycin sharply elevated SAPK phosphorylation (Fig. 3). Figure 4a reveals that the action of anisomycin on JNK phosphorylation was lower than that of ATP; in contrast, anisomycin was a more potent activator of p38 phosphorylation than ATP. However, these differences were not statistically significant. We observed that 40-min exposure to anisomycin resulted in the inhibition of NKCC to about the same level as was detected in ATP-treated cells (Fig. 4b).
Fig. 3Representative blots revealing the effects of SP600125 (a), MPA (b), SB202190 (c), and SB202474 (d) on JNK and p38 phosphorylation in C11 cells under control conditions and in the presence of 100 μM ATP or 0.1 μM anisomycin. ATP and anisomycin were added during the last 40 min of preincubation of cells in Cl−-depleted medium. Compounds SP600125, MPA, SB202190, and SB202474 were added at the indicated concentrations 30 min before ATP or anisomycinFig. 4Effect of ATP and anisomycin on JNK and p38 phosphorylation (a) and NKCC (b) in C11 cells. 100 μM ATP and 0.1 μM anisomycin were added during the last 40 min of preincubation of cells in Cl−-depleted medium. Phosphoprotein content and NKCC activity in the absence of ATP and anisomycin were taken as 1.0 and 100%, respectively. Mean values from three (a) or four (b) independent experiments are shown
With cell-free systems, it was demonstrated that several newly synthesized cell-permeable compounds inhibited purified or recombinant JNK and p38 without significant action on other protein kinases. Thus, compound SP600125 inhibited recombinant JNK1-3 with an IC50 of 0.05–0.20 μM, whereas half-maximal inhibition of other members of the MAPK superfamily, such as Erk and p38 as well as PKA and cyclin-dependent kinases, was observed at concentrations higher than 5 μM [22, 23]. It should be underlined that SP600125 was a much less potent inhibitor of JNK in intact cells. Indeed, inhibition of JNK in the anisomycin-treated KB-3 carcinoma cell line was detected after 1-h preincubation with this compound at concentrations higher than 3 μM [23]. To decrease JNK phosphorylation by two- to threefold in PMA-treated Jurkat cells, SP600125 should be added at a concentration of 50 μM [22]. Surprisingly, we observed that at a concentration of 20 μM SP600125 and SB202190, i.e., potent inhibitors of p38 SAPK in cell-free systems, increased JNK and p38 phosphorylation to the level comparable with their phosphorylation in the presence of ATP and anisomycin (Fig. 3a and c). Moreover, like other potent activators of JNK and p38 phosphorylation, such as ATP and anisomycin, both SP600125 and SB202190 sharply decreased NKCC activity in C11 cells (Fig. 5). In contrast, neither JNK/p38 phosphorylation (Fig. 3b and d) nor NKCC activity in the presence and absence of ATP (data not shown) was affected by the inactive structural analogues of SP600125 and SB202190, compounds MPA and SB202474, respectively.
Fig. 5Effect of ATP, SP600125, and SB202190 on NKCC activity in C11 cells; 100 μM ATP was added during the last 40 min of preincubation of cells in Cl−-depleted medium. Compounds SP600125 and SB202190 were added at 20 μM concentration 30 min before ATP. NKCC activity in control cells was taken as 100%. Mean ± SE values from experiments performed in quadruplicate are shown
Increased phospho-JNK and phospho-p38 levels in SP600125- and SB202190-treated C11 cells, revealed in our study, are in contrast with the inhibitory action on these SAPK in cell-free systems [22, 23]. Indeed, the Calbiochem catalogue notes that SP600125 and SB202190 inhibit purified JNK and p38 with IC50 of 50 and 16 nM, respectively. It should be underlined that both compounds are ATP-competitive kinase inhibitors, and the data reported were obtained at an ATP concentration of 5 μM, i.e., 1,000-fold lower than the intracellular ATP concentration. For this reason, in experiments with intact cells, these inhibitors are used at much higher concentrations where diverse side effects cannot be excluded. For more details, see [24].
Discussion
The data obtained in our study show that NKCC inhibition in ATP-treated C11 cells is accompanied by activation of JNK and/or p38. We also report here that activation of these SAPK with three distinct stimuli, such as actinomycin, SP600125, and SB202190, results in NKCC inhibition. Additional experiments should be performed to establish the causal relationship between SAPK activation and NKCC inhibition. It should also be noted that the modest inhibitory action of ATP on NKCC was preserved in the presence of the above-listed activators of SAPK. This observation suggests that the signaling cascade is not limited to the activation of these kinases. Recent studies have demonstrated that the with no lysine (K) kinase member WNK4 downregulates NKCC1 via the STE20-related kinase PASK interacting with the conserved domain in the NKCC1 N-terminus [25–27]. The role of WNK kinases in ATP-induced NKCC1 inhibition limited to intercalated cells deserves further investigation.
Two questions should be answered to evaluate the role of purinergic-dependent ion transporters in the regulation of renal function in vivo.  First, are concentrations of ATP and other extracellular nucleosides sufficient for activation of P2Y receptors on the apical and basolateral surfaces of collecting duct epithelia?  Second, what is the physiological consequence of purinergic regulation of NKCC and other ion transporters in principal and intercalated cells comprising collecting ducts?
In peripheral blood, ectonucleotidases maintain circulating levels of ATP < 10 nM [28], a concentration at which renal P2 receptors with ID50(ATP) > 1 μM [29] cannot be activated. However, nucleosides can act in paracrine and autocrine ways, reaching high extracellular concentrations after sympathetic [30] and cholinergic [31] stimulation or exposure to shear [32, 33], osmotic [34–36], and ischemic stresses [33, 37–39]. Sympathetic innervation is probably the major source of nucleosides for basolateral purinergic receptors [38] whereas shear and osmotic stresses, occurring in proximal tubules and the juxtaglomerular apparatus, contribute to ATP release through the apical membrane in tubular fluid [20, 40]. Using chimeric Staphylococcus aureus protein A-luciferase bound to endogenous antigens on the human airway epithelium, it was shown that hypotonic shock leads to elevation of ATP concentration in surface fluid from ~0.001 to 1 μM [41]. Released ATP is rapidly metabolized by ectoenzymes to adenosine. Importantly, modest elevation of UTP [33] and adenosine [42] sharply increased ATP release from MDCK cells, suggesting a positive feedback loop. It is well documented that osmolality of tubular fluid in collecting ducts varies from ~400 to 1,500 mOsm [43]. Baseline concentrations of ATP and other P2Y agonists in collecting duct tubular fluid and their modulation by osmotic perturbations remain unknown.
Both C7 and C11 cells, resembling principal and intercalated cells, are highly abundant with P2Y1 and P2Y2 receptors [13, 17]. In these cells, P2Y1 and P2Y2 receptors have been shown to mainly reside on the basolateral and apical membranes, respectively [44]. Using monolayers of C7 cells, it was noted that basolateral P2Y1 receptors activate Cl− secretion via PLA2-PKA-mediated activation of Cl− channels [17]. Our studies demonstrated that P2Y2 receptors contribute to Cl− secretion via Ca2+-calmodulin-mediated activation of NKCC1 [14] (Fig. 6a). Glanville and coworkers reported that in mouse collecting ducts, basolateral NKCC possesses the same affinity for K+ and  (K0.5 ~1.5 mM) [45]. Because bumetanide markedly decreased the acidification rate in NH4Cl-treated cells, they proposed a key role for NKCC1 in H+ secretion and  handling by intercalated cells. With intercalated-like C11 cells, we observed that apical P2Y2 receptors transiently activated NKCC1 via Ca2+-calmodulin-mediated signaling, whereas sustained activation of basolateral P2Y1 receptors inhibited this carrier [13]. The present data suggest that NKCC1 inhibition in intercalated cells evoked by P2Y1 agonists is caused by activation of SAPK (Fig. 6b). Further studies should be performed to examine the relative contribution of purinergic signaling in the final adjustment of salt reabsorption and acid-base balance by principal and intercalated cells in vivo as well as the pathophysiological implications of these regulatory pathways.
Fig. 6Possible mechanisms of purinergic signaling in the regulation of Cl− secretion and acid-base homeostasis by principal (a) and intercalated (b) cells from collecting ducts in vivo. 1 Cl− channels, 2 H+-ATPase, 3 anion exchange, PLC phospholipase C, IP3 inositol 1,4,5-triphosphate, COX cyclooxygenase, PGE1 prostaglandin E1, CAMK (Ca2++CaM)-dependent protein kinase, CA carbonic anhydrase, a.m. and b.m. apical and basolateral membranes, respectively. For other abbreviations and more details, see text

k+
stress-activated protein kinases
intercalated cells
purinergic receptors
cl− cotransport
na+


Does self-monitoring of blood glucose improve outcome in type 2 diabetes? The Fremantle Diabetes Study

Aims/hypothesis To assess whether self-monitoring of blood glucose (SMBG) is an independent predictor of improved outcome in a community-based cohort of type 2 diabetic patients.
Introduction
Intensive glycaemic control is a cost-effective way of reducing the complications associated with type 2 diabetes [1]. Whether self-monitoring of blood glucose (SMBG) can improve glycaemia is subject to debate [2, 3]. The Retrolective Study ‘Self-monitoring of Blood Glucose and Outcome in Patients with Type 2 Diabetes’ (ROSSO) investigators recently reported that SMBG was associated with decreased diabetes-related morbidity and all-cause mortality in type 2 diabetes, despite the SMBG group having a higher mean baseline fasting plasma glucose than the non-SMBG group [4]. In an Australian community-based cohort of type 2 patients we found that SMBG was not a determinant of glycaemic control [5], and now investigate whether, as in ROSSO, those who performed SMBG had better outcomes than those who did not.
Subjects and methods
Subjects The Fremantle Diabetes Study (FDS) was a longitudinal observational study in a community of 120,097 people in the state of Western Australia. We identified 2,258 subjects between 1993 and 1996, using all available sources, and recruited 1,426 (63%) to attend annual assessments, of whom 1,294 (91%) had type 2 diabetes [6]. The FDS protocol was approved by the Fremantle Hospital Human Rights Committee and all subjects gave informed consent. The present study included (1) 1,280 type 2 patients (mean age 64.1 ± 11.3 years, 48.8% men) with complete diabetes treatment and mortality data who reported SMBG status at FDS entry, and (2) a subset of 531 patients (mean age 62.4 ± 9.4 years, 54.2% men) who attended six or more consecutive annual assessments.
Clinical assessment At baseline and annual reviews, a comprehensive history was taken and a physical examination was performed. Complications were identified using standard criteria [5, 7]. Microalbuminuria was defined as an urinary albumin:creatinine ratio (ACR) ≥3.0 mg/mmol on a first morning sample, neuropathy as a score >2/8 on the clinical portion of the Michigan Neuropathy Screening Instrument, and retinopathy as any grade in one/both eyes on direct and/or indirect ophthalmoscopy and/or detailed specialist assessment. Self-report and hospitalisations were used to identify cerebrovascular disease (CVD; stroke, transient ischaemic attack) and CHD (myocardial infarction, angina, coronary revascularisation). Peripheral arterial disease (PAD) was defined as an ankle:brachial index ≤0.9 or diabetes-related amputation. Blindness in one or both eyes, foot amputation and end-stage renal disease were also defined to allow comparison with the ROSSO findings [4].
Hospital morbidity and mortality Western Australian government registers record details of all deaths and hospital admissions, and make up part of the Western Australian Data Linkage System (WADLS) [8], which provided endpoint data to the end of June 2006. The Confidentiality of Health Information Committee approved linkage with the FDS database. All hospitalisations for endpoints were identified using the International Classification of Diseases (ICD)-9-CM and ICD-10-AM diagnosis/procedure codes. Causes of death were classified independently by two authors (D.G. Bruce, T.M.E. Davis) as ‘cardiac’ or ‘other’ [7].
Statistical analysis Data were analysed using SPSS for Windows (version 11.5) and are presented as proportions or means±SD. Comparison of two independent proportions was by Fisher’s exact test. Freedom from non-fatal endpoints, all-cause mortality or cardiac death was analysed by baseline SMBG status using the Kaplan–Meier method and compared with the log-rank test. For outcomes with sufficient events, Cox proportional hazards modelling (forward conditional variable entry and removal with p < 0.05 and p > 0.05, respectively), was used to determine: (1) independent predictors of first ever occurrence of endpoints, with all clinically plausible univariate variables with a p value of less than 0.20 considered for entry; and (2) whether, after adjusting for these variables, either SMBG at baseline or the time-dependent covariate SMBG was independently associated with outcome. A p value of less than 0.05 was considered significant.
Results
At baseline, 898 type 2 patients (70.2%) performed SMBG. During 12,491 patient-years of follow-up (mean 9.8 ± 3.5 years), 486 (38.0%) died, of which 196 (15.3%) deaths were from cardiac causes. SMBG was significantly less prevalent in those who died during follow-up than in those who were alive at the end of June 2006 (65.4 vs 73.0%, p = 0.005).
SMBG and mortality In unadjusted survival analysis (Model 1), SMBG was associated with a significant 24% reduction in all-cause mortality (Table 1). After adjusting for age, sex and diabetes duration (Model 2), this became a non-significant 11% increased risk. Additional adjustment for independent risk factors for all-cause mortality (Model 3) did not alter this finding. In patients on diet±oral hypoglycaemic agents (OHAs) and in those on insulin, baseline SMBG was associated with significant unadjusted 24% and 54% reductions in the risk of death, respectively, which became non-significant after full adjustment.
Table 1Hazard ratios for all-cause and cardiac mortality in 1,280 type 2 diabetes patients by SMBG status for: (1) all diabetes treatment types; (2) those who were treated by diet with or without OHAs; and (3) those taking insulin with or without OHAs No SMBGSMBG  Number of patientsNumber of eventsAbsolute riskaNumber of patientsNumber of eventsAbsolute riskaLog-rank p valueHR (95%CI)All patients All-cause mortality  Model 1 (unadjusted)38216846.789831835.80.0040.76 (0.63–0.92)  Model 2 (adjusted)b1.11 (0.92–1.35)  Model 3 (adjusted)c1.15 (0.93–1.44) Cardiac mortality  Model 1 (unadjusted)3826117.089813515.20.460.89 (0.66–1.21)  Model 2 (adjusted)b1.35 (0.99–1.86)  Model 3 (adjusted)d1.55 (1.07–2.24)Diet ± OHAs All-cause mortality  Model 1 (unadjusted)35414542.677325232.50.0080.76 (0.62–0.93)  Model 2 (adjusted)b1.15 (0.93–1.43)  Model 3 (adjusted)e1.20 (0.94–1.52) Cardiac mortality  Model 1 (unadjusted)3545115.077310813.90.650.93 (0.66–1.29)  Model 2 (adjusted)b1.51 (1.06–2.14)  Model 3 (adjusted)f1.79 (1.19–2.69)Insulin ± OHAs All-cause mortality  Model 1 (unadjusted)2823119.31256658.40.0010.46 (0.29–0.75)  Model 2 (adjusted)b0.68 (0.41–1.14)  Model 3 (adjusted)g0.73 (0.43–1.26) Cardiac mortality  Model 1 (unadjusted)281051.91252723.90.0260.45 (0.22–0.93)  Model 2 (adjusted)b0.58 (0.27–1.27)  Model 3 (adjusted)h0.52 (0.22–1.19)aEvents per 1,000 person–yearsbModel 2 includes age, sex and duration of diabetes in each case. Model 3 includes all variables in Model 2, plus:cprior CHD, CVD, PAD, neuropathy, retinopathy, ln[ACR], any exercise in past 2 weeks (negative), abdominal obesity (negative), on lipid-lowering medications (negative), Australian Aboriginal, Asian (negative), current smokerdprior CHD, CVD, PAD, neuropathy, retinopathy, ln[ACR], systolic BP (negative), total serum cholesterol, Australian Aboriginal, current smokereprior CHD, CVD, PAD, neuropathy, retinopathy, ln[ACR], abdominal obesity (negative), on lipid-lowering medication (negative), Australian Aboriginal, current smokerfprior CHD, PAD, neuropathy, retinopathy, ln[ACR], systolic BP (negative), total serum cholesterol, current smokergprior CHD, diabetes education (ever; negative), HbA1c, Australian Aboriginalhprior CHD, retinopathy, HbA1c, Australian AboriginalIn unadjusted models for cardiac death, there was a significant 55% risk reduction in insulin-treated patients (Table 1); after full adjustment, this became non-significant. SMBG was associated with a 55% increased risk of cardiac death in Model 3 as a result of a significant 79% increased risk in patients not treated with insulin.
Five-year cohort Consistent with intensification of diabetes therapy, and as reported previously [5], the proportion of the 5-year longitudinal cohort using SMBG increased over a mean of 5.4 ± 0.5 years, from 75.2% at entry to 85.5% at third review, with little change thereafter.We ascertained the incidence of first ever occurrence of micro- and macrovascular complications by baseline SMBG status (Table 2). Unadjusted survival analysis showed no significant benefit of baseline SMBG (p ≥ 0.11) for any endpoint, single or combination. Neither adjustment for age, sex and diabetes duration (Model 2), nor relevant additional variables independently associated with the outcomes in Cox proportional hazards modelling (Model 3), improved this result.
Table 2Hazard ratios of first-ever non-fatal complications in the 531 type 2 diabetes patients in the longitudinal arm for single and combined micro- and macrovascular events, and the ROSSO Study non-fatal endpoint, by SMBG status at: (1) baseline; and (2) each annual assessment (time-dependent covariate) No SMBG at baselineSMBG at baseline  Time-dependent SMBG useNo. of patientsNo. of eventsAbsolute riskaNo. of patientsNo. of eventsAbsolute riskaLog-rank p valueHR (95% CI)HR (95% CI)RetinopathyModel 1 (unadjusted)1164790.034911268.40.110.76 (0.54–1.07)0.51 (0.36–0.72)Model 2 (adjusted)b0.80 (0.57–1.13)0.52 (0.37–0.74)Model 3 (adjusted)c0.82 (0.58–1.16)0.52 (0.37–0.73)NeuropathyModel 1 (unadjusted)8462199.3256186203.00.761.05 (0.78–1.40)0.77 (0.57–1.03)Model 2 (adjusted)b1.16 (0.87–1.55)0.79 (0.59–1.07)Model 3 (adjusted)d1.30 (0.97–1.74)0.89 (0.66–1.20)Microalbuminuria Model 1 (unadjusted)8237104.125810390.60.380.85 (0.58–1.23)0.77 (0.52–1.14)Model 2 (adjusted)b0.88 (0.60–1.28)0.78 (0.53–1.15)Model 3 (adjusted)e0.91 (0.62–1.33)0.74 (0.50–1.10)Any microangiopathy Model 1 (unadjusted)5850280.5172153302.60.591.09 (0.79–1.51)0.99 (0.71–1.38)Model 2 (adjusted)b1.22 (0.89–1.68)0.96 (0.69–1.33)Model 3 (adjusted)f1.34 (0.97–1.86)0.98 (0.71–1.37)Myocardial infarction Model 1 (unadjusted)13168.6386188.80.971.02 (0.41–2.57)0.58 (0.24–1.41)Model 2 (adjusted)b1.13 (0.44–2.89)0.63 (0.26–1.54)Stroke Model 1 (unadjusted)13322.739820.90.280.35 (0.05–2.52)0.19 (0.03–1.36)Model 2 (adjusted)b0.82 (0.10–6.60)0.30 (0.04–2.28)Peripheral arterial diseaseModel 1 (unadjusted)10151119.330713499.30.290.84 (0.61–1.16)0.74 (0.53–1.04)Model 2 (adjusted)b0.97 (0.70–1.36)0.80 (0.57–1.13)Model 3 (adjusted)g1.13 (0.79–1.62)0.89 (0.62–1.28)Any macroangiopathyModel 1 (unadjusted)9954133.7297133103.80.110.77 (0.56–1.06)0.67 (0.48–0.93)Model 2 (adjusted)b0.88 (0.63–1.21)0.71 (0.51–0.99)Model 3 (adjusted)h0.88 (0.63–1.22)0.74 (0.52–1.04)All vascular diseaseModel 1 (unadjusted)4441222.0141128230.10.950.99 (0.69–1.41)1.38 (0.89–2.15)Model 2 (adjusted)b1.15 (0.80–1.64)1.43 (0.91–2.23)Model 3 (adjusted)i––ROSSO endpointModel 1 (unadjusted)110813.83282615.30.761.13 (0.51–2.50)0.54 (0.26–1.14) Model 2 (adjusted)b1.29 (0.58–2.87)0.58 (0.27–1.23)aEvents per 1,000 person–yearsbModel 2 includes age, sex and duration of diabetes in each case. Model 3 includes all variables in Model 2, plus:cfasting plasma glucosedAsian (negative), other European, mixed/other ethnicity (negative)eBMI, ln[ACR], aspirin-use, any exercise in past 2 weeks (negative), married (negative)fln[ACR]gsystolic BP, aspirin-use, diabetes education (ever; negative), smoker, √daily alcohol consumptionhsystolic BP, aspirin-use, smoker, √daily alcohol consumptioninone after adjustment forbTime-dependent Cox models were included to allow for commencement (or discontinuation) of SMBG during follow-up. In both unadjusted and adjusted models, SMBG during follow-up was associated with a reduction in the risk of retinopathy of approximately 50%. In unadjusted analysis, the risk of the development of any macrovascular disease was reduced by 33% in patients who used SMBG, but this became non-significant after full adjustment.SMBG was not associated with the first occurrence of the composite ROSSO endpoint (which occurred infrequently), either at baseline or as a time-dependent covariate.
Discussion
The present study represents the second observational assessment of the relationship between SMBG and outcome in type 2 diabetes. With regard to all-cause mortality in the full FDS sample, our unadjusted data showed that baseline SMBG was associated with a significant risk reduction, a relationship observed in both non-insulin-treated and insulin-treated subgroups. After adjustment for other potential explanatory variables, this apparent benefit was lost in both subgroups. For both combined micro- and macroangiopathy outcomes, SMBG was not associated with a first ever event in the 5-year longitudinal cohort in either unadjusted or adjusted models, with or without time-dependent analyses. These findings are inconsistent with those of the ROSSO study [4], in which all-cause mortality and diabetes-related morbidity were reduced by 32 and 51%, respectively, in patients employing SMBG.
What is the explanation for these discrepancies? ROSSO had a larger number of patients than the FDS (3,268 vs 1,280), followed for a similar period. However, while the FDS involved a prevalent, prospectively-studied, community-based cohort and all patients had access to subsidised SMBG [5], ROSSO was ‘retrolective’—newly-diagnosed patients aged ≥45 years were selected—and only those receiving insulin were reimbursed for SMBG [4]. Whereas there was a low rate of migration of the FDS sample out of Western Australia [9] (96% had been captured on the WADLS by the end of June 2006 [10]), the retention rate of the ROSSO primary care sample is unknown. The range of potential explanatory variables (including diabetes education, marital status, education, alcohol consumption, exercise and urinary ACR) and the completeness of data collection (e.g. HbA1c and serum HDL-cholesterol results from a single laboratory were available for ≥98.8% of FDS patients compared with only 45.5 and 30.2%, respectively, from multiple laboratories in ROSSO [4]) were both much greater in the FDS. The present analyses are therefore likely to have assessed the independent contribution of SMBG to diabetes-associated morbidity and mortality with greater validity. A healthy survivor effect may also have confounded the relationship between SMBG and non-fatal outcome in ROSSO, while these analyses were undertaken in FDS participants alive at the end of 5 years of follow-up.
After adjustment, SMBG was associated with an increased risk of cardiac death in patients not treated with insulin. Although the ROSSO investigators suggest that, based on apparent benefits, SMBG may be a surrogate for greater patient empowerment and physician interest, with consequently improved compliance [4], it is equally plausible that SMBG may represent a belated attempt to improve glycaemic control by mostly non-insulin-treated patients who become aware of symptoms of coronary insufficiency and heart failure. Retinopathy was the only non-fatal complication with which SMBG was independently associated, with a significantly lower relative risk in our 5-year cohort in both unadjusted and adjusted time-dependent models. This could be a chance finding, but could also reflect the effect of other variables not measured in the FDS. It is not due to better glycaemic control in patients who perform SMBG, since SMBG use did not improve glycaemia [5]. The ROSSO study report does not contain a similar disaggregated analysis [4].
The results of the present study do not support a relationship between SMBG and improved survival in a well-characterised community-based sample of type 2 diabetic patients. We found evidence that SMBG was associated with an increased risk of cardiac death and a reduced risk of retinopathy. These conflicting results might reflect complex interactions between patient, physician and disease factors in particular circumstances, but may also represent the effects of confounding, incomplete covariate adjustment or chance.

self-monitoring of blood glucose
outcomes
type 2 diabetes
cohort study
epidemiology


Evidence of molecular alignment fluctuations in aqueous dilute liquid crystalline media

Protein dynamics can be studied by NMR measurements of aqueous dilute liquid crystalline samples. However, the measured residual dipolar couplings are sensitive not only to internal fluctuations but to all changes in internuclear vectors relative to the laboratory frame. We show that side-chain fluctuations and bond librations in the ps–ns time scale perturb the molecular shape and charge distribution of a small globular protein sufficiently to cause a noticeable variation in the molecular alignment. The alignment variation disperses the bond vectors of a conformational ensemble even further from the dispersion already caused by internal fluctuations of a protein. Consequently RDC-probed order parameters are lower than those obtained by laboratory frame relaxation measurements.
Introduction
Native proteins are customarily pictured as highly structured entities; a view that is supported by many lines of evidence. Nonetheless, all proteins exhibit internal motions on various time scales and amplitudes. Fast internal motions, i.e. bond vibrations and librations as well as peptide plane movements, take place in picoseconds to nanoseconds (Bouvignies et al. 2005; Kay et  al. 1989; Lakomek et al. 2006). Slow internal motions, i.e. secondary structure and protein domain motions, occur at a sedate pace in microseconds to milliseconds. NMR coherence decays have revealed that bond librations and side-chain reorientations amount only to a small reduction in structural order. The associated Lipari–Szabo order parameter (SLS2) (Lipari and Szabo 1982a, b) in well-structured parts of a protein is typically above 0.85 for amide moieties (Kay et al. 1989), 0.9 for backbone bonds (Ferrage et al. 2006) and 0.2–0.9 for side-chain methyl groups depending on the length of the side-chain (Millet et al. 2002; Skrynnikov et al. 2002). On slower time scales, in the μs-regime and above, relaxation dispersion and exchange rate measurements (Palmer 2004) have revealed larger but infrequent excursions away from the most populated states. Recently, residual dipolar coupling (RDC) data has revealed (Lakomek et al. 2005; Meiler et al. 2001; Tolman et al. 2001) in the intervening time-scale, below μs and above τc, anisotropic and correlated motions, that have been associated with order parameters in the range 0.6–1.0.
RDCs (Tjandra and Bax 1997) can be regarded as ideal motional probes as they respond explicitly to all motions of internuclear vectors. Non-zero RDCs arise first when molecular tumbling in solution is slightly anisotropic. To that end a dilute liquid crystalline (LC) medium is used to impose a subtle alignment on a biological macro-molecule solute via steric and electrostatic interactions (Prestegard et al. 2004). RDC observables are, of course, averages over a dynamic conformational ensemble (Bax et al. 2001; Louhivuori et al. 2006). The high structural similarity among the most populated states prompts to think that on average all conformations would, to an excellent approximation, adopt a similar alignment. This is the implicit assumption underlying the many structural and dynamic data analyses of weak alignment NMR spectroscopy—a method that is widely used today in structural biology (Bax and Grishaev 2005; Blackledge 2005). Indeed it seems reasonable to imagine that the dispersion of molecular alignments among the conformations is small, perhaps negligible, in particular as no apparent inconsistencies have been noticed in protein structure determination. Furthermore it may also seem reasonable to use in the analysis of dynamics an effective alignment frame because usually the conformations are in a fast exchange compared to molecular reorientation.
In this study we inspect critically the concept of a common alignment frame for a protein structure on the basis of experimental data. We find that a variation in molecular alignment due to the ps–ns fluctuations can contribute significantly to the RDC-probed order parameter in the case of a small globular protein.
Theory
Protein motions captured by RDCs
A residual dipolar coupling reports essentially on the average direction of an internuclear vector, e.g. a chemical bond, relative to the main magnetic field of a NMR spectrometer. Both fast and slow internal motions perturb the directions of a chemical bond and since RDCs are measured as an average over a longer time period this reflects directly into the observed RDC. Besides internal motions, the molecule experiences also rotational and translational diffusion in the liquid sample. Anisotropic orientational sampling in a dilute liquid crystalline medium is the basis for observing a residual of a dipolar coupling (Tjandra and Bax 1997). In practice, it may not be possible to de-convolute the observed data to distinct motional modes, but their identification will clarify the analysis.
From a molecular perspective, i.e. from the perspective of an observer on a single molecule, external motions are incidental and of no particular interest—at least if we assume that molecule–medium interactions are negligible. It would therefore be sufficient to consider any disorder present in molecular structures only in terms of internal motions (Fig. 1). The averaging due to the fast ps–ns bond fluctuations when inspected in the local sub-molecular frame is given by the aforementioned  values. The recently proposed (Bernadó and Blackledge 2004; Lakomek et al. 2006; Meiler et al. 2003; Peti et al. 2002; Tolman 2002) substantial and frequent fluctuation of secondary structures, i.e. α-helices or β-sheets, relative to a molecular frame will cause additional down-scaling of the order parameter. We denote the corresponding order parameter as  and note that it has been reported to range from 0.6 to 1.0.Fig. 1In the molecular perspective internal motions like (a) bond fluctuations, (b) peptide plane motions and (c) secondary structure movements all contribute to disperse a chemical bond away from its average direction. (d) In the ensemble perspective both internal and external motions contribute to fill the available state space. In a RDC measurement the available state space is reduced to effective alignment tensors of the constituent conformations
From an ensemble perspective, i.e. from the perspective of an observer regarding every molecule in the sample, both internal and external motions are manifestations of the state space of the system (Louhivuori et al. 2006). Internal motions cause the conformational diversity and external motions are responsible for the various orientations and positions the molecules inhabit. A conformational change is, from the ensemble perspective, tied to a change in the effective alignment of that molecule. This connection between the alignment and the conformation seems like non-sense from the molecular perspective since a conformational change does not instantly change the orientation of the molecule. Since RDCs are a time and ensemble average the ensemble perspective is an appropriate choice. In the vicinity of a liquid crystalline particle conformations are not energetically equivalent (Louhivuori et al. 2006) and therefore e.g. some side-chain conformations are not in a fast exchange. This means that conformational changes due to internal motions couple to the alignment and appear as fluctuations in the effective alignment frame of a molecule. We mark the corresponding order parameter as  It should be emphasised that the alignment is not a property of a free molecule, i.e. a molecule in void, but rather a property of the whole sample including the LC-particles.  does not therefore correspond to any intrinsic molecular motion, even though it might appear as one if the molecular perspective is chosen for RDC data interpretation.
Finally, we remind that molecular tumbling in solution is the most effective averaging process with respect to the laboratory frame. The associated order parameter Sτ2 is on the order of 10−6 in a dilute LC-medium (Bax et al. 2001; Tjandra and Bax 1997). The order parameters are usually normalized relative to Sτ2.
Dynamics modulated alignment
We reason that the variation in molecular alignment originates from the dynamics-induced modulation of van der Waals and Coulomb interactions between the protein conformations and the nematogens (Louhivuori et al. 2006). It may appear paradoxical that a transient conformation could align as the internal ps–ns fluctuations are much faster than the molecular translational and rotational diffusion. This quandary is resolved as explained above in what is formally referred to as Boltzmann’s ergodic hypothesis of the equivalence between time and ensemble averages. The suppression of some of the molecules internal fluctuations by the presence of the LC-particle imposes an unique alignment on each transient conformation. In other words, a transient conformation does not align per se but rather may only exist if it is energetically allowed. It is thus mistaken to justify the use of a single, effective alignment frame solely on the fact that internal fluctuations are much faster than translational or rotational diffusion.
It is clear then that dynamics modulated alignment (DMA) is a conceivable phenomenon and could very well be expressed in experimental data. We will therefore proceed to search for experimental evidence of it. Our analysis will be restricted to the ps–ns protein dynamics that is well characterized by NMR relaxation measurements. On such short time scales primarily bonds fluctuate and side-chains reorientate while motions of secondary structure elements are negligible (Palmer 2004).
Signature of dynamics modulated alignment
Of the many motions captured by residual dipolar couplings, bond fluctuations and the overall molecular tumbling are well understood. These two contributions should be removed from the total order to expose any additional motion attributable to either DMA or additional intrinsic motion, e.g. in the form of secondary structure fluctuations, or to both. If the additional motion is an exclusive property of the protein, i.e. independent of the LC-medium that was used in measurements, then it is likely evidence of the reported sub-μs intrinsic secondary structure fluctuations. On the other hand, if a change in LC-media makes a difference then the excess motion may just as well result from the alignment frame dispersion in DMA. Obviously neither secondary structure nor alignment fluctuations are mutually exclusive and indeed it is quite conceivable that both contribute to the observed additional motions. The presence of DMA does not therefore disprove for example the recent findings (Lakomek et al. 2005) of correlated backbone motions.
All order examined by RDCs is captured by an overall molecular measure called the general degree of order (GDO) (Tolman et al. 2001). We employ its squared value,  The components ϑij are obtained from the measured RDCs, e.g. by singular value decomposition (SVD) (Losonczi et al. 1999), using a set of equationswhere the refined structural model is given by cicj, i.e. internuclear vectors in the Cartesian {x, y, z} frame. The constants for the two nuclei A and B are the gyromagnetic ratios γA and γB, the internuclear distance rAB and as usual Planck’s constant h and the permeability of vacuum μo.
We aim to determine the ps–ns fluctuations, i.e. the SLS2 contribution to ϑ2, on the basis of the intrinsic difference between amide (NH) and backbone (CαCO) bond fluctuations. This difference is not large, but it is significant (Ferrage et al. 2006). We find from molecular dynamics (MD) simulations of ubiquitin and the B3 domain of protein G that the NH order parameter squared is on the average  and the corresponding CαCO value  when flexible tails and loops are excluded from the evaluation. These values are consistent with the average model-free order parameter SLS2 for NH obtained from laboratory frame relaxation measurements (Hall and Fushman 2003; Tjandra et al. 1995). The fluctuations of CαCO bonds are being investigated by cross correlated relaxation measurements (Chang and Tjandra 2005; Ferrage et al. 2006; Wang et al. 2003).
It follows that when ϑ2 is determined using only the NH couplings the result should be slightly smaller than when only the CαCO couplings are used, provided that no other motions play a role. Conversely, any additional motion will partially mask the intrinsic bond fluctuations and perturb the difference  Asymmetric motions, i.e. motions that affect the two bonds unequally, will either increase or decrease this difference, or even keep it invariant, whereas symmetric motions will only decrease it. Any change in  that is due to the intrinsic secondary structure fluctuations, i.e. Sαβ2, will be independent of the alignment medium. Any medium-dependent variation in  cannot arise from medium-independent intrinsic molecular motions and must be attributed either to DMA or to some unknown, novel medium-induced motion.
In order to remove contributions from Sτ2 and such superfluous factors as LC-medium density and sample temperature that are present equally in ϑNH2 and  we normalize  by Furthermore we should note that the sample conditions of the data sets used in this study to illustrate DMA are so similar (cf. Clore and Schwieters 2004a, b; Cornilescu et al. 1998; Tossavainen et al. 2003; Ulmer et al. 2003) that we cannot see how they could have any significant effect on the results to be presented. Alignment tensors are determined from RDC data e.g. by using SVD (Losonczi et al. 1999). The δ parameter and its error are then obtained using Eq. 2.
Amount of intrinsic bond fluctuations
Based on the MD simulation derived  and  values, the normalized difference between the average NH and CαCO bond fluctuations is without any additional motion  If the measured  differs substantially from the simulated value of 0.955, this value should be adjusted accordingly. It should also be noted that if the internal bond fluctuations were highly molecule dependent, it might be more sensible to consider δo separately for each molecule rather than the general δo shown here. Nevertheless, a δo parameter derived from a system outside the influence of any alignment medium serves to establish a numeric value that includes all intrinsic motions and nothing else. Any deviation from this baseline is then a sign of additional motions.
Local alignment frames
It may appear strange to use the general degree of order, a molecular quantity, as a motional probe because local order parameters are more informative. A locus-specific measure of RDC-probed dynamics is obtained by combining, via transformations, a non-redundant data set of at least five alignments (Tolman et al. 2001). The obtained local GDOs provide a clear measure of residue-specific dynamics, but unfortunately the procedure would also mix up the eventual fluctuations in alignments—the very effect we aim to address. To differentiate between internal and alignment fluctuations it only matters to observe whether δ varies from one medium to another. To this end ϑ2 is a robust measure that samples various bond directions. By taking care to account for alignment frame fluctuations and population differences between the conformations (Louhivuori et al. 2006), one could arrive at more precise local order parameters.
Methods
The conformational ensembles of GB1 (850 members), GB3 (150 members) and ubiquitin (2,400 members) were generated by molecular dynamics using the CHARMM22 parameter set (MacKerell et al. 1998) with a modified backbone potential (MacKerell et al. 2004) and generalized Born model (Im et al. 2003). The proteins were first heated to 300 K during 200 ps and then equilibrated for 2 ns at this temperature before sampling for 48, 34 and 3 ns for ubiquitin, GB1 and GB3, respectively. Subsequently the alignment of each conformation was simulated using the PALES program (Zweckstetter 2006; Zweckstetter and Bax 2000; Zweckstetter et al. 2004) in order to achieve the alignment dispersion present in a real sample. The alignments were predicted in the presence of a neutral planar obstacle as well as using positively and negatively charged bacteriophages carrying a surface charge density 0.047 e/nm2 and 10 mM ionic strength of monovalent salt in pH 6.5 with a LC concentration of 1 mg/ml.
The refined structures 1P7F (Clore and Schwieters 2004a; Ulmer et al. 2003), 1D3Z (Clore and Schwieters 2004b; Cornilescu et al. 1998) and 1NYA (Tossavainen et al. 2003) of the B3 domain of protein G, ubiquitin and calerythrin were used to analyse experimental RDC data available in various LC media (Table 1). The temperature dependence of the experimental parameters were reported (Cornilescu et al. 1998; Ulmer et al. 2003) to be minimal in the temperature range used in the experiments.Table 1ϑ2 and δ of GB3, ubiquitin and calerythrin in various alignment media. ϑ2 are shown in units of 10−6ProteinMediumT (K)GB3 (1P7F)aCTAB2.0732.3490.117 ± 0.006302PEG0.6140.7500.181 ± 0.009304PAG+1.0791.2420.131 ± 0.007298PAG−1.1131.3200.158 ± 0.006298Pf11.3771.7310.205 ± 0.005302UBI (1D3Z)bBicelles0.6950.7880.118 ± 0.005304CTAB2.2692.6850.155 ± 0.003304CAL (1NYA)cPf14.4514.7280.059 ± 0.001318Experimental temperature is shown for an easy comparison with δ variationapH 6.5, ɛNH =  0.26 Hz,  Pf1 11 mg/ml, 100 mM NaClbɛNH =  0.15 HzcpH 6.0; Pf1 30 mg/ml
An effective alignment tensor for each ensemble was calculated using SVD (Losonczi et al. 1999). Either the experimental or average PALES predicted RDCs were used. Besides the refined structures (Table 1) experimental RDCs were used also with the MD simulated—PALES aligned structures in what we call the hybrid model (Table 5). PALES predicted average RDCs were used in conjunction with the MD simulated–PALES aligned structures (Table 2).Table 2 and δ parameters for simulated conformational ensembles of the B1 and B3 domain of protein G and human ubiquitin based on PALES-predicted average RDCsProteinMedium (°) (°)δdirectδSVDGB1Negative31.7 ± 0.231.1 ± 0.20.172 ± 0.0070.109 ± 0.008Neutral11.82 ± 0.047.38 ± 0.020.114 ± 0.0010.116 ± 0.008Positive40.36 ± 0.0536.23 ± 0.030.385 ± 0.0040.208 ± 0.002GB3Negative12.60 ± 0.109.54 ± 0.060.082 ± 0.0030.097 ± 0.009Neutral11.82 ± 0.097.75 ± 0.050.096 ± 0.0030.100 ± 0.009Positive23.0 ± 0.420.6 ± 0.40.119 ± 0.0100.107 ± 0.007UBINegative13.21 ± 0.049.87 ± 0.020.090 ± 0.0010.106 ± 0.003Neutral16.95 ± 0.0913.16 ± 0.080.080 ± 0.0020.085 ± 0.005Positive15.84 ± 0.0612.86 ± 0.050.092 ± 0.0020.104 ± 0.007
Results and discussion
Analysis of experimental data
The wealth of high-quality residual dipolar coupling data of the B3 domain of protein G (GB3) and ubiquitin (UBI) and their refined structures (1P7F and 1D3Z, respectively) allow us to determine GDOs and to calculate δ values for several cases (Table 1). The data for calerythrin (1NYA), a significantly larger protein (∼20 kDa v. less than 10 kDa), is also available (Tossavainen et al. 2003). The quality of the refined structures in terms of an RMSD measure, referred to as the quality factor (Clore and Garrett 1999), is in all these cases good. Accordingly, in the evaluation of δ we employ only the NH and CαCO couplings that were used to refine the GB3, ubiquitin and calerythrin structures. The GDO values were calculated from the experimental dipolar coupling data using a SVD algorithm against the average structures of the refined ensembles.
The two most important observations are that  and that δ varies from one medium to another. Therefore it seems that there are motions in addition to bond fluctuations and we attribute them, owing to their medium-dependency, at least partly to dynamics modulated alignment.
The results are subject to the following uncertainties. The errors in δ were obtained by a Monte-Carlo (MC) method from the experimental errors in RDCs. The largest ambiguity originates from the ratio between the NH and CαCO bond length. We used rNH = 1.02 Å (Jeffrey 1992, p. 270) and  (Engh and Huber 1991; Lehmann et al. 1972). An eventual error in the bond length ratio is systematic and affects the absolute δ value, but it does not abolish the variation in δ from one medium to another. It should be noted that customarily when a structure is refined against RDCs rNH is set to 1.04 Å (Ottiger and Bax 1998) to obtain the average directions of the more mobile amides consistent with less mobile bonds, e.g. those of the CαCO vectors. In other words, it is desirable to obtain the same magnitude of alignment tensor irrespective of the couplings employed. Further uncertainty stems from the discrete sampling of directions by the non-uniform and dissimilar distributions of NH and CαCO bonds, but as will be shown later on it does not seem to matter that much. Furthermore, as mentioned above, no motional effects including the consequences of discrete non-uniform sampling can be seen in δ when the excess fluctuations are small compared to SLS2. Large alignment fluctuations on the other hand lead categorically to nearly uniform sampling of directions.
Simple model of wobble
A simple approximation of internal bond motions considers fluctuations about the mean direction only as axial, i.e. including just the zero-order spherical harmonics Y00.Assuming that the internal bond (θ) and alignment () fluctuations are independent and on different timescales, they can be separated and treated independently (Brainard and Szabo 1981; Petersen and Chan 1977). If an isotropic sampling of bond directions is also assumed, then the combined effect of the fluctuations will be  It would then be clear from Eq. 2 that δ values remain invariant even in the presence of alignment fluctuations.
However, it is evident from Table 1 that the δ parameter varies from one medium to another implying a coupling between the alignment fluctuations and δ. It is therefore reasoned that  cannot be factorised. This is not a contradiction since neither of the previously mentioned assumptions are entirely valid in this case. (i) The two fluctuations are not completely independent since there is a slight coupling between the alignment and the ps–ns bond motions (Louhivuori et al. 2006). (ii) Since alignment fluctuations are not a real motion, but rather a manifestation of the state space available in the system, alignment fluctuations can be said to affect all time-scales.
Therefore we propose the following simple ad hoc model to describe the combined effect of internal bond motions and alignment frame fluctuations.Essentially this means that we are considering a model where a molecule’s wobble about an average orientation amplifies the effect of internal bond motions. As a consequence the effective opening angle of internal bond motions is increased from the initial value of θo by
In the absence of alignment fluctuations () θo can be calculated using Eq. 5 from the GDO values determined for the MD simulated ensembles.Accordingly, θo for NH and CαCO were calculated from the previously mentioned MD-derived  and  and found to be 11.82° and 9.99°, respectively. Then  and  were calculated using Eq. 4 and plotted as functions of increasing  (Fig.  2). Finally, δ versus  was calculated from  and  using Eq. 2.Fig. 2Calculated effective order parameters  for the NH (dotted) and CαCO (dashed) bonds and the δ parameter calculated from them as the molecule is subject to a fluctuating alignment of increasing angle  in addition to internal bond fluctuations
When the internal bond and alignment fluctuations combined reach the magic angle the alignment is lost all together. The normalized difference in the NH and CαCO probed order, denoted by δ, is available from measurements and shown in Fig. 2 for the B3 domain of protein G in CTAB doped bicelles, positively and negatively charged stretched polyacrylamide gels, polyethylene-glycol lamella and filamentous phages, and for ubiquitin in bicelles and CTAB doped bicelles. The δ value of calerythrin was smaller than the proposed general minimum of 0.081 and is therefore omitted. The symbols in the figure are larger than the errors obtained by MC analysis. It needs to be stressed that  should not be interpreted as a real measure of molecular motion since alignment fluctuations are only an ensemble property affecting RDC data.
When the experimental δ values are placed on the (δ, Ω)-curve we see that for both GB3 and ubiquitin in bicelle medium the effect of DMA is small enough to be comparable to that of bond fluctuations. Non-axial bond fluctuations will naturally complicate the situation and might invalidate such a comparison. The precision in δ is modest owing to the functional form of the -curve. In other media, particularly in those carrying surface charges on the nematogen, the alignments fluctuate noticeably. For example in filamentous phages the alignment of GB3 fluctuates even more than 13°. Thus ϑ2 may fall even below 0.7. However, this does not mean that the molecule is somehow reorienting itself with such large-amplitude motions. It only tells that quite likely there are more than one major alignment modes separated from each other sufficiently to give the impression of large-scale motions if the alignment fluctuations are interpreted simply as real, molecular fluctuations around an average direction as was done in Eq. 4. Nonetheless, the medium-dependent variation in δ shows that DMA contributes to RDC-probed dynamics.
Analysis of simulation data
The sources of the medium-dependent variation in δ found from the experimental data can be many. Fortunately, they can be assessed via computer simulations. We consider the variation in the protein shape and charge distribution due to the ps–ns dynamics. These fluctuations give rise to a family of conformations whose members do not all align in exactly the same way. Simultaneous variation in the shape and net electric dipole moment may lead to a substantial alignment fluctuation. However, it is also conceivable that dynamics of the nematogen contributes to the protein alignment dispersion. Other microscopic heterogeneity in obstructing nematogens may also add to the variation of protein alignments but we are unequipped to examine these effects. Neither are the possible effects of hydration fully explored in this study.
The average NH and CαCO RDCs of the simulated conformational ensembles were calculated and the GDO values were determined using SVD against the average structure of the ensemble. The average of the cosine matrix cicj of Eq. 1 was taken to present the best possible average structure. In this way we aimed to mimic the experimental procedure where GDOs were determined from the measured couplings against the refined structure. Alternatively, the knowledge of all NH and CαCO vectors of the dispersed ensembles allowed us to calculate directly  of Eq. 3 and hence also the order parameters and δ for the various cases. The two methods, direct and SVD, give similar δ values, with only GB1 in positive alignment medium showing a larger discrepancy (Table 2). Since both exhibit similar overall tendencies the methods can be assumed to be qualitatively consistent.
The alignment simulations in the neutral, positively and negatively charged media were not intended to match specifically any of the experimental conditions, but nevertheless the values are somewhat comparable. The simulated δ values are more uniform, but comparable in size (Table 2). Only the B1 domain of protein G seems to be significantly susceptible to DMA. The lack of dispersion in δ was unexpected in light of the clear dispersion found from experimental data (Table 1). Because of this and since the limited sampling of GB3 leaves a shadow of doubt on the exact values reported in Table 2, further work was done to understand the source for the lack of dispersion in δ. First, non-uniform sampling of bond directions was addressed. Second, the possible increase in PALES-predicted alignment dispersion due to hydration was considered. Third, a hybrid model was used to circumvent any shortcomings in PALES-predicted RDCs.
Non-uniform bond direction sampling
The finite sampling of bond directions may influence the δ parameter. Therefore, we calculated the fraction of vectors oriented along the three principal directions as suggested by Fushman et al. (2000). The fractions fi are a convenient measure of the uniformity of the bond direction sampling. In the case of uniform sampling  whereas a significant deviation from 1/3 is a sign of non-uniform sampling. The Fushman-fractions calculated from the MD ensembles show gratifyingly that the sampling is rather uniform (Table 3).Table 3The fraction of vectors oriented along the three principal axis calculated for the MD simulated ensembles of GB1, GB3 and ubiquitinProteinBondfzfyfxGB1NH0.540.260.20CαCO0.450.290.26GB3NH0.550.260.19CαCO0.450.290.26UBINH0.490.310.21CαCO0.370.340.29
Nevertheless, the possible effects of discrete, non-uniform sampling of bond directions was tested using the MD ensembles. The CαCO bonds of every conformation were first rotated one residue at a time to point, on average, at the same direction as the NH bonds did, on average. A new set of RDCs was then calculated using the twisted structures and the original Saupe matrices. Singular value decomposition was then used to obtain an effective GDO value using the twisted structures and the new average RDCs. From the NH and CαCO GDO values the δ parameter was calculated using Eq. 2. As can be seen from Table  4, δ parameters changed, but mostly within the margins of error. Both GB1 in positively charged medium and ubiquitin in neutral medium experience a sharp drop in δ. It seems that this is due to the fact that the CαCO bonds rotated pre-dominantly closer to the magic angle diminishing CαCO couplings sufficiently to render them much more labile in the face of the applied MC error. However, non-uniform bond direction sampling does not seem to influence δ significantly in most cases.Table 4Alignment fluctuations in the MD ensemble before (δSVD) and after (δ′SVD) CαCO bonds were superimposed on NH bondsProteinMediumδSVDδ′SVDGB1Negative0.1090.113 ± 0.008Neutral0.1160.116 ± 0.007Positive0.2080.130 ± 0.008GB3Negative0.0970.116 ± 0.006Neutral0.1000.106 ± 0.006Positive0.1070.113 ± 0.006UBINegative0.1060.109 ± 0.003Neutral0.0850.018 ± 0.008Positive0.1040.092 ± 0.007Table 5Hybrid modelProteinMediumδδhybridGB3CTAB0.1170.133 ± 0.005Pf10.2050.265 ± 0.004UBIBicelles0.1180.093 ± 0.008CTAB0.1550.139 ± 0.006Alignment fluctuations in simulated conformational ensembles of the B3 domain of protein G and human ubiquitin based on experimental RDCs (δhybrid) compared to those in the refined, experimental structures (δ)
Hydration shell
Since the MD simulated conformational ensembles seemed to be less susceptible to DMA than NMR-based ensembles, it was plausible that PALES failed to impose a realistic dispersion of alignments. We hypothesised that the addition of a hydration shell to the molecules would make them more globular and therefore more susceptible to alignment dispersion due to steric interactions. To test whether this hypothesis could account for the observed lack of dispersion, a hydration layer needed to be added to the molecules. This was done with a rough model of the hydration shell in the form of increased atomic radii in PALES. Atomic radii were increased by 1, 2, 3, and 4 Å, but no increase in DMA was observed neither for GB1 nor for ubiquitin. The lack of a hydration shell does not therefore seem to be the cause for the difference in DMA between simulations and experiments.
Hybrid model
Another possibility was that PALES dispersed the alignments sufficiently, but that the predicted RDCs had some issues that were reflected in the effective alignment and ultimately in δ. To overcome this, we calculated the effective alignments of the MD ensembles using experimental RDCs instead of PALES-predicted ones. This was done for GB3 in Pf1 and CTAB-doped bicelle media and for ubiquitin in bicelle media with and without CTAB. As can be seen from Table 5 and Fig. 3 this seems to be the major cause for the lack of δ-dispersion. What this suggests is that when experimental RDCs are available DMA can be estimated consistently from a MD-simulated conformational ensemble even without structures refined against experimental RDCs.Fig. 3Hybrid model. Calculated effective order parameters  for the NH (dotted) and CαCO (dashed) bonds and the δ parameter calculated from them as the MD simulated conformations are subject to a fluctuating alignment of increasing angle  in addition to internal bond fluctuations
Competition of interactions
In the neutral medium only the molecular shape fluctuations contribute and DMA is comparable to the internal bond vector fluctuations. Neither in the simulation nor in the experimental data is there a lot of difference between the smaller molecules GB3 and ubiquitin. Calerythrin resembles more an oblate ellipsoid (Fig. 4) and is expected to fluctuate less than the smaller, globular proteins in the absence of electrostatic interactions.Fig. 4Causes of molecular alignment fluctuations for (a) GB1, (b) GB3, (c) ubiquitin and (d) calerythrin illustrated for example conformations. The variation in the molecular shape and charge distribution are simplified by showing the molecular principle axes (grey) and net electric dipole moments (orange)
It is more intricate to rationalize the reduced order parameters in a charged media as both molecular shape and charge distribution fluctuations are present. It may not be possible to simplify the dynamics of a complex set of interactions to provide lucid understanding to the causes of DMA. To begin with, GB1, GB3, ubiquitin and calerythrin carry under experimental conditions a net charge of −1.22, −3.09, +1.39 and −8.45, respectively. In the repulsive electrostatic potential the alignment is, to a crude approximation, governed by the direction and size of the net electric dipole moment in relation to the molecular shape tensor.
A competition between the steric and electrostatic alignment arises when the dipole moment is mostly along the long axis of the molecule and comparatively small. GB1 is a good example of this (Fig. 4). In GB1 dipole moments are pointing roughly to the middle of the long and short axis in two slightly separated groups. The situation is similar in GB3, but with the dipole moments concentrated in a single, tight group. Furthermore, the dipole moments in GB3 are larger than in GB1 and dominate the alignment. In the globular ubiquitin the dipole moments are directed along the shortest of the molecular axes. Thus both the shape and electrostatics act in synergy to narrow the cone of alignments. In spite of this the globular shape of ubiquitin ensures that modest alignment fluctuations remain in charged media. In the refined structures of calerythrin the dipole moments are spread out slightly more than in the MD simulated ensemble of GB1 and directed similarly roughly to the middle of the long and short axis. Alignment fluctuations in calerythrin are small (Table 1), even though the combination of an oblate ellipsoidal shape with dipole moments directed away from the short axis suggest a competition between the steric and electrostatic alignments. Calerythrin contains a flexible hinge-region that has a slightly ambiguous structure in 1NYA (Tossavainen et al. 2003). It is thus possible that the lack of restraints in the structural refinement has led to somewhat more compact structures with slightly distorted net dipole moments. Analysis of an attractive potential is more involved and not easily reduced to the analysis of the net electric dipole moment relative to the shape tensor. The variation in the position of the net charge influences the alignment that can fluctuate substantially as exemplified by the aforementioned δ values for GB3.
In general a small globular protein that carries many charges and yet only a small net electric dipole moment is the most vulnerable to molecular alignment fluctuations. The shape as well as the charge distribution is perturbed by dynamics of long side-chains at the molecular surface. For large proteins the effects are expected to have less impact on the overall molecular alignment.
Conclusions
Internal motions are, at least to a good approximation, medium-independent. Therefore one would expect that e.g. relative differences between NH and CαCO bond fluctuations, denoted by the δ parameter, would remain the same from one medium to another. However, we find that this is not the case for protein G and ubiquitin. The changes in δ from one medium to another imply the presence of an additional medium-dependent ensemble property involved in the alignment phenomenon.
The simulations suggest that the effective alignment of a molecule fluctuates as a response to the variation in molecular shape and charge distribution caused by protein dynamics. This alignment modulation is at its highest when steric and electrostatic interactions compete for the dominance in a small globular protein. Also flexible loops and termini may cause a fluctuating alignment. The alignment fluctuation as a phenomenon is known and e.g. used to point out domain–domain motions in modular proteins (Bewley and Clore 2000; Fischer et al. 1999) and flexibility of polypeptides (Chou et al. 2001; Goto et al. 2001).
Here we were able to consider only alignment effects due to protein dynamics that occur in the ps–ns range owing to the limitations of molecular dynamics simulations. Other states accessible via slower motions are of course also expected to contribute to the variation in molecular alignment depending on their population, shapes and charge distributions. We also expect that dynamics of nematogens and other variation in the media contribute as well to the bond vector dispersion underlying the observed RDC signals.
It is perhaps perplexing that molecular alignment fluctuations do not show up as clear inconsistencies in refinement of protein structures in particular when a number of RDCs obtained from measurements in various media are used simultaneously. It should be emphasized that the dynamics modulated alignment is a process that is nearly uncorrelated from the intrinsic vector fluctuations. Consequently it will, to an excellent approximation, only scale RDCs but leave their directional information content, used in the structure determination, intact. Thus the averaging due to DMA appears much as the averaging due to the molecular tumbling. Even a substantial wobble in the alignment can be compensated by increasing the strength of LC-medium to acquire large enough couplings for quantitative analyses. It should be stressed that the presence of DMA does not, for example, invalidate the recent finding (Bernadó et al. 2005) of long-range interactions in α-Synuclein nor the previously mentioned supra-τc backbone motions, but gives a rationale for the low S2 values. Perhaps indicative of fluctuating alignments are the challenges in predicting precisely RDC-data on the basis of a single structure.
In studies of dynamics where the dispersion of a vector direction rather than its average direction is of interest we expect that the alignment fluctuations are worth to consider as they will inevitably incorporate into the RDC-based order parameter. In this study the evidence of molecular alignment fluctuation was established using the average molecular parameter, general degree of order. Consequently the site-specific variation in the order and its correlation to the secondary structure that have been reported in the RDC-probed dynamics studies was not addressed. It is conceivable that an alignment fluctuation could result in some site-specific variation in ϑ2 depending on the direction of the bonds, but unlikely in any correlated variations.

protein dynamics
residual dipolar couplings
liquid crystal nmr spectroscopy