Javier M Hernández

Marianne Gaborieau

Patrice Castignolles

Michael J Gidley

Alan M Myers

author. Professor Robert G Gilbert
email: b.gilbert@uq.edu.au

Mechanistic investigation of a starch branching enzyme using hydrodynamic volume SEC analysis

Keywords: starch, enzyme, branching, size exclusion chromatography (SEC), molecular weight distribution, hydrodynamic volume

Two linear α-(1,4) -D-glucans substrates, of degrees of polymerization DP ~ 150 and 6,000, were exposed to maize starch branching enzyme IIa (mSBEIIa) in vitro. The resulting branched α-glucans and their constituent chains (obtained by debranching) were analyzed by nuclear magnetic resonance (NMR) and size-exclusion chromatography (SEC). SEC data for the debranched species are presented as chain-length distributions, while those for branched species are presented as hydrodynamic volume distributions (HVDs), which is the most meaningful way to present such data (since SEC separates by size, not molar mass, and a sample of branched polymers with the same size can have a range of molar masses). A rigorous interpretation of the HVDs of the substrate and its branched product show that at least part of the branching is an inter-chain transfer mechanism in both the short and long-chain substrate cases. A bimodal HVD of the in vitro branched α-glucan derived from the short-chain substrate was observed, and it is postulated that the divergence of the two populations is due to very small chains being unable to undergo branching. In the case of the in vitro branching of the long-chain substrate, the formation of maltohexaose during the reaction and the presence of a monomodal HVD were observed, suggesting a distinct mode of action of mSBEIIa on this substrate. Quantification of the branching level by NMR showed the branched glucans from both substrates had substantial amounts of branching (2.1-4.5 %), ascribed to the intrinsic nature of the action of mSBEIIa on the two substrates. It is postulated that differences in the degrees of substrate association affect the pattern of branching catalyzed by the enzyme, and a putative active site structure is proposed based on the appearance of maltohexaose. The molar mass distribution of the constituent chains of the in vitro branched α-glucans obtained by isoamylase treatment reveals the transfer of chains of specific size and supports the supposition given in the literature that mSBEIIa is responsible page 3 for short-chain branching in amylopectin. It is suggested that hydrodynamic volume SEC analysis should be used as a tool for the mechanistic investigation of SBEs, allowing SEC data of in vitro branched α-glucans to be both comparable and quantitative.

Introduction

Starch consists of one polymer with two types of structure, amylose (AM) and amylopectin (AP). It is chemically a simple macromolecule, consisting of only one monomer (glucose) and two connecting glycosidic α- (1,4) and α- (1,6) linkages that give rise to the polymer backbone and branching points, respectively. Starch has higher 'supramacromolecular' structural levels that make characterization difficult. Amylose (AM, weight-average molar mass -M w ~10 4 -10 5 ) is typically "essentially" linear, with a few long branches. 1 Amylopectin (AP, -M w ~10 6 -10 9 ) is branched at approximately 5% of the glucose units, with a highly specific and non-random structure. The exact nature of the branching distribution has not yet been fully described. The most widely accepted model is the cluster model put forward by Hizukuri, which proposes that branches are organized into tightly packed clusters that give rise to crystalline regions. 2 The higher levels of supramacromolecular order are responsible for the crystallization of AP into insoluble starch granules (for reviews see 3,4 ).

The molecular mechanisms by which AP is synthesized with its specific branching structure are not well understood. Starch branching enzymes (SBEs) are responsible for the formation of α- (1,6) glycosidic linkages, and genetic and biochemical studies have shown that they are required for the normal biosynthesis of starch. [5][6][7] In addition to SBEs, physiological and genetic evidence also suggest that starch debranching enzymes (DBE), which catalyze the hydrolysis of branch points, are critical for the final branching structure of AP and its subsequent page 4 crystallization into granules. 8,9 However, the exact roles of these enzymes in starch biosynthesis

have not yet been fully elucidated from in vivo studies, and to date the in vitro synthesis of insoluble starch granules has not been reported. SBEs mediate branch formation by cleaving an α- (1,4) glycosidic bond in a donor chain and transferring the cleaved fragment to a primary hydroxyl group (carbon 6) on an acceptor chain to form an α- (1,6) glycosidic bond, i.e., a branch point. 10 If the donor chain and the acceptor chain are the same, the reaction is termed intra-chain, and if different it is referred to as inter-chain. A specific class of an intra-chain reaction scheme is an intra-cyclization reaction (Figure 1).

Mechanism A in that figure has been reported by Rydberg and co-workers for potato SBE I and II. 11 Mechanism B has been shown to occur in potato SBEI, 12,13 and reported by Antrim for a maize SBE, although the latter has apparently not been published in the open literature. 14 Mechanism C has been demonstrated by Takata and co-workers for Bacillus stearothermophilus BE. 15 A number of studies have addressed the branching of α-glucans in vitro as catalyzed by SBEs 11,[16][17][18][19] and at least two classes of SBEs differing in biochemical properties, referred to as SBEI and SBEII, have been identified in maize, pea, potato, rice and wheat. 5,[20][21][22][23][24] An approach that has been widely used for the mechanistic investigation of SBEs in vitro is the determination of the molar mass distribution (or equivalently, and as more widely referred to in the starch literature, the chain-length distribution) of the constituent chain segments of an αglucan exhaustively branched by a SBE. These analyses involve complete hydrolysis of the branch linkages in the glucan by treatment with a bacterial isoamylase (DBE), then separating the resultant linear chains by capillary electrophoresis (CE), high performance anion-exchange chromatography (HPAEC) or size-exclusion chromatography (SEC, sometimes also referred to as gel permeation chromatography, GPC). This approach allows indirect inferences to be made on the size of chains transferred by the SBE, as well as gaining information regarding the whole distribution of transferred chain sizes.

page 5 An approach that has been considerably less employed is the size characterization of the in vitro branched α-glucan product itself. Problems with fully dissolving and completely eluting the glucan without degradation or loss of chains in the SEC columns, combined with the need for accurate calibration to convert elution time to a physically meaningful size parameter, as well as the problem of band broadening, suggest that there are at present no accurate size distribution analyses of branched α-glucan.

The SBE-catalyzed in vitro branching of linear α- (1,4) glucans provides a model system for the mechanistic investigation of SBEs in general. The main reason for this is the ability to control the reaction conditions and the relatively straightforward (although by no means free of experimental limitations) procedure of size separation with SEC. It is thus possible to determine the size distribution of the precursor linear α- (1,4) glucan, the in vitro branched product, and the corresponding debranched α-(1,4)-glucan. Comparison of the distributions allows evaluation of the effect of the action of SBE on the substrate, making inferences regarding the mechanism possible. For instance, investigating possible intra-or inter-chain transfer mechanisms require examining these distributions since these two mechanism cannot be distinguished from the distributions of the debranched α-glucans alone.

This study addresses the in vitro branching of linear α- (1,4)-glucans of different sizes as catalyzed by maize starch branching enzyme IIa (mSBEIIa). The substrates provided are produced by the action of rabbit phosphorylase a and thus are entirely linear. 16 This is an approach that contrasts to previous studies that used native amylose as the substrate, and in which the low frequency of branches in the substrate may have influenced the results and its interpretation. For accurate and unambiguous mechanistic interpretation of SEC data from the different α-glucans, such as the ability to assess the importance of the chain length of the substrate on branching, it is essential that the substrate is linear and well characterized.

Andersson and co-workers 17 used 1 H NMR to quantify branching of potato SBE-catalyzed branched α-glucans using an enzymatically-degraded starch as the substrate. However, they did page 6 not report the level of branching that the substrate had prior to incubation with SBEs. Many previous studies used native amyloses as substrate, which contain some branching. Praznik and co-workers 16 used linear α-(1,4) glucans enzymatically synthesized with phosphorylase a from glucose-1-phosphate. We also employ this strategy in order to eliminate the possibility that the presence of branches on the substrate can cause mSBEIIa to behave differently.

A new method is used for analyzing SEC data of the branched α-glucan products by presenting size distributions in the form of hydrodynamic volume distributions (HVDs), 25,26 and NMR spectroscopy is used as a sensitive means of determining branching level. HVDs of the branched product molecules, and molar mass distributions (MMDs) of the constituent linear chains from the molecules, are interpreted to gain mechanistic information of the mode of action of mSBEIIa.

The mechanistic investigation of the mSBEIIa isoform has been the subject of a number of important studies, showing for instance its effects on amylose and AP, and the use of SEC and other size separating techniques to look at its transfer pattern. 5,19,27,28 In the present study, we extend earlier work using recent improvements in starch and α-glucan characterization, such as SEC equipment and data analysis, and also discuss the advantages of these improvements for in vitro studies of SBEs from different origins.

Distributions from SEC

Definition of HVDs

Some background on the theory of SEC is given because this is the first application of some of these tools to linear and branched α-glucans; details are found elsewhere, e.g. 25,26,29 .

Recall that SEC separates by a size parameter, not molar mass. For complex branched polymers (such as starch), there is no unique relation between size and molar mass (except under special circumstances 30 ): polymers of different molar masses can have the same size if they also have different branch connectivity. There is evidence that SEC separates macromolecules according to hydrodynamic volume, V h . [31][32][33] V h is defined as the hypothetical volume of an page 7 equivalent impenetrable sphere having the same contribution as the polymer molecule to the viscosity of the solution in a shear flow, and thus takes into account both size and hydrodynamic effects. [START_REF] Flory | Principles of Polymer Chemistry[END_REF] For a monodisperse macromolecule of molar mass M and intrinsic viscosity [η], V h is not a geometric size (or volume) as defined by a geometric parameter such as the radius of gyration, but is given by:

V h = 2[η] M 5 N A for a monodisperse polymer in a solvent (1)
Here N A is Avogadro's constant. For iso-V h mixtures of branched polymers, eq (1) is replaced by: 26,35,36

V h = 2 5 - [η] w (V h ) - M n (V h ) N A (2)
Here - [η] w (V h ) is the weight-average intrinsic viscosity and -M n (V h ) the number-average molar mass of chains with hydrodynamic volume V h .

The MMD of a polymer can be expressed as a weight distribution W(M) or as a number distribution N(M). The "SEC distribution" w(logM) of a linear polymer is defined as:

w(log M) = M W(M) = M 2 N(M) (3)
For complex branched polymers, separation by SEC is 'incomplete' in terms of M (i.e., different molar mass and branching structures can have the same V h ), and thus different detection methods yield only average molar masses at a given V h (i.e. MALLS and LALLS yield local weight-average molar mass, while viscometry and universal calibration yield local numberaverage molar mass at a given V h ). 26 For branched polymers, one thus has analogous expressions to eq (3) wherein M is replaced by V h ; while the V h expressions are valid also for linear chains, expressing distributions in terms of M for branched chains is not. Replacing eq (3) one has the hydrodynamic volume distributions (HVDs):

w(log V h ) = V h W(V h ) = V h - M n (V h ) N(V h ) (4) page 8
The value of V h corresponding to an SEC elution time can be found with online viscometry or through Mark-Houwink-Sakurada parameters for particular standards in a given eluent at a given temperature. 26 In this work the MMDs are expressed in terms of DP X (or number of monomeric units) rather than in terms of molar mass M, related for starch by M = 162.2 X + 18.0 (162.2 is the molar mass of the monomeric unit of starch and 18.0 that of the additional water in the end group).

HVDs as a tool for starch branching enzyme studies

MMDs and HVDs can provide mechanistic information on polymer processes. 37 A problem with studying branched polymers with SEC is choosing a meaningful way of comparing distributions of different polymer branching structures, e.g., a linear α-glucan substrate and its in vitro branched product. A molar mass calibration curve generated for linear α-glucans cannot be used for the calibration of SBE-catalyzed branched α-glucans, since the branching structure will alter their hydrodynamic properties. To overcome this problem, we use the 'universal calibration' assumption 31,32 that SEC separates solely by hydrodynamic volume: any two polymer molecules with the same V h , irrespective of type and conformation, should elute at the same time. Thus, for a polymer standard S and any unknown polymer P (linear, branched, di-block, etc):

V h S = V h P at elution time t el (5)
From eqs (1) and ( 5), any set of monodisperse standards with known molar mass and intrinsic viscosity can be used to construct a general calibration curve if V h is used as the calibration parameter, thereby expressing the size populations of the branched glucans as HVDs. This permits the comparison of the three stages of the SEC analysis of the SBE-catalyzed reaction (linear starting chains, subsequently branched chains formed from this starting population, and debranched chains formed in turn from these branched ones) in a physically meaningful way.

There are other reasons why using HVDs is advantageous for branching reactions. It is common in the literature to present SEC data of branched α-glucans as simple elution profiles, which can only be interpreted with no real reference to size (glucans that elute earlier are "larger" than glucans eluting later). Such information is set-up dependent and prevents comparison between studies. On the other hand, HVDs only require that α-glucans be separated in the same solvent and temperature, and is set-up-independent.

Methods

Expression of maize SBEIIa

Rosetta E. coli (Novagen) cells were transformed with vector pHC16 encoding recombinant maize SBEIIa•S-tag fusion protein. 38 phosphorylase a were respectively 50.00 mM, 0.100 mM and 0.001 mM to obtain a weightaverage DP -X w of 215, and 39.00 mM, 0.0015 mM and 0.0020 mM to obtain -X w of 5,950.

Evaluation of the synthesis and determination of the average DP were performed by SEC immediately after termination to minimize retrogradation. After termination, the reaction was cooled in ice and the α-(1,4) glucan precipitated with ethanol (95 % v/v). The precipitate was filtered with a 0. For the long-chain branching experiments, a 2.1 mg⋅mL -1 master suspension of the α-glucan was initially prepared. The long-chain substrate only partially solubilized, although it was observed that the dissolved component was stable for many hours. The undissolved components were filtered through 0.45 µm Millex®-HV. An aliquot (80 µL) of the master suspension was then analyzed by SEC to take into account the degradation of the substrate during solubilization (the SEC distribution of this sample was taken as the size distribution of the linear substrate prior to branching). Maize SBEIIa (∼3 µM, 60 µL) was then added to 720 µL aliquots of the master suspension (the upper bound estimate of final concentration of α-glucan was 1.95 mg⋅mL -1 ).

After 1 to 9 h of incubation with mSBEIIa, the reaction was terminated by heating in boiling water for 5 min. Aliquots (80 to 100 µL) were taken for SEC analysis and debranching experiments. The remaining part of the sample was freeze-dried overnight for characterization by 1 H NMR.

The first experiments were performed with linear α-(1,4) glucans of number-average DP, -X n , 35 and 90. These first experiments were unsuccessful in maintaining the α-(1,4) glucans in solution for more than a few hours before they precipitated. In a number of experiments it was page 12

noticed that retrogradation was initiating on edges of the vial surface, which were probably inducing nucleation. A flat-base glass vial was replaced with a round-bottom tube (∼ 2 mL) to reduce potential nucleation sites. This strategy significantly reduced retrogradation occurring at the surface. However, it did not prevent retrogradation from occurring in the bulk. Linear α- (1,4) glucan chains of -X n 110 or less have been observed to aggregate most rapidly. 41 Linear α- (1,4) glucan chains with -X n ~ 150 were then synthesized and solubilized. Control experiments of α-

(1,4) glucans of this chain length produced stable solutions of the order of 10 h, which was considered sufficient time for mSBEIIa to branch phosphorylase a-catalyzed glucans to an extent that was detectable by 1 H NMR.

Debranching was carried out using standard procedures. 42 Glacial acetic acid (32 µL) was added to 0.05 M sodium acetate buffer (1 mL). The buffer ( 20 rate corrected using ethanol as a marker. The Mark-Houwink-Sakurada parameters for linear α- (1,4) glucans in this particular eluent and at 60 °C have been determined to be K = 0.0544 mL⋅g -1 and α = 0.486, while those of pullulan have been determined to be K = 0.0126 mL⋅g -1 and α = 0.733. 29 The calibration curve for linear α-(1,4) glucan using pullulan standards (Shodex) range was between 504 and 710,000 g⋅mol -1 (529 and 2,490,000 g⋅mol -1 linear α-(1,4) glucan equivalent), which covers fully the range of molar mass or size of the injected samples (see the typical chromatogram and calibration curves given in the Supporting Information). The traces were not corrected for band broadening since no high molar mass water-soluble broad standards are yet available needed to implement a new general correction procedure for this purpose. 43 Data were acquired using Astra software. Data treatment (normalization, baseline subtraction, calculation of the hydrodynamic volume distributions and molar mass distributions) was performed using the software packages in Excel and Origin.

The recovery of native starch under similar SEC conditions to those used in this work has been measured to be 80 %. 25 The recovery in the present work could not be measured accurately because of the very low quantity of sample. However, it should be noted that eluents based on dimethylsulfoxide 44 are better though not yet common for SEC of α-(1,4) glucans.

Nuclear magnetic resonance (NMR)

Optimization of the NMR experimental conditions

In this work, 1 H NMR is used to quantify the degree of branching of in vitro branched α- (1,4) glucans, taking advantage of the fact that the anomeric protons located in free reducing ends and in α-(1,4) and α-(1,6) glycosidic linkages exhibit different chemical shifts. 45 The ). This is consistent with known chemical shift values in different solvent systems. 45,46 page 14

For the quantification of branching in amylopectins and some degraded starches, detection of the α-(1,6) anomeric proton signal with good signal-to-noise ratios is obtainable for short recording times due to their high branching level. In order to select the best magnetic field at which to conduct the NMR experiments, an appropriate relaxation delay between scans was chosen by recording an 8-scan spectrum with a very long relaxation delay (greater than 30 s), followed by a series of 8-scan spectra with decreasing relaxation delays. The shortest relaxation delay of the series that showed no significant difference with the very long relaxation delay in the signal of the anomeric α- (1,4) proton was chosen. A spectrum run at 300 MHz reveals an approximately 3-fold increase in the signal-to-noise ratio with respect to spectra run at 200 MHz in the same time (Supporting Information, Table S1). The poor signal-to-noise ratio of the spectra run at 200 MHz is probably the main cause of the discrepancy between the integration values of the signal of interest.

The longitudinal relaxation times T 1 for both anomeric protons was then accurately determined using the inversion-recovery technique [START_REF] Claridge | High-Resolution NMR Techniques in Organic Chemistry[END_REF] delay must be at least 5 times the value of T 1 , [START_REF] Claridge | High-Resolution NMR Techniques in Organic Chemistry[END_REF] and hence a relaxation delay of 6.5 s was used for branching quantification.

Optimized experimental conditions and data treatment

1 H NMR spectra were recorded using a Bruker Avance 300 spectrometer operating on a 1 H Larmor frequency of 300.13 MHz. The solvent was DMSO-d 6 :D 2 O 80:20 (v/v) mixture (Cambridge Isotope Laboratory). All spectra were acquired at 360 K, using an ethylene glycol standard to calibrate the temperature. [START_REF] Ammann | [END_REF] The chemical shift scale was calibrated using a trisilylpropanoic acid (TSP) internal standard (0 ppm). An optimized 90 ° pulse of approximately 7 µs was used. An acquisition time and a total relaxation delay between scans of 1.5 and 6.5 s

were used for all recorded spectra, respectively.

Data were processed using XWIN-NMR software. All spectra were manually phased and baseline-corrected. The integration limits were 5.00-5. established for the quantification of branching in polyethylene by melt-state 13 C NMR: 50 SD (%) = 238 SNR 1.28 (7)

Results and Discussions

Maize SBEIIa-catalyzed in vitro branching of a short-chain α-glucan substrate

Repeatability of the experiment and analysis

Two sets of w(logV h ) HVDs containing the linear α-glucan substrate ( -

X

Possible causes for the increase in hydrodynamic volume

The HVD of the branched glucan exhibits both a decrease and a small but significant increase in V h relative to the HVD of the parent linear substrate. A reduction in size has been observed in some SBE studies 15,16,51 and is consistent with the cleavage of chains as a result of inter-chain branching, although other explanations have been suggested, such as a change in size due to a structure conformational change or a cyclization reaction. 15 On the other hand, an increase in size has been observed previously for a potato SBE isoform, although this observation did not receive any further remarks. 16 To obtain a mechanistic understanding of the significance of this increase, an accurate description of how SEC separates macromolecules is required.

page 17

Recall that the size parameter by which SEC separates is V h . For complex branched polymers, chains consisting of different intrinsic viscosities and molar masses can have the same V h . In this case, averages of the intrinsic viscosity and molar mass must be used to calculate V h . If universal calibration holds, then the increase in V h observed in Figure 2 must correspond to an increase in the product -

[η] w (V h ) - M n (V h ) (eq (2)).
It is known that model branched polymers, such as regularly-branched or star-shaped polymers, are hydrodynamically and geometrically contracted compared to their linear counterparts having the same molar mass (see 52 and references therein). However, since no accurate theoretical models currently exist that relate the geometric size and structure of complex branched polymers, such as the subject of this study, to their hydrodynamic properties, it cannot be certain whether the branching catalyzed by mSBEIIa results in a decrease in intrinsic viscosity or in the compound quantity V h , as expected for model polymers. Thus, empirical relations are required to relate the effect of branching on V h . Evidence for hydrodynamic contraction as a result of branching is available in the literature (typically as a Mark-Houwink plot comparing branched polymer to its linear equivalent) for model branched polymers, such as end-linked stars, 53 centipedes and regular combs. 54,55 Plots of intrinsic viscosity vs. -M n or M for fractionated polymers demonstrate a V h contraction compared to equivalent linear polymers of the same molar mass in both theta and good solvents. 56 The same effect is observed in complex branched polymers such as statistically long-chain branched polyethylene 57,58 and poly(vinyl alcohol). 59 Thus, the V h contraction caused by branching appears to be a feature of branched polymers with a wide range of branching structures.

Evidence for inter-chain mechanism

The foregoing observations show that the expansion of the hydrodynamic volume observed in Figure 2 cannot be due solely to the effect of branching on intrinsic viscosity. The only other possible explanation is that V h expands as a result of a mass increment (the -M n (V h ) term). Given that there is no mass transfer occurring in an intra-chain transfer reaction, this increase in V h is page 18

inconsistent with a completely intra-chain transfer scheme, and therefore we interpret this as evidence for an at least partly inter-chain transfer mechanism.

In their work on potato SBE I and II, Andersson and co-workers stated that in a completely intra-chain transfer process the MMD remains unaltered. 11,17 According to their interpretation, the observation that no significant change occurred in the MMD of the in vitro branched product relative to the original linear substrate implies the occurrence of intra-chain transfer. However, this conclusion may be misleading, since it makes the assumption that SEC separates by molar mass, whence the MMDs calculated for the in vitro branched glucans may well be incorrect. This effectively means that only meaningful comparison between the branched product and the linear substrate is either through the raw chromatograms obtained in exactly the same experimental conditions, or of HVDs. Unfortunately the separation presented by Andersson and coworkers may suffer from a too low resolution to observe any significant differences between the two chromatograms. It would be interesting to repeat those experiments making use of higher resolution SEC together with the HVD analysis used in the present paper, to compare the branching pattern of potato SBEs with the behavior we observe here with maize SBEIIa.

HVD bimodality

Figure 2 shows a clearly bimodal HVD exhibiting a pronounced shoulder at 31 nm 3 , indicating the presence of two distinct populations of chains in the branched α-glucan. One possibility for the origin of these two populations is the presence of two distinct mechanisms for branching: for example, branching via intra or inter-chain transfer producing two distinguishable forms of the branched product. This hypothesis is merely speculative at this stage and requires further testing.

An alternative hypothesis is the ability of mSBEIIa to act differently on chains of distinct sizes and/or structures. There is evidence from previous in vitro studies that suggest that chains may become what we here term 'eunuch': chains that are so small that they can no longer be cleaved, and thus can only act as chain acceptors and not as chain donors, once their lengths reach a critical size. This eunuch chain length can be as long as DP 40 12,60,61 or as short as 12. 27 It is likely page 19 that a eunuch substrate must meet the tight structural requirements needed for the formation of the enzyme/substrate complex; thus a distinction can be made with regards to the action pattern of mSBEIIa in terms of the substrate being above or below the eunuch chain length.

The short substrate used for these set of branching experiments contained some chains below DP 40 and above 200. Thus, it is conceivable that, as a result of different substrate conformational constraints, short chains will become eunuch at an early stage of the reaction, while longer chains will continue to be substrates for mSBEIIa for an extended period of time.

The action of the enzyme on two distinct substrate lengths would eventually produce two divergent populations.

Further evidence for the presence of two distinct populations of α-glucans with distinct structures comes from debranching experiments with isoamylase. Figure 4 shows that the population at higher V h disappears almost completely upon debranching, while the peak of the population of lower V h does not vanish completely but is slightly shifted. This result suggests that the former population consists almost entirely of branched chains (otherwise intact linear chains would remain in this population), while the latter contains a mixture of branched and linear molecules. These findings are in agreement with a recent study of the in vitro mechanism of potato SBEI. 51 By tagging the reducing end of an amylose substrate (bimodal distribution with peaks at DP 890 and 400) with a fluorophore and using multiple-detection SEC, the formation of two clearly distinct populations was observed. Enzymatic treatment of the branched product with β-amylase revealed that the population consisting of shorter α-glucans was a mixture of linear and branched chains with a peak DP of 30, which is consistent with the model proposed here to explain the appearance of two populations based on the eunuch chain effect hypothesis.

MMD of the debranched product

The weight and number MMDs of the debranched mSBEIIa-catalyzed α-glucans from Figure 2 are depicted in Figure 5. While in many systems it is useful to present such data as lnN(M), 62 such a mode of presenting the data, which assumes that both chain growth and termination occur page 20

simultaneously over the lifetime of the polymer chain, is inappropriate here, because the growth (synthesis of the substrate) and termination (i.e. branching) steps are separated in the present study.

The weight MMD shows that the broadness of the distribution of chains that remain after cleavage spans the DP range from 5 to 250. Compared to the linear α-glucan substrate, most of the weight has been shifted towards lower DP. The number MMD reveals a peak at DP 7, showing that mSBEIIa preferentially transfers chains of that DP, which is consistent with the supposition that mSBEIIa may be responsible for short-chain branching of AP. This agrees with the findings of Takeda and co-workers, 19 who used a reduced form of a slightly branched amylose substrate (quoted average DP 850; the authors do not specify which average they used). The shapes of both distributions in the present work are also similar to those obtained by Takeda and co-workers.

The number MMD observed here in Figure 5 is skewed and contains at least two visible populations at DP 7 and 11 (seen as a shoulder), which are also noticeable on the weight MMD.

An additional two populations, with peaks at DP 23 and 77, are also observed on the weight MMD, although not clearly apparent in the number MMD. Hanashiro and co-workers, based on work on potato SBEI, 51 proposed an explanation for the observed periodicity in the number MMD of the constituent chains. They found peaks at DP 6, 11, 18 and 29, which, within experimental error, coincide with the lengths of a helical turn in a single helix. This is consistent with the hypothesis that a helix is the unit substrate structure for SBEs. 12 Interestingly, the peaks observed at DP 7, 11 and 23 on the weight MMD observed in this study also coincide approximately with helical turns.

Maize SBEIIa-catalyzed in vitro branching of a long-chain α-glucan substrate

To investigate the dependence of the action of mSBEIIa on substrate size, the enzyme was incubated with a long α-(1,4)-glucan substrate ( -X w = 5950) and the product analyzed with SEC at 1 and 6 h. The degree of branching determined by 1 H NMR of the products incubated for 1 page 21 and 6 h were 3.3 ± 0.3 and 4.5 ± 0.1 %, respectively, which are comparable to the 3.7 % obtained in vitro using potato SBE isoforms. 17 It is worth noting that these branching densities approach those of native AP. A control reaction where water was added instead of mSBEIIa was analyzed after 19 h, the results showing that some hydrolysis of the substrate occurs. However, it must be noted that since the molar mass of the substrate is large, any minor hydrolytic degradation will alter the weight MMD (e.g. if a chain with DP 5000 is hydrolyzed into two equal halves, this will change the weight MMD significantly, but the number MMD is not affected as dramatically).

The small amounts of hydrolytic product is further corroborated from the 1 H NMR analysis of the branched samples, which shows no detectable increase in reducing end signal in conditions where the increase in branching signal is well above the limit of detection (e.g. in Figure 7, considered later). In any case, the extent of the degradation observed does not alter the conclusions that follow.

Significant reduction in V h provides evidence for an inter-chain transfer and/or a cyclization transfer mechanism

The HVDs of the mSBEIIa-catalyzed branched α-glucans exhibit an overall decrease in V h compared to the linear substrate (Figure 6). Interestingly, no significant amount of linear αglucans with V h from 7×10 2 to 7×10 3 nm 3 remain after incubation with mSBEIIa. There are four ways in which this could happen: (i) the chains conserve their mass, as occurs in an intra-chain transfer reaction, but experience at least a 10-fold V h contraction; (ii) the chains are cleaved but there is no transfer of mass, as occurs in a cyclization reaction (Figure 1); (iii) the chains have been cleaved and transferred to an extent such that the molar mass is reduced significantly, which occurs in an inter-chain transfer mechanism; (iv) any combination of these previous reactions.

Note that the position of the cleaved glycosidic bond at external (exo) or internal (endo) position may also play a role, and is discussed later in the text.

In the first case, where the molar mass of all chains is conserved, the V h contraction would have to be caused by a reduction in intrinsic viscosity. The intrinsic viscosity contraction factor, g´, is page 22

the ratio between the intrinsic viscosity of a branched polymer [η] br and that of its equivalent linear polymer of same molar mass [η] lin , and is dependent on polymer branching structure:

g´ = [η] br /[η] lin (8)
Thus, in order for the data to be consistent with an intra-chain reaction, a contraction factor of 0.1 or less is required to cause the 10-fold hydrodynamic reduction. Values of g´ of 0.45 have been measured for comb polymers having high side-chain weight fractions (> 0.6) with degree of branching of the order of 2.4 %. 54 Short-chain branching of polyethylene can cause contraction by a factor of 0.45 at side-chain weight fractions of 0.7. 63 Thus, even these examples of densely branched structures have contraction factors much higher than 0.1. Only extreme branching structures, for example, star-shaped polymers with 64 arms or more, have been known to have intrinsic viscosity contraction factors of less than 0.1 (see 52 , and references therein). We find it unlikely, particularly with degrees of branching of the order of 3 to 4 %, that intra-chain branching can cause compact structures with g´ values of 0.1.

Since the degree of branching obtained by NMR is an average, it is still possible that branched α-glucans contain some highly branched chains that have g´ values of the order of 0.1. However, this would imply that most of the chains remain linear. This is not consistent with a considerable shift of the HVD after debranching, which suggests that branches are homogenously distributed throughout the sample (see Supporting Information Figure S-6). Hence we consider that it is highly improbable that mechanism (i) alone can account for the decrease in V h observed.

The alternative mechanisms that can account for this observation are (ii) cyclization and (iii)

inter-chain transfer. These two reaction mechanisms are similar, in that both produce residual chains through cleavage, significantly reducing the size of the substrate if cleavage occurs at internal linkages of the substrate. In cyclization, each transfer event results in the creation of a new residual fragment chain, thus reducing the size of the donor chain. In an inter-chain transfer mechanism, each transfer event results in the reduction in molar mass of the donor chain, whereas the acceptor chain gains mass. However, if enough cycles of transfer occur, it would be expected page 23

that the effect of mass exchange between short and long chains would cause an overall decrease in V h of most, if not all, chains. This would explain why no chains with an increment in their V h are observed. Thus, we believe the presence of an inter-chain transfer and/or a cyclization mechanism is more likely to significantly reduce the size of the long α-(1,4)-glucan substrate in the conditions studied. Furthermore, although both mechanisms can reduce V h (in different ways), the magnitude of the decrease is likely to be of the same order; hence in principle they cannot be distinguished solely by simple SEC separation. Multiple-detection SEC or multi-dimensional HPLC could reveal the presence or absence of cyclic α-glucans (a cyclic structure has a different viscosity than its linear counter-part of same molar mass or can also be discriminated by labeling of the end-groups).

It is worth noticing that Takata and co-workers have identified the formation of cyclic αglucans produced by the action of a BE from Bacillus stearothermophilus on an amylose substrate. 15 They interpreted that the retardation in the SEC elution time of the branched α-glucan was due to a cyclization reaction, and that SEC data from other in vitro studies showing similar behavior could be best explained by this mechanism and not an intra-chain transfer mechanism (see e.g. 64 ). However, in light of our interpretation of SEC data using the concepts of V h and g´, we believe the magnitude of the changes of these parameters must first be evaluated before discarding the possibility of an intra-chain transfer mechanism.

Another interesting observation is that the addition of ca 1.2 % more branches does not cause any significant changes in polymer structure, as demonstrated by the similarities in the HVDs (Figure 6) and the MMDs (Figure 7) of the constituent chains of the samples at the two incubation times. This suggests that the addition of branches represents an "add on" to the unit core structure that is initially formed.

Appearance of maltohexaose as residual fragments

Small but significant amounts of maltohexaose (DP 6) (and lesser amounts of maltoheptaose (DP 7)) are produced during the branching of the long-chain substrate as determined by using a page 24 linear α-(1,4) glucan molar mass calibration curve in this size range (data not shown; maltohexaose is indicated in the HVDs on Figure 6). The control run did not show any degradation of the substrate into these oligosaccharides and their appearance was not detected in the branching of short-chain substrate using mSBEIIa from the same batch; hence the oligosaccharides were not produced by α-amylase contaminants from the preparation but must originate from the intrinsic nature of mSBEIIa action on the substrate.

Other explanations for the appearance of maltohexaose could be that it is formed by cleavage to form a short donor fragment but is released into the medium due to a weak substrate/enzyme interaction, or alternatively that it originates from a double scission event in internal regions of the substrate chain. These mechanisms imply the generation of new reducing sugars. However, comparison of the 1 H NMR spectra of the α-(1,4) glucans obtained after 1 and 6 h of branching of the long substrate shows no increase in reducing end signal, in conditions where the increase in branching signal is well above the limit of detection (Supporting Information Figure S7). A number of other studies were also not able to detect significant amounts of reducing sugars from in vitro studies of SBE isoforms from maize, potato and bacteria, making these alternatives unlikely to occur. 12,15,19 Eliminating these mechanisms supports the notion that maltohexaose derives from a chain end of the substrate, although which end cannot be deduced from the data. Interestingly, an in vitro study of potato SBE I also revealed the appearance of maltohexaose and maltoheptaose during the branching reaction and showed that these oligosaccharides were derived from the reducing end of the amylose substrate (DP > 400). 51 Thus the findings of this study suggest that the maltohexaose observed here are reducing-end residual fragments that remain after the nonreducing side of the donor chain is transferred (Figure 1).

An important question that stands out from these results is why no maltohexaose is produced in the branching of the short-chain α-glucan substrate (Figure 2). Given that similar weight concentrations were used for branching experiments of long and short-chain substrate, there are page 25

significantly more reducing ends in a short-chain substrate than in a long one; thus it would be expected that, for comparable degrees of branching and for the same mechanism of action of mSBEIIa, the production of oligosaccharides would be more significant in the short substrate.

Since the degrees of branching between both products are not significantly different, we therefore attribute this discrepancy to the differential action of mSBEIIa on the two substrates.

MMD of the debranched product

The MMDs of the two debranched mSBEIIa-catalyzed α-glucans from Figure 6 are depicted in Figure 7. Both the weight and number MMDs share similar features with that of the in vitro branched short-chain substrate (Figure 4), exhibiting four populations on the weight MMD and two on the number distribution. The product incubated for 6 h shows that the creation of more branches has shifted the population at high molar mass towards smaller weights, while increasing the proportion of chains for the populations at DP 7, 11 and 23. Figure 7 illustrates that there is a higher concentration of longer chains on a weight basis, that is, the weight of longer chains is higher than the weight of short chains. This is unsurprising since the starting chains are quite big and it would only take a few long chains that remain relatively unaltered by the enzyme for the weight of chains to be mostly on these. On a number basis the distribution approximates what is typically found in debranched AP, ass shown on the lower graph.

Mechanistic inferences from maize SBEIIa-catalyzed in vitro branching of a short-and long-chain α-glucan substrate

Substrate-size-dependence

One major difference we observe in the HVDs of mSBEIIa-catalyzed branched α-glucans derived from a short and long substrate is the formation of two populations in the former: one consisting of entirely branched α-glucans and another consisting of a mixture of branched and linear α-glucans. We postulate that the divergence of these two populations are due to size/steric factors that affect the binding of enzyme to the substrate, and that there is a eunuch chain length where the binding affinity is significantly affected.

page 26

The action of mSBEIIa on a long α-glucan substrate produced a product with a monomodal HVD. Upon debranching this product, we consistently found that most of the weight of the αglucans shifted to lower hydrodynamic volume, indicating that a greater proportion of chains were branched in this longer substrate (Figure S-6). These observations indicate the presence of a more homogenous population of α-glucans produced by the action of We postulate that the chains in this longer substrate are considerably above the eunuch chain length, so only one pattern of action is observed in these conditions.

This pattern of branching, however, must be different to the mode of action observed for the short-chain substrate, since maltohexaose is not formed during the mSBEIIa-catalyzed branching of this substrate. Therefore, the eunuch chain effect cannot be the only one influencing the action of mSBEIIa, suggesting that other properties of the substrate are likely to play a role. One possibility is that chain associations in the substrate affect how mSBEIIa catalyzes branching.

Borovsky and co-workers proposed that the substrate for the potato Q-enzyme (SBEI) is a double helix, 12 although not enough experimental evidence is available to confirm this hypothesis.

Indeed, the short and long-chain substrates used here will differ significantly in degrees of chain association, since the concentration of substrate on a number basis (as opposed to a weight basis) is higher for the short-chain α-glucan. We believe that this chain association effect deserves further attention and should be experimentally investigated, for instance, by systematically varying the substrate concentration and using SEC analysis to look for differences in the HVDs and MMDs of the branched α-glucans and its constituent chains.

Another possibility for the observed differences that cannot be discarded is that the enzyme is able to interact with the substrate in more than one site. It may be that a short-chain α-glucan could not be extended enough to interact to additional non-catalytic binding sites present on mSBEIIa, resulting in a different mechanism of action. Again, structural studies exploring the different interactions possible between the enzyme and substrate would be invaluable in trying to understand this size-dependent action of mSBEIIa.

page 27

Putative active-site structure An interesting inference that can be made from the appearance of maltohexaose as reducingend residual fragments in the branching of the long-chain substrate is the estimation of the eunuch chain length or minimum DP of the donor chain required for branching. Debranching of all in vitro branched products demonstrates that the minimum DP that was transferred in significant amount was 6 (Figures 5 and7). if our interpretation of maltohexaose consisting of reducing-end residual fragments is correct, the minimum length of a donor chain required for branching by mSBEIIa must be 12. This number coincides exactly by that found by Guan and co-workers for a maize SBE II isoform. 27 Another interesting question that follows from the appearance of maltohexaose relates to the specificity of the fragment size. We propose that the specificity of the fragment is directly related to the structure of the active site of the enzyme and suggest that the specific arrangement of the loops surrounding the catalytic active site of mSBEIIa form glucosyl pockets or subsites that accommodate the glucosyl units (Figure 8). This putative active site contains exactly six subsites on the reducing-end side of the cleaved glycosidic bond, whereas there are at least six subsites on the non-reducing side of the substrate, although it is likely that a more complex array of subsites exist on this side that controls the specificity of transferred chains observed in the present study.

The subsite recognizing the reducing end glucosyl unit is further proposed to have a different structure to the rest, so that, although it can bind less strongly to other glucosyl units, it preferentially recognizes and accommodates the reducing glucosyl unit at the sixth subsite. This would allow mSBEIIa to have some chain-end recognition capability that may be important for the control of branching structure. This postulated preferential binding may be enhanced under certain conditions, such as in the branching of a substrate with characteristics resembling those of the long-chain substrate used in this work. Although the molecular basis of this chain-end recognition is unknown, it is plausible that the more flexible structure of the reducing unit allows it to adopt a specific conformation which makes binding to the sixth subsite more favorable.

page 28

Interestingly, distorted ring conformations have been shown to be essential for the occupation of subsites in some α-amylases (see 65 and references therein).

Our model implies that a form of mSBEII shares mechanistic properties similar to those enzymes belonging to the α-amylase family that act on their substrates at their external ends (exo action). α-amylases are generally considered to be endo-acting enzymes, that is, they are able to hydrolyze glycosidic bonds at internal positions of a long-chain substrate, producing a broad distribution of chain lengths. However, some α-amylases are exo-acting, and these produce mainly small chains of a single length. 66 The determining factor for whether an enzyme is endo or exo-acting is the active site structure and its ability to accommodate and recognize glucosyl units at unique positions of the substrate. For example, the crystal structure of a mutant exo-amylase from Pseudomonas stutzeri co-crystallized with maltopentaose (DP 5) reveals that the nonreducing glucose unit is tightly hydrogen-bonded with three key amino-acid side chains, and that this strong interaction is vital for non-reducing end recognition and exo-acting activity. 67 Borovsky and co-workers were the first to demonstrate the endo-acting mechanism of potato SBEI. 12 Later, Takeda and co-workers showed that maize SBE isoforms transferred a broad range of chain DPs at early reaction times, 19 which is also consistent with an endo-type mechanism. We do not find anything in our SEC data that is inconsistent with an endo-type mode of action of mSBEIIa; in fact, the absence of significant amounts of branched α-(1,4) glucan product of comparable size to the original long chain substrate (Figure 6) can be interpreted as being indicative of an endo-acting mechanism. However, the unique observation of the appearance of maltohexaose during the branching reaction in a long-chain α-glucan substrate in this present work and in the work of Hanashiro and co-workers 51 for potato SBEI prompts us to propose that mSBEIIa, and possibly other SBEs from different origins, may well have a propensity for recognizing the reducing end of the substrate under certain conditions. Thus, our proposal implies that in vitro mSBEIIa is able to operate in a resilient manner, being able to simultaneously act on its substrate with characteristics resembling both endo and exo-acting enzymes.

page 29

It is quite plausible that the appearance of maltohexaose and the mechanism we propose by which it is formed is a unique feature of the in vitro system studied. In the clusters of AP, where mSBEIIa is more likely to be involved, there are no reducing ends and hence the exo-acting activity we propose here may not be physiologically relevant. In the in vitro treatment of native potato AP with potato SBEI (formerly known as Q-enzyme), the specific appearance of maltohexaose in the debranched product was observed (an oligosaccharide which was not present in the debranched untreated sample) and its attributed to a residual fragment resulting from the transfer of a donor chain that itself constituted a branch chain. From this the authors proposed that the ability for that enzyme to cleave bonds depends on its distance to a branch point with a minimum of 6 glucose units. 68 It is tempting to conjecture if our postulated active site subsite structure and the nature of the biochemical interaction of subsite 6 (Figure 8) with the reducing end glucosyl unit in an in vitro system has any relation to a putative branch point recognition mechanism which could have physiological relevance.

Conclusions

We have used hydrodynamic volume SEC analysis to gain information regarding the mechanism of action of maize SBEIIa. By using a well characterized linear α-(1,4) glucan substrate, we gained insight on how maize SBEIIa shows distinct behavior on substrates of different length. It is essential to interpret SEC data for the branched polymers in these systems in terms of hydrodynamic volume distributions, to avoid artifacts arising because branched polymers can have a range of molar masses eluting at a given time. It was thus possible to infer the presence of an inter-chain transfer and/or a cyclization mechanism in substrates of significantly different molar mass, although the simultaneous occurrence of an intra-chain transfer reaction cannot be excluded. Our findings also suggest that other factors, such as substrate association or concentration, may also affect the way in which maize SBEIIa can catalyze branching in vitro. In addition to the use of SEC, the quantification of branching using page 30

HVDs are not due to different degrees of branching, but due to the differential action pattern of the enzyme, emphasizing that accurate branching level determination is also important for proper characterization of the in vitro branched α-(1,4) glucans.

We cannot tell at this point how relevant the in vitro substrate-dependent behavior of mSBEIIa is in vivo. However, the mechanistic trends we observe here should be kept in mind when evaluating data that is derived from in vivo experiments. SEC hydrodynamic volume analysis is a powerful tool to investigate the mechanism of action of SBE isoforms from maize and other sources in a wide range of conditions, which among other things can help in making different studies of SBEs in vitro much more comparable. The hexagonal rings represent glucose units from the substrate and the black circle is the reducing end, while the white oval cavities are subsites created by residues in the enzyme loops.

Figures

The subsite accommodating the reducing end is proposed to have a different structure to the rest of the subsites, allowing it to recognize reducing end glucosyl units, although it can still bind to other glucose units less strongly. This differential binding affinity confers the enzyme some resilience with regard to the nature of the enzyme/substrate interaction. The glycosidic bond to be cleaved lies in between units 1 and -1. In the

1 µm

 1 Durapore® poly(vinylidene fluoride) membrane filter (Millipore), and washed with Milli-Q water and ethanol (repeated × 3). The α-(1,4) glucan was vacuum dried and stored at -33 °C until used. The MMDs of the two linear α-glucans are compared in the Supporting Information (Figure S-3). page 11In vitro branching and debranchingPhosphorylase-catalyzed α-(1,4) glucans (∼2.0 mg for short chains, 6-8 mg for long chains) was solubilized in NaOH (1 M, 200 µL for short chains, 800 µL for long chains) at 70 or 80 °C with gentle shaking for 10 min (short chains) or 20 min (long chains). The pH of the solution was adjusted to pH ∼7.4 with HCl (1 M). In the case of the short-chain branching experiments, 3-(Nmorpholino) propanesulfonic acid (MOPS) buffer and MilliQ water were added (final MOPS concentration 0.05 M) followed by addition of mSBEIIa solution (∼3 µM, 150 µL; final α-glucan concentration 2.0 mg•mL -1 ) and the solution cooled to 30 °C. A 70 µL aliquot was taken from this preparation immediately after mSBEIIa addition, heated in boiling water (5 min), and analyzed with SEC (the SEC distribution of this sample was taken as the size distribution of the linear substrate prior to branching).

  µL) was added to in vitro synthetic branched α-(1,4) glucan (100 µL). The α-(1,4) glucan solution was debranched by incubating with isoamylase (Megazyme; 5 µL, 500 or 1000 units) at 40 °C for 3 h. The reaction was terminated by heating in boiling water for 3 to 5 min. BaNO 3 (5 to 10 mg) was added and the resulting precipitate spun in an analytical centrifuge (less than 3 s). The supernatant was transferred into a new tube and the precipitation was repeated with BaNO 3(5 mg). An aliquot of the supernatant was analyzed by SEC.Size-Exclusion Chromatography (SEC)SEC was performed on a Shimadzu system (LC-10ATVP pump, DGU-14AVP degasser, SIL-10ADVP autoinjector, CT10ACVP column oven) connected online with a Wyatt OPTILAB EOS interferometric refractometer (temperature 30 ˚C). Two Waters column sets were used: Ultrahydrogel 250, Ultrahydrogel 120 and Ultrahydrogel Guard column connected in series for low molar mass polymers, Ultrahydrogel 500, 250,120 and Guard column connected in series for high molar mass polymers. The eluent used was 0.05 M ammonium acetate filtered with a 0.1 µm Durapore® poly(vinylidene fluoride) membrane filter (Millipore) (0.05 % NaN 3 ; pH 5.2) at a flow rate of 0.5 mL⋅min -1 with the column oven set to 60 °C. All SEC traces obtained were flowpage13

  1 H NMR spectrum of a Shimuzi Mochi rice starch variety revealed the chemical shift range of the α-(1,4) and reducing end anomeric protons (5.4-5.0 ppm, relative to TMS) and α-(1,6) anomeric proton (4.8 ppm) in an 80:20 (v/v) DMSO-d 6 :D 2 O mixture at 360 K (ca 4 mg.mL -1

  with the following conditions: 80:20 DMSO-d 6 :D 2 O page 15 solvent, 300 MHz, T = 360 K, 20 s relaxation delay, 256 scans, 9 recovery delays ranging from 10 µs to 20 s. The T 1 values obtained were 880 ms for the α-(1,6) anomeric proton and 940 ms for the α-(1,4) anomeric protons. To quantify the integral of the signals of interest, the relaxation

  40 ppm for the α reducing end and α-(1,4) glycosidic anomeric signals and 4.75-4.85 ppm for α-(1,6) glycosidic anomeric signal, respectively. Note that the former range does not cover the β reducing end signal coming out at 4.7 ppm, but its integration is negligible compared to that of the large α-(1,4) glycosidic anomeric signals (Figure 3). Degrees of branching, expressed in percents of the anhydroglucose units, were calculated using: DB (%) = 100 α-(1,6) proton signal α-(1,4) + α reducing end proton signals (6) Signal-to-noise ratios of the α-(1,6) glycosidic anomeric signals were calculated with XWIN-NMR software (noise range: 5.4-6.6 ppm; signal range: 4.75-4.85 ppm). Long acquisitions times (overnight) made multiple runs of samples impractical. Standard deviations (SD) of the degree of branching were estimated using the empirical formula relating it with signal-to-noise ratio (SNR)

  w = 215) and the branched product are shown in Figure2. The two experiments were conducted four months apart, with the SEC set-up recalibrated for the second experiment. The almost exact match of the duplicates of the two sets of HVDs demonstrates the good repeatability of both the reaction and the SEC analysis in the conditions studied. The degrees of branching were also not significantly different: 2.3 ± 0.1 % and 2.1 ± 0.1 % as measured by1 H NMR for the two samples shown (Figure3). A control reaction where water was added instead of mSBEIIa was also employed and showed no changes on the size distributions of the substrate (see Supporting Information Figure S-5).

Figure 1 :

 1 Figure 1: Schematics of three possible branching mechanisms as catalyzed by a starch

Figure 2 :

 2 Figure 2: Hydrodynamic volume distributions of a short linear α-(1,4) glucan substrate ( -X w

Figure 3 : 1 H

 31 Figure 3 : 1 H NMR spectrum of the SBE-catalyzed branched α-glucan obtained from the short

Figure 4 :

 4 Figure 4 : Hydrodynamic volume distributions of the in vitro branched product from Figure 2

Figure 5 :

 5 Figure 5: Weight (upper, solid line) and number (lower) MMDs, as functions of degree of

Figure 6 :

 6 Figure 6: Hydrodynamic volume distributions of a long linear α-(1,4) glucan substrate ( -X w =

Figure 7 :

 7 Figure 7: Weight (upper) and number (lower) MMDs, as functions of degree of

Figure 8 :

 8 Figure 8: Schematic representation of occupied subsites at a putative active site of mSBEIIa.

  Figure a substrate with the proposed minimum degree of polymerization required for a donor chain is about to be cleaved, eventually transferring the segment of DP 6 on the non-reducing side (negative numbers) of the chain to form a new α-(1,6) linkage and leaving behind a reducing-end residual fragment of DP 6(positive numbers). The subsites proposed here represent only essential pockets that must be occupied for catalytic activity, but additional subsites on the non-reducing side of the substrate page 39 are likely to control the size specificity of the transferred chain segments (these are represented as an extended dotted line).

Figure S4: 1 H 7 Figure S5 : 8 Figure S6 :

 17S58S6 Figure S4: 1 H NMR Spectra of an amylopectin sample ozonolyzed for 60 min 2 at 300 (dotted line) and 400 MHz (solid line) in DMSO-d 6 :D 2 O 20:20 at 80 °C. Experimental temperature was approximately 360 K, 64 scans were acquired.

  45,47 A sample consisting of linear α-(1,4)

	glucans (2 mg) incubated with mSBEIIa for 15 h and 40 min that partially retrograded was
	freeze-dried and redissolved in 80:20 DMSO-d 6 :D 2 O mixture (0.5 mL). A typical 1 H spectrum (ca
	20 min, 300 MHz, 256 scans, T ≈ 360 K) of this branched α-(1,4) glucan sample was unable to
	detect a signal at 4.8 ppm corresponding to the chemical shift of the α-(1,6) anomeric proton in
	these conditions. However, an increase in the number of scans (ca 80 min, 1028 scans) revealed a

small signal at this chemical shift. To properly quantify the level of branching by NMR, some operating parameters were optimized with this sample. The composition of the DMSO-d 6 :D 2 O solvent mixture was first optimized for best resolution. 100 % DMSO-d 6 significantly decreased the apparent resolution compared to the 80:20 DMSOd 6 :D 2 O mixture, because all hydroxyl groups of the α-(1,4) glucan are observed as well. On the other hand, too high D 2 O content resulted in incomplete solubility. The 80:20 DMSO-d 6 :D 2 O mixture originally used proved to give the best compromise between solubility and resolution.

Acknowledgements

The support of both a Discovery grant and a LIEF grant from the Australian Research Council are gratefully acknowledged. We greatly appreciate discussions and suggestions from Dr Melissa Fitzgerald (International Rice Research Institute), Dr David Lamb and Dr Jeffrey Castro. JMH gratefully acknowledges financial support from an Australian Postgraduate Award.

Supporting Information

SDS-polyacrylamide gel electrophoresis of SBIIa, SEC elugram of the substrate together with pullulan calibration curve, ,olecular weight distributions of the substrate, NMR experimental parameters and NMR spectrum of amylopectin, elugram for the control reaction, hydrodynamic volume distributions of the branched and debranched glucans.

Supporting information for

Mechanistic investigation of a starch branching enzyme using hydrodynamic volume SEC analysis S4. Those spectra were run as a test to try to obtain higher signal-to-noise ratio at 400 MHz, which was not conclusive. This showed an increase in signal-to-noise ratio of ca.

G6 and G-1-P peaks?

The following checks were done to see if the G6 and G-1-P peaks could not be an eluent peak. SEC traces from a pullulan standard and also a run containing ethanol (shown below), which does not interfere with any peaks, show that the peak is seen many minutes after the water peak. Thus the peak under question is not an eluent peak or any kind of anomalous peak, but comes from the linear starch synthesis preparation.

Above: green and blue are resolubilized linear starches, red is part of the trace for the ethanol run in the marker. The peak under question is the one at 33.5 min.

Above: A resolubilized linear starch (blue) and a pullulan standard in buffer (red). In this case the peak under question is at 51 min, but even the peak for this linear starch sample is small.


Nabi Hermann

Singh-Manoux Archana

Jane E Ferrie

Melchior Maria

Kivim Ki Ä Mika

-Perine

Hostility and depressive mood: results from the Whitehall II prospective cohort study

Keywords: hostility, depressive mood, psychosocial vulnerability, stressful life events, social support

Background

The psychosocial vulnerability model of hostility posits that hostile individuals, given their oppositional attitudes and behaviours, are more likely to have increased interpersonal conflicts, lower social support, more stressful life events and higher likelihood of depression. However, little research has tested this hypothesis using large scale prospective samples. The present study aims to assess the predictive value of hostility for depressive mood.

Methods

Data are from 3,399 participants in the Whitehall II cohort study, aged 35 55 years at baseline (phase 1 1985 1988). Cynical hostility -= was measured at phase 1 using the Cook-Medley-Hostility-scale. Depressive mood was assessed at phase 7 (2002 2004) using the -Center-for-Epidemiologic-Studies Depression-Scale (CES-D). Sociodemographic characteristics, health related-behaviours, common mental disorders and antidepressant medication intake were assessed at phase 1. Stressful live events (SL-E) and confiding/emotional support were measured at phases 1, 2 (1989 1990), and 5 (1997 1999).

--

Results

Compared

to participants in the lowest quartile of cynical-hostility, those in the highest quartiles were more likely to have depressive mood (second quartile: OR 1.58, 95 CI; 1.14 2.20; third quartile: OR 2.78; 95 CI, 2.03 3.77; fourth quartile: OR 4.66; 95 CI, = % -= % -= %

3.41 6.36) in analysis adjusted for sociodemographic characteristics. This graded association was somewhat attenuated (up to 18 )

-% but remained robust to adjustments for the covariates measured at baseline and follow-up. The association was also evident in participants free of mental health difficulties at baseline.

Conclusions

Cynical-hostility is a strong and robust predictor of depressive mood. Consideration of personality characteristics may be crucial to the understanding and management of depression.

INTRODUCTION

Depression is a major public health issue worldwide (

). Projections of the Global Burden of Disease (GBD) [START_REF] Moussavi | Depression, chronic diseases, and decrements in health: results from the World Health Surveys[END_REF] suggest that depression will account for 10 of the total disease burden in high-income countries by 2030 (

).

% [START_REF] Mathers | Projections of global mortality and burden of disease from 2002 to 2030[END_REF] The psychosocial vulnerability model of hostility posits that hostile individuals, given their oppositional attitudes and behaviours, are more likely to have increased interpersonal conflicts, lower social support, more stressful life events and higher likelihood of depression ( , ). Research suggests that stressful life-events (SL-E) may be independent risk factors [START_REF] Kivimaki | Hostility, unemployment and health status: testing three theoretical models[END_REF]et al. Miller , 1996 et al. Kendler et ) for depression with several studies showing SL-E to be associated with an increased risk of both the onset ( ) , 1999 al. [START_REF] Caspi | Influence of life stress on depression: moderation by a polymorphism in the 5-HTT gene[END_REF]

recurrence of depression (

). Another well established factor in the aetiology of depression is social support. [START_REF] Bifulco | Lifetime stressors and recurrent depression: preliminary findings of the Adult Life Phase Interview (ALPHI)[END_REF] According to the stress-buffering hypothesis (

), social support may protect from the negative effects of stressors " " [START_REF] Cohen | Stress, social support, and the buffering hypothesis[END_REF] such as SL-E, hence protecting against depression. Indeed, a large body of evidence has shown that a low level of social support predicts future depression and recovery from depressive episodes ( , ). [START_REF] Brown | Clinical and psychosocial origins of chronic depressive episodes. II. A patient enquiry[END_REF][START_REF] Johnson | Social support and the course of bipolar disorder[END_REF] We argue that cynical hostility, a personality trait characterized by general cynicism and interpersonal mistrust, may increase the risk of depressive disorders because hostility is related to both SL-E and social support ( , ). [START_REF] Hardy | Cynical hostility and vulnerability to disease: social support, life stress, and physiological response to conflict[END_REF][START_REF] Hardy | Cynical hostility and vulnerability to disease: social support, life stress, and physiological response to conflict[END_REF][START_REF] Smith | What s so unhealthy about hostility? Construct validity and psychosocial correlates of the Cook and Medley Ho scale[END_REF] However, there is little research on the predictive value of hostility for depressive disorders using large scale prospective samples. A small scale cross-sectional study (

) conducted among undergraduate students found cynical hostility to be strongly associated with [START_REF] Felsten | Hostility, stress and symptoms of depression[END_REF] depressive mood. Another study (

) examining the longitudinal effects of hostility on depressive tendencies among [START_REF] Heponiemi | The longitudinal effects of social support and hostility on depressive tendencies[END_REF] 1413 men and women found cynical hostility to be related to an increase in depressive tendencies after 5 years. Depressive mood may reinforce hostile feelings and behaviours toward others ( ), or influence the assessment of cynical hostility ( Painuly , 2005 et al. Kendler et ); a longer time lag between assessment of hostility and the measurement of depression would allow the examination of whether , 2006 al.

the influence of cynical hostility on depressive mood persists over time. The aim of the present study is to examine the predictive value of cynical hostility measured in midlife age on depressive mood 19 years later by controlling for baseline common mental disorders, antidepressants medication intake as well as for SL-E and confiding/emotional support at the baseline and during the follow-up. a clinical examination and a self-administered questionnaire. Subsequent phases of data collection have alternated between postal questionnaire alone Phases 2 (1989 1990), 4 (1995 1996), 6 (2001) and 8 (2006) and postal questionnaire accompanied by a clinical

MATERIAL & METHODS

Data

[ - - ]
examination Phases 3 (1991 1993), 5 (1997 1999) and 7 (2002 2004) . The University College London ethics committee approved the

[ - - - ]
study.

Measures

, defined as a personality trait characterized by general cynicism and interpersonal, was assessed using the Cynical hostility

Cook-Medley Hostility scale (

) at phase 1 (1985 1988 : 19, 183, 237, 253, 386, 394, 410, Hathaway and McKinley, 1943 455, 458, 485, 504, and 558). Cynical Hostility levels were determined based on the quartile distribution lowest (0 6), middle lowest ( 7[ --10), middle highest (11 15) and highest (> 16) . The lowest quartile was the reference category.

-]

We also used the eight-item Cynical Distrust Scale (alpha 0.72), an alternative short-form measure of cynical hostility, derived by ). Radloff, 1977 Covariates included age, sex, ethnicity, and socioeconomic position (SEP) assessed by British civil service grade Sociodemographic measures of employment taken from the phase 1 questionnaire. assessed at phase 1 included smoking status (never, ex, and current), exercise ( 1.5 or <1.5 hours of Health-related behaviours ≥ moderate or vigorous exercise/week), heavy alcohol consumption in units of alcohol consumed per week (>22 for men and >15 for women), and body mass index (BMI) (<20, 20 24.9, 25 29.9, or 30 kg/m ).

--≥ 2 at baseline was assessed using the self-administered 30-item General Health Questionnaire at phase 1. In Common mental disorder each GHQ item an enquiry is made about the presence or absence of a specific symptom. On the basis of receiver operating characteristics analysis and previous studies, we defined people with a GHQ sum score of 5 or more as cases and those scoring 0 4 as non-cases ( -). In the present study in which GHQ scores were validated against a Clinical Interview Schedule, the Stansfeld and Marmot, 1992b sensitivity (73 ) and specificity (78 ) using this measure of caseness was acceptable (

).

% % ' ' Stansfeld and Marmot, 1992b at phase 1 was assessed by asking participants whether in the last 14 days they had taken antidepressants Antidepressant medication prescribed by a doctor (yes/no). at phases 1, 2, and 5 included the number stressful life events (0, 1, 2 and more) derived from an eight item Stressful life events self-reported question concerning experiences in the previous 12 months. The instruction "The following is a list of things that can happen to people. Try to think back over the past 12 months and remember if any of these things happened to you and, if so, how much you were was followed by a list of events: 1) Personal serious illness, injury or operation; 2) Death of a close relative; 3) upset or disturbed by it?" Serious illness, injury or operation of a close relative or friend; 4) Major financial difficulty; 5) Divorce, separation or break-up of personal intimate relationship; 6) Other marital or family problem; 7) Any mugging, robbery, accident or similar event; 8) Change of job or residence.

at phases 1, 2, and 5 was assessed using the Close Persons Questionnaire ( Confiding/emotional support Stansfeld and Marmot,

) which included a 7-item scale measuring wanting to confide, confiding, sharing interests, boosting self-esteem and reciprocity 1992a relative to the first close relationship. Each item of the scale was evaluated using a Likert scale ranging from 1 to 4 with higher scores indicating more confiding/emotional support. The final confiding/emotional support scores were divided in three groups based on tertiles representing different levels of exposure to confiding/emotional support (low, middle, high).

Statistical analysis

Differences in cynical hostility score levels and depressive mood status as a function of the baseline covariates were assessed using a chi-square test.

The association between cynical hostility and the depressive mood at follow-up was assessed using logistic regressions in 5 serially adjusted models. In Model 1 no adjustment was made. Model 2 adjusted the likelihood of depressive mood for sex, age, ethnicity and socioeconomic position. In model 3, the analysis was additionally adjusted for smoking, BMI, alcohol consumption and physical exercise.

Model 4 had two elements: model 4a was additionally adjusted for baseline stressful life events and confiding/emotional support score (phase 1) and model 4b for stressful life events and confiding/emotional support score at baseline and during the follow-up (phases 1, 2, and 5). Model 5 had further adjustments for antidepressant medication and common mental disorders at baseline. The same serial analyses were undertaken to examine the association between cynical distrust and depressive mood at follow-up. The interaction between cynical hostility with sex in relation to depressive mood was notstatistically significant (P >0.05), leading us to combine men and women in the analyses.

RESULTS

Only 75 of the 10308 participants were asked to complete the hostility scale at phase 1 due to this measure being introduced after the % start of the baseline survey. 6484 participants responded to the hostility questions (84 of those asked). 6012 participants at Phase 7 % responded to the CES-D Scale; 3639 of these had data cynical hostility. Finally, 3399 participants had complete data on cynical hostility, depressive mood and the 13 covariates. The mean age (SD) at baseline was 44 (5.9) years. The prevalence of depressive mood among these participants at phase 7 was 15.1 .

%

shows the associations between covariates (Phase 1), cynical hostility (Phase 1) and depressive mood (Phase 7). Higher Table 1 cynical hostility scores were associated with younger age, lower socioeconomic position, being non-white, higher BMI, antidepressant medication intake, having common mental disorders, higher number of SL-E, lower social network size, and higher social isolation (all p ≤ 0.007). The presence of depressive mood at Phase 7 was associated with being female, younger age, lower socioeconomic position, being non-white, lower alcohol consumption, lower exercise, antidepressant medication, having common mental disorders, higher number of SL-E, and lower confiding/emotional support score at baseline (all <0.001). p presents the association between cynical hostility at baseline (Phase 1) and depressive mood over 19 years later (Phase 7). In Table 2 model 2, adjusting for sex, age, ethnicity and socioeconomic position, participants in the second quartile of cynical hostility had 1.58 times greater odds (95 CI; 1.14 2.20) of depressive mood compared to those in the first quartile. Those in the third (OR 2.78; 95 CI, 2.03

% - = % -
3.77) and fourth quartile (OR 4.66; 95 CI, 3.41 6.36) also had a greater likelihood of depressive mood when compared to those in the = % lowest quartile. Further adjustment for health-related behaviours in model 3 (BMI, alcohol consumption and exercise) did not much change these associations. In Model 4a, when further adjustment was made for baseline SL-E and confiding/emotional support score, the associations were attenuated, particularly for participants in the highest cynical hostility quartile (16 compared to model 2). In model 4b, % when further adjustment was made for SL-E and confiding/emotional support score at the baseline and during the follow-up, a similar percentage of attenuation was observed. Finally, further adjustments (model 5) for antidepressant medication intake and common mental disorders at baseline, the odds of depressive mood at follow up was reduced, particularly for participants in the highest cynical hostility level (17 compared to model 3). However, the dose response association between cynical hostility levels and depressive mood was % preserved even in the fully adjusted models. In the table we also present the association between cynical distrust and depressive mood. As with cynical hostility, we found evidence of a dose-response association between levels of cynical distrust and the likelihood of depressive mood at follow-up.

Sensitivity analyses

To test the robustness of the present findings, we examined the predictive value of hostility on depressive mood among participants with no mental health difficulties (common mental disorders or antidepressant medication) at study baseline (phase 1). After excluding participants who reported common mental disorders and antidepressant medication at baseline (phase 1), the number of participants with depressive mood at follow decreased by 49 to 260. Nevertheless, the magnitude of the association between cynical hostility and % depressive mood at follow-up was similar to that observed in the full sample. Participants in the second quartile of cynical hostility had 1.41 times greater odds (95 CI; 0.93 2.12) of depressive mood compared to those in the first quartile. Those in the third (OR 2.30; 95

% - = %
CI, 1.57 3.37) and fourth quartile (OR 3.39; 95 CI, 2.27 5.07) also had greater likelihood of depressive mood, suggesting that cynical

- = % -
hostility is a strong predictor depressive mood even in individuals free of mental health difficulties at baseline.

Cynical distrust was also assessed at phase 5 of the study; analysis with this measure revealed that it also predicted depressive mood at follow-up, despite the shortened follow-up time (9 years instead of 19): participants in the second quartile of cynical distrust at phase 5 had 1.58 greater odds (95 CI; 2.21 2.05) of depressive mood compared to those in the first quartile. Those in the third (OR 2.03; 95 CI,

% - = %
1.61 2.56) and fourth quartile (OR 4.06; 95 CI, 3.19 5.17) also had a greater likelihood of depressive mood, suggesting that cynical

- = % -
distrust is a strong and consistent predictor of depressive mood.

DISCUSSION

In this study we sought to examine the longitudinal association between cynical hostility assessed in midlife and depressive mood in early old age. The risk of depressive mood 19 years later increased in a dose-response relationship by level of cynical hostility. This graded association was preserved after controlling for sex, age, ethnicity, socioeconomic position, health-related behaviours (BMI, alcohol consumption, and exercise), common mental disorders, and antidepressant medication at baseline as well as SL-E, confiding/emotional support score at baseline and during the follow-up; all these factors were found to be associated with hostility or depressive mood or with both of them.

Comparison with previous studies

To the best of our knowledge, this is the first longitudinal cohort study to examine the predictive value of cynical hostility on depressive mood over a 19-year period. Both cynical hostility and depressive mood were assessed using standardized tools. We were able to control for a wide range of confounders that have been found to be important both for hostility and depressive mood. We were also able to control for common mental disorder at baseline. Previous studies have shown cross-sectional ( ) and prospective [START_REF] Felsten | Hostility, stress and symptoms of depression[END_REF] associations over a 5-year follow-up ( ) between neurotic or cynical hostility and depressive mood. Our findings [START_REF] Heponiemi | The longitudinal effects of social support and hostility on depressive tendencies[END_REF] show the effects of cynical hostility on depressive mood to persist over 19 years. Cynical hostility as personality trait is assumed to be relatively stable during adulthood.( ) In our sample the short-form 8-item cynical distrust scale showed moderate McCrae and Costa Jr, 1994 stability over 10 years (correlation coefficient 0.53). The prospective association over the 19-year follow-up could imply that cynical = hostility is relatively stable across the lifecourse and predicts depressive mood over time. It is also possible that the observed association is the product of a mutually reinforcing cycle between hostility and depression. Some evidence for the latter explanation comes from the stronger association of depression with short-form cynical distrust measured at Phase 5 compared to Phase 1. With either interpretation, our results clearly show cynical hostility to be risk factor for depressive mood.

Our results also show that the cynical distrust scale, a short-form measure of cynical hostility scale developed by Everson and colleagues, shown to be associated with mortality and myocardial infarction,(

) is also associated with depressive mood [START_REF] Everson | Hostility and increased risk of mortality and acute myocardial infarction: the mediating role of behavioral risk factors[END_REF] in a similar way to the longer version of the questionnaire. Thus, our results provide further validation of this shortened version of the cynical hostility scale; a finding that will be of interest to other researchers in the field.

The psychosocial vulnerability model of hostility ( , ) suggests that hostile individuals may be at [START_REF] Kivimaki | Hostility, unemployment and health status: testing three theoretical models[END_REF][START_REF] Miller | A meta-analytic review of research on hostility and physical health[END_REF] greater risk for depressive mood because they are more likely to have lower social support and experience more stressful life events. In the present study we found that participants who scored higher on cynical hostility scale were more likely to have a higher number of SL-E and reduced confiding/emotional support score. These factors have also been found to be related to the presence of depressive mood, making them potential mediators of the association between cynical hostility and depressive mood. However, statistical adjustment for baseline number of SL-E and confiding/emotional support score explained at best 16 of the association, providing only partial support % for the psychosocial vulnerability model of hostility. Similar attenuation (at best 17 ) was observed when the association between cynical % hostility and depressive was adjusted for previous common mental disorders (depression and anxiety) and history of antidepressants intake.

We were able to model potential mediators of the association between cynical hostility and depressive mood, particularly SL-E and confiding/emotional social support as time-dependent variables. However, controlling for the cumulative number of SL-E and exposure to confiding/emotional support did not strengthen their status as mediators between cynical hostility and depressive mood. The percentage of attenuation in the association was 16 at best.

%

Adjustment for socioeconomic position attenuated the association between hostility and depressive mood, suggesting that it is a possible confounder. On the other hand, we found no significant interactions between socioeconomic position and hostility in predicting depressive mood. However we cannot conclude that social context is of little importance for the development of hostility and ultimately the liability of depressive mood. Although personality is often seen as a relatively stable individual attribute, it is likely that socioeconomic circumstances affect personality, both in childhood and adulthood (

). [START_REF] Bifulco | Predicting depression in women: the role of past and present vulnerability[END_REF]Brown , 1990aet al. Brown , 1990bet al. Brown , 1990c[START_REF] The | Sociodemographic and psychosocial factors in childhood as predictors of adult mortality[END_REF] personality characteristic and self-esteem for instance, are partially rooted in environmental conditions in childhood, (learning) experiences, and rearing styles and that the development of hostility is, in part, explained by factors such as parental behaviour that is overly strict, critical and demanding of conformity. It is also plausible that adult circumstances, such as work-related stressors, contribute to the development or promotion of personality traits, such as hostility. The parental behaviour pattern described above (i.e., overly strict, critical and demanding of conformity) may be viewed as a reflection of the parents occupational and other life experiences, which are ' characterized, for example, by job-strain ( ). strength of the association between cynical hostility and depressive mood. Results (not shown) revealed no significant interaction effects of age on this association, again supporting the finding that cynical hostility is a long-term vulnerability factor for depressive mood, irrespective of the effects of aging.

Study limitations

the present results, it is important to note some limitations. First, our cohort of civil servants included neither blue collar workers, nor individuals who were unemployed or retired; thus it is not representative of the general population, which may limit the generalisability of our findings. Second, we assessed depressive mood instead of clinical depression. However, it has been suggested that significant depressive symptomatology could be a risk for clinical depression (

). For example, findings from longitudinal Radloff, 1977 data on 9,900 adults drawn from four sites in the United States showed depressive mood to be strongly associated with first-onset of major depression (

). In that study, it was estimated that more than 50 of cases of first-onset of major depression were Horwath , 1992 et al.

% associated with prior depressive mood (

). Thus, it is possible that cynical hostility is also associated with major Horwath , 1992 et al.

depression although this needs to be confirmed in further studies. Third, only 3639 participants had data on cynical hostility (phase 1) and depressive mood (phase 7). As all analyses were based on complete data, only 3 399 (44 ) participants were included in the present study. % However, this did not compromise the statistical power of our analysis. In addition, compared to participants included in this study, those who did not respond to the CES-D and hostility scales were more likely to be: women (37.3 vs. 25.4 , p<0.001), non-white (13.4 However, controlling for age, sex, ethnicity and socioeconomic position did not alter the graded association between cynical hostility and depressive mood as presented in . We repeated our analyses modelling the association between hostility and depression mood table 2 stratified by sex, age groups, ethnicity and socioeconomic position. We found no significant interaction between these variables and hostility in relation to depressive mood, supporting therefore the validity of these findings.

Conclusions and implications

In summary, the present study based on a large occupational cohort, suggests that cynical hostility is a strong and robust predictor of depressive mood, even after a 19-year period. These findings emphasize the importance of considering individual-level psychological factors, alongside with social-cultural and biogenetic factors, in understanding the predictors of depressive mood or depression. If the relationship between cynical hostility and clinical depression is confirmed, it might have implications for the management of depression as understanding the role of hostility in the aetiology of depressive disorders might allow better assignment of a treatment.

Ackowledgements:

AS-M is supported by a EURYI award from the European Science Foundation. MGM is supported by an MRC Research Professorship. MK

Footnotes:

None to declare † Role of cumulative stressful life events and confiding/emotional support (phases 1, 2 and 5) in the association between hostility (phase 1) and depressive mood (phase 7).

± Cynical distrust is a short-form 8-item subscale of the Cook-Medley Hostility Scale.

  are drawn from the Whitehall II study, established in 1985 as a longitudinal study to examine the socioeconomic gradient in health and disease among 10,308 civil servants (6,895 men and 3,413 women). All civil servants aged 35 55 years in 20 London based departments were invited to participate by letter, and 73 agreed. Baseline screening (Phase 1) took place during 1985 1988, and involved % -

  the quartile distribution lowest (0 8), middle lowest (9), middle highest (10 11) and highest (> 11) was assessed using the Center for Epidemiologic Studies Depression Scale (CES-D, Cronbach alpha Depressive mood = 0.83) at phase 7 (2002 2004). The CES-D, a widely used and validated instrument, is a 20-item self-report questionnaire designed to measure depressive mood in community studies ( ). A score 16 from a total possible score of 60 reflects significant Radloff, 1977 ≥ depressive mood and risk for presence of clinical depression (

  the Academy of Finland (grants 117604, 124322). The Whitehall II study is supported by grants from the Medical Research Council; British Heart Foundation; Health and Safety Executive; Department of Health; National Heart Lung and Blood Institute (HL36310), US, NIH: National Institute on Aging, US, NIH; Agency for Health Care Policy Research (HS06516); and the John D and Catherine T MacArthur Foundation Research Networks on Successful Midlife Development and Socio-economic Status and Health.

  ). Internal consistency, test retest reliability, and construct Cook and Medley, 1954 original 50-item instrument. Item savings were necessary because of the extreme length of the original questionnaire; the Minnesota

			-	-
	validity of this scale have been demonstrated (	Smith, 1992	). Participants completed an abridged 38-item version (Cronbach alpha 0.83)
	Multiphasic Personality Inventory (		) (numbers of the omitted items are

=

of the

  vs.

	%	%	%
	6.4 ; p<0.001), older (24 vs. 19.5 aged 50 years, p<0.001), and from lower socioeconomic position (27.5 vs.13.8 , p<0.001),. % % % ≥ % %

Table 1

 1 Bivariate associations of sample characteristics at baseline (phase 1) with cynical hostility score levels (phase 1) and depressive mood (phase 7), n 3399 .

	=	†

%

Table 2

 2 Association of hostility (phase 1) with depressive mood (phase 7), n of depressive participants/n all participants 513/3399.

=

Depressive

mood at phase 7 predicted by Cynical hostility Cynical distrust ±

		OR	95 CI %		OR	95 CI
	Lowest quartile	1	Ref		1	ref
	Middle lowest	1.55	(1.11 2.15) -	**	1.06	(0.79 1.42) -
	Middle highest	2.91	(2.15 3.94) -	***	1.65	(1.28 2.12) -	***
	Highest quartile	5.09	(3.75 6.91) -	***	3.14	(2.42 4.08) -	***
	Model 2 (adjusted for sex, age, ethnicity, SEP)
	Lowest quartile	1	Ref		1	ref
	Middle lowest	1.58	(1.14 2.20) -	**	1.04	(0.77 1.40) -
	Middle highest	2.78	(2.05 3.77) -	***	1.58	(1.22 2.05) -	***
	Highest quartile	4.66	(3.41 6.36) -	***	2.80	(2.14 3.67) -	***
	Model 3 (

%

Model 1 (Unadjusted model)

Model 2 + BMI, alcohol consumption, physical activity)

	Lowest quartile	1	ref		1	ref
	Middle lowest	1.59	(1.14 2.20) -	**	1.06	(0.79 1.43) -
	Middle highest	2.84	(2.09 3.86) -	***	1.61	(1.24 2.08) -	***
	Highest quartile	4.74	(3.47 6.48) -	***	2.85	(2.17 3.74) -	***
	Model 4 a (

Model 3 + stressful life events, confiding/emotional support at phase 1)

	Highest quartile	4.15	(3.03 5.69) -	***	2.55	(1.93 3.35) -	***
	Model 4 b † (
	Lowest quartile	1	ref		1	ref
	Middle lowest	1.52	(1.09 2.12) -	*	0.99	(0.73 1.34) -
	Middle highest	2.59	(1.91 3.53) -	***	1.49	(1.15 1.94) -	**
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			Psychol Med. Author manuscript

Model 3 + stressful life events and confiding/emotional support at phases 1, 2, and 5)

	Lowest quartile	1	ref		1	ref
	Middle lowest	1.49	(1.07 2.08) -	*	1.00	(0.74 1.35) -
	Middle highest	2.57	(1.89 3.51) -	***	1.50	(1.16 1.96) -	**
	Highest quartile	4.13	(3.01 5.68) -	***	2.55	(1.93 3.36) -	***
	Model 5 (

Model 4 + antidepressants medication intake + common mental disorder at baseline )

	Lowest quartile	1	ref		1	ref
	Middle lowest	1.45	(1.03 2.02) -	*	0.97	(0.72 1.33) -
	Middle highest	2.34	(1.72 3.19) -	***	1.45	(1.11 1.89) -	**
	Highest quartile	3.62	(2.63 4.98) -	***	2.32	(1.76 3.07) -	***
	* p<0.05,

** p<0.01 *** p<0.001

Conflict of interest: