Improvement of the liquid-chromatographic analysis of protein tryptic digests by the use of long-capillary monolithic columns with UV and MS detection

Optimisation of peak capacity is an important strategy in gradient liquid chromatography (LC). This can be achieved by using either long columns or columns packed with small particles. Monolithic columns allow the use of long columns at relatively low back-pressure. The gain in peak capacity using long columns was evaluated by the separation of a tryptic bovine serum albumin digest with an LC–UV–mass spectrometry (MS) system and monolithic columns of different length (150 and 750 mm). Peak capacities were determined from UV chromatograms and MS/MS data were used for Mascot database searching. Analyses with a similar gradient slope for the two columns produced ratios of the peak capacities that were close to the expected value of the square root of the column length ratio. Peak capacities of the short column were 12.6 and 25.0 with 3 and 15 min gradients, respectively, and 29.7 and 41.0 for the long column with 15 and 75 min gradients, respectively. Protein identification scores were also higher for the long column, 641 and 750 for the 3- and 15-min gradients with the short column and 1,376 and 993 for the 15- and 75-min gradients with the long column. Thus, the use of long monolithic columns provides improved peptide separation and increased reliability of protein identification.
Introduction
Identification of unknown proteins is a key step in proteome analysis. The standard method of protein identification consists of enzymatic digestion of the protein(s), usually by using trypsin, followed by mass spectrometry (MS) analysis of the resulting digest. When analysing a single protein, e.g. from an excised 2D gel spot, a peptide separation is usually unnecessary. When analysing a digest of a mixture of proteins, the resolution of only a mass spectrometer is usually not sufficient. In those cases, a peptide separation is applied before MS detection. In shotgun proteomics, where a whole proteome is digested without prior protein fractionation, 2D liquid chromatography (LC) is the method of choice owing to the high complexity of the peptide mixtures. A drawback of multidimensional methods is the time frame, as typical analysis times for 2D-LC analysis of peptide mixtures range from several hours to more than 1 day [1–4]. 1D-LC methods can be used for separation of less complex samples (digests of fewer than 100 proteins), but this requires very efficient separations in combination with the additional separation power of MS detection. Such high-efficiency separations have been reported for analyses with long, packed columns, using (ultra-) high pressure (about 70 MPa) systems [5, 6].
Peak capacity is the primary parameter for evaluation of efficiency in gradient chromatography [7]. Peak capacity was first defined by Giddings [8] as the maximum number of peaks that can be separated by a phase system. This theory was later adapted for gradient chromatography by Horváth and Lipsky [9]. There are generally two approaches for optimisation of the peak capacity. The first is increase of the gradient length; gradients of up to 10 h have been reported on single columns [10]. However, according to theory, peak capacity increases to a maximum and then decreases as the gradients become longer [11, 12]. The second approach is the use of longer columns, as the peak capacity increases linearly with the square root of the plate number and thus with the square root of the column length. Wang et al. [13] illustrated this by connecting several columns packed with a pellicular stationary phase. They showed that the ratio of the peak capacity and the square root of the column length was constant for columns ranging in length from 7.5 to 60 cm. The main limitation to simply increasing column length is the higher back-pressure. A possible solution for this problem is the use of monolithic columns. Monolithic columns have a higher permeability compared with packed columns, facilitating fast separations or the use of long columns [10, 14–16]. Wang et al. [13] reported a back-pressure of 28.5 MPa for a 60 cm × 2.1 mm packed column at a linear flow of 0.94 mm/s. In contrast, other groups have obtained 3 times higher linear flow rates for monolithic columns of similar length [10, 17]. The higher permeability of monolithic columns makes it possible to operate long columns at higher flow rates while using conventional LC equipment. Luo et al. [10] separated a bacterial protein digest at a linear flow rate of 2.4 mm/s with a 70-cm monolithic column at 34.5 MPa (5,000 psi). Tolstikov et al. [17] analysed plant metabolomic extracts with a flow rate of 2.6 mm/s for a 60-cm column.
The quality of the analysis of a protein digest can be expressed in different ways. One way is to describe chromatographic efficiency in terms of parameters like peak width, peak capacity and resolution. Another way is to use the reliability of protein identification, like SEQUEST [18] or Mascot (http://www.matrixscience.com) [19] scores. Such an approach is also useful, as the mass spectrometer adds a second dimension to the separation, which is overlooked when only chromatographic efficiency is measured.
In this paper the evaluation of capillary monolithic silica columns of different lengths for the LC-UV-MS analysis of a bovine serum albumin (BSA) tryptic digest is described. Columns of 150- and 750-mm length were investigated using gradient times varying from 3 to 75 min. Chromatographic peak capacities, based on UV detection, and protein identification, based on Mascot scoring data of the MS detection, were determined as a measure for the efficiency of peptide separation.
Theoretical aspects
The peak capacity is defined as the maximum number of bands that will fit within a chromatogram with a resolution of Rs = 1.0 [20]. In gradient LC, where peak width is about constant throughout the separation, the theoretical peak capacity is given by Eq. 1:
where tg is the gradient time and wav is the average baseline peak width. For large values of PC, this approaches tg/wav. Because sample peaks often do not occupy the entire length of the gradient, the sample peak capacity can then be defined as
where ta and tz are the retention times of the first and last eluting peaks, respectively.
Wang et al. [13] investigated the effect of column length on peak capacity in packed columns and found that the peak capacity is proportional to  if the gradient slope is proportional to L. The gradient slope can be defined as
where Δϕ is the change in organic modifier fraction during the gradient (0≤ϕ≤1) and t0 is the column dead time. If the linear flow rate is constant, t0 is only dependent on column length; therefore, tg should be varied proportionally to column length in order to keep the gradient slope constant.
Experimental
Materials and reagents
BSA, trypsin (porcine, type IX-S, EC 3.4.21.4) and 1,4-dithiothreitol (DTT) were purchased from Sigma (St. Louis, MO, USA), and iodoacetamide (IAA) and NH4HCO3 from Fluka (Buchs, Switzerland). Acetonitrile was high-performance LC gradient grade (Biosolve, Valkenswaard, the Netherlands), and spectroscopy-grade trifluoroacetic acid (TFA) was obtained from Merck (Darmstadt, Germany). All solutions were prepared using water from a Milli-Q water-purifying system (Millipore, Bedford, MA, USA).
All reagents for the digestion of BSA were prepared in 200 mM NH4HCO3 buffer, pH 8. The tryptic digest was prepared as follows. A 100-μl aliquot of BSA stock solution (3.5 μg/μl in water) was set to pH 8 by adding 25 μl of 1 M NH4HCO3 buffer (pH 8). After addition of 25 μl of 10 mM DTT solution, the sample was incubated at 50 °C for 30 min to reduce disulfide bonds. After cooling to room temperature, 25 μl of 30 mM IAA solution was added and the sample was incubated in the dark for 60 min to alkylate the free thiols. Trypsin was dissolved in 10 μl of buffer to reach a trypsin-to-protein mass ratio of 1:50 in the final solution; the trypsin solution was added to the sample, which was incubated at 37 °C overnight for 15 h. The digestion was stopped by addition of 15 μl of 10% TFA. The sample was diluted to 200 ng/μl (3 μM) with LC mobile phase A (water plus 0.05% TFA) and injected without further purification.
Apparatus and LC columns
All analyses were performed with an Agilent 1100 nanoLC system (Agilent Technologies, Waldbronn, Germany), consisting of a vacuum degasser, a binary Nano-Pump, a μ-well plate sampler and a column switching module with a trapping column in the 1–4 position of the six-port column-switching valve. The trapping pump was a Gynkotek model 480 (Gynkotek, Germering, Germany). Detection was performed by UV and MS detection, with the detectors connected in series. The UV detector was an MU 701 UV–VIS detector (ATAS GL International, Veldhoven, the Netherlands), equipped with an external optical-fibre flow cell (6 nl, 3-mm light path); peptides were detected at 215 nm. The mass spectrometer was an Agilent LC/MSD Trap XCT (Agilent Technologies, Waldbronn, Germany) ion-trap mass spectrometer, equipped with an orthogonal electrospray ionisation (ESI) interface. The external flow cell of the UV detector allows minimal time delay and band-broadening between UV and MS detection.
The monolithic columns were provided by GL Sciences (Tokyo, Japan). The columns were a 150 mm × 0.1 mm MonoCap for nano-flow C18-silica monolithic column and a 750 mm × 0.2 mm MonoCap high resolution C18-silica monolithic column. For trapping of the digest a 5 mm × 0.3 mm column packed with 5 μm Zorbax 300 SB-C18 (Agilent Technologies, Waldbronn, Germany) was used.
Method and data analysis
LC solvent A was water plus 0.05% TFA (v/v); solvent B was acetonitrile plus 0.04% TFA (v/v). The trapping solvent was a mixture of 5% (v/v) solvent B in solvent A. After injection of the digest (0.25 μl for the 0.1-mm inner diameter column and 1.0 μl for the 0.2-mm inner diameter column), the sample was trapped on the trapping column at a flow rate of 5 μl/min. After 5 min, the trapping column was switched on-line with the separation column and the gradient was started. All separations were performed at room temperature using a gradient of 5–50% solvent B with gradient times varying from 3 to 75 min. MS spectra were acquired in the positive ion mode over the 400–2,000 m/z range, after which the two most intense ions (with a preference for doubly charged ions) were selected for fragmentation. MS/MS fragmentation spectra were acquired over the 100–2200 m/z range. An ESI spray voltage of -3 kV was used for all experiments.
The effect of separation efficiency on protein identification was evaluated using the Mascot search engine [19]. LC-MS/MS data were converted to the Mascot generic format (.mgf file) using the data-analysis software, and the .mgf files were searched against the MSDB database using Mascot’s MS/MS ion search module. The database was searched for tryptic peptides from all entries in the database, allowing one missed cleavage per peptide and containing carbamidomethyl cysteine as a variable modification. Mass tolerances were set to default values: peptide mass tolerance ±2.0 Da, MS/MS tolerance ±0.8 Da.
Results and discussion
Liquid chromatography–UV analysis
Because of the difference in diameter, the 150 mm × 0.1 mm and the 750 mm × 0.2 mm columns were used with different flow rates. For the 150- and the 750-mm columns, the flow rates were 0.5 and 2.0 μl/min, respectively, resulting in a linear flow rate of 1.06 mm/s. Injection volumes were also proportional to the square of the column diameter, 0.25 μl of the digest for the 0.1-mm column and 1.0 μl onto the 0.2-mm column. During the gradient, the maximum back-pressure of the 750-mm column was below 20 Mpa, which is well below the manufacturers limit of 30 Mpa.
Figure 1 shows the LC-UV chromatograms of 3- and 15-min gradients run on the 150-mm column and 15- and 75-min gradients run on the 750-mm column. When the chromatograms of the analyses with similar gradient slopes are compared (Fig. 1a,b, and Fig. 1c,d), it is clear that an increase in column length improves the peptide separation. In order to quantify the efficiency of the separation, the sample peak capacity was calculated for all analyses. Because of the incomplete resolution of the digest, the peak capacity was estimated by using the average peak width of a selected number of peaks that appeared to contain only a single peptide. Using this method, we calculated peak capacities for all analyses and the results are summarised in Table 1. The peak capacities found for the short column are comparable to those found in the literature for similar columns [21, 22]. As expected, the peak capacities of the long column are higher than those of the short column, but they are relatively low compared with the values reported in [10]. However, when gradient time is taken into consideration, the difference is significantly less: PC**/tg is 0.55 peaks per minute with the present system (75-min gradient on the 750-mm column) and 1.62 peaks per minute for the 260-min gradient reported in [10]. A possible negative effect on the resolution of our system is the use of a trapping column, filled with a different stationary phase, which probably has a selectivity that differs from that of the analysis column.
Fig. 1Liquid chromatography (LC)–UV chromatograms of a tryptic bovine serum albumin (BSA) digest, separated by monolithic silica capillary columns of 150 mm × 0.1 mm (a, c) and 750 mm × 0.2 mm (b, d) using a gradient of 5–50% acetonitrile (0.04% v/v trifluoroacetic acid, TFA) in water (0.05% v/v TFA). a A 3-min gradient (15%/min); b, c 15-min gradient (3%/min); d 75-min gradient (0.6%/min). The run times include 5 min of trapping and 2.5 and 12.5 min of gradient delay time for the 150-mm and 750-mm columns, respectivelyTable 1Chromatographic parameters from liquid chromatography–UV analysis 150 mm × 0.1 mm750 mm × 0.2 mmtg (min)wavΔtPC**wavΔtPC30.243.0512.60.283.1611.2150.4411.025.00.3911.629.7751.2737.629.60.7731.741.0tg gradient time, wav average width of seven to 12 peaks which were found to contain only a single peptide (based on mass spectrometry data), Δt elution window between the first and last eluting peptides, PC** sample peak capacity (Δt/wav)
If chromatograms with the same gradient slope are compared (3- and 15-min gradients for the 150-mm column and 15- and 75-min gradients for the 750-mm column, respectively), the ratio of the peak capacities should be close to the square root of the column length ratio (2.24). For the 3-/15-min gradient pair this ratio is 29.7/12.6 = 2.37; for the 15-/75-min gradient pair the ratio is 41.0/25.0 = 1.64. For the short column, no increase in peak capacity is observed with a gradient of more than 15 min, but for the long column the peak capacity increases up to a 75-min gradient. A possible explanation can be found in the study of Stadalius et al. [11, 12], who demonstrated that peak capacity will increase with gradient time, until it reaches a maximum, after which it will even decrease. The gradient time at which this maximum is obtained is greater for longer columns.
Liquid chromatography–mass spectrometry analysis
To assess the influence of peptide separation on protein identification, MS/MS data were investigated by database-searching using the MS/MS ion search module from the Mascot search engine. The results are expressed as a Mascot score, the number of unique identified peptides and the percentage of the BSA amino acid sequence covered by these peptides (Table 2). These results were compared with the scores obtained for direct infusion of the BSA digest at the same flow rates as for the LC separations. The protein identification parameters for the infusion experiments were about similar for both flow rates. All database searches gave bovine albumin as the top protein match and the only other significant matches were albumins from other species.
Table 2Mascot® database search results 150 mm × 0.1 mm750 mm × 0.2 mmtgMascot scorePeptidesaCoverageb (%)Mascot scorePeptidesaCoverageb (%)Infusionc31791737610183 min6411428787132515 min75016291,376243875 min69316299931929aNumber of unique peptides identified by database search.bPercentage of amino acid sequence covered by identified peptides.cDirect infusion of the digest into the mass spectrometer.
Figure 2 shows base peak chromatograms of separations with the same gradient slope, a 15-min gradient for the 150-mm column and a 75-min gradient for the 750-mm column. The Mascot scores for the 750-mm column are higher than those for the 150-mm column. The average score per identified peptide is between 52 and 60 for the long column and between 42 and 51 for the short column. Combined with the larger number of identified peptides on the long column, this adds up to a higher Mascot score (Table 2).
Fig. 2LC–mass spectrometry (MS) base peak chromatograms and mass spectra of tryptic BSA digest, separated by monolithic silica columns of 150 mm × 0.1 mm (a) and 750 mm × 0.2 mm (b), using a gradient of 5–50% acetonitrile (0.04% v/v TFA) in water (0.05% v/v TFA). a A 15-min gradient (3%/min); b 75-min gradient (0.6%/min). The run times include 5 min of trapping and 2.5 and 12.5 min of gradient delay time for the 150-mm and 750-mm columns, respectively
Despite providing only a low resolution, even the shortest gradients cause a considerable increase in the reliability of protein identification. Increase in gradient length leads to cleaner mass spectra (Fig. 3) and a higher number of identified peptides and sequence coverage, compared with direct infusion. These numbers, however, decrease beyond a certain gradient time. This could possibly be attributed to the lower peak heights for longer gradient times. This result indicates that there is a gradient slope where an increase in chromatographic separation no longer improves protein identification.
Fig. 3Averaged mass spectra of peptide YICDNQDTISSK (m/z 722.32, M2H2+) as identified from extracted ion chromatograms in the LC-MS analysis of a tryptic BSA digest. a A 150-mm × 0.1-mm silica monolithic column, 15-min gradient of 5–50% acetonitrile (0.04% v/v TFA) in water (0.05% v/v TFA); b 750-mm × 0.2-mm column, 75-min gradient
Conclusions
The use of long silica-based capillary monolithic columns provides a clear advantage over use of shorter columns, i.e. an increase of chromatographic efficiency and reliability of protein identification. As expected from chromatography theory, a factor 5 longer column gives a 1.6–2.4 times increase in peak capacity for separations with similar gradient slope. The use of longer gradients also leads to an initial improvement of the protein identification score, but the score seems to have a maximum at longer gradient times.
While the use of longer columns for the separation of peptides has a clear advantage because of the gain in chromatographic efficiency, this also gives a longer analysis time. As maximum protein identification scores for rather simple digests are reached at relatively short gradient times, it is important to find a compromise between chromatographic efficiency and analysis time. However, if the sample is more complex, the use of longer columns is more attractive as longer gradients are necessary to achieve sufficient separation.
In the near future, short and long columns of the same diameter (0.1-mm inner diameter) will be compared. Further improvement of the separation might be obtained by optimisation of the combination of the trap column and the analysis column. Moreover, the potential of longer monolithic capillary columns will be demonstrated by the analysis of more complex and real samples.

monoliths
peak capacity
gradient liquid chromatography
long columns
protein identification


Structure of the Tandem Fibronectin Type 3 Domains of Neural Cell Adhesion Molecule

Activation of the fibroblast growth factor receptor (FGFR) by neural cell adhesion molecule (NCAM) is essential for NCAM-mediated neurite outgrowth. Previous peptide studies have identified two regions in the fibronectin type 3 (FN3)-like domains of NCAM as being important for these activities. Here we report the crystal structure of the NCAM FN3 domain tandem, which reveals an acutely bent domain arrangement. Mutation of a non-conserved surface residue (M610R) led to a second crystal form showing a substantially different conformation. Thus, the FN3 domain linker is highly flexible, suggesting that it corresponds to the hinge seen in electron micrographs of NCAM. The two putative FGFR1-binding segments, one in each NCAM FN3 domain, are situated close to the domain interface. They form a contiguous patch in the more severely bent conformation but become separated upon straightening of the FN3 tandem, suggesting that conformational changes within NCAM may modulate FGFR1 activation. Surface plasmon resonance experiments demonstrated only a very weak interaction between the NCAM FN3 tandem and soluble FGFR1 proteins expressed in mammalian cells (dissociation constant > 100 μM). Thus, the NCAM–FGFR1 interaction at the cell surface is likely to depend upon avidity effects due to receptor clustering.
Introduction
The neural cell adhesion molecule (NCAM) is the prototype and founding member of the immunoglobulin (Ig) superfamily cell adhesion molecules (CAMs).1–4 NCAM is present on the cell surface of neurons, astrocytes and oligodendrocytes, where it mediates homophilic and heterophilic cell adhesion. NCAM is involved in neuronal migration, axon growth and guidance, as well as in synaptic plasticity associated with learning and memory.5–7 Alternative splicing of the NCAM1 gene results in isoforms of three size classes that differ in their membrane attachment and cytosolic regions but have in common an extracellular domain consisting of five Igs and two fibronectin type 3 (FN3) domains.8 The two larger isoforms have a transmembrane helix and cytosolic domains of different sizes, while the smallest isoform has a glycophosphatidylinositol membrane anchor. Variable use of alternative exons in the extracellular domain results in small insertions into Ig4 or between the FN3 domains.9–12 NCAM function is further regulated by an unusual posttranslational modification, namely, the addition of polysialic acid to Ig5.13
The molecular basis of homophilic adhesion by NCAM has been a subject of intense study, and the results from biochemical and biophysical studies have not always been consistent.14,15 A crystal structure of NCAM Ig1–Ig3 has led to a zipper model of adhesion, which postulates both cis and trans interactions by the Ig1–Ig3 region of NCAM.16 While a conclusive picture has yet to emerge, it appears that there may be multiple modes of homophilic interaction.17
NCAM is also engaged in heterophilic interactions. There is now convincing evidence that NCAM-mediated neurite outgrowth, as well as tumour development and progression, critically involves the activation of fibroblast growth factor receptor 1 (FGFR1), through a cis interaction of NCAM and FGFR1.18–22 The four FGFRs and their 23 growth factor ligands control a variety of cellular processes, including development, angiogenesis, hematopoiesis and tumourigenesis.23–26 Alternative splicing of the four FGFR genes results in at least 48 receptor isoforms that vary in their ligand binding profiles and kinase domains. The longest FGFR1 ectodomain variant consists of three Ig domains, D1–D3, with a stretch of acidic amino acids (the “acid box”) inserted into the D1–D2 linker; shorter forms lack D1 and the acid box. The NCAM–FGFR1 interaction was originally proposed based on indirect biological evidence,27 but biochemical evidence has been obtained to suggest that the interaction is mediated by direct binding of the FN3 domains of NCAM to FGFRs (Fig. 1).20,28,29 We and others identified NCAM-derived peptides capable of stimulating FGFR1 signalling and inducing neurite outgrowth. One peptide (FRM peptide) is derived from the first FN3 domain of NCAM (1FN3),30 while another (FGL peptide) is derived from the second FN3 domain (2FN3).28 The structures of the 1FN3 and 2FN3 domains in isolation have been determined,28,31 but how the two domains cooperate in FGFR1 activation is unclear. In this study, we determined the crystal structure of the NCAM FN3 tandem (1FN3–2FN3) in two crystal forms. We report that 1FN3–2FN3 assumes a bent conformation in both forms, with evidence of substantial flexibility of the domain linker. In direct binding experiments with fully glycosylated proteins of mammalian origin, we observed only a very weak interaction (dissociation constant > 100 μM) of NCAM 1FN3–2FN3 with two FGFR1 ectodomain constructs. We conclude that the NCAM–FGFR1 interaction at the cell surface may be transient or stabilised by avidity effects resulting from receptor clustering and that conformational changes within NCAM may have a profound role in FGFR1 activation.
Results
Crystal structure of the NCAM FN3 tandem
To obtain insight into the relative orientation of the two FN3 domains of NCAM, we determined the crystal structure of 1FN3–2FN3 at 2.3-Å resolution (Table 1). Both 1FN3 and 2FN3 adopt the typical β-sandwich fold of all FN3 domains consisting of seven strands arranged in two antiparallel sheets (ABE and GFCD) (Fig. 2a and b). Preceding strand A in both domains are short proline-rich segments that are integrated into the FN3 fold, with the proline tetrahydropyrrole rings pointing into the hydrophobic core (Pro500 and Pro503 in 1FN3; Pro601 and Pro604 in 2FN3). A similar feature has been observed in other FN3 domains (e.g., in gp13033 and titin34). 1FN3 contains an unusual α-helix situated between strands D and E, as reported previously.31
1FN3 in our FN3 tandem structure matches the crystal structure of 1FN3 in isolation,31 with an r.m.s.d. of 0.50 Å for 100 Cα atoms. 2FN3 in our FN3 tandem matches the solution structure of 2FN3 in isolation,28 with an r.m.s.d. of 1.3 Å for 92 Cα atoms (the main differences are concentrated in the B–C and C–D loops).
The relative orientation of the two FN3 domains in the 1FN3–2FN3 tandem is characterised by an unusually bent conformation with an interdomain angle of ∼ 80° (calculated between the long axes of the two FN3 domains). The interface between 1FN3 and 2FN3 buries 630 Å2 of solvent-accessible surface (calculated with the CCP4 program AREAIMOL), which is in the typical range for rigid FN3 (and Ig) domain interfaces. The interface is dominated by polar interactions, between the A–B loop of 1FN3 on the one hand and the domain linker and B–C loop of 2FN3 on the other (Fig. 2c) The key interface residues (Tyr511, Ser513, Thr514, Pro601 and Asp625) are strictly conserved in all vertebrate NCAM sequences (Fig. 2d), suggesting that the bent conformation may be physiologically relevant. However, residues 511–514 have also been implicated in FGFR1 binding,30 and the linker region could assume a very different structure when NCAM is bound to FGFR1 (see below).
The conformation of multidomain proteins often is influenced by the crystal lattice. In this regard, we noted that the asymmetric unit of our crystals contains two 1FN3–2FN3 molecules arranged in a tightly interlocked dimer (Fig. 2b). There are two main contacts responsible for dimer formation: the α-helix of 1FN3 packs against the GFCD sheet of 2FN3 of the other molecule, and the 2FN3 domains of the two molecules interact via their A and G strands. Altogether, these contacts bury as much as 3040Å2 of solvent-accessible surface. We do not think that the dimer observed in our crystals is physiologically relevant, as dimer interface residues are only poorly conserved (Fig. 2d) and NCAM dimers were never observed in electron microscopy studies.35,36 Because we were concerned that the tight association of 1FN3–2FN3 molecules in the dimer may have forced the unusually bent interdomain conformation, we sought to disrupt the dimer by mutagenesis and crystallise a mutant 1FN3–2FN3 protein in a different crystal form.
Structure of the M610R mutant
We expressed three point mutants of NCAM 1FN3–2FN3 (M610R, Y672E and R690E), all of which should be incompatible with the dimer structure seen in crystals of the wild-type protein. Importantly, all three mutations target surface residues and are unlikely to have an effect on NCAM folding. Whether the mutations are functionally neutral could not be determined due to a lack of suitable assays. When examined by size-exclusion chromatography, all three mutants eluted as a mixture of monomers and dimers, similar to the wild-type protein (data not shown). We obtained crystals of the M610R mutant and determined its structure at 2.7-Å resolution by molecular replacement (Fig. 3a). The asymmetric unit of the crystals contains a hexamer of NCAM 1FN3–2FN3 M610R. The hexamer can be regarded as a trimer of dimers, with the dimers having a completely different mode of association compared with the wild-type structure (the individual domains are very similar, as expected, with r.m.s.d. values of 0.93 and 1.1 Å for 1FN3 and 2FN3, respectively). The relative orientation of the two FN3 domains in the M610R mutant is less severely bent than in the wild-type structure but still far from fully extended (interdomain angle ∼ 120°; Fig. 3b). The FN3 pair opens up in the M610R mutant, and 1FN3 is additionally twisted about its long axis. The combined hinge opening and 1FN3 twisting amounts to a pure rotation of 73° for 1FN3 when the structures are superimposed on their 2FN3 domains. There is no domain interface to speak of in the mutant (240 Å2 buried), and the conformation appears to be stabilised entirely by the crystal lattice. Thus, crystal lattice forces can have a profound influence on the conformation of the NCAM FN3 tandem, suggesting that the 1FN3–2FN3 linker may act as a flexible hinge in native NCAM.
We compared the NCAM FN3 tandem with other FN3 tandems of known structure. Tandems from the extracellular matrix proteins fibronectin and tenascin generally assume an extended conformation.38–40 In contrast, many cytokine and hormone receptors (e.g., gp130)33 feature bent FN3 pairs that are superficially similar to the NCAM structures reported here (data not shown). Of particular interest is the structure of the FN3 pair of neuroglian, a Drosophila CAM.37 The neuroglian tandem, which has an extensive domain interface that incorporates a bound sodium ion, adopts a conformation that is intermediate between the two conformations we report for the NCAM tandem. Thus, a similar bend in the FN3 linker(s) may be a general feature of other animal CAMs containing FN3 domains in their membrane-proximal region.
Location of putative FGFR1 binding site
Previous studies have implicated two NCAM 1FN3–2FN3 regions in FGFR1 binding. Kiselyov et al. identified a bioactive peptide from the F–G loop of 2FN3 (FGL peptide),28 and we identified a bioactive peptide from the A–B loop of 1FN3 (FRM peptide).30 In agreement with our earlier prediction,30 the FRM and FGL loops are indeed located in close proximity on the same face of the wild-type NCAM molecule (Fig. 4a). However, in the M610R mutant, the opening up of the two domains combined with rotation of 1FN3 places the FRM and FGL loops much farther apart. Thus, conformational changes at the NCAM 1FN3–2FN3 hinge may modulate the interaction of NCAM with FGFR1.
Surface plasmon resonance analysis of the NCAM–FGFR1 interaction
We wanted to map the FGFR1 binding site on the NCAM FN3 tandem by structure-based mutagenesis and first sought to establish a suitable binding assay. A solid-phase assay with immobilised NCAM and Fc-tagged FGFR1 proteins did not show any appreciable interaction (data not shown). We therefore used surface plasmon resonance (SPR) to analyse the binding of NCAM 1FN3–2FN3 to two FGFR1 ectodomain constructs. The FGFR1 D1–D3 construct used (residues 22–364) spans essentially the full ectodomain and contains the acid box situated between domains D1 and D2. The FGFR1 D2–D3 construct used (residues 151–364) lacks D1 and the acid box but retains the binding site for FGFs; this construct is similar to the construct previously used by Kiselyov et al. in SPR studies.28 Both soluble FGFR1 proteins were produced by the 293-EBNA cells in good yields. Due to the presence of multiple N-linked glycosylation sites in FGFR1 (see below), the purified recombinant proteins migrate as diffuse bands of higher-than-calculated molecular mass on SDS-PAGE (Fig. 5). In a first set of experiments, the two FGFR1 constructs were immobilised on a CM5 sensor chip [8000 resonance units (RU) of D1–D3 and 3850 RU of D2–D3]. Recombinant FGF1 injected at a concentration of 100 nM produced sensorgrams characteristic of a high-affinity interaction, confirming that the immobilised proteins are functional (Fig. 6a and b). In contrast, wild-type NCAM 1FN3–2FN3 up to a concentration of 70 μM did not produce a signal on the FGFR1 D1–D3 surface and showed only very weak binding to FGFR D2–D3 (Fig. 6c and d). In a second set of experiments, the order of proteins was reversed. NCAM 1FN3–2FN3 proteins were immobilised on a CM4 sensor chip (1800 RU of wild-type protein and 1900 RU of M610R mutant), and the two soluble FGFR1 constructs were used as analytes up to a concentration of 100 μM. We again observed only weak interactions for all pairings (Fig. 6e–h). Wild-type and M610R NCAM 1FN3–2FN3 behaved almost identically in these experiments, and, as before, it appeared that the affinity of NCAM for FGFR1 D2–D3 was higher than that for FGFR1 D1–D3. The fast association and dissociation steps in the sensorgrams prevented the fitting of kinetic constants. We used the plateau values at equilibrium to estimate a dissociation constant of > 100 μM for the interaction of NCAM 1FN3–2FN3 with FGFR1 D2–D3 (not shown), but we emphasise that this value is very approximate given the weak resonance signals obtained. In view of the weakness of the NCAM–FGFR1 interaction in our assay, we were unable to pursue our initial plans of mapping the binding site(s) by mutagenesis.
Discussion
NCAM ectodomain structure
The current view of the NCAM ectodomain structure is based on early studies by rotary shadowing electron microscopy of tissue-derived NCAM.35 Electron microscopy visualised the NCAM ectodomain as ∼ 28-nm rods bent at a flexible hinge located ∼ 10 nm from the C-terminus; the angle between the two arms varied from 50° to 140° (average = 100°). The hinge was attributed to the proline-rich linker between Ig5 and the first FN3 domain, and the long and short arms were described as rigid domain tandems (Ig1–Ig5 and 1FN3–2FN3, respectively) in extended conformations.35 A crystal structure of NCAM Ig1–Ig3 indeed showed a largely extended structure.16 In sharp contrast, the present crystal structure analysis of the NCAM 1FN3–2FN3 domain pair has revealed a prominent bend between the two domains in two independent crystal forms (Fig. 3b). This finding is difficult to reconcile with the uniformly straight appearance of the short arm in the electron micrographs and suggests that the hinge point may actually lie between 1FN3 and 2FN3. In this respect, we note that the Ig5–1FN3 linker is actually rather short and may well be rigid (in the Ig5–1FN3 linker sequence, ILVQADTPSSP, the isoleucine and valine residues are predicted to contribute to the hydrophobic core of Ig5 and the first proline is already part of the 1FN3 fold). If the hinge is instead situated in the 1FN3–2FN3 linker, the juxtamembrane domain of NCAM must have contributed to the short arm seen in electron micrographs, as a single FN3 domain would only account for half of the short arm length. The serine/threonine-rich juxtamembrane domain of NCAM (sequence TSAQPTAIPANGSPTSGLSTGA) is predicted to be extensively modified by O-linked glycosylation (NetOGlyc 3.1 server†) and could easily assume the extended and rigid conformation required to span the remaining ∼ 5 nm.41 Further structural analysis, in particular, of the Ig5–1FN3 pair, is required to conclusively pinpoint the site of articulation within the NCAM ectodomain.
NCAM splice variants
Interestingly, the flexible hinge linking the two FN3 domains of NCAM 1FN3–2FN3 is known to be modified by alternative splicing. Our structure is of the shortest isoform (linker sequence TQPVREPSAP), whereas the underlined arginine residue is replaced by QG, HSPPPQG or even longer sequences in other variants.9,10,12 The HSPPPQG insertion has been suggested as a potential hinge region.1 The biological relevance of these splicing events is not well understood, but in vitro experiments have demonstrated that the NCAM isoforms differ in their capacity to support cell adhesion and spreading,42,43 as well as myoblast fusion.44 We prepared NCAM 1FN3–2FN3 proteins with QG or HSPPPQG inserts in the domain linker, but, unfortunately, these proteins were very prone to aggregation in physiological buffers and could not be used for SPR or structural analysis (data not shown).
Interaction of NCAM with FGF
Hinge bending and alternative splicing at the 1FN3–2FN3 junction could affect NCAM function by modulating either its homophilic binding properties or its heterophilic interactions with other proteins. There is currently no evidence for the former scenario. In contrast, the functional interaction of NCAM with FGFR1 in cis (i.e., at the same cell membrane) is well established1,22,30 and would appear to be an attractive candidate for regulation by alternative splicing. Peptides from two regions of the NCAM 1FN3–2FN3 tandem, one in each domain, have been shown to modulate FGFR1-dependent neurite outgrowth.28,30 Intriguingly, the corresponding loop regions are in close proximity in the acutely bent conformation observed in crystals of the wild-type protein, suggesting that they are part of a larger FGFR1 binding site extending over the domain junction. In crystals of the M610R mutant, the two putative FGFR1-binding loops are farther apart and no longer on the same face of the 1FN3–2FN3 structure (Fig. 4). Thus, changes in the 1FN3–2FN3 conformation, either by biomechanical forces resulting from cell–cell contact or by alternative splicing, could have a profound effect on the NCAM–FGFR1 interaction. Other NCAM activities that might be affected by alternative splicing of the 1FN3–2FN3 linker include interactions with polysialyltransferases31 and prion protein,45 which both bind to the FN3 domains.
Previous biochemical studies reported a dissociation constant of ∼ 10 μM for the interaction between NCAM 1FN3–2FN3 and FGFR1 D2–D3.28,29 Another study, using cell-based assays, concluded that the acid box in the FGFR1 D1–D2 linker was essential for the NCAM–FGFR1 interaction.20 We wanted to use SPR binding experiments to identify NCAM residues involved in FGFR1 binding and test the effect of splice inserts in the 1FN3–2FN3 linker. Unfortunately, using our recombinant proteins expressed in mammalian cells (Fig. 5), we were unable to detect substantial binding between soluble FGFR1 and NCAM proteins, regardless of whether FGFR1 or NCAM was immobilised on the sensor chip. A very weak interaction (estimated dissociation constant > 100 μM) was evident between FGFR1 D2–D3 and NCAM 1FN3–2FN3, but we observed no NCAM binding to the FGFR1 D1–D3 construct containing the acid box (Fig. 6). The most likely explanation for the discrepancy between our findings and those of Christensen et al.29 is the difference in glycosylation of the FGFR1 proteins used. Christensen et al. expressed FGFR1 D2–D3 in insect cells, which produce N-linked oligosaccharides of the high-mannose type, whereas our expression system (human embryonic kidney cells) produces complex-type oligosaccharides, which more closely resemble the glycan present on mammalian FGFRs. Human and rodent FGFR1 proteins are highly glycosylated, and the glycan is known to influence ligand binding.46 We think that there may be electrostatic repulsion between the acidic NCAM 1FN3–2FN3 protein (isoelectric point 5.0) and the terminal sialic acids present on FGFR1 expressed in human cells. The very weak interaction we observed between soluble NCAM and FGFR1 proteins does not preclude a critical role of this interaction at the cell surface, where avidity effects due to receptor clustering may be substantial. An attractive hypothesis is that the functional state of NCAM (i.e., whether it is engaged in a homophilic contact or not) is linked to FGFR1 binding and activation. It is tempting to speculate that the flexible 1FN3–2FN3 linkage revealed by our structural analysis could provide the molecular means for such a regulatory mechanism.
Experimental Procedures
Expression vectors
NCAM constructs were made by PCR amplification from a bacterial expression vector coding for the FN3 pair of human NCAM. Our NCAM numbering scheme corresponds to SwissProt entry P13591 up to residue 598 but differs by − 1 from P13591 for all subsequent residues due to the replacement of Gln599–Gly600 by Arg, a naturally occurring splice variant in the brain and muscle.12 The M610R mutation in NCAM was introduced by strand-overlap-extension PCR. FGFR1 constructs were made by PCR amplification from a complete cDNA clone of human FGFR1 (IIIc isoform; our numbering scheme corresponds to SwissProt entry P11362). The PCR products were cloned into a modified pCEP-Pu vector47 coding for proteins with a C-terminal His6 tag. The insert sequences of all expression vectors were verified by DNA sequencing. The domain boundaries of the constructs are as follows: NCAM 1FN3–2FN3, QADTP…VFRTS (496–692); FGFR1 D1–D3, RPSPT…EALEE (22–364); and FGFR1 D2–D3, VAPYW…EALEE (151–364). Vector-derived APLA and AAAHHHHHH sequences are additionally present at the N- and C-terminus, respectively.
Protein expression and purification
All proteins were purified from the conditioned medium of episomally transfected 293-EBNA cells. Cells were cultured in Dulbecco's modified Eagle's medium supplemented with 10% fetal calf serum (Invitrogen), transfected using Fugene reagent (Roche Applied Science) and selected with 1 μg/ml of puromycin (Sigma). Proteins were purified by a combination of affinity and size-exclusion chromatography performed on an Äkta platform (GE Healthcare). Typically, 1.5 l of conditioned serum-free medium was loaded onto a 5-ml HisTrap column (GE Healthcare) equilibrated in phosphate-buffered saline (PBS) buffer, pH 7.45 (140 mM NaCl, 10 mM Na2PO4 and 3 mM KCl), and eluted with 500 mM imidazole in PBS. The eluate was concentrated using Vivaspin centrifugal devices (Sartorius AG) and further purified on a 24-ml Superdex 200 size-exclusion chromatography column (GE Healthcare) with Tris-buffered saline (TBS) buffer, pH 7.4, as the running buffer. Purified proteins were analysed by SDS-PAGE, quantified by measuring their absorption at 280 nm, concentrated to the final desired concentrations and flash-frozen in liquid nitrogen for storage at − 80 °C. Final yields were 10–20 mg of pure protein per litre of cell culture medium.
Crystallisation and structure determination
NCAM 1FN3–2FN3 was concentrated to 13 mg/ml in TBS, and crystals were obtained by hanging drop vapour diffusion at room temperature using 2.2 M ammonium sulfate, 0.1 M sodium citrate, pH 5.2, 0.2 M potassium/sodium tartrate and 3–5% ethanol as precipitant. Crystals grew within 2 days and belong to space group P212121 with unit cell dimensions a = 52.77 Å, b = 71.35 Å and c = 98.22 Å. There are two 1FN3–2FN3 molecules in the asymmetric unit, resulting in a solvent content of ∼ 38%. Crystals were flash-frozen in liquid nitrogen after brief soaking in mother liquor supplemented with 20% glycerol. A crystal was soaked in mother liquor supplemented with 300 mM potassium iodide for 30 s before freezing to obtain a heavy atom derivative. Diffraction data from native and KI derivative crystals were collected at 100 K on station 14.1 at the Synchrotron Radiation Source (SRS) Daresbury and on station ID29 at the European Synchrotron Radiation Facility Grenoble, respectively. The NCAM 1FN3–2FN3 M610R mutant was concentrated to 14 mg/ml in TBS, and crystals were obtained by sitting drop vapour diffusion at room temperature using 2 M ammonium sulfate and 0.1 M sodium acetate, pH 4.6, as precipitant. Crystals grew within 3–4 days and belong to space group P212121 with unit cell dimensions a = 92.74 Å, b = 107.49 Å and c = 161.18 Å. There are six copies of mutant 1FN3–2FN3 in the asymmetric unit, resulting in a solvent content of ∼ 42%. Crystals were flash-frozen in liquid nitrogen after brief soaking in mother liquor supplemented with 20% glycerol, and diffraction data were collected at 100 K on station 10.1 at the SRS Daresbury. The diffraction data were processed with MOSFLM‡ and programs of the CCP4 suite.48 The structure of NCAM 1FN3–2FN3 was solved by single-wavelength anomalous dispersion phasing of a KI-soaked crystal using SHARP (Globalphasing Ltd., Cambridge) in full automatic mode. The structure was rebuilt with O49 and refined with Crystallography & NMR System.50 The structure of the NCAM M610R mutant was solved with some difficulty by molecular replacement with PHASER,51,52 using the isolated FN3 domains of the NCAM 1FN3–2FN3 structure as search models. Data collection, phasing and refinement statistics are summarised in Table 1. The figures were made with PyMOL§.
SPR experiments
Binding experiments were performed on a Biacore 3000 instrument (GE Healthcare) at 25 °C. Proteins were immobilised on activated CM4 or CM5 chips using standard amine coupling procedures following the manufacturer's instructions. Briefly, flow cells were activated with 20 μl of a mixture of 0.2 M 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide and 0.05 M N-hydroxy-sulfosuccinimide at a flow rate of 5 μl/min. The proteins to be immobilised (50–100 μg/ml in 10 mM sodium acetate, pH 4.5–5.5) were allowed to pass over activated flow cells to reach ∼ 2000–8000 RU, after which unreacted groups were blocked with 20 μl of 1 M ethanolamine, pH 8.5. Reference flow cells without protein were treated identically. The chips were equilibrated in 10 mM Hepes, pH 7.4, 150 mM NaCl, 50 μM ethylenediaminetetraacetic acid and 0.005% surfactant P20 (HBS-EP buffer), and serial dilutions of analyte proteins in PBS (our recombinant proteins or FGF1 from PeproTech) were injected at 20 μl/min for 300 s, followed by 200 s of pure buffer to monitor dissociation. Chips were regenerated using HBS-EP with 1 M glycine–HCl, pH 3.5. The sensorgrams were analysed with the BiaEvaluation 4.1 software.
Protein Data Bank accession codes
Coordinates and structure factors for wild-type and M610R NCAM 1FN3–2FN3 have been deposited in the Protein Data Bank with codes 2vkw and 2vkx, respectively.

cell adhesion
domain linker
cam, cell adhesion molecule
ncam, neural cell adhesion molecule
ig, immunoglobulin
fn3, fibronectin type 3
fgfr, fibroblast growth factor receptor
spr, surface plasmon resonance
ru, resonance units
pbs, phosphate-buffered saline
tbs, tris-buffered saline
crystal packing
protein interaction
x-ray crystallography


The influence of psychosocial factors at work and life style on health and work ability among professional workers

Objectives The purpose of this article is to explore the associations of psychosocial factors at work, life style, and stressful life events on health and work ability among white-collar workers.
Introduction
Many western countries face the challenge of an aging population, which also affects the workforce. From the biological perspective, aging means a progressive deterioration in various physiological systems, which is accompanied by changes in physical and mental capacities of workers (Ilmarinen, 1997). Aging of the workforce will result in an increased prevalence of work-relevant symptoms and diseases. Therefore, the role of (functional) health in working life is of interest, especially since modern welfare states are prolonging working life by increasing the statutory retirement age. A recent study on the relation between health and working life showed that a perceived poor health predicts staying or becoming unemployed (Schuring et al. 2007). This calls for better adjustments of the working life demands with the individual’s health as a crucial element for a longer career at work. Within this framework, the concept of work ability has been developed as an important tool to identify workers at risk for imbalance between health, capabilities and demands at work.
The work ability concept is based on the assumption that work ability is determined by an individual’s perception of the demands at work and the ability to cope with them. The work ability index (WAI) is a well-accepted instrument to conceptualize work ability. Several studies have shown that a low score on the index is highly predictive of work disability during follow-up (Liira et al. 2000; Tuomi et al. 1991). Previous research, predominantly in physically demanding jobs, showed that the WAI is negatively influenced by older age, high physical work demands, high psychosocial work demands (e.g. lack of possibilities to control one’s own work), unhealthy lifestyle (lack of physical activity), and a poor physical fitness (Alavinia et al. 2007; Ilmarinen et al. 1997; Pohjonen, 2001a; Tuomi et al. 2001).
Few studies have addressed determinants of work ability in occupational populations with predominantly mental demands at work. Among office workers Sjögren-Rönkä (2002) showed that low stress at work and a better self-confidence were directly related to higher work ability. Seniority in the job and job satisfaction were also associated with a better work ability among office workers (Martinez and Latorre 2006). However, the knowledge of determinants of work ability in mentally demanding occupations is scarce and hence, it remains unclear whether in these jobs the relative importance of personal and work-related factors is similar to their well-known contribution in physically demanding jobs.
The purpose of this study was to explore the associations of psychosocial factors at work, stressful life events, and life style on health and work ability among white-collar workers.
Methods
Subjects
In the period between 2003 and 2007 a total of 2,666 white-collar workers from six companies in commercial services were invited for a health examination. Twenty percent of the subjects were employed at three consultancy firms, 62% at two insurance companies and 18% at an information technology company. The health examination consisted of two parts, i.e. a questionnaire and a physical examination. Both parts were offered independently to workers and their participation was entirely voluntary. The response to the questionnaire was 69.4% (n = 1,850). The response to the physical examination was 67.8% (n = 1808). Selection of subjects with both a filled out questionnaire and a physical examination comprised the study population of 1141 (42.8%) subjects.
Work ability
Work ability was measured with the work ability index (WAI). The WAI consists of an assessment of the physical and mental demands on an individual in relation to his work, previously diagnosed diseases, limitations in work due to disease, sick leave, work ability prognosis, and psychological resources. The WAI consists of seven dimensions and the index is derived as the sum of the ratings on these dimensions. The range of the summative index is 7–49, which is classified into a poor (7–27), moderate (28–36), good (37–43), or excellent (44–49) work ability (Tuomi et al. 1998).
Functional health status
Functional health status was assessed using the Short-Form Health Survey (SF-12) version 2, the shortened alternative for the 36-item health survey. This measure provides two weighted summary scores assessing physical function (physical health component summary, PCS) and mental well-being (mental health component summary, MCS) (Ware et al., 2002). The mental health summary score ranges from 8 to 74, whereas the physical health summary score ranges from 4 to 73, with a higher score indicating a better health state.
Psychosocial factors at work
Psychosocial factors at work were measured by the stress monitor (Petri et al. 2001). The original monitor consists of four dimensions, whereas three dimensions (teamwork, stress handling, and self-development) were used in the current study. The three dimensions consist of 27 items on a five-point scale varying from “totally disagree” to “totally agree”. The dimension teamwork (Cronbach’s alpha = 0.85) reflects social support and work spirit and consists of 12 items, e.g. “I can rely on my colleagues and trust them” and “We are not a team at work”. The stress-handling dimension (Cronbach’s alpha = 0.77) reflects active coping and self-efficacy and consists of seven items, such as “In difficult situations I do not wait and see, but take action” and “I can cope well with the demands of my job”. The dimension self-development (Cronbach’s alpha = 0.82) reflects possibilities for self-fulfilment and consists of eight items. Examples are: “My abilities are full employed” and “I need a new challenge”. The scores on items within each dimension were transformed to a 0–100 scale with a higher score indicating good teamwork, better stress handling, and more opportunities for self-development in work. The sum scores for the variables teamwork, stress handling, and self-development were not normally distributed. Tertiles were calculated to assign subjects into low, intermediate and high levels per dimension.
Stressful life events
The occurrence of stressful life events in the past 12 months was measured using a shortened social readjustment rating questionnaire (SRRQ) (Holmes and Rahe 1967). The original SRRQ consists of 43 life events (e.g., divorce, job change, death of family members and so forth), listed by rank order based on their mean life change values. Life change values classify the impact of the events and were obtained by scaling the life events based on the amount of coping required to deal with the event. The total score counts the life change values of all events in the past 12 months. In the current study the 25 events most appropriate for the population under study were selected. In theory, the total score can range from none of these events (0) up to all events (1077).
Life style factors
Life style factors were measured with the Dutch version of the Stanford wellness inventory (Sallis et al.  1985). Lifestyle factors of interest concerned moderate physical activity, vigorous activity, smoking, and alcohol use. The questionnaire has single questions on regular participation in moderate activities for 30 minutes or more and participation in vigorous activities for 20 min or more, both on a five-point scale ranging from “never” to “5 days or more per week”. Those who reported moderate physical activity on at least 5 days per week were considered in agreement with the recommendation on moderate-intensity physical activity, and subjects with vigorous exercises at least 3 times per week were considered in agreement with the recommendation on vigorous-intensity physical activity (Pate et al. 1995). Current smoking was assessed with the question “Do you smoke?”. A five-point-response scale was used to assess alcohol drinking by average number of alcohol drinks per week (1–7, 8–14, 15–21, 22–28, more than 28). Problematic drinkers were defined as those who consumed more than 14 units of alcohol per week for women and more than 21 units for men (Health Council Netherlands    2006).
Physical examination
Physical examinations were performed using MicroFit equipment in accordance with the protocol of the American College of Sports Medicine (ACSM, 1975). During the physical examination biometry was recorded, including weight, height, biceps strength, and cardio respiratory fitness. The body mass index (BMI) was used to define subjects as normal (BMI ≤ 25), overweight (BMI 25–30), or obese (BMI ≥ 30). Maximal isometric muscular strength of the biceps was measured after one practice trial with a calibrated dynamometer with the subjects in standing position with 90° flexion in the elbows for 3 s. The isometric biceps strength was calculated as the average of several hundred readings over the 3 s period. Cardio respiratory fitness was assessed by a 12 min sub-maximal bicycle ergometer test, supervised by instructors. Subjects pedaled at 60 rev. min−1 for 12 min on the cycle ergometer, at an exercise intensity designed to produce a heart rate between 120 and 170 beats min−1, in order to reach a level of 80% of the theoretical maximal heart rate of the participant for 3 min, after a warming up period of minimal 3 min. This level was sustained for 3 min and the heart rate was measured at the end of each minute. The VO2 max (mL min−1 kg−1) was calculated by the work intensity (watts) and heart rates at the end of all the stages at exercise level.
Statistics
The effects of individual characteristics (age and sex), life style, psychosocial factors at work, stressful life events, life style, and physical condition on the outcome variables work ability, and mental and physical health were investigated with linear regression analysis. Probability plots and Kolmogorov–Smirnov tests showed that none of the determinants measured at continuous level were normally distributed. However, the evaluation of the distributions of residuals in the regression analyses showed that for those variables measured at ratio scale (i.e. age, VO2 max, and biceps strength), the assumption of linearity was not violated. These variables were included in the linear regression analyses as continuous variables. Due to considerable ceiling effects for the psychosocial variables and skewed distribution for life stress events, these variables were treated as categorical variables, defined by cut-off values based on tertiles.
The analysis started with univariate regression models to determine the single effects of all determinants of interest. A backward regression technique was used to determine the multivariate model with the best overall fit. In this analysis, independent variables with a P-value of 0.05 or less were retained in the final model.
The results of the regression analyses are presented by the regression coefficients and associated standard errors. A regression coefficient is an expression of the change in the work ability score due to a change in one unit of measurement of the independent variable of interest. For categorical variables, this reflects the effect on the work ability score of the presence of this determinant.
The regression analysis on determinants of work ability was stratified for three age groups. All significant determinants in the multivariate model for one age group were included in the models for other age groups as well in order to provide an appropriate comparison.
All analyses were carried out with the statistical package for social sciences version 11.0 for Windows (SPSS, 1999).
Results
The study population included 769 men (67%) and 372 (33%) women in a variety of jobs (Table 1). The median for age was 35.7 years (18–63). The distribution of excellent, good, moderate, and poor work ability was 42.8, 45.4, 9.7, and 2.1%, respectively. Subjects scored almost equal on mental health as on physical health, whereas the Pearson correlation coefficient between both measures of health was −0.20. The Pearson correlation coefficients between WAI and mental and physical health were 0.49 and 0.35, respectively. The three psychosocial factors at work were strongly interrelated with Pearson correlation coefficients varying from 0.45 to 0.57.
Table 1Characteristics of 1,141 commercial workers who participated in a voluntary medical examinationCharacteristicsCasesMedian (min–max)Frequency (%)Individual characteristics Age (year)1,14135.7 (18–63)67.4 Male769Work ability Excellent (44–49)48842.1 (9–49)42.8 Good (37–43)51845.4 Moderate (28–36)1119.7 Poor (7–27)242.1Health Mental health component summary (MCS) (8–74)1,14154.2 (10.9–67.9) Physical health component summary (PCS) (4–73)1,14153.4 (18.2–70.6)Psychosocial factors at work Teamwork (0–100)1,13681.0 (27–100) Stress-handling (0–100)1,13668.0 (11–100) Self-development (0–100)1,13678.0 (9–100)Stressful life events (0–100)1,1365.5 (0−38.4)Life style Lack of moderate physical activity (<5 days week−1)79870.2 Lack of vigorous physical activity (<3times week−1 )88678.0 Current smoker14512.8 Problematic alcohol use424.5Physical examination Overweight (BMI 25–30 kg m−2)37135.9 (11.4–61.7)34.6 Obesity (BMI ≥ 30 kg m−2)5737.0 (8.0–94.0)5.3 VO2max (ml kg −1 min−1)1,117 Biceps strength (kg)1,134
Table 2 shows mental health was statistically significant influenced by psychosocial factors at work, stressful life events, and, life style factors, whereas physical health was influenced by lifestyle factors and physical condition in the univariate model. The multivariate model explained 22% of the variance in mental health. An increase in age with one year increased the mental health score with 0.1 point, and decreased the physical health score with 0.1 point. In the multivariate analysis most determinants remained statistically significant, albeit with a lower regression coefficient, especially for teamwork and self-development. The multivariate model explained only 5% of the variance in physical health. It is of interest to note that neither problematic alcohol use nor overweight or obesity was associated with physical health.
Table 2Results of backward regression analysis: effects of psychosocial factors at work, stressful life events, lifestyle and physical condition on mental health and physical health among workers in commercial services (n = 1141)Mental Health (MCS)
Physical health (PCS)Univariate modelMultivariate modelUnivariate modelMultivariate modelβSEβSEβSEβSEIndividual characteristics Age (year)0.09*0.030.07*0.02–0.07*0.02–0.09*0.02 Male2.41*0.511.52*0.471.66*0.381.91*0.38Psychosocial factors at work Low versus high teamwork –5.90*0.56–2.71*0.66–0.92*0.43n.s. Intermediate high versus teamwork –2.39*0.57–0.700.58–0.680.45n.s. Low versus high stress-handling –6.94*0.56–4.71*0.62–0.260.45n.s. Intermediate versus  high stress-handling –2.39*0.58–1.42*0.590.010.46n.s. Low versus  high self-development –5.44*0.60–2.12*0.65–0.730.46n.s. Intermediate versus  high self-development –2.19*0.58–0.590.57–0.810.45n.s.Stressful life events High versus  low stressful life events–3.13*0.59–3.13*0.54–0.620.44n.s. Intermediate versus  low stressful life events–1.91*0.59–1.98*0.53–0.050.44n.s.Life style Lack of moderate physical activity–0.140.53n.s.–0.010.39n.s. Lack of vigorous physical activity–1.27*0.58–1.37*0.52–1.79*0.43–1.71*0.42 Current smoker–2.02*0.72–1.96*0.65–1.14*0.54n.s. Problematic alcohol use–1.761.27n.s.–1.610.92n.s.Physical examination Obesity (BMI ≥ 30) versus normal (BMI < 25)–0.841.14n.s–1.540.83n.s. Overweight (BMI 25–30) versus normal 0.340.54n.s–0.300.39n.s. VO2 max (ml/kg/min)–0.010.03n.s0.05*0.02n.s. Biceps strength (kg)0.030.02n.s0.04*0.02n.s.n.s not significant, P > 0.05
Table 3 shows work ability was statistically significant influenced by psychosocial factor at work, stressful life events, lack of vigorous physical activity, and obesity in the univariate model. The multivariate model explained 29% of the variance in work ability. Again, in the multivariate model most determinants remained statistically significant, although with lower regression coefficients. The influence of stressful life events increased in the multivariate model.
Table 3Results of backward regression analysis: effects of psychosocial factors at work, stressful life events, lifestyle and physical condition on work ability among workers in commercial services (n = 1141)Work abilityWork abilityUnivariate modelMultivariate modelβSEβSEIndividual characteristics Age (year)–0.07*0.02–0.09*0.01 Male2.13*0.312.08*0.28Psychosocial factors at work Low versus high teamwork–4.02*0.32–1.32*0.40 Intermediate versus high  teamwork–1.52*0.34–0.200.35 Lowversus versus high stress-handling–4.39*0.34–2.75*0.35 Intermediate versus high  stress-handling–1.41*0.35–0.79*0.35 Low versus high  self-development–4.11*0.35–2.20*0.39 Intermediate versus high  self-development –1.67*0.34–0.91*0.34Stressful life events High versus low stressful life  events–1.36*0.36–2.01*0.32 Intermediate versus low stressful  life events–0.97*0.36–1.14*0.32Life style Lack of moderate physical  activity0.490.32n.s0.31 Lack of vigorous physical  activity–0.71*0.35–0.71* Current smoker–0.680.44n.s. Problematic alcohol use–0.520.74n.s.Physical examination Obesity (BMI ≥ 30) versus  normal (BMI < 25)–2.02*0.68–1.21*0.59 Overweight (BMI 25–30) versus  normal (BMI < 25)–0.490.32–0.320.28 VO2 max (ml −1kg −1min)0.030.02n.s. Biceps strength (kg)0.030.01n.s.n.s  not significant, P > 0.05
No significant interaction was observed for age, sex, and psychosocial factors at work.
Table 4 shows that in each age group sex, stress handling, and self-development were associated with the work ability index. Lifestyle factors were associated with work ability only in the oldest age group of workers, over 45 years. Obesity no longer was statistically significant.
Table 4Results on backward regression analysis per age-group: effects of psychosocial factors at work, stressful life events, lifestyle and physical condition on work ability among workers in commercial services (n = 1141)Work abilityMultivariate modelAge ≤ 32 year (n = 335)Age 32–45 year (n = 366)Age > 45 year (n = 200)βSEβSEβSEIndividual characteristics Male1.43*0.421.37*0.493.19*0.83Psychosocial factors at work Low versus high teamwork–0.440.61–1.40*0.63–1.070.98 ntermediate versus high  teamwork–0.490.50–0.390.60–0.580.89 Low versus high  stress-handling–2.85*0.55–3.22*0.63–2.44*0.95 Intermediate versus high  stress-handling–0.960.52–1.080.61–0.830.86 Low versus high  self-development–2.59*0.64–1.64*0.59–3.57*1.01 Intermediate versus high  self-development –1.27*0.51–0.920.57–1.630.84Stressful life events High versus low stressful life  events–1.25*0.49–1.67*0.51–2.60*0.84 Intermediate versus low  stressful life events–0.640.53–1.78*0.52–0.340.72Life style Lack of moderate  physical activity–0.280.470.020.511.45*0.70 Lack of vigorous physical  activity–0.580.49–0.560.53–1.62*0.79 Problematic alcohol use1.700.93–0.851.19–2.62*1.33P > 0.05
Discussion
This study showed that work ability of white-collar workers in commercial services industry was strongly associated with psychosocial factors at work, such as teamwork, stress handling, self-development, and, to a lesser extent, with stressful life events, lack of physical activity, and obesity. Work ability was strongly associated with mental and physical health. Determinants of mental health were very similar to those of work ability, whereas physical health was influenced primarily by lack of life physical activity.
Some limitations must be taken into account in this study. First, the cross-sectional design does not permit exploration of causal relationships between the determinants and work ability. Therefore, it remains unknown whether, for example, poor stress handling will decrease work ability or decreased work ability will cause a poorer stress handling. Nevertheless, the results are still of interest as they give a first insight into important factors for interventions among white-collar workers. Second, data were drawn from voluntary participants. Information on non-response for both measures showed that age and sex did not bias response. Non-response differences between questionnaire and physical examination did not show any bias; none of the questionnaire variables were associated with not participating in the physical examination; and also none of the physical examination variables were associated with not participating in the questionnaire. Third, the reliability of the physical examination highly depends on the professional skills of the instructor and the standardization of the examination. The maximum oxygen uptake was indirectly calculated using the heart rate, which can be easily increased by minor distractions, such as room temperature, and talking during the test.
In this study among white-collar workers in commercial services industry, the proportion of workers with poor work ability was 2.1% and the mean WAI was 41.1 (SD = 5.1). These results are slightly higher than the Finnish reference data in mentally demanding work (mean 39) (Tuomi et al. 1998).
Work ability in this study population was influenced by sex, age, psychosocial factors at work, stressful life events, and life style factors. These factors together explained 29% of the total variance in work ability in this study population. Male sex increased work ability with two points, which means 4% of the maximum score. An increase in age of 40 years decreases the WAI score with four points, which is 7.3% of the maximum score, which indicates a rather modest influence of age on work ability. Psychosocial factors each had an effect on WAI comparable to sex, whereas the combined effect of the psychosocial factors is approximately 1.5-fold the effect of 40 years of aging. Lack of vigorous physical activity decreases the WAI score with only 0.7 points, which is no more than 1.5% of the maximum score. Obesity (5% of the population) compared to normal weight decreases the WAI score with 1.2 points, which is 2.4% of the maximum score.
Each psychosocial factor at work was negatively associated with work ability. Univariate results showed comparable strength in associations, while the multivariate model showed lower regression coefficients, especially for teamwork. It seems that the association between teamwork and work ability was more influenced by other determinants included in the multivariate model, than the associations between work ability and stress handling and self-development.
In previous research, inconsistent results were found regarding the influence of psychosocial factors at work on work ability. For example, in the metal industry an increase in teamwork and increase in opportunities for development was not predictive of an increase in work ability during a 2-year follow-up (Tuomi et al. 2004). Negative associations between mental stress and work ability have been found among office workers (β = −0.17), but this association was minimized when including age in the regression model (Sjögren-Rönkä, 2002). Among bus drivers, significant associations were observed for high control by superiors and lack of responsibility at work with lower WAI scores (Kloimüller et al. 2000).
The negative association of stressful life events with work ability in the current study is in agreement with earlier findings by Pohjonen (2001b), who found an increased risk for poor work ability [OR = 3.62 (2.2–5.9)] for a hard life situation outside of work.
The results showed that a lack of vigorous physical activity was associated with decreased work ability, whereas associations between work ability and biceps strength and maximum oxygen uptake were not found in the multivariate model. The lack of significant results for maximum oxygen uptake and biceps strength is in line with findings of Eskelinen et al. (1991), Nygard et al. (1991), and Pohjonen (2001a). It may be hypothesized that in mentally demanding jobs a good physical condition is not required to meet the work demands and, thus, will have no influence on work ability.
Stratification by age showed the importance of lifestyle in the oldest age group, but not among younger workers. This effect may be explained by the fact that health problems due to an unhealthy lifestyle, most notably diabetes mellitus and cardiovascular disease, occur primarily at older age. In the total study population, obesity was significantly associated with lower work ability, whereas no significant associations were found in the stratified analyses. This is partly due to lack of statistical power in these strata with smaller numbers of workers, since the magnitude of the regression coefficients were comparable but the standard errors increased substantially.
The Pearson correlation coefficient of mental and physical health was −0.20, which was in line with results of van Duijn et al. (2004). In a univariate analysis both mental health and physical health were associated with work ability. However, determinants of work ability were similar to determinants of mental health. This finding can be explained by the fact that the work setting of the white-collar workers in the current study is characterized by high mental demands. An exception to the similarity in factors influencing both mental health and work ability was, smoking. Smoking was related to mental health, but not to work ability.
The results of the current study outline the importance of work-related factors in white-collar workers, with regard to work ability. The combined impact of psychosocial factors is much stronger than is for individual factors, and is amenable to change, in contrast to individual factors as age, and sex.
In conclusion, among white-collar workers in commercial services industry psychosocial factors at work, stressful life events, lack of vigorous physical activity, and obesity were significant with regard to work ability. The strong associations between psychosocial factors at work and mental health and work ability suggest that in this study population health promotion should address working conditions rather than individual life style factors, although the importance of life style factors seems to increase with aging of the worker.

psychosocial factors
work ability
physical activity
functional health