Coping and sickness absence

Objectives The aim of this study is to examine the role of coping styles in sickness absence. In line with findings that contrast the reactive–passive focused strategies, problem-solving strategies are generally associated with positive results in terms of well-being and overall health outcomes; our hypothesis is that such strategies are positively related to a low frequency of sickness absence and with short lengths (total number of days absent) and durations (mean duration per spell).
Introduction
A strong association exists between ill health and sickness absence, particularly for long absence spells (Marmot et al. 1995; Hensing et al. 1997). However, the decision of an employee to go on sick leave or to stay at work is not just the result of his or her (ill) health status alone (Aronsson et al. 2000; Rosvold and Bjertness 2001; Sandanger et al. 2000; Whitaker 2001; Anonymous 1979; Johansson and Lundberg 2004) but depends also on a number of demographic, social, and economic determinants (Johansson and Lundberg 2004; Voss et al. 2001; Eshoj et al. 2001). For instance, age (Sandanger et al. 2000), gender (Evans and Steptoe 2002), marriage (Mastekaasa 2000), level of education (Eshoj et al. 2001), salary (Chevalier et al. 1987), and sickness absence history (Landstad et al. 2001) are known to be associated with sickness absence behaviour. In addition, the way the individual deals with stressful situations (at work) is likely to affect his or her decision to report ill. In this article we focus on the role of this kind of so-called employee coping behaviour.
The relationship between coping and illness behaviour has been a major research focus over the past two decades (Somerfield and McCrae 2000). A variety of conceptual coping-frameworks have been proposed and numerous measures have been developed to assess ways of coping (McWilliams et al. 2003). Pioneering work in the field of coping has been carried out by Folkman and Lazarus (1980) who define coping as “the cognitive and behavioral efforts made to master, tolerate, or reduce external and internal demands and conflicts among them”. In their opinion, coping has to be considered as a behaviour that is primarily determined by environmental demands, that is, coping is an individual response to a stressful environment. In contrast, other scholars (Holahan et al. 1996; Moos and Holahan 2003) consider coping primarily as a trait or as a resource. The former refers to a relatively stable personal characteristic: that is, similar coping strategies are used across a wide variety of situations (Parker and Endler 1992; Carver and Scheier 1994). The latter refers to the use of particular social and personal characteristics: that is, personal resources on which the individual may draw upon when dealing with stressful situations (Pearlin and Schooler 1978). This trait- or dispositional approach of coping implies a stable coping style or a coping resource regularly used.
As early as four decades ago, Kahn et al. (1964) distinguished between two general coping strategies: problem-solving strategies and reactive–passive strategies. Their idea of two general coping strategies has been worked out by Lazarus and Folkman (1984) in what nowadays is probably the most popular and widely accepted conceptualization of coping behaviour. Problem solving-coping refers to active strategies that are directly targeted at solving the problem at hand, whereas reactive–passive focused coping refers to those strategies that reduce the negative emotions that are evoked by the stressful situation (Elfering et al. 2005).
Much research on coping strategies reveals that both reactive–passive strategies and avoidance strategies result in psychological and physical symptoms (Terry et al. 1996; Pisarski et al. 1998; Penley et al. 2002), whereas active, problem-solving coping generally has a positive impact on well-being and overall health outcomes (Penley et al. 2002). However, in their recent review, Austenfeld and Stanton (2004) criticized this popular and almost generally accepted conclusion. They identified over a hundred articles examining the relationship between reactive–passive coping and adjustment (Stanton et al. 2002b) and found that hardly any of the coping instruments contained the same set of coping strategies, which made it practically impossible to aggregate the findings. Furthermore, the association between reactive–passive strategies and psychological and physical symptoms appeared to be related to the way these strategies had been operationalized (Stanton et al. 2002a). It appeared that corruption of the original coping items as well as the use of item formulations that include the expression of emotional distress or self-deprecation result in spurious correlations.
Studies on coping and sickness absence are scarce. Kristensen (1991) was among the first to investigate this relationship and he asserted that sickness absence itself should be regarded as coping behaviour reflecting the individual’s perception of health or illness. Sickness absence itself, in his opinion, is a functional coping strategy, used by employees to reduce work-related strain by avoiding the workplace and thus creating for themselves the opportunity for recuperation. Kristensen was one of the first not to primarily focus on determinants of sickness absence, but rather tried to understand sickness absence from a coping perspective. By doing so, he went beyond existing concepts of coping by considering sickness absence “a type of coping behaviour” (Kristensen 1991). As he stated: “sickness absence can well be a rational coping behaviour seen in the light of a person’s wish to maintain his/her health and working capacity: as such it is the opposite of withdrawal behaviour”. Clearly, this approach differs from considering coping as a personality trait or resource.
In the present study, coping is conceptualized and measured as a trait or disposition i.e. it is assumed that individuals tend to use rather similar coping strategies across a wide variety of situations. The Utrecht coping list (UCL) (Schreurs et al. 1993) was selected to assess the employees’ coping style. This well-validated self-report questionnaire is the most widely used coping inventory in the Netherlands, both in research and in practice (Schreurs et al. 1993; Schaufeli and Van Dierendonck 1992; Norberg et al. 2005; Buitenhuis et al. 2003). Like the COPE questionnaire of Carver et al. (1989), the UCL asks individuals how they deal with stressful situations; that is, how often they engage in various exertions encountering problems or unpleasant occurrences. The UCL distinguishes between five coping styles that can be grouped together into two higher-order coping styles: active, problem-solving and a reactive–passive style (Schaufeli and Van Dierendonck 1992). Hence, the UCL offers the possibility to investigate employees’ coping styles at a more detailed level, at the same time taking into account the conceptual distinction between problem-solving and reactive–passive coping.
Sickness absence has been measured in terms of frequency, (total) length of sickness absence, (mean) duration of sickness absence spells as well as by the sickness absence free interval. These sickness absence measures are defined in accordance with recommendations of Hensing et al. (1998) who pleaded for a more standardized international description of sickness absence measures. In their literature review, Hensing et al. pointed out the multi-interpretability of sick leave indicators and recommended basic measures to encompass the full spectrum of the sickness absence phenomenon to make studies more accessible for international comparisons. Recently, a study by Landstad et al. (2001) confirmed this line of reasoning by concluding that different forms of absenteeism need to be studied simultaneously, in order to distinguish changes in sickness absence pattern correctly.
In summary then, the aim of the study is to examine the role of coping styles in sickness absence. Based on the fact that, contrary to reactive–passive strategies, problem-solving strategies are generally associated with positive results in terms of well-being and overall health outcomes, our hypothesis is that such strategies are positively related to a low frequency of sickness absence and with short lengths and durations. Reactive–passive strategies, on the other hand, are not expected to be related to sickness absence.
Subjects and methods
Study population and participants
Participants were employees of a large Dutch telecom company. An occupational health survey was sent to all 7,522 employees, including an assessment of coping strategies (response rate 51%; N = 3,852). Sickness absence of the participants was followed up for 1 year after the survey. Due to missing sickness absence data, the sample was reduced to 3,628 employees [3,302 men (mean age 44.7 years, SD = 7.5) and 311 women (mean age 39.7 years, SD = 8.7)]. A description of the sample is shown in Table 1. During the first quarter after the start of the study, 64% of the participants have not been absent because of sickness, whereas, 7% of the participants have been absent for more than 14 days (length).
Table 1Demographics and absenteeism of participants Variable Male91% Age, mean (SD) (min–max) years44.2 (7.7) (22–63)Marital status Married or cohabiting79% Single17% Divorced or separated4%Educational level Lower vocational education27% Intermediate vocational education50% Higher vocational education and university21% Missing/something else2%Working years present job 1 year30% >1–5 year43% >5–10 year14% >10 year14%Sickness absence first quarter 0 days 64% 1–7 days22% 8–14 days6% >14 days7%Function Blue collar (executive)41% Office workers (administrative)30% Supervisors6% Consultants16% Managerial staff7%
Compared to non-participants, participants were predominantly male, older, better paid, and were less absent for sickness (see Table 2).
Table 2Demographics and absenteeism of participants and non-participantsParticipants (n = 3,628)Non-participants (n = 3,670)T Test pχ2pGender (%women)8.614.10.000Age, mean (SD) in years44.2 (7.7)40.7 (9.3)0.000Salary (%)0.000 Low40.653.2 Medium42.933.3 High16.513.5Absenteeism Length mean (SD) days14.9 (39.9)22.9 (59.3)0.000 Frequency 1.20 (1.31)1.31 (1.46)0.000
Measures
Coping style
We assessed the coping strategy of the participants using the shortened 19-item version of the original 30-item Utrecht Coping List (UCL) (Schreurs et al. 1993). This questionnaire was designed to measure the coping strategies people use in stressful situations, either life events or daily hassles. Each item is rated on a four-point Likert scale ranging from one (never) to four (very often).
The UCL includes five dimensions; (1) active problem-focusing (five items, e.g. thinking of different possibilities to solve a problem), (2) seeking social support (5 five items, e.g. seeking comfort and sympathy), (3) palliative reaction pattern (four items, e.g. looking for distraction), (4) avoidance behavior (three items, e.g. complying to avoid problematic situations) and (5) expression of emotions (two items, e.g. showing frustrations). The first three coping styles were found to cluster into a second-order active problem-solving factor, whereas both final styles clustered into a reactive–passive factor (Schaufeli and Van Dierendonck 1992). According to the test manual, the internal consistencies as well as the test–retest reliability are satisfactory (Schreurs et al. 1993). In order to assess the factorial validity of the shortened UCL in our employee sample, a confirmative factor analysis was carried out.
Sickness absence
Sickness absence data were taken from the sickness absence records of the employees filed in the database of ArboNed, an occupational health service (OHS) serving the telecom company. All spells of absence for medical reasons were centrally reported and registered by the executive manager of the company. Absence spells longer than 2 weeks were verified by an occupational physician by inviting the employee on sick leave for an interview. Therefore, the validity of the absence data is assumed to be high.
Measures used are (1) (total) length of sickness absence in current and new spells during the study period (1 year) per sick listed person (i.e. total number of days absent) (2) frequency of sickness absence (new sick-leave spells during the study period (1 year) and (3) (mean) duration of sickness absence (sick-leave days in new spells during the study period (1 year) per spell). The duration of sickness absence is classified into more or less than 7 days. In our sample, short-term sickness (less than 7 days) accounts for 75% of the absences and mainly represents minor ailments. Finally, we assessed the median time before the onset of a new sick leave period after the occupational health survey.
Statistical analysis
Confirmatory factor analysis (CFA), using the AMOS 5 software program (Arbuckle 2003) was used to test the fit of two competing models: M1 that assumes that all 19 items load on one general coping factor, and M2 that assumes that the items load on the five hypothesized correlated factors. Maximum likelihood estimation methods were used and the input for each analysis was the covariance matrix of the items. The goodness-of-fit of both models was evaluated using the χ2 goodness-of-fit statistic and the root mean square error of approximation (RMSEA). However, χ2 is sensitive to sample size so that the probability of rejecting a hypothesized model increases when sample size increases, even if the difference between the fitted model and the “true” underlying model is very small. To overcome this problem, the computation of relative goodness-of-fit indices is strongly recommended (Bentler 1990). Three relative goodness-of-fit indices were computed: the normed fit index (NFI), the non-normed fit index (NNFI) and the comparative fit index (CFI). The latter is particularly recommended for model comparison purposes (Goffin 1993). For all relative fit-indices, as a rule of thumb, values greater than 0.90 are considered as indicating a good fit (Byrne, 2001, pp. 79–88), whereas values smaller than .08 for RMSEA indicate acceptable fit (Cudeck and Browne 1993). Next, Cronbach alphas were calculated for the UCL-subscales.
In a next step, scale scores for different coping strategies were calculated and transformed into scale scores ranging from 0 to 100. Finally, tertiles of the distribution of the 0–100 scale scores were used to distinguish between low-, medium- and high levels of the coping strategies.
To examine the relationship between coping and sickness absence, odds ratios and corresponding 95% confidence intervals were calculated using logistic regression analysis.
Stepwise multiple logistic regression analysis was used to study the (confounding) influence of sociodemographic factors and other determinants on the relationship between coping and sickness absence. The magnitude of the (confounding) effects was assessed by calculating the proportion of the excess risk (OR minus 1.0) explained when fitting these terms in the model.
Finally, the period between the health surveillance and the onset of a new period of absenteeism was evaluated using survival analysis. Since we wish to estimate the probability of absenteeism at a designated time interval (conditional probability) the Kaplan–Meier methodology (Kaplan and Meier 1958) has been applied. With this statistical technique, means, medians and confidence intervals of the ‘survival’ (in this study: the onset of absenteeism) are calculated without making assumptions about the survival distribution.
Results
UCL factor structure
As can be seen in Table 3, confirmatory factor analysis (EFA) corroborated the underlying five-factor structure of the short form of the UCL. More particularly, all fit-indices of M2—the hypothesized model with five correlated factors—sufficed their respective criteria, except NNFI that approached its criterion of 0.90. The mean correlation between the five factors was 0.24, ranging from −0.04 to 0.45. Moreover, the fit of M2 was superior to that of M1 that assumed that all items load on one undifferentiated coping factor (Δχ2 = 10146.22; df = 10; P < 0.001). Hence the factorial validity of the UCL-15 was demonstrated.
Table 3Fit indices of one-factor (M1) and two-factor (M2) models of coping (UCL-19)Modelχ2dfGFIAGFIRMSEANFINNFICFIM11030.291520.690.610.140.430.360.43M2184.071420..950.930.060.900.880.90Null model17976.601710.540.490.17–––GFI goodness of fit index, AGFI adjusted goodness of fit index, RMSEA root mean square estimate of approximation, NFI normed fit index, NNFI non-normed fit index, CFI comparative fit index; all χ2, P < 0.001
The Cronbach alphas for the subscales avoidance behaviour, expression of emotions, seeking social support, active problem-focusing and palliative reaction in this study were 0.67, 0.65, 0.76, 0.81 and 0.68, respectively. Although some values are slightly below 0.70, which is recommended for established scales, all values are well above 0.60, which is deemed satisfactory for newly developed scales (Nunnaly and Bernstein 1994).
Sickness absence and demographics
As can be seen from Table 4, length (total number of days absent) and duration (mean duration per spell) of sickness absence are associated with gender (i.e. women), being divorced or single, having an intermediate or lower education, a shorter period working in the present (current) job, lower salary, higher age, and a history of sickness absence both for length and frequency. Likewise, a higher frequency of sickness absence was associated with gender (i.e. women), being divorced, an intermediate salary and a history of sickness absence both for length and frequency. In our sample there is no association between absence frequency and level of education, the period working in the current job, or age.
Table 4Associations of demographics and sickness absenceSickness absenceLength >14 daysDuration >7 daysFrequency >2xOR95% CIOR95% CIOR95% CIGender Woman1.001.001.00 Man0.490.38–0.620.660.51–0.850.420.32–0.55Married Married1.001.001.00 Single0.830.66–1.031.251.03–1.521.050.82–1.36 Divorced1.731.24–2.412.180.54–8.811.541.04–2.28Education University1.001.001.00 Higher vocational education0.880.59–1.331.130.72–1.781.000.63–1.59 Interm. vocational education1.421.00–2.001.931.30–2.861.160.78–1.72 Lower vocational education2.071.45–2.962.841.90–4.241.300.86–1.96Present (current) job >10 years1.001.001.00 5–10 years0.980.74–1.281.020.78–1.341.040.74–1.46 <5 years0.670.54–0.830.610.49–0.760.910.69–1.19Salary Low 4–61.001.001.00 Intermediate 7–90.500.43–0.600.490.41–0.591.711.28–2.28 High >90.380.30–0.490.330.25–0.441.070.79–1.44Age <35 years1.001.001.00 35–45 years1.381.07–1.781.541.17–2.021.200.90–1.60 >45 years1.481.16–1.881.791.38–2.320.930.70–1.22History sickness absence in days (length) 1 year before 01.001.001.00 1–71.571.24–2.011.190.95–1.503.072.18–4.31 8–143.682.85–4.742.652.08–3.396.304.44–8.95 > 149.727.75–12.24.263.43–5.2912.99.40–17.8History frequency (1 year before) 0x1.001.001.00 1–2x2.772.26–3.411.911.58–2.323.962.91–5.40 >2x8.666.81–11.023.833.04–4.8417.4712.6–24.2n ranges between 3,575–3,606 due to missing values
Sickness absence and ways of coping
As displayed in Table 5, a greater length (total number of days) of sickness absence is predicted by low- or medium-active problem-focusing, avoidance behaviour and a medium- or high palliative reaction. The frequency and the duration of sickness absence are associated in a similar way, however, the latter showing a relation with low seeking social support rather than a palliative reaction. Table 5b summarizes the significant associations between various sickness absence measures and ways of coping (Table 5). It can be seen from this table that the crude ORs of the active and avoidant coping styles show the most consistent patterns of associations across all sickness absence measures.
Table 5Associations of coping and sickness absenceSickness absenceLength >14 daysDuration >7 daysFrequency >2xOR95% CIOR95% CIOR95% CIProblem-solvingActive problem-focusing Low1.001.001.00 Medium0.840.70–0.990.830.69–0.990.840.68–1.04 High0.610.49–0.750.690.56–0.860.780.53–0.87Seeking social support Low1.001.001.00 Medium0.980.82–1.170.970.80–1.161.01−0.80–1.26 High0.920.75–1.120.810.66–0.991.150.90–1.45Palliative reaction Low1.001.001.00 Medium1.221.01–1.491.150.94–1.411.431.13–1.81 High1.331.11–1.591.190.99–1.431.401.12–1.74Reactive–passiveAvoidance behaviour Low1.001.001.00 Medium1.110.92–1.321.140.94–1.371.220.98–1.51 High1.351.10–1.651.321.07–1.631.391.09–1.67Expression of emotions Low 1.001.001.00 Medium1.050.87–1.281.210.99–1.480.910.72–1.14 High1.190.95–1.501.130.89–1.441.290.99–1.69Summary table 5Sickness absenceLengthDurationFrequencyProblem-solvingActive problem-focusingXXXSeeking social support–X–Palliative reactionX–XReactive–passiveAvoidance behaviourXXXExpression of emotions–––
Sickness absence, and demographics and ways of coping
Of course, the question arises whether or not the association between coping and sickness absence could be explained by previous sickness absence and by demographics. Therefore, Table 6 displays the ORs for the three sickness absence measures with coping strategies after adjustment for previous sickness absence and the demographics mentioned in Table 4.
Table 6Odds ratios (95% CI) for sickness absence (length > 14 days, frequency >2x, duration >7 days) associated with different coping styles measured at the start of a 1-year follow-up study (n = 3575)AdjustmentsLength >14 daysDuration >7 daysFrequency >2xOR95% CIOR95% CIOR95% CIProblem-solvingActive problem- focusingNo adjustments (crude OR)0.63(0.51–0.77)0.71(0.57–0.80)0.69(0.54–0.89)History sickness absence length0.71(0.56–0.88)0.78(0.63–0.97)0.71(0.55–0.93)History sickness absence length + gender (female)0.72(0.58–0.90)0.79(0.63–0.98)0.73(0.55–0.95)History sickness absence length + gender + salary (high)0.77(0.61–0.97)0.86(0.69–1.07)0.74(0.57–0.98)History sickness absence length + gender + salary + education (high)0.79(0.63–0.99)0.88(0.71–1.10)0.74(0.57–0.98)History sickness absence length + gender + salary + education + marital status (married)0.79(0.62–0.99)0.880.70–1.10)0.74(0.57–0.98)Seeking social supportNo adjustments (crude OR)0.90(0.73–1.09)0.80(0.65–0.98)1.14(0.90–1.45)History sickness absence length0.95(0.78–1.16)0.81(0.66–1.00)1.06(0.83–1.37)History sickness absence length + gender (female)0.83(0.67–1.03)0.78(0.63–0.97)1.01(0.78–1.30)History sickness absence length + gender + salary (high)0.87(0.70–1.08)0.83(0.67–1.02)1.03(0.80–1.33)History sickness absence length + gender + salary + education (high)0.88(0.71–1.10)0.84(0.68–1.04)1.03(0.80–1.34)History sickness absence length + gender + salary + education + marital status (married)0.88(0.71–1.10)0.84(0.68–1.04)1.03(0.80–1.34)Palliative reactionNo adjustments (crude OR)1.32(1.10–1.58)1.19(0.99–1.43)1.37(1.10–1.72)History sickness absence length1.24(1.02–1.50)1.16(0.96–1.41)1.18(0.93–1.50)History sickness absence length + gender (female)1.20(0.99–1.45)1.14(0.94–1.38)1.15(0.91–1.46)History sickness absence length + gender + salary (high)1.20(0.99–1.46)1.14(0.94–1.38)1.15(0.91–1.45)History sickness absence length + gender + salary + education (high)1.21(1.00–1.47)1.15(0.95–1.40)1.15(0.91–1.46)History sickness absence length + gender + salary + education + marital status (married)1.22(1.00–1.48)1.16(0.96–1.41)1.15(0.91–1.46)Reactive–passiveAvoidance behaviourNo adjustments (crude OR)1.36(1.11–1.66)1.33(1.07–1.64)1.37(1.07–1.75)History sickness absence length1.24(0.99–1.54)1.29(1.04–1.60)1.37(1.05–1.78)History sickness absence length + gender (female)1.23(0.99–1.53)1.28(1.03–1.59)1.36(1.04–1.77)History sickness absence length + gender + salary (high)1.22(0.98–1.52)1.27(1.02–1.57)1.35(1.04–1.75)History sickness absence length + gender + salary + education (high)1.21(0.97–1.51)1.26(1.02–1.57)1.35(1.03–1.75)History sickness absence length + gender + salary + education + marital status (married)1.22(0.97–1.52)1.27(1.02–1.58)1.35(1.03–1.75)Expression of emotionsNo adjustments (crude OR)1.17(0.97–1.40)1.01(0.83–1.22)1.41(1.13–1.75)History sickness absence length1.07(0.88–1.31)0.98(0.80–1.19)1.34(1.06–1.68)History sickness absence length + gender (female)1.06(0.87–1.29)0.97(0.79–1.18)1.32(1.05–1.67)History sickness absence length + gender + salary (high)1.07(0.87–1.30)0.98(0.80–1.19)1.33(1.05–1.67)History sickness absence length + gender + salary + education (high)1.08(0.88–1.31)0.99(0.81–1.20)1.33(1.05–1.67)History sickness absence length + gender + salary + education + marital status (married)1.08(0.88–1.31)0.99(0.81–1.20)1.33(1.05–1.67)All odds ratios are based each time on the same 3,575 employees without missing values on each variable in the model
Length
Adjustment for sickness absence history increases the excess risk to be absent for more than 14 days in one year by 22% for active problem-focusing (thus, sickness absence history reduces the effect of active coping), while reducing it by 25 and 33% for palliative reaction and avoidance coping, respectively. After adjustment for gender and sickness absence history, the excess risk for length in addition to palliative reaction and sickness absence history decreases by 17%.
The excess risk for length adjusted for salary in addition to active problem-focusing, sickness absence history and gender increases by 18%. In summary, adjusted for several confounding variables, the length of sickness absence is effectively influenced by active problem-focusing and palliative reaction.
Frequency
Adjustment for sickness absence history barely minimizes the risk for frequency by coping considering active problem-focusing and avoidance behaviour. For palliative coping, the reduction for the excess risk amounts to 51%. When adjusted for gender, in addition to sickness absence history, the risk of high frequency in association with palliative reaction reduces by another 16%. In sum, adjusted for several confounding variables the frequency of sickness absence is effectively influenced by active problem-focusing, avoidance behaviour and expression of emotions.
Duration
Adjustment for sickness absence history reduces the excess risk of active problem focusing by 24%, of seeking social support and palliative reaction by 16%, and of avoidance behaviour by 12%. Adjustment for gender in association with seeking social support affects the excess risk of duration by 16%. In summary, adjusted for several confounding variables, the duration of sickness absence is effectively influenced by active problem focusing, avoidance behaviour and seeking social support.
Effects on the onset of a new period of absenteeism
During the first year, the median time before the onset of a new episode of absenteeism is significantly shorter for those low in active problem-focusing, high in avoidance, and high in a palliative response. For the two remaining coping styles, no significant results were found (Table 7). This means that employees who are used to solving problems actively instead of avoiding problems or engaging in alternative behaviours, enter sick leave later, the next time.
Table 7Kaplan–Meier: the relation between different coping styles and the onset of absenteeism in the year after coping assessmentLog rankMedian (days)SE95% CIStatdfSignProblem-solvingActive problem-focusing Low1527137–167 Medium17013145–195 High17614149–2039.4420.01Seeking social support Low16810148–188 Medium1658148–182 High15517122–1880.4520.80Palliative reaction Low18214155–209 medium15512132–178 High1467131–16113.6520.00Reactive–passiveAvoidance behaviour Low18211160–204 Medium15111130–172 High14410125–16314.620.00Expression of emotion low16510146–184 Medium16711145–189 High15612133–1790.9820.61
Discussion
In accordance with our hypothesis, and after adjustment for potential confounders, employees with an active problem-solving coping strategy are less likely to drop out because of sickness absence in terms of frequency, length (total number of days absent, longer than 14 days), and duration (mean duration per spell, more than 7 days) of sickness absence. This positive effect is observed in the case of ‘seeking social support’ only for duration of sickness absence, and in the case of ‘palliative reaction’ only for length and frequency of sickness absence. In contrast, an avoidant coping style, representing a reactive–passive strategy, significantly increases the likelihood of frequent absences, as well as the duration of sickness absence. Expression of emotions, representing another reactive–passive strategy, has no effect on sickness absence. The median time before the onset of a new episode of absenteeism, finally, is significantly extended for active problem-solving and reduced for avoidance and for a palliative response.
In summary, we conclude that in accordance with our hypothesis, a problem-solving coping strategy, in contrast to a reactive–passive coping strategy, significantly reduces sickness absence. This result seems to corroborate other research findings that showed that problem-solving coping is associated with well being and overall health outcomes (Kohn 1996). On the other hand, our results are at odds with research findings that document a positive relationship between reactive–passive coping and health (Austenfeld and Statton 2004; Coyne and Racioppo 2000). Austenfeld and Statton (2004) have argued that the negative effect of reactive–passive coping on health may partly be attributed to the operationalizaton of this construct, and therefore recommended a clear description of the reactive–passive coping items used. The idea is that reactive–passive coping can be separated into two factors, namely emotional expression and emotional processing (Lazarus 1993). The former factor is an active attempt to acknowledge, explore meanings or come to an understanding of one’s emotions. Items measuring emotional processing, however, focus on the acknowledgement of emotions, the validness and importance of feelings, the delving into the feelings. Especially, emotional processing has a positive association with health, although how the influencing occurs is still unclear. The items that tap reactive–passive coping in the UCL refer to the expression of emotions and not to their processing. This probably explains the indifferent and negative effect on sickness absence by ‘emotional expression’ and ‘avoidance behaviour’, respectively. A second possible explanation can be that reactive–passive strategies have a positive relationship to health but not necessarily with sickness absence.
Our study partly refutes the assumption of Kristensen (1991) that sickness absence is a coping strategy by itself. Kristensen claimed that employees who use sickness absence as a coping strategy would experience less work-related strain, especially in jobs with poor decision latitude. Accordingly, because they are no longer exposed to their stressful jobs, employees would recuperate during sickness absence, especially in the case of psychosomatic symptoms. In our study, sickness absence history that can be considered a proxy of the coping strategy of sickness absence had only a minor impact on sickness absence given a general coping style. And although the effect is less strong, the measured coping strategies of the UCL still have an effect on sickness absence.
The favourable outcome of problem-solving coping in relation to sickness absence can be attributed to being engaged in active transactions between person and environment with the aim of alleviating stress-inducing situations (Lazarus 1993; Huizink et al. 2002; Roesch and Weiner 2001). Efforts to remove the stressor, gathering information, and finding possible solutions for the problems are a few examples. In general, these strategies are associated with self-confidence and perceived control, and are observed in individuals who are persistent and assertive, self-efficacious, and less anxious and depressed (Heppner 1988; Heppner and Baker 1997).
Two factors in the evaluation of problem-solving coping should be commented upon. Men are believed to be more likely to confront a problem with active coping, whereas women are believed to exhibit a more reactive–passive response (Pearlin and Schooler 1978; Hamilton and Fagot 1998). For instance, a meta-analysis of Tamres et al. (2002) showed that compared to men, women are more likely to use indirect strategies that involve verbal expression or to seek emotional support. Huizink et al. (2002), however, argue that the presumed effectiveness of problem-solving strategies is based on the assumption that male-gender role behaviour is superior. She suggests that studies, as a result of gender bias, have failed to identify other styles of coping as potentially effective. In our study, however, considering several styles, the adjustment for gender barely affects the influence of coping on sickness absence measures.
Another complicating factor in the evaluation of the effectiveness of problem-solving coping may be that reviewers group several distinct coping behaviours under this one single coping category in an effort to simplify the findings (Tamres et al. 2002). For instance, problem-solving coping may be composed of different behaviours. This is underscored by our finding that different problem-solving strategies have different outcomes on sickness absence. Seeking social support, for example, affects only duration (marginally), whereas active problem-focusing affects length, duration and frequency. The difference in outcome of different sickness absence measures in the case of seeking social support may be clarified by Stansfeld et al. (1997) who argues that social support may influence absence-related behaviour and encourage a person to take absence at a time of illness. Contrarily, one may postulate that social support also shortens sickness absence. Both postulations may result in the absence of a substantial effect.
To the authors’ knowledge, this is the first study with four sick leave outcome measures in relation to coping that reveals a more comprehensive picture of changes in the sick leave pattern. In line with Isacsson et al. (1992), we can conclude that “adding more measures gives a more comprehensive picture of sickness absenteeism and of differences between groups”. For instance, the present study demonstrates a relation between a palliative coping reaction and length of sickness absence in contrast with the duration of sick leave. Without the differential pattern for sickness absence, the differential effects of several coping strategies would remain invisible. Another, and perhaps even more important, argument to use different measures of sickness absence is the accessibility of this study for international comparisons in future research.
Finally, the multi-factorial aetiology of sickness absence requires discussion. Alexanderson (1998) pointed out that different disciplines and scientific traditions deal in different ways with absenteeism. In medical science, for instance, the focus of research is on occurrence, etiology and intervention, whereas the focus in medical sociology is on interacting factors within a pre-circumscribed model. She and other authors (Whitaker 2001; Alexanderson 1998), therefore categorized the many factors of sickness absence in three levels: macro/national level (Alexanderson 1995) (e.g. insurance systems), organizational level (Jeurissen and Nyklicek 2001; Vahtera et al. 1996) (e.g. job demands, resources) and individual level (e.g. gender, education). Recognizing this phenomenon, our analyses were adjusted for several known risk factors at the level of the individual. Since the present study was conducted in one Dutch company, the influence of organizational and socioeconomic factors was equally present in all groups and in this sense controlled for.
A strong point of our study is the detailed way in which sickness absence is assessed, using objective archival data. Thus far, relatively little attention has been paid to the implications of different quantitative measures of sickness absence. Moreover, a prospective design was used that allowed for predicting future sickness absenteeism.
A limitation of the study is the non-recurring measurement of coping in our study. Therefore, we cannot rule out the possibility that sickness absence might influence the way employees cope with stressful situations. Although, coping styles, as measured with the UCL, have proven to be relatively stable in time (Norberg et al. 2005), reversed causation cannot be ruled out.
A second limitation could be the Cronbach’s alpha of some subscales of the UCL (slightly below the 0.70). However, the criterion of 0.70 is an arbitrary value that is not universally accepted as the minimum level of acceptability. As an example of the arbitrariness of this criterion, Nunnally (1967) mentioned that αs ranging from 0.50–0.60 would be acceptable, but in the second edition of that book he suggests that 0.70 is the minimally acceptable value—without further justification (Nunnally 1978). Moreover, the minimally required degree of reliability is a function of the research purpose; for individual-level, diagnostic research α should be much higher than for the basic, group-level research reported in our study (Peterson 1994). Hence we used a minimum threshold for coefficient α of 0.65 as was recently proposed by De Vellis (2003).
In spite of these limitations, the results of the present study support the notion that problem-solving coping and reactive–passive strategies are inextricably connected with frequency, duration, length and onset of sickness absence. Especially ‘active problem-focusing’ decreases the chance of future sickness absence.

coping
sickness absence
frequency
length
duration
ucl


Brief Report: Normal Intestinal Permeability at Elevated Platelet Serotonin Levels in a Subgroup of Children with Pervasive Developmental Disorders in Curaçao (The Netherlands Antilles)

This study investigated the relationship between platelet (PLT) serotonin (5-HT) and intestinal permeability in children with pervasive developmental disorders (PDD). Differential sugar absorption and PLT 5-HT were determined in 23 children with PDD. PLT 5-HT (2.0–7.1 nmol/109 PLT) was elevated in 4/23 patients. None exhibited elevated intestinal permeability (lactulose/mannitol ratio: 0.008–0.035 mol/mol). PLT 5-HT did not correlate with intestinal permeability or GI tract complaints. PLT 5-HT correlated with 24 h urinary 5-hydroxyindoleacetic acid (5-HIAA; p = .034). Also urinary 5-HIAA and urinary 5-HT were interrelated (p = .005). A link between hyperserotonemia and increased intestinal permeability remained unsupported. Increased PLT 5-HT in PDD is likely to derive from increased PLT exposure to 5-HT. Longitudinal studies, showing the (in)consistency of abnormal intestinal permeability and PLT 5-HT, may resolve present discrepancies in the literature.
Introduction
Autism has been linked to gastrointestinal (GI) disturbances (White 2003). It is, however, questionable whether GI anomalies in children with autism are specific (Erickson et al., 2005). An increase of chronic diarrhea, constipation, abdominal bloating and food regurgitation has been found in some studies, but could not be confirmed in more recent studies (reviewed by Erickson et al. 2005). Increased GI permeability, as established through the differential sugar absorption test (SAT), was demonstrated in 9/21 (43%) (D’Eufemia et al., 1996) and 19/26 (76%) (Horvath & Perman, 2002) of patients diagnosed with autism. The SAT measures the integrity of the intestine by the ingestion of two indigestible saccharides that after GI uptake become fully excreted in urine. One of these (usually lactulose) passes the intestinal wall through paracellular transport (‘leakage’), while the other (usually mannitol) passes by paracellular and transcellular transport. The urinary lactulose/mannitol ratio is used as a measure of intestinal integrity and permeability (van Elburg et al., 1995). In addition, a recent study found a high prevalence of congenital GI anomalies (adjusted odds ratio 5.1, 95% confidence interval 1.8–14.1), notably pyloric stenosis, in autism, which may be linked to the high rate of GI dysfunction reported by their parents (Wier, Yoshida, Odouli, Grether, & Croen, 2006).
The implication of the GI tract in autistic pathophysiology warrants more detailed investigation of the gut-brain axis (Erickson et al., 2005). Especially the role of serotonin (5-hydroxytryptamine; 5-HT), as a messenger within this axis (Gershon, 2005), deserves attention. Many different aspects of the 5-HT system in autism have already been studied (Burgess, Sweeten, McMahon, & Fujinami, 2006; Croonenberghs, Verkerk, Scharpe, Deboutte, & Maes, 2005; Janusonis, 2005; Mulder et al., 2004). A recent report (Mulder et al., 2004) on platelet (PLT) 5-HT in PDD showed PLT hyperserotonemia in approximately 36% of patients with autism and in 58% of patients with PDD not otherwise specified (NOS). Using mixture-modeling analysis Mulder et al. (2004) derived an empirical cut-off value that enabled dichotomization of patients with PDD into normo- and hyperserotonemic. Extensive behavioral assessments did, however, not show significant correlates with PLT 5-HT or hyperserotonemic status.
A common (developmental) factor, causing both an autistic brain and deregulated 5-HT release from the GI tract, years after birth, may be involved in the etiology of PDD (Janusonis, 2005). The primary site of the hyperserotonemia in autism is likely to be located in the GI tract. Serotonin is a biogenic amine that derives from the essential amino acid tryptophan (Tryp) by hydroxylation and subsequent decarboxylation. The GI tract contains about 80% of bodily 5-HT, which is unevenly distributed among the enterochromaffin cells (90–95%) and neurons (5–10%) (Houghton, Atkinson, Whitaker, Whorwell, & Rimmer, 2003). The main functions of 5-HT are in smooth muscle contraction, blood pressure regulation, and peripheral and central neurotransmission. Serotonin localized in the basolateral stores of enterochromaffin tissue is released upon neuronal, chemical or mechanical stimulation. Several 5-HT receptors control GI motility, sensation and secretion (Gershon, 2005). Following its release, 5-HT is removed from the interstitial space by 5-HT selective reuptake transporters (Gershon, 2005). However, part of the 5-HT enters the portal blood and systemic circulation where it is either rapidly taken up and accumulated by PLT, or metabolized by the liver, lung and kidneys into its major metabolite 5-hydroxyindoleacetic acid (5-HIAA) (Houghton et al., 2003). Platelets store and transport the majority (99%) of circulating 5-HT (Ortiz, Artigas, & Gelpi, 1988).
Elevated PLT 5-HT levels observed in subgroups of patients with PDD, may be related to increased GI motility. This notion is supported by higher PLT 5-HT in patients with diarrhea predominant irritable bowel syndrome (d-IBS), as compared with healthy controls (Houghton et al., 2003), although this was not consistently found (Atkinson, Lockhart, Whorwell, Keevil, & Houghton, 2006). Patients with d-IBS have augmented GI motility, which is likely to cause increased exposure of their circulating PLT to 5-HT (Atkinson et al., 2006; Gershon, 2005; Houghton et al., 2003). Increased PLT 5-HT is also observed in patients with carcinoid tumors (Kema et al., 2001). Carcinoid tumors derive from enterochromaffin cells and are characterized by high 5-HT production with diarrhea as a frequent symptom (Modlin, Kidd, Latich, Zikusoka, & Shapiro, 2005). Consequently, measurement of PLT 5-HT is used as a sensitive marker for the early diagnosis and the subsequent follow-up of patients with carcinoid tumors (Kema et al., 2001).
The aim of the present study was to investigate whether the subgroup of children with PDD having increased PLT 5-HT levels, is the same as the one exhibiting increased intestinal permeability as established by a SAT.
Methods
Patients
Parents of patients with PDD (n = 31) according to the DSM-IV TR (American Psychiatric Association, 1994) were asked for the participation of their affected children via the local patient society and pediatricians. Oral and written informed consent were obtained. Information regarding comorbidity, medication, nutritional supplements and the prevalence of GI related complaints was obtained from medical records and with the aid of assisted questionnaires. The study was performed in Curaçao (The Netherlands Antilles) in the summer of 2004. All collected urine and blood samples were transported in dry ice to the Netherlands for further analyses in the University Medical Center Groningen (UMCG). The study was approved by the Medical Ethical Committee of the St. Elisabeth Hospital in Curaçao.
Sugar Absorption Test
The SAT was performed according to van Elburg et al. (1995). Shortly, the patients ingested a sugar (lactulose, mannitol and sucrose) containing test fluid after an overnight fast. All urine voidings during the following 5 h were collected and pooled. Urinary sugars were analyzed by gas chromatography as previously described (Jansen, Muskiet, Schierbeek, Berger, & van der Slik, 1986). A urinary lactulose/mannitol (L/M) ratio above 0.090 was considered to be indicative for abnormal GI integrity/increased intestinal permeability (van Elburg et al., 1995).
Serotonin Assays
For estimation of 5-HT turnover and exposure of PLT to 5-HT, we examined 24 h urinary excretion of 5-HIAA and total 5-HT (Kema et al., 2001). For this, parents were asked to abstain their child completely from 5-HT containing foods (e.g. banana, pineapple, kiwi, walnuts) during collection and during the preceding 12 h. The volumes of the urine samples were measured before storing at −20°C. Urinary 5-HIAA and total 5-HT concentrations were determined as previously reported (Kema et al., 2001). Urinary 5-HIAA values were evaluated with the use of age-dependent reference values (American Association for Clinical Chemistry, 2005).
Non-fasting venous blood (for serum antibodies) and EDTA-anticoagulated blood (all other assays) were collected from children with PDD. EDTA-anticoagulated blood was placed on melting ice. Hematological indices were measured immediately after sampling. Within 1 h after collection a 1:1 mixture of K2EDTA and Na2S2O5 was added to PLT-rich plasma (PRP) to prevent oxidation of indoles. Plasma and serum were stored at −80°C. Simultaneous analysis of indoles [Tryp, 5-hydroxytryptophan (5-HTP), 5-HT and 5-HIAA] in PRP was performed as previously described (Kema et al., 2001). PLT 5-HT data were compared with both a local cut-off value of 5.4 nmol/109 PLT (Meijer, Kema, Volmer, Willemse, & de Vries, 2000) and an empiric cut-off value of 4.55 nmol/109 PLT (Mulder et al., 2004). The local cut-off value represents the 97.5th percentile of a reference group of healthy adults, that is employed in our laboratory for the diagnosis of carcinoid tumors. The empirical cut-off value represents the bottom of the valley of the PLT 5-HT bimodal distribution, as exhibited by patients with PDD. This value allows optimal classification into those who are normoserotonemic and hyperserotonemic. Platelet-rich-plasma Tryp data were evaluated with the use of age-dependent reference values (American Association for Clinical Chemistry, 2005).
Exclusion of Celiac Disease
Serum IgA anti-endomysium titers and HLA genotype were assessed to rule-out celiac disease, which is an established cause of increased GI permeability (van Elburg, Uil, Mulder, & Heymans, 1993).
Statistics
All data were analyzed using the Statistical Product and Service Solutions package, version 11.5 (SPSS Inc. Chicago). Data were tested for normality using the Shapiro–Wilk W test. Group comparisons (normo- and hyperserotonemic) were performed with the Mann–Whitney U test (non-parametric). Spearman (non-parametric) tests were used to evaluate correlations at α = 0.05, to minimize type-II errors.
Results
Patients
We enrolled the first 24 (77%) of the 31 patients with PDD whose parents agreed to participate. Patient characteristics are reported in Table 1. The parents reported 13/23 (57%) of their affected children to have one or more GI symptoms.
Table 1Characteristics of patients with pervasive developmental disorders in Curaçao tested for platelet serotonin and intestinal permeabilityParameterGender (male/female)18 (75%) / 6 (25%)Age (years)9.9 (±3.9)DSM-IV TR diagnoses    299.00 (autistic disorder)8 (33%)    299.80 (PDD-NOS)16 (67%)Ethnicity    Caucasian8 (33%)    African–American13 (54%)    Other3 (13%)Comorbidity    Epilepsy5 (22%)    Allergy2 (9%)    Asthma1 (4%)    Intestinal yeast infection1 (4%)Medication for comorbidity9 (38%)Nutritional supplements (vitamins/ω3-oils)9 (38%) / 2 (4%)Diet (gluten and casein free)a2 (9%)Physical complaints related to GI tracta13 (57%)    Nausea0 (0%)    Vomiting1 (4%)    Diarrhea4 (17%)    Constipation4 (17%)    Bloating and gaseousness8 (35%)an = 23. PDD-NOS pervasive developmental disorder—not otherwise specifiedData represent number (percentage) or mean (±SD) for 24 patients, unless otherwise specified
Because of cleanliness problems, no urine samples were obtained from one patient, while from another we only received urine for SAT. Blood sampling from yet another patient was problematic. Consequently, our study comprised urine for SAT from 23/24 patients, 24 h urine from 22/24 patients and blood samples from 23/24 patients. Table 2 shows the indices related to 5-HT turnover and intestinal permeability of these patients.
Table 2Indices of serotonin (5-HT) turnover and intestinal permeability in patients with pervasive developmental disorders in CuraçaoParameterReference values<RV≥RVPLT 5-HT (nmol/109 PLT)a3.4 (2.0–7.1)<4.55b6 (26%)<5.4c4 (17%)Tryp (μmol/l)a50.0 (±10.9)2–18y: 0–79d0 (0%)0 (0%)2y: 35–73d}6y: 37–76d3 (13%)0 (0%)16y: 54–93d5-HIAA (μmol/24 h)e8.4 (3.9–36.4)3–8y: 2.1–29.3d}9–12y: 5.2–32.9d1 (5%)1 (5%)13–17: 4.7–34.0d>18y: 5.2–36.6d5-HT (nmol/24 h)e305 (±92)Lactulose (mmol/5 h)f0.024 (±0.11)Mannitol (mmol/5 h)f1.32 (±0.59)L/M ratio (mol/mol)f0.019 (±0.007)<0.090g23 (100%)0 (0%)aPlatelet-rich plasma, n = 23bEmpiric cut-off derived from Mulder et al. (2004)cUpper reference value derived from apparently healthy adults Meijer et al. (2000)dFrom reference American Association for Clinical Chemistry (2005)eAbout 24 h urine, n = 22fAbout 5 h urine from sugar absorption test, n = 23gFrom reference van Elburg et al. (1995)Data represent number (percentage), median (range) or mean (±SD).RV; reference value; PLT, platelet; Tryp, tryptophan; 5-HIAA, 5-hydroxyindoleacetic acid; L/M, lactulose/mannitol
Sugar Absorption Test
Intestinal permeability, reflected by the L/M ratio [median (range) 0.017 mol/mol (0.008–0.035)], indicated that none of the patients had increased intestinal permeability (i.e. L/M ratio ≥ 0.090).
Serotonin Assays
PLT 5-HT [median (range): 3.4 (2.0–7.1) nmol/109 PLT] was elevated in 4 (range: 5.7–7.1 nmol/109 PLT) and 6 [range 4.6–7.1 nmol/109 PLT] patients if compared to the local cut-off value ((Meijer et al., 2000); <5.4 nmol/109 PLT) or the empirical cut-off value ((Mulder et al., 2004); <4.55 nmol/109 PLT), respectively. The sole patient exhibiting detectable levels of plasma 5-HIAA (26.0 μmol/l), also exhibited increased PLT 5-HT (6.4 nmol/109 PLT). However, this patient did not exhibit increased urinary total 5-HT or 5-HIAA excretions. Urinary excretion of 5-HIAA was within normal range.
Exclusion of Celiac Disease
None of the patients were positive for serum IgA anti endomysium and 8/23 (35%) patients had a genotype positive for either HLA-DQ2 (n = 5) or HLA-DQ8 (n = 3). Based on the results of serology, none of the patients seemed to have celiac disease, although we did not perform further tests to exclude this.
Statistics
There was no correlation between PLT 5-HT and L/M ratio (p = .663; r = −.098). Patients exhibiting GI tract complaints did not have higher PLT 5-HT, higher L/M ratios, or higher 5-HIAA and total 5-HT 24 h urinary excretions (p > .4). Platelet 5-HT correlated with 24 h urinary 5-HIAA excretion (p = .034; r = .465). Also the 24 h excretion rates of 5-HIAA and total 5-HT showed a positive correlation (p = .005; r = .580).
Discussion
In this study of children with PDD we did not observe a relation between PLT 5-HT and intestinal permeability, as derived from the urinary L/M ratio. The number of children with PDD exhibiting increased PLT 5-HT was lower compared with reports of others. For instance, the recent study of Mulder et al. (2004) showed 23/81 (28%) of Dutch children with PDD to exhibit increased PLT 5-HT, using the same analytical method and a cut-off value of 5.4 nmol/109 PLT. Also our data on intestinal permeability contrast with previous reports (D’Eufemia et al., 1996; Horvath et al., 2002) showing that 43–76% of children with autism have increased intestinal permeability, as established by a SAT. A weakness of the current study is its small size and the lack of a local age- and gender matched control group. We have, on the other hand, no indications for deviant reference values for PLT 5-HT or L/M ratios in Curaçao, as compared with The Netherlands. Age and gender do not appear to affect PLT 5-HT (Mulder et al., 2004), but it must be noted that PLT 5-HT reaches highest levels during childhood and gradually decreases during adulthood (Flachaire et al., 1990). Dependent on the cut-off values employed, we nevertheless found 4 and 6 patients with increased PLT 5-HT. Neither of these patients had abnormal intestinal permeability or L/M ratios residing in the upper range of normality. However, the positive correlation between PLT 5-HT levels and 24 h urine 5-HIAA, and also the relation between urinary 5-HIAA and urinary 5-HT, suggest that, also in PDD, exposure of PLT to 5-HT determines PLT 5-HT and its consistently found increase in a subgroup. It is possible that increased PLT 5-HT and GI permeability are not consistent features of children with PDD in time. Long term, e.g. monthly, monitoring of a well defined patient and control group may shed more light on this potential source of variance as a cause of the conflicting results found by several investigators. Differences in the activity of the 5-HT transporter based on genetic polymorphisms are unlikely, since these seem to have minor effects, if any, on PLT 5-HT levels (Mulder, 2006).
In conclusion, the finding of a subgroup of children with PDD exhibiting hyperserotonemia was replicated. None of the children exhibited increased intestinal permeability, while PLT 5-HT was unrelated to both intestinal permeability and GI symptoms. Additional studies are needed to elucidate the etiology of increased PLT 5-HT in PDD and to establish its relation with intestinal pathology, if any.

permeability
platelets
serotonin
pervasive
gastrointestinal
child development disorders


Assessment of technological options and economical feasibility for cyanophycin biopolymer and high-value amino acid production

Major transitions can be expected within the next few decades aiming at the reduction of pollution and global warming and at energy saving measures. For these purposes, new sustainable biorefinery concepts will be needed that will replace the traditional mineral oil-based synthesis of specialty and bulk chemicals. An important group of these chemicals are those that comprise N-functionalities. Many plant components contained in biomass rest or waste stream fractions contain these N-functionalities in proteins and free amino acids that can be used as starting materials for the synthesis of biopolymers and chemicals. This paper describes the economic and technological feasibility for cyanophycin production by fermentation of the potato waste stream Protamylasse™ or directly in plants and its subsequent conversion to a number of N-containing bulk chemicals.
Introduction
Plants have the ability to use the incident sunlight for the biosynthesis of a tremendous variety of compounds that may contain a number of functionalized atoms or groups. An important group of these functionalized compounds are proteins and especially the individual amino acids that contain one or more nitrogen atoms. When starting from crude oil or naphtha, the incorporation of functionalities (e.g., −NH2) into derived bulk chemicals (such as 1,2-ethanediamine and 1,4-butanediamine) requires considerable amounts of energy and catalysts. However, some amino acids appear to be very suitable starting materials for highly functionalized bulk chemicals (Scott et al. 2007).
The biorefinery concept is a rapidly emerging field of research and commercial activities aiming at the integral use of all components of agricultural crops. In addition to the main product such as starch or oil, also, other side stream fractions including protein, free amino acid, and fiber fractions have high potential for valorization. An example of such waste stream fraction, Protamylasse™ that remains after starch and protein extraction from potato and its possible application as a substrate for microbial fermentation and production process for cyanophycin (Elbahloul et al. 2005a,b, 2006), will be described in detail in the current paper.
Cyanophycin (multiarginyl-poly[l-aspartic acid]; CGP, cyanophycin granule peptide) is a non-ribosomal protein-like polymer which consists of equimolar amounts of aspartic acid and arginine arranged as a poly-aspartic acid backbone to which arginine residues are linked to the β-carboxyl group of each aspartate by its α-amino group. In nature, cyanophycin is produced by most, but not all, cyanobacteria as a temporary nitrogen reserve material during the transition of cells from the exponential phase to the stationary phase. The polymerization reaction is catalyzed by only one enzyme, which is referred to as cyanophycin synthetase (CphA). Because of the low polymer content and the slow growth of cyanobacteria resulting in only low cell densities, cyanobacteria are not suitable for large-scale production of cyanophycin. Therefore, the cphA genes from a number of cyanobacteria have been expressed in several bacteria, and, more recently, also in plants. Furthermore, the polymer isolated from recombinant strains contained lysine as an additional amino acid constituent.
Now that cyanophycin can be produced in sufficient amounts by pilot scale fermentations for studying its material properties, it appears of biotechnological interest because purified cyanophycin can be chemically converted into a polymer with a reduced arginine content, which might be used like poly-aspartic acid as a biodegradable substitute for synthetic polyacrylate in various technical processes. In addition, cyanophycin might also be of interest for other applications when the hitherto unknown physical and material properties of this polymer will be revealed. On the other hand, cyanophycin is a convenient source of the constituent amino acids that may be regarded as nitrogen-functionalized precursor chemicals.
In the current paper, conditions will be discussed for the technological and economic feasibility of cyanophycin production by microbial fermentation and by cyanophycin production directly in plants. The conditions for fermentative cyanophycin production will be based upon the use of cheap substrates derived from agricultural waste streams and the possible cyanophycin production simultaneously with other fermentation products like ethanol. This aspect is denoted process integration.
Biorefinery and its place in the production of chemicals
The depletion in fossil feedstocks, increasing oil prices and the ecological problems associated with CO2 emissions, are forcing the development of alternative resources for energy, transport fuels, and chemicals: the replacement of fossil resources with CO2 neutral biomass.
Potentially, biomass may be used to replace fossil raw materials in several major applications: heat, electricity, transport fuels, chemicals, and other industrial use. Each of these groups represents about 20% of the total fossil consumption in the industrialized countries (Oil Market Report of the International Energy Agency 2004). Large variations in the cost of these products at the wholesale level, based on their energy content, are evident (Table 1). When one considers the contribution to costs by the raw materials (expressed per GJ end product), large differences are also seen. Heat can be produced from coal for around 3 €/GJ due to utilizing inexpensive feedstocks with high conversion efficiency (about 100%), while the raw material costs for electricity is double (6 €/GJ) due to a conversion yield of about 50%. Most notable is the high raw material costs for chemicals. Here, expensive raw materials (oil) are used with low(er) conversion yields (Sanders et al. 2005, 2007).
Table 1Different applications and contributions of biomassContributionIntegral cost prices (€/GJ end product)Raw material cost fossil (€/GJ)Percentage of total energy in the Netherlands (3.000 PJ) consumed per application (%)Heat43 (Coal)±20Electricity226 (Coal)±20Transport fuel108 (Oil)±20Average bulk chemicals7530 (Oil)±20Rest of industry±20
To obtain a good net income for biomass, an effective biorefinery system is required for the separation of the harvested crop into fractions for use in (several of) these applications. These may be used directly as the desired product or undergo conversion by chemical, enzymatic, and/or microbial means to obtain other products. Biorefinery systems are well established for a number of crops. For example, soybeans are the raw materials for large biorefineries to produce oil (for biofuels), proteins and valuable nutraceuticals.
Less well explored is the use of biomass to make industrial chemicals. Effort to produce chemicals with constant quality and performance (such as lactic acid) has been addressed, but has mainly focused on the use of carbohydrates as raw materials and use of biotechnology for conversion. However, for effective biorefinery approaches, other biomass fractions should also be considered for the production of chemicals. It is anticipated that the substitution of petrochemical transportation fuels with biofuels will rise significantly in the coming years. This means that a rise in the production of biodiesel will lead to large volumes of glycerol as a residual stream, and indeed, some companies are already investigating the use of glycerol to produce chemicals. While the awareness of large volumes of glycerol from biofuel production is apparent, one should not overlook other waste streams. Indeed, from biofuel production, an immense concomitant waste stream of protein will also be generated. Sources of proteins and amino acids are not limited to those generated from biofuel production, but also from other industries such as potato starch production. For example, during AVEBE’s processing of potatoes for starch extraction, the main waste stream is Protamylasse™, which mainly contains sugars, organic acids, proteins, and free amino acids and has currently no major market use. Some of the amino acids present in such sources could be very suitable raw materials for preparing (highly) functionalized chemicals traditionally prepared by the petrochemical industry (Sanders et al. 2007).
Generally, the conversion of crude oil products utilizes primary products (ethylene, etc.), and their conversion to materials or (functional) chemicals makes use of co-reagents such as ammonia and various process steps to introduce functionalities such as −NH2. Conversely, many products found in biomass, such as proteins and amino acids, often contain these functionalities. Therefore, it is attractive to exploit this to bypass the use, and preparation, of co-reagents as well as eliminate various process steps by utilizing suitable biomass-based precursors for the production of chemicals. Thus, the production of chemicals from biomass takes advantage of the biomass structure in a more efficient way than the production of fuels or electricity alone and can potentially save more fossil energy than producing energy alone (Scott et al. 2007). When used in combination with environmentally sound production and processing techniques across the whole biomass production chain, i.e., from cultivation and harvest, its (pre)treatment and conversion to products, the use of biomass is considered a sustainable alternative to conventional feedstocks, which is reflected by sound economic advantages in both raw material and investment costs.
General introduction on NRPs and especially cyanophycin
Cyanophycin (also referred to as CGP, cyanophycin granule polypeptide) that belongs to the family of bacterial poly-amino acids together with poly-γ-glutamic acid and poly-ɛ-lysine, was discovered in 1887 by Borzi (1887) during microscopic studies of cyanobacteria and was later found in all groups of cyanobacteria (Oppermann-Sanio and Steinbüchel 2002). The cyanophycin molecule structure is related to that of poly(aspartic acid)s, but, unlike synthetic poly-aspartic acid, it is a comb-like polymer with α-amino-α-carboxy-linked l-aspartic acid residues representing the poly(α-l-aspartic acid) backbone and l-arginine residues bound to the β-carboxylic groups of aspartic acids (Simon and Weathers 1976; for recent review, see Obst and Steinbüchel 2004; Fig. 1). Cyanophycin is synthesized by most, but not all, cyanobacteria as a temporary nitrogen reserve material during the transition of cells from the exponential phase to the stationary phase (Mackerras et al. 1990). At neutral pH and physiological ionic strength, cyanophycin is insoluble and deposited in the cytoplasm as membraneless granules (Lawry and Simon 1982).
Fig. 1Chemical structure of the cyanophycin monomer
Cyanophycin isolated from Cyanobacteria is highly polydisperse and shows a molecular weight range of 25–100 kDa as estimated by sodium dodecylsulphate polyacrylamide gel electrophoresis corresponding to a polymerization degree of 90–400 (Simon 1971; Simon and Weathers 1976). Cyanophycin is a transiently accumulated storage compound which is synthesized under conditions of low temperature or low light intensity. Its accumulation can be artificially enhanced by the addition of chloramphenicol as an inhibitor of ribosomal protein biosynthesis (Simon 1973). Cyanophycin plays an important role in the conservation of nitrogen, carbon, and energy and, as indicated by its biosynthesis in presence of chloramphenicol, is non-ribosomally synthesized by CphA. Cyanophycin is accumulated in the cytoplasm of cyanobacteria as membraneless granules (Allen and Weathers 1980) in the early stationary growth phase (Mackerras et al. 1990; Liotenberg et al. 1996). When growth is resumed, for example due to a change in cultivation conditions, cyanophycin is reutilized by the cells (Mackerras et al. 1990). Krehenbrink et al. (2002) and Ziegler et al. (2002) showed that cyanophycin occurs even in heterotrophic bacteria like Acinetobacter sp. and Desulfitobacterium hafniense and therefore confirmed the wide distribution of this biopolymer and its function in nature as a general storage compound.
Cyanophycin is of biotechnological interest because the purified polymer can be chemically converted into a polymer with reduced arginine content (Joentgen et al. 1998), which might be used like poly-aspartic acid as a biodegradable substitute for synthetic polyacrylate in various technical processes. In addition, cyanophycin might also be of interest for other applications if the unknown physical and material properties of this polymer are revealed. Because of the low polymer content and the slow growth of cyanobacteria resulting in only low cell densities, cyanobacteria are not suitable for large-scale production of cyanophycin (Schwamborn 1998), and sufficient amounts of cyanophycin were hitherto not available.
The polymerization reaction is catalyzed by only one enzyme, which is referred to as CphA (Ziegler et al. 1998). The cphA genes from Anabaena variabilis ATCC 29413, Anabaena sp. strain PCC7120, Synechocystis sp. strain PCC6803, Synechocystis sp. strain PCC6308, Synechococcus elongatus, Synechococcus sp. strain MA19 and others were cloned and expressed in Escherichia coli (Aboulmagd et al. 2000; Berg et al. 2000; Hai et al. 1999; Oppermann-Sanio et al. 1999; Ziegler et al. 1998). More recently, heterologous expression of cphA was also demonstrated at a small scale in recombinant strains of Ralstonia eutropha, Corynebacterium glutamicum, and Pseudomonas putida (Aboulmagd et al. 2001). Whereas in cyanobacteria the molecular mass of the polymer strands ranged from 25 to 100 kDa (Simon 1976), the polymer from recombinant strains harboring cphA as well as in vitro-synthesized polymer exhibited a much lower range (25 to 30 kDa) and polydispersity. Furthermore, it was found that the polymer isolated from recombinant strains contained lysine as an additional amino acid constituent (Aboulmagd et al. 2001; Ziegler et al. 1998). Recently, the results of a detailed in silico analysis of the occurrence of enzymes involved in cyanophycin metabolism was published (Füser and Steinbüchel 2007).
Recently, the earlier postulated instability of recombinant E. coli strains employed for cyanophycin production was also confirmed in both DH1 and DH5α. This instability may be caused by loss of the plasmid during fermentation. However, as cyanophycin production continues in cultures that rapidly appear to loose the ampicillin resistance employed for selection and plasmid maintenance, other explanations are also under consideration such as competition for Arg and Asp by both cyanophycin and the ampicillin resistance protein and the theoretical possibility that the ampicillin resistance protein could be trapped into the cyanophycin granule or at least be made inaccessible to the ampicillin.
Due to the wide knowledge of its metabolism and available genetic tools, E. coli is one of the most commonly used bacterial hosts for the production of recombinant proteins (Lee 1996). Several expression systems have been developed for technical-scale production of recombinant proteins in E. coli based on the regulated trp, lac, or lambda PL promoter (Hannig and Makrides 1998). The cultivation of recombinant E. coli strains harboring cphA from Synechocystis sp. strain PCC6803 at the 500-l scale for the production of cyanophycin has been described (Frey et al. 2002). As the previously described method for the purification of cyanophycin (Simon and Weathers 1976) is not applicable to a large scale, a simplified method for isolation of the polymer at the technical scale was elaborated.
Biosynthesis of cyanophycin was extensively studied in the 1970s by Simon and coworkers (Simon 1971, 1976; Simon and Weathers 1976). Later, this led to the identification of cyanophycin synthetase enzymes and the encoding genes (cphA) in various organisms (Ziegler et al. 1998; Aboulmagd et al. 2000; Berg et al. 2000; Hai et al. 2002). Subsequently, the enzymes involved in the degradation of cyanophycin by intracellular cyanophcyinases of cyanobacteria (cphB) and extracellular depolymerases, like hydrolase (cphE) and cyanophycinase (cphI) genes, were identified (Obst et al. 2002, 2004; Obst and Steinbüchel 2004). Elbahloul et al. (2005a,b) have found that inactivation of the cyanophycinase gene in Acinetobacter resulted in significantly less cyanophycin accumulation than the wild type presumably due to a shortage of cyanophycin primer molecules. On the contrary, cyanophycin is highly resistant against hydrolytic cleavage by proteases such as trypsin, pronase, pepsin, carboxypeptidases B, carboxypeptidase C, and leucin-aminopeptidase, and cyanophycin is also resistant against arginases (Simon and Weathers 1976).
Because the cyanophycin synthetase genes (cphA) of many cyanobacteria, and recently, also of other microorganisms were identified, cloned, and heterologously expressed in other bacteria (Aboulmagd et al. 2001) conferring the ability to produce comparably large amounts of cyanophycin (up to 50% of CDW) in a much shorter period of time (1–2 days) as compared to cyanobacteria (about 4 weeks) and cyanophycin production was demonstrated at the 30- to 500-l scale (Aboulmagd et al. 2001; Frey et al. 2002; Voß and Steinbüchel 2006), the interest in cyanophycin as a potential raw material has constantly increased over the last few years. Improvement of fermentation conditions, feeding regimes, and the possibility to produce cyanophycin now by the employment of many genetically engineered bacteria with industrial relevance like R. eutropha, C. glutamicum, or P. putida (Aboulmagd et al. 2001) in complex but also in defined media make it appear likely that further improvement of cyanophycin production in bacteria will be achieved during future studies. These studies may include elementary mode analyses (Diniz et al. 2006) or more conventional approaches involving experimental design using fermenter arrays and principal component analyses.
In conclusion, large-scale fermentation processes for cyanophycin production and downstream processing are available for a number of different microorganisms able to grow in different substrates, including the potato waste stream Protamylasse™, and also, low cyanophycin yields were reported in plants.
Production and economic aspects of fermentative cyanophycin production
An important contribution to sustainability can be made by the use of a considerable plant waste stream for the production of renewable, biodegradable, and biocompatible polymers and/or valuable chemicals that are now produced on large scale from petroleum. Some of the polymer classes to be developed may be expected to replace some existing mineral petroleum-based polymers as soon as competitive production prices can be obtained and/or supporting measures will be taken to promote the use of renewable resources. On the other hand, completely novel types of biopolymers may be developed for completely novel applications.
AVEBE, located in the northern part of The Netherlands, is the largest potato-starch-producing company in the world involved in extraction, processing, and sales of starch and starch-derived products. During processing of potatoes for starch extraction, the main waste stream is Protamylasse™. Annually, AVEBE produces about 120,000 m3 of Protamylasse™ containing about 70,000 tons of dry matter, mainly consisting of sugars (14,000 tons), organic acids (13,300 tons), proteins and free amino acids (18,000 tons). The amino acids arginine and aspartic acid amount to about 1,000 tons each and lysine to about 700 tons. Currently, there is no proper outlet for this Protamylasse™ other than low value epandage (e.g., by Bos Agra-Service, NL in which the salts present, such as potassium, are used as fertilizer), whereas rough calculation shows that the total intrinsic gross value of the valuable components in the Protamylasse™ is about 45 million euros. A number of research objectives is now in progress to add substantial value to the entire potato starch production chain (and thus its economical feasibility). Protamylasse™ may be considered a model for other agricultural waste streams, such as grass juice and beet residue. It is not certain whether concentrations of all medium components in the Protamylasse™ will be optimal to sustain microbial growth and cyanophycin production, and it is anticipated that additional medium components need to be identified and/or tailor-made production strains constructed. This will require a detailed analysis of Protamylasse™ components before and after the cyanophycin production phase.
To calculate the economic feasibility of cyanophycin production using Protamylasse™, several aspects need to be included as follows:
Necessary steps from potato starch and protein extraction, yielding Protamylasse™, to the final purified product cyanophycin (which is considered an intermediate product for derived polymer types and N-containing bulk chemicals) include: shipment to a fermentation plant, dilution to 5–6% (v/v) using tap water, removal of potato particles by filtration, disposal (or alternative use) of particle fraction (60% DM), loading of step up fermenters [to allow an microbial inoculum concentration of 10% (v/v) per step; e.g., 10→100→1,000→10,000→100,000 l], sterilization, cooling, addition of ampicillin, inoculation, (batch) fermentation, addition of acid and/or base for pH control, cell harvesting and concentration, disposal or recycling of spent Protamylasse™, cell disruption (not for E. coli), cyanophycin extraction using pH 2, neutralization, cyanophycin crystallization, precipitation, purification, and storage.Yearly Protamylasse™ supply, 120,000 m3; 70,000 tons (60% DM)Protamylasse™ dry matter composition: amino acids (18,000 tons), sugars (14,000 tons), organic acids (13,300 tons), ash (22,200 tons). For further details on the composition, see Elbahloul et al. (2005a, b).Small-scale fermentation process data: Protamylasse™ concentration, 5–6% (v/v); fermenter volume, 25 l; strain E. coli DH1 (pMa/c5-914::cphA); temperature 37°C; maximal OD and cyanophycin yield reached after 15 h; optimal pH 7.5–8.0; biomass yield 5–10 g/l (CDW); cyanophycin content, 25% (w/w); cyanophycin composition, Asp, 50%; Arg, 45%; Lys, 5%; E. coli occasionally (5–10%) incorporates lysine in stead of arginine; poly(Asp–Arg) is non-soluble in water at neutral pH; poly (Asp–Arg–Lys) is soluble in water.
Assuming that most of the solid particles will be removed from the Protamylasse™ by filtration, a yearly amount of 48,000 m3 of Protamylasse™ liquid juice will become available, which is used at a 5% dilution during fermentation, thus, providing a yearly amount of 960,000 m3 of diluted fermentation broth. This volume would be enough to run 9,600 × 100 m3 fermenter volumes. Further assuming a 1 week’s run time (including cleaning, sterilization, fermentation, and harvest) a park of, e.g., 185 × 100 m3 or preferably 20 × 1,000 m3 fermenter units (step up units included) could be operated continuously. With the current E. coli biomass yield of 5 g/l (CDM) with a cyanophycin content of 25% (w/w DM), this would yield a yearly amount of 1,200 tons of purified cyanophycin. At an estimated market price of € 1,000 per ton cyanophycin, the Protamylasse™ juice fraction (40% v/v) would yield a yearly income of only 1.2 million euros. However, by only increasing the amount of microbial biomass from the actual 5 g/l (CDM, E. coli) to a realistic value of 100 g/l (CDM, S. cerevisiae) with the same cyanophycin content of 25% (w/w DM), this yearly income could be raised to 24 million euros. If within the same fermentation run via so-called process integration, in addition to cyanophycin, ethanol also [yielding from 960,000 m3 at 5% (v/v) about 48,000 m3 ethanol and an additional yearly income of 27.4 million euros at $2.8 per US gallon, € 0.57 per liter) could be produced using semi-aerobic fermentation with S. cerevisiae, this yearly income could be further raised to about 50 million euros. The Protamylasse™ particle fraction can, in principle, also be used for cyanophycin and ethanol production. Preliminary calculations using the model of Golden Grain Energy, LLC (http://sec.edgar-online.com/ 2004/06/14/0001104659-04-016859/Section7.asp) suggest that the break-even point could roughly be reached within about 3–5 years.
Further process improvements may be obtainable and are necessary to make this process economically feasible. These obtainable figures provide estimates as to the investments and costs that will be necessary to start a cyanophycin production fermentation facility. However, before fermentative cyanophycin production on industrial scale can be started, a number of bottlenecks should be overcome. Table 2 lists current bottlenecks and proposed measures for optimal and economically feasible cyanophycin production.
Table 2Economical and technological bottlenecks and proposed measuresBottleneckProposed measure(s)Investments, including costs for fermentation and downstream processing equipmentThe calculation provided here suggests that these may be acceptableCosts for the production of cyanophycin, cyanophycin-derived products and for downstream processing of biomassConstruction of a sufficiently productive microbial strain to convert or simply utilize constituents of plant waste streams like Protamylasse™ and to incorporate these compounds, presumably amino acids, into the cyanophycin polymer chain during cyanophycin biosynthesisPhenotypic instability of E. coli production strains used until now, DH1 and DH5α, containing plasmid pMa/c5-914::cphA6803Construction of stable strains with integrated copies of the cyanophycinsynthesis genesLow biomass yields of the E. coli strains usedSince not all components present in the current source of Protamylasse™ may have the proper concentration for current laboratory strain(s), an optimization may require the addition of substrates other than Protamylasse™, for example other plant waste streams. Sufficient provision of amino acids like arginine should be ensured during the production phaseOptimization of microbial biomass formationBy using yeasts as alternative production organisms biomass yields could be increased to 100 g/l CDM for S. cerevisiae [factor 20×] or 150 g/l CDM for Pichia pastoris [factor 30×] if in Protamylasse™ the same yields can be obtained as in dedicated growth mediaSub-optimal fermentation processesFermentation technology and feeding regimes have to be developed for optimum amino acid utilization or biosynthesis from Protamylasse™ or other plant waste streamsGeneration of valuable side stream particle fraction of Protamylasse™Alternative use of the side stream particle fraction of Protamylasse™, e.g. by using cyanophycin producing filamentous fungiCo-production with, e.g., ethanolWhen using S. cerevisiae as the production organisms and (semi-) anaerobic fermentation both cyanophycin and ethanol could possibly be produced during the same runCosts for cyanophycin extractionDevelopment of alternative cheap cyanophycin extraction methods using, e.g., hydro-cyclone equipment for the non-soluble fractionCost-efficient production of cyanophycin in plantsThe transfer of the bacterial cyanophycin synthetase gene (cphA) into eukaryotic hosts, mostly plants and its effective expression in suitable organs or cell compartments is a major step (see below)Efficacy of downstream processingDownstream processing has to be adapted and optimized for cyanophycin or cyanophycin derivatives containing biomass, which will be either bacterial cells or eukaryotic (mostly plant) cells or tissuesLack of insight in possible modifications of cyanophycin, their impact on cyanophycin properties and market potentialThe diverse possibilities to modify the cyanophycin molecule chemically or enzymatically has to be exhaustingly explored to identify all potential key applications for cyanophycin-derived products and to find the most suitable products with regard to market potential and the possibility of their commercializationLack of knowledge concerning properties of known cyanophycin synthetases and their genetic engineeringThe possibility to modify the active sites of the cyanophycin synthetases in order to change its substrate specificity and to allow the production of cyanophycin derivatives has to be determinedInsufficient insight in all possible applications for cyanophycin as a polymer or as a starting material for chemical synthesesThe exploitation of cyanophycins and cyanophycin-derived molecules as substitutes for well established industrial products or as renewable raw materials has to be determined precisely
Cyanophycin production in plants
Transgenic plants can be utilized to produce renewable resources for industrial purposes in a CO2-neutral, environmentally acceptable, and competitive way. Poly-3-hydroxybutyrate (PHB) was the first plastic-like compound produced in plants (Poirier et al. 1992), followed by, e.g., poly-3-hydroxyalkanoate (PHA; Poirier 2002) and medium chain-length PHA in potato (Romano et al. 2003, 2005) and showed the feasibility to produce biopolymers in plants. Recently, it has been shown that it is also possible to produce cyanophycin in plants (Neumann et al. 2005). For this, the Thermosynechococcus elongatus BP-1 cyanophycin synthetase gene was expressed constitutively under a 35S promoter in tobacco and potato plants. It was shown that approximately 1.14 and 0.24% dry weight could be accumulated in the cytosol of tobacco and potato leaves, respectively. The size (35 kDa), amino acid composition (Asp/Arg/Lys = 1:1.05:0.1), and structure of the plant-produced polymer was similar to that produced in transgenic E. coli expressing the same gene; however, the amount and molecular weight of the cyanophycin produced in plants was much lower than that observed in bacteria (up to 50% dry weight and 125 kDa in bacteria). The experiments have provided proof of concept for the potential of producing cyanophycin in plants.
Production of the cyanophycin biopolymer in potato is of high interest to the potato starch industry. Production in this plant does not require any additional infrastructure. After processing of the potatoes, cyanophycin can be isolated from the Protamylasse™. However, for commercial application, the efficiency of cyanophycin accumulation in potato has to be significantly improved.
Neumann et al. (2005) already indicated that directing the cyanophycin synthetase into several other compartments, such as the chloroplasts, could lead to increased accumulation of cyanophycin. However, chloroplasts in the cyanophycin-producing cells differ morphologically from wild-type chloroplasts. There are fewer and smaller grana stacks, and the growth rate is slower. One of the possible explanations for these properties is depletion of amino acid resources as a result of cyanophycin production (Neumann et al. 2005). The pioneering experiments by Neumann et al. (2005) have opened up a new field for the production of cyanopycin in agricultural crops and show that more research is needed before being introduced into agricultural production (Conrad 2005).
Additional strategies
Priming cyanophycin elongation
In in vitro studies, it has been shown that cyanophycin synthetase works more efficiently with a (β-Asp/Arg)3/Arg primer. In planta production of such a primer may enhance cyanophycin biosynthesis. Cyanophycinases encoded by the chpB and chpE genes can degrade cyanophycin into arginine–aspartic acid dipeptides (Asp–Arg) which cannot be used as primer for cyanophycin biosynthesis.
The cphI gene encodes a plant-type asparaginase able to hydrolyze β-Asp/Arg bonds and that, thus, may be responsible for the last step in cyanophycin degradation. Bacterial studies have indicated that cphI expression contributes to a higher cyanophycin level.
It might be possible to use a poly-Asp backbone as primer for cyanophycin biosynthesis. This peptide can be produced by ribosomal protein biosynthesis. The gene should be under the control of a low-level promoter to prevent the production of many peptides, and thus, the production of many low molecular weight polymers.
Optimization of amino acid biosynthesis
It has been shown that cyanophycin synthetase uses arginine and aspartic acid as its major amino acid source but that it also can incorporate lysine in the cyanophycin polymer (Berg et al. 2000). It is unclear how this affects the properties of the polymer. Based on the chemical composition of Protamylasse™ and the affinity of the enzyme for arginine and lysine, it can be proposed that lysine accounts for 1.5% of the total cyanophycin. To reduce this amount, three strategies are possible, i.e., improve biosynthesis of arginine, reduce the level of lysine, and/or transform lysine into arginine.
It is possible that the availability of substrates (Asp and Arg) in plants is limiting or off-balance. Therefore, it is important to identify the organs that have the highest concentrations of available substrates and to investigate whether the substrate supply can be enhanced by introduction of genes involved in substrate production.
Comparison of economics of cyanophycin production by fermentation or in plants
As for some other commodities, depending on production price, market volume and final product price (Fig. 2) for cyanophycin specialty product applications, fermentation is the preferred production method (roughly below 20.000 tons annually), whereas for bulk quantities of cyanophycin, the production directly in plants is preferred (above 20,000 tons). Therefore, for the production of cyanophycin-derived bulk quantities of nitrogen-containing chemicals, plants are considered the best production organisms, whereas for specialty polymers, fermentation may be the preferred production technology.
Fig. 2Cyanophycin production in planta or by fermentation. Gray square: raw material costs, filled square: fermentation costs, open square: recovery and purification costs
Assuming that a typical fermentative production of a bulk product, such as lysine, citric acid or glutamic acid costs about € 1,500 per ton and that these costs consist of: € 500 for the raw materials, € 500 for the fermentation process, and € 500 for recovery and purification. The advantage of producing in plants is that both the raw material costs and the fermentation costs can almost be neglected. On the other hand, recovery costs could be much higher. For the sake of the reasoning, it is assumed that recovery costs for cyanophycin production in plants will be the same as in the case of fermented production, i.e., € 500 per ton. In case of a fermentation process, a typical production volume for a company would be in the order of magnitude of 100,000 tons/year. The turnover would then be 150 million euros per year (i.e., € 1,500 per ton × 100,000 tons/year). In the case of production in plants, as raw material and fermentation costs are negligible, cost savings would be 100 million euros per year. This would be the maximum advantage, as this calculation does not include any costs for the production of the crop nor any additional costs for biorefining of the crop and treatment of side products. A similar reasoning would bring a maximum advantage for a product with a typical company production volume of 20,000 tons per year, such as the case with a medium-sized monomer. In this case, cost savings per ton would be higher, but as the volume is much smaller, the maximum advantage could be around 70 million euros. For specialty products with a volume of 300 tons per year (enzymes), a maximum advantage is estimated to be in the order of 5–10 million euros. For pharmaceutical production with a volume of 10 tons per year, the advantage would be around 0.5–1.5 million euros, as the volume would be small and production costs would mainly be ascribed to recovery.
Other plant side streams
In addition to the possible exploitation of Protamylasse™, a large number of other plant side streams may be used for cyanophycin production, including grass juice, which remains after protein extraction or beet or cane molasses remaining after sugar extraction. It is foreseen that with the production of biodiesel and bioethanol, large volumes of side streams will become available, all containing major protein quantities. Such streams will include dried distillers grain and solubles (DDGS) from corn and wheat, press cake from palm oil and rape seed oil. These streams all have in common a very low cost price, which implies that when used as a microbial growth medium, a major contribution to fermentation process costs (about 30%) can be eliminated. However, it is important to note that none of any waste stream (to be) considered has exactly the same chemical or element composition as the production organism and its product(s), including, e.g., CO2. This implies that in each fermentation process to be developed using plant waste streams as substrates, limiting components should be supplemented. For the identification of such limiting components, fermentation test runs should be performed, preferably supported by statistic analyses such as elementary mode analysis (Diniz et al. 2006). Only after performing such exercises can reliable estimates of integral project costs be obtained.
Cyanophycin-derived bulk chemicals
Cyanophycin can be hydrolyzed to its constituent amino acids, aspartic acid, and arginine. These amino acids may be utilized directly in food and pharmaceutical applications. However, based on the chemical structure of these amino acids and the presence of functionalized nitrogen-containing groups, it is possible to anticipate their conversion to a number of industrial chemicals, including:
Arginine may be converted to 1,4-butanediamine. 1,4-diaminobutane, derived from petrochemistry, is currently used as a co-monomer in the production of nylon-4,6. The volume of production is not known, but is estimated to be in the range of 10,000s tons per year with a value of >€ 1,600 per ton.Conversion of aspartic acid to acrylonitrile is also envisaged. Using current petrochemical technology, the current worldwide market volume of acrylonitrile is 2.7–5 × 106 tons per year and a price of about € 800–1,000 per ton.
Other chemicals which could be obtained from cyanophycin but are currently prepared from fossil resources include, e.g., 1,4-butanediol and urea.
The production of cyanophycin by plants will drastically reduce the cost price, potentially below € 1,000 per ton. This will enable the production of functionalized bulk chemicals such as 1,4-diaminobutane, and possibly, also acrylonitrile. The transition from mineral oil to plant-based precursor production has a considerable impact. The use of ammonia for the incorporation of nitrogen into chemicals is very important, but also very energy-intensive. Therefore, if the incorporation of nitrogen can be realized in systems based on plant (rest) streams in the form of protein or amino acid precursors, then this will yield considerable energy savings.
Cyanophycin-based biopolymers
Poly-aspartic acid is derived from cyanophycin after the hydrolytic removal of arginine. This polymer has properties that are very similar to poly-acrylic acid. The cost price of this polymer can be set on 1,000€/ton in the cost calculations for arginine here above. As the volumes of these products will be similar, only 1,000 tons/year will be manufactured. Higher market prices might be obtained for special applications in food and/or pharmaceutical applications. This might change the cost structure of arginine in a way that market volumes might double or triple.
Cyanophycin as such might have applications as a polymer. Furthermore, derivatives obtained by enzymatic/genetic and/or chemical modifications might give valuable properties. Without thorough investigations, we cannot anticipate on the value of these polymers. Utilization in this area can be expected after 10 to 12 years after the beginning of the proposed research approaches for research and development.
Possible cyanophycin modifications, applications for bulk chemicals and for polymers
By incorporating other amino acids, different types of polymers can be made. So far, several cyanophycins have been produced in recombinant strains of E. coli up to 50% dry weight. However, especially for health care medical and food packaging applications, E. coli may not be the best commercial production organism. Therefore, the development of alternative food-grade production organisms is also one of the objectives of the current activities. One suitable candidate may be the bakers yeast Saccharomyces cerevisiae (and others: see above). In addition, being stable at a pH between 3 and 9, cyanophycin can also be hydrolyzed in concentrated volumes into its pure components, arginine and aspartic acid. This would make the whole process a novel biological extraction procedure for the selected amino acids.
Outlook
Given the anticipated cost development for fossil energy carriers and environmental regulations, the chemical industry is facing increasing financial pressure and is thus looking for possibilities to broach new resources as a basis for polymer production. Important considerations in this search are to lower energy costs and prices of raw material and to develop cheaper and more sustainable production processes.
Unlike poly-γ-glutamic acid and poly-ɛ-lysine, cyanophycin has not been commercialized yet. Cyanophycin can be broken down into the individual amino acids that can be used as building blocks in various industrial processes. Because of its homogeneous structure and composition, the cyanophycin polymer and its derivatives also appear to be good candidates as starting materials for the production of nitrogen-rich commodity products, which are based on nitrogen-rich chemicals, like, for example, nylons. For example, for poly(aspartic acid) which is the polymer backbone of cyanophycin, various applications have been developed ranging from water-softening or detergent applications to applications in the paper, building material, petroleum or leather industry, in cosmetics, as well as many dispersant applications.
Cyanophycin can be chemically converted into a polymer with a reduced arginine content, which might be used like poly-aspartic acid as a biodegradable substitute for synthetic polyacrylate in various technical processes (Schwamborn 1998). Thus, cyanophycin may find applications in cyanophycin-derived bulk chemicals and in cyanophycin-based biopolymers.
It can be expected that economical activities can be developed within the following areas, such as: fermentation industry, biopolymer production, processing, modification and product development (also for medical technology), packaging industry, food and feed supplementation industries and, last but not least, state-of-the-art technology (which, in turn, will attract additional financial sources and economical activities). It should be emphasized, however, that the mentioned applications are still uncertain and that these are so far only potential applications.
On the one hand, this development will lead to the substitution of chemicals that are now produced at the cost of fossil raw materials, such as oil. As oil may be depleted in about 50 years, and as there seems to be a correlation of the use of fossil raw materials with climate changes, it is essential to develop alternatives. The anticipated alternatives can be produced by fermentation and, in principle, by plant production systems, and so, giving a new economic and knowledge intensive value to the fermentation industry and/or to agriculture. On the other hand, novel types of polymers will be developed that do not simply replace existing applications but that will enter novel product markets.
Elements of the contents of this paper are included in a dedicated patent application (Elbahloul et al. 2006).

cyanophycin
biorefinery
bulk chemicals
n-functionality
protamylasse
non-ribosomal
plant waste, rest stream